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African Journal of 

Agricultural Research 

Volume 7 Number 17 5 May, 2012 

ISSN 1991-637X

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International Journal of Medicine and Medical Sciences 

African Journal of Agricultural Research 

Table of Contents: Volume 7 Number 17 5 May, 2012 

ences 

ARTICLES 

Review 

ENVIRONMENTAL SCIENCE 

Is there a relationship between floristic diversity and carbon stocks in 

Tropical vegetation in Mexico? 2584 

Jose Luis Martinez-Sanchez and Luisa Camara Cabrales 

SOIL SCIENCE 

Comparison of automated and manual landform delineation in semi 

detailed soil survey procedure 2592 

Kamran Moravej, Mostafa Karimian Eghbal, Norair Toomanian and 

Shahla Mahmoodi 

Effect of nitrogen and potassium fertilizer on yield, quality and some 

quantitative parameters of flue-cured tobacco cv. K326 2601 

Ali Reza Farrokh, Ibrahim Azizov, Atoosa Farrokh, Masoud Esfahani, 

Mehdi Ranjbar Choubeh and Masoud Kavoosi 

Vermicompost induced changes in growth and development of 

Lilium Asiatic hybrid var. Navona 2609 

Ali Reza Ladan Moghadam, Zahra Oraghi Ardebili and Fateme Saidi


ences 

ARTICLES 

Contribution of soil organic carbon levels, different grazing and converted 

rangeland on aggregates size distribution in the rangelands of Kermanshah 

Province, Iran 2622 

Mohammad Gheitury, Mohammad Jafary, Hossein Azarnivand, Hossein 

Arzani, Seyed Akbar Javady and Mosayeb Heshmati 

ANIMAL SCIENCE 

Effects of Gynostemma pentaphyllum (Thunb.) Makino polysaccharides 

supplementation on exercise tolerance and oxidative stress induced 

by exhaustive exercise in rats 2632 

Hongfang Wang, Changjun Li, Xiaolan Wu and Xiaojuan Lou 

Climate change and adaptation of small-scale cattle and sheep 

farmers 2639 

B. Mandleni and F.D. K. Anim 

POULTRY SCIENCE 

Village chicken production practices in the Amatola Basin of the 

Eastern Cape Province, South Africa 2647 

N. M. B. Nyoni and P. J. Masika 

AGRONOMY 

The relationship between agricultural intensification and sustainability 

in China 2653 

Hong-Wei Chen and Da-Fu Wu


ences 

ARTICLES 

Water movement and retention in a mollic andosol mixed with raw mature 

chickpea residue 2664 

Isaiah I. C. Wakindiki and Morris O. Omondi 

PLANT BREEDING 

In vitro propagation of native Ornithogalum species in West 

Mediterrenean region of Turkey 2669 

Ozgul Karaguzel, Ayse Kaya, Beyza Biner and Koksal Aydinsakir 

Determination of associations between three morphological and two 

cytological traits of yams (Dioscorea spp.) using canonical correlation 

Analysis 2674 

P. E. Norman, P. Tongoona and P. E. Shanahan 

Distribution of nuclei and microfilaments during pollen germination in 

Populus tomentosa Carr. 2679 

Yuan Cao, Rui-Zhi Hao, Mei-Qin Liu, Xin-Min An and Yan-Ping Jing 

AGRICULTURAL ENGINEERING 

Development of an automatic cutting system for harvesting oil palm 

fresh fruitbunch (FFB) 2683 

Hamed Shokripour, Wan Ishak Wan Ismail, Ramin Shokripour and Zahra 

Moezkarimi 

African Journal of Agricultural Research Vol. 7(17), pp. 2584-2591, 5 May, 2012

Available online at http://www.academicjournals.org/AJAR 


ences 

AGRICULTURAL ECONOMICS 

ARTICLES 

Total factor productivity growth and convergence in Northern Thai 

agriculture 2689 

Sanzidur Rahman, Aree Wiboonpongse, Songsak Sriboonchitta and 

Kritsada Kanmanee 

CROP SCIENCE 

Dynamics of on-farm management of potato (Solanum tuberosum) 

cultivars in Central Kenya 2701 

C. Lung’aho, G. Chemining’wa, S. Shibairo and M. Hutchinson 

Effect of organic and inorganic fertilizer on maize hybrids under agro- 

environmental conditions of Faisalabad-Pakistan 2713 

Wajid NASIM, Ashfaq AHMAD, Tasneem KHALIQ, Aftab WAJID, 

Muhammad Farooq Hussain MUNIS, Hassan Javaid CHAUDHRY, 

Muhammad Mudassar Maqbool Shakeel AHMAD and Hafiz 

Mohkum HAMMAD

African Journal of Agricultural Research Vol. 7(17), pp. 2584-2591, 5 May, 2012 


DOI: 10.5897/AJAR11.599 

ISSN 1991-637X ©2012 Academic Journals 

Full Length Research Paper 

Is there a relationship between floristic diversity and 

carbon stocks in tropical vegetation in Mexico? 

Jose Luis Martinez-Sanchez* and Luisa Camara Cabrales 

División Académica de Ciencias Biológicas, Universidad Juárez Autónoma de Tabasco. km 0.5 Carr. Villahermosa- 

Cardenas, Villahermosa, Tabasco, 86990, México. 

Accepted 21 December, 2011 

Tropical vegetation plays an important role in the terrestrial carbon stocks, and Mexico has a 

considerable amount of this vegetation that may store a large amount of carbon. However, an important 

variable of vegetation is its floristic diversity. Floristic diversity influences in a great extent the biomass 

of an ecosystem and hence carbon stocks. The purpose of this study was to determine whether there is 

a relationship between floristic diversity and carbon stocks in the case of tropical vegetation in Mexico. 

Floristic species richness, Shannon´s index and above-ground biomass were estimated for eight 

localities with arboreal and herbaceous vegetation. The biomass in the humid and sub-humid tropical 

forests was estimated using two specific allometric formulas for trees. In the case of grassland, it was 

estimated by harvesting the vegetation. A positive relationship was observed by a straight line (r 2 = 

0.82, P = 0.001) and a significant relationship was observed in the inverse U polynomial model (r 2 = 0.84, 

P = 0.002) between species richness and carbon stocks. The diversity estimated by Shannon´s index 

also presented a significant relationship (r 2 = 0.65, P = 0.05). This proves what was expected and 

indicates that there is a relationship between floristic diversity and carbon stock in these ecosystems. 

The conservation of floristic diversity may represent an important factor in the mitigation of global 

warming, through the storage of large amounts of carbon. 

Key words: Biomass, carbon budget, diversity index, global warming, species richness, sub-humid forest, 

tropical rain forest. 

INTRODUCTION 

Among terrestrial plant ecosystems, tropical systems are 

the most diverse, productive and, at the same time, 

vulnerable to land use change (Sala et al., 2000; Siche 

and Ortega, 2008). Nowadays, and worldwide, they play 

a main role in the reduction of atmospheric CO2 

concentrations through carbon sequestration (Brown et 

al., 1989). 

Mexico still has a considerable amount of tropical 

ecosystems that may function as carbon sinks. These 

ecosystems differ, among many other aspects, in their 

floristic diversity as a result of natural or anthropogenic 

conditions. In view of all the implications of the 

management and conservation of terrestrial plant 

ecosystems, it is important to determine the relationship 

*Corresponding author. E-mail: jose.martinez@ujat.mx. 

there is between floristic diversity and carbon stocks, in 

order to provide a useful tool for decision makers. The 

way in which the diversity of species affects the 

productivity of an ecosystem will in turn, affects the 

assets and services it can provide. This will be important 

to determine whether ecosystems in future, be less 

productive and effective as carbon sinks, should a 

progressive reduction in global biodiversity occur. The 

above-ground biomass of an ecosystem constitutes a 

basic indicator of its productivity (Pearson et al., 1989). 

The organic carbon in vegetation represents half its dry 

biomass (Pearson et al., 2005). The amount and 

variability of the above-ground biomass, and thus of the 

carbon that an arboreal ecosystem may store, respond to 

the diversity and relative abundance of the species 

(Tilman et al., 1996; Hector et al., 1999; Bunker et al., 

2005). Despite many theoretical and experimental studies 

carried out to date, the relationship between species

diversity and ecosystem productivity continues to be one 

of the most controversial subjects in Ecology (Garnier et 

al., 1997; Guo and Berry, 1998; Mittelbach et al., 2001). 

In the case of plant communities, this relationship has 

been studied mostly for grassland and temperate forests. 

In the case of grassland, Tilman et al. (1996) and Hector 

et al. (1999) found that a reduction in biodiversity causes 

a reduction in productivity (biomass). In the case of 

temperate forests, An-ning et al. (2008) found a negative 

relationship between species richness and biomass for 

two communities of Quercus in China. Regarding to 

humid tropical forests, to date there are not yet 

documented case studies. 

The diversity-biomass relationship is affected by the 

environment (Guo and Berry, 1998). When environments 

are homogeneous, lineal relationships are present, and 

when environments are heterogeneous, inverted U-type 

curvilineal relationships occur. Lineal relationships may 

be positive or negative (Pianka, 1967; Silvertown, 1980). 

Guo and Berry (1998) found that the species richnessbiomass 

relationship in homogeneous environments 

characterised by shrubs was positive, negative or not 

related. Mittelbach et al. (2001) found positive 

relationships between species richness and biomass of 

vascular plants, however, the inverted U-type curvilineal 

relationship was more frequent (65%). Tropical humid 

forests include relatively homogeneous environments that 

may be compared, and in which a linear relationship 

between species diversity and biomass may be expected. 

It is a fact that at present it is necessary to conserve 

forest areas as carbon stocks, and it will always be 

important to conserve areas with the greatest possible 

diversity. But, what is the magnitude of the total carbon 

storage considering the actual diversity in the 

ecosystems that are available for this purpose? 

The questions of this study were: what is the 

relationship between the floristic diversity of tropical plant 

ecosystems and the carbon storage? The more diverse 

plant ecosystems store more carbon than the less 

diverse ecosystems? 

METHODS 

Eight localities were selected (Table 1). Plot replicates were 

randomly selected throughout in all localities. The above-ground 

biomass in the grasslands was harvested from ten 2 × 2 m subplots 

in each plot, dried at 70° C for 72 h, and the dry weight was 

estimated. The above-ground species in the localities of Rieles de 

San Jose and Veteranos de la Revolución were identified by the 

local people and verified with herbarium vouchers. The names of 

the species were obtained from Magaña (2006) and Ochoa-Gaona 

et al. (2008). The identification of the species in Yumka was carried 

out at the Universidad Juárez Autónoma de Tabasco. For La 

Cuchilla and Los Tuxtlas, leaf samples were collected and identified 

with the aid of herbarium specimens. The non-identified species 

were considered as morpho-species. 

In the case of the arboreal vegetation, the above-ground biomass 

of each tree was calculated (>10 cm dap) for each plot with the 

formulas for humid (total annual rainfall >3,500 mm) and sub-humid 

Sanchez and Cabrales 2585 

(total annual rainfall 1,500 to 3,500 mm) forests, and the lowest 

value of mean error, according to Chave et al. (2005). The formulas 

used were, for the sub-humid tropical forest: 

ln(AGB)= -1.562 + 2.148 ln(D) + ln(D) 2 + ln(D) 3 + ln(q) (1) 

For the humid tropical forest: 

ln (AGB) = -1.302 + 1.98 ln (D) + 0.207(ln(D)) 2 – 0.0281(ln(D)) 3 + 

ln(q) (2) 

Where AGB = above ground biomass, D = diametre at breast 

height (cm) and q = wood density of 0.6 g/cm 3 . 

For the case of palms, lianas and Cecropia obtusifolia, the 

following formulas were used (Pearson et al., 2005): 

Palms: AGB = 6.666 + 12.826 / height [0.5(ln(height))] (3) 

Lianas: AGB = exp[0.12 + 0.9(log (Basal area at D))] (4) 

C. obtusifolia: AGB = 12.764 + 0.2588 [D(2.0515)] (5) 

The species richness (total number of species ha -1 ), Shannon´s 

diversity index (Krebs, 1998), and the above-ground biomass were 

obtained for each locality. Data were checked out for normality and 

homogeneity of variances. The regression models between 

diversity (species richness and diversity index) and above-ground 

biomass were obtained with the values of the seven localities. 

Simple and polynomial (inverse U) regressions were tested. The 

use of the polynomial regression was accordingly to the most 

approximate regression model to the inverse U line previously 

discussed. Statgraphics Plus 4.0 was used for the analyses. 

RESULTS 

The sub-humid tropical forest 1 presented a total of 20 

species per hectare, with an above-ground biomass of 

135 t/ha. The sub-humid tropical forest 2 and 3 

(regeneration site) presented a total of 41 and 30 species 

per hectare, with an above-ground biomass of 199.7 and 

79.4 t/ha respectively. The humid tropical forests 

presented a range from 62 to 88 tree species per hectare 

and a biomass from 198.8 to 261.4 t/ha. Shannon´s index 

for the sub-humid tropical forests ranged from 3.7 to 4.0, 

and for the humid tropical forests from 4.08 to 5.31 (Table 

2). The grasslands presented 12 and 10 herbaceous 

species per hectare (> 20% area coverage), with a 

biomass of 4.5 and 5.5 t/ha respectively. The dominant 

species were the grasses Brachiara decumbens and 

Paspalum notatum respectively. 

Biomass distribution amongst the families and 

species of the sites 

Species biomass of the grasslands could not be 

obtained. Overall dominant families in biomass in the 

arboreal vegetation of all the localities were Moraceae 

(four communities), Bombacaceae (two communities), 

Tiliaceae (two communities), Anacardiaceae and 

Caesalpinaceae (Table 3). These families dominated

2586 Afr. J. Agric. Res. 

Table 1. Characteristics of the study sites in Mexico. 

Site Locality Vegetation type Location 

Sub-humid tropical forest 1 Yumka, Villahermosa, Tab. 40-year old Sub-humid tropical forest 

17° 59’ - 18° 00’ N, 92° 

47’ - 92° 49’ W 

Annual average 

temperature (°C) 

Annual average 

rainfall (mm) 

Number (and size) 

of sampled plots 

26.9 2,159.3 3 (50 × 50 m) 0.75 

Sub-humid tropical forest 2 La Cuchilla, Balancan, Tab. 20-year old Sub-humid tropical forest 17° 47’ N, 91º 13’ W 28.0 1,500.0 25 (10 × 10 m) 0.25 

Sub-humid tropical forest 3 La Cuchilla, Balancan, Tab. Mature Sub-humid tropical forest 17° 47’ N, 91º 13’ W 28.0 1,500.0 25 (10 × 10 m) 0.25 

Humid tropical forest 1 


Rieles de San Jose, Tenosique, 

Tab. 

Veteranos de la Revolución, 

Tenosique, Tab. 

Mixture of mature forest and 10-20 yearold 

Humid tropical forest 

Mixture of mature forest and 10-20 yearold 

humid tropical forest 

Humid tropical forest 3 Los Tuxtlas, Ver. Mature Humid tropical forest 

Grassland 1 Yumka, Villahermosa, Tab. Grassland 

Grassland 2 Yumka, Villahermosa, Tab. Grassland 

17° 19’ 00’’ N, 91º 21’ 

15’’ W 

17º 23’ 30’’ N, 91º 21’ 

00’’ W, 

18° 34’ - 18° 36’ N, 95° 

04’ - 95° 09’ W 

17° 59’ - 18° 00’ N, 92° 

47’ - 92° 49’ W 

17° 59’ - 18° 00’ N, 92° 

47’ - 92° 49’ W 

Table 2. Values of diversity and above-ground biomass for six arboreal communities and two 

grasslands in southeastern Mexico. 

Site No. of species ha -1 Shannon index Biomass (t/ha) 

Sub-humid tropical forest 1 27 4.00 135.0 



Humid tropical forest 1 66 4.55 198.8 



Grassland 1 12 4.5 

Grassland 2 10 5.5 

26.0 3,300.0 13 (30 × 30 m) 1.17 

26.0 3,300.0 13 (30 × 30 m) 1.17 

25.1 4,487 3 (50 × 50 m) 0.75 

Sampled 

area (ha) 

26.9 2,159.3 30 (2 × 2 m) 0.012 

26.9 2,159.3 30 (2 × 2 m) 0.012

almost with the same species elsewhere. In the 

sub-humid tropical forest 1, only two species 

accounted for more than 60% of the total 

biomass. In contrast, in the humid tropical forest 1 

for example, tree biomass spread over many 

species with small values. In all the communities 

biomass dominant species were not strictly tree 

density dominant species. Species with the 

highest tree densities had intermediate or low 

biomass values. 

The relationship between species richness 

(number) and biomass in these plant communities 

was significant with a positive linear relationship, 

and for the inverted U curve polynomial model 

(Figure 1a and b). The relationship for diversity 

and biomass obtained with Shannon´s index as 

an estimator of diversity may also be considered 

as significant (Figure 2). 

DISCUSSION 

A possible minor baized could occur in the 

species and biomass values owing to the different 

plot size used throughout the localities in the 

present study. However this baized may be 

negligible. In the grassland 30 × 30 m plots were 

set up. This was because the herbs are small-size 

plants and large trees that require large sampling 

plots were almost absent. In the secondary 

forests, the baized could be greater owing to the 

relatively small sampling plots (10 × 10 m). 

However due to the larger number of plots 

sampled this could be sufficiently overcompensated 

(Gentry, 1990). Two grassland 

localities were considered sufficient owing to the 

low biomass variation of this vegetation type. A 

second biased in the study could be owing to the 

use of a mean value of wood density (0.6 g/cm 3 , 

Pearson et al., 2005) to obtain the tree biomass. 

Since many particular species from these 

localities do not have a literature value yet and the 

reported mean value is obtained from a larger 

sample of species and worldwide accepted, it was 

decided to maintain it. 

Site biomass tended to concentrate in species 

of large trees. However, it seems that overall, 

biomass values among the vegetation types differ 

more because of species richness than because 

of species composition. Dominant species of the 

topical humid forests accounted for the highest 

community biomass values, but there were a large 

number of species with lower biomass values that 

overall, accounted more for the community total 

biomass than dominant species. 

A relationship was found between floristic 

diversity and biomass (carbon stocks) in the 

tropical plant communities. This relationship was 

positive and slightly more significant for the 

species richness than for the Shannon´s diversity 

index. It may be that a greater number of localities 

would also have provided a more significant result 

with the diversity index. These relationships were 

slightly more significant with the lineal regression 

model than with the inverted U quadratic model. A 

complete inverted U line was not observed, surely 

because a greater number of localities are 

required. As Guo and Berry (1998) indicated, 

ecosystems in homogeneous climates tend to 

present a lineal relationship between diversity and 

productivity. Tropical ecosystems have a 

homogeneous climate with relatively 

homogeneous temperature and rainfall values 

throughout the year. 

Given that carbon stocks is half of the biomass, 

the linear regression between biodiversity and 

biomass in the tropical ecosystems indicates that 

the conservation of biodiversity deserves the main 

importance, when considering a tropical 

ecosystem or a forest area with the purpose of 

carbon storage. This may contribute to the 

management of natural areas by simplifying 

decision making. 

In natural communities, the positive relationship 


between species diversity and productivity has 

been proved by many experiments, mainly for 

herbaceous species, aided by the advantage of a 

faster growth and easy manipulation (Wardle, 

1999; Schwartz et al., 2000; Diaz and Cabido, 

2001; Loreau et al., 2001; Schmid et al., 2002; 

Hooper et al., 2005; Spehn et al., 2005; Fargione 

et al., 2007). Positive relationships occur 

principally when there are new species that 

increase productivity (Mittelbach et al., 2001). 

A tendency may also be seen (P = 0.03, F = 

9.52) towards the commonly accepted inverse U 

relationship that is observed in most plant 

communities, probably because this type of 

relationship is more common in heterogeneous 

than in homogeneous environments (tropical 

areas) (Guo and Berry, 1998). In this case, the 

sub-humid tropical forest represents the greatest 

value of the slope by having, with only 20 and 41 

species (Yumka and La Cuchilla, Table 1), a 

slightly smaller or equal biomass than that of 

humid tropical forests, where the number of 

species is much greater. This also indicates the 

importance of conserving the presently reduced 

sub-humid tropical forests. The basic difference 

between these two types of tropical forests is the 

shorter dry season and greater rainfall in the 

humid tropical forest, compared with the subhumid 

tropical forest. 

It has been proposed that the non-observation 

of the peak in the inverted U relationship (straight 

line) in natural communities responds to the 

absence of a wide availability of resources (Marrs, 

1999; Mittelbach et al., 2001). In the case of 

natural communities, Gough et al. (2000) 

proposed that the inverted U relationship occurs 

after long ecological processes such as the 

colonization of new species, for which reason, in 

general, only the ascending part of the curve may 

be seen. However, Whittaker and Heegaard 

(2003) said that this widely accepted biomassdiversity 

relationship has been overestimated


Table 3. Biomass dominant species and families in the sites. 

Site Dominant families Dominant species Contribution of total biomass (%) 

Sub-humid tropical forest 1 






Grassland 1 

Grassland 2 

n.d. = not determined. 

Moraceae Brosimum alicastrum Sw. 41.0 

Caesalpinioidae Dialium guianense (Aubl.) Sandw. 20.7 

Bombacaceae Ceiba pentandra (L.) Gaertn. 21.0 

Combretaceae Bucida buceras L. 19.1 

Moraceae Ficus sp 16.5 

Combretaceae Bucida buceras 9.6 

Mimosoideae Acacia sp 8.0 

Anacardiaceae Spondias mombin L. 7.9 

Moraceae Brosimum alicastrum 6.8 

Bombacaceae Pseudobombax ellipticum (Kunth) Dugand 6.3 

Tiliaceae Trichospermum mexicanum (DC.) Baill. 5.4 

Tiliaceae Trichospermum mexicanum 16.2 

Moraceae Psuedolmedia oxyphyllaria Donn. Sm. 14.4 

Bombacaceae Pachira aquatica Aubl. 9.8 

Bombacaceae Pseudobombax ellipticum 8.0 

Moraceae Nectandra ambigens (S.F. Blake) C.K. Allen 18.8 

Anacardiaceae Spondias radlkoferi Donn. Sm. 11.2 

Poaceae 

Fabaceae Mimosa pigra 

Poaceae 

Bachiaria decumbens L. 

Axonopus compresus Sw. 

Cynodum dactylon (L.) Pers 

Eulosine indica 

Paspalum notatum Flugg 

Paspalum notatum Flugg 

Panicum 5aximum 

Andropogon bicornis L. 

Euphorbiacea Euphorbia harta 

Fabaceae Phaseolus latyroides 

n.d. 

n.d.

Biomass (t/ha) 

300 

250 

200 

150 

100 

50 

0 

0 20 40 60 80 100 

Species number 

Figure 1a. Linear relationship of the number of species per hectare and 

above-ground biomass in two tropical grasslands and six tropical forests. r 2 = 

0.82, P = 0.001, F = 34.0. Model: Biomass = 0.675896 + 3.20147 * No. of 

species. 


300 

250 

200 

150 

100 

50 

0 

0 20 40 60 80 100 

Species number 

Figure 1b. Polynomial relationship of the number of species per hectare and 

above-ground biomass in two tropical grasslands and six tropical forests. r 2 = 

0.84, P = 0.002, F = 25.5. Model: Biomass = -56.875 + 6.77745* No. of 

species - 0.03801 * No. of species ^ 2. 

by data analysis. Grime (1973) proposed that the 

descending part of the inverted U responds to 

environmental stress and a high inter-specific competition 

that decrease the productivity of a wide variety of 

species. 

The fact that the arboreal biomass increases 

progressively with an increase in the diversity of arboreal 

species has important practical implications, as for 

example: it is advisable to maintain an ecosystem with its 


greatest diversity to ensure a greater productivity and 

profit for the ecosystem. The more diverse an ecosystem, 

the more effective in providing important environmental 

services, as is the specific case of the carbon stocks. The 

relationship between greater ecosystem productivity and 

greater diversity has two possible explanations (Aarsen, 

1997; Huston, 1997; Tilman et al., 1996, 1997; Loreau, 

2000). One is “the sampling effect” in which a greater 

productivity responds to the greater probability of occur-



300 

250 

200 

150 

100 

50 

0 

3.7 4 4.3 4.6 4.9 5.2 5.5 

Shannon index 

Figure 2. Linear relationship for diversity (Shannon Index) and aboveground 

biomass in tropical grassland and six tropical forests. r 2 = 0.65, P = 

0.05, F = 7.34. Model: Biomass = -187.69 + 86.032 * Shannon index. 

ence of one or several dominant species in the sampling 

units. The other is “the complimentarily of the niche” in 

which the sharing of resources by the different species 

results in a greater productivity in the community. The 

possibility of use of the same resource by more species 

(sharing) gives the possibility of a higher number of 

species in the community and then of biomass. If the 

polynomial regression (inverted U curve) is considered 

the one that best explains this relationship in arboreal 

communities, the greatest value of the positive slope 

(lower number of species and higher arboreal biomass) 

was recorded for the sub-humid tropical forest in Yumka. 

This may indicate that the sub-humid tropical forests 

have a relatively greater productivity with a lower number 

of species, in comparison with the humid tropical forests. 

This is of great importance in the conservation of tropical 

ecosystems also as, in the case of Mexico, the subhumid 

tropical forests cover at present the smallest area 

and has been strongly eradicated from their original 

distribution (Rzedowski, 1978). From the point of view of 

arboreal ecosystems as carbon stocks, the sub-humid 

tropical forests are of great importance. 

It may be concluded that, as the theory states, a 

relationship between diversity and productivity was 

recorded for the humid tropical vegetation of Mexico. This 

relationship was directly proportional (ascending straight 

line) as predicted for homogeneous environments. This 

means that carbon stocks in these highly productive 

terrestrial ecosystems will be more efficient when there is 

a greater diversity of arboreal species, and that the 

environmental service of carbon sequestration will be 

markedly favored by the preservation of a greater floristic 

diversity. 

ACKNOWLEDGEMENTS 

The authors thank M.C. Jesús Ascencio (UJAT) for the 

identification of the arboreal species of the Yumka park. 

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DOI: 10.5897/AJAR11.728 



Comparison of automated and manual landform 

delineation in semi detailed soil survey procedure 

Kamran Moravej 1 , Mostafa Karimian Eghbal 1 *, Norair Toomanian 2 and Shahla Mahmoodi 3 

1 Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. 

P.O. Box: 14115-111, Tehran, Iran. 

2 Agricultural and Natural Resource Research Station, Isfahan, Iran. 

3 Department of Soil Science, Faculty of Soil and Water Engineering, Tehran University. Iran. 

Accepted 13 July, 2011 

ASTER DEM data was used to automate landform classification during soil survey in the Varamin area. 

For comparison, manual landform classification was done in the same area. Study area was located at 

South of Jajrood river watershed, Southeast of Tehran province (Iran). The main purpose of this study 

was to compare the effect of automated and manual landform classification methods in semi-detailed 

soil survey procedure. Eight geomorphometric parameters were extracted from DEM using the TAS and 

DiGem software. The Pearson correlation coefficient analysis elucidated that, the most effective of 

parameters were: analytical hill-shade, plan and profile curvature, and slope and divergenceconvergence 

index. In addition to these terrain attributes, principal component analyses (PCA) of 

primary geomorphometric parameters were produced to increase the quality of classification and to 

reduce modeled data. First three PCAs cover 97% of variance of the data. These PCAs and mentioned 

terrain parameters were selected for performing of K-means unsupervised landform classification 

model. Results indicated that unsupervised and manual classification can be complemented, such that 

conflation of the final maps obtained by these methods can produce a more accurate map. Also, the Kmeans 

algorithm with correct iterations, tolerance and suitable number of classes can be used for 

automated landform classification as well. Hybrid landform classification method is useful for soil 

survey and soil mapping especially, in watersheds and natural resource fields. 

Key words: Hybrid landform classification, geomorphometric parameters, K-means classifier, Pearson 

correlation coefficient. 

INTRODUCTION 

Landforms are considered a central concept for soil 

development. It influences soil distribution, properties and 

processes that occur in soil pedon and catena. In 

different parts of the world, many studies have been 

carried out to show the relationships between landform 

elements and soil distribution. In soil survey and soil 

mapping procedures, the geomorphologic processes in 

delineating the landscape is inferred to find the controller 

process and cause of different types of soil. Besides, 

extracting soil-landscape relationships helps surveyors to 

*Corresponding author. E-mail: mkeghbal@modarse.ac.ir. Tel: 

+98912-2143795, +98 21-44108794. Fax: +98 21-48292273. 

judge and infer their tacit knowledge into modeling steps. 

A fundamental objective in geomorphologic clustering is 

to extract and classify landform units. These units provide 

primary view about the distribution of soil units. Most of 

the environmental processes depend on topography 

(Hugget and Cheesman, 2002) and if topography 

remains uniform, then the processes affect mostly the 

earth crust. 

There are different approaches to define and describe 

the landscape divisions (geomorphic units). These terms 

used in different approaches are more or less the same 

(Wilson and Gallant, 2000). Landform units are formed 

due to different geomorphic, hydrologic and pedologic 

processes in each landscape. Thus, based on the 

approach of Zink (1988), large areas are divided to

geomorphologic units such as landscape, landform and 

geomorphic surfaces. Each of them may be composed of 

one or several types of soil unit. Contiguous 

environmental processes make delineation and mapping 

of geomorphic or soil units difficult. Similarity in 

geomorphic units indicates uniformity in soil forming 

processes, therefore, we will be able to delineate uniform 

soil mapping unit. This induces that mapping topography 

will help us to delineate the soil units also (Etzelmüller 

and Sulebak, 2000). Different techniques from automatic 

(supervised and unsupervised algorithm) to hybrid (semiautomated) 

classification methods were used in landform 

classification for modeling of soil characteristics and 

spatial distribution of types of soils. In this context, 

improvements in Geographic Information Systems (GIS) 

and terrain analysis techniques allow developing soil 

survey models based on different new concepts such as 

geomorphometry. DEMs are used for analysis of 

topography, landscapes and landforms and extracting 

terrain attributes (Bishop and Shroder, 2000; Tucker et 

al., 2001). Many geomorphometric parameters are 

derived from DEMs. 

These parameters are used for automated landform 

classification and are more correlated with current digital 

soil mapping than conventional soil surveying. These 

morphometric attributes are divided to primary terrain 

attributes (for example, slope, aspect, elevation, plan 

curvature, profile curvature, total curvature, tangential 

curvature, surface curvature index, and shaded relief etc) 

and compound terrain attributes (for example, 

topographic wetness index, sediment transport capacity, 

and composite relief model etc) (Gallant and Wilson, 

2000). Both of them can be used to predict surface and 

sub-surface processes through automated landform 

classification. Digital soil mapping can be performed in 

different scales: from large (1:5,000) to small scales 

(1:500,000) depending on vertical and horizontal 

resolution of produced DEMs (Gallant and Wilson, 2000). 

Recently, studies on these topics has increased due to 

availability of high-resolution DEMs produced by different 

satellite data and software packages of terrain analysis 

systems (McMillan et al., 2003; McBratney et al., 2003; 

Smith et al., 2006). Many studies have shown that 

changes in goemorphometric parameters can cause 

intrinsic differences in elucidating the spatial distributions 

of the landform and soil units (Gallant and Wilson, 2000). 

Ventura and Irvin (2000) classified landform by 

applying the iso-clustering unsupervised classification 

method using six geomorphometric parameters. Their 

study showed that automated segmentation landform can 

separate units more detail than the conventional method. 

Moreno et al. (2005) also classified landscape 

automatically using GIS into landform elements based on 

geomorphometry. The results indicated that it is less time 

consuming with a rewarding conclusion compared to 

manual methods. 

Mousavi et al. (2007) assessed in their studies the 

Moravej et al. 2593 

extracted geomorphometric parameters from ASTER 

DEM with PCI GEOMATICA software in Damavand 

region (Iran). They derived five parameters which are 

useful in identifying and describing process and 

geomorphologic units: height, slop, aspect, vertical 

curvature and tangential curvature. These researchers 

concluded that ASTER DEM data, according to their 

technical specification and features, are appropriate for 

interpretation and production of geomorphic data in 

macro and meso scales, and only provide the possibility 

of topography in medium scales (1:100000 and 1:50000). 

Barka et al. (2011) used landform classification in 

predictive soil mapping at the forest area in Slovakia. 

Their evaluation indicated that terrain classification is one 

of the methods which can be used suitably in delineation 

of pedological and forestry units. Hengl and Rossiter 

(2003) used maximum likelihood classifier for landform 

classification to enhance the process and replace aerial 

photo interpretation in semi-detailed soil surveys. They 

used nine geomorphometric parameters extracted from 

DEM with a 10 m cell size to model landform 

classification. The result indicated that their methodology 

can be applied to update current maps and to enhance or 

replace aerial photo interpretation for new surveys. 

Umali et al. (2010) used a simple method to predict 

spatial pattern of soil organic carbon (SOC) using a 

substitute variable, soil color and digital terrain analysis in 

East of Adelaide, South Australia. They derived seven 

geomorphometric parameters from the DEMs (specific 

catchment area, profile curvature, wetness index, slope, 

plan curvature, sediment transport capacity index and 

tangential curvature). Also, they identified one hundred 

random points across the study area and value 

component of soil color was obtained. Spearman rank 

correlation analysis showed that elevation, specific 

catchment area, profile curvature and wetness index 

affects value component of soil color. They also found 

that the application of logicalness algorithms to DEMs 

derived from contour line maps produced better 

correlation coefficients as compared to unsmooth DEMs. 

With this background, it is assumed that quantitative 

geomorphology can be used for delineating landform 

units within a soil surveying procedure. Thus, the 

objectives of this study are: 1) to what extent the 

geomorphometric parameters and automated landform 

classification method can be used to replace 

physiographic classification method traditionally used for 

Iranian soil survey and soil mapping? 2) to what extent 

can satellite data from Google Earth replace aerial photo 

interpretations in soil survey and geomorphological 

studies? 

MATERIALS AND METHODS 

Condition of study area 

The study area is located on the Southern slopes of Alborz range, 

40 km South-east of Tehran (Figure 1). It is part of the major alluvial


Figure 1. Location of the study area in Southeast of Tehran province, Iran. 

fan formed by Jajrood river watershed between 35˚20΄26˝ and 

35˚31΄09˝ N latitude and 51˚41΄52˝ and 51˚52΄32˝ E longitude. 

Mean elevation of region is 1250 m above sea level (1463 to 

950M). Variations of elevation were represented by Hypsometric 

map (Figure 2). Soil moisture and temperature regimes are weak 

aridic and thermic respectively. Topography of such regions is 

predominantly flat with the exception of the Northern part. General 

slope of the area runs from North to South. Soils of the region are 

generally classified (Moravej et al., 2003) in two orders of Entisols 

and Aridisols (USDA, 2010). The Varamin plain represents an intermountain 

basin that is bounded on the North by Alburz mountain 

range and on the South by Siah kuh range. Study area consists of 

Paleozoic and Mesozoic sediments and Eocene volcanic, which are 

covered with young Tertiary and Quaternary deposits of the Jajrood 

river. The river deposits, mainly from Pleistocene Epoch, are more 

than 300 m thick in some places and, through their sandy nature, 

represent an important reservoir of good quality groundwater. At the 

apex of the alluvial fan, the Jajrood river diverges into a large 

number of branches and its sediment-carrying capacity thus 

diminishes. Coarse and very coarse sediments therefore, occur at 

the apex; while farther downstream the sediments gradually 

becomes finer, passing into loam and silt in the lower parts of the 

fan. 

AUTOMATED LANDFORM CLASSIFICATION METHOD 

Pre-processing of the DEM 

The DEM of study area was downloaded form the ASTER GDEM 

web site. There are different acceptable procedures for producing, 

editing and correcting of DEM before starting the extraction of 

goemorphometric parameters (Lindsay, 2005; Liu et al., 2006). 

Improving methods more or less depends on the way the DEM is 

extracted (satellite data or contour lines). The procedures used in 

this research to increase the quality of DEM before extraction of 

geomorphometric parameters were: re-sampling of the ASTER 

DEM to 14 m to exploit full ortho image resolution. Then, DEM data 

was searched for sink. 9010 sinks were detected that filled by 

DiGem software to improve the quality of the DEM. Flat area 

drainage enforce command was performed to the DEM by TAS 

software. Without this, flow routing algorithms are unable to identify 

down slope neighbors in these areas and flow routing ceases. This 

command adds very small elevation adjustments to flat areas (that 

is, cells where the lowest neighbors have equal elevation) from the 

'pour point' backwards, so that flow routing algorithms can work 

properly. The algorithm used in this module is taken from Jenson 

and Dominique (1988). The study area is mostly flat to gently 

sloping except in the Northern part. So, it was important to know 

what the variation in elevation is within a grid cell size distance from 

the centre cell in a window. Consequently, a circular shaped 

window with 5 × 5 size was used for mean filtering of the raster 

DEM. A circular shape window seems more suitable than a square 

because the boundary of a circle will always be of equal distance to 

the focal point (Brabyn, 1998). 

Derivation of geomorphometric parameters 

All the important primary and secondary terrain attributes were

Figure 2. Hypsometric map of the study area. 

extracted using TAS Version 2.0.9 (Terrain Analysis Systems), a 

Software called White box GAT (Geospatial Analysis Tools) 

developed by Lindsay (2005) and DiGem software produced by 

Conrad (2002) of the Gottingen University. The extracted 

parameters which have been mapped separately are: Aspect, 

slope, curvature, Maximum Down Slope (MDS), Sediment 

Transport Capacity Index (STCI), Shaded Relief (SHR), Wetness 

Index (WI) or Topographic Index, Analytical Hill shade (AH), 

Divergence-Convergence Index (DCI), Plan Curvature (PL.C) and 

Profile Curvature (PR.C). 

Correlation among the geomorphometric parameters 

Some geomorphometric parameters have some correlation and 

contain similar information. When two parameters have positive 

correlation (between 0 to +1) it shows that increase in one 

parameter resulted from the increase in another one, and vice 

versa. Also, it shows that the presence of high correlated coefficient 

between two parameters means that there are some similarities in 

the data. To ordinate the geomorphometric parameters, the 


Pearson product-moment correlation coefficient (r) was computed 

between these parameters by White box GAT software. The 

primary correlation coefficients among mentioned extracted 

parameters vary from -0.09 to 0.99. So, high positive correlated 

parameters were omitted including: Curvature, MDS, and SHR. 

Pearson correlation coefficient was computed between the 

remained parameters [Aspect, Slope, Sediment Transport Capacity 

Index (STCI), Wetness Index (WI) or Topographic Index, Analytical 

Hill shade (AH), Divergence-Convergence Index (DCI), Plan 

Curvature (PL.C) and Profile Curvature (PR.C)]. For the second 

time (Table 1). As can be seen in this table, r varies from -0.291 to 

0.55. 

Principal components analysis (PCAs) of eight geomorphometric 

parameters was extracted to increase the quality of classification 

and to reduce the data. The outputs of correlation matrix extracted 

by PCA and Pearson Correlation Coefficient (r) are exactly similar. 

So, principal components analysis was done for producing images 

that had maximum variance data. Subsequently, three parameters 

that had relatively high correlation with other parameters (STCI, WI, 

Aspect) were omitted and replaced by PCA1,2,3 that had the highest 

information variance (97% approximately).


Table 1. The Pearson correlation coefficient (r) matrix. 

Parameter Aspect AH DCI PL.C PR.C Slope STCI WI 

Aspect 1 -0.291 0.210 0.051 0.036 0.357 0.209 0.550 

AH 1 0.037 0.029 0.008 0.261 0.177 0.219 

DCI 1 0.519 0.572 0.181 0.005 0.146 

PL.C 1 0.463 0.123 -0.069 0.113 

PR.C 1 0.035 -0.096 -0.002 

Slope 1 0.736 0.470 

STCI 1 0.384 

WI 1 

AH: Analytical hill shade, DCI: divergence-convergence index, PL.C: plan curvature, PR.C: profile curvature. 

Table 2. Geomorphic legend showing automated landform classification. 

Landscape Relief Lithology Landform Code 

Alluvial fan 

Hill land 

Low 

Coarse alluvial sediments over red marl with 

intercalations of well bedded sandstone. 

Middle-texture alluvial sediments over old gravel fan 

quaternary. 

Upper alluvial fan Af 111 

Middle alluvial fan Af 121 

Very low Fine alluvial sediments. Lower alluvial fan Af 211 

Low rolling hills 

Gray conglomerate with marl cement. Complex facet hillside Hi 111 

Light gray to light red alternation of conglomerate, 

sandstone with silt. 

Moderate (steeply dissected) Gray conglomerate with marl cement. 

Automated landform classification 

Selected terrain parameters (analytical hillshade, plan and profile 

curvature, slope and divergence-convergence index) and PCA1, 2, 3 

were treated as a single band images and using a K-mean 

unsupervised algorithm, the landforms were classified. This method 

finds statistically similar groups in multi spectral space during its 

analysis. The algorithm starts by randomly locating k clusters in 

spectral space. Each pixel in the input image group is then 

assigned to the nearest cluster centre and the cluster centre 

locations are moved to the average of their class values. This 

classification is then repeated until a stopping condition is reached. 

The stopping condition may either be a maximum number of 

iterations or a tolerance threshold which designates the smallest 

possible distance to move cluster centers before stopping the 

iterative process. In this approach, the determinative parameters 

were 10, 15 and 5 respectively, for the number of predictive class, 

maximum iteration and change tolerance. Post processing of this 

primary classification was done in ArcGIS software. Process was 

consisted of performance majority filtering and other cartographic 

rules. 

Manual landform classification 

Manual landform classification was done using Google Earth 

Complex facet hillside Hi 121 

Steeply dissected hillside Hi 211 

Moderately steep slope hillside Hi 212 

images, Geological maps (scale: 1:100000) and field works. To 

delineate the landforms and description of each unit, we have used 

the categories presented by Zink (1988) which clearly relates 

landforms to soil units. Legend of the delineated landforms is also 

defined. The primary map was exported to ArcGIS using KLM 

extension and final map was edited. 

Evaluation 

Classification algorithms can be evaluated by different methods. 

The most common methods are cross tabulation and error matrix 

analysis. The outputs of these tables are Kappa index, Chi square, 

Crammers V, Overall accuracy and etc. evaluation process was 

used to compare the frequency of cells belonging to all landform 

units within manual and automated landform classifications and for 

the purpose of accuracy assessment. 

RESULTS 

An automated landform classification map was produced 

at a scale of 1:50000. The result was a map with seven 

classes (Figure 3). Table 2 shows hierarchical landform

Land classification map 

Figure 3. Automated landform classification of study area. 

classification based on Zink method. The results 

indicated that diverged landforms mostly vary by variation 

of only two geomorphometric parameters (slope and hill 

shade). Although, some other parameters have more 

influence than others in different parts of the study area. 

For example, Af 121, Af 211 and Af 111 units were 

mostly separated by slope and partly hill shade factor in 

alluvial fan landscape. But, all of the used terrain 

parameters simultaneously affect the separation of 

landform units in hilly landscape with different intensities. 

Although, Analytical hill shade parameter has more 

influence in some landform units located in hilly 

landscape. 

In the manual method, eight soil-landscape units (three 

in the piedmont area and five in the hilly landscape) were 

identified using the Zink method (Figure 4 and Table 3). 

Cross tabulation was used for accuracy analysis and 

evaluation of manual and unsupervised classification 

(Table 4). Comparison of these methods indicates that: 1) 

Af 211 unit in manual method is divided into three 


different landform units (Af 111, Af 121 and Af 211) in the 

automated classification; 2) Hi 211 unit in unsupervised 

landform classification was not separated in the manual 

segmentation; 3) Hi 121 and Hi 212 units were delineated 

with relatively good accuracy in manual and automated 

methods, respectively; 4) Hi 211 and Hi 212 in the 

manual method were integrated to Hi 121 and Hi 111 in 

automated method, respectively; 5) Hi 111 unit in the 

manual method is divided into two units (Hi 111 and Hi 

121) in the automated classification mainly by differences 

in analytical hill shade parameter; 6) Af 111 unit in 

automated segmentation was divided to unit (Af 111 and 

Af 121) in manual segmentation. Although, Af 111 and Af 

121 are similar in terrain parameters, but they are 

different in evolution processes and it is better that they 

are separated. Important point to consider in comparing 

the two methods is manual and unsupervised 

classification can be used complementally for areas that 

are unavailable or study was limited point of cost and 

time.


Table 3. Geomorphic legend showing manual landform classification. 

Landscape Relief Lithology Landform Code 

Alluvial fan 

Hill land 

Low 

Coarse alluvial sediments over red marl with 

intercalations of well bedded sandstone. 

Very coarse-texture alluvial sediments over 

young gravel fan quaternary. 

Upper alluvial fan Af 111 

Young alluvial fan Af 121 

Very low Middle and fine alluvial sediments. Lower alluvial fan Af 211 

Low rolling hills 

Gray conglomerate with marl cement. Complex facet hillside Hi 111 

Light gray to light red alternation of 

conglomerate, sandstone with silt. 

Moderate (steeply dissected) Gray conglomerate with marl cement. 

Table 4. Cross-tabulation for manual and automated landform classification. 

Unsupervised classification 

Manual classification 

Complex facet hillside Hi 121 



Af 111 Af 211 Af 121 Hi 211 Hi 121 Hi 111 Hi 212 

Af 111 345 1353 1086 0 9 40 0 3563 

Af 211 0 13144 116 0 0 0 0 11260 

Af 121 0 3702 730 0 0 0 0 5702 

Hi 211 35 0 0 0 142 85 0 333 

Hi 121 0 0 0 71 908 290 14 147 

Hi 111 0 0 291 140 54 309 84 3962 

Hi 212 1 2071 0 0 794 4 0 852 

Total 381 20270 2224 211 1907 728 98 25819 

Kappa index: 0.507, overall accuracy: 0.596, Crammer's V: 0.7205. 

DISCUSSION 

It is logical to assume that increases of one 

geomorphometric parameter can cause a decrease or 

increase in other parameters; especially, compound 

terrain attributes that are extracted from primary terrain 

attributes. Some of these parameters correlate with each 

other and have positive or negative co-relationships. At 

the same time, some geomorphometric parameters have 

the same basic mathematical formulas which describe 

relationships between them. It is clear that, correlation 

between some geomorphometric parameters in one area 

can not be used for other areas (Debella-Gilo et al., 

2007). Thus, in each area, depending on the natural 

dominant process, one can find different relationships 

between geomorphometric parameters. 

The ASTER DEM data is a suitable source for 

derivation of geomorphometric parameters (Kamp et al., 

2003), automated landform classification and soil survey 

at a medium scale (1:50000) or order 3 to 4 and smaller 

Total 

scale soil surveys (Soil Survey Division Staff, 1993, 

Tables 2 and 1). However, in areas with less relief or flat 

topography, in addition to ASTER DEM, use of other data 

is suggested, such as remote sensing data and their 

indexes for increasing the accuracy of outputs. In this 

study, the accuracy of landform map decreases with 

reduction of relief. In order to obtain better results, field 

observations, consumption of more time and money is 

essential. The ASTER DEM data are available freely for 

many parts of the world including Iran. 

The Google Earth images have many advantages for 

landform classification as compared with traditional aerial 

photos: 1) its images are colored instead of black and 

white photos and as such, interpretation of landform data 

is easier; 2) Google earth images have more temporal 

resolution than aerial photos; 3) the images and its 

related data are available everywhere and every time; 4) 

it has capability link to ArcGIS software easily and 

produced layers that have coordinate system in Lat/Long 

and UTM format; 5) some of primary cartographic errors

Manual landform classification 

Figure 4. Landform classification map using the manual method. 

can be corrected for these images; 6) no stereoscope is 

needed for a 3D-View; 7) it is more economical, low cost 

and saves time as compared with aerial photos. Hengl 

and Rossiter (2003) used aerial photo interpretation for 

supervised landform classification. They gave some 

limitations and ways to overcome them. 

The advantages of the automated landform 

classification (Dikau et al., 1991) as compared with the 

manual method are: 1) it can provide a more detail 

geomorphic map, soil survey and soil classification if the 

data and parameters used for the classification are 

accurate and proportional to topographic variations of the 

study area; 2) if suitable algorithm classification was 

selected, classification can result in more accurate 

output; 3) it can be used for quantitative studies of the 

relationship between geomorphometric parameters and 

surface processes; 4) it can easily be processed into 

different GIS softwares, and it is easily exportable, 

importable and interpretable. Unfortunately, the terrain 

attributes to be used as parameters for the supervised 

and unsupervised classification do not have the capability 

for standardization for everywhere. So, the validity of the 


classification is dependent on its ability in showing the 

changes in geomorphic (Debella-Gilo et al., 2007); 5) 

automated landform classification and selected terrain 

parameters can be easily tested to other areas that have 

similar topography and geomorphology (Brabyn, 1998). 

Unsupervised classification algorithms without field 

observations cannot always result in correct 

classifications. The best method is to use the manual and 

unsupervised classification together (Hybrid 

classification). Hybrid landform classification method and 

use of Google Earth data as a color composite image of 

geomorphometric parameters are very useful for soil 

survey and soil mapping, especially, for developing 

countries. Automated classification makes more detailed 

output than traditional semi detailed survey. Some of the 

Iranian soil maps are old and they need to be updated, so 

that use of such methods can be very useful and 

economical. 

For many years, delineation of physiographic units has 

been the basis for soil survey in Iran. In such 

delineations, less attention is being paid to geomorphic 

landforms and processes. Geomorphologic studies are


very important and it is essential that soil scientists are 

aware of geomorphic relations to soil formations and 

distributions (Alavipanah et al., 2009). Geomorphometric 

parameters can be used successfully for soil survey and 

results can be more accurate if they were used in 

association with geomorphologic studies. 

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DOI: 10.5897/AJAR11.1206 



Effect of nitrogen and potassium fertilizer on yield, 

quality and some quantitative parameters of flue-cured 

tobacco cv. K326 

Ali Reza Farrokh 1 *, Ibrahim Azizov 2 , Atoosa Farrokh 3 , Masoud Esfahani 4 , Mehdi Ranjbar 

Choubeh 5 and Masoud Kavoosi 6 

1 Young Researchers Club, Rasht Branch, Islamic Azad University, Iran. 

2 Institute of Botany, National Academy of Science, Republic of Azerbaijan. 

3 Islamic Azad University, Branch Qazvin, Iran. 

4 Scientific Board of Guilan University, Iran. 

5 Tobacco Research Institute, Iran. 

6 Scientific Board of Rice Research Institute, Iran. 

Accepted 2 March, 2012 

In order to investigate the effect of nitrogen and potassium fertilizers on yield, quality and some 

quantitative parameters of flue-cured tobacco, 2 year experiment was carried out in tobacco research 

institute of Rasht located in Guilan province on factorial based with 3 replications. The applied fertilizer 

levels included 35 (N1), 45 (N2), 55 (N3) and 65 (N4) kg N/ha of pure nitrogen of urea source and 

potassium in two levels of 150 (K1) and 200 (K2) kg K/ha of potassium sulphate. The measured 

parameters in this experiment included leaf dry weight, leaf wet weight, stem height, stem diameter, leaf 

number in plant, leaf length, leaf width, stem dry weight, biomass, nicotine and sugar content. The 

effect of year on parameters such as leaf dry weight, leaf wet weight, stem height, leaf number, leaf 

length, stem dry weight, biomass and sugar content was significant (P


class of neutral (non aromatic) tobaccos which has a little 

direct relation with the name of Virginia State of USA. 

Dried flue- cured tobacco is the only source of a special 

British cigarette which is different from other European- 

American common tobacco (Virginian). The stronger 

degrees of tobacco mostly were used for pipe tobacco 

(Ranjbar, 2005). Western types of tobacco belong to fluecured 

tobacco which sufficient water and nutrients are 

needed for its cultivation and fertilizing lands by chemical 

fertilizers are necessary and inevitable. The main reason 

of using fertilizers is not just raising the crop yield and 

produced tobacco quality has also its own importance 

(Shamel, 1995). Fertilizer is a compound which directly 

and indirectly improves plant growth, tobacco quality 

degree and finally farmers’ income. The farmers should 

be very aware of the quality of used fertilizers. In tobacco 

cultivation contrary to other crops, disproportionate 

fertilizers usage has not only positive effect, but also to 

some extent decreases tobacco quality and imposes 

economical damage on farmers so the amount of added 

fertilizers is very critical. On the other hand, using the 

same definite amount of fertilizers for all different crop 

lands does not seem logical because the fertility, texture 

and type of soils would differ (Fajri, 1998). The goal of 

tobacco farmers should be improvement of fertilizer 

program for fulfilling plant nutritional needs and reducing 

fertilizer costs and environmental effects. To approach 

this objective, farmers require a fertilizer plan based on 

soil examination to determine the quantity and availability 

of soil minerals. The fertilizer selection should be 

designed according to plant needs and level of soil 

productivity, fertilizer costs and proper application of 

fertilizers. 

The excessive usage of fertilizers causes wasting of 

natural resources and nutrients and increases the 

probability of environment pollution outbreak as well as 

imposing high costs (Zargami, 2006). Nitrogen is one of 

the elements which is needed in all parts of plant life. 

Farmers are increasingly persuaded to use more nitrogen 

fertilizers in order to improve the crop yield because of 

the remarkable nutritional effect of nitrogen on yield and 

low nitrate nitrogen content in soils (Khodabandeh, 

1997). The usual nitrogen content is between 2 to 5%. 

The nitrogen deficiency symptoms appear when nitrogen 

content is below 1.5% (Sabeti and Mohammad, 2004). 

More nitrogen content reduces starch storage and 

intensifies more formation of leaves nicotine. Low and 

insufficient nitrogen content limits nicotine formation and 

increases starch storage. This type of tobacco is 

undesirable and chemically imbalanced and has high 

sugar to nicotine ratio and produces tasteless smoke 

(Zargami, 2006). Potassium is in silicate form in soil and 

exists in igneous stones (granite and feldespat) or 

particles from their decomposition and seen on the 

*Corresponding author. E-mail: ar_farrokh274@yahoo.com. 

surface of soil colloids or ions in soil solution (Ebrahimi, 

2001). Tobacco cultivar TT5 was fertilized by different 

level of nitrogen and in order to determine free amino 

acid compound, fresh leaves were sampled after 

transplantation in each 2 weeks intervals. The main acids 

included proline, glutamic acid, alanine and asparat acid 

which composed 65 to 75% of free amino acids. 

Remarkable changes happened in quantity of proline and 

glutamic acid during growth stage. Glutamic acid was 

dominant at transplantation time and its quantity became 

maximum until 4 weeks before topping. Proline is just 

found in a little amount in fresh. The total amino acids 

content except glutamic acid got maximum amount 

before topping. The free amino acids content increases 

by adding more nitrogen but usage excessive nitrogen at 

the early growth stages led to low amounts of free amino 

acids compared to other treatments. Among tested free 

amino acids, proline was highly affected by nitrogen. 

Proline content got 20 times greater when more nitrogen 

was applied. The ratio of each amino acid (except proline 

and glutamic acid) to the total free amino acids in the 

same growth stage was not too high (Hu et al., 1985). 

Compared to other tobacco growth stages, 45 days 

after transplanting leaves had the greatest nitrogen 

content. When nicotine and calcium was increased during 

tobacco growth, leaf nitrogen content was reduced. The 

correlation between leaf nicotine and potassium was 

negative (Patel et al., 1987). In order to investigate the 

effect of potassium on carbon metabolism and flue-cured 

tobacco quality, an experiment carried out in china. The 

results indicated that invertase and amylase activity 

during middle and final growth stages increased by 

adding proper amount of potassium. 60 days after 

transplanting, leaf carbohydrates content increased. Leaf 

sugar and starch content increased by adding more 

potassium in middle and final tobacco growth stages. 

Tobacco metabolism increased and more carbohydrate 

was stored by using more potassium in next tobacco 

growth stages and leaf potassium content increased at 

the same time (Gu et al., 1987). For investigating the 

effect of potassium (80, 200, 400, 600 and 800 kg K/ha) 

on topping on leaf potassium content, yield and quality of 

flue-cured tobacco cv. McNair12, a vase experiment 

conducted in India. Based on obtained results, leaf dry 

yield, green leaf yield, leaf degree value and burning rate 

increased by usage of 800 kg K/ha. The differences 

among nicotine, sugar and chloride content in topped 

plants and non topped ones were not significant. The 

upper and lower leaves had the most thickness due to 

applying 80 kg K/ha in topped plants and non topped 

ones. In control treatment, the middle leavers had the 

most thickness (Rao and Rao, 1993). In another 

experiment, different amounts of nitrogen and potassium 

fertilizers were applied and also the same nitrogen: 

potassium ratio was used for determining the level of CTK 

and ABA of flue-cured tobacco leaves in status of non 

absorptive bond enzyme. CTK level increase by leaf 

growth and gradually decreased by leaf ripening.

The most CTK level was gained in leaf blade. But CTK 

levels was greater when more nitrogen and potassium 

were used compared to other treatments. ABA level of 

leaf blade increased during leaf growth and ripening. As a 

result, providing enough nitrogen and proper N:P:K ratio 

is very effective in production of flue-cured tobacco (Lin 

and He, 1999). 


In order to investigate the nitrogen and potassium fertilizers effects 

on yield, quality and some quantitative characteristics of flue-cured, 

tobacco cv. K326, a two year experiment conducted in 2008 and 

2009 with 35 (N1), 45 (N2), 55(N3), 65 (N4) kg N/ha nitrogen from 

urea source and 150 (K1), 200 kg K/ha potassium from potassium 

sulphate source regarding common condition of region and experts 

advice in factorial design at Tobacco Research Institute of Rasht 

located in Guilan province at longitude 49˚ 3 ΄ east and latitude 37˚ 

16΄ north and 25 altitude from sea level. In March 2007 and 2008, 

the nursery of tobacco seedling was prepared and then disinfected 

using vapan (0.1 L/m 2 ) and covered by plastic after 20 days. The 

cover was removed and leveling and beating of nursery bed started 

at the end, fermented animal fertilizer was used in 0.5 to 1 cm 

thickness and seeds were scattered over 0.1 to 0.18 m 2 of nursery. 

From this time until transplanting of seedlings to the main field, all 

operations like irrigation, covering the nursery at nights, spraying 

pesticides were carried out. The field of experiment kept fallow the 

years before sowing and field providing activities such as fall tillage 

and rather deep spring tillage vertical to fall tillage were performed 

and 4 L/ha radical herbicide was used before sowing and mixed 

with soil through disking. In order to measure the physical and 

chemical parameters of soil, after providing of main land, a 

composed soil sample was taken from 0 to 30 cm depth. After 

ploughing and primary leveling by hoe, the seedlings were 

transplanted in 6 lines when they were 20 to 50 cm high. The space 

between rows was 110 cm and between plants on rows was 55 cm. 

The space between plots and replications were 1.5 and 2.5 m 

respectively. 50% of determined fertilizer level for each plot was 

applied before sowing and transplanting. Irrigation time was 

determined using a tensiometer based on suction power of 40 to 50 

cm bar. Weeding control was performed twice. 

In order to prevent Agrotis damages, Ambush and Noakran were 

used 1.4 L/ha respectively. 50% of remained fertilizer level was 

applied on two bands (the distance between bands was 10 cm) in 

10 cm depth of soil. Topping in tobacco is one of the most important 

performances for growth and evolution of remained leaves of plant 

and their quality. In this experiment, topping was performed when 

50% of plants reached flowering stage. For this purpose, all flowers 

plus 2 to 3 terminal leaves were cut. Afterwards, in order to 

prevent lateral buds from growing and evolving, maleic hydrasid 

containing potassium salt was sprayed over plants. The 

investigated parameters in this article include: leaf dry weight, leaf 

wet weight, stem height, stem diameter, plant leaf number, leaf 

length, leaf width, nicotine and sugar content. Tobacco leaves ripen 

gradually from bottom during growth stages. At industrial ripening, 

leaves are harvested through 4 picks. The harvested leaves at 

every picks are first measured after carrying to saloon and the 

green leaves weight were recorded. Afterwards, the leaves were 

separately setup at the petiole over the cassetes and transferred to 

the Balk Gurin hot –house for drying. The leaves passed three 

steps to give color, fixation and drying. The harvested stems were 

conveyed to the hot-house for three days for drying and then their 

weight was measured. The plant leaf number was recorded by 

counting of leaves. Leaf length was measured from soil surface to 

the top of inflorescence by ruler. 

In order to measure the stem thickness, 30 to 40 cm above the 

Farrokh et al. 2603 

soil surface was considered and measured by caliper. Leaf number 

was recorded by counting. Leaf length was measured from initiation 

of petiole to the tip of leaf by ruler and the widest part of leaf was 

considered as a leaf width and measured. The auto analyzer set 

was used for measuring sugar and nicotine content and the 

measurement method was based on Porine ring or Cyanojen 

formation. This complex produces yellow color in the vicinity of 

aniline buffer and could be measured by chlorimeter set in 460 nm 

wave-length. For measurement of reductive sugar percentage, 

reactions of reductive sugar were used, that the yellow color of 

Cyanid Ferric weakens through reductive sugars. The weakening of 

color depends on the amount of reductive sugars existed in extract 

which is measurable in chlorimeter set . In order to determine leaf 

sugar and nicotine content, 0.1 g milled tobacco leaf sample was 

solved in100 ml distilled water and stirred for 20 min by shaker set. 

The derived extract was poured into auto analyzer dishes and 

measured after filtering. Analysis of variance and means 

comparisons were done by SAS software. 

The effect of year 

Variance analysis indicated that the effect of year on parameters 

like dry leaf yield, wet leaf yield, stem height, plant leaf number, 

stem diameter, leaf length, stem dry weight, biomass, nicotine and 

sugar content was significant (P


Table 1. Average decomposition of variance squares of the studied qualities. 

Suger Nicotin Biomass 

Stalk dry 

weight 

Leaf width Leaf length Leaf number 

Stalk 

diameter 

Stalk height 

Green leaf 

yield 

Dry leaf yield 

Changes 

sources (s.o.v) 

467.8379441** 166145333.3** 6697602.083** 98623.802** 6453333.33 220.7634083** 544.5921333** 5.10255208 4554.03440** 216032345.0** 2544262.521** Year 

10.5395896** 0.15549722 1341879.944** 448396.561** 7.36833333* 40.6360750** 11.0224278 7.94229097* 414.117786 64663730.9** 252327.743* Nitrogen 

30.4868441** 0.36053333 29205.333 63267.997 0.40333333 4.1654083 5.2668750 6.49005208 215.222700 12629034.2 6417.187 Potassuym 

0.9054308 0.45508889 1239942.944** 352951.371* 10.89722222* 36.8558528** 28.5269139** 10.39511319* 466.166767 27800245.6* 313240.632* 

Potassium 

nitrogen 

× 

0.7006507 0.18553333 455475.583* 110173.170 8.03055556* 40.6360750** 12.0754833 6858957.64 188.600564 3475821.7 123646.854 Nitrogen × year 

0.8677941 0.37100833 81510.083 10111.149 0.27000000 23.1574083 0.0252083 6.49005208 22.742533 503275.5 3397.521 Potassium × year 

2.2954519 0.13547500 56263.368 3320.714 4.81944444 6.4878528 2.5895806 0.76733542 30.783267 2885628.4 40143.965 

Potassium × 

nitrogen × year 

Table 2. Comparison of average effect of year for the studied qualities. 

Suger Nicotin Biomass Stalk dry weight Leaf length Leaf number Stalk height Green leaf yield Dry leaf yield Year 

10/60 b 2.18 a 2336.2 b 820.7 b 43.98 a 24.01 b 113.64 b 12296 b 1515.5 b First 

16/84 a 1.00 b 3083.3 a 1107.3 a 29.69 b 30.75 a 113.64 a 16539 a 1976.0 a Second 

Table 3. Comparison of average effect of nitrogen for the studied qualities. 

Suger Biomass Stalk dry weight Leaf width Leaf length Stalk diameter Green leaf yield Dry leaf yield Nitrogen 

14/82 a 3172.7 a 1214.9 a 19.31 a 44.52 a 23.06 a 173671 a 1957.7 a 35 

13/96 a 2608.3 b 911.9 b 17.50 b 42.91 ab 22.22 ab 15168 a 1696.3 b 45 

13/55 ab 2679.9 b 980.0 ab 19.61 a 42.37 ab 23.24 a 12390 b 1699.9 b 55 

12/56 b 2378.1 b 749.1 b 17.99 ab 40.35 b 21.51 b 12746 b 1629.0 b 65 

The greatest amount of stem dry weight (1214.9 Kg/ha) is 

allotted to the treatment in which 35 Kg N/ha was applied. 

The average stem dry weight of 980 Kg/ha was in the 

second class and obtained when 55 Kg N/ha was applied. 

The least stem dry weight with average weight of 911.9 

and 749.1 Kg/ha was observed when 45 and 65 Kg N/ha 

was applied respectively. The greatest biomass with 

average weight of 3172.7 Kg/ha was obtained when 35 Kg 

N/ha was applied. The average biomass weights of 2679.9, 

2608.3 and 2378 Kg/ha were in the second class and 

obtained when 55, 45 and 65 Kg N/ha were utilized 

respectively. The highest sugar content with averages of 

14.82 and 13.96% were gotten when 35 and 45 Kg N/ha 

were applied respectively. 55 Kg N/ha level was in the 

second class by production of 12.56% sugar content. The 

least sugar content (12.56%) was determined when 65 Kg 

N/ha was applied. 

The effect of potassium 

Potassium showed significant effect on sugar content 

(P

Table 4. Comparison of average effect of potassium for the 

studied qualities. 

Suger Potassium 

12.93 b 150 

14.52 a 200 

at 5% statistical level was significant (Table 1). The greatest dry leaf 

yield with average weight of 2070 kg/ha was associated with using 

35 kg N/ha plus 200 kg K/ha. Applying 35 kg N/ha plus 150 kg K/ha, 

65 kg N/ha plus 150 kg K/ha and 55 kg N/ha plus 200 kg K/ha led to 

produce of 1845.3, 1838.7 and 1773.2 kg dry leaf yield per hectare 

respectively which lies on the second class. 

The fertilizer levels of 45 kg N/ha plus 200 kg K/ha and 55 kg 

N/ha plus 200 kg K/ha which led to 1619.5 and 1572 kg dry leaf 

yield per hectare respectively were in the third class. The least dry 

leaf yield (1419.3 kg/ha) was associated with usage of 65 kg N/ha 

plus 200 kg K/ha. The maximum green leaf yield with averages 

of 18094 and 16640 kg/ha belonged to fertilizer treatments of 35 kg 

N/ha plus 200 kg K/ha and 35 kg N/ha plus 150 kg K/ha 

respectively. Applying 45 Kg N/ha plus 200 kg K/ha, 65 kg N/ha plus 

150 kg K/ha led to produce of 15501 and 14835 kg green leaf yield 

per hectare respectively which lies on the second class. Fertilizer 

levels of 55 kg N/ha plus 150 kg K/ha and 55 kg N/ha plus 200 kg 

K/ha placed in third class (12823 and 11957 kg green leaf yield per 

hectare respectively). The least green leaf yield (10067 kg/ha) was 

associated with fertilizer level of 65 Kg N/ha plus 200 kg K/ha. The 

highest stem diameter (24.42 mm) produced when 35 kg N/ha plus 

200 kg K/ha was applied. Using 45 kg N/ha plus 200 kg K/ha, 45 kg 

N/ha plus 150 kg K/ha, 55 kg N/ha plus 200 kg K/ha, 35 kg N/ha 

plus 150 Kg K/ha, 65 kg N/ha plus 200 Kg K/ha led to average of 

22.32, 22.10, 22.03, 21.7 and 20.70 mm stem diameter which is 

placed in the second class. The greatest leaf number (30.21 

leaves) was associated with usage of 35 kg N/ha plus 200 kg K/ha. 

The average leaf number of 28.68 which was produced under 

fertilizer level of 55 kg N/ha plus 200 kg K/ha was in the second 

class. Level of 45 kg N/ha plus 150 kg K/ha led to average leaf 

number of 27.83 leaves was in the third class. The fertilizer levels of 

45 kg N/ha plus 200 kg K/ha, 35 kg N/ha plus 150 kg K/ha, 55 kg 

N/ha plus 200 kg K/ha, 65 kg N/ha plus 200 kg K/ha which lead to 

average leaf numbers of 27.25, 26.95, 25.58 and 24.72 leaves 

respectively were in next classes. The greatest leaf length with 

average of 46.28 cm obtained when 35 kg N/ha plus 200 kg K/ha 

was applied. Levels of 55 kg N/ha plus 200 kg K/ha and 35 kg N/ha 

plus 150 kg K/ha which led to 42.85 and 42.75 cm leaf length were 

in the second class. The third class of leaf length was associated 

with applying 45 kg N/ha plus 150 kg K/ha, 65 kg N/ha plus 200 kg 

K/ha, 45 kg N/ha plus 200 kg K/ha, 55 kg N/ha plus 150 kg K/ha 

which lead to average leaf lengths of 42.10, 42.03, 40.73 and 39.3 

cm respectively. The minimum leaf length (38.67 cm) was observed 

in fertilizer level of 65 kg N/ha plus 200 kg K/ha. The maximum leaf 

width (19.42 cm) was seen in fertilizer levels of 35 kg N/ha plus 200 

kg K/ha and 55 kg N/ha plus 200 kg K/ha. Fertilizer levels of 65 kg 

N/ha plus 150 Kg K/ha, 35 kg N/ha plus 150 kg K/ha and 45 kg N/ha 

plus 150 kg K/ha which led to average leaf width of 19.1, 18.68 and 

18.18 were all in the second class. 

The least leaf widths (16.87 and 16.82 cm) were associated with 

fertilizer level of 65 kg N/ha plus 200 kg K/ha and 45 kg N/ha plus 

200 kg K/ha. The maximum stem dry weigh with average weights of 

1332.2 and 1216.9 kg/ha were associated with fertilizer levels of 35 

kg N/ha plus 200 kg K/ha and 55 kg N/ha plus 200 kg K/ha. The 

level of 35 kg N/ha plus 150 kg K/ha which led to average stem 

dried weight of 1096.7 kg/ha was in a second class. The third class 

of stem dry weight belonged to usage of 45 kg N/ha plus 150 kg 

K/ha by average stem dry weight of 1043.5 kg/ha. All fertilizer levels 


of 65 kg N/ha plus 150 kg K/ha, 45kg N/ha plus 200 kg K/ha and 55 

kg N/ha plus 150 kg K/ha which respectively led to average weights 

of 827.4, 780.3 and 743.2 kg/ha were in forth class. The least stem 

dry weight (670.9 kg/ha) was associated with usage of 65 kg N/ha 

plus 200 kg K/ha. The greatest amount of biomass (3043.2 kg/ha) 

was associated with usage of 35 kg N/ha plus 200 kg K/ha. The 

next classes were associated with fertilizer levels of 55 kg N/ha plus 

200 kg K/ha, 35 kg N/ha plus 150 kg K/ha, 45 kg N/ha plus 150 kg 

K/ha, 65 kg N/ha plus 150 kg K/ha, 45 kg N/ha plus 200 kg K/ha and 

55 kg N/ha plus 150 kg K/ha which led to average weights of 

3044.5, 2942.2, 2816.8, 2666, 2399 and 2090.2 kg/ha respectively 

(Table 5). 

The interaction between year and nitrogen 

The interaction between year and nitrogen on leaf length was 

significant (P


Table 5. Comparison of average interaction effect of nitrogen and potassium fertilizers for the studied qualities. 

Biomass Stalk dry weight Leaf length Leaf number Leaf number Stalk diameter Green leaf yield Dry leaf yield Treatments 

2942.2 b 1096.7 ab 18.68 ab 42.750 a 26.950 bcd 21.70 b 16640 a 1845.3 ab N35K150 

3403.2 a 1333.2 a 19.42 a 46.283 a 30.210 a 24.42 a 18094 a 2070.0 a N35K200 

2816.8 bc 1043.5 abc 18.82 ab 42.100 abc 27.838 ab 22.10 b 14835 ab 1773.2 ab N45K150 

2399.8 cde 780.3 bc 16.82 b 40.73 bc 27.245 abcd 22.350 b 15501 ab 1619.5 bc N45K200 

2315.3 de 743.2 bc 17.80 ab 39.30 bc 25.580 cd 20.44 b 12823 bc 1572.0 bc N55K150 

3044.5 ab 12169.9 a 19.42 a 42.85 b 28.678 ab 22.03 b 11957 bc 1827.8 ab N55K200 

2666.0 bcd 827.4 bc 19.10 ab 42.03 bc 27.830 abc 22.32 b 15425 ab 1838.7 ab N65K150 

2090.2 e 670.9 c 16.87 b 38.67 c 24.715 d 20.71 b 10067 c 1419.3 c N65K200 

of these compounds is done at plants root but 

some times they are absorbed by plants leaves. 

Even Cyanamid compound which is poisonous for 

metabolism reactions can be absorbed by plants 

roots, leaves and stems. This compound prevents 

catalyses enzyme from activity. In normal 

condition, absorbed mineral nitrogen is digested 

quickly and changed into organic nitrogen 

compounds. 

Almost all nitrogenous compounds have fairly 

proper mobility, so the first nitrogen deficiency 

signal is appeared in old leaves because in 

nitrogen shortage status, the nitrogen which was 

replaced in old leaves in a shape of protein is 

Table 6. Comparison of average interaction effect of year and nitrogen fertilizers for the studied qualities. 

Biomass Leaf number Leaf number Treatments 

3057.2 a 20.483 a 49.17 35 kgN/ha × first year (2008) 

2266.2 b 18.417 ab 43.82 b 45 kgN/ha × first year (2008) 

2208.7 bc 18.550 ab 41.80 bc 55 kgN/ha × first year (2008) 

1812.8 c 17.417 b 41.15 bc 65 kgN/ha × first year (2008) 

3288.2 a 18.133 b 39.87 c 35 kgN/ha × second year (2009) 

2950.5 a 16.583 b 39.02 c 45 kgN/ha × second year (2009) 

3151.2 a 18.667 ab 40.35 bc 55 kgN/ha × second year (2009) 

2943.3 a 18.550 ab 39.54 c 65 kgN/ha × second year (2009) 

changed into soluble amino acid molecule under 

proteolyses process and transferred to the 

required location. Nitrogen is also called base 

physiologic fertilizer because increases pH after 

consumption and, makes cells acidic contrary to 

ammonium. Nitrates ions amplifies making 

organic acids in cells. It has been proved that 

organic acids quantity will be increased and 

decreased by nitrate and ammonium absorption 

respectively (Hagh, 1991). Nitrogen effect on 

tobacco is more than other elements but 

excessive usage of nitrogen increases leaf 

nicotine content and these leaves are not valuable 

for making cigarette. Additional, nitrogen in soil 

near the end of tobacco growth stage results in 

lateness of flowering, maturity and harvesting 

time. Basically, nitrogen deficiency reduces 

tobacco yield. On the other hand adding 

excessive nitrogen to the soil increases 

undesirable leaves nitrates. Reduction in nitrate 

levels of dried leaves would be possible by 

optimized management of nitrogen (Zargami, 

2006). Adding excessive nitrogen does not have 

significant effect on increasing of plant yield and 

decreases the quality. Total nitrogen content has 

reversed relation with carbohydrates content. 

Total nitrogen composes of 75% protein 

nitrogen, 10% alkaloids and 15% nitrogen which is

existed in amino acids and other protein compounds 

(Ranjbar, 2005). Since a large amount of nitrogen is 

absorbed by plants, type of nutrition by nitrogen has a 

great impact on plants cation and anion ratio. For 

example, feeding plants by ammonium caused less 

absorption of other cations but amplifies anions 

absorption like phosphates. On the contrary, if plants are 

fed by nitrates, more anions will be absorbed 

consequently and causes other anions absorption and 

amplifies absorption of other cations (Ebrahimi, 2001). 

Nitrogen is the most restrictive element agent in crop 

production. Some plants like maize require more nitrogen 

so it is called heavy consumer. Nitrogen is one of key 

components of important molecules likes amino acids, 

proteins, nucleic acids and some hormones and 

chlorophyll. More symptoms of potassium deficiency 

include gradually growth reduction and general yellow 

color of plant leaves. Nitrogen is very mobile in plant. 

When older leaves get yellow and die, nitrogen would be 

translocated from old leaves to the younger ones in 

shape of soluble amine and amides (Pooran and 

Rahnama, 2000). Nitrogen is a very important element in 

growth and evolution of tobacco leaves and crop 

improvement which added to the soil in mineral and 

organic status. Organic materials in soil act as a hormone 

in changing of soil physical factors (Salardini, 2006). 

Nitrogen is the main production restrictive factor in most 

crop plants and after carbon, its assimilation has great 

importance. In majority of plants, the most nitrogen 

absorption is done at early quick growth stage and it is 

reduced by initiation of oldness and stages (Hagh, 1991). 

Nitrogen has more effect on yield and quality of tobacco 

compared to other elements. Low amount of nitrogen 

reduces yield and makes plant leaves yellowish and 

decreases leaves processing in high temperatures. An 

excessive nitrogen usage would cause increases yield a 

bit but might make mechanical harvesting and processing 

more difficult and delay ripening and prolong leaves 

processing and lead to more undesirable processed 

leaves and decrease of quality consequently. 

An excessive quality also stimulates growth of lateral 

shoots which is fallowed by more usage of maleik 

hydrasid will be exacerbated. Nitrogen leaching ability is 

very much and excessive utilization of nitrogen leads to 

underground water pollution. More usage of nitrogen 

fertilizers results in accumulation of nitrates after 

harvesting. This situation could lead to increase of 

drinking water pollution (Ranjbar, 2005). One important 

effect of nitrogenous fertilizer increase is less storage of 

hydrocarbonic materials in plants. The absorbed nitrogen 

is rapidly bounded by hydro carbonic materials and uses 

them as energy and carbon source for production of 

amino acids and protein. As a result, in nitrogen shortage 

status, cell walls which contain calcium pektat, cellulosan, 

cellulose and legnin could not be built properly. 

Meanwhile, cell walls which have been built in this 

situation are very big and contain lots of water in their 


structures. Since nitrogen exists in many plant cell 

compounds like amino acids and nucleic acids, one of the 

nitrogen deficiency signals could be slow growth. In the 

beginning, potassium moves toward cell wall chambers of 

root cortex through mass flow and spread. Afterwards, 

potassium moves towards phloem through plasmatic 

membrane (Salardini, 2006). In addition to role of 

potassium to help plants transferring photosynthesis 

productions, it eases nitrogen movement and its 

changing into proteins. So potassium functions like 

nitrogen pomp and improve potassium abortion and 

consumption (Kavoosi, 2002). Potassium is absorbed in a 

shape of monovalency (K + ) and found abundantly in cells 

but does not have structural roles. Potassium 

concentration in phloem is very high. Stomata cells 

concentration is even more than potassium concentration 

but according to potassium ion is activator of many 

enzymes especially those involved in photosynthesis and 

respiration. Synthesis of starch and protein is affected by 

potassium deficiency. Potassium has very important role 

in controlling of plant cell potassium, plant movements 

such as opening and closing stomata, suit movement or 

daily leaves arrangements. Potassium concentration is 

higher in younger organs, roots (Ebrahimi, 2001). 

Presence of potassium helps translocation of glucid 

materials to different organs and increase crop yield. On 

the other hand, potassium enhances plant resistance to 

pests and diseases and drought stress and leads to leaf 

color uniformity and well burning of tobacco leaves. 

Potassium alleviates the negative effects of excessive 

nitrogen of soil (Mohsen, 2000). In investigation of soil 

and plant and nutrients relations, potassium is very 

importance. Potassium forms a great percentage of 

earth's crust and minerals existing in soils and plants. 

Some plants such as tobacco absorbs potassium more 

than 5 times of their weight. Earth’s crust and crop soils 

contain around 2.3 and 1.4% K2O respectively which is a 

significant quantity compared to other main elements. 

Among plant required cations, potassium is the biggest 

cation regarding its 1.33 angestrum atomic radius. K-O 

bond is not very stable in minerals structure because 8 to 

14 molecules surround it. Polar ability of potassium is 

more than calcium, magnesium, lithium, sodium and less 

than ammonium, rubidium and barium. If other situations 

are the same, the more polar ability leads to the more 

exchangeable activities (Salardini, 2006). An experiment 

was done by 4 different seed levels (200, 300, 400 and 

500 mg/m -2 ) and 4 nitrogen amounts on randomized 

block design with 3 replications. Up to 400 mg seed per 

each meter square did not show any significant decrease 

but use of 500 mg seed per each meter square. On the 

other hand, using nitrogen up to 30 kg/ha led to linear 

increase in leaf area. The lowest number of seedling 

(174) and the highest number of seedling (263) obtained 

when 200 and 400 mg/m 2 seed was used respectively. 

An increase in nitrogen utilization up to 30 kg/ha led to 

increase in healthy seedling number. Compound effect of


usage of 400 mg/m 2 seed plus 30 kg/ha nitrogen caused 

the greatest number of healthy seedling (Tripathi and 

Bhattacharya, 1983). In an experiment which was carried 

out in Pakistan, 3 different ways of applying fertilizers 

were investigated. Using ammonium nitrate led to 

relatively better quality of tobacco leaves with low 

nicotine and protein content and high amount of 

carbohydrate content. 

The lowest value of nicotine and total protein in 

gathered leaves was seen when fertilizer scattered 

superficially (Khan et al., 1981). 

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Ebrahimi H (2001). Plant physiol. Tehran university publication. 

Fajri H (1998). Determining amount of neccary chemical fertilizers for 

Virginia and Barly. Tob. Res. Inst. 

Gu MH, LI T, Zou K, Wany (1987). Study on the effect on potassium 

nutrition in carbon metabolism and quality of flue – cured tobacco. 

China. 

Hagh PTM (1991). Plant physiol. Guilan University Publication. 

Hu HY, Chen LH, Tsal CF (1985). Influence of nitrogen fertilization on 

the amino acid composition of tobacco leaves at Various growing 

stage. I. Free amino acida. Bull. Taiwan Tob. Res. Inst., 22: 23–40. 

Kavoosi, M (2002). Study of intraction effects between nitrogen and 

potassium on rice. Rice Res. Inst. Iran. 

Khan H, Qazi MZ, Alam M (1981). Effects of different nitrogen sources 

and methods of application on the quality of Virginia flue – cured 

tobacco. Pak. Tob., – 5 – L, p. 29–32. 

Khodabandeh N (1997). Ind. Plant Agron. Publication of Tehran 

University. 

Lin KZ, He Z (1999). Influence of N and K levels on cytokinin and 

abscisic acid levels in flue – cured tobacco. Tob. Res., 25(2): 67–71. 

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of six Variety of tobacco. MSC. Agriculture Faculty, Mashhad 

University. 

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tobacco at Varying stages of growth. Tob. Res., 13: 126–133. 

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Ranjbar CM (2005). Production, to ripen and evaluation of flue cured 

tobacco. Rasht Tob. Res. Inst. 

Rao KN, Rao BVK (1993). Effect of potash levels and topping on leaf 

potassiun, yield and quality in flue – cured tobacco. Indian Journal of 

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Tob. Res. 9–1. p. 39–44.



DOI: 10.5897/AJAR11.1806 



Vermicompost induced changes in growth and 

development of Lilium Asiatic hybrid var. Navona 

Ali Reza Ladan Moghadam 1* , Zahra Oraghi Ardebili 2 and Fateme Saidi 3 

1 Department of Agriculture, Garmsar Branch, Islamic Azad University, Garmsar, Iran. 

2 Department of Biology, Garmsar Branch, Islamic Azad University, Garmsar, Iran. 

3 Garmsar Branch, Islamic Azad University, Garmsar, Iran. 

Accepted 17January, 2012 

Organic agriculture minimizes the negative effects of agricultural activities. The present study was 

carried out in the factorial experiment on the basis of Complete Randomized Design (CRD) with 4 

treatments and 3 replications. The vermicompost used was prepared using bovine manure and the 

earthworm (Eisenia foetida). Applied vermicompost levels were V0 = 0%, V1 = 10%, V2 = 20%, V3 = 30%. 

The obtained results from the present research indicated that applied vermicompost especially, at 30% 

level had significantly improving effects on the accumulation of macro nutrients, Ca and K, and 

micronutrients, Zn and Fe in both stems and root tissues. Applied vermicompost resulted in better 

growth and development of vermicompost treated plants as they had higher number of leaves, leaf dry 

mass, fresh stem and dry weight, stem height and diameter, root number and length. Observed 

increased contents of gibberellic acid in root tissues resulted from the application of vermicompost. 

Vermicompost treatments at suitable levels of 20 and 30% had stimulating effects on the number of 

flowers and their diameters and reducing effects on time of flowering. The result of the present study 

showed that the use of vermicompost, particularly, with a 30% content had favorable results on the 

growth and development of the Lilium asiatic hybrid var. Navona. 

Key words: Biofertilizer, flowering, Gibberellins, humic acid, ornamental plant. 

INTRODUCTION 

Nowadays, according to the importance of environmental 

issues, more attention is being paid to the substituting of 

chemical fertilizers with biological ones (Hu and Barker, 

1998). Using the biological fertilizers such as vermicomposts 

increases the quality and sustainability, in 

addition to preserving of the environment (Kader et al., 

2002). Vermicomposts are produced through interactions 

between earthworms and micro-organisms in the 

breakdown of organic wastes (Edwards et al., 2010). 

Depending on the origin, vermicomposts differ in 

chemical composition (Handreck, 1986). Vermicomposts 

are finely divided peat-like materials with high porosity, 

aeration, drainage, and water-holding capacity (Edwards 

*Corresponding author. E-mail: 

alirezaladanmoghadam@yahoo.com. Tel: +989121789162. 

Fax: +982324229969. 

and Burrows, 1988). Vermicomposts are usually more 

stable than their parent materials with increased 

availability of nutrients and improved physio-chemical 

and microbiological properties (Edwards and Burrows, 

1988; Albanell et al., 1988; Orozco et al., 1996; Atiyeh, 

2000). Vermicomposts have the same reported benefits 

as conventional composts such as a source of organic 

matter, increased moisture-holding capacity, and 

enhanced nutrient uptake and plant hormone-like activity 

(Tomati et al., 1988; Galli et al., 1990; Atiyeh et al., 2002; 

Bachman and Metzger, 2008). Vermicompost is a 

sustainable source of macro- and micro-nutrients and 

have a considerable potential for improving plant growth 

significantly when used as components of horticultural 

soils or container media (Sahni et al., 2008). Vermicompost 

is formed from pits with lots of pores with high 

potential of airing, draining, and water retention which 

prepares an optimum condition in soil (Atiyeh et al., 

2001b). Vermicompost in potting media has no detrimental


but rather a stimulatory effect on the emergence and root 

growth of seedlings and has thus, a considerable 

potential for substituting peat in horticultural potting 

substrates (Zaller, 2007a, b). Replacing part of the 

ground soil by different amounts of vermicomposts with 

different origins leads to increased germination, growth 

and flowering in laboratory and greenhouse condition in a 

vast variety of ornamental plants and vegetables such as 

marigold (Atiyeh et al., 2001a), tomato (Atiyeh et al., 2001b) 

and pepper (Arancon et al., 2004). 

Lilium belongs to the Liliaceae family. Lilies are of 

special economic importance because they possess big, 

beautiful and attractive flowers. This plant is known as 

one of the important bulbous products and has 

possessed the 7 th position among the cut flowers of the 

world (Varshney et al., 2000). 

Despite the proposed enhancing effects of 

vermicompost, it is stated that high level of it could have 

inhibiting effect on the plant growth and development, this 

could be probably due to plant stress by its high soluble 

salt concentration (Wang et al., 2010). The results of 

some studies indicated that the application of some levels 

of vermicompost did not result in significantly increased 

plant growth and linear relationship between applied 

vermicompost levels and growth and development of 

treated plants have not been observed. Atiyeh et al. 

(2000) reported that lower concentrations of vermincomposts 

(

Table 3. Characteristics of the used water. 

Measured indexes 

Soil tissue Loam 

pH 7.5 

EC 0.045 dS m -1 

SAR 0.3 meqL -1 

LR 0.4 meq L -1 

Mg 41 mgL -1 

Ca 720 mqL -1 

So4 

52.836 mgL -1 

Cl 14.2 mg L -1 

HCo3 -1 164 mg L -1 

CO3 -2 0 mg L -1 

SAR: Sodium absorbation rate; LR: Leaching 

requirement. 

weight of stem, number of flowers and some other physiological 

characteristics including amount of Fe, Zn, Ca and K of stem and 

root, and gibberellins (GA) content of root were measured at the 

end of the growth period. 

Ash solution was prepared by wet ash method. Determination by 

atomic absorption spectroscopy, Varian specter-AA200 was used to 

measure the elements, Fe, Zn, Ca and K. Finally, the concentration 

of each element was measured per each gram of dry mass. 

Gibberellic acid (GA) concentration was measured as previously 

described by Shengjie et al. (2008). A solid-phase extraction (SPE) 

pre-treatment method was used to concentrate and purify 

hormones from plant samples. 1 g of fresh tissue was put in the 

solution of methanol, water and acetic acid with a proportion of (30: 

70: 1) and the solution was homogenized by homogenizer. The 

supernatant was separated with centrifuge and after being placed in 

SPE column C18, the solution of ethanol, water, acetic acid with the 

proportion of (80: 20: 1) was used. This solution was dried, resolved 

in methanol and used for the determination of GA content. The 

separation was carried out on a C18 reversed-phase column, using 

methanol/water containing 0.2% formic acid (50: 50, v/v) as the 

isocratic mobile phase at the flow-rate of 1.0 ml min −1 . 

Analysis of variance was performed on all data sets. Duncan test 

with a probability of 0.05 was used to show significant differences 

between treatments. All data are presented as mean±SE. 

RESULTS 

The applied vermicompost especially, at 30% level had 

significantly improving effects on the accumulation of 

macro-nutrients, Ca and K in root and stem tissues 

(Figures 1 and 2). The effect of application of 

vermicompost on Fe and Zn content of stem tissues was 

significant (Figures 3 and 4) where the highest amounts 

of them were detected at 30% level of applied treatments. 

Applied vermicompost especially, at 20 and 30% had 

significantly enhancing effects on the Fe and Zn contents 

of root tissues (Figures 3 and 4). 

The application of vermicompost especially, at 20 and 

30% treatments resulted in significantly increased root 

number and length as they shown in Figures 5 and 6. 

Application of vermicompost resulted in improved GA 

Moghadam et al. 2611 

contents of root tissues with the highest observed amount 

at 30% treatment (Figure 7). 

Based on our results, the effect of vermicompost on the 

stem height, diameter and dry weight was significant 

(Figures 8, 9 and 10). The highest amounts of them were 

found at 30% vermicompost treatment. 

All used levels of veremicompost at 10, 20 and 30% 

respectively, had significantly enhancing effects on the 

number of leaves (Figure 11). The applied treatments of 

20 and 30% respectively, had significantly increasing 

effects on leaf dry mass (Figure 12). 

The stimulating effect of applied vermicompost on the 

number of flower was observed only at 30% level of 

significance (Figure 14). The results indicated that impact 

of vermicompost on flower diameter was significant 

(Figure 13). Flower diameter in V2 and V3 treatment 

groups was significantly increased (Figure 13). The time 

of flowering was significantly reduced by the application 

of 30% vermicompost (Figure 15). 

DISCUSSION 

The obtained results from the present research indicated 

that the applied vermicompost especially, at 30% level of 

significance had significantly improving effects on the 

accumulation of macro nutrients, Ca and K, and 

micronutrients, Zn and Fe in both stem and root tissues. 

It could result from vermicompost containing humic acid 

and minerals in suitable and available forms and 

vermicompost-induced changes in root system and 

metabolism. 

It is recorded that compared to conventional composts, 

vermicompost was 40 to 60% richer in humic compounds 

(Dominguez et al., 1997). Humic acid increases nutrient 

accumulation in conditions of limited nutrient availability 

(David et al., 1994). Treatment with humic acids derived 

from vermicompost enhanced growth of tomato and 

cucumber (Atiyeh et al., 2002). Treatments with vermincompost 

showed increased accumulation of N, P, K, S, 

Mn and Fe in chickpea seedlings (Sahni et al., 2008). 

Vermicomposts contain nutrients in forms that are readily 

taken up by the plants such as nitrates, exchange-able 

phosphorus and soluble potassium, calcium, and 

magnesium (Edwards and Burrows, 1988; Orozco et al., 

1996; Atiyeh, 2001). The high nitrate content of the 

mature vermicompost (Atiyeh, 2001) and presence of 

available forms of minerals led to enhanced growth of 

tomato plants in vermicompost derived from pig wastes 

(Atiyeh et al., 1999). 

At the present research, the application of vermincompost 

resulted in induced morphological changes of 

root and increased GA content of root tissues. The 

observed changes in root system could be as a result of 

induced hormonal changes, especially, GA, the hormonelike 

activities of vermicompost and improved nutrition. In 

addition to GA effect on root system, it could affect


V0 0 V1 10 V2 20 V2 30 

Figure 1. The effect of different levels of vermicompost on the Ca content of stem and root tissues ( X ±SE) 

in Lilium Navona. The vertical bars indicate standard errors of three replications. 

VO 0 

V1 10 

V2 20 

Vermicompost concentrations (%) 

(%) 

V3 30 

Figure 2. The effect of different levels of vermicompost on the K content of stem and root tissues 

( X ±SE) in Lilium Navona. The vertical bars indicate standard errors of three replications.

V0 0 V1 10 


V2 20 V3 30 

Figure 3. The effect of different levels of vermicompost on the Fe content of stem and root tissues ( X ± SE) in 

Lilium Navona. The vertical bars indicate standard errors of three replications. 

V0 0 V1 10 V2 20 V3 30 


Figure 4. The effect of different levels of vermicompost on the Zn content of stem and root tissues 

( X ± SE) in Lilium Navona. The vertical bars indicate standard errors of three replications. 

Moghadam et al. 2613


Root (number) 

Root length (cm) 

V0 0 V1 10 V2 20 V3 30 


Figure 5. The effect of different levels of vermicompost on the root length ( X ± SE) in Lilium Navona. 

The vertical bars indicate standard errors of three replications. 

V0 0 V1 10 V2 20 V3 30 


Figure 6. The effect of different levels of vermicompost on the number of root ( X ± SE) in Lilium Navona. 

The vertical bars indicate standard errors of three replications.

Stem height (cm) 

V0 0 V1 10 V2 20 V3 30 


Figure 7. The effect of different levels of vermicompost on the GA content of root tissues ( X ±SE) 

in Lilium Navona. The vertical bars indicate standard errors of three replications. 

V0 0 V1 1 0 V2 20 V3 30 


Figure 8. The effect of different levels of vermicompost on stem height ( X ±SE) in Lilium Navona. The 

vertical bars indicate standard errors of three replications. 

Moghadam et al. 2615


Stem diameter (cm) 

Stem Diameter (Cm.) 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

a 

V0 0 

a 

V0 V0 (0%) 0 

V1 V1 (10%) 10 V2 (20%) 20 V3 V3 (30%) 30 

Vermicompost concenrations 

concentrations (%) 

Figure 9. The effect of different levels of vermicompost on stem Diameter ( X ±SE) in Lilium Navona. The 

vertical bars indicate standard errors of three replications. 

V0 0 V1 1 0 V2 2 0 V3 3 0 


Figure10. The effect of different levels of vermicompost on stem dry mass ( X ±SE) in Lilium 

Navona. The vertical bars indicate standard errors of three replications. 

a 

b

Leaf (number) 

V0 0 V1 10 V2 20 V3 30 


Figure 11. The effect of different levels of vermicompost on the number of leaves ( X ±SE) in 

Lilium Navona. The vertical bars indicate standard errors of three replications. 

V0 0 

V1 10 

V2 20 



V3 30 

Figure 12. The effect of different levels of vermicompost on the leaf dry mass ( X ±SE) in Lilium Navona. 

The vertical bars indicate standard errors of three replications.


The diameter of the flower (mm) 

V0 0 

V1 1 0 V2 20 V3 30 


Figure 13. The effect of different levels of vermicompost on the flower diameter ( X ±SE) in Lilium 

Navona. The vertical bars indicate standard errors of three replications. 

Flower (number) 

V0 0 

V1 10 

V2 20 


V3 30 

Figure 14. The effect of different levels of vermicompost on the number of flowers ( X ±SE) in Lilium 

Navona. The vertical bars indicate standard errors of three replications.

Flowering (days) 

V0 0 

V0 0 V1 10 V2 20 V3 30 



Figure 15. The effect of different levels of vermicompost on the time of flowering ( X ±SE) in Lilium Navona. 

The vertical bars indicate standard errors of three replications. 

growth and development of shoot tissues. The alteration 

in different aspect of cellular metabolisms including 

content of phytohormones could be arising from the 

different compounds present in the used vermicompost. 

It is proposed that the humic acid present in 

vermicompost may affect biochemical processes in plants 

(Sahni et al., 2008). Applications of vermicompost 

significantly increased the contents of vitamin C, phenols 

and flavonoids of treated plants (Wang et al., 2010). The 

gibberellic acid (GA) is involved in many aspects of 

development throughout the life-cycle of higher plants. 

They also mediate certain environmental effects on plant 

development (Hedden, 1999). Gibberellins (GAs) are 

signaling molecules that regulate and integrate developmental 

processes during the entire life-cycle of higher 

plants, including shoot elongation and root development 

(Lange et al., 2005).There is growing evidence for the 

presence of root based GA biosynthesis from many plant 

species, including pumpkin, pea, Arabidopsis and rice 

(Lange et al., 2005). GA biosynthesis and signaling is 

limited to the root tip (Kaneko et al., 2003). One of the 

highest GA1 expressions was observed in root tips 

(Silverstone et al., 1997). Regulation of gibberellin (GA) 

biosynthesis by endogenous and environmental stimuli is 

an important factor in the control of plant morphogenesis 

(Hedden, 1999). It has been shown that GA promotes 

root growth in Arabidopsis (Lange et al., 2005). GA 

signaling may enable integration of aerial and root 

development (Gou et al., 2010). The hormone-like 

activities of vermicompost leads to increased rooting, root 

biomass, plant growth and development (Edwards, 1983, 

Satchell et al., 1984; Edwards et al., 1985; Tomati et al., 

1988; Sainz et al., 1998; Bachman and Metzger, 2008). 

Vermicompost shows hormone-like activity and increased 

the number of roots, and consequently, improved nutrient 

uptake and plant growth and development (Alvarez and 

Grigera, 2005). Humic acids isolated from earthworm 

compost enhanced root elongation, lateral root 

emergence, and H+-ATPase activity of the plasma 

membrane of maize roots (Canellas et al., 2002). 

The obtained results from the present research 

indicated that the application of the used vermicompost 

led to better growth and development of vermicompost 

treated plants as they were shown with higher number of 

leaves, leaf dry mass, fresh stem and dry mass, stem 

height and diameter, root number and length. The 

increased number of leaves induced by the applied 

vermicompost could lead to stimulated photosynthesis 

and increased leaf dry mass. Thus, the observed 

enhanced growth and development in vermicompost 

treated plants, especially, at 20 and 30% level of 

significance could result from the improved nutrition, 

stimulated rooting, induced changes of metabolic process 

and humic acid present in vermicompost.


The application of vermicompost increased plant leaf 

area, dry matter and total fruit yield in strawberries (Singh 

et al., 2008). Sheep-manure vermicompost as a soil 

supplement increased plant heights significantly 

(Gutiérrez-Miceli et al., 2007). Vermicompost treated 

plants had increased leaf area and biomass, especially, 

in the 10% vermicompost: soil mix (Warman and 

AngLopez, 2010). The enhancement of the marketable 

weight, leaf numbers and leaf areas by vermicompost 

treatments may be due to the plant growth regulators and 

humic acids present in the vermicompost (Wang et al., 

2010). 

Study on the effects of different levels of vermicompost 

on the time of flowering and flowers revealed that 

vermicompost treatments at suitable levels of 20 and 

30% respectively, had improving effects on the number of 

flowers and their diameter and reducing effects on the 

time of flowering. More GA content, enhanced root 

system, stimulated development shown by increased 

number of leaves, improved photosynthesis which is 

concluded from more dry mass, and better nutritional 

status due to the applied vermicompost at optimal 

concentrations could lead to improved and accelerated 

flowering, as it was indicated by induced flower numbers 

and reduced time of flowering. 

Incorporation of vermicompost of pig manure origin into 

germination media up to 20% v/v enhanced shoot and 

root mass, leaf area, and shoot: root ratios of both tomato 

and French marigold seedlings (Bachman and Metzger, 

2008). Enhancement in plant growth was attributed to 

modifications in soil structure, access to water, increased 

availability of macro and micro nutrient elements, 

stimulation of microbial activity, augmentation of the 

activities of critical enzymes, or production of plant 

growth-promoting substances by micro-organisms (Atiyeh 

et al., 2001; Sahni et al., 2008). It is possible that 

vermicompost, in a similar way to compost, can affect 

plant growth by modifying the physio-chemical and 

microbiological characteristics of the plant growth 

medium (Sahni et al., 2008). Anwar et al. (2005) stated 

that vermicompost not only increases the availability of 

nutrient elements needed by plant, but also provides an 

optimum condition of growth and availability of nutrients 

by improving physical condition and functions of microorganisms. 

The enhancement of plant growth by mature 

vermicompost may not only be nutritional but may also be 

due to its content of biologically-active plant growth 

influencing substances (Atiyeh et al., 1999; Arancon et 

al., 2004; Warman and AngLopez, 2010). Foliar application 

of vermicompost leachates improved the growth 

parameters like leaf area and dry weight of strawberry 

because of the presence of humic acid (Singh et al., 

2010). The results of the current study matched with the 

results of vermicompost effects in red clover, barley 

(Krishnamoorthy and Vajrabhiah, 1986), tomato (Zaller, 

2007a, b) and papaya (Shivaputra et al., 2004). 

However, the best results of the applied vermicompost 

were found in V3 group (30%), the observed differences 

between V2 (20%) and V3 (30%) treatment groups were 

not considerable. As It was proposed that high level of 

vermicompost have inhibiting effects on plant growth and 

development and the applied vermicompost has high EC, 

therefore, it does not seem that the application of 40% 

vermicompost would result in better economic results 

than that of the 30%. Thus, according to the findings of 

the present research, it can be stated that using 

vermicomposts up to 30% level, is optimum and 

economic to producing Lilium Navona. This level of 

vermicompost could be examined for organic planting. 


This study is supported by the Islamic Azad University, 

Garmsar branch. Authors would like to thank Dr. Hamdi, 

Dr. Jabbarpoor and Dr. Bugar for their warming help. 

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DOI: 10.5897/AJAR11.2060 



Contribution of soil organic carbon levels, different 

grazing and converted rangeland on aggregates size 

distribution in the rangelands of Kermanshah Province, 

Iran 

Mohammad Gheitury 1 *, Mohammad Jafary 2 , Hossein Azarnivand 2 , Hossein Arzani 2 , Seyed 

Akbar Javady 1 and Mosayeb Heshmati 3 

1 Department of Rangeland, Faculty of Agriculture and Natural Resources (FANR), Science and Research Branch, 

Islamic Azad University, Tehran, Iran. 

2 Faculty of Natural Resources, Tehran University, Iran. 

3 Department of Watershed Management, Agriculture and Natural Research Center, Kermanshah, Iran. 

Accepted 20 January, 2012 

Soil Organic Carbon (SOC) plays a major role in nutrient cycling as the primary sink and source of plant 

nutrients, water holding, soil infiltration, soil aggregation and soil health. This study was conducted in 

the rangelands of Kermanshah, Iran within five land-use practices including normal rangeland (NR), 

overgrazed rangeland (OR), fired rangeland (FR), converted rangeland in rain-fed orchard (CRO) and 

converted rangeland in rain-fed (CRR). 57 soil samples were taken from these sites and subjected to 

soil samples analyses, especially soil organic carbon (SOC) and aggregate size distribution (ASD). 

Results showed that the respective mean SOC in the NR, OR, FR, CRO and CRR include 3.32, 1.16, 1.02, 

2.13 and 1.22%. There was significantly (P≤ 0.05%) higher in NR than others. Course aggregate class in 

the NR and CRO were significantly (P≤ 0.05%) more than others due to light grazing and higher SOC 

while fine aggregate size was found significantly different from each other. Fine aggregate size was 

higher in the CRR (20.42 %) and OR (18.40 %) compared to NR. It occurs through, up to down the slope 

plough and lower SOC value. The fire and overgrazing are second and third improper activities which 

negatively affect SOC and soil aggregation. 

Key words: Aggregate size distribution, normal rangeland, soil organic carbon, different grazing, converted 

rangeland. 

INTRODUCTION 

Soil Organic Carbon (SOC) plays a major role in nutrient 

cycling as the primary sink and source of plant nutrients, 

water holding, soil infiltration, soil aggregation and soil 

*Corresponding author. E-mail: m_ghatori50@yahoo.com. Tel: 

+989188301350. ‏ 

Abbreviations: CRR, converted rangeland in rain-fed; CRO, 

converted rangeland in rain-fed orchard; FR, fired rangeland; 

NR, normal rangeland; OR, overgrazed rangeland; SOC, soil 

organic carbon. 

health (Lal, 1998; Youjun et al., 2007). Due to its multi- 

functions, SOC depletion results from both on- and offsite 

impacts of land degradation, especially carbon 

dioxide (CO2) emission. The CO2 emission contributes to 

serious climate changes such as global warming, making 

anthropogenic carbon flux a main concern among 

relevant experts and decision makers during last and 

current decades. There are several sources for CO2 

emission such as fossil fuel combustion and agricultural 

activities, as well as soil degradation. It is estimated that 

CO2 concentration in the atmosphere has been increasing 

from 285 ppm at the end of the nineteenth century to

about 366 ppm in 1998 (FAO, 2001). Plant residue is the 

main source of SOC in the semi-arid regions which can 

enhance macro aggregates (0.25 to 50 mm) of the soil. 

Baladok (2000) reported that small micro aggregates 

(


Figure 1. Location of study area (Kermanshah province) in Iran. 

grazed rangeland (OR); iii) fired rangeland (FR); iv) converted 

rangeland in rain-fed orchard (CRO); and v) converted rangeland in 

rain-fed (CRR). 

Soil sampling and analyses 

57 soil samples were taken from 0 to 20 cm depth during 

September to November 2010 in the five land-use patterns as was 

described in site selection and land use patterns. The soil samples 

were air-dried and sieved through 2 mm mesh for physico-chemical 

analyses including particles size distributions, aggregate size 

distribution, soil organic carbon, pH, EC and calcite. The 

hydrometer method (FAO, 1974) was used for determination of soil 

particles size distributions as outlined by Ryan et al. (2001). Soil 

aggregate distribution was determined using wet sieving. 50 g soil 

(< 5 mm) was subject to soil aggregates analysis. The aggregates 

were categorized into five size fractions including very coarse (5.0 

to 2.0 mm), coarse (2.0 to 1.0 mm), moderate (1.0 to 0.250 mm), 

fine (0.250 to 0.05 mm) and very fine (

Statistical analysis 

The statistical analyses including ANOVA and regression carried 

out using SPSS software (version 19). Means comparing analysis 

was done between soil variables of each site and index site (normal 

rangeland) through one-way ANOVA (NSK procedure) at P = 0.05. 

RESULTS AND DISCUSSION 

Land-use practices properties 

Normal rangeland (NR) is characterized by natural 

vegetation cover, relatively good condition, light animal 

grazing and low soil erosion. Respective elevation 

ranges, average slope, main slope direction and mean 

annual precipitation are 1500 to 2245 m (above sea 

level), 30%, northern aspect and 500 mm. Soil depth in 

this site is shallow mainly about 40 cm: 

i) Overgrazed rangeland (OR) that suffered livestock 

overgrazing, diminishing of desirable plant species such 

as Festuca ovina and Medicago sativa (plant biodiversity 

reduction), compacted soil due to heavy animal traffic 

(through animal’s hooves), high potential of run-off, soil 

erosion hazard, especially inter-rill and rill erosions. The 

vegetation cover of this is dominated by annual grasses. 

Soil is relatively deeper, but stoniness and gravels at the 

surface soil are considerable. Average annual 

precipitation is 550 mm and minimum and maximum 

altitudes above sea level include 1533 to 2153 m with 

both northern and southern slope directions. 

ii) Fired rangeland (FR) that during last 10 years was 

subject to annual fire incidence especially arson fires. 

During field verification, it was seen that both vegetation 

and surface soil have been negatively affected by fire. 

Bare soil and gravels were more apparent than plant 

cover. Most parts of biannual and perennials plants were 

diminished by fire while annual plants species dominated 

in the site. Topographical conditions are almost the same 

as OR site. 

iii) Converted rangeland in rain-fed orchards (CRO) which 

for more than 10 years are planted by almond trees and 

vineyards. These areas mainly located in vicinity of 

smallholder’s lands. Field survey showed that surface 

water is harvested through simple micro catchments for 

supplemental irrigation. Average elevation is 1547 m 

above sea level and is dominated by northern slope 

aspect. The slope steepness varies from 10 to 40% 

which is highest in Paveh site and gentle in Ravansar site 

(Figure 1). 

iv) Converted rangeland in the rain-fed (CRR) that carried 

out mainly during recent 10 years. Land-use pattern in 

this site characterized by up-down the slope tillage, 

annual crops (wheat, barley, and chickpea) cultivation 

and low crops yield due to shallow soil depth and hill 

slope. It was seen that this site contribute to siltation 

problem due to improper tillage practices. It is a common 

Gheitury et al. 2625 

activity in the western part of Iran which is due to 

considerable annual precipitation (about 450 mm/yr) 

contributing to soil erosion hazard (inter-rill and rill 

erosion) and high runoff coefficient. The distributions of 

these sites are shown in Figure 1. 

Effects of land-use practices on soil characteristics 

Soil organic carbon 

Soil organic carbon (SOC) levels in the different land-use 

practices are presented in Table 2. Mean level of SOC in 

the NR, OR, FR, CRO and CRR are 3.32, 1.16, 1.02, 

2.13 and 1.22% respectively. The difference between 

minimum and maximum levels of SOC were found as 

0.68 and 3.93% in OR and NR, respectively. The ANOVA 

analysis of SOC among land-use practices explored that 

there were three significant levels of SOC for land-use 

practice (P≤ 0.05%). The lower SOC value belong to OR, 

FR and CRR while this value in the NR is significantly 

higher than others. In addition, this value for CRO was 

moderate among these sites (Table 1). The highest level 

of SOC in the NR (3.32%) is related to considerable 

vegetation cover that was found more than 60% during 

field verification as well as low soil erosion hazards. The 

study of Schuman et al. (2002) showed that proper 

grazing management in the USA enhanced 100 to 300 

kg/ha/year soil organic carbon which was up to 600 

kg/ha/year for new grasslands. In contrast, fire, heavy 

grazing and up-down slope tillage practices resulted in 

significant reduction of SOC in FR, OR and CRR sites, 

respectively which negatively affect balance between 

input and fluxes of SOC. 

The effect of different land use type on organic matter 

content is dependent on a balance between organic 

matter inputs and the degradative effect of the way of 

tillage and reaping (Liu et al., 2006). Also, an 

investigation by Li et al. (2008) showed that heavy sheep 

grazing decreased about 16.5 kg of OC per ha. 

Calcite 

The respective average level of calcite in CRO, OR, NR, 

CRR and FR were 11.76, 13.25, 16.94, 18.37 and 

19.66%. The minimum and maximum calcite content 

were found in the rain-fed orchard and fired rangeland, 

respectively. Statistical analyses showed that there was 

no significant difference (P ≤ 0.01%) among all sites for 

soil calcite variable (Table 3). It is due to geological 

properties which are dominated by calcareous 

sedimentary formations. High level of calcite 

consequently affects pH that is alkaline in all sites. 

Soil pH 

As given in Tables 2 and 3, mean soil pH in the study


Table 1. Mean, minimum, maximum, SD, CV and Skewness of soil variables of rangeland in Kermanshah province, Iran. 

Soil variable Land-use Mean Min. Max. SD CV (%) Skewness 

OC (%) 

Calcite (%) 

pH 

EC (dSm -1 ) 

Aggregate size distribution (%) 

2-5 (mm) 

1-2 (mm) 

0.250-1 (mm) 

0.05-0.25 (mm) 

>50 (mm) 

NR 3.32 1.94 3.93 0.64 0.41 -1.08 

OR 1.16 0.68 1.63 0.28 0.08 0.02 

FR 1.02 1.14 2.47 0.44 0.19 0.97 

CRO 2.13 1.23 3.73 0.81 0.66 0.67 

CRR 1.22 0.84 2.01 0.35 0.12 1.02 

NR 16.94 3.00 54.80 16.47 271.13 1.24 

OR 13.25 3.80 21.70 5.58 31.19 -0.19 

FR 19.66 4.00 47.40 15.70 246.57 0.78 

CRO 14.99 6.50 48.00 12.08 145.86 2.71 

CRR 18.38 6.00 32.50 9.57 91.585 0.31 

NR 7.41 7.14 7.79 0.18 0.03 0.71 

OR 7.64 7.43 7.90 0.16 0.02 0.44 

FR 7.61 7.51 7.78 0.08 0.08 0.69 

CRO 7.65 7.47 7.76 0.09 0.09 -0.82 

CRR 7.63 7.38 7.82 0.12 0.01 -0.56 

NR 0.59 0.40 0.78 0.12 0.01 0.12 

OR 0.47 0.29 0.62 0.11 0.01 -0.24 

FR 0.59 0.50 0.67 0.06 0.01 -0.20 

CRO 0.46 0.30 0.60 0.11 0.01 -0.36 

CRR 0.49 0.35 0.71 0.13 0.02 0.96 

NR 30.81 11.81 72.23 17.31 299.58 0.96 

OR 24.04 15.24 32.83 4.28 18.30 -0.13 

FR 30.53 6.20 47.63 12.11 146.66 -0.34 

CRO 7.21 0.90 13.16 3.76 14.12 0.06 

CRR 7.41 3.46 16.90 3.65 13.35 1.38 

NR 13.67 4.80 20.65 5.41 29.26 -0.16 

OR 16.01 10.10 19.94 3.07 9.42 -0.41 

FR 35.50 15.92 65.58 16.34 266.83 0.41 

CRO 18.39 10.82 28.06 6.75 45.52 0.29 

CRR 16.43 8.07 23.83 5.35 28.59 -0.26 

NR 13.61 3.73 30.14 8.80 77.37 0.71 

OR 21.11 13.24 29.02 4.84 23.41 -0.15 

FR 40.83 33.57 58.84 7.76 60.14 1.54 

CRO 11.92 4.72 27.00 7.50 56.23 1.14 

CRR 12.53 5.58 20.02 4.75 22.56 0.35 

NR 21.55 7.63 36.60 11.05 122.10 -0.04 

OR 22.16 13.52 31.18 6.58 43.24 -0.09 

FR 33.15 21.77 53.82 10.94 119.61 0.76 

CRO 13.59 2.66 24.64 7.92 62.69 -0.16 

CRR 9.55 7.40 18.82 3.40 11.55 2.71 

NR 11.80 5.32 21.38 5.44 29.60 0.39 

OR 15.29 12.37 21.90 2.95 8.71 1.30 

FR 34.37 17.54 58.44 15.20 231.03 0.53 

CRO 20.42 9.63 32.53 8.63 74.52 0.06 

CRR 18.12 7.35 24.60 5.57 31.04 -0.99

Table 2. Soil EC, pH, calcite, SOC, SPD and aggregate size distribution in different land-use practices of rangeland in Kermanshah province, Iran. 

Land-use 

Normal rangeland 

Overgrazed rangeland 

Fired rangeland 

Converted rangeland into rain-fed orchard 


Ec 

(mDm -1 ) 

pH 

Calcite 

(%) 

SOC 1 

(%) 

SPD 2 (%) 

Sand Silt Clay 

2-5 

(mm) 

Aggregate size distribution (%) 

1-2 0.25-1 0.05-0.50 < 0.05 

(mm) (mm) (mm) (mm) 

0.78 7.04 3 3.85 14.2 .51.0 34.8 15.96 20.00 38.35 12.91 12.77 

0.4 7.5 3.2 3.84 26.2 55.4 18.4 26.96 22.06 32.16 11.12 7.70 

0.75 7.36 3 3.2 11.2 48 40.8 72.23 15.24 6.20 0.90 5.42 

0.55 7.73 17 2.66 9.6 51 39.4 19.18 23.79 40.74 6.37 9.92 

0.64 7.68 22.5 1.94 9.2 44 46.8 16.55 18.13 35.25 13.16 16.90 

0.62 7.45 5 3.74 13.2 44 42.8 52.44 23.12 15.22 3.16 6.06 

0.59 7.61 4.5 2.22 11.6 48 40.4 35.45 32.83 20.84 7.35 3.52 

0.6 7.76 6.5 3.93 17.6 50 32.4 13.14 22.70 45.22 11.07 7.86 

0.52 7.76 6.5 3.89 18 46 36 38.32 25.27 25.86 4.97 5.58 

0.48 7.6 8.5 3.7 10 32 58 45.04 24.58 23.10 2.30 4.98 

0.75 7.59 14 3.8 22 40 38 11.81 25.46 47.63 7.02 8.08 

0.47 7.76 26.5 3.68 24 40 36 14.42 25.77 44.93 7.10 7.79 

0.72 7.6 34.8 3.16 19.2 40 40.8 22.28 25.22 36.23 8.73 7.54 

0.24 7.17 54.8 3.36 14.8 42.4 42.8 39.61 27.71 21.22 8.00 3.46 

0.57 7.21 44.3 2.89 22 33.5 44.5 38.82 28.72 24.97 3.96 3.52 

0.38 7.59 20 1.25 3.2 43.4 53.4 11.59 14.44 51.49 10.82 11.65 

0.29 7.9 21.7 0.9 2.2 40.4 57.4 4.80 10.10 65.58 11.45 8.07 

0.36 7.81 14.5 0.96 1.1 38.4 60.4 20.02 18.76 38.42 11.60 11.20 

0.42 7.8 6.75 0.68 1.2 69.6 29.2 7.95 13.76 40.76 18.09 19.44 

0.62 7.53 11.2 1.25 3.2 38.4 58.4 11.67 19.94 42.80 14.02 11.57 

0.56 7.54 16 1.47 4.2 39.4 56.4 9.68 15.59 40.69 14.92 19.13 

0.46 7.43 3.8 1.63 8 36 56 19.21 14.86 19.77 23.99 22.16 

0.56 7.48 13.7 1.05 13 27 60 20.65 15.17 15.92 24.42 23.83 

0.48 7.6 15.4 1.1 23 29 48 14.86 17.77 20.55 28.06 18.77 

0.55 7.7 9.5 1.29 20 43 37 16.23 19.75 19.06 26.49 18.47 

0.65 7.51 47.4 1.14 11.2 49.4 39.4 7.01 13.24 41.60 18.13 20.02 

0.67 7.56 5.2 1.45 21.2 29.4 49.4 3.73 17.91 58.84 9.58 9.94 

0.65 7.52 9.5 1.25 17 34 49 4.94 15.07 38.43 27.00 14.56 

0.56 7.59 19 1.21 13.2 38.4 48.4 15.07 20.70 47.48 8.76 7.98 

0.63 7.71 40.5 1.49 15.2 46.4 38.4 30.14 25.60 33.95 4.73 5.58 

0.59 7.78 33 2.12 17.2 38.8 44 20.25 23.58 37.29 9.65 9.23 

0.57 7.56 4 2 9.2 40 50.8 15.13 29.02 38.24 7.09 10.53 

0.52 7.66 22 1.56 11.6 43 45.4 23.40 23.68 34.79 4.72 13.40 

0.58 7.62 7 2.47 9.6 45 45.4 9.72 22.95 44.08 8.59 14.66 

0.5 7.64 9 1.42 11 44 45 6.74 19.36 33.57 20.95 19.38 

0.32 7.47 0.5 2.05 4 37.2 58.8 36.60 30.58 21.77 2.66 8.39 

0.3 7.6 2.5 2.2 3.6 30 66.4 34.38 31.18 23.70 3.22 7.52 

0.51 7.69 1.25 2.51 2 37.2 60.8 32.24 26.39 26.65 4.63 10.08


Table 2. Contd. 

Converted rangeland into rain-fed crop 

0.54 7.71 48 2 24 48 28 19.12 18.70 41.64 13.14 7.40 

0.35 7.66 15 0.83 30 26 44 7.63 13.73 53.82 16.83 7.99 

0.4 7.61 16.4 0.97 22 30 48 18.27 23.26 26.02 24.64 7.81 

0.55 7.68 12.5 1.08 20 31 49 9.10 15.20 41.52 24.04 10.14 

0.6 7.76 6.5 3.73 18 50 32 7.70 13.52 42.98 16.98 18.82 

0.52 7.76 6.5 3.39 18 46 36 27.38 25.01 23.93 14.57 9.12 

0.48 7.6 8.5 2.52 11 32 57 23.05 24.04 29.47 15.17 8.26 

0.71 7.48 15.3 1.18 3.2 51 45.8 5.99 17.11 58.44 11.10 7.35 

0.45 7.38 8.3 1.22 2.2 49 48.8 6.1 13.12 38.38 19.98 22.42 

0.44 7.57 10.2 1.56 5.2 40.4 54.4 5.32 19.64 45.69 11.03 18.33 

0.46 7.65 12.5 1.16 3.6 54 42.4 8.74 13.89 54.74 14.25 8.38 

0.71 7.66 9 1.39 9.6 54 36.4 6.60 12.97 53.89 11.96 14.58 

0.69 7.56 6 2.01 7.2 46 46.8 21.38 21.90 27.96 9.63 19.15 

0.43 7.82 31 1.1 12 46 42 16.74 12.96 29.04 20.30 20.95 

0.5 7.76 21.5 1.52 15.2 39 45.8 13.02 15.74 25.07 29.07 17.10 

0.35 7.64 21.25 0.93 10.4 37 52.6 10.67 12.37 22.57 29.96 24.43 

0.4 7.64 21.75 0.84 7.2 44 48.8 19.01 15.08 17.54 28.56 19.80 

0.37 7.66 31.25 0.85 5.4 39 55.6 13.64 15.36 18.17 32.53 20.31 

0.37 7.7 32.5 0.93 8.4 44 47.6 14.38 13.38 20.92 26.72 24.60 

*SOC = soil organic carbon, **SPD = soil particles distribution and ***soil sampling at the top-soil (0 to 20 cm). 

Table 3. The ANOVA analyses of soil variables in the different land-use practices of rangeland in Kermanshah province, Iran. 

Soil variable 

NR 

Land-use practice 

Sig. 

1 

OR 2 

FR 3 

CRO 4 CRC 5 

SOC (%) 3.3240 (b) 1.1580 (a) 1.2242 (a) 2.2630 (ab) 1.6110 (a) Calcite (%) 11.7650 

0.000 

(a) 13.2550 (a) 16.9400 (a) 18.3792 (a) 19.6600 (a) 0.613 (NS) 

pH 7.5213 (a) 7.6150 (a) 7.6267 (a) 7.6380 (a) 7.6540 (a) 0.212 (NS) 

EC (mDm -1 ) 0.4570 (a) 0.4680 (a) 0.4900 (a) 0.5787 (a) 0.5920 (a) 0.020 

Aggregate size distribution (%) 2-5 (mm) 30.814 (b) 13.666 (a) 13.614 (a) 21.547 (b) 11.799 (a) 0.000 

1-2 (mm) 24.043 (b) 16.014 (a) 21.111 (b) 22.161 (b) 15.293 (a) 0.000 

0.25-1 (mm) 30.528 (a) 35.505 (a) 40.827 (a) 33.151 (a) 34.368 (a) 0.414 (NS) 

0.05-0.50 (mm) 7.208 (a) 18.386 (bc) 11.920 (ab) 13.588 (abc) 20.424 (c) 0.000 

< 0.05 (mm) 7.407 (a) 16.429 (cd) 12.528 (bc) 9.553 (ab) 18.116 (d) 0.000 

1 NR = Normal rangeland, 2 OR = overgrazed rangeland, 3 FR = fired rangeland, 4 CRO = converted rangeland into orchard, 5 CRC = converted rangeland into rain-fed, NS = no 

significant.

area is 7.4 indicating the moderate alkaline soil and there 

was no significant difference (P ≤ 0.05%) in all the sites. 

However, average pH was 7.41 (NR), 7.64 (OR), 7.61 

(FR), 7.65 (CRO) and 7.63 (CRR). Alkaline pH value in 

Kermanshah Province is related to geological formation 

properties which mainly comprise limestone especially in 

surface layers. The semi-arid regions of Iran soils are 

moderately alkaline with pH value of 7.4 to 8.4 (Marx et 

al., 1999; Heshmati et al., 2011). 

Soil EC 

The minimum and maximum soil EC levels in the different 

management practices are 0.45 to 0.78 mDm -1 indicating 

low EC in the study area (Table 2). The respective soil 

EC for NR, OR, FR, CRO and CRR are 0.59, 0.47, 0.59, 

0.46 and 0.49 mDm -1 with no significant difference (P ≤ 

0.05%) among these land-use practices. This result 

showed that susceptibility of these soils to salinity hazard 

is low. The soil with low EC (less than 2 dSm -1 ) is 

categorized as the non-saline soil whose salinity effects 

are mostly negligible (Hazelton and Murphy, 2007). 

Aggregate size distribution 

Aggregate size distribution including 2.0 to 5.0, 1.0 

to 2.0, 0.250 to 1.0, 0.050 to 0.250 and


aggregates within each land-use practice and among all 

sites is roughly the same as fine aggregates. There is 

lowest value for NR while it is highest level in CRR site. 

The critical levels (18.12 and 16.43%) occurred through 

tillage and animal traffic (animal hooves) affect CRR and 

OR. The study by Hevia et al. (2003), in the agricultural 

areas of Argentina showed that linear decrease in SOC 

correlated with loss of fine soil aggregates due to erosion 

in continuous conventionally tilled. 

Conclusion 

Both soil aggregation and SOC are affected by different 

land-use practices in the rangelands of Kermanshah 

Province. Although, tillage practice is an illegal activity in 

the rangelands of Iran, it contributes to damage soil 

physical properties. The CRR activity causes significant 

reduction of SOC compared to other land-use practices. 

It also negatively influences soil aggregate size 

distribution reducing coarse and very coarse aggregates 

and adversely enhancing the fine aggregate size. 

Furthermore, after tillage practice, fire and overgrazing 

are second and third improper activities which negatively 

affect both SOC and soil aggregation. It is concluded that 

respective proper land-use practices were found in 

normal rangelands (characterized by light grazing) where 

orchard construction resulted in enhancing the SOC 

value as well as course soil aggregate ratio. 

ACKNOWLEDGMENTS 

The authors would like to acknowledge Soil conservation 

and Watershed Management Institute of Iran, Agricultural 

Research and Education Organization of Iran (AREO) 

and Department of Watershed Management, Agriculture 

and Natural Resources Research Center of Kermanshah, 

Iran for financial and technical supports. 

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DOI: 10.5897/AJAR11.1206 



Effects of Gynostemma pentaphyllum (Thunb.) Makino 

polysaccharides supplementation on exercise tolerance 

and oxidative stress induced by exhaustive exercise 

in rats 

Hongfang Wang 1 , Changjun Li 2 *, Xiaolan Wu 2 and Xiaojuan Lou 3 

1 Hunan Institute of Engineering, Xiangtan, 411104, China. 

2 Huangshan University, No 39, Xihai Road, Tunxi District, Huangshan City, Anhui Province, 245041, China. 

3 Donghua University, Shanghai, 200051, China. 


The purpose of this study was to evaluate the effects of Gynostemma pentaphyllum (Thunb.) Makino 

polysaccharides (GPMP) supplementation on exercise tolerance and oxidative stress induced by 

exhaustive exercise. Male rats were divided into 5 groups of 10 animals each. The first, second, third 

and forth groups designated as PGP treatment group was administered with GPMP of 50, 100, 200 and 

400 mg/kg body weight by gavage every day, respectively. The fifth group designated as control group 

was administered with the equal volume of distilled water. After 30 days, exhaustive swimming exercise 

of rats was performed, and then the exhaustive swimming time, liver glycogen level, antioxidant 

enzymes activities and MDA concentrations were determined. Results of the aforementioned study 

showed that GPMP supplementation prolonged exhaustive swimming time and improved liver glycogen 

reserve, which suggested that GPMP supplementation improved exercise tolerance. Furthermore, 

GPMP supplementation could promote increases in the activities of super oxide dismutase (SOD), 

catalase (CAT) and glutathione peroxidase (GPH-Px), and reduce MDA concentrations, which suggested 

that PGP supplementation reduced oxidative stress induced by exhaustive exercise. 

Key words: Gynostemma pentaphyllum (Thunb.) Makino, polysaccharides, exercise tolerance, oxidative 

stress, rat. 

INTRODUCTION 

Gynostemma pentaphyllum (Thunb.) Makin (botanical 

name) or Jiao-gu-lan (Chinese name), a perennial 

creeping herb distributed in Japan, Korea, China, and 

Southeast Asia, is praised in China as Xian-cao (the herb 

of immortality) (Yin et al., 2004). For hundreds of years, 

this plant has been regarded as a traditional Chinese 

medicine or a folk medicine used for heat clearing, 

detoxification, and as an anti-tussive and expectorant for 

relieving cough and chronic bronchitis (Zhang and Sun, 

*Corresponding author. E-mail: lichangjunm@yeah.net. Tel: 

+86-13905596767. Fax: +86-0559-2546550. 

1994; Megalli et al., 2005; Liu et al., 2008). In recent 

decades, pharmacological studies have revealed that G. 

pentaphyllum (Thunb.) Makin has many bioactivities, 

including antimicrobia, anti-cancer, anti-aging, antifatigue, 

anti-ulcer, hypolipidemic and immuno-modulatory 

qualities (Wang et al., 2002; Rujjanawate et al., 2004; 

Megalli et al., 2006; Yeo et al., 2008; Srichana et al., 

2011; Schild et al., 2010; Long, 2010). G. pentaphyllum 

(Thunb.) Makin contains saponins, polysaccharides, 

flavonoids, organic acids and trace elements and other 

chemicals (Zhang et al., 2007). To date, its biological 

activities are mainly attributed to saponins (triterpene 

glycosides or gypenosides) (Kao et al., 2008; Xie et al., 

2010). However, recent studies have suggested that the 

polysaccharide from G. pentaphyllum (Thunb.) Makin

(GPMP) also exhibit significant bioactivities, including 

anti-aging, anti-fatigue and improving immune 

competence (Luo and Wang, 2005; Chi et al., 2008; Yang 

et al., 2008). In addition, GPMP showed scavenging 

activity against superoxide radicals and inhibitory effects 

on selfoxidation of 1,2,2-phentriol (Wang and Luo, 

2007a), suggesting its potential as an antioxidant. 

Exhaustive exercise is often associated with an 

increase in the production of free radicals and reactive 

oxygen species (ROS) in various tissues, which results in 

oxidative stress (Sen et al., 1994; Aguilóet al., 2005; 

Morillas-Ruiz et al., 2006). Oxidative stress can induce 

adverse effects on health and well being. Even moderate 

exercise may increase ROS production exceeding the 

capacity of antioxidant defences (Aguiló et al., 2005). 

Over the last 30 years, it has been indicated that ROS 

can lead to the destruction of tissue and cell 

macromolecules such as lipids, proteins and nucleic 

acids (Perse et al., 2009; Miyazaki et al., 2001). The 

mechanisms implicated in ROS formation during 

exhaustive exercise are thought to include catecholamine 

autoxidation, the reperfusion of ischemic tissues, 

prostanoid metabolism, altered calcium homeostasis, and 

mechanical stress (particularly in the case of eccentric 

exercise, which has been shown to initiate cytokine 

activity, thus, playing a causal role in inflammation (Jówko 

et al., 2011). Growing evidence has indicated that 

exogenous antioxidants, primarily obtained as nutrients 

or nutritional supplements, may help to counteract the 

exhaustive exercise-induced oxidative stress (Peake and 

Suzuki, 2004; Watson et al., 2005; Gomez-Cabrera et al., 

2006). 

GPMP has been reported to be a potential antioxidant 

(Wang and Luo, 2007a; Shi et al., 2009). However, the 

effects of GPMP supplementation on oxidative stress 

induced by exhaustive exercise are still poorly 

understood. Therefore, the purpose of this study was to 

investigate the effects of GPMP supplementation on 

exercise tolerance and oxidative stress induced by 

exhaustive exercise. 


Plant materials and reagents 

The commercial diagnostic kits of liver glycogen, super oxide 

dismutase (SOD), catalase (CAT), glutathione peroxidase (GPH- 

Px) and malondialdehyde (MDA) were purchased from the 

Jianchen Bioengineering Institute (Nanjing, China). Other chemicals 

and biochemicals were of analytical grade and were purchased 

from Sigma Chem. Co. (St. Louis, MO, USA) and Changsha 

Pharmaceutical Co. (Changsha, China) unless otherwise indicated. 

Dried G. pentaphyllum (Thunb.) Makin was purchased from the 

Huangshan Pharmaceutical Company (Huangshan, China) and 

authenticated by Dr. Fang J. Y. A voucher specimen (No. 037009) 

was deposited in the herbarium of the Laboratory of Pharmaceutical 

Sciences, Huangshan University (Huangshan, China). The 

materials were ground separately into powder using a miller before 

extraction of the crude polysaccharides. 

Li et al. 2633 

Gynostemma pentaphyllum (Thunb.) Makin polysaccharides 

preparation 

The G. pentaphyllum (Thunb.) Makin powder (250 g) was extracted 

with 95% ethanol at 50°C for 6 h, dried, and then extracted with 

distilled water at 95°C for 1.5 h twice. After each extraction, the 

soluble polymers were separated from residues by Wltration, and 

extracts were combined, concentrated and dialyzed against running 

water for 48 h. The aforementioned extract was submitted to 

graded precipitation with four volumes of ethanol and the mixture 

was kept overnight at 4°C to precipitate the polysaccharides. The 

precipitate was collected by centrifugation, washed successively 

with ethanol and ether, and dried at reduced pressure (Wang and 

Luo, 2007b). Then, the crude polysaccharides from G. pentaphyllum 

(Thunb.) Makin (GPMP) was obtained. The content of 

polysaccharide was determined by the phenol-sulphuric acid 

method (Dubois et al., 1956) and expressed as glucose 

equivalents. The glucose equivalent was 217.4 μg/mg of GPMP. 

Animals and grouping 

Male rats each weighing 180 to 220 g of Sprague Dawley strain 

were obtained from the Experimental Animal Center of Anhui 

Province, China (SPF grade) and acclimated for 1 week. They were 

housed in a standard animal facility under controlled environmental 

conditions at room temperature of 22 ± 2°C and 12-h light-dark 

cycle, and received a standard pellet diet and water ad libitum. All 

animal (used in this experiment) handling procedures were 

performed in strict accordance with the P. R. China legislation for 

the use and care of laboratory animals, with the guidelines 

established by Institute for Experimental Animals of Huangshan 

University, and were approved by the College committee for animal 

experiments. The rats were divided into 5 groups of 10 animals 

each. The first, second, third and forth group designated as PGP 

treatment group was administered with GPMP of 50, 100, 200 and 

400 mg/kg body weight by gavage daily for 30 consecutive days, 

respectively. GPMP in the present study were dissolved in a small 

amount of distilled water. The fifth group designated as control 

group was administered with the equal volume of distilled water by 

gavage daily for 30 consecutive days. Body weights were 

measured by electronic balance at 0 (pre-trial) and 30 days after 

the administration of GPMP. 

Exhaustive swimming exercise 

Exhaustive swimming exercise was carried out as described in the 

literature (Yildiz et al., 2009; Perse et al., 2009). Thirty consecutive 

days later, the rats exercised in acrylic plastic pool (90 × 45 × 45 

cm) filled with water (28 ± 1°C) to a depth of 37 cm. The rats were 

loaded with a steel washer weighing approximately 7% of their body 

weight attached to the tails, which forced the rats to maintain 

continuous rapid leg movement. The uncoordinated movements 

and staying under the water for 10 s without swimming at the 

surface were accepted as the exhaustion criteria of the rats 

(Dawson and Horvath, 1970). Exhaustive swimming time was 

recorded as minute for each rat. 

Tissue preparation 

All animals were sacrificed by decapitation while under ketaset 

anaesthesia (20 mg/kg body weight) immediately at the end of 

swimming exercise on the 30th day. The gastrocnemius muscle and 

liver tissues were collected. The liver tissue was immediately rinsed 

with ice-cold 0.9% NaCl solution, dried with paper towels and 

weighed for determining the liver index. Then, all the tissues were


Body weights (g) 

300 

250 

200 

150 

100 

50 

0 

A 

0 days 30 days 

First Second Third Forth Fifth 

Groups 

140 

120 

100 

80 

60 

40 

20 

0 

* 

* 

* 


Figure 1. Effects of GPMP on the body weights and exhaustive swimming time of the rats. Body weights (A) and exhaustive swimming time 

(B). Note, values are expressed as means ±SD of ten; *P < 0.05, compared with the fifth (control) group. 

refrigerated at -20°C and within 2 h of refrigeration, the tissues were 

processed for determining the liver glycogen level, antioxidant 

enzymes activities and MDA concentrations in muscle tissue. 

Analytical method 

The liver glycogen level, SOD, GSH-Px, CAT activities and MDA 

concentrations were determined using commercial diagnostic kits 

following the manufacturer’s instructions. The liver index as 

calculated according to the following formula: 

Liver weight (g) 

Liver index (LI) = × 100% 

Body weight (g) 


All values were presented as the means ± SD. Statistical 

comparisons of the differences were performed using one way 

analysis of variance for repeated measures combined with the 

Newman-Keuls post hoc test. P values below 0.05 were considered 

statistically significant. 

RESULTS 

Effects of GPMP on the body weights and exhaustive 

swimming time of the rats 

As shown in Figure 1, the body weights in all GPMP 

treatment groups were of no significant difference 

compared with the fifth (control) group (P > 0.05) at 0 

(pre-trial) and 30 days after the treatment of GPMP, which 

meant GPMP had no effect on body weights. After 30 

Exhaustive swimming time (min) 

B 

Group 

days of treatment with GPMP, the exhaustive swimming 

time was much longer in all GPMP treatment groups 

compared with the fifth (control) group (P < 0.05), and the 

increase ratios were 21.54% (first group), 29.64% 

(second group), 38.32% (third group) and 48.69% (forth 

group), respectively. 

Effects of GPMP on the liver index and liver glycogen 

levels of the rats 

As shown in Figure 2, after 30 days of treatment with 

GPMP, the liver index in the experimental groups were of 

no significant difference compared with the fifth (control) 

group (P > 0.05), so GPMP had no significant effect on 

the liver index. The liver glycogen levels were much 

higher in all GPMP treatment groups compared with the 

fifth (control) group (P < 0.05), and the increase ratios 

were 27.63% (first group), 39.47% (second group), 

47.37% (third group) and 56.58% (forth group), 

respectively. 

Effects of GPMP on the antioxidant enzymes 

activities in muscle tissue of the rats 

As shown in Figure 3, the SOD activities of the second, 

third and forth groups were significantly higher than that 

of the fifth (control) group (15.92, 23.46 and 36.14% 

greater, respectively) (P < 0.05), while the first group had 

no significant differences (P > 0.05), compared with the 

fifth (control) group. The GSH-Px activities were much 

higher in all GPMP treatment groups compared with the 

*

Liver index (%) 

4.5 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

A 


Groups 

1.6 

1.2 

0.8 

0.4 

0 

* 

* 

* 



Figure 2. Effects of GPMP on the liver index and liver glycogen levels of the rats. liver index (A) and liver glycogen (B). Note, values 

are expressed as means ±SD of ten; *P < 0.05, compared with the fifth (control) group. 

SOD activity (U/ mg prot) 

50 

40 

30 

20 

10 

0 

A 

* 


CAT activity (U/ mg prot) 

* 

Group 

4 

3 

2 

1 

0 

C 

* 

* 

* 

Liver glycogen level (mg/g) 

GSH-Px(U/mg prot) 


500 

400 

300 

200 

100 

0 

* 

B 

B 

* 

Group 

* 


Figure 3. Effects of GPMP on the antioxidant enzymes activities in muscle tissue of the rats. SOD (A), GSH-Px (B) and CAT (C). 

Note, values are expressed as means ±SD of ten; *P < 0.05, compared with the fifth (control) group. 

* 

Group 

* 

Group 

* 

*


MDA concentrations (nmol/mg 

prot) 

6 

5 

4 

3 

2 

1 

0 

* 

* 

* * 


Group 

Figure 4. Effects of GPMP on the MDA concentrations in muscle tissue of the rats. Note, 

values are expressed as means ±SD of ten; *P < 0.05, compared with the fifth (control) 

group. 

control group (P < 0.05), and the increase ratios were 

19.97% (first group), 25.01% (second group), 33.09% 

(third group) and 35.95% (forth group), respectively. The 

CAT activities were much higher in all GPMP treatment 

groups compared with the control group (P < 0.05), and 

the increase ratios were 44.91% (first group), 39.20% 

(second group), 39.20% (third group) and 41.82% (forth 

group), respectively. 

Effects of GPMP on the MDA concentrations in 

muscle tissue of the rats 

As shown in Figure 4, the MDA concentrations were 

much lower in all PGP treatment groups compared with 

the control group (P < 0.05), and the decrease ratios 

were 19.42% (first group), 39.57% (second group), 

46.30% (third group) and 48.69% (forth group), 

respectively. 

DISCUSSION 

The current study determined the effects of G. 

pentaphyllum (Thunb.) Makino polysaccharides (GPMP) 

supplementation on exercise tolerance and oxidative 

stress induced by exhaustive exercise. This premise is 

based on the fact that recent studies have demonstrated 

the antioxidant effects of PGP. Swimming exercise was 

chosen as a suitable model since it is a natural behaviour 

of rodents. The method causes less mechanical stress 

and injury, and leads to a better redistribution of blood 

flow among tissues without significant variations in 

cardiac output and heart rate which in turn may minimize 

the magnitude of injury caused due to the generation of 

ROS (Aydin et al., 2007). The present study 

demonstrated that the GPMP supplementation prolonged 

exhaustive swimming time, which suggested that GPMP 

supplementation influenced the performance of 

exhaustive exercise and improved exercise tolerance. 

Furthermore, GPMP supplementation improved liver 

glycogen reserve. It was known that endurance capacity 

of body was markedly decreased if the energy was 

exhausted. As glycogen was the important resource of 

energy during exercise, the increase of glycogen stored 

in liver is an advantage to enhance the endurance of the 

exercise (Ding et al., 2009). In this study, the 

prolongation of the exhaustive swimming time exhibited 

by the rats administered with GPMP may be related to 

the improvement in the physiological function or the 

activation of energy metabolism. Exhaustive exercise is 

associated with accelerated generation of reactive 

oxygen species (ROS) that results in oxidative stress. To 

combat the deleterious effects of ROS, the body has 

some complex internal protective mechanisms like 

enzymatic defenses, which include primary antioxidative 

enzymes like super oxide dismutase (SOD), catalase 

(CAT), glutathione peroxidase (GPH-Px) and nonenzymatic 

defenses like vitamin C, vitamin E, ubiquinol 

co-enzyme Q-10 and reduced glutathione (Gupta et al., 

2009). SOD dismutates superoxide radicals to form H2O2 

and O2. GPH-Px is an enzyme responsible for reducing 

H2O2 or organic hydroperoxides to water and alcohol, 

respectively. 

CAT catalyses the breakdown of H2O2 to form water 

and O2 (Shan et al., 2011). It is known that antioxidant 

enzymes exhibit synergistic interactions by protecting 

each other from specific free radical attacks (Perse et al., 

2009). The significant decrease in the activities of SOD, 

GPH-Px and CAT in the muscle tissue after forced 

swimming may be an indication of exercise-induced 

oxidative threat (Misra et al., 2009). Malondialdehyde

(MDA) has been the most widely used parameter for 

evaluating oxidative damage to lipids, although, it is 

known that oxidative damage to amino acids, proteins 

and DNA also causes release of MDA. Previous studies 

had indicated that exhaustive exercise causes an 

increase in MDA and the MDA increasing due to excess 

oxygen radical reacting polyunsaturated acid in the 

muscle (Misra et al., 2009; Sun et al., 2010). The present 

study demonstrated that the GPMP supplementation can 

promote increases in the activities of these antioxidant 

enzymes (SOD, GPH-Px and CAT) and reduce lipid peroxidation. 

These observations suggested that GPMP 

supplementation had beneficial effects on attenuating the 

oxidative stress induced by exhaustive exercise. 

Conclusion 

The present study clearly showed that GPMP 

supplementation influenced the performance of 

exhaustive exercise and improved exercise tolerance. 

Moreover, GPMP supplementation could promote 

increases in the activities of SOD, GPH-Px and CAT, and 

reduce lipid per-oxidation, which suggested that GPMP 

supplementation was beneficial in enhancing the 

antioxidant status and inhibiting oxidative stress induced 

by exhaustive exercise. 

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DOI: 10.5897/AJAR10.747 



Climate change and adaptation of small-scale cattle and 

sheep farmers 

B. Mandleni and F.D. K. Anim* 

Department of Agriculture, Animal Health and Human Ecology, College of Agriculture and Environmental Sciences, 

University of South Africa, Florida Campus, Private Bag X6 Florida 1710, South Africa. 

Accepted 24 November, 2010 

The study was conducted in the Eastern Cape Province of South Africa during the period 2005 to 2009 

to investigate factors that affected the decision of small-scale farmers who kept cattle and sheep. The 

Binary Logistic Regression model was used to investigate farmers’ decision. The results implied that a 

large number of socio-economic variables affected the decision of farmers on adaptation to climate 

change. It was concluded that the most significant factors affecting climate change and adaptation were 

non-farm income, type of weather perceived, livestock ownership, distance to weather stations, 

distance to input markets, adaptation strategies and annual average temperature. It was recommended 

that a comparison of the decision to adapt to climate change be investigated further in other areas of 

similar agro-ecological conditions to ascertain the findings of the study. 

Key words: Climate change, small-scale cattle and sheep farming, Binary logistic model. 

INTRODUCTION 

Several studies have shown significant and alarming 

negative impacts of climate change and adaptation of 

livestock farmers in different parts of the world (Hassan 

and Nhemachena, 2008; Deressa et al., 2005; Kabubo- 

Mariara, 2007). Various research findings indicate that 

the damaging effects of global temperature is increasing 

and most damages are predicted to occur in sub-Saharan 

Africa where the region already faces average high 

temperatures and low precipitation, frequent droughts 

and scarcity of both ground and surface water (IPCC, 

2001). In developing countries of Africa, including South 

Africa, global warming studies predict that by year 2100, 

increase in temperature is estimated to be in the region of 

4°C. Previous studies on climate change and adaptation 

of livestock farmers have shown that climate change 

affects livestock farming directly and indirectly (Kabubo- 

*Corresponding author. E-mail: animfdk@unisa.ac.za. Tel. 011- 

471-3231. Fax: 011-471-2260. 

Mariara, 2008). Direct effects have been observed to 

include retardation of animal growth, low quality animal 

products including hides and skins and animal production 

in general. Indirect effects have included general decline 

in quantity and quality of feedstuffs for example, pasture, 

forage, grain severity and distribution of different species 

of livestock and other effects such as increase in 

livestock diseases and pests. In particular, extreme 

temperatures resulting in drought have had devastating 

effects on livestock farming and the vulnerable rural poor 

have been left with marginal pasture and grazing lands 

(Kabubo-Mariara, 2005). 

The vulnerability of livestock farming to climate change 

is an important concern in the world and in many African 

countries and in particular South Africa where many rural 

households depend on livestock as a store of wealth. 

Over the last decade when global warming was found to 

be detrimental to fauna and flora in the world, the relative 

contribution of the agricultural sector, including livestock 

numbers, had declined. There are studies on the impact 

of climate change in agriculture in South Africa and other


Figure 1. Map of South Africa showing the study area. 

developing countries; however, there is limited research 

on its impact on livestock production particularly, cattle 

and sheep farming. Moreover, few studies have been 

undertaken especially at the provincial and district levels 

(Hassan and Nhemachena, 2008). This study addresses 

the research gaps and examines cattle and sheep 

(livestock) farmers’ decision to adapt or not to climate 

change in three district municipalities of the Eastern Cape 

Province of South Africa. The main objective of this study 

was to investigate factors that affected the decisions to 

adapt to climate change by small-scale cattle and sheep 

farmers in order to guide policy makers on adaptation 

decisions. 


This study was based on a cross-sectional household survey data 

collected from 500 household heads during the 2005 - 2009 farming 

season in three district municipalities in the Eastern Cape of South 

Africa namely: Amathole, Chris Hani and Oliver Tambo (Figure 1). 

The 500 households surveyed were from the three selected district 

municipalities based on representative agro-ecological zones and 

livestock farming systems in each municipality. The sample districts 

were selected purposefully to cover uniform or homogeneous 

characteristics of the three areas, namely: agro ecological zones, 

intensity of livestock (cattle and sheep) farming activities, average 

annual rainfall and household characteristics. The dependent 

variable in the empirical model was the two choices: the decision to 

adapt or not adapt, mentioned by households. The 500 households 

were proportionally selected according to the information on 

household sizes given by the Department of Agriculture and Rural 

Development Office. The choice of exogenous variables used in the 

analysis was guided by available literature and economic theory. 

EMPIRICAL MODEL 

The Binary Logistic Regression model (BLR) model was used to 

determine cattle and sheep (livestock) farmers’ decision to adapt or 

not to climate change. The method has been used by researchers

to analyse similar studies on livestock farmers’ choices in decision 

making on the impacts of climate change (Seo et al., 2005). The 

main advantage of the BLR over other models of discrete and 

limited dependent variables is that it allows the analysis of 

decisions across two categories, allowing the determination of 

choice of probabilities from different categories. In addition, its 

likelihood function, which is globally concave, makes it easy to 

compute. However, the main limitation is the independence of 

irrelevant alternative properties, which states that the ratio of the 

probabilities of choosing any two alternatives is independent of the 

attributes of any other alternatives in the available choice selections 

(Deressa et al., 2009). 

In BLR, a single outcome variable Yi (i=1, ...,n) follows a Bernoulli 

probability function that takes on the value 1 with probability Pi and 

0 with probability 1-Pi. Pi/1-Pi and is referred to as the odds of an 

event occurring. Pi varies over the observations as an inverse 

logistic function of a vector Xi, which includes a constant and K 

explanatory variables (Greene, 2003). The Bernoulli probability 

function can be expressed as: 

Yi� Bernoulli ( Yi 

/ Pi 

) 

(1) 

or 

� Pi 

( Yi 

� 1) 

� 

� 

� 

�1� 

Pi 

( Yi 

� 1) 

� 

k 

In = In (Odds) = 

k 

k ik 

X � 0 � � � 

(2) 

�1 

Equation (2) above is referred to as the log odds and also the logit 

and by taking the antilog of both sides, the model can also be 

expressed in odds rather than log odds, that is: 

� P ( Y � 1) 

� 

� 

� 

�1� 

Pi 

( Yi 

� 1) 

� 

i i 

Odds = � 

or 

� � 

= e 

k 

k ik 

k 

X � � 

�1 

k 

k � 

� 

exp � � � k ik � 

� k� 

� 

X 

0 � 

1 

� ( 3) 

= � � k 

�0 

�k 

X k �0 

�k 

e * �e 

� e * � e 

k�1 

k 

k �1 

There are several alternatives to the BLR that might be just as 

plausible in a particular case. However, as stated above, the BLR is 

comparatively easy from a computational point of view. There are 

many tools available which can be used to estimate logistic 

regression models but in practice the BLR tends to work fairly well. 

If either of the odds or the log odds is known it is easy to figure out 

the corresponding probability which can be written as: 

' 

� odds � � exp( � � 

0 � � X ) 

P � 

= 

� 

�1� 

odds� 

� 

' � 

� �1� 

exp( � 0 � � X ) � 

The unknown α0 is a scalar constant term and β’ is a K x 1 vector 

with elements corresponding to the explanatory variables. In this 

study, the parameters of the model were estimated by maximum 

likelihood. That is, the coefficients that make the observed results 

most likely were selected. The likelihood function formed by 

X 

(5) 

(4) 

Mandleni and Anim 2641 

assuming independence over the observations can be written as: 

L( 

� 

� 

n 

1�Y 

i 

Yi 

� , ) � Px 

( 1� 

P 

i x ) 

(6) 

i 

i�1 

To random sample (xi, yi), i=1, 2,...,n, by taking logs and using 

equation (2), the log-likelihood simplified to: 

n 

� 0 , �)] 

� ��y( � � �x) 

� In( 

1� 

exp( � � x) 

} 

i�1 

(7) 

In[ 

L( 

� 

The estimator of unknown parameter α and β can be gained from 

the following equations by means of maximum- likelihood 

estimation. 

�In[ 

L( 

� , �)] 

�� 

0 

0 

�In[ 

L( 

� , �)] 

�� 

0 

0 

n 

� � 

i�1 

n 

� � 

i�1 

exp 

yi 

� 

1� 

exp 

exp 

yi 

� 

1� 

exp 

�� x� 

�� x� 

�� x� 

�� x� 

� 0 

� 0 

Since equations (8) and (9) are non-linear, the maximum likelihood 

estimators must be obtained by an iterative process, such as the 

Newto-Raphson or Davidson-Flecher-Powell or Berndt-Hall-Hall- 

Hausman algorithm (Greene, 2003). 

A statistical model based on likelihood ratio (LR) was deemed 

appropriate. This ratio was defined as follows: 

2( R 

U ) LogL 

LogL 

LR � � 

Where LogLu was defined as the log-likelihood for the unrestricted 

model and LogLr was the log-likelihood for the model with k 

parametric restrictions imposed. The likelihood ratio statistic follows 

2 

a chi-square ( � ) distribution with k degrees of freedom. 

RESULTS 

The descriptive statistics of the variables used in the 

model are presented in Table 1. The table gives the 

mean values, standard deviation and variance of the 

dichotomous endogenous variable (adaption and no 

adaption) and the exogenous variables used in the binary 

logistic model. 

Table 2 presents the results of the estimated model. 

The estimated model indicated classification rates of 

85.4% for no adaptation, 90.6% for adaptation and an 

overall classification rate of 88.7%. These results indicate 

the degree of accuracy of the model and therefore the 

reliability of the resulting estimated coefficients with their 

accompanying statistics. From the data, the dependent 

variable would explain between 56.5 and 77.4% of the 

variation in results as indicated by the diagnostics. The 

(8) 

(9)


Table 1. Descriptive statistics of variables used in the analysis. 

Variable Mean Std. dev. Variance 

Adaptation Yes = 1; No = 0 0.43 0.496 0.246 

Primary farm operation Cattle =1; Sheep =2 1.63 0.483 0.233 

Access to extension services Yes = 1; No = 0 0.25 0.435 0.189 

Total size of farming area (ha) 78.81 250.91 62957.02 

Total number of people in household 6.05 3.22 10.39 

Age group (yrs) 1= 16 - 24; 2 = 25 - 34 3= 35-49; 4= 50-64, 5= > 65 3.59 0.992 0.984 

Gender Male = 1; Female = 2 1.28 0.450 0.203 

Non- farm income per annum (R and × 10 3 )1= 16-24; 2 = 25 – 34 3 = 

35 - 49; 4 = 50 – 64 5 = > 65 

4.70 3.19 10.20 

Type of weather during 2005-2009 1 = Drought; 2 = Wind 1.84 0.371 0.137 

Temperature during 2005 – 2009 1 = Increased; 2 = Decreased 

3=Stayed the same 

Livestock production and ownership 1 = Increased; 2 = Decreased 3 = 

Numbers stayed the same 4 = n/a 

2.39 0.591 0.349 

3.79 0.683 0.466 

Access to credit 1 = Yes; 2 = No 1.38 0.487 0.237 

Access to information on climate 1.80 0.400 0.160 

1 = Yes; 2 = No Years of education (yrs) 1.62 0.977 0.954 

Distance to weather station Km 26.56 28.91 835.91 

Distance to input market (Km ) 24.06 23.00 529.27 

Barriers to adaptation 1= Lack of information; 2 = Lackof credit 3= 

Shortage of labour; 4= Land tenure system5= Poor grazing land 

Adaptation strategies 1= Planted supplementary feed;2 = Plant 

windbreaks 3 = Sold livestock; 4 = Different livestock species; 5 = 

Vaccination, 6 = Culling; 7= Migration; 8 = Changed to mixed farming 

1.35 1.690 2.857 

7.16 5.95 35.34 

Temperature °C (annual average 2005 - 2009) 12.66 81.26 9.01 

District dummy 1= Amatole; 2=Chris Hani; 3= Oliver Tambo Sample 

size = 500; Valid N (list wise) = 133 

non significance of the goodness of fit indicates that the 

model fits the data well. 

Primary farm operation had positive effect on 

adaptation. The t-value of more than unity also indicated 

10% significance of the coefficient. The mean value of 

1.63 indicated the presence of more sheep farmers than 

cattle in the study area. Judging from the coding of the 

variable “Primary farm operation’’ a plausible explanation 

of the results is that sheep farmers in the area are able to 

adapt to climate change more than cattle farmers. 

Access to extension services was positively related to 

adaptation. Among the exogenous variables it was the 

1.62 1.262 1.594 

only variable that had the highest weighting coefficient. 

The result indicated that having access to extension 

services increased the likelihood of farmers’ adaptation to 

climate change. Total size of farm area also had positive 

effect on climate change but the likelihood of farmers’ 

adaptation to climate change varied by only 0.8%. Total 

number of people in household was also positively 

related to climate change and adaptation but the 

coefficient was not statistically significant even at the 

10% level of significance. The results implied that large 

family sizes increased awareness and use of climate 

change and adaptation.

Table 2. Parameter estimates of the binary logistic model of climate change and adaptation. 


Variable β SE Wald df Sig Exp (β) 

Primary farm operation 2.583 1.573 2.696 1 0.101 13.237 

Access to extension services 34.887 2769.280 0.000 1 0.990 1.417E15 

Total size of farming area (ha) 0.008 0.004 3.386 1 0.66 1.008 

Total number of people in household 0.044 0.107 0.169 1 0.681 1.045 

Age group (yrs) -0.142 0.408 0.122 1 0.727 0.867 

Gender -0.372 0.835 0.199 1 0.656 0.689 

Non- farm income per annum (R and x 103) -0.559 0.237 5.578 1 0.018 0.572 

Type of weather during 2005-2009 -3.418 1.928 3.143 1 0.076 0.033 

Temperature during 2005 – 2009 -2.083 1.354 2.367 1 0.124 0.125 

Livestock production and ownership 1.350 0.781 2.987 1 0.084 3.857 

Access to credit 1.541 1.267 1.479 1 0.224 4.670 

Access to information -2.023 2.013 1.010 1 0.315 0.132 

Years of education -0.774 0.584 1.754 1 0.185 0.461 

Distance to weather station (Km ) -0.088 0.032 7.535 1 0.006 0.916 

Distance to input market (Km) 0.061 0.032 3.670 1 0.055 1.063 

Barriers to adaptation selections -0.467 0.631 0.549 1 0.459 0.627 

Adaptation strategies -0.311 0.164 3.604 1 0.058 0.733 

Temperature 0C(annual average 2005-2009) 0.168 0.095 3.141 1 0.076 1.182 

District dummy 0.278 0.400 0.484 1 0.487 1.321 

Constant 8.692 8.181 1.129 1 0.288 5953.741 

Diagnostics Classification Goodness of fit 

-2 Log likelihood = 63.279 No adaptation = 85.4% � 2 = 1.234 

Cox and Snell R square = 0.565 Adaptation = 90.6% df = 1 

Nagelkerke R Square =0.774 Overall = 88.7% Sig. = 0.996 

N = 500; Dependent variable = Adaptation Yes = 1; No = 0. 

DISCUSSION 

Extensive literature indicates that households with large 

sizes tend to embark upon labour intensive technology 

(Featherstone and Goodwin, 1993). Alternatively, 

research has proved that a large family is mostly inclined 

to divert part of its labour force into non-farm activities to 

generate more income and reduce consumption 

demands (Mano and Nhemachena, 2006). However, 

according to Hassan and Nhemachena (2008), the 

opportunity cost might be too low in most smallholder 

farming systems as off-farm opportunities are difficult to 

find in most cases. Households that had large sizes were 

therefore expected to have enough labour to take up 

adaptation measures in response to climate change 

(Hassan and Nhemachena, 2008). The results indicated 

that household size increased the probability of adapting 

to climate change by 4.4% although the coefficient was 

not significant. 

As mentioned by Galvin et al. (2001) the influence of 

age on farmers’ decision has mixed results. Some 

researchers have found negative relationship between 

age and farmers’ decision to choice selection (Seo et al., 

2005; Sherlund et al., 2002) while others have found 

positive relationships (Imai, 2003; Gbetibouo and 

Hassan, 2005). In this study it was hypothesised that old 

age would be associated with old farmers who wanted to 

maintain the status quo in farming and therefore resisted 

change and expected age to be negatively related to 

climate change and adaptation measures. The results 

suggested that the likelihood of old farmers responding to 

climate change and adaptation decreased by 14.2%. 

Gender is an important variable in decision taking 

among farmers. Bayard et al. (2007) have indicated that 

female farmers have been found to be more likely to 

adopt natural resource management and conservation 

practices than their male counterparts. However, studies 

have shown that the variable has no significant value in 

decision making process (Bekele and Drake, 2003). In 

this study, the results of the analysis indicated a negative 

relationship between the decision to adapt to climate 

change by farmers and the likelihood decreased by 37.2%.


The results showed that non-farm income significantly 

affected adaptation choice (P < 5%) and was also a 

strong predictor of results. Farm income represents 

additional wealth for livestock farmers. Higher income 

farmers may however be less risk averse and have 

enough access to information. For this reason, non-farm 

income showed a negative effect on the likelihood of 

adaptation. The results indicated that when livestock 

farmers have the option for nonfarm incomes, they can 

afford not to adapt to climate change. 

Type of weather and the resulting temperature 

observed during 2005 and 2009 appeared to be 

negatively correlated to climate change and adaptation. 

This variable also had significant effect on adaptation (P 

< 10%) and a relatively high predictor among the 

independent variables. Households with windy and higher 

temperatures over the survey period were less likely to 

adapt to climate change through adoption of different 

practices. Furthermore, households who perceived great 

differences in seasonal temperatures during the survey 

period were less likely to adapt to climate change. 

Empirical studies on the impact of climate change on 

agriculture indicated that climate attributes significantly 

affect net farm income and reduced adaptation (Mano 

and Nhemachena, 2006). 

As expected, livestock production and ownership 

positively affected climate change and adaptation with 

high marginal impact. The variable also had significant 

effect on adaptation (P < 10%). Livestock ownership 

plays a major role as a store of wealth in the households 

and also provides traction and manure required for 

grazing maintenance. Thus in this study the variable was 

hypothesised to have an increase in the likelihood of 

climate change and adaptation of farmers (Smith et al., 

2001). 

Access to credit had a positive impact on climate 

change and adaptation. Having access to credit 

increased the likelihood of adaptation by farmers. The 

results implied that institutional support in terms of the 

provision of credit was an important factor in promoting 

adaptation options to reduce the negative effects of 

climate change (Deressa et al., 2009). Several studies 

have shown that access to credit by farmers is an 

important determinant of the adoption of various 

technologies (Kandlinkar and Risbey, 2000). In this study 

it was hypothesised that the availability of credit to 

livestock farmers would be positively related to climate 

change and adaptation. Access to credit has been found 

to assist farmers to pay for information on agriculture. In 

this study such farmers were assumed to have been able 

to make comparative decisions on climate change and 

adaptation. Availability of financial resources would 

enable farmers to buy new breeds of livestock and other 

important inputs that they may require for the adaptation 

choices. The results suggested that access to information 

and years of education had negative impacts on famers’ 

likelihood to adapt to climate change. Education has 

been found to be negatively correlated with farmers’ 

decisions on climate change and adaptation measures 

(Gould et al., 1989) while access to information has been 

found to have mixed impacts on the decision making of 

farmers (Dolisca et al., 2006). 

Distance to weather station had a negative but 

significant (P < 1%) impact on adaptation. The results 

from this study indicated that long distances decreased 

the likelihood of adaptation by 8.8%. Distance to input 

markets was also positively and significantly (P < 10%) 

related to adaptation choices. Market access has been 

found to be an important factor in determining technology 

adoption choices among farmers (Luseno et al., 2003). 

Access to input markets allowed farmers to acquire 

inputs needed for adaptation choices such as planting of 

supplementary feed, windbreaks, purchase of new 

livestock species, vaccination etc. Zhang and Flick 

(2001) however, found that long distances to input 

markets decreased the likelihood of adaptation. 

The presence of barriers to adaptation had negative 

impact on adaptation. Choice of adaptation strategies 

had negative and significant (P < 10%) effect on 

adaptation indicating that households with proper choices 

of adaptation strategies needed not to adapt to climate 

change. Farmers who perceived higher annual mean 

temperatures over the survey period were more likely to 

adapt to climate change. The variable was also 

significant (P < 10%) determinant of the likelihood of 

adaptation. The results showed that a rise in temperature 

1°C higher than the mean increased the likelihood of 

adaptation by 16.8%. The results indicated that with more 

warming, farmers would employ various adaptation 

measures to compensate for the loss of water associated 

with increased temperatures (Deressa et al., 2009). 

Differences in agro-ecological zones in the three district 

municipalities had positive influence on adaptation 

decisions of farmers. Empirical studies on climate change 

and adaptation of farmers in Africa have shown that 

climate attributes in different agricultural zones 

significantly affected adaptation (Kurukulasuriya and 

Mendelsohn, 2006). Regional studies have also shown 

that the choice of livestock species is sensitive to climate 

change (Seo et al., 2005). 

SUMMARY 

This study examined small-scale cattle and sheep 

(livestock) farmers’ decision to adapt to climate change in 

three district municipalities of the Eastern Cape Province 

of South Africa. The main objective was to investigate 

factors that affected the decisions to adapt to climate 

change by small-scale livestock farmers. The study was

ased on a cross-sectional household survey data 

collected from 500 household heads during the 2005 - 

2009 farming season. The Binary Logistic Regression 

Model was used to determine livestock farmers’ decision 

to adapt or not to climate change. 

The results indicated that primary farm operation had 

positive effect on adaptation decision. A plausible 

explanation for the results was that the predominant 

sheep farmers in the area were able to adapt to climate 

change more than cattle farmers. Access to extension 

services was positively related to climate change and had 

the highest weighting coefficient. From the results it was 

concluded that having access to extension services 

increased the likelihood of adaptation to climate. Total 

size of farm area also had positive effect on climate 

change but the likelihood of farmers’ adaptation to 

climate change varied by only 0.8%. Total number of 

people in household was positively related to climate 

change and adaptation and the coefficient was not 

statistically significant. The results implied that large 

family sizes increased awareness of climate change and 

adaptation. 

From the results of the study it was suggested that 

household size increased the probability of farmers 

adapting to climate change. Also that the likelihood of old 

farmers responding to climate change and adaptation 

decreased by 14.2%. The results of the analysis 

indicated a negative relationship between gender and the 

decision in adapting to climate change by farmers and 

the likelihood decreased by 37.2%. Households who 

perceived great differences in seasonal temperatures 

during the survey period were less likely to adapt to 

climate change. 

Differences in agro-ecological zones in the three district 

municipalities had positive influence on adaptation 

decisions of farmers. This study confirms other empirical 

studies on climate change and adaptation of farmers in 

Africa that have shown that climate change in different 

agricultural zones significantly affected adaptation. The 

study also confirmed other regional studies that have also 

shown that the choice of livestock species is sensitive to 

climate change. 

CONCLUSIONS AND RECOMMENDATIONS 

In this study the most significant factors that affected 

climate change and adaptation were non-farm income, 

type of weather perceived, livestock production and 

ownership, distance to weather stations, distance to input 

markets, adaptation strategies and annual average 

temperature. Non-farm income significantly affected 

adaptation. From this it was concluded that households 

with non farm income could afford not to adapt to climate 

change because of other sources of income that they 


had. Type of weather and temperature perceived by the 

households during the period of study were negatively 

related to climate change and adaptation. Households 

that experienced windy and higher temperatures over the 

period of survey were less likely to adapt to climate 

change. The conclusion is that those households did not 

see the need to adapt to climate change. 

Livestock production and ownership positively affected 

adaptation with high marginal impact. This variable also 

had significant effect on adaptation. Distance to weather 

station had a negative but significant impact on 

adaptation. Conclusions were that long distances to 

weather stations decreased the likelihood of adaptation. 

Distance to input markets were also positively and 

significantly related to adaptation choices. Choice of 

adaptation strategies had negative and significant effect 

on adaptation indicating that households with proper 

choices of adaptation strategies needed not to adapt to 

climate change. Farmers who perceived higher annual 

mean temperatures over the survey period were more 

likely to adapt to climate change. This variable was also a 

significant determinant of the likelihood to adapt. From 

the results it was concluded that with more climate 

warming, farmers would employ various adaptation 

measures to compensate for the loss of water associated 

with increased temperatures. It was therefore 

recommended that a comparison of adaptation to climate 

change by small-scale livestock farmers be investigated 

in other areas of similar agro-ecological conditions to 

confirm the outcome of this study. 

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DOI: 10.5897/AJAR11.1689 



Village chicken production practices in the Amatola 

Basin of the Eastern Cape Province, South Africa 

N. M. B. Nyoni 1 and P. J. Masika 2 * 

1 Department of Livestock and Pasture Sciences, University of Fort Hare, Alice 5700, South Africa. 

2 Agricultural and Rural Development Research Institute, Faculty of Science and Agriculture, University of Fort Hare, 

P/Bag X 1314, Alice 5700. South Africa. 

Accepted 16 February, 2012 

A majority of rural households in South Africa-own village chickens which contribute significantly to 

their livelihoods, yet, there is dearth of information on production practices of this enterprise. Thus, this 

study was conducted to determine the village chicken production practices in the Amatola Basin of the 

Eastern Cape Province. Data were gathered using a questionnaire survey of 81 households. They were 

identified from seven villages using snowball’s sampling technique. Village chickens were mostly 

(60.5%; n = 49) owned by women and mainly raised to meet household food requirements. Some 

farmers (28.4%; n = 23) also occasionally sold their chickens to neighbours at an average of R50 

(USD7.55) per bird. Most chicken flocks (96.3%; n = 78) were provided with supplementary feeds and 

drinking water. Majority (93.8%; n = 76) of their households also provided some form of shelter for their 

chickens. Although, most respondents (93.8%; n = 76) confirmed the use of alternative remedies to 

control parasites and treat diseases; most chicken keepers (81.5%; n = 66) experienced chicken losses 

due to predation and health related problems. Since this study was limited to the documentation of 

village chicken production, there is the need for a further research to ascertain the extent to which 

chicken management practices and environmental variables affect village chicken production in this 

area. 

Key words: Ethno-veterinary medicines, free-range, resource-limited farmers, rural, scavenging chickens. 

INTRODUCTION 

Poultry production is an important agricultural activity for 

most rural communities in Africa. It provides rural 

households with scarce animal protein in the form of 

meat and eggs as well as being a reliable source of petty 

cash (Kalita et al., 2004; McAinsh et al., 2004; Njenga, 

2005). Rural poultry have also been reported to be used 

for traditional ceremonies and festivals in some cultures 

(Alders et al., 2007), hence, they contribute significantly 

to the livelihoods of the most vulnerable rural households 

in developing countries (Mack et al., 2005). 

It is estimated that up to 70% of poultry products in the 

*Corresponding author. E-mail: pmasika@ufh.ac.za. Fax: +27 

40 602 2583. Tel: +27 40 602 2317, +27 40 653 1154. 

developing world are produced by resource-limited 

farmers and in family-managed poultry systems (Sonaiya, 

2000), of which 80% are found in rural areas under the 

free range system (Alders and Spradbrow, 2001). 

However, rural poultry production is not rated high in the 

mainstream of national economies because of the lack of 

measurable indicators of output (Alders and Spradbrow, 

2001). Productivity levels of rural poultry in many African 

countries fall far below desirable levels. Output in terms 

of number of eggs per hen per year and flock sizes are 

low with relatively high mortality rates when compared to 

commercial poultry production (Gondwe and Wolly, 2007; 

Mapiye et al., 2008). Due to the low value resourcelimited 

farmers attached to poultry in relation to other 

livestock, farmers often are ignorant of small changes 

that could enhance the quality, health and productivity of


Table 1. Other livestock owned by Amatola basin village chicken 

farmers in the Eastern Cape Province of South Africa. 

Livestock Ownership % (n) 

Cattle and goats 27.2 (22) 

Cattle, goats and pigs 16.1 (13) 

Cattle, goats, pigs and sheep 7.4 (6) 

Pigs 4.9 (4) 

Cattle 4.9 (4) 

Goats 3.7 (3) 

Other poultry (geese and ducks) 4.9 (4) 

their flocks (Acavomic et al., 2005). An extra effort in the 

management of poultry housing, feeding, and animal 

health care will increase village chicken productivity 

significantly (Sonaiya, 2007). Furthermore, strategic 

increases in the production of rural poultry flocks will 

greatly assist in addressing the challenge of fighting 

poverty and malnutrition (Sonaiya, 2007; Gillespie and 

Flanders, 2009). 

Although, other poultry species which include ducks, 

turkeys, guinea fowl, quail, and pigeons are important in 

village systems; village chickens are the most important 

and major poultry species (Acamovic et al., 2005). 

Research on indigenous knowledge and associated 

traditional production practices of village chicken is 

limited in South Africa and yet in principle, this system 

contributes to the lives of many rural people (Swatson et 

al., 2002). Although, some studies have been conducted 

in the Limpopo and Kwa-Zulu Natal Provinces, the fact 

that village chicken production varies from area to area 

depending on the socio-economic, cultural and biological 

factors (Muchadeyi et al., 2007), makes an investigation 

imperative in the Eastern Cape Province. This will 

broaden the understanding of the significance of village 

chickens in the study area and also outline the 

challenges that farmers face. The main objective of this 

study, therefore, was to determine the village chicken 

production practices. 


Study area 

The study was conducted in the Amatola basin of the Amathole 

district situated in the Eastern Cape Province of South Africa. Out 

of 13 villages, 7 were randomly selected to participate in the study. 

Consequently, about 30% of the households were sampled per 

village. The area has an altitude of 1 807 m above sea level, and 

lies within latitude 32°31.00 -32º 45.00 S and longi tude of 26°57.00- 

27º02.00 E on the Eastern slopes of the Amatola mountain range. 

The winter season temperatures range from 7 to 20ºC, while 

summer temperatures range from 16 to 31ºC. The Amatola basin 

receives an average annual rainfall of about 580 to 800 mm (ISCW, 

2008). 

Sampling procedure and data collection 

A total of 81 structured questionnaires were administered by 

personal interviews with households which owned chickens. These 

households were identified using the snowball sampling technique, 

where respondents were asked to give referrals to other persons 

believed to fit the study requirements. Only those households who 

owned chickens and were willing to participate in the research were 

considered. Information on village chicken production was gathered 

under the following categories: household demography, livestock 

inventory, roles of village chickens, chicken nutrition, housing and 

health management, and agricultural extension services. Interviews 

were conducted with the farmers and key individuals, namely 

chairpersons of villages, herbalists and agriculture extension 

officers. Farmers’ perceptions of village chicken production 

constraints were also gathered. 


The collected data were analyzed using the Statistical Package for 

the Social Sciences (SPSS, 2009). Descriptive statistics and cross 

tabulations were computed. Chi-square (χ²) for association values 

were computed to determine the relationships between the 

ownership of village chickens and farmer’s age, and chicken flock 

sizes and ownership of larger livestock (cattle). 

RESULTS 

Household demography 

Many household heads (49.4%; n = 40) were over 60 

years of age. Most households were female headed 

(53.1%; n = 43). Although, the majority of household 

heads (85.2%; n = 69) were not employed, they had 

attained some form of education-at least up to primary 

level (58.0%; n = 47). Household sizes ranged from as 

low as 1 to 13, with an average of 7. A majority of the 

families (61.7%; n = 50) received some monthly financial 

income in the form of old age pension and government 

grants. A great portion of village chicken flocks (60.5%; n 

= 49) were owned by women, 35.8% (n = 29) were 

owned by men, and a few (3.7%; n = 3) were owned by 

children. Most village chicken flocks were owned by 

persons above 60 years of age (P < 0.05; χ² = 6.7). 

Although, ownership translated to chicken management 

in terms of decision making, other household members 

such as children played a role in looking after the 

chickens. Respondents that owned the most cattle also 

had large chicken flocks (P < 0.05; χ² = 13.2). 

Livestock inventory 

Households owned on average of 17 (±2 S.E.M.) 

chickens, with a range of 3 to 45. Most farmers (67.9%; n 

= 55) also owned cattle, goats, pigs, sheep, and other 

poultry, such as geese and ducks, as shown in Table 1. 

However, village chickens were ranked as most important 

livestock species by most farmers (60.5%; n = 49). On 

average each hen laid 11.3 eggs per clutch, with a 

hatchability of close to 68.0%. Hatchability levels were 

reported to be influenced by the effect of external

parasites (1.2%; n = 1), predation (8.6%; n = 7), 

management (32.1%; n = 26) and effects of weather 

(27.2%; n = 22). Most farmers actually preferred buying 

commercially produced eggs instead of eating those laid 

by their own chickens. Dogs were reported to eat some of 

the eggs, especially from chickens that incubated eggs 

outside in bushes or in the cattle kraals. On the average, 

5.2 chicks reached maturity. Most chicks were lost due to 

predation and ill-health (24.7%; n = 20 and 33.3%; n = 

27, respectively). Chicken production was not considered 

an economic venture by most respondents (60.5%; n = 

49). Instead they saw it as a means to cater for 

household food requirements. Most farmers (91.4%; n = 

74) did not introduce new chickens to old flocks, but the 

few who did neither inspected, vaccinated nor treated 

new chickens for diseases or parasites before introducing 

them to the flocks. 

Farmers used different criteria when selecting chickens 

to be retained for production. The majority considered 

size (63.0%; n = 51), others the breed (40.7%; n = 33), 

color (16.1%; n = 13) and yet some considered cost 

(13.6%; n = 11). Old birds and those with poor productive 

performance were consumed as a way of culling the 

flocks. 

Roles of village chickens 

Village chickens were mainly raised for consumption. 

Respondents considered village chicken meat a delicacy. 

However, there were a few (28.4%; n = 23) farmers who 

occasionally sold some of their chickens to neighbours to 

get some income. The price for a matured chicken was 

R50 (USD7.55) on the average. Most farmers (74.1%; n 

= 60) acknowledged that the market for village chickens 

was available throughout the year. However, none of the 

farmers reported selling chicken eggs but many (43.2%; 

n = 35) acknowledged consuming a few and reserving 

the rest for incubation. In most cases (64.2%; n = 52) 

village chicken eggs were regarded as only important for 

incubation purposes as a strategy to increase production. 

A few chickens (13.6%; n = 11) were used for gifts and 

donations to relatives and friends. Chicken manure was 

mostly (62.9%; n = 51) used by respondents to fertilize 

their home gardens, where they grew a range of 

vegetables. Village chickens were, however, not used in 

any rituals or traditional ceremonies. 

Nutrition 

All chicken flocks scavenged for feed; however, the 

majority of households (96.3%; n = 78) provided feed 

supplements. In some instances (21.0%; n = 17), specific 

feed were prepared for chicks. Supplementary feeds 

given to chicks were ground into smaller particles for easy 

consumption. Almost all (96.3%; n = 78) respondents 

Nyoni and Masika 2649 

threw the supplements to the ground for chickens to 

peck, while the rest used improvised feeding troughs. 

Village chickens were usually given supplementary feed 

(74.1%; n = 60) twice a day in the morning and evening, 

but in some cases (21.0%; n = 17) were fed just once a 

day, in the morning. There was, however, one 

respondent who gave supplementary feed three times a 

day (morning, noon and evening). The supplementary 

feeds were comprised of yellow maize, kitchen wastes, 

sunflower cake, grower’s mash for chicks, and/or wheat. 

Most farmers (87.7%; n = 71) bought yellow maize to 

supplement their chickens. The quantities given as 

supplementary feed, however, were based on the 

individual farmers’ judgment and varied from household 

to household. It ranged from as little as one handful 

(approximately, 100 g) of yellow maize grain to about five 

handfuls (approximately, 500 g) per day. Furthermore, 

most chicken flocks (96.3%; n = 78) were provided with 

water. This came from different sources, including wells 

(2.5%; n = 2), boreholes (7.4%; n = 6), streams (9.9%; n 

= 8), ponds (11.1%; n = 9), and taps (65.4%; n = 53). 

Housing 

Different forms of housing structures were provided for 

the chickens. However, in a few cases chickens roosted 

on trees overnight (3.7%; n = 3) and/or in open spaces 

(3.7%; n = 3), especially, in the kraals. Chicken houses 

were constructed using a wide range of materials. All 

structures were roofed with iron sheets. A few structures 

(8.6%; n = 7) had solid walls; some had wire mesh 

(14.8%; n = 12), whilst most (76.5%; n = 62) had a 

combination of iron sheets and wire mesh. Most of the 

floors were simply compacted soil (82.7%; n = 67), while 

some were either unaltered (11.1%; n = 9) or cemented 

(6.2%; n = 5). A few of the farmers provided bedding in 

the form of dry grass and/or crop residues (4.9%; n = 4). 

Most chicken houses (96.3%; n = 78) were cleaned 

approximately once a month on average. 

The type of chicken shelters provided by the farmers 

depended on availability of resources (75.3%; n = 61) 

and were designed is such a way that farmers could 

enter without complications (6.2%; n = 5). In some 

instances (18.5%; n = 15), however, the shelter provided 

was influenced by both availability of resources and 

security from theft. A majority of farmers (59.3%; n = 48) 

were of the opinion that the chicken house structures 

adversely affected the growth and development of their 

chicken flocks. However, many did not have the financial 

means to make the necessary improvements. 

Health management 

Most farmers (81.5%; n = 66) acknowledged that health 

related problems were a challenge. These ranged from


diseases (21.0%; n = 17), parasites (27.2%; n = 22), a 

combination of parasites and diseases (49.4%; n = 40), to 

wounds (2.5%; n = 2). In this context, disease refers 

specifically to a clinically evident condition resulting from 

the presence of pathogenic microbial agents, excluding 

helminths and ectoparasites. Most of the respondents 

(93.8%; n = 76) used alternative remedies, also referred 

to as ethno-veterinary medicines (EVM), to control and/or 

treat diseases and parasitic infections. The rest either did 

not know about the remedies (2.5%; n = 2) or were not 

interested in using them (3.7%; n = 3). 

Extension services 

Government agricultural extension workers have the task 

of bringing scientific knowledge to rural farmers. The 

object of their task is to improve the efficiency of 

agriculture, for instance, in chicken production. Only 6.2% 

(n = 5) of the village chicken farmers in the current study 

acknowledged having had a chance to access some 

advice or information on chicken husbandry from 

extension officers. However, the current study revealed 

no association between advice or information received by 

respondents and village chicken flock sizes (P>0.05; χ² = 

5.4). Villagers shared some relevant information with 

neighbors, usually when there was a disease outbreak or 

when marketing the chickens. 

DISCUSSION 

The average number of village chickens owned per 

household was consistent with previous studies (Aning, 

2006; Muhiye, 2007; Mwale and Masika, 2009). The 

small flock sizes may be mainly ascribed to the slow 

growth rate and poor egg production of village chickens, 

as reported by Phiri et al. (2007). In addition, predation 

and ill-health may also be preventing increases in flock 

sizes (Mapiye and Sibanda, 2005). Although, some 

farmers also owned cattle, goats and sheep, these 

livestock were generally relatively low in numbers as 

compared to chickens; hence, the latter were regarded as 

very important by most farmers. 

Ownership of chickens were predominantly by women, 

a finding consistent with Halima (2007) and Mwale and 

Masika (2009), which could be ascribed to the high 

number of female-headed households. However, in the 

male-headed households, men were the principal owners 

of village chickens, which disagrees with Mwale and 

Masika (2009) and Moreki et al. (2010). This deviation 

from the previous findings may be due to the fact that 

most men in the current study area were not employed 

and they did not have other larger livestock to 

concentrate on. Thus, to try and fulfil their responsibilities 

as principal household providers, men would retain the 

ownership of chickens. However, those men who also 

had other livestock in relatively large numbers also coowned 

village chickens with other household members. 

Selection of chickens was based on phenotypic 

characteristics similar to findings in earlier studies 

(Njenga, 2005; Mogesse, 2007). Farmers valued the size 

of the chicken because it was translated to the quantity of 

meat per bird, thus, reflecting the main role of these 

chickens - consumption. Although, village chickens were 

mainly kept for food security, they could be sold in cases 

of cash emergencies, a finding also affirmed in previous 

studies (Njenga, 2005; Mapiye et al., 2008; Mwale and 

Masika, 2009). This could be attributed to the fact that it 

is much easier to slaughter a chicken for consumption 

than other livestock such as cattle (Mwale and Masika, 

2009). In addition, other livestock in the study area were 

few in number, hence, the villagers found it imprudent to 

slaughter some for consumption. However, a study to 

quantify the chicken that farmers consume per annum will 

be worth undertaking. 

Village chickens were not used in rituals or traditional 

ceremonies, in contrast to earlier reports (Mafu and 

Masika, 2003; Mack et al., 2005). Respondents, however, 

indicated that cattle and goats were the livestock 

normally used during cultural ceremonies, a finding 

consistent with the reports from the coastal region 

(Centane district) of the Eastern Cape (Mwale and 

Masika, 2009). However, village chickens were used for 

gifts, a finding similar to that of Mwale and Masika (2009) 

in Centane. Farmers acknowledged that meat from 

village chickens was a delicacy compared to that from 

broiler (commercial) chickens. This could explain why 

they are used as gifts. 

As also reported by Mapiye et al. (2008), productivity in 

terms of number of eggs laid per clutch, chicks hatched 

per clutch and chick survival to maturity were very low. 

The reported low hatchability could have resulted from 

the effect of external parasites which tended to bite and 

irritate chickens during incubation. When chickens are 

affected by external parasites they tend to leave their 

eggs often, and may abandon them completely in some 

cases (Banjo et al., 2009). Low hatchability may have 

also resulted from production of infertile eggs, poor egg 

handling and both incorrect storage and improper 

incubation environment, as supported by Cooper (2001). 

Furthermore, microbial infection of chicken eggs caused 

by contaminated nests and poor sanitation, results in low 

hatchability (Cooper, 2001). Chicken eggs in the current 

study were regarded as important only for incubation 

purposes and not for consumption, which may have been 

a strategy to counter the low hatchability so as to grow 

their flocks. 

Although, supplementary feed was provided, village 

chickens depended mainly on scavenging for their 

nutritional needs, a finding consistent with Njenga (2005), 

Muchadeyi et al. (2007) and Mwale and Masika (2009). 

Feed supplementation was mainly maize grain, as 

observed in similar studies in Zimbabwe (Muchadeyi

et al., 2004), Ethiopia (Halima, 2007) and South Africa 

(Mwale and Masika, 2009). Not only did scavenging 

affect nutrition, it also exposed the chickens to predation, 

diseases and parasites, as also found by Acamovic et al. 

(2005). In addition, chickens at different stages of growth 

were left to compete for the same feed, a finding 

consistent with Muchadeyi et al. (2004) who reported that 

the provision of supplementary feed was indiscriminate 

and all age groups typically competed for the 

supplement. This non-preferential feeding might result in 

weaker groups, such as chicks, getting sub-optimal 

nutrition (Tadelle and Ogle, 2001). Moreover, since the 

supplements were thrown to the ground, feed losses 

(especially, of small grains) were inevitable and the 

chances of chicken exposure to internal parasites were 

increased. 

The finding in the current study that the quantities given 

as supplementary feeds were based on the individual 

farmers’ judgment and varied from household to 

household as was also observed by Mapiye et al. (2008). 

Chickens are known to require different amount of 

nutrients depending on the production stage (Tadelle and 

Ogle, 2001; Ogle et al., 2004). It is not clear, however, 

whether the chickens got enough nutrients through 

scavenging and supplementary feeding. Adequate hen 

nutrition is vital for ensuring fertility, increasing the 

number of eggs laid, and ensuring good survival rates of 

hatched chicks (Cooper, 2001). The fluctuations in the 

supply of feed resources require appropriate strategic 

supplementation programmes (Muchadeyi et al., 2005). 

Frequency of feeding in terms of when, what, and how to 

feed and the quantity to feed are important aspects to 

consider in developing strategies to improve the nutrition 

of village chickens (Mapiye and Sibanda, 2005; Mapiye et 

al., 2008). Most farmers in the current study provided 

clean water for their chickens, a finding in agreement with 

Mwale and Masika (2009). This could be due to the 

proximity and availability of clean water in the area of 

study. 

Village chickens are vulnerable to theft and easily 

predated upon when not sheltered. The finding of this 

study that most chicken flocks were provided with 

housing is consistent with some recent studies 

(Muchadeyi et al., 2007; Mwale and Masika, 2009). 

Provision of shelter for chickens mainly during the night 

was in agreement with previous reports (Muchadeyi et 

al., 2004; Mwale and Masika, 2009). Most chicken 

keepers resorted to cheap and locally available materials 

such as mud, wooden poles, and corrugated sheets, as 

also reported by Mapiye et al. (2008). 

Although, these village chickens contributed 

significantly towards the livelihoods of rural people in the 

study area in terms of food security, they were highly 

susceptible not only to parasite infestation, as Mwale and 

Masika (2009) reported, but also diseases. The disease 

challenge has previously been attributed to different ages 

in a flock, possible transfer from wild birds, and constant 

Nyoni and Masika 2651 

use of the land by poultry thereby, facilitating the 

numbers of parasites build up (Acamovic et al., 2005). 

Various conventional drugs for controlling parasites and 

treating diseases of chickens have been effectively 

developed globally (Maphosa et al., 2004), however, 

most respondents were resource-limited and could not 

afford to purchase these drugs, a finding which is also 

supported by Mwale et al. (2005). Thus, most of them 

resorted to the use of alternative remedies when a 

disease or parasitic infection presented itself as a 

measure of control or treatment, respectively (Mathius- 

Mundy and McCorkle 1989; Mwale et al., 2005). 

However, information in the Eastern Cape Province of 

South Africa on the use of Ethno-Veterinary Medicine 

(EVM) in village chickens are very limited (Mwale and 

Masika, 2009). EVM can play a significant role in 

grassroots development, which seeks to empower people 

by enhancing the use of their own knowledge and 

resources (Mwale and Masika, 2009). It will be therefore, 

imperative for researchers to validate these EVM to 

ascertain their efficacy and document the findings for 

current and future use. 

Village chicken production was carried out with no 

extension support, a finding consistent with a study 

conducted in Limpopo Province (Swatson et al., 2002). 

Farmers in the current study made use of their 

indigenous poultry rearing knowledge acquired over a 

long period of time, which is consistent with the findings 

of Swatson et al. (2002). Although, farmers shared some 

information on chicken production, there were no farmer 

organizations from which households could obtain 

chicken village chicken husbandry information or 

education. Village chicken production has not been 

accorded the recognition it requires in terms of 

development and policy support by governmental 

institutions and non-governmental organizations, yet, it 

contributes significantly to the livelihoods of rural people. 

Conclusion 

The current study revealed that village chickens play a 

very important role in the livelihoods of rural farmers by 

meeting their family food needs. 

Chicken flocks were provided with supplementary 

feeds, clean water and some form of shelter. Predation 

and health related problems were the main causes for 

chicken losses. 

Farmers used alternative remedies to control parasitic 

infestations and treat diseases but they did not have any 

chicken husbandry education which may have led to 

mismanagement of flocks. Since this study is limited to 

the documentation of village chicken production in 

Amatola basin, there is the need for a further research to 

ascertain the extent to which chicken management 

practices and environmental variables affect village 

chicken productivity in the area of study.



The financial assistance from the National Research 

Foundation of South Africa (Project number UID 62298) 

is acknowledged with gratitude. Authors, also thankfully 

acknowledged the support from the Agricultural and Rural 

Development Research Institute (ARDRI), the 

Department of Livestock and Pasture Science and the 

farmers in the Amatola Basin, Eastern Cape Province. 

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DOI: 10.5897/AJAR11.385 



The relationship between agricultural intensification 

and sustainability in China 

Hong-Wei Chen 1,2 and Da-Fu Wu 2,3 * 

1 College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China. 

2 Henan Institute of Science and Technology, Xinxiang, 453003, China. 

3 Institute of Soil Science, the Chinese Academy of Sciences, Nanjing, 21008, China. 


According to the principles of agricultural sustainability, combining with utilizing the methods of 

principal components analysis, we made full use of statistical data from 1949 to 1998, Yujiang County, 

Jiangxi province. At the same time, the assessing system included 28 formed. The results showed that 

agricultural sustainability rose increasingly with agricultural intensification growth. However, both of 

them kept the same pace. The production sustainability index has been increased from 0.0808 in 1949 

to 0.1496 in 1998. The annual raising rate has reached 1.265% during 50 years in Yujiang County; the 

economic sustainability index leaped from 0.0166 in 1949 to 0.4093 in 1998. It is enlarged 24.65 times 

compared with that of 1949. The ecological sustainability index has augmented 0.125% yearly since 

1949. In general, the sustainability index reached 0.7533 in 1998. When the agricultural intensification is 

raised 1 unit, the sustainability would be enhanced by rate of 0.0001. 

Key words: Yujiang County, sustainability, agriculture, intensification. 

INTRODUCTION 

Many definitions about sustainable agriculture and 

sustainability have been put forward since 1987. A widely 

accepted definition for sustainable agriculture was the 

one adopted in 1988 by The American Society of 

Agronomy (1988), namely, the sustainable agriculture 

enhances environmental quality and the resource on 

which the development of agriculture depends, and 

provides for basic human food and fiber needs, and is 

economically viable, and enhances the quality of life for 

farmers and society as a whole. The term sustainability 

was first advanced in 1980 by the International Union for 

the Conservation of Nature and National Resources 

(Lele, 1991). While sustainability is a complex and wideranging 

concept and sustainability properties dimensions 

vary widely (Filson, 2004). Agricultural intensification can 

be technically defined as an increase in agricultural 

production per unit of inputs (which may be labour, land, 

*Corresponding author. E-mail: bshxld@gmail.com. Tel: 

+86 373 3693629. 

time, fertilizer, seed, feed or cash). For practical 

purposes, intensification occurs when there is an 

increase in the total volume of agricultural production that 

results from a higher productivity of inputs, or agricultural 

production is maintained while certain inputs are 

decreased (FAO, 2004). They therefore employ relatively 

larger investments in land, labor, and capital than was 

traditionally the case when smaller, more mixed farming 

operations predominated (Filson, 2004). 

Nearly 100% of farmers in China use improved 

varieties of rice, wheat and maize (Huang et al., 1999), 

together with subsequent investments in water 

controlling, intensification of chemical input use. 

According to the state statistical data, the fertilizer 

application reached 298 kg·hm -2 in 2004. Officially, the 

percentage of irrigated arable land has risen from 16% in 

1950 to nearly 50% in 1990’s (Conway, 1997). Since 

1990s, it has been paid high attention to the sustainable 

development of intensive Chinese agriculture which 

follows the same definition of FAO, Earth Summit 

document. During the same period, some scientists have 

voiced concerns that the intensification of farming


systems may not be sustainable because of systematic 

degradation of the resource base and environment 

(Pingal et al., 1994). Brown (Brown, 1995; Brown and 

Halweil, 1998) addressed “who will feed China?” and 

“China’s water shortage could shake world food security”. 

China is not only the world’s most populous country, but 

also its economy grows fast. China is faced with an 

extraordinary challenge (Brown, 2005). Could not the 

intensive China’s agriculture develop sustainably? 

Concerning reports have not been found, so the author 

according the principles of agricultural sustainability 

collected the data of Yujiang county, Jiangxi province, 

adopted the SPSS method to get the weight of index, 

obtained the value of agricultural sustainability since 1949 

by comparing the sustainability every year. 

At last it revealed the relationship between the 

intensification and sustainability of agriculture in china. 

RESEARCH METHODS 

General condition of research region 

The researched area was Yujiang County, Jixiang province. Its sites 

are 116° 41' ~ 117° 09' E, 28° 04' ~ 28° 37' N. In 2008, the 

population was 335500, and the total area of arable land was 20400 

ha. The total land area is 927 km 2 , 78.2% of which is covered with 

the mountain area and only 21.8% of which is the plain. The 

average of sunshine duration is 1809 h per year. The mean annual 

temperature is 17.6°C. The number of frost-free season is 262 

days. The average annual precipitation could reach 1700 mm. 

Moreover, the level of agricultural intensification was high, for 

instance, total machine power reached 2.94 kW/ha, and the rate of 

fertilizer application was 675.8 kg/ha. Therefore, these conditions 

would play a key role in intensive agricultural development. 

The standardization of original data 

There were diversities of dimensions and quantitative levels in the 

original data. For the sake of analysis, it was essential to standard 

original data. Therefore, both of the dimensions would be unified 

and the gap of quantitative levels of indexes could be eliminated. 

The formula of standardization for original data was as follows: 

x 

x 

i 

man 

� x 

� x 

min 

min 

Where: Xi = original data on every index, Xmin = the minimum datum 

of original data, Xmax = the maximum datum of original data. 

After the standardization of original data, the value of data would 

be between 0 and 1. 

Selecting the method to confirm the evaluation indexes weight 

The weight vectors were confirmed by the method of principal 

components analysis. The standardization data should be used and 

analysed with method of principal components analysis, the 

contribute rate (CR) and factor loading matrix (FLM) would be 

calculated. The accumulation of CR multiplying by FLM of main 

components could express the effects on total information by each 

index. This method was proposed by Wu and Chen (1996a). The 

formula was as: 

WKi = 

n 

� 

j�1 

n n 

�� 

i�1 

j�1 

CR( 

j) 

* FLM( 

ji) 

CR( 

j) 

* FLM( 

ji) 

Where: Wk = index weight; i = the serial number of index; j = main 

component; n = the number of index; CR = the contribute rate of 

main component; FLM = factor loading matrix. 

According to the afore-mentioned formula, the weight per index 

would be easily confirmed. 

Weight of evaluation indexes confirming 

According to the aforementioned method, the weight of index would 

be decided, the results was showed in Table 2. 

EVALUATION SYSTEMS 

The data collection 

The statistics data were furnished by statistics office of Yujiang 

county, Jiangxi province. A few data were gotten by forum with 

some offices of agriculture office, Forestry Office of Yujiang County. 

Production sustainability 

The production sustainability is that the produce could meet the 

need of economic growth and increasingly improve the living 

standard. It is affected by many factors such as industrial input 

including inorganic fertilizers, pesticides and mechanization of 

agriculture (Chi, 1990; Shen, 1996; Niu, 1997; Luo, 2001; Chen, 

2003). 

Economic sustainability 

The economic sustainability involves both economic beneficial and 

efficiency aspects. The economic benefits are the primary content 

of agricultural production; they are the core of agricultural 

sustainability. The economic sustainability focuses on the aspects of 

increasing growth of economic benefits and efficiency (Niu, 1997; 

Luo, 2001; Chen, 2003; Ren, 1995). 

Ecological sustainability 

The ecological sustainability is defined as making full use of 

resources, protecting natural environment and improving the 

environmental quality. However, better environment is the crucial 

basis of production and economic sustainability. So the ecological 

sustainability means a responsibility for the environment - a 

stewardship of our natural resources (Luo, 2001; Wu and Chen, 

1996b; Niu, 1997; Chen et al., 1993). Amongst production, 

economic, and ecological sustainability, they are integration, and 

they promote the intensive agriculture sustainable development. 

Therefore, production, economic and ecological sustainability play 

the same role of the development of intensive agriculture.

Table 1. The evaluation system of intensive agriculture in Yujiang County. 

The first level of indexes The second level of indexes The third level of indexes 

Production sustainability (PS) 

Economic sustainability (ES1) 

Ecological sustainability (ES2) 

The evaluation index and systems 

Natural resources index (NRI) 

Agricultural intensification index (AII) 

Production index (PI) 

Production efficiency index (PEI) 

Production benefit index (PBI) 

Economic efficiency of production index (EEPI) 

Resources utilization index (RUI) 

Agricultural calamity- resistance index (ACRI) 

The evaluation system was established (Table 1). It is consistent 

with afore-mentioned principle of sustainability. The evaluation 

system would be divided into three levels (He and Bi, 1986; Hou, 

1999). The first level included production, economic and ecological 

sustainability indexes. The second one was composed of different 

index groups that indicated three sustainabilities, namely, the 

production sustainability indexes were formed by natural resources 

index, agricultural intensification index, production index; economic 

sustainability indexes were formed by production efficiency index, 

production benefit index, and economic efficiency of production; the 

ecological sustainability indexes were constructed by index of 

resources utilization and agricultural calamity-resistance index. The 

third one included 28 index. The detail contents were showed in 

Table 1. 

Chen and Wu 2655 

Area of arable land per capita (AALPC) 

Area of paddy rice per capita (APRPC) 

Amount of fertilizer application per area (AFAPA) 

Agricultural intensification (AI) 

Commercial ratio of agricultural production (CRAP) 

Amount of grain per capita (AGPC) 

Amount of meat per capita (AMPC) 

Yield of rice per area (YRPA) 

Yield of grain per area (YGPA) 

Commercial ratio of pig (CRP) 

Total value of agricultural (TVA) 

Value of plantation (VP) 

Value of livestock (VL) 

Value of forestry (VF) 

Value of aquaculture (VA) 

Net Income per capita (NIPC) 

Output value per labor (OVPL) 

Output value per capita (OVPC) 

Productivity (P) 

Nitrogen balance index (NBI) 

Phosphorus balance index (PBIp) 

Potassium balance index (PBIk) 

Planting index (PI) 

Ratio of energy output to input (REOI) 

Rate of light utilization of grain (RLUG) 

Rate of light utilization of rice (RLUR) 

Irrigation rate of arable land (IRAL) 

Rate of calamity- resistance (RCR) 

RESULTS AND ANALYSIS 

The relationship between intensification and the 

production sustainability 

Among the components of production sustainability index, 

the natural resources index has been heavily cut down 

with the population size increasingly growth. On the 

contrary, the agricultural intensification index has been 

zoomed with the rate of machine, electronic power, 

artificial fertilizer application per area of arable field 

growing; the agricultural production index has been 

enlarged with the crop yields rising (Figure 1). From 1949


value of NRI/AII/API/PS 

Table 2. The weight of evaluation index of intensive agriculture in Yujiang County. 

Index Weight Index weight Index weight Index weight 

AALPC 0.0386 YRPA 0.0361 VA 0.0400 PBIk 0.0340 

APRPC 0.0367 YGPA 0.0368 NIPC 0.0403 PI 0.0315 

AFAPA 0.0398 CRP 0.0393 OVPL 0.0404 REOI 0.0382 

AI 0.0399 TVA 0.0395 OVPC 0.0242 RLUG 0.0288 

CRAP 0.0388 VP 0.0371 P 0.0363 RLUR 0.0216 

AGPC 0.0168 VL 0.0386 NBI 0.0361 IRAL 0.0395 

AMPC 0.0350 VF 0.0396 PBIp 0.0368 RCR 0.0397 

Value of NRI/AII/API/PS 

0.18 

0.16 

0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

NRI 

AII 

API 

PS 

0.00 

1949 1959 1969 1979 1989 1999 

year 

Figure 1. The relationship between PS and NRI, AII and PI in Yujiang County for 50 years. 

to 1998, the NRI was gradually cut down by population 

size augment. The AALPC and APRPC reached the 

minimum in 1998. The natural resources index had been 

reduced at the rate of 0.1506% during 50 years in Yujiang 

County. The corresponding period, the AALPC and 

APRPC had been decreased by 67.53 and 68%, 

respectively. The former decreasing annual rate was 

0.2592% and the latter was cut down by the rate of 

0.2316% annually (Figure 2). In the same period, AII has 

been continuously grown; the annual increasing rate 

reached 0.1594% (Figure 2). AFAPA had outgrown from 

1.3 kg·ha -1 in 1949 to 786.44 kg·ha -1 in 1998. As for the 

AI, it climbed about 38 times from 140.38 CNY in 1949 to 

5327.03 in 1998 (Figure 3). Moreover, PI has been 

aggrandized, the rate of agricultural production index 

arrived at 0.1286% per year (Figure 1). Since CRAP was 

raised from 18.83% in 1949 to 42.30% in 1998; the 

AGPC reached 431 kg and AMPC was 81.8 kg in 1998, 

but the AGPC was only 238.7 kg in 1949, the AMPC was 

15.23 kg in 1978 (Figure 4). 

In general, the PS index has been increased as 

85.15% from 0.0808 in 1949 to 0.1496 in 1998. The 

annual raising rate reached 1.265% for 50 years in 

Yujiang County. 

The relationship between economic sustainability 

index and production efficiency, production benefit 

and economic efficiency of production index 

The economic sustainability index was affected by 

production efficiency, production benefit and economic 

efficiency of production index. However, the production 

efficiency was affected by YRPA, YGPA and CRP. Both 

YRPA and YGPA increasingly grow with AI. In comparison 

with production efficiency of 1949, it enhanced almost 2 

times and reached 0.1105 (Figure 5). Figure 5 indicated 

that the economic sustainability soared from 0.0166 in 

1949 to 0.4093 in 1998. It enlarged 24.65 times 

comparing with that of 1949. The annually leaping rate

AI(CNY) 

AI (CNY) 

area (ha) 

Area (ha) 

0.25 

0.20 

0.15 

0.10 

0.05 

AALPC 

APRPC 

0.00 

1949 1959 1969 1979 1989 1999 

year 

Figure 2. The variation of AALPC and APRPC during 50 years in Yujiang County. 

6000 

5000 

4000 

3000 

2000 

1000 

AI 

AFAPA 

0 

1949 1969 1989 

year 

Figure 3. The variation of AI and AFAPA during 50 years in Yujiang County. 

was 6.7596% as a result of net income increase. YRPA 

and YGPA increasingly rose. The former enlarged from 

1275 kg·ha -1 in 1949 to 8048 kg·ha -1 in 1998; the latter 

leap more than 7 times (from 990 to 7667 kg·ha -1 ). The 

commercial rate of pig rose from 74.26% in 1949 to 

145.29% in 1998. It has been benefited from “Green 

Revolution”. Thereby production efficiency index 

increasingly rose at the annual rate of 0.188% (Figure 6). 


900 

800 

700 

600 

500 

400 

300 

200 

100 

During the same term, the production benefit index 

enhanced in 1784 times. The increasing rate reached 

16.51% annually. The TVA, VP, VL, VF, and VA outgrow 

were 52.38, 25.16, 75.97, 327.25 and 208.2 times for 50 

years, respectively (Figure 7). 

As to the economic efficiency of production index, it 

reached 0.1204 in 1998. The NIPC, OVPL, OVPC, and P 

raised to 2082, 5217.16 and 1986.81 CNY, and 1133 kg 

0 

AFAPA (kgha -1 ) 

AFAPA(kgha -1 )


AFGPC/AMPC(kg) 

AFGPC/AMPC (kg) 

600 

500 

400 

300 

200 

100 

AGPC 

AMPC 

CRAP 

0 

0 

1949 1959 1969 1979 1989 1999 

year 

Figure 4. The variation of AGPC, AMPC and CRAP during 50 years in Yujiang County. 

value of PEI/PBI/EEPI/ES1 

Value of PEI/PBI/EEPI/ES1 

0.45 

0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

PEI 

PBI 

EEPI 

ES1 

0.00 

1949 1959 1969 1979 1989 1999 

year 

Figure 5. The variation of ES1, PEI, PBI and EEPI during 50 years in Yujiang County. 

in 1998, respectively; compared with that of 1949, they 

raised about 56.3, 22.1, 17.7 and 1.5 times, respectively 

(Figure 8). 

The relationship between ecological sustainability 

index and resources utilization and agricultural 

calamity- resistance index 

The ecological sustainability index was affected by 

45 

40 

35 

30 

25 

20 

15 

10 

resources utilization and agricultural calamity- resistance 

index. The resources utilization index was determined by 

NBI, PBIP, PBIK, PI, REOI, LURFG and LURR. The NBI, 

PBIP, PBIK were defined that input of N and/or P and/or 

K to output of N and/or P and/or K in agriculture systems. 

But the calamity- resistance index rested with the IRAI 

and RCR. Therefore, the ecological sustainability index 

augmented 0.125% yearly since 1949 (from 0.1319 in 

1949 to 0.1944 in 1998) (Figure 9). From 1949 to 1998, 

the resources utilization index increased from 0.0932 to 

5 

CRAP(%) 

CRAP (%)

YRPA/YFGPA(kg/hm 2 ) 

YRPA/YFGPA (kg/hm 2 ) 

YRPA/YFGPA (kg/hm2) 

YRPA/FGPA (kg/hm2 ) 

9000 

8000 

7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

YGPA 

YRPA 

CRP 

1949 1959 1969 1979 1989 1999 

年份 Years 

Year 

Figure 6. The variation of YRPA, YGPA and CRP during 50 years. 

TVA/VP/VL/VA (10kCNY) 

TVA/VP/VL/VA (10kCNY) 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

TVA 

VP 

VL 

VA 

VF 

0 

0 

1949 1959 1969 1979 1989 1999 

year 

Figure 7. The variation of TVA, VP, VL, VF and VA during 50 years. 

0.1217. The nitrogen nutrition turned from the state of 

shortage into the state of surplus with the chemical 

fertilizer application. The nitrogen balance was 1.16 in 

1949 and 0.47 in 1998. However, as for the phosphorous 

balance and potassium balance, the conditions were on 

the contrary. The phosphorus was still plenitude; the 

phosphorous balance was 0.23 in 1949 and 0.15 in 1998. 

The potassium nutrition still changed from grievous 

shortage to slight surplus. It was shown by that potassium 

balance (3.35 in 1949 and 0.90 in 1998). The REOI still 


3000 

2500 

2000 

1500 

1000 

500 

VF(10kCNY) 

160 

140 

120 

100 

slightly decreased from 3.42 in 1949 to 2.89 in 1998 

(Figure 10). As to the RLUG, RLUR rose from 0.0778 and 

0.1003% in 1949 to 0.6029 and 0.6328% in 1998. The 

planting index enhanced 120.3 from 154.8% in 1949 to 

275.1% in 1998 (Figure 11). 

On the other hand, with the rate of arable land 

increasing (from 4.91% in 1949 to 92.13% in 1998), and 

rate of calamity-resistance decreasing (from 90.53% in 

1949 to 81.91% in 1998), the calamity- resistance index 

grow to 0.034 (from 0.0387 in 1949 to 0.0727 in 1998) 

VF (10kCNY) 

80 

60 

40 

20 

0 

CRP(%) 

CRP (%)


value of RUI/ACRI/ES2 

(CNY) 

Value of RUI/ACRI/ES2 

(CNY) 

6000 

5000 

4000 

3000 

2000 

1000 

P 

OVPC 

OVPL 

NIPC 

0 

1949 1959 1969 1979 1989 1999 

year 

Figure 8. The variation of P (kg), OVPC, OVPL and NIPC (CNY) during 50 years. 

0.25 

0.20 

0.15 

0.10 

0.05 

RUI 

ACRI 

ES2 

0.00 

1949 1959 1969 1979 1989 1999 

year 

Figure 9. The variation of ES2, RUI and ACRI during 50 years in Yujiang County. 

(Figure 12). Figure 12 showed that the RCR was the 

lowest during 1960 to 1980s. We thought that the 

population rapidly increased and needed much more 

lands to convert into farmlands; the agricultural 

intensification level was low, the agricultural machines 

were poor, thereby the agricultural production was 

threatened by drought and flood disasters, also the 

application of pesticide was little, the pest or disease 

might decrease the yield, even get nothing. 

The variation of sustainability since 1949 in Yujiang 

County 

Since 1949, the sustainability has been increasingly 

grown in Yujiang County. The sustainability was merely

NBI/PBIK/REOI 

NBI/PBIK/REOI 

6.0 

5.0 

4.0 

3.0 

2.0 

1.0 

0.0 

1949 1959 1969 1979 1989 1999 

year 

Figure 10. The variation of NBI, PBIP, PBIK and REOI during 50 years. 

PI(%) 

PI (%) 

350 

300 

250 

200 

150 

100 

50 

PI 

RLUG 

RLUR 

0 

1949 1969 1989 

year 

NBI 

PBIK 

REOI 

PBIP 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

Figure 11. The variation of PI, RLUG and RLUR (%) during 50 years in Yujiang County. 

0.2294 in 1949, but it was 0.7533 in 1998. It climbed 3.28 

times (Figure 13). 

The relationship between sustainability and 

agricultural intensification in Yujiang County 

In order to study the relationship between sustainability 

RLUR/RLUFG(%) 

RLUR/RLUFG (%) 


0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

and agricultural intensification in Yujiang County, the 

formula of sustainability (y) and agricultural intensification 

(x) was established: 

y = 0.0001x + 0.2184, R 2 = 0.9268, r = 0.9627**, r0.01 = 

0.328, r0.05 = 0.235 

According to the formula, it is obviously found that the 

PBIP 

PBIP


sustainability 

Sustainability 

IRAL/RCR(%) 

IRAL/RCR (%) 

100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

IRAL 

RCR 

0 

1949 1959 1969 1979 1989 1999 

year 

Figure 12. The variation of IRAL (%) and RCR (%) during 50 years in Yujiang County. 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

1949 1959 1969 1979 1989 1999 

year 

Figure 13. The variation of sustainability since 1949 in Yujiang County. 

sustainability increased with agricultural intensification 

rising. Because they have linear relationship, and 

agricultural intensification raised 1 unit, the sustainability 

would be enhanced to 0.0001. 

DISCUSSION 

Agricultural sustainability has become an increasingly 

important issue in the latter half of the 20th Century, and 

in particular how this can be matched with intensification 

(Morse et al., 2002). Today, concerns about sustainability 

centre on the need to develop agricultural technologies 

and practices that do not have adverse effects on the 

environment, and lead to both improvements in food 

productivity and have side effects on environmental 

goods and services. Some report showed that they were 

inconsistent, even they were opposite. But the 

development of Yujiang County, Jiangxi province 

displayed that the production sustainability index has 

been increased from 0.0808 to 0.1496 in 50 years. 

Meanwhile, the annual raising rate has reached 1.265% 

and the sustainability index reached 0.7533 in 1998. 

When the agricultural intensification raised 1 unit, the 

sustainability would be enhanced by rate of 0.0001. So, 

we found the relationship between sustainability and 

agricultural intensification was linear one in Yujiang 

County. As a more sustainable agriculture seeks to make 

the best use of nature’s goods and services, technologies 

and practices must be locally adapted and fitted to place 

(Pretty, 2007). At the same time, the chemical fertilizer 

was applied more and more, the soil fertility could not be

only improved but also increased. 

The state of soil nitrogen turned from shortage to over 

plus, the state of potassium could be evident change. 

These advances were fuelled by modern plant breeding, 

improved agronomy and development of inorganic 

fertilizers and modern pesticides (Hazel and Wood, 

2008). Therefore, in China, growing population, shrinking 

arable land demand more attention to improve 

sustainability and intensification of agricultural 

development. 

CONCLUSION 

The study concludes that agricultural sustainability can 

increase easily with agricultural intensification growing in 

the meantime in China. The relationship between 

sustainability and agricultural intensification was linear 

one; both theory and practice from the point of view, there 

may be intensive and sustainable synchronization; but 

there may be separate. Result of the separation of 

interaction is mutual restraint. If the two complement 

each other, they can continue to progress together. 

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DOI: 10.5897/AJAR11.1270 



Water movement and retention in a mollic andosol 

mixed with raw mature chickpea residue 

Isaiah I. C. Wakindiki 1 * and Morris O. Omondi 2 

1 Department of Agronomy, University of Fort Hare, Private Bag X1314 King William’s Town Road 5700 Alice, 

South Africa. 

2 Department of Crops, Horticulture and Soils, Egerton University, P.O. Box 536-20107 Egerton, Njoro, Kenya. 

Accepted 11 October, 2011 

Organic manures affect soil properties, however, little is known about raw mature crop residue effects 

on water movement and storage in soils of low density like Andosols. It was hypothesized that water 

movement and storage in an andosol would be affected if the soil was mixed with raw mature crop 

residue. Therefore, the objective was to determine the water status in an andosol mixed with raw 

mature chickpea (Cicer arietinum) residue. Two treatments; amended and unamended soils were 

investigated. Saturated hydraulic conductivity was determined following the constant head method. 

Plant available water was estimated after determining water retention using the pressure plate 

apparatus at 3 and 1500 kPa water tension. The saturated hydraulic conductivity was approximately 

two-fold higher in the amended compared to the unamended soil. Water retention and plant available 

water were not affected by the raw mature chickpea residue. Since the bulk density was �0.9 Mg/m 3 in 

both treatments, porosity was similar. Therefore, increased aggregate stability was the most likely 

reason for the increased water movement in the amended soil. Addition of crop residues into such as a 

soil could be desirable in situations where it is necessary to enhance water flow in the soil profile 

without affecting water storage and plant available water, for example to improve the bearing capacity 

of wet mollic Andosols during tillage. 

Key words: Aggregate stability, soil texture, soil organic matter, water retention, bulk density. 

INTRODUCTION 

Manures are indispensable boosters for soil fertility and 

many studies have demonstrated that plant nutrients like 

nitrogen, phosphorus and potassium are released upon 

their decomposition (Danga et al., 2009). Besides soil 

fertility, manures also improve soil physical properties. 

Studies have adduced evidence that in coarse textured 

soils, CO2, which is a simple product of decomposition, 

may be used as a rapid indicator of soil aggregate 

stability and water movement. High CO2 evolution 

coincided with high aggregate stability and water 

movement (Wakindiki and Yegon, 2011). Whereas the 

benefits of green manure and decomposition are widely 

acknowledged, literature on the physical behavior of soil 

*Corresponding author. E-mail: iwakindiki@ufh.ac.za. Tel: 

+27406022096. Fax: +27867582117. 

immediately after mixing with undecomposed manure is 

limited. Wesseling et al. (2009) performed an experiment 

on the effect of raw organic amendment on the 

hydrological behaviour of coarse-textured soils and found 

out that addition of peat significantly decreased hydraulic 

conductivity and increased water retention. Conclusions 

from many early experiments assumed that raw organic 

matter affected soil water status indirectly through 

modification of structure of coarse textured soils. Feustal 

and Byers (1936) added varying amounts of raw peat 

moss to sand and clay loam and reported increased 

available water content for sand but not for clay loam soil. 

Likewise, Kelley (1954) included soil structure and 

texture but not organic matter in a review of the factors 

affecting available water content. Later, Jamison and 

Kroth (1958) evaluated 271 soil samples and found 

significant positive correlation between soil organic 

matter (SOM) content and available water content but

discounted this direct relationship and instead attributed it 

to soil texture. More experimental evidence showing a 

direct relationship between SOM and water holding 

capacity became common in 1990s. Hudson (1994) 

demonstrated that water holding capacity was improved 

by SOM. Further advances were made at the beginning 

of this decade as interest moved from the static view of 

soil water parameters like available water and water 

holding capacity to dynamic terms of soil water potential. 

Buytaert et al. (2002) and Rawls et al. (2003) reported 

that SOM was an important soil property influencing soil 

water retention compared to soil texture. Organic matter 

has a lower density compared to that of mineral soil and 

so it has direct impact on the soil physical properties like 

bulk density and porosity (Hillel, 1998). 

On the other hand, Andosols are characterised by low 

bulk density, typically less than 1.0 mg/m 3 in the surface 

horizon (FAO/ISRIC/ISSS, 1998). Andosols are generally 

fertile and occur in many volcanic regions of the world but 

their productivity is often limited by phosphate fixation. To 

alleviate the phosphate fixation, manures are often 

incorporated (IUSS Working group WRB, 2006). The 

unique fractal structure of the dominant clay, allophane, 

make Andosols excellent sequesters of soil organic 

carbon (Chevallier et al., 2010). Despite the phenomenal 

influence of raw organic inputs on the soil physical 

properties, few investigations have paid attention to the 

effect of undecomposed crop residue on water movement 

and retention in Andosols. Wesseling et al. (2009) 

cautioned that addition of raw organic matter into soils 

increases the risk of ponding and runoff if the hydraulic 

conductivity is significantly reduced. It was hypothesized 

that water movement and storage in soils with inherent 

low bulk density like Andosols would be affected if the 

soil is mixed with raw mature crop residue. Therefore, the 

objective was to determine saturated hydraulic 

conductivity, water retention and plant available water in 

Andosols immediately after mixing with raw mature 

chickpea (Cicer arietinum) residue. 


Site management and sampling 

Soils were obtained from the top 0.15 m layer in an experimental 

site in Njoro in Kenya. The site is located at latitude 0°23′ S, 

longitude 35° 35′ E, and 2200 m above sea level. In this humid area 

rain falls in two distinct seasons, commonly referred to as long rain 

season (LRS) and short rain season (SRS). The LRS is between 

April and August and the SRS is between late October and 

December. The soils are fertile, well-drained mollic Andosols 

(Jaetzold and Schmidt, 1983). In this region, wheat is grown during 

the LRS and the land is left fallow during the SRS. So in five 

consecutive cropping seasons, between 2003 and 2006, we tested 

the possibility of introducing chickpea as a SRS crop and use its 

residue to enhance the soil productivity (Danga et al., 2009). So 

land that had been fallow for at least five years was ploughed at the 

start of the SRS in 2003 and divided into two 0.5 ha portions. 

One portion was planted with chickpea and the other portion was 

left fallow. These portions are hereafter referred to as “amended” 

Wakindiki and Omondi 2665 

and “unamended” respectively. At the end of the crop growing 

season (SRS, 2003), mature chickpea was harvested and its dry 

residue incorporated using hand tools back into the upper 0.15 m 

soil layer in readiness for the wheat growing season during the LRS 

in 2004. The incorporated dry mature chickpea residue had a C:N 

ratio of 20:1 (Wakindiki and Yegon, 2011). Immediately after 

incorporating the residue two batches; undisturbed and disturbed 

soil samples were collected from the amended and unamended 

portions. Undisturbed samples for the determination of saturated 

hydraulic conductivity were collected in aluminium cylinders with 

0.05 m diameter and 0.05 m height while samples for the 

determination of moisture retention were collected in aluminium 

cylinders with 0.05 m diameter and 0.03 m height. Each cylinder 

was secured with lids on both open sides during transportation. The 

disturbed soil samples, for the determination of soil texture and 

aggregate stability, were collected using a spade from 0 to 0.15 m 

layer and put in paper bags. 

Saturated hydraulic conductivity, moisture retention and plant 

available water 

The lids were carefully removed from the undisturbed soil cores. 

One side of each core was then covered with cheesecloth. The 

samples were slowly saturated with tap water from below for 48 h. 

Saturated hydraulic conductivity (Ksat, cm/h) was then determined 

using the constant head method (Reynods and Elrick, 2002) and 

estimated using Equation 1: 

Q K sat 

A 

�h � L� 

L 

t 

� (1) 

Where Q was the volume of water that passed through each core in 

time t, L was the length of the core, A was the cross section area of 

the core, and h was the height of water on the core. Afterwards, 

core samples were oven-dried at 105°C for 48 h and weighed to 

determine the dry bulk density of the soil as described by Grossman 

and Reinsch (2002). The bulk density �b, mg/m 3 was then 

calculated using Equation 2: 

M 

s � b � 

(2) 

Vt 

Where Ms was the mass of oven dry soil and Vt was the total 

volume of the core sample. Water retention at 3 and 1500 kPa was 

determined using the pressure plate apparatus (Dane and 

Hopmans, 2002). The soil samples were removed from the 

pressure plate apparatus after equilibration and their respective 

moisture contents�, mg/mg, was determined gravimetrically using 

Equation 3: 

M 

M 

w � � 

(3) 

s 

Where Mw was the mass of water and Ms was the mass of dry soil. 

The gravimetric water content was then converted to volumetric 

water content by multiplying it with the bulk density. The difference 

between water content at 3 and 1500 kPa moisture tensions was 

assumed to be the plant available water. 

Soil texture and aggregate stability 

The soil texture was determined on the disturbed samples following


Volumetric water content (cm 3 /cm 3 ) 

Saturated hydraulic conductivity (cm/h) 

Figure 1. Saturated hydraulic conductivity in the unamended and amended soils. Different letters 

above the treatment values indicate significant difference (P � 0.05). Bars indicate standard error. 

Figure 2. Volumetric water content in the unamended and amended soils at 3 and 1500 kPa. 

Different lowercase letters above the treatment values at the same matric suction and different 

uppercase letters above the treatment values at different matric suctions indicate significant 

difference (P = 0.05). Bars indicate standard error. 

the hydrometer method as described by Gee and Or (2002). The 

aggregate fraction sizes were determined by sieving the soil 

samples in a net of sieves of 2, 1, 0.5, 0.25 and 0.1 mm diameter. 

The soil in each sieve was then weighed and expressed as a 

percentage of the total weight of the soil. The aggregate stability 

was expressed by calculating the mean weight diameter (MWD, 

mm) of the six classes: 

6 

� � i i 

i�1 

w x 

MWD (4) 

Where wi was the weight fraction of aggregates in the size class i 

with mean diameter x (Le Bissonnais, 1996). 


The means were analyzed using the t-test (Steel and Torrie, 1980). 

RESULTS 

The saturated hydraulic conductivity in the amended soil 

was approximately two-fold higher compared to that in 

the unamended soil (Figure 1). Therefore, addition of raw 

mature chickpea residue made it easier for the water to 

flow in the soil under saturated conditions. However, 

water retention (Figure 2) and plant available water 

(Figure 3) were not significantly affected by the incorpora-

Plant available water (%) 

Wakindiki and Omondi 2667 

Figure 3. Plant available water in the unamended and amended soils. Different letters above the 

treatment values indicate significant difference (P = 0.05). Bars indicate standard error. 

Table 1. Some physical properties of the mollic andosol used in the study. 

Soil 

Bulk density 

Mg/m 

% Mean weight diameter 

3 Clay Silt Sand 

Unamended 0.96 35 43 22 0.35 a 

Amended 0.96 35 43 22 1.25 b 

Different letter following mean weight diameter value indicate a significant difference. (P = 0.05). 

-tion of the chickpea residue. The aggregate stability in 

the amended soil was significantly higher than in the 

unamended soil, while the bulk density, which was �0.9 

mg/m 3 was not affected by the amendment (Table 1). 

DISCUSSION 

Saturated hydraulic conductivity indicates the ease with 

which soil pores in saturated soil permit water to move. 

Quantitatively, it measures the capacity of a saturated 

soil to transmit water under the influence of hydraulic 

gradient (Hillel, 1998). The same hydraulic gradient was 

maintained in both treatments. Therefore a possible 

reason for the increased saturated hydraulic conductivity 

in the amended soil was increased aggregate stability 

(Table 1). Since the bulk density was approximately 0.9 

mg/m 3 in both treatments, there was no increase in 

porosity. Similarly, Lado et al. (2004) found that in the 

absence of raindrop impact, the saturated hydraulic 

conductivity of the soil was enhanced by organic matter. 

However, contradicting results were reported in sand 

mixtures by Wesseling et al. (2009), suggesting that 

organic matter decreased saturated hydraulic 

conductivity. In our experiment the soil was a silty clay 

loam (Table 1), suggesting that in finer soils, raw 

manures enhance saturated hydraulic conductivity. 

In general, the amount of water retained in any soil at 

low values of moisture tension depends primarily upon 

the gravitational potential, while at higher moisture 

tension, adsorptive potential become dominant (Hillel, 

1998; Dane and Hopmans, 2002). In our study, water 

retention for both treatments was not significantly 

different (Figure 2). Likewise, the plant available water for 

both treatments was similar (Figure 3). Hitherto, many 

experiments have demonstrated enhanced water 

retention when SOM is increased in soils (Jamison and 

Kroth, 1958; Rawls et al., 2003; Wesseling et al., 2009) 

but our results contrast these earlier findings probably 

because the soil texture was finer. Therefore, addition of 

crop residues into fine-textured soil could be desirable in 

situations where it is necessary to enhance water flow in


the soil profile without affecting the plant available water 

capacity. For example, Andosols are known to be highly 

susceptible to compaction due to their low bearing 

capacity when highly hydrated (IUSS Working group 

WRB, 2006). So it might be reasonable to incorporate 

crop residues to enhance water transmission and release 

and facilitate operations like tillage. Nonetheless there is 

little evidence to support this hypothesis. 


We acknowledge the provision of chickpea seeds and the 

pressure plate apparatus by the International Crop 

Research Institute for Semi-Arid Tropics (ICRISAT), 

Nairobi and the Kenya Agricultural Research Institute 

(KARI), Njoro, respectively. 

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DOI: 10.5897/AJAR11.454 



In vitro propagation of native Ornithogalum species in 

West Mediterrenean region of Turkey 

Ozgul Karaguzel*, Ayse Kaya, Beyza Biner and Koksal Aydinsakir 

Bati Akdeniz Agricultural Research Institute, Antalya, Turkey. 

Accepted 17th April, 2012 

Ornithogalum is a popular genus especially, as an ornamental plant. In this study, eight native 

Ornithogalum species grown in West Mediterrenean Region of Turkey were cultured on MS media 

supplemented with combinations of BAP (1.0, 2.0, 4.0 mg L -1 ) and NAA (0.25, 0.50 mg L -1 ). The results 

indicated that the highest rate of proliferation was induced on Murashige and Skoog (MS) to which 

4 mg L -1 BAP + 0.5 mg L -1 NAA (4.97 bulblets/explant) while the lowest rate of bulblet proliferation was 

obtained in 2 mg L -1 BAP + 0.25 mg L -1 NAA (2.27 bulblets/explant). Ornithogalum umbellatum showed 

the highest bulblet regeneration within the species. The regenerated bulblets were transferred into 

plastic viols containing mixture of peat moss and perlite (1:1) after 5 months. 

Key words: Ornithogalum spp, bulb scale explant, bulblet regeneration, in vitro propagation. 

INTRODUCTION 

The genus, Ornithogalum which belongs to family 

Hyacinthaceae (Şabudak, 1999), was widely grown in 

Africa, Mediterrenean, Europa and Asia naturally and 

contains nearly 150 species in the world (Petanidou and 

Vujic, 2007). Fourty four species has been represented in 

Turkey and 17 species of them are endemic (Davis, 

1984; Ekim et al., 2000; Dusen and Deniz, 2005; Uysal et 

al., 2005). Ornithogalum species is an impressive 

ornamental plant due to its attractive white flowers and 

used as medicinal plants (Asimgil, 2003). Some 

Ornithogalum species (Ornithogalum comosum L., 

Ornithogalum lanceolatum Labill., Ornithogalum latifolium 

Baker, Ornithogalum pyrenaicum L., O. narborense L., 

Ornithogalum oligophyllum Clarke and Ornithogalum 

sibthorpii W.Greuter) have potentially an economic 

importance since it is consumed as vegetable in Turkey 

*Corresponding author. E-mail: tezkara@yahoo.com. 

Abbreviations: BA, Benzyl adenin; BAP, benzyl amino purin; 

NAA, naphthalene acetic acid; NaOCl, sodium hypochlorite; 

KOH, potassium hydroxide; HCl, hydrochloric acid. 

(Baytop, 1997), whereas, it has not sufficently been 

considered to propagation. 

Ornithogalum species are vegetatively propagated 

using mother bulbs, but the rate of propagation of these 

bulbs is very slow. Approximetly 4 to 6 bulblets are 

annualy produced from a mother bulb (Naik and Nayak, 

2005). Therefore in vitro techniques were used for rapid 

propagation of bulbs. Several studies have been carried 

out in vitro micro-propagation protocols of some culture 

Ornithogalum species (Hussey, 1976; Nel, 1981; 

Yanagawa and Ito, 1988; Rensburg et al., 1989; Landby 

and Neiderwieser, 1992; Ziv and Lilien-Kipnes, 2000; 

Karuiki and Kako, 2003; Malabadi and van Staden, 2004; 

Naik and Nayak, 2005; Suh et al., 2005). At the same 

time, micro-propagation of some native Ornithogalum 

species such as O. umbellatum L. (Nayak and Sen, 

1995), O. ulouphyllum Hand-Mazz (Ozel et al., 2008), O. 

plathyllum Boiss (Ipek et al., 2006), O. oligophyllum E. 

D.Clarke (Ozel and Khawar, 2007) has been reported. 

However, there are no reports in vitro for the propagation 

a large number of the native Ornithogalum species up to 

now. In the present study, a new propagation protocol for 

bulblet regeneration from bulb scale explants of eight 

native Ornithogalum species in the West Mediterranean


Region of Turkey was investigated. 


Plant material and explant source 

The Ornithogalum species (O. umbellatum L., O. oligophyllum E. C. 

Clarke, O. sigmoideum Freyn. Sint., O. narborense L., O. 

pyrenaicum L., O. lanceolatum Labill., O. isauricum O. D. Düşen 

and H. Sümbül ‘endemic’ and O. nutans L.) were collected between 

April and June, 2006 in West Mediterranean Region of Turkey. 

Ornithogalum bulbs were washed under running tap water for 

half an hour to remove the mud and dirt. Firstly, the bulbs were 

sterilized by treatment for 10 min in a 10% commercial bleach (5 to 

6% NaOCl) + 172 ml Tween 20 per 100 ml. Then for 30 min in 80% 

commercial bleach (5 to 6% NaOCl) + 172 ml Tween 20 per 100 ml 

commercial bleach with continuous stirring using magnetic stirrer. 

Finally, they were rinsed in sterile distil water thrice. After removing 

the outer scales of the bulbs, explants were longitudianally cut 

under sterile conditions to obtain about 1 to 2 cm four scale 

segments including the basal plate. 

Medium and culture conditions 

The explants were cultured on MS medium (Murashige and Skoog, 

1962), supplemented with 1.0, 2.0, 4.0 mg L -1 BAP and 0.25, 

0.50 mg L -1 NAA. The pH values of all media was adjusted to 5.7 

with 1 N KOH or 1 N HCl before adding 30 g L -1 sucrose and 7 g L -1 

agar (Sigma, 1296) and autoclaved at 1.2 atm and at 121°C for 20 

min. Petri dishes were wrapped with strech film and then placed at 

25 ± 1°C by 16 h photoperiod under 100 µmol m -2 s -1 light intensity 

supplied with white fluorescent light. The bulblets were subcultured 

every month onto fresh medium for aproximately three months and 

later transferred to the MS medium containing no plant growth 

regulators for induction of rooting. Each treatment consisted of 5 

explants and the experiments were repeated thrice. 

Data collection and statistical analysis 

The number of bulbs per explant and survival rate of bulblet 

transfered to soil were determined in eight different Ornithogalum 

species at the end of the experiment. All data were statistically 

analyzed according to a completely randomized design (Gomez 

and Gomez, 1984) and variance analysis (ANOVA) was done. 

Means were calculated and Duncan’s multiple range test at a 

significance level of 5% was compared. 

RESULTS 

Explants cultured on MS medium supplemented with 

different combinations of plant growth regulators 

displayed proliferation within 15 to 20 days, by small 

bulblets which were developed on the scales. Bulblets 

were produced directly on the bulb scales or the basal 

plates (Figure 1). The results indicated that the effect of 

plant growth regulators on the number of bulblet 

regeneration for each species were statistically different 

(P

Figure 1. Bulblet regeneration from 4 scale explants eight different 

Ornithogalum species “a) 4.0 mg L -1 BAP+0.50 mg L -1 NAA O. umbellatum, b) 

1.0 mg L -1 BAP+ 0.25 mg L -1 NAA O. oligophyllum, c) 1.0 mg L -1 BAP+ 

0.25 mg L -1 NAA O. sigmoideum, d) 4.0 mg L -1 BAP+0.25 mg L -1 NAA O. 

narborense, e) 0 mg L -1 BAP+ 0.50 mg L -1 NAA O. lanceolatum, f) 1.0 mg L -1 

BAP+ 0.50 mg L -1 NAA O. isauricum, g) 1.0 mg L -1 BAP+ 0.50 mg L -1 NAA O. 

nutans, h) 1.0 mg L -1 l BAP+ 0.25 mg L -1 NAA O. pyrenaicum”. 

Karaguzel et al. 2671


Table 1. Effects of growth regulators on bulblet regeneration from 4 scales and mean number of bulblets per explant on Ornithogalum species. 

Species 

Mean number of bulblets per explant 

Plant growth regulators (mg L -1) 

1 BAP + 0.25 NAA 1 BAP+0.5 NAA 2 BAP + 0.25 NAA 2 BAP + 0.5 NAA 4 BAP + 0.25 NAA 4 BAP + 0.5 NAA 

Mean of species 

Survival rate of bulblet 

transfered to soil (%) 

O. umbellatum 19.00 Aax 7.00 Ba 3.67 Ba 6.67 Ba 6.67 Ba 21.67 Aa 10.78 ** 51 

O.oligophyllum 14.33 Ab 5.33 Bac 4.00 Ba 5.00 Bab 2.00 Bbc 4.33 Bbc 5.83 29 

O.sigmoideum 3.00 Ac 2.33 Abd 2.67 Aa 1.67 Abc 2.33 Ab 4.33 Ab 2.72 58 

O.narborense 3.67 ABc 6.33 Aab 4.00 Aba 4.33 ABac 1.67 Bb 6.33 Ab 4.39 67 

O.pyrenaicum 1.33 Ac 1.67 Acd 2.67 Aa 1.67 Abc 2.67 Abc 2.33 Abc 2.06 41 

O.lanceolatum 3.00 Bc 7.33 Aa 2.33 Ba 2.67 Bac 2.67 Bb 5.67 ABb 3.94 28 

O.isauricum (endemic) 0.00 Ac 1.33 Acd 0.00 Aa 1.33 Abc 2.67 Ac 0.00 Ac 0.89 20 

O.nutans 0.00 Ac 0.00 Ad 0.00A a 0.00 Ac 0.00 Ac 0.67 Ac 0.11 100 

Mean of plant growth regulators 4.67 ** 3.97 2.27 2.77 2.30 4.97 3.49 

Mean separation within columns by Duncan’s multiple range test, at 0.05 level; ns No statistical difference at P < 0.05 and P < 0.01, * Statistical difference at P < 0.05, ** Statistical difference at P < 0.01, X : 

capitals show the comparison between the averages given horizontally (along the line) and the small characters show the comparison between the averages given vertically (along the column). 

Figure 2. Plants growth from bulblets in growing media containing peat 

moss and perlite (1:1).

obtained with a medium containing 2.0 mg L -1 

BAP + 0.50 mg L -1 NAA. Number of bulblet per plant was 

4.83. (Ozel et al., 2008). In our study, number of bulblet 

per plant in O.umbelatum was 21.67 while, 2.06 to 5.83 

values were recorded in the other excluded species that 

encountered infection. The most constructive results 

were also obtained from MS medium with 2.0 mg L -1 

BA + 1.0 mg L -1 NAA (10.4 bulblets/explant) in O. virens 

as reported by Naik and Nayak (2005). Correspondingly, 

a remarkable increase in the regeneration of cultured O. 

arabicum bulblets by 5.0 mg L -1 BA + 0.01 mg L -1 NAA on 

solid White’s medium was detected (Yanagawa and Ito, 

1988). In other studies, development of the highest 

number of shoots from bulb scale explants on 

Ornithogalum hybrid cultured on MS medium with 

1.5 mg L -1 BA + 0.50 mg L -1 NAA was also reported (Suh 

et al., 2005). In our present study, the highest bulblet 

formation was achieved by 4.0 mg L -1 BAP + 0.50 mg L -1 

NAA at eight different Ornithogalum species. These 

obtained incoherent findings can be associated with 

differences of the genotypes, explants and 

concentrations of growth regulators used in mediums. 

During the studies, bacterial and fungal contaminations 

were observed on O. nutans and O. isauricum culture 

medium and limited number of bulblets were obtained 

from these species. The presence of heavy bacterial and 

fungal contamination risks were reported if the bulbs will 

be used as a source of explant (Langens-Gerrits et al., 

1998; Ziv and Lilien-Kipnis, 2000; Mirici et al., 2005). 

Also, Karaoğlu (2010) stated that despite all surface 

sterilizations done in some bulbous plants, diseases were 

not prevented and they resulted from endogenic plants. 

In this study, it was found that the number of bulblets 

were significiantly increased with the addition BA and 

NAA into medium culture. In conclusion, from the ongoing 

results, it may be concluded that a simple and rapid 

protocol can be established for propagation of eight 

Ornithogalum species which were grown in West 

Mediterranean Region of Turkey. 


This research was supported by the Scientific and 

Technical Research Council of Turkey (TUBITAK; Project 

no TOVAG 104O327). The authors wish to thank Prof. 

Dr. İbrahim BAKTIR, Prof. Dr. Osman Karagüzel and 

Associate Prof. Dr. Ö. Baysal. 

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DOI: 10.5897/AJAR11.1299 



Determination of associations between three 

morphological and two cytological traits of yams 

(Dioscorea spp.) using canonical correlation analysis 

P. E. Norman 1 *, P. Tongoona 2 and P. E. Shanahan 3 

1 Njala Agricultural Research Center (NARC), PMB 540, Freetown, Sierra Leone. 

2 Africa Center for Crop Improvement (ACCI), University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, 

South Africa. 

3 Agricultural Plant Sciences, University of KwaZulu-Natal, South Africa. 


Agro-morphological traits of plants may directly or indirectly depend on cytological traits. Thus, the 

determination of associations between morphological traits (absence or presence of wings, number of 

stems per plant and wing colour of stem) and cytological traits (DNA content and ploidy level) of yams 

were investigated using canonical correlation analysis. This multivariate technique is used in wide 

fields of study to quantify the mathematical relationships between multiple sets of independent and 

dependent traits or properties. Canonical weights and loadings indicated that DNA content (pg) had the 

highest contribution to the variation of the morphological traits (presence of wings, number of stems 

per plant and wing colour) compared with ploidy level. It was found that cytological traits accounted for 

0.09 to 0.17% of the variation in the selected morphological traits. The first and second canonical 

correlations exhibited 60.91 and 39.09% overlapping variance of the canonical variate sets respectively. 

The first and second canonical variates extracted 0.57 and 4.43% of the total variance in the cytological 

trait set. The study demonstrated the successful determination of complex inter-relationships between 

morphological and cytological traits. 

Key words: Canonical correlation, morphological traits, cytological traits, yam. 

INTRODUCTION 

Canonical correlation analysis (CCA) is one of several 

multivariate analysis techniques used to determine the 

overall correlation between two sets of traits (X and Y). 

Canonical correlation is a generalization of multiple 

regression analysis with more than one trait in the 

independent and dependent trait sets. The basic principle 

of the technique is to determine how much variance in 

one set of traits is accounted for by the other set along 

one or more axes (Tabachnick and Fidell, 2001). In 

contrast to many other techniques, any of the two sets of 

traits is a potential candidate to be used as dependent or 

independent traits. Canonical correlation makes possible 

several combinations of two trait sets. The number of 

combinations depends on the number of traits in the 

*Corresponding author. E-mail: penorman2008@yahoo.com. 

smaller trait set (Tabachnick and Fidell, 2001; Keskin and 

Yasar, 2007). Canonical correlation analysis has been 

widely applied in various fields such as the plant 

sciences, biology, chemistry, social and management 

sciences. However, there is scant information available 

on the interrelationship between morphological and 

cytological traits of yams. The main aim of this study was 

to determine the level of association between 

morphological and cytological traits of yams using 

canonical correlation analysis. The hypothesis being 

tested was that correlation exists between the agromorphological 

and cytological traits used in the two 

methods of classification. 


A total of five traits including three morphological (absence or

Table 1. Descriptive statistics of the cytological and morphological traits. 

Traits Mean ± SE Minimum Maximum 

DNA (pg) 1.858 ± 0.013 1.588 2.118 

Ploidy 3.962 ± 0.091 2.000 6.000 

APW 0.846 ± 0.051 0.000 1.000 

NS 1.846 ± 0.108 1.000 5.000 

WC 1.404 ± 0.117 0.000 3.000 

DNA: Deoxyribonucleic acid content (pg); Ploidy level; APW: absence or presence of wings; NS: number of 

stems per plant; and WC: wing colour. 

presence of wings, number of stems per plant and wing colour of 

stem) and two cytological (DNA content and ploidy level) traits were 

used. The morphological traits were considered as the dependent 

Y-trait set, whereas the cytological traits were taken as the 

independent X-trait set. To obtain the maximum correlations 

between two sets of traits, two linear combinations were designed 

as follows: 

Wi = ai1X1 + ai2X2 + -------- + aipXp (1) 

Vi = bi1Y1 + bi2Y2 + -------- + biqYq (2) 

The symbols W and V represents canonical variates; a and b are 

canonical coefficients of the X and Y trait sets; and p (two traits) 

and q (three traits) are the number of traits in the X and Y trait sets, 

respectively. The estimation of the vector coefficients, a and b, was 

done according to Tabachnick and Fidell (2001). 

To generate the canonical correlation for both sets of traits, the 

following formulae were used: 

var (W) = aʹCov (X) a (3) 

var (V) = bʹCov (Y) b (4) 

Where var (W) represents variance of the canonical variate W; var 

(V) is the variance of the covariate V; Cwv is the canonical 

correlation between the X and Y trait sets; Cov (Y) and Cov (X) are 

the covariances of the traits in the X and Y trait sets, respectively 

(Keskin and Yasar, 2007). 

The relationship of a set of canonical variate is maximized when 

the correlation (r-value) of the p and q is small. The first set of 

canonical variate (W1 and V1) gives the highest correlation and is 

considered the most important. The correlation between W2 and V2 

is only maximized where the traits measured are uncorrelated to W1 

and V1. Similarly, the correlation between W3 and V3 is maximized if 

traits are not correlated with W1, V1, W2 and V2 (Manly, 1994). The 

canonical correlation analysis procedure (cancorrelation procedure) 

in Genstat version 12.1 was used to generate the relationships 

between sets of traits (Payne et al., 2009). The squared canonical 

correlation (also known as canonical roots or eigen-values) 

represents the amount of variance in one canonical variate 

accounted for by the other canonical variate (Hair et al., 1998). The 

standardized coefficients are similar to the standardized regression 

coefficients in multiple regression, which gives an indication of the 

relative importance of the independent traits in determining the 

value of dependent traits. In order to determine the amount of 

variance in one set of traits that is accounted for by another set of 

traits, Sharma (1996) suggested the estimation of the redundancy 

(5) 

Norman et al. 2675 

measure (RM) for each canonical correlation. The equation for the 

RM is shown as follows: 

RMvi/wi = AV (Y/Vi) x Ci 2 (6) 

AV (Y/Vi) = [∑ q LYij 2 /q] (7) 

Where AV (Y/Vi) = the averaged variance in Y traits that is 

accounted for by the canonical variate Vi. LYij 2 = the loading of the 

jth Y trait on the ith canonical variate Vi; q = the number of traits in 

canonical variates; Ci 2 = the shared variance between Vi and Wi; Wi 

and Vi are canonical variates of Y and X trait sets, respectively. 

This estimate is necessary because a large canonical correlation 

does not always imply powerful relationship between two sets of 

traits. Canonical correlation maximizes the estimate of correlation 

between linear combinations of traits in the two sets, but does not 

maximize the amount of variance accounted for in one set of traits 

by the other set of traits. Thus, the variance in one set of traits 

accounted for by the other set is obtained through the RM (Akbas 

and Takma, 2005). 

RESULTS 

The descriptive statistics and Pearson correlation 

coefficient (r) for the six traits are presented in Tables 1 

and 2, respectively. The Pearson correlation coefficients 

for the traits ranged between -0.3928 and 0.7798 and 

were statistically significant (p


Table 2. Pearson correlation coefficients between cytological and morphological traits. 

DNA Ploidy APW NS 

Ploidy 0.7798** 

APW -0.3928** -0.2715 ns 

NS 0.2882* 0.3468** -0.4727** 

WC -0.3578** -0.2895* 0.7143** 0.1755 ns 

*: p< 0.05, **: p< 0.01, ns: not significant. DNA: deoxyribonucleic acid content (pg); Ploidy level; APW: 

absence or presence of wings; NS: number of stems per plant; and WC: wing colour. 

Table 3. Canonical correlations between canonical variates. 

Canonical 

variates 

Canonical 

correlations 

Squared canonical 

correlation 

% 

correlation 

Cumulative % 

correlation 

Likelihood 

ratio 

Probability Pr 

> F 

W1V1 0.4441 0.1972 69.91 69.91 0.79 0.001 

W2V2 0.2850 0.0812 39.09 100.0 0.89 0.005 

Table 4. Non-standardized coefficients of the respective traits of the canonical variates. 

Traits W1 W2 Traits V1 V2 

DNA 0.9605 -2.2161 APW -0.0436 0.5411 

Ploidy 0.0909 0.3209 NS 0.1046 0.1724 

WC -0.0999 -0.0931 

DNA: Deoxyribonucleic acid content (pg); Ploidy level; APW: absence or presence of wings; NS: number of stems 

per plant; and WC: wing colour. 

( ) of overlapping variance of the 

first canonical variate. The second canonical correlation 

W2V2, which exhibited 0.2850, represents 39.9% 

( ) overlapping variance of the 

second canonical variate set (Table 3). The coefficients 

of canonical variates from the original data are presented 

in Table 4. These coefficients of canonical equations are 

not unique since the DNA coefficient value is more than 

1.0. Therefore, the coefficients were standardized to give 

canonical variates with zero mean and unit variance. 

The standardized canonical coefficients for the X and Y 

trait sets are presented in Table 5. The magnitude of 

each canonical coefficient represents the relative 

contribution of each trait to its respective canonical 

variate. From Equations 1 and 2, the following canonical 

variates can be obtained from the standardized 

coefficients (Table 5): 

W1 = 0.0890 DNA + 0.0591 Ploidy 

V1 = -0.0159 APW + 0.0815 NS – 0.0845 WC 

W2 = -0.2052 DNA + 0.2157 Ploidy 

V2 = 0.1971 APW + 0.1344 NS – 0.0787 WC 

From the equations aforementioned, W1 estimates the 

additive effect between DNA amount and ploidy level; 

whereas V1 estimates the contrast between number of 

stems per plant on one hand and the other traits (wing 

colour and absence or presence of wing). This indicates 

that the large variation in morphological traits (wing 

colour and absence or presence of wing) compared to 

number of stems per plant was possibly due to the 

additive influence between the cytological traits, like DNA 

amount and ploidy level. However, the second canonical 

variate, W2 estimates a contrast between DNA amount 

and ploidy level; whereas V2 measures the difference 

between wing colour and the other traits (number of stem 

per plant and absence or presence of wing). This 

indicates that the variation in morphological traits 

(absence or presence of wing and number of stem per 

plant) compared to wing colour was possibly due to the 

influence of the cytological traits (DNA amount and ploidy 

level). Of the three morphological traits used, number of 

stems produced per plant and wing colour were more 

stable (their signs did not change in both canonical 

variates) compared to absence or presence of wing 

(Table 5). Ploidy level was also more stable compared to 

DNA content. This implies that a group of genotypes with 

similar ploidy level may not necessarily contain the same 

DNA content and consequently, the number of loci will

Table 5. Standardized coefficients of the respective traits of the canonical variates. 

Traits W1 W2 Traits V1 V2 

DNA 0.0890 -0.2052 APW -0.0159 0.1971 

Ploidy 0.0591 0.2157 NS 0.0815 0.1344 

WC -0.0845 -0.0787 

DNA: Deoxyribonucleic acid content (pg); Ploidy level; APW: absence or presence of wings; NS: number of 

stems per plant; and WC: wing colour. 

vary. The proportion of the total variance extracted from a 

set of traits by a canonical variate of that set is equal to 

the quotient of the sum of square of loadings and the 

number of traits in the set. Thus, the first canonical 

variates, W1 in the X trait set; and V1 in the Y trait set, 

were estimated as 0.0057 [(0.0890 2 + 0.0591 2 )/2] and 

0.0047 [(-0.0159 2 + 0.0815 2 + (-0.0845 2 )/3] respectively. 

Therefore, the first canonical variate (W1) extracted 

0.57% in the X trait set and 0.47% in the Y trait set. 

The second canonical variates (W2) in the X trait set 

and (V2) in the Y trait set were estimated as 0.0443 [(- 

0.2052 2 + 0.2157 2 )/2] and 0.0210 [(0.1971 2 + 0.1344 2 + (- 

0.0787 2 )/3], respectively. The redundancy index in a 

canonical variate is expressed as the percentage of 

variance it extracts from its own set of traits. Thus, the 

first canonical variate (V1) extracted 0.09% (0.0047 × 

0.4441 2 ) of the variance in the X trait set; whereas, the 

second variate (V2) extracted 0.17% (0.0210 × 0.2850 2 ) 

of the variance in the X trait set. The results suggest that 

traits in the Y trait set (APW, NS and WC) are influenced 

by those in the X trait set (DNA and ploidy). 

DISCUSSION 

The first pair of canonical variates (W1V1) had the highest 

(0.4441) estimated canonical correlation compared to the 

second pair of canonical variates [W2V2 (0.2850)]. The 

correlation between the first pair of canonical variate 

indicates that morphological traits: absence or presence 

of wing, number of stems per plant and wing colour are 

associated with cytological traits: DNA and Ploidy level. 

The signs of the standardized coefficients reflect the 

effects of DNA and ploidy on absence or presence of 

wing, number of stems and wing colour. Wright et al. 

(2008) suggested that the total amount of DNA in the 

genome (genome size) roughly reflects an estimate of the 

number of genes within a genome. Thus, an 

understanding of allelic diversity within germplasm is 

relevant in association with observed phenotypic 

variation. The redundancy estimates for the first and 

second canonical correlation suggested that 0.09 and 

0.17% of the variance in the Y trait set (APW, NS and 

WC) was accounted by the X trait set (DNA and ploidy). 

Although, the percentages were small, the variation in 

each of the morphological traits showed a significant 

(p


Payne RW, Murray DA, Harding SA, Baird DB, Souter DM (2009). 

Genstat for Windows (12 th Edition) Introduction VSN International, 

Hemel Hempstead. 

Sharma S (1996). Applied Multivariate Techniques. John Willey and 

Sons, Inc., Canada. pp. 391-404. 

Tabachnick B, Fidell LS (2001). Using Multivariate Statistics. A Pearson 

Education Company, Needham Heights, USA. pp. 966. 

Wright SI, Ness RW, Foxe JP, Barette SCH (2008). Genomic 

consequences of out-crossing and selfing in plants. Inter. J. Plant 

Sci., 169: 105-118.



DOI: 10.5897/AJAR11.1734 



Distribution of nuclei and microfilaments during pollen 

germination in Populus tomentosa Carr. 

Yuan Cao, Rui-Zhi Hao, Mei-Qin Liu, Xin-Min An and Yan-Ping Jing* 

National Engineering Laboratory for Tree Breeding, NDRC, College of Biological Science and Biotechnology, Beijing 

Forestry University, Tsinghua East Road No. 35, Beijing, 100083, P.R. China. 


Pollen tubes transport nuclei to the ovules for fertilization. The distribution of microfilaments and the 

nuclei were investigated by fluorescent phalloidin labeling and DAPI (4', 6-diamidino-2- phenylindole) 

during pollen germination and pollen tube growth of Populus tomentosa Carr., a Chinese native tree 

species. The coexistence of trinucleate and binucleate pollen was confirmed, which had been 

previously considered binucleate. Three distinct typical microfilament structures were found, and Factin 

was present at the periphery of both the vegetative and the sperm nuclei. The investigation 

indicated that movement of sperm nuclei and vegetative nucleus is related to the microfilament system. 

Key words: Generative nucleus, vegetative nucleus, actin cytoskeleton, pollen tube growth. 

INTRODUCTION 

Pollen germination and pollen tube growth are two of the 

most important physical activities in the sexual 

reproduction of plants, and both are complicated dynamic 

process. Pollen germination on a compatible stigma 

involves the pollen cell extending to form the pollen tube, 

which functions to send the sperm nuclei to the embryo 

sac (Higashiyama et al., 2003). 

Microfilaments form a substantial component of the 

cytoskeleton and play important roles in pollen 

germination and pollen tube growth (Taylor and Hepler, 

1997; Cai et al., 2000). The movement of the generative 

cell and the vegetative nucleus are mediated by myosin 

and transported along microfilaments (Vidali and Hepler, 

2001). The dynamic structure of microfilaments was 

reported to be closely related to the movement of the 

vegetative and generative nuclei and the generative and 

sperm cells at different stages in pollen development 

(Zee et al., 2003). 

Populus as an economically important timber tree in 

China is a model plant for tree research. Research on 

pollen germination and pollen tube growth in Populus is 

significant for its contributions to plant reproductive the 

*Corresponding author. E-mail: ypjing.bjfu@gmail.com. Fax: 86- 

10-62336248. 

movements of the nucleus and microfilaments in biology 

and to tree breeding. We know of no reports on Populus 

pollination. In this study, we used live fluorescent labeling 

and in vitro culturing of Populus pollen to investigate the 

movements of nuclei and microfilaments during pollen 

germination and pollen tube growth in Populus tomentosa 

Carr., Chinese white poplar, which belongs to the genus 

Populus, and is an important fast-growing timber species 

native to China. 


Pollen collection and preservation 

Batches of branches of LM-50, a male clone of P. tomentosa Carr 

from Guan County, Shandong Province, were cut off in January, 

2009, 2010 and 2011 and grown hydroponically in a greenhouse. 

Pollen was collected from each batch and preserved in sterilized 

dry glass bottles containing silica gel at -20°C. 

Pollen germination and pollen tube growth in vitro 

Pre-hydrated pollen grains were cultured at a concentration 

of0.01g/mL in liquid germination fluid (0.8 mmol/L MgSO4, 1 mmol/L 

KNO3, 6 mmol/L Ca(NO3)2, 4 mmol/L H3BO3, 15% PEG-4000, 10% 

sucrose), which was determined by our earlier studies in a shaker 

(100 rpm) at 26°C for 6h. Culture was sampled for staining every 30 

min.


Figure 1. Mature pollen grains in anthers before dehiscence (A-C); bar = 10 µm; A) Binucleate and trinucleate pollen grains 

in the loculus; B) Binucleate pollen; and C) Trinucleate pollen. 

Fluorescent observation of microfilament and nuclei 

Anthers before dehiscence were fixed with Carnoy fixative, then 

paraffin sections were stained with 1 µg/ml DAPI for 5 min after 

dewaxing and rehydration followed by washing twice with 

phosphate buffer (137 mmol/L NaCl, 2.7 mmol/L KCl, 8 mmol/L 

Na2HPO4, 1.5 mmol/L KH2PO4, pH 7.2). The slide was sealed with 

50% glycerol and observed with a fluorescence microscope 

(OLYMPUS BX51, Tokyo, Japan). 

Referring to Li et al. (2001), we used fluorescent phalloidin to 

investigate actin arrangement. At room temperature, microfilaments 

in living pollen and pollen tubes were labeled in the dark for 15 min 

with 165 nmol/mL Alexa Fluor® 488 phalloidin dye (Invitrogen, 

California, USA) containing 1.5% DMSO and 0.01% NP-40. Nuclei 

were stained with 0.5 µg/mL DAPI for 5 to 10 min. After staining, 

one drop of culture liquid was placed on a slide sealed with 50% 

glycerol and examined with a 3D laser section using the LSCM 

system (Laser Scanning Confocal Microscope TCS SP5, LEICA, 

Buffalo Grove, IL, USA). 


Mature pollen that was observed before dehiscence 

contained not only binucleate pollen which had a brighter 

generative nucleus and a larger paler vegetative nucleus 

(Figure 1A and B), but also trinucleate pollen, which had 

two smaller brighter sperm nuclei produced by the 

division of the generative nucleus and a paler vegetative 

nucleus (Figure 1A and C). Prior studies have suggested 

that the pollen of Populus is binucleate. However, 

trinucleate pollen has ever been found in several Populus 

species such as Populus yunnanensis (Hamilton and 

Langridge, 1976). We also found not more than 40% 

trinucleate pollen in the anther before dehiscence (Figure 

1A), in Chinese special local tree species- P. tomentosa 

Carr. Further investigation is necessary to determine the 

reasons for the coexistence of binucleate pollen and 

trinucleate pollen in P. tomentosa Carr. 

Under appropriate in vitro culture conditions, the 

generative cell of binucleate pollen is divided into two 

sperm cells within the pollen grain prior pollen 

germination. The dynamic assembling of microfilaments 

is critical for pollen germination and pollen tube tip growth 

(Fu et al., 2001; Lee et al., 2008). The sperm nuclei and 

vegetative nucleus were each surrounded with short 

fragments of microfilament (Figure 2A). As germination 

progressed, large amounts of actin assembled to form a 

microfilament meshwork throughout the pollen grain and 

the vegetative and sperm nuclei were enveloped by a 

dense network of microfilaments (Figure 2B). As the 

pollen tube emerged, the three nuclei moved towards the 

base of the pollen grain opposite the pollen tube 

orientation (Figure 2C); subsequently, they began to 

move out of the grain and into the growing pollen tube 

(Figure 2D). The pollen tube continued to extend, and 

ultimately all three nuclei entered the elongated pollen 

tube (Figure 2E1, E2 and G). At this point, the parallel 

bundles of microfilaments were distinctive, especially, 

around the nuclei. Throughout the germination process, 

microfilaments were always observed around the 

vegetative nucleus (Figures 2C and E2). There were also 

microfilaments around the sperm nuclei except in the 

early germination stage when the nuclei moved towards 

the base of the pollen grain, when there was no 

microfilament staining (dark areas) around the sperm 

nuclei (Figure 2C). 

Subapically, the microfilaments usually formed a 

network (Figure 2H) or a cortical fringe (Figure 2I). 

Apically, long actin filaments were lacking; instead there 

were very short fragments (Figure 2H) or a dark area with 

no fluorescence (Figure 2I). But some abnormalities were 

observed, such as only one sperm nucleus has entered 

the pollen tube, while the other sperm nucleus and the 

vegetative nucleus remain in the pollen grain (Figure 2F). 

In the pollen tube, the microfilaments around the sperm 

nucleus appear thin and end-arranged, while the 

microfilaments in the grain where the two nuclei will move 

towards disorderly (Figure 2F). However, it has been

Figure 2. F-actin and the movement of nuclei during pollen 

germination and pollen tube growth in P. tomentosa Car.; bar = 10 

µm; Arrow head: microfilament; arrow: nucleus; A) Microfilaments 

forming spindle- and needle-shaped granules around the nuclei 

(after hydration); B) Dense network of microfilaments around the 

nuclei (before germination); C) Nuclei positioned basally in the 

pollen grain, with distinct microfilaments around the vegetative 

nucleus (early germination); D) Microfilaments around the sperm 

nuclei as they moving into the growing pollen tube; E1) 3D image 

of end-arranged microfilaments near the nuclei in a long pollen 

tube; E2) Merged image from two channels at one single layer of 

pollen tube showing microfilaments around the nuclei at the same 

position shown in E1; F) Microfilaments around the sperm 

nucleus in the pollen tube appear thin and end-arranged, while 

microfilaments in the paths of the nuclei movement within the 

grain were disorderly; G) End-arranged microfilaments in a pollen 

tube containing three nuclei. SN, sperm nuclei; VN, vegetative 

nucleus. H-I) Structure of microfilaments in pollen tube; note the 

parallel bundles in the shanks of the pollen tubes; H, bracket: 

subapical F-actin network, arrow head: apical short fragments; I, 

bracket: subapical cortical fringe of microfilament fragments, 

arrow head: no visible apical F-actin. 

Cao et al. 2681


found that microfilaments enveloped the vegetative 

nucleus of mature pollen, while there were no 

microfilaments around the generative cell (Gervais et al., 

1994; Hause et al., 1992) and the movement into the 

pollen tube of the vegetative nucleus and sperm cells was 

thought to depend on the microfilament-associated 

myosin motive system (Heslop-Harrison and Heslop- 

Harrison, 1989). Our results of microfilaments around 

both the vegetative and sperm nuclei (Figure 2A to G) 

indicated that the movement of the nuclei in P. tomentosa 

Carr. is also dependent on the microfilament-associated 

myosin motive system and the existence of 

microfilaments in the sperm cells may differ among 

species. Our result also suggests that thedisruption of 

microfilament distribution and/or structure could prevent 

the nuclei from entering the pollen tube (Figure 2F). 

The in vitro pollen germination ratio of P. tomentosa 

Carr. was relatively low, and stored pollen grains do not 

live long. Thus, not all aspects of the movement of 

vegetative and sperm nuclei have been revealed. 

Furthermore, could the disruption of the microfilament 

network be a component of incompatibility in Populus? 

Further research, including in vivo reporter gene 

strategies and real-time dynamic observation should be 

carried out to investigate these issues. 

Conclusion 

Our investigation confirms the coexistence of trinucleate 

and binucleate pollen instead of only binucleate pollen in 

Chinese native species P. tomentosa Carr. which 

indicated that trinucleate pollen, is more widely in 

Populus than is previously acknowledged. As in other 

species, pollen tubes in P. tomentosa Carr. have three 

distinct microfilament regions. The observation of nuclei 

during pollen germination and pollen tube growth 

supports the argument that movement of sperm nuclei in 

the pollen tube is related to the microfilament system 

similar to the movement of the vegetative nucleus. We 

therefore, propose that a disruption in the arrangement of 

microfilaments in P. tomentosa Carr. pollen may 

adversely affect the normal transportation of nuclei from 

the pollen grain to the embryo sac, and may be a cause 

of incompatibility. More attention should be paid to this 

new aspect of tree breeding as microfilaments are 

important to the regulation of pollen tube growth and to 

signal transduction. 

ACKNOWLEDGMENT 

This study was supported by the Nature Science 

Foundation of China (30700636, 31170631). 

REFERENCES 

Cai G, Del Casino C, Cresti M (2000). Cytoskeletal basis of organelle 

trafficking in the angiosperm pollen tube. Ann. Bot-London, 85:69-77. 

Fu Y, Wu G, Yang Z (2001). Rop GTPase–dependent dynamics of tiplocalized 

F-actin controls tip growth in pollen tubes. J. Cell Biol., 

152:1019-1032. 

Gervais C, Simmonds DH, Newcomb W (1994). Actin microfilament 

organization during pollen development of Brassica napus cv. Topas. 

Protoplasma, 183:67-76. 

Hamilton D, Langridge P (1976). Trinucleate pollen in the genus 

Populus. Cell Mol. Life Sci., 32:467-468. 

Hause G, Hause B, Van Lammeren A (1992). Microtubular and actin 

filament configurations during microspore and pollen development in 

Brassica napus cv. Topas. Can. J. Bot., 70: 1369-1376. 

Heslop-Harrison J, Heslop-Harrison Y (1989). Myosin associated with 

the surfaces of organelles, vegetative nucleus and generative cells in 

angiosperm pollen grains and tubes. J. Cell. Sci., 94: 319-325. 

Higashiyama T, Kuroiwa H, Kuroiwa T (2003). Pollen-tube guidance: 

beacons from the female gametophyte. Curr. Opin. Plant Biol., 6: 36- 

41. 

Lee YJ, Szumlanski A, Nielsen E, Yang Z (2008). Rho-GTPase– 

dependent filamentous actin dynamics coordinate vesicle targeting 

and exocytosis during tip growth. J. Cell Biol., 181: 1155-1168. 

Li Y, Zee SY, Liu YM, Huang BQ, Yen LF (2001). Circular F-actin 

bundles and a G-actin gradient in pollen and pollen tubes of Lilium 

davidii. Planta, 213: 722-730. 

Taylor LP, Hepler PK (1997). Pollen germination and tube growth. 

Annu. Rev. Plant Biol., 48: 461-491. 

Vidali L, Hepler PK (2001). Actin and pollen tube growth. Protoplasma, 

215: 64-76. 

Zee S, Ye X, Wang L, Yau CP, Yip WK (2003). F-actin visualization in 

generative and sperm cells of living pollen of rice using a GFPmouse 

talin fusion protein. Acta Bot. Sin., 45(008): 949-958.



DOI: 10.5897/AJAR11.1648 



Development of an automatic cutting system for 

harvesting oil palm fresh fruit bunch (FFB) 

Hamed Shokripour 1 *, Wan Ishak Wan Ismail 1 , Ramin Shokripour 2 and Zahra Moezkarimi 3 

1 Department of Biological and Agriculture Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 

Serdang, Selangor, Malaysia. 

2 Software Engineering Laboratory, Faculty of Computer Science and Information Technology, University of Malaya, 

50603 Lembah Pantai Kuala Lumpur, Malaysia. 

3 Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Hafez Ave, 

Tehran, Iran. 

Accepted 17 April, 2012 

The purpose of this project was to design, fabricate and test a harvesting mechanism for oil palm fresh 

fruit bunches (FFB). A carrier machine was designed and fabricated which can move around the tree 

trunk smoothly while carrying the cutting system. A mechanize motor system also was designed and 

assembled on the carrier machine for moving the cutting machine forward and backward along the tree 

trunk radius. For a successful and smooth cutting process, two direct current (DC) motors were used 

for carrier machine. Cutting machine consists of a mechanism for cutting and a cutting blade. A 

reciprocating mechanism was used in this project because of the added advantages of this method as 

compared to others. Design of the blade tooth for doing a fast and clean cut was an important 

parameter in this project. An HM-TR Transparent Wireless Data Link Module and an ATmega8 

microcontroller were used to control the cutting system. This system was tested successfully in both 

laboratory and field condition. 

Key words: Cutter blade, fruit harvesting, oil palm, reciprocating cutting, remote controller. 

INTRODUCTION 

The goal of agricultural robotics does not only apply to 

robotics technologies in the field of agriculture, it also 

applies to using agricultural challenges to develop new 

techniques and systems (Ito, 1990). An agricultural robot 

must deal with an unstructured, unknown, uncertain and 

varying environment. Fruits are randomly located on 

trees and are difficult to detect and reach (Jimenez et al. 

1999). In recent years, harvester robots have been 

among the noteworthy topics studied by researchers. 

Until now, different robots have been designed for 

harvesting fruits, such as apples and oranges (Baeten et 

al., 2008; Sanders, 2005). 

The production of palm oil and palm kernel oil in 2005 

reached 36 million tons, and palm derived oil has become 

*Corresponding author. E-mail: hamedshokripour@gmail.com. 

Tel: 0060-173724849. 

the most abundant and consistently supplied oil among 

vegetable oils of the world (Kanoh et al., 2008). The oil 

palm is a tree without branches but with many wide 

leaves at its top. The fruits are compactly packed in 

bunches which are hidden in leaf axils in crowns that may 

be over 12 m above the ground. In traditional harvesting 

methods, the short oil palm trees within arm-reach are 

harvested using the chisel to cut the fruit bunches and 

fronds. The tall trees above 9 m in height are harvested 

using a curved knife with the sharp edge which is 

attached to the end of a bamboo pole. The worker stands 

on the ground while the pole and knife are raised to the 

tree crown in order to harvest bunches (Adetan et al., 

2007). 

Timeliness of harvesting is very crucial to the quality 

and quantity of oil palm fresh fruit bunches (FFB). The 

design and fabrication of the harvester robot for oil palm 

bunches are subjects that have stimulated the interest of 

many researchers to work on better method (Muhamad,


Figure 1. Oil palm climbing robot. 

2008). The proposed cutting system consists of two main 

parts: (1) a system for cutting the leaves, bunches and 

fronds and (2) a system for carrying the cutter blade near 

to the fruit bunches. 

This project was carried out to design and fabricate a 

light and powerful cutting system that can be carried by a 

four wheeled tree climbing robot (Shokripour et al., 2010). 

The climbing robot used in this project can carry the 

weight of a cutting system with a maximum of 7 kg while 

keeping its balance. Previous oil palm harvesting 

machines use a hydraulic power source; hence, these 

machines are powerful, big and heavy. An electric power 

source and electrical components were used to make it a 

light and powerful cutting system. After considering and 

testing different mechanisms of cutting the oil palm 

leaves and bunches, the reciprocating motion of a 

rectangular blade was found to be the best option. An 

alternative current (AC) motor was used as the driver of 

cutting machine. An inverter device was used to convert 

the direct current (DC) from the battery to an appropriate 

AC current for the motor of the cutting machine. 


The cutting system comprises of three main parts: cutting machine, 

carrier machine and the electronic control system. The function of 

the cutting machine is to cut the fronds and the stem of the fruit 

bunches. The cutting process is carried out by fast and continual 

reciprocating motions of a rectangular saw blade. The cutting 

machine was installed on the carrier machine. Carrier machine can 

generate a circular motion for cutting machine to go around the tree 

trunk and also a forward and backward motion on the tree trunk 

circumference. This system makes the cutting machine able to cut 

the leaves and bunches on any point of the tree trunk. The control 

system of the robot analyses the received commands from the 

operator’s remote controller to send an appropriate command to 

each electric motors. 

The whole components of the cutting system are assembled on a 

four wheels tree climbing robot. Designing the cutting system with 

minimum weight was one of the most important parameter in this 

project, because the climbing robot can carry a load with a 

maximum weight of 7 kg. The climbing robot carries the cutting 

system to and near the leaves and fruit bunches. Figure 1 shows 

the climbing robot. The mechanism and operations of the climbing 

robot can be obtained from (Shokripour et al., 2010). 

Cutting machine 

Two essential parameters for designing a cutting machine for any 

material are the cutting mechanism and the cutter blade design. 

Common types of cutting machines include reciprocating saws, 

horizontal endless band saws, universal tilt frame band saws, 

abrasive saws, and cold saws (Ko and Kim, 1999). 

Rotary saw cutting is a cutting method which is versatile and 

effective for a variety of industrial applications. The advantages of 

using the rotary saw method are: faster cutting times, closer 

tolerances, better finishes, less kerf, easier tool changes, better tool 

life, and an overall wider range of applicability (Varvatsoulakis, 

2009). 

The reciprocating saw mimics the back and forth motion of a 

common hacksaw. The gearing system inside the reciprocating saw 

will cause the saw blade to move back and forth across the material

Figure 2. Reciprocating mechanism. 

Figure 3. The alternate set of style and its angles. 

Figure 4. Top view of the carrier machine. 

that needs to be cut. This saw is usually used to cut wood, plaster, 

plastic or some other soft material (Brumbach and Clade, 2003). 

The wire saw can successfully cut the leaves; however, finding an 

appropriate wire, assembling the wire, repairing or changing the cut 

wire are some problems that operators encounter in the field. 

Round saw blade for cutting the fruit bunches was tested, and it 

failed. The diameter of the round saw blade for cutting the leaves 

Shokripour et al. 2685 

needs to be more than 26 cm, which is twice the diameter of the 

leaves. It causes strong vibrations during the cutting process. 

Furthermore, after cutting the oil palm leaves, the leaf fibers stuck 

around the blade shaft and cause the motor to stop. Cleaning these 

fibers is a difficult and time consuming process for operators. 

After testing and comparing different cutting methods, the 

reciprocating method was used in this project for cutting the leaves 

and bunches. Reciprocating motion is produced in various ways. 

The helical pinion gear that powers a flat drive gear at right angles 

was used to transfer the rotary motion of the motor in to a 

reciprocating linear motion. The drive gear has an offset roller 

bearing that acts as a crank. In conjunction with a channel mounted 

on the reciprocating shaft, also known as a slider, the circular 

motion is converted to reciprocating motion. Figure 2 shows the 

components of the reciprocating machine used in this project. 

A 350 W AC motor was used to drive this system. DC motors that 

can generate the same speed and power are found to be big and 

heavy but were not recommended. Since the power source was 

used for climber robot and other components are a direct current 

battery, we used an inverter device to convert the 12 V DC to 220 V 

and 500 W AC for this motor. The speed of the motor, and 

accordingly, the number of strokes can be adjusted within the range 

of 200 to 3000 min -1 for the saw blade. 

The important parameters to be considered when choosing a 

blade for cutting are the type of metal, width, blade set, thickness, 

tooth form and length of the blade. Most saws are designed to 

create a kerf that is wider than the saw blade. This difference is 

called the side clearance which is the most important parameter for 

doing a curve cut (Duginske, 1989). 

The teeth of the blade used in this project were bent alternately in 

opposite directions to create a kerf wider than the body. The 

amount of bending is usually proportionate to the thickness of the 

blade body and is usually 25% of the blade thickness. The angle at 

which the points of the saw tooth make contact with the material is 

an important factor of the effective cutting performance of a saw 

blade. Figure 3 shows the design of the alternate tooth style and 

teeth angles of the blade used in this project for cutting the oil palm 

leaves. 

The radial lengths of the circles that can be cut by a blade 

depend on the ratio of the width of the blade to the width of the saw 

kerf. Since the diameter of the tree trunk is not constant, the blade 

was designed based on the minimum size of the oil palm tree trunk 

which is 32 cm. The appropriate size of the blade width is 

calculated using: 

W 

2 

2 

� 

� � � �2 2 

R � K � R � T 

Where W is the blade width, R is the radius of the tree trunk, K is 

the size of the kerf width and T is the thickness of the blade. 

Carrier machine 

One of the most important objectives of this project was to design a 

mechanism that can move around the tree to place the blade of the 

cutting machine at any point around the tree and at any distance 

from the surface of the tree trunk. A special rail was designed for 

the carrier robot to guide the cutting machine on a fixed circular 

path around the tree. The carrier machine comprises of one DC 

motor, four ball transfers, three ball bearings and one sprocket. 

Figure 4 shows the top view of the transporter robot. 

The ball transfers were used as the wheels of the machine. Using 

the ball transfers as the wheel enable the carrier machine to 

smoothly navigate itself around the tree trunk, because it generates 

only a small amount of friction and have three degree of freedoms.


Figure 5. Carrier machine. 

Figure 6. DC motor, plate, sprocket and chain. 

The power required to drive the robot is generated by a DC micro 

gear motor. A gearbox was installed on this motor to reduce its 

speed from 2500 to 5 rpm. The slow motion of the cutting system 

causes smooth and accurate cutting. Furthermore, decreasing the 

motor speed causes an increase in the power and torque of the 

motor which lead to more force to push the cutting blade into the 

branch. The power of the motor is transferred to a 4 cm diameter 

sprocket. The speed of the carrier machine moving around the tree 

is calculated as: 

V = 5 rpm � (4 � 3.14) = 51.52 cm. min -1 

The motor rotation direction is controlled by the operator via a 

remote controller. 

To do an accurate cutting, a gear system was added on carrier 

machine to move the cutting machine forward and backward along 

the tree trunk circumference. It comprised of a DC motor with a 

sprocket, a set of two ball bearing drawer slides and two limit 

switches. Figures 5 and 6 show the schematic and fabricated 

system after they have been installed on the carrier machine. 

The DC motor was installed in such position so that the teeth of 

its sprocket were placed inside the chain. The rotation of the DC 

motor causes the sheet metal to move forward and backward. One 

limit switch was installed at the head of the cutting machine. When 

the cutting machine moves forward, the limit switch touches the tree 

surface and stops the motor. Controlling of the DC motors is done 

by the operator using the remote controller. 

REMOTE CONTROLLER 

A remote controller system comprises of hardware and software 

designed and fabricated for operator to control the cutting system. 

The remote controller and the cutting system each have one HM- 

RF transmitter receiver device and an ATmega32 microcontroller as 

the main hardware components. A specific program was written for 

each microcontroller to receive the data, analyze them and finally 

generate and send the appropriate commands to other 

components, such as DC motors and HM-RF devices. 

A HM-TR Transparent Wireless Data Link Module consists of 

two parts, both of which can send and receive data wirelessly. The 

first part is used by the remote controller to send the operator’s 

commands and the second part is installed on the cutting system to 

receive the commands from the remote controller. The data

Table 1. Program for controlling 

the cutting system functions. 

R1 = Udr 

If Flag1 = 0 Then 

If R1 = 10 Then 

Cs1 = 10 

Flag1 = 1 

Count1 = 1 

End If 

Else 

Sdata1(count1) = R1 

If Count1 = 2 Then 

If Cs2 = Sdata1(2) Then 

Setpoint1 = Sdata1(1) 

Count1 = 0 

Flag1 = 0 

End If 

End If 

Incr Count1 

Cs2 = Cs1 + R1 

End 

Figure 7. Carrier system and cutting machine. 

transferred between these parts uses a radio frequency that ranges 

between 310.24 and 929.27 MHz. This paper focuses more on the 

processes needed to transfer the data from the HM-TR Module to 

the microcontroller, and the analysis of them. 

The HM-TR Transparent Wireless Data Link Module uses RS232 

logic level for transferring data to the microcontroller. The RS232 

physical specification produces a logic 1 at receiver input as -3 to - 

25 V and logic 0 as +3 to +25 V. Since the ATmega64 

microcontroller can receive and analyze the data on the transistortransistor 

logic (TTL) logic level, a MAX232 integrated circuit (IC) is 

used for converting the data of the HM-TR Module to TTL logic 

level. It can generate the necessary RS-232 voltage levels of 

approximately -10 and +10 V internally from a single +5 V power 

supply. It can reduce RS-232 inputs which may be as high as ±25 V 

to standard TTL levels 5 V. 

Software 

Shokripour et al. 2687 

When the operator pushes a button of the remote controller, the 

remote controller sends three numerical codes to the receiver of the 

robot. The first number informs the microcontroller that a new 

command is being sent by remote controller and it readies the 

microcontroller to receive the next two numbers. The second 

number is the code of the command that is allocated to a button of 

the remote controller. By analyzing this number, the microcontroller 

understands what the operator wants the robot to do. The third 

number is a summation of the first and the second numbers and is 

called the check sum number. The microcontroller also adds up the 

first and the second received numbers and compares the result with 

the check sum. If these numbers are the same, the microcontroller 

understands that it has received the data from the remote controller 

completely and correctly. Table 1 shows the main part of the remote 

control program that was written for controlling the functions of 

cutting system. 

In the next program, the microcontroller sends the appropriate 

command to each DC motor based on the value of the second 

number. To change the motors rotation directions, a miniature 

intermediate power relay was used for each motor. The operator 

can start and stop the DC motors for moving the cutting system 

around the tree trunk and also move it forward and backward. 

Furthermore, turning the AC motor of the cutting machine on and 

off is controlled by the remote controller. 

Since the power supply of this robot is a 12 V DC, an inverter 

device is used to convert the 12 V DC to 220 V and 500 W AC. To 

decrease the weight of the system, the inverter device is placed on 

the ground. 


Different parts of this cutting system were tested in 

laboratory condition. The cutting machine was tested by 

cutting the oil palm leaves. The blade cut the leaves 

successfully and created a suitable kerf but it was 

suggested to design and fabricate a new type of blade 

with round shaft like round fine with larger and sharper 

teeth for developing this system. Figure 7 shows this 

machine after assembling the cutting system on carrier 

machine. 

The center of the oil palm leaves is made of very hard 

wood. During the cutting process this part caused intense 

vibrations to be generated in the carrier robot. This 

problem was solved by adjusting the carrier robot to 

make it more stable during the cutting process. The 

speed of moving the blade through the leaves was slow 

enough to carry out an accurate and successful cutting. 

Fabricating and adjusting the carrier machine was a 

difficult part of this project because of having large 

number of components. For developing this system can 

redesign some parts of the carrier system to make it 

more stable against the vibrations. 

The maximum size of the oil palm leaves and bunches 

were 14 cm in this tests and the speed of the cutting 

system is 51.52 cm/min. The time required for cutting a 

leaves is 14 × 60/51.52 = 16.3 s. Figure 8 shows the 

cutting system after assembling on the climbing robot. 

The weight of the cutting system was 6.3 kg, so the 

climbing robot can carry it successfully to and near the


Figure 8. Cutting system after assembling on climbing robot. 

leaves and bunches. The total weight of climbing robot 

and cutting system is 18.5 kg, so the operator can move 

it easily from tree to tree in the farm. The power supply of 

the climbing and cutting system is 12 V DC which can be 

generated by automobile battery. Different functions of 

the remote control of the system were tested 

successfully. Since it was designed to be user friendly, 

the operator can control the robot easily after short 

training. 

REFERENCESS 

Adetan DA, Adekoya LO, Oladejo KA (2007). An Improved Pole-and- 

Knife Method of Har vesting Oil Palms. Agricultural Engineering 

International: The CIGR E J. p. 60. 

Baeten J, Donne K, Boedrij S, Beckers W , Claesen E (2008). 

Autonomous Fruit Picking Machine: A Robotic Apple Harvester. Field 

Serv. Rob., 531-539. 

Brumbach ME, Clade JA (2003). Industrial Maintenance, Published by 

Delmar Learning. ISBN0-7668-2695-3 

Duginske M (1989),band saw handbook published by sterling publishing 

Co, New York, ISBN 0-8069-6398-0. 

Ito N (1990). Agricultural robots in Japan. 'Towards a New Frontier of 

Applications', Proceedings. IROS '90. IEEE International Workshop 

on ssue Date: 3-6 Jul 1990. 

Jimenez AR, Ceres R, Pons JL (1999). A machine vision system using 

a laser radar applied to robotic fruit harvesting. (CVBVS '99) 

Proceedings. Workshop on Computer Vision Beyond the Visible 

Spectrum: Methods and Applications. IEEE Computer Society 

Washington, DC. 

Kanoh T, Iwabuchi H, Hoshida Y, Yamada J, Hikosaka T, Yamazaki 

A, Hatta Y, Koide H (2008). Analyses of electro-chemical 

characteristics of Palm Fatty Acid Esters as insulating oil. Dielectric 

Liquids, 2008. ICDL 2008. IEEE International Conference on. 

Ko TJ, Kim HS (1999). Mechanistic cutting force model in band sawing. 

Inter. J. Mach. Tools Manufacture, 39(8): 1185-1197. 

Muhamad JMRW (2008). "Design and fabrication the Prototype of a 

Motorized Cutter for Harvesting Palm fruit" University Malaysia 

Pahang, Thesis. 

Sanders KF (2005). Selective Picking Head for Citrus Harvester. 

Biosyst. Eng., 90(3): 279-287. 

Shokripour H, Ismail WIW, Moes KZ (2010)."Development of an 

automatic self balancing control system for a tree climbing robot" 

Afri. J. Agric. Res., pp. 5-21. 

Varvatsoulakis MN (2008). Design and implementation issues of a 

control system for rotary saw cutting. Control Eng. Pract., 17: 198– 

202.



DOI: 10.5897/AJAR11.1234 



Total factor productivity growth and convergence in 

Northern Thai agriculture 

Sanzidur Rahman 1 *, Aree Wiboonpongse 2 , Songsak Sriboonchitta 3 and Kritsada Kanmanee 4 

1 School of Geography, Earth and Environmental Sciences, University of Plymouth, 

Plymouth, PL4 8AA, United Kingdom. 

2 Department of Agricultural Economics, Faculty of Agriculture, Chiang Mai University, Chiang Mai, Thailand 50200. 

3 Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand. 

4 Institute of Self-sufficiency in Research and Promotion, Chiang Mai University, Chiang Mai, 50200, Thailand. 


The paper estimate s Total Factor Productivity (TFP) growth in six agro-economic zones of Northern 

Thailand covering a 23 year period (1977 to 1999) and te sts convergence among st regions using a 

stochastic production frontier approach. Results revealed that all the inputs excluding fertilizer 

significantly contribute to agricultural productivity. Increa sing returns to scale prevail in these regions. 

Land, labor, irrigation and loan capital have substitution relationships but all are within the inelastic 

range. The mean technical efficiency level is low (0.88). The overall TFP declined slightly due to 

technical regress and mode st improvement in technical efficiency change over time. However, 

convergence in productivity ha s been reached in all regions towards the end. The government’s 

initiative to support investment through Bank of Agriculture and Agricultural Cooperative loan had a 

significant influence on technical efficiency improvements. Policy i mplications include provision of 

capital through loan, investments in irrigation, and proper functioning of land and labor markets to 

improve agricultural productivity. 

Key words: Thailand, stochastic production frontiers, total factor productivity growth, convergence, technical 

efficiency change, technical change, agro-economic zone. 

INTRODUCTION 

Agricultural productivity and efficiency improvements 

have been the priority concern of the government in 

developing countries due to severe pressure imposed by 

declining agricultural prices as well as, prevailing highly 

competitive trade environment. Therefore, performance 

evaluation of the agricultural sector at the national, 

regional or provincial level is important for policy 

planning. Given the unprecedented food crisis in recent 

years, the importance of measuring performance of the 

agricultural sector became a topmost priority of many 

*Corresponding author. E-mail: srahman@plymouth.ac.uk. Tel: 

+44-1752-585911. Fax: +44-1752-585998. 

JEL classification: O33, Q18 and C21. 

developing economies in order to ensure food security of 

its growing population base. 

Traditionally, the rice industry has played an important 

role in the Thai economy by supplying main staple food, 

employing a large portion of the labor force, and 

contributing towards government revenue and foreign 

exchange earnings (Choeun et al., 2006). Thailand has 

experienced high rate of growth in agricultural sector over 

the past four decades mainly through expansion of areas 

(for example, by clearing forests) which however, cannot 

be continued from the 1990’s onward (Krasachat, 2001). 

As a result, government promoted the use of modern 

inputs such as fertilizers and irrigation facilities to boost 

agricultural productivity. Nevertheless, the policy makers 

and economists have raised questions on the impact of 

modern input usage as well as, the availability of new 

lands on productivity growth of the Thai agricultural


sector (Krasachat, 2001). 

Studies on Total Factor Productivity (TFP) growth of 

Thai agriculture are highly limited. So far, only a few 

studies are available, perhaps largely due to data 

limitations but are characterized with similar overall 

conclusions. Luh et al. (2008) analyzed TFP growth using 

a DEA approach jointly for eight East Asian economies 

including Thailand using national level aggregate data for 

the period of 1961 to 2001. They concluded that the 

agricultural productivity in Thailand has deteriorated 

largely due to technical regress which in turn had a 

dominating effect on the improvement in technical 

efficiency. Although, this study provides a detailed 

examination of TFP growth in Thailand, the limitation in 

the study of Luh et al. (2008) is that the performance of 

Thailand is measured in relation to a multi-country 

production frontier. Therefore, intra-provincial or regional 

performance cannot be identified due to aggregate nature 

of the data. Suphannachart and Warr (2010) used 

national aggregate data for the period of 1970 to 2006 

and measured agricultural TFP using the growth 

accounting method and concluded that TFP in crop 

sector grew at an annual rate of 0.68%. The obvious 

problem with this study is the limited number of 

observations (that is, only 36 observations) and the 

inability to provide information on TFP growth 

components as well as, results at a lower level of 

aggregation, that is, at the provincial or regional level. 

Krasachat (2001) estimated productivity growth for four 

agricultural regions of Thailand over a 22 year period of 

(1972 to 1994) using a cost function framework which 

implies perfect efficiency of production units, and 

therefore, inherently biased. This is because it is a 

common knowledge that developing country agriculture 

operates with considerable low level of technical 

efficiency ranging from 72.4 to 84.5% (Bravo-Ureta et al., 

2007). Krasachat (2001) concluded that the TFP index 

declined at an annual rate of -0.4% partly explained by 

low level of technical progress, which in turn is biased 

towards saving labor, fertilizer and capital. Finally, 

Krasachat (2000) applied a DEA approach to measure 

technical efficiency change in Thai agriculture using the 

same dataset and concluded that technical efficiency has 

been very low in general, and there has been a decline in 

all types of efficiency (that is, overall technical efficiency, 

scale efficiency and pure technical efficiency) over time. 

In this study, we select a panel -data of six Agroeconomic 

Zones (AEZs) of Northern Thailand (covering 

17 provinces) for a 23 year period (1977 to 1999) to: (a) 

measure agricultural TFP growth and its components: 

technical change and technical efficiency change; and (b) 

test for the existence of convergence in agricultural 

productivity amongst regions. We applied the stochastic 

production frontier approach in order to circumvent some 

of the inherent problems of DEA methodology used by 

Krashachat (2000) and Luh et al. (2008) as well as, to 

examine whether similar results hold for the Northern part 

of Thailand, which is essentially the most productive 

region of the country. The parameters of the stochastic 

frontier provide estimates of the changes in technical 

efficiency and technical progress as well as, TFP allowing 

policy implications to be inferred (Coelli et al., 2003). 

METHODOLOGY 

Stochastic production frontier 

Since one of the objectives of this study is to estimate technical 

efficiency of these AEZs in Northern Thailand, w e applied the 

stochastic production frontier method as proposed by Aigner et al. 

(1977). The stochastic frontier production f unction for panel data 

can be w ritten as: 

Yit = exp(xit�+ Vit – Uit) (1) 

Where Yit is production in year t (t = 1, 2, …, 31) for region i (i = 1, 

2, ….., 16); 

� is the vector of parameters to be estimated; 

Vits are the error component and are assumed to follow a normal 

distribution N (0, �2v); 

Uits are non-negative random variables, associated w ith technical 

inefficiency in production, w hich are assumed to arise from a 

normal distribution w ith mean, � and variance, �2u, w hich is 

truncated at zero. 

The model used here incorporates a simple exponential 

specification of the time-varying inefficiencies, follow ing Battese 

and Coelli (1995). The technical efficiency of production for the ith 

region at the tth year can be predicted using Equation 2 (Coelli et 

al., 1998): 

TEit = E[exp(- Uit)|(Vit- Uit)] (2) 

Measuring productivity change 

Productivity change occurs w hen the rate of change in output 

differs from the rate of change in the use of an index of inputs 

(Kumbhakar and Lovell, 2000). TFP can be measured by 

constructing the Malmquist productivity index, a measure of TFP of 

a unit based on the ratio of total output quantity to an index of all 

input quantities. Unlike partial productivity measures (simple 

output/input ratios), TFP prov ides an overall measure of productivity 

(Helvoigt and Adams, 2009). Given the measure of TEit in Equation 

2, technical efficiency change (ECit) is then calculated as: 

ECit = TEit/TEis (3) 

An index of technological change (TCit) betw een tw o adjacent 

period s and t for the ith region can be directly calculated fro m the 

estimated parameters of the stochastic production frontier. The 

partial derivatives of the production function are evaluated w ith 

respect to time at xit and xis. We then converted these into indices 

and calculated their geometric mean. Follow ing Coe lli et al. (1998, 

2003), the calculation of the technical change index is given as:

TC 

it 

�� 

�f 

( xis, 

s, 

�) 

� � �f 

( xit, 

t, 

�) 

�� 

� �� 

1� 

� 

� 

� 

1� 

�� 

�� 

�s 

� � �t 

�� 

The indices of technical efficiency change and technological 

change obtained by using Equations 3 and 4 respectively can be 

multiplied to obtain a TFP index (Coelli et al., 2003) such as: 

TFPit = ECit * TCit (5) 

This is equivalent to the decomposition of the Malmquist index 

suggested by Fare et al. (1985). 

Data issues and variables 

A major draw back to conducting research on a long-term 

productiv ity performance of the agricultural sector is the lack of 

time-series panel data for most of the developing economies, and 

Thailand is no exception. Most often, data are available at a highly 

aggregated form w hich is of no use and as such unless a multicountry 

analysis is attempted (Luh et al., 2008), many relevant 

variables required for such assessment of performance 

disaggregated at the regional or provincial level over time are not 

available. Consequently, estimation of econometric models w ithout 

relevant variables tends to be biased. 

The Office of Agricultural Ec onomics (OA E) of Thailand is 

responsible for providing agricultural statistics to facilitate planning. 

In general, output data is available at A EZ levels for some period 

and w as discontinued in later years (for example, 2000 onw ard). 

Moreover, data on key production inputs such as labor and 

fertilizers, disaggregated at A EZ levels are even harder to come by. 

Rahmaan et al. 2691 

0. 

5 

(4) 

( 4) 

Farmers are hypothesized to incorporate natural environments in 

their response to economic factors (especially, price changes) 

which influence their decision on the use of modern inputs and 

adoption of technologies. Consequently, productiv ity and efficiency 

differ amongst regions. Hence, it is of utmost importance that 

performance evaluation of the agricultural sector of any economy 

needs to be examined at a disaggregated level so that policies can 

be directed to areas w here it is most needed (Table 1). 

Given these limitations, annual data of six AEZ w ere finally 

collated from the available OA E statistical yearbooks (various 

issues) for the period of 1977 to 1999. The series cannot be 

updated further due to a lack of disaggregated information on 

inputs. The 17 provinces included in these six A EZs of Northern 

Thailand are: 

Zone 8 = Nakhon Saw an, Phetchabun 

Zone 9 = Tak, Kamphang Phet, Sukothai 

Zone 10 = Phrae, Nan, Uttaradit 

Zone 11 = Phitsanulok, Phichit 

Zone 12 = Chiang Rai, Lampang, Phayao 

Zone 13 = Chiang Mai, Lamphun, Mae Hong Son. 

The empirical model 

To measure technical efficiency, TFP grow th and its components 

from the stochastic production frontier, w e have used the follow ing 

fully flexible translog function: 

5 

5 5 

5 

1 

1 2 

ln Yit � � � �� 

k ln X kit � �tt 

� �� kj (ln X kit )(ln X jit ) � �ttt 

� �� 

kt ln X kit * t � uit 

� v 

2 

2 

0 it 

k�1 

k�1 j�1 

k�1 

(6) 

Where; 

t represents the year of the observation (1977=1); 

yit is the agricultural gross domestic product at constant prices 

(million baht) in the ith region in year t; 

xit is the land area (rai) of the ith region in year t; 

x2it is the total labor used (persons) in the ith region in year t; 

x3it is the amount of fertilizers used (ton) in the ith region in year t; 

x4it is the share of irrigated area (percentage) in the ith region in 

year t; 

x5it is the amount of loan capital disbursed (million baht) in the ith 

region in year t; 

vit and uit are respectively, the sy mmetric and one-sided random 

error terms denned earlier. 

The inefficiency effects model is specified as: 

uit = δ0 + δ1time + δ2rainfall + δ3BAAC + ςit (7) 

Where; 

Rainfall = the amount of average rainfall per year (mm) 

BAAC = the share of BAAC in total loan capital 

ςit = the sy mmetric error. 

RESULTS 

The parameter estimates of the stochastic production 

frontier model (Equation 6) estimated jointly with the 

inefficiency effects model (Equation 7) using the 

Maximum Likelihood Estimation (MLE) procedure is 

presented in Table 3. Prior to discussing the results of the 

production frontier, we report the series of hypothesis 

tests conducted to select the functional form and to 

decide whether the frontier model is an appropriate 

choice rather than a standard mean-response or average 

production function as used by Krasachat (2001). The 

results are reported in Table 2. Sauer et al. (2006) raised 

the importance of checking theoretical consistency, 

flexibility and choice of the appropriate functional form 

when estimating stochastic production frontiers. 

However, given the purpose of our research, we 

concentrate on the choice of an appropriate functional 

form that is also flexible. The first test was conducted to 

( 6)


Table 2. Hypothesis tests. 

Table 1. Summary statistics of the variables. 

Variable Measurement Mean Std. deviation 

Deflated agricultural GDP Million baht 7,783,022.00 1,632, 899.00 

Land area Rai 3,589,754.00 1,377,851.00 

Labor Thousand persons 63,315.60 144,863.40 

Loan capital (commercial + BAAC) Million baht 3,855.26 3,324.45 

Fertilizer Tons 3,283.76 1,814.66 

Irrigation Percent of land area 33.00 22.96 

BAAC share Percent of loan 47.61 14.50 

Time years 12 6.66 

Number of regions 6 

Number of years 23 

Number of observations 138 

Tests Parameter restrictions 

LR test 

statistic 

Degree of 

freedom 

χ 2 Critical value 5% Outcome 

Functional form test H 0: all βjk = 0 110. 7 15 24.99 Reject H 0: CD is inadequate 

Frontier test 

H 0: M3T = 0 (that is, no 

inefficiency component) 

z statistic = 

40.12 

p value of z = 0.000 Reject H 0: Frontier not OLS 

No technical change H 0: βt = βtt = βkt = 0 719. 90 7 14.07 Reject H 0 

No inefficiency effects H 0: δ0 = δ1 = δ2 = 0 23.45 3 7.82 Reject H 0 

Constant returns to scale (CRTS) H 0: Σβk = 1 17.67 1 3.84 Increasing RTS 

Regularity conditions check 

Monotonicity (dy/dxi>0) for ever y 

input) 

Diminishing marginal productivity 

(d 2 y/dx 2 i

Table 3. Parameter estimates of the stochastic production frontier and 

inefficiency effects model. 

Production frontier function Coefficient t-ratio 

Constant 15.7543 414.33*** 

ln land 0.3427 5.41*** 

ln labor 0.8719 16.84*** 

ln irrigation 0.0844 2.11** 

ln fertilizer 0.0072 0.14 

ln loan capital 0.1681 4.51*** 

0.5 * (ln land) 2 -1.5702 -2.21** 

0.5 * (ln labor) 2 1.1642 5.27*** 

0.5 * (ln fertilizer) 2 0.1573 0.95 

0.5 * (ln irrigation) 2 -0.5959 -3.01*** 

0.5 * (ln loan capital) 2 -0.1446 -1.06 

ln land * ln labor 0.4354 1.38 

ln land * ln fertilizer 0.8809 4.69*** 

ln land * ln irrigation -1.8395 -3.73*** 

ln Land * ln loan capital 0.2632 0.75 

ln Labor * ln Fertilizer -0.6554 -5.10*** 

ln Labor * ln Irrigation 0.4618 1.74* 

ln labor * ln loan capital 0.5470 2.71*** 

ln fertilizer * ln irrigation 0.6719 3.64*** 

ln fertilizer * ln loan capital -0.3005 -2.62*** 

ln irrigation * ln loan capital 0.2687 1.03 

Time -0.0219 -4.42*** 

Time * ln land -0.0291 -0.65 

Time * ln labor -0.1087 -3.82*** 

Time * ln fertilizer -0.0050 -0.45 

Time * ln Irrigation -0.0341 -0.91 

Time * ln loan capital 0.0491 2.53** 

Time * time -0.0050 -2.58*** 

Model diagnostics 

Log likelihood 166.677 

σu 2 

0.0127 36.89*** 

σv 2 0.0017 15.58*** 

γ 0.99 

No inefficiency effects (H0: δ0 = δ1 = δ2 = 0) 23.45*** 

Inefficiency effects function 

Constant -0.0742 -0.80 

time 0.0274 3.52*** 

Rainfall 0.0001 0.79 

BAAC loan share -0.0001 -4.51*** 

Number of observations 138 

*** Significant at 1% level (p


Table 4. Production elasticities and returns to scale. 

Inputs Value t-ratio 

Land 0.3427 5.41*** 

Labor 0.8719 16.84*** 

Irrigation 0.0844 2.11** 

Fertilizer 0.0072 0.14 

Loan 0.1681 4.51*** 

Returns to scale 1.4743 


alone. The efficiency scores are particularly high for the 

years 1977, 1981, 1982, 1996, 1997 and 1999 (Figure 1). 

However, in the later period, from the 1990’s, the 

variation between regions has reduced but overall 

technical efficiency level somewhat fluctuated and then 

rose to a maximum level during the last few years. The 

range commensurate with the average reported for 

developing economies (for example, Bravo-Ureta et al., 

2007). Figure 2 presents regional distribution of technical 

efficiency scores over time. As evident from Figure 2, 

region 10 has the highest level of fluctuation in technical 

efficiency scores with the lowest average. It should be 

noted that observation of high efficiencies with wide 

variation across regions may also reflect the use of 

aggregated data with reduced variability and a potentially 

lower efficient frontier (Helvoigt and Adams, 2009). 

Factors affecting technical efficiency 

Whereas the time variable in the frontier production 

function captures technical change over time (that is, 

shifting of the production frontier), in the inefficiency 

equation the time variable is intended to capture 

inefficiency change (that is, changes in the distance of 

the average unit from the sector production frontier). The 

positive sign on the coefficient of the time variable in the 

inefficiency equation indicates that the distance of the 

typical production region from the technical frontier 

increased significantly over the study period. However, it 

is encouraging to see that the investment channeled 

through BAAC loan has a significantly positive impact on 

improving technical efficiency. 

Productivity growth, technical change and technical 

efficiency change 

Finally, Table 6 and Figure 3 present the average levels 

of technical change, technical efficiency change and 

overall TFP growth by year. It is clear from Figure 3 that 

TFP initially declined until 1987 and then picked up but 

dropped slightly in later years, a pattern also mirrored by 

technical efficiency change index. Specifically, overall 

TFP declined at an annual rate of -0.6% per annum due 

to technical regress at a rate of -2.1% while technical 

efficiency improved at a modest rate of 1.5%, 

respectively. The overall conclusion is remarkably similar 

to those of Krasachat (2001, 2000) and Luh et al. (2008). 

Although, Figure 3 shows a clear and smooth pattern, it 

hides the level of fluctuation experienced by individual 

regions. Table 7 provides these measurements by region. 

Only Region 8 experienced positive growth in TFP, 

technical change and efficiency change, though, at a very 

modest rate. The highest level of technical regress is in 

Region 12. In fact, F-test confirmed that significant 

differences amongst region exist with respect to technical 


change (Table 7). Figure 4 shows the actual level of 

fluctuation in TFP index among regions which tend to be 

smoothened towards the end. The implication is that 

lagging regions are catching-up with the leading ones, 

that is, converging to a common level of productivity 

growth. 

Testing convergence among regions 

Convergence occurs when regions with poorer 

productivity level during the initial period grow more 

rapidly than regions with high initial level of productivity 

implying that the poorer regions are catching up. Table 7 

and Figure 4 suggest that none of the regions are 

producing at a significantly higher level of productivity as 

evident from a narrow range of deviation between 

regions, hence, TFP growth is contributed by all of the 

regions. In other words, there is no evidence of significant 

divergence among the regions. However, more 

conclusive results can only be obtained by formally 

testing for convergence as discussed subsequently. We 

applied the cross-sectional method which examines the 

tendency of regions/countries with initial low levels of 

productivity to grow relatively faster in order to catch-up 

with those of high initial level productivity. Therefore, if 

the growth rates are regressed on the initial level of 

productivity and the coefficient is negative, there is said 

to be Beta convergence. The average growth rate of 

productivity for each region i between year 0 and T can 

be defined as: 

gi,T = T-1 (yi,t – yi,0) 

Then, a test of Beta convergence is conducted by a 

regression of growth rate as the dependent variable with 

the initial level of productivity as the regressor as follows: 

g �� 

� �y 

� � 

i, 

t 

i, 

0 i, 

T 

Where; � and � are parameters and �i,T is an error term 

with a zero mean and finite variance. Convergence exists 

if the value of � is negative and significant. The result of 

this exercise is presented in Table 8. The estimated 

parameter �, which is the coefficient of the initial level of 

productivity level is negative and significant at 1% 

confidence level. This provides strong evidence that 

agricultural productivity in Northern Thailand has 

converged. In other words, regions with initial poor level 

of productivity grew faster and are catching up with the 

high productivity regions. 

Another simple cross-sectional test for convergence is 

the Sigma convergence, which holds if the crosssectional 

standard deviations of the log of TFP decrease 

over time. In other words, it tests whether the productivity 

differences among regions are narrowing over time. 

(8) 

(


Figure 1. Distribution of technical efficiency by year (Box-plots). 

Figure 2. Distribution of technical efficiency by region (Box-plots).

Indices 

TFP index 

1.20 

1.10 

1.00 

0.90 

0.80 

0.70 

0.60 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

Year 

1990 


1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

Technical change Efficiency change Malmquist index 

Figure 3. Technical change, efficiency change and productivity grow th in Northern Thai agriculture, 1977 to 1999. 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

Year 

1989 

Region 8 Region 9 Region 10 Region 11 

Region 12 Region 13 

Figure 4. Total factor productivity grow th by region, 1978 to 1999. 

Technically, a necessary condition for Sigma 

convergence is the existence of Beta convergence 

although Beta convergence does not guarantee a 

reduction in the distribution of dispersion among TFP 

growth rates (Thirtle et al., 2003). Figure 5 shows that the 

cross-sectional standard deviations for the log of TFP 

over time are in fact fluctuating within a narrow range, 

1990 

1991 

1992 

1993 

1994 

which further corroborate the result obtained from Beta 

convergence test. 

Evidence of productivity convergence in Thailand 

should not be treated as exceptional. There are 

evidences of convergence in agricultural productivity and 

its components in Asia. Wu (2000) found that overall TFP 

growth in China has shown signs of convergence since 

1995 

1996 

1997 

1998 

1999


Standard deviation of log of TFP 

Stdev of log of TFP 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

Year 

Figure 5. Sigma convergence: standard deviations of the logarithm of TFP index. 

Table 6. TFP grow th and its components by year. 

Year Technical change Efficiency change TFP change (Malmquist index) 

1977 1.0000 1.0000 1.0000 

1978 0.9156 0.9722 0.8901 

1979 0.9240 0.9970 0.9213 

1980 0.9260 1.0411 0.9640 

1981 0.9301 0.9909 0.9216 

1982 0.9394 1.0265 0.9643 

1983 0.9511 0.9958 0.9471 

1984 0.9621 0.9630 0.9265 

1985 0.9648 0.9317 0.8989 

1986 0.9614 0.9105 0.8753 

1987 0.9601 0.9795 0.9404 

1988 0.9630 1.0057 0.9686 

1989 0.9683 1.0337 1.0009 

1990 0.9784 1.0696 1.0465 

1991 1.0007 1.0621 1.0628 

1992 1.0190 1.0714 1.0918 

1993 1.0236 1.0824 1.1080 

1994 1.0231 1.0566 1.0810 

1995 1.0247 1.0505 1.0764 

1996 1.0304 1.0473 1.0791 

1997 1.0322 1.0339 1.0672 

1998 1.0216 1.0506 1.0733 

1999 1.0088 1.0009 1.0096 

Mean 0.9788 1.0152 0.9937 

the 1990’s with technical efficiency across regions having 

converged as early as the 1980’s. Rahman (2007) 

1989 

1990 

1991 

1992 

1993 

1994 

showed that productivity growth in Bangladeshi regions 

has converged during the mature stage of Green 

1995 

1996 

1997 

1998 

1999

Table 7. TFP grow th and its components by region. 


Region Technical change Efficiency change TFP change (Malmquist index) 

8 1.0125 1.0076 1.0203 

9 1.0027 0.9985 1.0011 

10 0.9805 1.0014 0.9819 

11 0.9734 0.9986 0.9721 

12 0.9292 1.0083 0.9369 

13 0.9707 1.0006 0.9713 

Mean 0.9788 1.0152 0.9937 

Difference among regions 

F-value (5,126) 11.35*** 0.02 1.14 



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DOI: 10.5897/AJAR11.488 



Dynamics of on-farm management of potato (Solanum 

tuberosum) cultivars in Central Kenya 

C. Lung’aho 1 *, G. Chemining’wa 2 , S. Shibairo 2 and M. Hutchinson 2 

1 Kenya Agricultural Research Institute -Tigoni, P.O. Box 338-00219 Limuru, Kenya. 

2 Department of Plant Science and Crop Protection, University of Nairobi, P. O. Box 29053-00625 Kangemi, 

Nairobi, Kenya. 

Accepted 7 December, 2011 

Studies to understand the dynamic nature of farmers’ management of potato and assess the extent of 

genetic erosion and farmers’ perceptions of genetic erosion in potato were conducted in Kiambu West 

district in 2006. A stratified random sampling procedure was used to draw a sample of 302 farmers for 

the study. Majority of the farmers interviewed obtained seeds from informal sources. Farmers identified 

29 varieties which were once widely grown in the study area. Of these, only 9 are still grown while 

another 11 have been introduced. The most commonly grown varieties were Zangi (69.4%), Tigoni 

(41.4%), Thima Thuti (30.8%) and Karuse (20.9%). Twenty cultivars including Amin, Anett, Cardinal, 

Feldeslohn, Gituru, Kiraya, Kibururu, Kenya Baraka, Kenya Dhamana, Karora Iguru, Maritta, Mirka, Njae, 

Njine, Patrones, Romano, Roslin Bvumbwe, Roslin Gucha, Suzanna and Furaha were the most affected 

by genetic erosion. The most important causes for abandonment of varieties were low yields, rapid 

greening, susceptibility to late blight, strong dormancy, sensitivity to drought, and susceptibility to 

bacterial wilt, susceptibility to potato tuber moth, poor storability and poor cooking quality. The 

emergence of new and better varieties, lack of markets and lack of seed were the three most cited nonvarietal 

reasons for abandoning varieties. Farmers were not bothered by the loss of varieties. When 

comparing varieties currently cultivated to formerly available varieties, a genetic erosion of 31.0% was 

computed suggesting that genetic loss has occurred in the study area. Results of this study suggested 

that it is necessary to initiate collection, characterization and conservation studies of potato varieties 

across the country. There is also the need for awareness creation on the importance of potato genetic 

resources and their conservation. 

Key words: Conservation, genetic erosion, farmers’ perceptions, Kenya, potato, seed sources, Solanum 

tuberosum, variety abandonment. 

INTRODUCTION 

The continuing need for improved crops to cope with new 

environmental and changing consumer demands creates 

a constant requirement for genetic diversity, but the pool 

of natural diversity is shrinking with time largely, because 

of the negative actions of humans (Guarino, 1999). The 

loss of genetic diversity results in increasing vulnerability 

of crops to changing abiotic and biotic stresses and 

threatens global food security (Hawkes et al., 2000). The 

concept of genetic erosion in agriculture can be applied 

*Corresponding author. E-mail: lungahocs@yahoo.com. 

at three different levels of integration: at crop level as an 

impoverishment in the assemblage of crops used in 

agriculture, at the level of varieties of a specific crop or at 

the level of alleles (van de Wouw et al., 2009). 

The present threats to biodiversity from genetic erosion 

and extinction were recognized by the Convention on 

Biological Diversity’s (CBD’s) Global Strategy for Plant 

Conservation (CBD, 2002) which in Target 9 called for 

conservation of 70% of the genetic diversity of crops and 

other major socioeconomically valuable plant species. 

Further, the 2010 biodiversity target committed the 

parties ‘to achieve by 2010 a significant reduction of the 

current rate of biodiversity loss at the global, regional and


national levels as a contribution to poverty alleviation and 

to the benefit of all life on earth’. From the dawn of 

modern plant breeding, there has been apprehension that 

replacement of traditional crop varieties with improved 

varieties poses a risk to biological diversity (Harlan, 1975; 

Vellve, 1993). It has previously been suggested that plant 

breeding is a strong force in the reduction of genetic 

diversity’ (Gepts, 2006), and introduction of modern 

cultivars has been cited as evidence of genetic erosion 

(Bennett, 1973). It is however, unclear to what extent the 

onset of modern breeding efforts has really affected 

diversity levels in crops (van de Wouw et al., 2009). 

Frankel and Bennett (1970) referred to such a decrease 

in crop diversity as genetic erosion (GE). The traditional 

perception of genetic erosion is that of the loss of a stable 

and diverse set of locally adapted landraces resulting 

from the adoption of a small number of modern varieties 

(Hawkes, 1983; Brush, 1999). On the basis of this 

perspective, genetic erosion is considered to be the 

disappearance of named varieties in the regions where 

they were previously grown (for example, Hammer et al., 

1996). 

Maxted and Guarino (2006) defined genetic erosion as 

the permanent reduction in richness (or evenness) of 

common local alleles, or the loss of combinations of 

alleles over time in a defined area. Brown (2008) defined 

genetic erosion as a process that refers to a change in 

genetic diversity over time, and considered it to be 

difficult to specify in an index or indicator since monitoring 

changes in the rate of genetic erosion strictly requires a 

direct comparable if not identical measures of the state of 

a system at several points in time. More recently, van de 

Wouw (2009) reviewed the concept of genetic erosion 

and concluded that genetic erosion as reflected in a 

reduction of allelic evenness and richness appears to be 

the most useful definition, but has to be viewed in 

conjunction with events at variety level. To date, there is 

no simple technique available that can adequately 

measure the genetic erosion of crop diversity. This is 

partly attributable to the huge data requirements needed 

to cover all the disciplines involved (for example, 

agronomy, plant protection, genetics, population biology, 

ecology, economics, sociology, ethno-botany), and the 

paucity of time series data on landraces. The dynamic nature of 

crop evolution, whereby genetic diversity is added and lost 

from plant populations through time (Wood and Lenne, 

1997; Brush, 1999; Tunstall et al., 2001) also complicated 

the study of genetic erosion. There is also a dearth of 

information on the selection and maintenance of 

landraces by traditional farmers (Zeven, 2002). According 

to Wood and Lenne (1997) and Brush (1999), information 

on the conservation and use of genetic diversity in 

traditional agricultural systems remain largely empirical 

and anecdotal. Therefore, most of the available 

information regarding genetic diversity of landraces is not 

consistent and/or not adequately explored. 

The degree of genetic erosion faced by a particular 

crop in a certain region over time can be estimated using 

a number of approaches including making comparisons 

between the number of species/cultivars still in use by 

farmers at the present time and those found in previous 

studies or interviewing farmers about varieties that used 

to be grown in a particular area (van de Wouw et al., 

2009). The latter approach has been applied to estimate 

the extent of GE in wheat (Teklu and Hammer, 2006; 

Tsegaye and Berg, 2007), sorghum (Mekbib, 2007) and 

cassava (Willemen et al., 2007). 

There is, however, little information on the level of 

genetic erosion of potato in Kenya. This is despite the 

fact that the crop has been grown in the country since the 

late nineteenth century (Durr and Lorenzl, 1980). In a 

study on evaluation, choice and use of potato varieties in 

Kenya, Crissman (1989) reported that some older 

varieties had been rejected by farmers, but no attempt 

was made to estimate the extent of genetic erosion. The 

objectives of this study were to: i) understand the 

dynamic nature of farmers’ management of potato; and ii) 

assess the extent of genetic erosion (GE) and farmers’ 

perceptions of genetic erosion in Kiambu West district in 

Kenya. 


Study area 

The study was done in three divisions: Limuru, Ndeiya and Tigoni of 

Kiambu West district in central province of Kenya between February 

and March, 2006. The altitude in the study area varies between 

1800 and 3000 m above sea level. Agroecological zones include 

Upper Highland (UH0 to UH2), Lower Highlands (LH1 to LH5) and 

Upper Midlands (UM3, UM5 and UM6). The district borders, Lari 

district to the North, Naivasha district to the West, Kikuyu and 

Kajiado districts to the South and Kiambu East district towards the 

east. The farming system of the study area is a typical crop-based 

mixed, crop–livestock mixed production. Production is rain-fed with 

most households growing a number of crop types and varieties. 

The three divisions are readily accessible to markets. 

Sampling procedures 

Kiambu West district was purposively chosen as the study area 

because of; (i) its importance in potato production; (ii) its long 

history of growing potatoes hence, has ideal sites for study on farm 

GE; (iii) the diverse cropping systems; (iv) the area has been 

exposed to market forces and other externalities that influence onfarm 

diversity of potato; and (v) its proximity to the Kenya 

Agricultural Research Institute (KARI) Tigoni Research Centre, 

which permits access to many varieties since this is the institute 

mandated with potato variety development in the country. 

Farmers were selected using stratified sampling techniques 

whereby the three major potato growing locations in each division 

were chosen and within each location, farmers were selected at 

random from a list of potato-growers obtained from the local 

agricultural extension offices across all locations and sub-locations 

within a division. 

Questionnaire development 

Information about varieties grown in 1989 was obtained from

secondary literature and key informant interviews. The key 

informant interviews were conducted with special potato knowledge 

holders in the farming community and involved interviewing 

agricultural extension officers, key farmers, potato traders and 

market vendors in the target area. A questionnaire was then 

formulated based on information from the key informant interviews 

and secondary literature. The questionnaire captured information 

on i) farmer’s seed sources, ii) identification and naming of 

varieties, iii) varieties grown, iv) varieties no longer grown and the 

reasons for not growing them, and v) farmers’ perception on genetic 

erosion. The questionnaire was pretested with 15 farmers in each 

of the three divisions and then revised accordingly. The final 

version consisted of both open-ended and closed questions. 

Field survey 

Interviews were conducted in farmers’ potato fields to permit crosschecking 

of their answers with field observations where applicable. 

A total of 302 farmers comprising 100 farmers from Limuru, 110 

from Ndeiya and 92 from Tigoni division, respectively, were 

interviewed. Enumerators were drawn from extension personnel 

from the Ministry of Agriculture and the KARI-Tigoni Research 

Centre. The interviews were conducted either in the national 

language (Kiswahili) or the local language (Kikuyu), depending on 

the language competencies of the respondents. 

Data analysis 

Survey data was analyzed using descriptive statistics, data 

explorations and cross-tabulations based on the Statistical Package 

for Social Scientists (SSPS) version 16 computer software. Pearson 

chi-square (χ 2 ) tests were used to determine whether there were 

significant associations between variables at p≤0.10 (Steel et al., 

1997). The extent of genetic erosion, expressed as loss of potato 

cultivars grown by farmers was computed as the ratio of the number 

of varieties currently available to their former number using two 

parameters; genetic erosion and genetic integrity as indicated by 

Hammer et al.(1996) but as modified by Mekbib (2007). Genetic 

erosion (GE) = 100% - Genetic Integrity (GI). Genetic integrity (GI) 

= C2006/C1989 × 100, where C2006 is the number of varieties grown by 

farmers in 2006 (the year the survey was conducted) and C1989 is 

the number of varieties grown by farmers in 1989. The year, 1989 

was chosen for comparison because there was a documented 

evidence of many of the varieties that were grown in this area 

during that time (Crissman, 1989) while the year 2006 was the year 

when the survey was done. In using names of potato cultivars as a 

proxy indicator for diversity in the study area, consideration was 

made of the following factors: (i) potato is clonally propagated and 

genetic integrity is maintained over relatively long periods of time, 

and (ii) the community members in the study area belonging to the 

same ethnic group share a common history, and have similar socioeconomic 

and cultural environments. The farming system and 

cropping patterns are also fairly similar thus; information shared 

concerning crop species and cultivar names, most likely, remains 

consistent. It was therefore, assumed that names of potato cultivars 

identified by farmers could be used as a proxy indicator for genetic 

diversity within the species. 

RESULTS 

Farmer attributes 

The characteristics of the potato farmers interviewed are 

Lung'aho et al. 2703 

presented in Table 1. Majority of the farmers surveyed in 

the three divisions were females (67.5%). Most of the 

farmers surveyed were 30 to 50 years (36.1%) and over 

51 years old (60.3%). The farmers had stayed in the 

respective areas for relatively long periods, with most of 

them having been resident in their divisions for over 20 

years (57.0%). Average farm sizes were generally small 

with 37.1% of the farmers owning less than 1 acre and 

36.8% owning 1 to 5 acres. Only 1.3% of the farmers had 

acreages greater than 20 acres. On the average, most of 

the farmers were experienced in potato growing with 

41.1% of the farmers having grown the crop for over 20 

years and 29.5% having grown the crop for between 16 

and 20 years. Only 0.7% of the farmers had grown the 

crop for periods ranging from 1 to 5 years. 

Sources of seed 

Table 2 lists the sources of potato seed identified by 

farmers. Only 23, 8.2 and 13.2% of the farmers in Limuru, 

Ndeiya and Tigoni, respectively, obtained seeds from 

sources likely to produce high quality seed such as seed 

growers and research institutions. Use of own seed 

saved from previous harvest was the most important 

source of seed for farmers in all the divisions. The 

second most important source of seed in all the three 

divisions was the market while neighbours comprised the 

third most important source of seed. In nearly all the 

cases (95%), only small sized tubers were saved as 

seed. None of the farmers interviewed had a specialized 

plot for seed production purposes. 

Varieties known and planted by farmers 

Details about farmers’ knowledge and awareness of 

varieties are presented in Table 3 and were generally 

similar across the three divisions. Seventeen of the 40 

varieties named by farmers had local names with 13 of 

them being in the local dialect. All the farmers’ given 

names referred to dominant criteria such as 

morphological characteristics, productive capacity, an 

important person or event that coincided with the time the 

variety was introduced, the person who introduced the 

cultivar and similarity with other cultivars. 

Except for older varieties such as Amin, Gituru, Kiraya, 

Mirka, Njine, Njae, Patrones and Roslin Gucha, 32 other 

varieties were known by more than 69.9% of the 

respondents. None of the respondents had grown or 

knew anyone growing some 20 varieties (Amin, Anett, 

Cardinal, Feldeslohn, Furaha, Gituru, Kenya Baraka, 

Kenya Dhamana, Karora Iguru, Kibururu, Kiraya, Maritta, 

Mirka, Njae, Njine, Patrones, Roslin Bvumbwe, Roslin 

Tana, Romano and Suzanna) that had been previously 

grown in the survey area in the past five years prior to the 

survey. The varieties known by respondents ranged from


Table 1. Farmer characteristics in the three divisions surveyed (percentage of respondents). 

Attribute Limuru (n = 100) Ndeiya (n = 110) Tigoni (n = 92) Total (n = 302) 

Sex 

Male 35.0 30.9 31.5 32.5 

Female 65.0 69.1 68.5 67.5 

Age 

18-29 years 6.0 4.5 0.0 3.6 

30-50 years 36.0 35.5 37.0 36.1 

Over 50 years 58.0 60.0 63.0 60.3 

Period of stay 

11-15 years 14.0 16.4 16.3 15.6 

16-20 years 24.0 30.9 27.2 27.5 

Over 20 years 62.0 52.7 56.5 57.0 

Farm size 

< 1 acre 9.0 32.7 72.8 37.1 

1-5 acres 36.0 45.5 27.2 36.8 

6-10 acres 26.0 19.1 0.0 15.6 

11-20 acres 25.0 2.7 0.0 9.3 

Over 20 acres< 4.0 0.0 0.0 1.3 

Household size 

1-2 persons 10.0 9.1 7.6 8.9 

3-5 persons 59.0 48.2 60.9 55.6 

Over 5 persons 31.0 42.7 31.5 35.4 

Experience in potato growing 

1-5 years 2.0 0.0 0.0 0.7 

6-10 years 12.0 7.3 8.7 9.3 

11-15 years 15.0 19.1 25.0 19.5 

16-20 years 33.0 26.4 29.3 29.5 

Over 20 years 38.0 47.3 37.0 41.1 

Table 2. Sources of seed for potato farmers in Limuru, Ndeiya and Tigoni (in percentage). 

Division 

Sources of seed (%) a 

Own seeds Market Neighbour Traders Seed grower/research 

Limuru (n = 100) 100 57.0 47.0 6.0 23.0 

Ndeiya (n = 110) 100 76.4 32.7 1.8 8.2 

Tigoni (n = 92) 100 79.3 34.8 0.0 13.0 

Total (n = 302) 100 70.9 38.1 2.6 14.6 

a Multiple responses possible. 

23 to 40. Table 4 shows that about 42% of the farmers 

knew 35 to 40 varieties while 38.1% were familiar with 31 

to 35 varieties. Approximately 19.0% of the farmers knew 

26 to 30 varieties. Table 4 also shows that older farmers 

tended to know more varieties. The longer the growing 

period of potatoes the more the varieties a farmer tended 

to know (Table 5). About ninety eight percent of the 

farmers who had grown potatoes for over 20 years knew 

35 to 40 varieties. None of the farmers who had grown 

potatoes for less than 6 years could identify more than 30 

varieties. The number of varieties planted by farmers 

since the beginning cultivation of potatoes ranged from

Table 3. Farmers’ knowledge and awareness of varieties (percentage of respondents). 

Variety 

Limuru 

(n = 100) 

Known by farmer 

Ndeiya 

(n = 110) 

Tigoni 

(n = 92) 

Total 

(n = 302) 


Planted by farmer or person known to the farmer 

Limuru 

(n = 100) 

Ndeiya 

(n = 110) 

Tigoni 

(n = 92) 

Total 

(n = 302) 

Roslin Gucha 40.0 50.0 50.0 46.7 0.0 0.0 0.0 0.0 

Feldeslohn 69.0 73.6 66.3 69.9 0.0 0.0 0.0 0.0 

Njine abc 41.0 59.1 48.9 50.0 0.0 0.0 0.0 0.0 

Mukori abc 100 100 100 100 31.0 25.5 26.1 27.5 

Desiree 100 100 100 100 100 100 100 100 

Nyayo ae 100 100 100 100 100 100 100 100 

Kerr’s Pink 100 100 100 100 61.0 48.2 48.9 52.6 

Maritta 94.0 93.6 93.5 93.7 0.0 0.0 0.0 0.0 

Anett 100 100 100 100 0.0 0.0 0.0 0.0 

Kenya Baraka 100 100 100 100 0.0 0.0 0.0 0.0 

Roslin Tana 100 100 98.9 99.7 31.0 22.7 22.8 25.5 

Njae abc 41.0 38.2 31.5 37.1 0.0 0.0 0.0 0.0 

Suzanna ae 76.0 71.8 63.0 70.5 0.0 0.0 0.0 0.0 

Arka 93.0 97.3 95.7 95.4 24.0 40.9 30.4 32.1 

Dutch Robijn 100 100 100 100 100 100 100 100 

Roslin Bvumbwe 98.0 92.7 91.3 94.0 0.0 0.0 0.0 0.0 

Cardinal 95.0 92.7 91.3 93.0 0.0 0.0 0.0 0.0 

Gituru abc 48.0 47.3 37.0 44.4 0.0 0.0 0.0 0.0 

Amin ae 59.0 70.0 57.6 62.6 0.0 0.0 0.0 0.0 

Kiraya abc 64.0 50.0 44.6 53.0 0.0 0.0 0.0 0.0 

Kihoro abc 100 100 100 100 61.0 48.2 48.9 52.6 

Kibururu abc 100 100 100 100 0.0 0.0 0.0 0.0 

Karora –Iguru abc 98.0 92.7 91.3 94.0 0.0 0.0 0.0 0.0 

Tana Kimande abc 99.0 100 100 99.7 100 100 100 100 

Tigoni 100 100 100 100 100 100 100 100 

Asante 100 100 100 100 100 100 100 100 

Karuse abe 100 100 100 100 100 100 100 100 

Thima Thuti abd 100 100 100 100 100 100 100 100 

Kenya Dhamana 98.0 100 100 99.3 0.0 0.0 0.0 0.0 

Furaha 100 100 100 100 0.0 0.0 0.0 0.0 

Zangi ae 100 100 100 100 100 100 100 100 

Kenya Sifa 100 100 100 100 56.0 66.4 56.5 59.9 

Kenya Mavuno 100 100 100 100 100 100 100 100 

Kenya Karibu 100 100 100 100 86.0 89.1 79.3 85.1 

Romano 98.0 99.1 91.3 96.4 0.0 0.0 0.0 0.0 

Roslin Eburu (B53) 98.0 100 100 99.3 2.0 0.0 0.0 0.7 

Meru Mugaruru abfg 99.0 100 100 99.7 39.0 51.8 51.1 47.4 

Ndera Mwana abd 100 100 100 100 100 100 100 100 

Mirka 34.0 42.7 31.5 36.4 0.0 0.0 0.0 0.0 

Patrones 30.0 39.1 28.3 32.8 0.0 0.0 0.0 0.0 

a- refers to local name given by farmers; b- refers to name in the local dialect-Kikuyu; c- refers to a morphological characteristics; d- refers to 

productive capacity, e- refers to an important person/event that coincided with introduction of the variety; f- refers to the person who introduced the 

cultivar and g- refers to morphological similarity with another cultivar. 

11 to 17. Most of them had planted between 15 to16 

varieties (Table 6). There was no relationship between 

the number of varieties known by a farmer and the 

number of varieties they had ever planted (r 2 = -0.58, p = 

0.316). On the average, majority of the farmers had 

planted two varieties (83.4%) during the survey (Table 6). 

None of the farmers surveyed had planted more than 3 

varieties.


Table 4. Relationship between age of farmer and number of varieties known (percentage of 

respondents). 

Number of varieties known 

18-29 years 

(n = 11) 

Age of farmer 

30-50 years 

(n = 109) 

Over 51 years 

(n = 182) 

Total 

(n = 302) 

20-25 0.0 2.8 0.5 1.3 

26-30 72.7 28.4 9.3 18.5 

31-35 27.3 51.4 30.8 38.1 

35-40 0.0 17.4 59.3 42.1 

Total 100 100 100 100 

Table 5. Relationship between experience in potato growing and number of varieties known 

(percentage of respondents). 

Number of varieties known 

1-5 

(n = 2) 

Experience in growing potatoes (years) 

6-10 

(n = 28) 

11-15 

(n = 59) 

16-20 

(n = 89) 

20< 

(n = 124) 

Total 

(n = 302) 

20-25 50.0 10.7 0.0 0.0 0.0 1.3 

26-30 50.0 75.0 57.6 0.0 0.0 18.5 

31-35 0.0 14.3 42.4 94.4 1.6 38.1 

35-40 0.0 0.0 0.0 5.6 98.4 42.1 

Table 6. Number of varieties planted by a farmer or person known to farmer, number of varieties currently planted by the farmers and 

characteristics used to distinguish varieties (percentage of respondents). 

Variety 

Limuru 

(n = 100) 

Division 

Ndeiya 

(n = 110) 

Tigoni 

(n = 92) 

Total 

(n = 302) 

Number of varieties planted by farmers since the beginning of potato growth 

10-12 14.0 10.9 20.7 14.9 

15-16 85.0 89.1 79.3 84.8 

17-18 1.0 0.0 0.0 0.3 

Number of varieties currently planted by farmers 

One 0 0 0 0 

Two 82.0 83.6 84.8 83.4 

Three 18.0 16.4 15.2 16.6 

Characteristics used to distinguish varieties 

Tubers 79.0 75.5 78.3 77.5 

Foliage 13.0 11.8 5.4 10.3 

Tubers + foliage 8.0 12.7 16.3 12.3 

Identification of varieties 

There was no difference in the characteristics farmers 

used by farmers to distinguish varieties across the three 

divisions (χ 2 = 5.903; d.f = 4; p = 0.207). Varieties were 

mainly distinguished according to tuber characteristics 

across the three divisions. Table 6 shows that the use of 

tuber characteristics (77.5%) was the most common 

method employed by farmers to distinguish varieties. 

Foliage characteristics were used by only 10.3% of the 

farmers while 12.3% indicated that they used both tuber 

and foliage characteristics to identify varieties. Tuber 

attributes included the shape, skin colour and sprout 

characteristics while foliage characteristics mainly

Table 7. Varieties grown by farmers in the three divisions surveyed (percentage of respondents). 

1 Variety Tuber skin colour 

Proportion of farmers growing (%) 

Limuru (n = 100) Ndeiya (n = 110) Tigoni (n = 92) 

Tigoni White 67.0 16.8 42.1 

Zangi Red 82.0 65.4 62.1 

Karuse Red 0.0 38.3 23.2 

Meru Red 0.0 6.5 8.4 

Nyayo White 17.0 5.6 15.8 

Ndera Mwana Red 2.0 39.3 16.8 

Asante Red 0.0 9.3 15.8 

Tana Kimande White 3.0 4.7 1.1 

Thima Thuti White 48.0 22.4 22.1 

Dutch Robijn Red 0.0 5.6 10.5 

1 All farmers grew more than one variety. 

Table 8. Reasons cited by farmers for abandoning eight of the most commonly abandoned varieties (percentage of respondents). 

a Reason 

Tigoni 

n = 501 

Nyayo 

n = 783 

Desiree 

n = 582 

Roslin tana 

n = 78 

Tana kimande 

n = 381 

Anett 

n = 42 


Roslin eburu 

(B53) n = 27 

Dutch robjin 

n = 309 

Rapid greening 29.3 10.0 - 16.7 - - 14.8 - 

Low yields 32.7 32.4 33.3 26.9 33.1 33.3 18.5 33.3 

Susceptible to late blight disease 25.3 26.6 22.9 16.7 3.4 33.3 - 19.4 

Strong dormancy 0.6 0.8 24.9 - 26.0 16.7 25.9 - 

Sensitive to drought conditions - 0.9 2.7 10.3 1.6 - 33.3 7.8 

Long maturity period - - - - 24.1 - - - 

Susceptible to bacterial disease 8.2 19.8 16.0 1.3 9.2 2.4 - 26.2 

Susceptible to moth infestation 3.8 4.0 0.2 - 2.6 14.3 - 13.6 

Poor storability - 5.6 - 28.2 - - - - 

Poor cooking qualities - - - - - - 7.4 - 

a Multiple answers possible. 

consisted of the flower colour and nature of the foliage. 

Predominant varieties and number of varieties grown 

During the survey period, only ten varieties were found in 

farmers’ fields (Table 7). The most common varieties 

across the three divisions were Zangi (69.4%), Tigoni 

(41.4%), Thima Thuti (30.8%) and Karuse (20.9%). Only 

three of the ten varieties (Tigoni, Dutch Robijn and 

Asante) were improved varieties developed by the 

Kenyan potato programme. 

The varieties grown as indicated by the respondents 

differed significantly across the divisions. Varieties 

Asante, Dutch Robijn, Karuse, Meru and Ndera Mwana 

were common in Ndeiya and Tigoni but not in Limuru. 

Varieties like Thima Thuti, Tigoni and Zangi were grown 

across the three divisions but tended to be more common 

in Limuru. Nyayo was more common in Limuru and 

Tigoni than in Ndeiya. There was no clear pattern for 

farmers’ preference for either red or white skinned 

varieties across the three divisions. 

Variety abandonment 

Reasons for variety abandonment 

Farmers reported that they had rejected some varieties 

and replaced them with others. Reasons for rejection 

were grouped into two: varietal reasons and non-varietal 

reasons. 

Varietal reasons: Farmers cited eight reasons as being 

the most important in rejection of varieties (Table 8). 

These included: rapid greening; low yields, susceptibility 

to late blight, strong dormancy, sensitivity to drought 

conditions, long maturity period, susceptibility to bacterial 

wilt, susceptibility to tuber moth, poor storability, and poor 

cooking qualities. The reason for rejecting a variety was


Table 9. Non-varietal reasons cited by farmers for abandoning varieties (in percentage). 

Reason 

Limuru (n = 100) 

Ndeiya (n = 110) 

Tigoni (n = 92) 

Total (n = 302) 

Rank 1 Rank 2 Rank 3 Rank 1 Rank 2 Rank 3 Rank 1 Rank 2 Rank 3 Rank 1 Rank 2 Rank 3 

Better varieties came 61.0 4.0 35.0 55.5 5.5 39.1 56.5 14.1 29.3 57.6 7.6 34.8 

Poor markets 11.0 69.0 20.0 20.9 43.6 35.5 4.3 64.1 31.5 12.6 58.3 29.1 

Lack of seeds 28.0 27.0 45.0 23.6 50.9 25.5 39.1 21.7 39.1 29.8 34.1 35.1 

Multiple responses possible. 

dependent on the variety itself, although, many of 

the varieties were rejected for similar reasons. 

Most of the farmers who rejected variety Tigoni 

cited low yields (32.7%), rapid greening (29.3%) 

and susceptibility to late blight (25.3%) as the 

most important reasons for rejecting it. Nyayo was 

rejected because of low yields (34.2%), 

susceptibility to late blight (26.6%) and 

susceptibility to bacterial wilt (19.8%). Desiree 

was rejected for low yields (33.3%), strong 

dormancy (24.9%) and susceptibility to late blight 

(22.9%). Roslin Tana was rejected because of low 

yields (26.9%), rapid greening (16.7%) and 

susceptibility to late blight (16.7%). The most 

important reasons for rejecting Tana Kimande 

were low yields (33.1%), strong dormancy 

(26.0%) and long maturity period (24.1%). Anett 

was rejected because of low yields (33.3%), 

susceptibility to late blight (33.3) and strong 

dormancy (16.7%). Roslin Eburu was rejected 

because of low yields (27.8%), sensitivity to 

drought (22.2%) and rapid greening (22.2%). 

Table 10. Method for variety abandonment used by farmers (percentage of respondents). 

Method for variety abandonment Limuru (n = 100) Ndeiya (n = 110) Tigoni (n = 92) Total (n = 302) 

Consume or sell all seed 97.3 98.9 98.6 98.3 

Leave seed to deteriorate and throw away 2.7 1.1 1.4 1.7 

Dutch Robijn was rejected because of low yields 

(33.0%), susceptibility to bacterial wilt (26.2%) 

and late blight susceptibility (19.4%). None of the 

farmers interviewed reported that they rejected 

any variety due to small sized tubers. 

Most of the farmers indicated that discarding of 

an older variety will only occur after the older and 

new varieties have been grown together for 

several seasons, permitting comparison. During 

the observation period, the acreage of the older 

varieties will be progressively reduced in favor of 

the new variety. Non-varietal reasons: Majority of 

the farmers ranked the displacement of older 

varieties by new varieties as the number one 

reason (57.6%) for rejecting varieties while 12.6% 

regarded poor markets as the second most 

important reason for rejecting a variety (Table 9). 

Lack of seed was considered the second most 

important reason for rejecting a variety by 29.8 % 

of the farmers. The reasons for rejecting the 

varieties did not differ significantly between the 

divisions. 

Method of variety abandonment 

Majority of the farmers surveyed (97.3%) 

indicated that the principal method they used to 

discard a variety was to either sell or consume all 

the tubers of the variety to be discarded (Table 

10). A small proportion of the farmers (2.7%) 

indicated that they left the seeds of the variety to 

be discarded to deteriorate during storage, after 

which the material was thrown away. 

Perceived losses of cultivars and 

quantification of genetic erosion 

From an initial 29 varieties grown in 1989 in the 

study area, a total of 20 varieties were no longer 

grown by farmers as of the year 2006. Another 11 

varieties (Asante, Furaha, Karuse, Kenya Karibu, 

Kenya Mavuno, Kenya Sifa, Mugaruru, Ndera 

Mwana, Thima Thuti, Tigoni and Zangi) had been 

introduced in farmers’ fields bringing the total

Table 11. Reasons why farmers do not care about loss of varieties (percentage of respondents). 

Reason 

Better 

varieties 

came 

Poor 

markets 

Lack of 

seeds 

Rank 

1 

Limuru (n = 100) 

Rank 

2 

Ndeiya (n = 110) 

Tigoni (n = 92) 

Rank 3 Rank 1 Rank 2 Rank 3 Rank 1 Rank 

2 

Rank 3 


Rank 

1 

Total (n = 302) 

69.0 3.0 11.0 64.5 5.5 20.0 84.8 1.1 6.5 72.2 3.3 12.9 

8.0 27.0 45.0 2.7 43.6 43.6 0.0 35.9 58.7 3.6 35.8 48.7 

8.0 55.0 19.0 22.7 40.9 26.4 9.8 57.6 29.3 13.9 50.7 24.8 

Multiple responses possible; ranking scale 1-3, where 1-most important, 2-second most important and 3 is least important. 

number of cultivated varieties in the survey area in 2006 

to 20. All the varieties that were introduced after 1989 

were still being grown to some degree by farmers. Thus, 

the GI is 69.0% while the GE was 31.0%. 

Farmer’s perception of genetic erosion 

There were differences in farmers perceptions about loss 

of varieties across the three divisions (χ 2 = 4.773; d.f = 2; 

p = 0.092). A very high proportion of the farmers that 

surveyed (89.7%) were not bothered by the loss of 

varieties. Majority of the farmers ranked the appearance 

of higher yielding varieties (72.2%) followed by the 

preference of newer varieties by the market (13.9%) as 

the most important reasons for not caring about the 

disappearance of the varieties (χχ 2 = 25.940; d.f = 6; p = 

0.001) (Table 11). Lack of seeds was ranked third (3.6 

%). 

DISCUSSION 

This study found potato farmers to be ageing lot with 

most of them aged over 50 years. The implication of this 

finding is that the ageing farmers participated prominently 

in potato farming production in the study area, while a 

large proportion of the young and able bodied men might 

have migrated to the urban centers in search of more 

lucrative jobs. This is a negative influence not only on 

conservation of potato varieties but also on the 

sustainability of potato farming with potentially negative 

effects on food security situation of Kenya. It is important 

that the youths are encouraged to take up potato farming 

through appropriate incentives and policy measures. 

Most studies have pointed out that yield is the most 

important criterion for the choice of a variety by a farmer 

(Heisey and Brennan, 1991). When the yields of a 

particular variety decline because of degeneration, 

farmers needed to replenish their seed stocks to restore 

the yields (Lung’aho et al., 2007). In the absence of 

Rank 

2 

Rank 

3 

sources of good quality seed, farmers may prefer to 

change a variety and plant a different variety that has 

high yields rather than a variety that is considered good 

but low yielding due to diseases. Thus, varietal 

composition in the informal seed system is dynamic. Over 

time, varieties are lost and new ones are introduced from 

elsewhere (Louette et al., 1997). Commonly, improved 

varieties are incorporated into the informal system 

(Almekinders et al., 1994), a process that is known as 

creolization (Bellon and Risopoulos, 2001). These 

creolized varieties are often given local names, becoming 

part of what farmers consider to be their local varieties. 

Majority of farmers in this study sourced their seed from 

informal seed sources. Studies from elsewhere 

(Cromwell, 1990; Ndjeunga et al., 2000) reported farmerto-farmer 

seed exchange to be an effective means of 

exchanging seed and a means of diffusing new varieties 

to small holder farmers. Farmer seed systems should 

therefore be strengthened so that they can provide local 

farmers with seeds of varieties that they require. 

Sustainable seed supply amongst farmers can be 

achieved by supporting local seed networks, injection of 

clean seeds and capacity building. 

Other than varieties officially named by the Kenyan 

potato programme or those introduced into the country 

with distinct names, many of the other names of varieties 

known in the survey area are derived from the local 

dialect - Kikuyu. Examples include Ndera Mwana, 

Kibururu, Gituru, Karora Iguru, Kiraya, Kihoro, Thima 

Thuti and Mukori. 

Both the factors related to variety and non-varietal 

characteristics influenced the degree to which varieties 

were replaced or abandoned by farmers. The cultivar 

characteristics included susceptibility to diseases and 

pests, agronomic performance and postharvest attributes 

while non-varietal characteristics that influenced 

abandonment of varieties by farmers included lack of 

seed, appearance of newer or better varieties and poor 

market for the older varieties. Results of this study 

showed that farmers have a logical preference for 

cultivars that produce higher yields and explains why


Zangi, Tigoni and Thima Thuti and Karuse were most 

commonly grown varieties. Similar observations were 

made by Crissman (1989). The fact that many farmers 

would not grow the varieties that have disappeared even 

if good quality seed was offered to them suggests that 

farmers attach little value to lost varieties. This 

observation has serious implications on the conservation 

of local potato germplasm and calls for deliberate efforts 

to conserve germplasm that is in danger of getting lost. 

The findings of our study are in agreement with those 

of FAO (1988) who reported that the main cause of GE in 

crops, as reported by most countries, was the 

replacement of farmer varieties by improved varieties. 

The results however, differ from those of Mekbib (2007) 

who reported that for sorghum, the most important 

reasons for variety loss were reduced benefit from the 

abandoned varieties, drought, reduced land size and 

introduction of other food crops. With respect to GE, our 

results are different from those of Mekbib (2007) who 

found that there was no genetic erosion of sorghum in the 

centre of diversity in Ethiopia and those of Hermadez 

(1993) who disproved GE of maize in Mexico in the 

centre of diversity. 

As has been previously pointed out (Quiros et al., 1990; 

Rao et al., 2002), the use of variety names to represent 

genetic diversity requires some precaution. It is possible 

that the same cultivar might be known by different names 

in different localities. Conversely, cultivars with different 

morphological and physiological characteristics might be 

called by the same name. The former type is commonly 

encountered when dealing with different ethnic groups 

(Tsegaye, 1991). The latter type of misclassification can 

arise when farmers use only one trait (for example, tuber 

skin colour) to distinguish between local varieties and 

disregard the other differences. Folk taxonomy exercised 

by traditional seed experts usually involves a hierarchy of 

classification criteria that combines various traits. For 

instance, Rao et al. (2002) identified three elements used 

in naming rice varieties (basic name, root name and a 

descriptor). Similarly, Tsehaye (2004) documented 

successive levels used by farmers to refine the 

classification of finger millet landraces (first inflorescence 

morphology, secondly, seed colour classes, then 

agronomic, and finally end-use characteristics). 

Farmers’ knowledge of their cultivars is reported to be 

fairly consistent. Teshome et al. (1997) examined 

sorghum landraces and confirmed that the landraces 

named by farmers were distinct plant populations, while 

Quiros et al. (1990) studied Andean potato varieties and 

found a high level of agreement between folk variety 

names and genetic distinctness identified by molecular 

markers. Similarly, diversity studies using DNA in taro 

cultivars (Caillon et al., 2004) revealed that each cultivar 

named by farmers corresponded to a separate genotype. 

Work by Lung’aho et al. (2011) which analyzed some of 

the potato cultivars mentioned in the present study 

demonstrated that the cultivars studied were indeed 

distinct from each other. 

Data on the loss of varieties may provide a good 

indicator of loss diversity particularly, if accompanied by 

data on genetic distances. Diversity could even increase 

if newer or improved varieties are genetically more 

heterogeneous than older varieties or if they offer traits 

that are not present in older varieties (Wood and Lenne, 

1997; Louette and Smale, 2000). Under such 

circumstances, the disappearance of named varieties 

may not be sufficient proof that loss of diversity has 

occurred. 

The nature of the informal seed system makes the 

designation of discrete entities somewhat difficult 

(Cromwell, 1990; Almekinders et al., 1994; Louette et al., 

1997) and local names may not necessarily reflect the 

genetic history of crops. Different names may be given to 

identical varieties and conversely, a single name may 

apply to heterogeneous material (Crissman, 1989; Jarvis 

et al., 2008). In such cases, DNA-marker techniques 

have provided tools for directly measuring genetic 

diversity hence, testing for occurrence of genetic erosion 

(Almanza-Pinzon et al., 2003). 

Although, viruses will not usually kill the crop, their 

presence can result in reduced yields (Lung’aho et al., 

2007) and the abandonment of the affected variety by 

farmers. Assessment of virus infection, cleaning of 

infected cultivars and providing local farmers with clean 

seed has been suggested as one way of maintaining 

potato diversity among growers (Clausen et al., 2005). It 

is however, doubtful if these systems would work with 

commercially oriented farmers since most of the farmers 

interviewed indicated that they would not grow such 

varieties as it would be difficult to market them and some 

may not be as high yielding as newer varieties. However, 

this may be an option for farmers practicing subsistence 

potato production. 

Conclusions 

Results of this study showed that loss of varieties has 

occurred with 20 of the 40 varieties encountered in this 

study being most affected by genetic erosion. The main 

reason for abandoning varieties was a decline in utility 

derived from a variety, with low yields and susceptibility 

to a host of biotic and abiotic stresses being rated as the 

most important reasons for abandoning a variety. The 

study also revealed that farmers were not concerned 

about loss of varieties. This may mean that they are 

unaware of the dangers of losing varieties or that they are 

aware but do not consider the threat to be significant. 

There is, therefore, the need for awareness creation on 

the importance of potato genetic resources and their 

conservation. There is also an urgent need to collect and 

preserve existing varieties as a reduction in their number 

has already taken place and it is only a matter of time 

before more varieties are lost.

ACKNOWLEGEMENTS 

The authors are grateful to agricultural extension 

personnel who assisted during the field survey. The 

cooperation of farmers in the study area is highly 

appreciated. The valuable comments on the earlier 

version of the manuscript by an anonymous reviewer are 

appreciated. This study was financed by the Kenya 

Agricultural Productivity Project (KAPP). 

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DOI: 10.5897/AJAR11.2255 



Effect of organic and inorganic fertilizer on maize 

hybrids under agro-environmental conditions of 

Faisalabad-Pakistan 

Wajid NASIM 1,2 *, Ashfaq AHMAD 3 , Tasneem KHALIQ 3 , Aftab WAJID 3 , Muhammad Farooq 

Hussain MUNIS 1 , Hassan Javaid CHAUDHRY 1 , Muhammad Mudassar Maqbool 4 Shakeel 

AHMAD 5 and Hafiz Mohkum HAMMAD 3 

1 Department of Plant Sciences, Quaid-i-Azam University, Islamabad-45320, Pakistan. 

2 Department of Environmental Sciences, COMSATS Institute of Information Technology, (CIIT), Vehari -61100, Pakistan. 

3 Agro-Climatology Laboratory, Department of Agronomy, University of Agriculture, Faisalabad-38040, Pakistan. 

4 College of Agriculture, Dera Ghazi Khan, sub-campus, University of Agriculture, Faisalabad-38040, Pakistan, 

5 Department of Agronomy, Bahauddin Zakariya University Multan-60800, Pakistan. 


Organic agriculture combines tradition, innovation and science to benefit the shared environment and 

promote fair relationships and a good quality of life for all involved. Furthermore, maize (Zea mays L.) 

crop is the 3 rd cereal crop of Pakistan after wheat and rice. According to the economic survey of 

Pakistan, it is cultivated on the area of approximately, 1.11 million hectare and production from this 

area was 4.04 million tone s. A field experiment was conducted at Agronomic Re search Area, University 

of Agriculture, Faisalabad-Pakistan to examine the effect of organic and inorganic fertilization on maize 

productivity. The experiment was laid out in Randomized Complete Block De sign (RCBD), with four 

replications. Two maize hybrids were used in this experiment. The results showed that maize yield and 

its component such a s cobs per plant, cob length, number of grains per cob, 1000 -grain weight were 

maximum when the plots were fertilized at 100 kg N ha -1 as urea + 100 kg N ha -1 as poultry manure. 

Further re search is desired to inve stigate maximum yield by using organic source of fertilizer than 

inorganic source of fertilizer to avoid lethal effects on human health created by inorganic fertilizers. 

Key words: Organic farming, maize productivity, inorganic fertilizer, semi-arid conditions, Pakistan. 

INTRODUCTION 

Maize is one of the most widely distributed crops of the 

world (Kaul et al., 2011). This crop being the highest 

yielding cereal crop in the world is of significant 

importance for countries like Pakistan, where rapidly 

*Corresponding author. E-mail: wajidnaseem2001@gmail.com, 

wajidnasim@ciitvehari.edu.pk. Tel: +92-333-991-1881. 

Abbreviations: GOP, Government of Pakistan; RCBD, 

randomized complete block design; FYM, farm yard manure; 

NPK, nitrogen-phosphorus-potash; TSP, triple super 

phosphate; SOP, sulphate of potash; PM, poultry manure; PH, 

plant height; LAI, leaf area index; PP, plant population. 

increasing population has already out stripped the 

available food supplies. In Pakistan, maize is the third 

important cereal after wheat and rice (Bukhsh et al., 

2011). It accounts for 4.8% of the total cropped area and 

3.5% of the value of agricultural output (Khaliq et al., 

2011). Furthermore, it is grown on about 1118 thousand 

hectares with annual production of 4036 thousand tones 

of grain and average yield of 3598 kg ha -1 (GOP, 2010). 

Intensive cropping system requires highly fertilized soils 

and those soils should be maintained through integrated 

plant nutrient management system (Bationo and Koala, 

1998). Organic manure provides many advantages such 

as improves soil tilth, aeration, water holding capacity


and stimulates micro-organisms in the soil that make 

plant nutrients readily available (Choudhary and Bailey, 

1994; Lal, 1997). The fertilization can affect enzymatic 

activities inside the soil profile (Yang et al., 2008; Zhu et 

al., 2008). Proper application of organic and inorganic 

fertilizers canincrease the activities of soil microorganisms 

and enzymes and soil available nutrient 

contents (He and Li, 2004; Saha et al., 2008). He and Li 

(2004) indicated that combined application of organic and 

inorganic fertilizers can increase the activities of soil 

invertase and available nutrient content. Furthermore, the 

application of organic manure mixed up with chemical 

fertilizer can prove to be an excellent procedure in 

maintaining and improving the soil fertility, and increasing 

fertilizer use efficiency. For this reason, it could be helpful 

to study the effect of application of organic manure 

combined with chemical fertilizer by using integrated 

nutrient management system, which has been the 

research focus all over the world (Reganold, 1995; Liu et 

al., 1996). In addition, application of organic manure 

could improve the soil quality and is more profitable in 

environment protection when compared with application 

of chemical fertilizer alone (Reganold, 1995). The soil 

with organic manure continually applied had lower bulk 

density and higher porosity values, porous and buffering 

capacities (Edmeades, 2003). The influence of different 

nutrients applied to soil on farmland ecosystem was 

different (Yang et al., 2004). Therefore, the present study 

was carried out to evaluate the effect of different corn 

hybrids under the integrated use of organic and inorganic 

fertilizers under the agro-climatic conditions of 

Faisalabad-Pakistan. 

MATERIALS AND M ETHODS 

Experimental site 

The experiment w as carried out at Agronomic Research Area, 

University of Agriculture, Faisalabad (31.26°N, Longitude 73.06°E 

and Altitude 184 m), and Pakistan during the autumn season of 

2008. Faisalabad shows the semi-arid environmental conditions 

Nas im et al. (2011). 

Experimental design and treatments 

The experiment w as laid out in randomized complete block design 

(RCBD) w ith split plot arrangement hav ing three replications 

keeping net plot size as 7.0 × 3.8 m. Tw o maize hybrids (H1 = 

Pioneer-3062, H2 = Pioneer-3061) w ith best qualities (sow n in may 

till mid July, takes 90 to 100 days for maturity, cobs are long w ith 

full size of grains resistant to diseases and finally bear the capacity 

of drought tolerance (Ali et al., 2004; Salah et al., 2011), w ere sow n 

in main plots and different fertilizer levels (F1 = control, F2 = w hole N 

as urea, F3 = w hole N as farmyard manure (35.31 t ha -1 ), F4 = w hole 

N as poultry manure (P.M) (12.98 t ha -1 ), F5 = half N as FY M (19.01 

t ha -1 ) + 100 kg N ha -1 as urea, F6 = half N as P. M. (7.01 t ha -1 ) + 

100 kg N ha -1 as urea) w ere kept in the sub plots. Whole N 

indicated 200 kg N ha -1 w hile half N show s 200 kg N ha -1 in the 

fertilizer treatments. 

Crop management strategy 

The crop w as sow n on 29 th of July, 2008 (Figure 1) manually w ith 

the help of dibbler keeping recommended distance of ridges and 

plants (P × P = 20 c m, R × R = 70 c m) w ith seed rate of 25 kg ha -1 . 

A recommended fertilizer amount of NPK (200: 100: 75, kg ha -1 ) 

was applied. The sources of fertilizer w ere urea, TSP, SOP, FY M 

and PM used and amounts of P and K w ere adjusted w ith TSP and 

SOP including quantity of P and K received by FY M and PM. The 

fertilizers w ere used in such a w ay that half the amount of N from 

urea and full amount of P, K, FY M and PM w ere applied at the time 

of sow ing, w hile the remaining half amount of the urea w as applied 

w ith second irrigation according to the treatments. All the cultural 

practices (hoeing, w eed management, irrigation and plant 

protection measures etc) w ere kept normal for the crop. 

Final harvest data 

The crop w as harvested w hen it completed its physiological 

maturity at (2 nd November, 2008). The parameters that w ere 

recorded during the course of study included days taken to 50% 

tasseling and silking, leaf area index (LAI) at tasseling, total dry 

matter (g m -2 ) at tasseling, plant population (m -2 ), number of grains 

(m -2 ), mean grain w eight (g), grain yield (kg ha -1 ) and harvest index 

(%). 

Total number of plants per plot w as counted at final harvest from 

an area of 1 m 2 , the number of grains w as counted from randomly 

selected sample of ten cobs plot -1 and then average number of 

grains (m -2 ) w as calculated. Furthermore, after threshing of crop, 

thousand grains w ere taken from each plot and w eighed, as w ell 

as, total grain w eight w as recorded from each plot and finally, grain 

yield (on hectare basis) w as calculated, respectively. As the harvest 

index (%) w as measured as it is the ratio of economic yield to the 

biological yield in percentage. In addition, quality parameters (grain 

oil and protein contents) w ere also observed at the final harvest 

(Nasim et al., 2012). 


The data collected during the season w ere statistically analyzed by 

using the computer statistical program MSTA T-C. Analysis of 

variance technique w as employed to test the overall significance of 

the data, w hile the least significance difference (LSD) test at P = 

0.05 w as used to compare the differences among treatment means 

(Steel et al., 1997). 


Soil, growing media and environmental conditions 

The chemical analysis of experimental site was carried 

out prior to sowing of the crop. All the characteristics of 

experimental soil such as soil texture, structure and soil 

pH etc and other parameters are shown in Table 1. 

The analysis of FYM and PM were carried out (Table 

2). The detailed weather conditions (minimum and 

maximum temperature, solar radiation and precipitation) 

are explained in Figures 1 and 2 that were observed 

during the crop cycle. 

50% ta sseling and silking (days) 

Data (Table 3) showed that maize hybrids had non-

Precipitation (mm) 

55 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Table 1. The chemical analysis of experimental location under agroclimatic 

conditions of Faisalabad- Pakistan. 

Characteristics Unit Value 

Soil texture Silty clay loam 

Organic matter % 1.21 

Total nitrogen % 0.06 

Phosphorus (available) ppm 0.99 

Potassium (available) ppm 178 

Soil pH - 7.6 

Soil EC dS m -1 1.5 

Table 2. The chemical analysis of organic manures under agro-climatic conditions 

of Faisalabad-Pakistan. 

Characteristics Unit 

*FYM 

Values 

**PM 

Dry matter % 19.99 47.0 

Moisture % 78.00 51.0 

Nitrogen % 0.57 1.51 

Phosphorus % 0.26 0.78 

Potassium % 0.63 0.37 

*Farm yard manure, **poultry manure. 

Precipitation (mm) 

Solar Radiation (MJ/m 2 /d) 

Aug Sep Oct Nov Dec 

Month 

a) 2008 

b) Mean (20 years) 

Jan Aug Sep Oct Nov Dec Jan 

Month 

Figure 1. Mean monthly solar radiation and precipitation under agro-environmental conditions of Faisalabad Pakistan 

(2008 and mean of 20 years). 

significant effect on days taken to 50% tasseling on an 

average that this period extended from 48.26 to 52.95 

days. The organic and inorganic fertilizers that were 

applied to maize significantly affected the duration of 

tasseling. The assessment of individual treatment means 

indicated that maximum number of days to tasseling 

(52.95) was taken where maize crop was fertilized with 

Nasim et al. 2715 

30 

25 

20 

15 

10 

5 

0 

Solar Radiation (MJ/m 2 /d) 

100 kg N ha -1 as poultry manure + 100 kg N ha -1 as urea 

(F6) but statistically at par with other treatments except 

control (F1). The period of silking was delayed in Pioneer- 

3061 as compared to Pioneer-3060. Furthermore, 

fertilizer application affected days taken to 50% silking as 

it was observed in tasseling. Maximum number of days to 

50% silking (56.55) was noted in plots where maize crop

Temperature (°C) 


Temperature ( o C) 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Average maximum temperature 

Average minimum temperature 

Aug Sep Oct Nov Dec 

a) 2008 b) Mean (20 years) 

Month 

Average maximum temperature 

Average minimum temperature 

Jan Aug Sep Oct Nov Dec Jan 

Month 

Figure 2. Mean monthly temperature (minimum & maximum) under agro-environmental conditions of Faisalabad-Pakistan 

(2008 and mean of 20 years). 

Table 3.The effect of organic and inorganic fertilizers on phenology and grow th of maize hybr ids under agroclimatic 

conditions of Faisalabad-Pakistan. 

Treatment 

(A) Hybrids 

50% tasseling 

(days) 

50% silking 

(days) 

Plant 

height (cm) 

LAI (at 

tasseling) 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 



Temperature ( o C) 

Total dry matter (at 

tasseling)(g/m 2 ) 

H1 49.63 56.55 a 197.01 a 4.87 1759.27 

H2 52.36 52.21 b 190.18 b 4.79 1726.26 

Significance NS * * NS NS 

(B) Fertilizers 

F1 48.26 b 51.59 b 169.58 d 3.95 d 1169.26 d 

F2 50.21 a 54.26 a 17.7.39 bc 4.62 c 1999.43 a 

F3 51.15 a 54.75 a 190.72 c 4.72 bc 1770.29 c 

F4 51.76 a 54.99 a 194.99 b 4.76 abc 1812.86 b 

F5 51.99 a 55.12 a 196.78 b 4.74 ab 1825.86 b 

F6 52.95 a 56.10 a 207.45 a 4.89 a 1870.96 ab 

Significance NS * * * * 

A x B NS NS NS NS NS 

Mean 51.94 53.07 190.29 4.26 237.26 

Means sharing at 5% level (P) same letter did not differ significantly; *= Significant; NS = Non-significant. 

was fertilized at the rate of 100 kg N ha -1 as poultry 

manure + 100 kg N ha -1 as urea. All other treatments 

were statistically at par with each other. 

Plant height (cm) 

Pioneer-3062 showed significantly more pH (197.01 cm) 

than Pioneer-3061 (190.18 cm). Both the organic and 

inorganic fertilizers affected pH significantly, the 

maximum (207.45 cm) height plant was found in F6 

treatment (plot fertilized with mixture of 100 kg N ha -1 as 

urea + 100 kg N ha -1 as poultry manure) followed by F5 

(100 kg N ha -1 as urea + 100 kg N ha -1 as FYM) which 

was statistically at par with F2 treatment (200 kg N ha -1 

as urea) as well as, F4 (200 kg N ha -1 as poultry manure). 

Minimum pH was recorded in F1 treatment (no fertilizer 

and manure was applied (Table 3). These results also 

confirmed the findings carried out by Borin and Sartori 

(1989); Thomassims et al. (1995) and Tamayo et al. (1997).

The increase in pH in the treatment (½ urea + ½ poultry 

manure) was observed as nitrogen was available from 

both urea as well as PM so it enhanced the growth of 

plants. 

Leaf area index at tasseling 

Non significant differences in LAI between two maize 

hybrids were found, while fertilizer treatments had 

significant differences and maximum LAI at tasseling 

(4.89) was observed in F6 (fertilized with a combination 

of 100 kg N ha -1 as urea + 100 kg N ha -1 as PM) followed 

by F5 (100 kg N ha -1 as urea + 100 kg N ha -1 as FYM) 

that was statistically at par with treatments F2 (200 kg N 

ha -1 as urea) and F4 (200 kg N ha -1 as PM). The 

minimum LAI was produced by the crop in control plots 

where no fertilizer was applied (Table 3). 

Total dry matter at tasseling 

The maize hybrids did not affect by total dry matter 

(TDM), when the crop was at tasseling stage (Table 3). 

The maximum TDM at tasseling (1999 gm -2 ) was 

observed by F2 (200 kg N ha -1 was applied as urea) 

which was statistically at par with F6 (combination of 100 

kg N ha -1 as urea + 100 kg N ha -1 as PM) where fertilizer 

was (1871 g m-2) as shown in the Table 4. Similar 

findings were also observed from the studies carried out 

by He and Li (2004) and Saha et al. (2008). Variations in 

grain number might be due to differences in genetic 

potential of maize hybrids. 

The findings also confirmed the results reported by 

Chaudhary et al. (1998); Sharma and Gupta (1998) and 

Younas et al. (2002). The levels of organic and inorganic 

fertilizers significantly influenced the number of grains 

(m -2 ). The treatment F6 (100 kg N ha -1 as urea + 100 kg N 

ha -1 as PM) produced more number (3301) of grains m -2 

than F4 (200 kg N ha -1 as PM) which produced 3012 

grains m -2 but it (F6) was not statistically different from 

plot fertilized with 200 kg N ha -1 as FYM (F5), while 

minimum number of grains was recorded from plots 

where no fertilizer and manure was applied, that is, 

control plots (Table 4). 

Mean grain weight (g) 

It is considered that grain weight contribute significant 

impacts on final yield of a crop. It is clear from Table 4 

that the corn hybrids, Pioneer-3062 and Pioneer-3061 

differed significantly from each other. Pioneer-3062 

produced more mean grain weight (0.451 g) than 

Pioneer-3061 (0.254 g). 

These results also corroborates the findings of Younas 

et al. (2002) who also observed that genetic potential had 

significant effect on mean grain weight. The maximum 


mean grain weight (0.299 g) was observed from F6 that 

was statistically similar to F2, F5 and F4 which produced 

0.282, 0.279 and 0.281 g, respectively (Table 4). While 

minimum mean grain weight (0.236 g) was obtained from 

control plot (F1). These findings are in line with the results 

of Rutunga et al. (1998); Sevaram et al. (1998) and Ma et 

al. (1999). Furthermore, the increase in mean grain 

weight was mainly due to reasonable sufficient supply of 

nutrients from both urea and PM throughout the duration 

of grain filling and development. 

Grain yield (kg ha -1 ) 

Grain yield is the main output for which the crop was 

sown. Grain yield differed significantly among different 

maize hybrids; Pioneer-3062 produced more grain yield 

(5612 kg ha -1 ) than Pioneer-3061 (5325 kg ha -1 ). The 

findings also have the similar approaches with the results 

explained by Ma et al. (1999) and Younas et al. (2002), 

who also observed that genetic potential had significant 

effect on mean grain weight and finally, grain yield. The 

treatment F6 produced maximum grain yield (6058 kg ha - 

1 ) which was statistically at par with F2 (Table 4). F5 also 

produced statistically similar yield as that of F2 (Table 4). 

Whereas, control (F1) plot gave minimum yield (4340 kg 

ha -1 ). The increase in grain yield in the treatments was 

mainly due to maximum number of grains per cob as well 

as, number of cobs per plant. This result is also in line 

with the findings carried out by Tamayo et al. (1997). 

They observed that combined use of mineral and 

organic manure gave maximum yield. 

Harve st index (%) 

Harvest index is the ratio of economic yield to biological 

yield represented in percent. The data presented in Table 

4 showed that the corn hybrids differed significantly from 

each other in their harvest index. Pioneer-3062 gave 

more harvest index (23.01%) than Pioneer-3061 

(21.87%). 

Furthermore, various levels of organic and inorganic 

fertilizers had significant effect on harvest index. The 

comparison of treatment means showed that maximum 

harvest index (27.09%) was recorded from F6. 

Treatments F2 and F5 have almost the same trend with 

each other as regards harvest index. Moreover, F3 and F4 

were also statistically at par with each other. 

The lowest harvest index (17.72%) was recorded in 

control treatment as shown in Table 4. Results also 

corroborate with Shah and Arif (2001) who also noted the 

positive effects of fertilizers (organic and inorganic) 

combined with each other. 

Grain protein and oil content (%) 

Grain protein and oil content of the maize hybrids were


Table 4. Effect of organic and inorganic fertilizers on yield and yield components of maize hybrids under agro-climatic conditions of Faisalabad- 

Pakistan. 

Treatments 

(A) Hybrids 

Plant 

population (m 2 ) 

Number of 

grains (m 2 ) 

Mean grain 

weight (g) 

Grain yield 

(kg ha -1 ) 

Harvest 

index (%) 

Grain protein 

content (%) 

Grain oil 

content (%) 

H1 6.79 3201 a 0.451 a 5612 a 23.01 a 10.02 4.12 

H2 6.67 3129 b 0.254 b 5325 b 21.87 b 9.81 4.05 

Significance NS * * * * NS NS 

(B) Fertilizers 

F1 6.22 2499 d 0.236 b 4340 d 17.72 d 9.01 d 4.02 b 

F2 6.60 3243 a 0.282 a 5867 ab 24.26 b 11.09 b 7.97 a 

F3 6.65 2855 c 0.260 b 5201 c 18.99 c 9.29 c 3.95 b 

F4 6.68 3012 b 0.281 a 5354 c 22.26 c 9.95 c 3.99 b 

F5 6.71 3126 ab 0.279 a 5720 b 24.35 d 11.16 b 4.95 a 

F6 6.69 3301 a 0.299 a 6058 a 27.09 a 12.01 a 4.92 a 

Significance NS * * * * * * 

Interaction (A x B) NS NS NS NS NS NS NS 

Mean 6.82 3015 0.289 5404 21.99 8.99 4.93 

Means sharing at 5 % level (P) same letter did not differ significantly; *= Significant, NS = Non-significant. 

also determined. The quality of the crop is reliant on the 

existence of protein and oil in its seeds/grains. The maize 

hybrids showed non-significant differences for quality 

parameters. As in yield and yield component paramete rs, 

the F6 treatment gave maximum (12.01%) protein content 

but the highest oil content was observed in the F5 

treatment, and this was statistically at par with F6 and F2 

treatments (Table 4). Similar results were also clarified by 

Rutunga et al. (1998); Sevaram et al. (1998) and Ma et 

al. (1999). 

Conclusion 

The hybrid Pioneer-3062 gave excellent results with 

respect to grain yield and yield components as well as, 

grain protein and oil contents than Pioneer-3061. As 

such, Pioneer-3062 is recommended to grow under agroclimatic 

of Faisalabad. Among different fertilizer 

treatments, maximum yield was observed in the case of 

F6 treatment (plots in which 100 kg N ha -1 as urea + 100 

kg N ha -1 as PM) gave outstanding results as compared 

to other treatments. Furthermore, from our results, it 

seems that use of organic and inorganic fertilizers in 

proper combination (50:50) received higher yields than 

the sole application of either of the fertilizer or manure 

particularly, in hybrid corn under agro-climatic conditions 

of Faisalabad (Semi arid environment), Pakistan. 

ACKNOWLEDGMENT 

The authors are grateful to all the members of Agro- 

Climatology Laboratory, Department of Agronomy, 

University of Agriculture, Faisalabad-Pakistan, for their 

manual and spiritual support. 

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