Livestock Research for Rural Development 24 (11) 2012 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Agroforestry technologies involving fodder production and implication on livelihood of smallholder livestock farmers in Zimbabwe. A case study of Goromonzi District

J Mutambara, I V Dube and B M Mvumi*

University of Zimbabwe, Department of Agricultural Economics and Extension, P.O Box MP 167, Harare, Zimbabwe
* University of Zimbabwe, Department of Soil Science and Agricultural Engineering, P.O Box MP 167, Harare, Zimbabwe.


A study was conducted to determine factors affecting adoption of agroforestry technologies involving fodder trees and evaluate the impacts of the technology on livelihoods of farmers/livestock keepers in Zimbabwe. The study interviewed 30 agroforestry practitioners producing fodder plants and 25 non-agroforestry practitioners who were purposively selected in Chikwaka communal lands in Goromonzi district, Mashonaland East Province of Zimbabwe. A standard structured questionnaire was used to obtain information from interviewees on agroforestry and farming practices as well as socio-economic information including livelihoods indicators. Data were analyzed using descriptive statistics, Student t- test and logistic regression.

Adopters of agroforestry practices had more cattle, goats, maize output, ploughs, scorch carts and income from farming than non adopters. Logistic regression results showed that the likelihood to adopt agroforestry technologies was influenced significantly by age (-2.39), level of education (1.90) and land size (2.10) at 1%, 10% and 10% levels respectively. This research proved that adoption of agroforestry practices leads to an improvement in livelihoods of farmers as indicated by the disparities in livestock ownership, farm assets, crop production and incomes between practitioners and non-practitioners. It is therefore recommende that farmers should continue practicing and adopting agroforestry technologies for better livelihoods.

Keywords: age, education, descriptive, land, livestock, logistic, practitioners


Agroforestry, is a collective name for all land-use systems and practices where woody perennial plants are deliberately grown on the same land management unit as agricultural crops and/or animals, either in spatial mixture or in temporal sequence (FAO 2005). Agroforestry has long been a recognized in sustainable development models throughout the world due to the benefits they bring not only to the economy and society but also to the ecosystem (Rocheleau et al 1989; Thanh et al 2005). A number of factors have contributed to a rising increase in agroforestry since the 1970s and these are deteriorating economic situation in many developing countries, increased deforestation and scarcity of land because of population pressures, interest in farming systems, intercropping and the environment (Nair 1993).

There are different types of agroforestry practices that can be used, these includes improved fallow, taungya (systems consisting of growing annual agricultural crops along with the forestry species during the early years of establishment of the forestry plantation), home gardens, alley cropping, growing multipurpose trees and shrubs on farmland, boundary planting, farm woodlots, orchards or tree gardens, plantation/crop combinations, shelterbelts, windbreaks, conservation hedges, fodder banks, live fences, trees on pastures and apiculture with trees (Nair 1993; Siclair 1999). The different types of agroforestry technologies have been found to address specific human and environmental needs.  One of the important benefits is production of fodder to feed livestock. Farmers in other parts of the world have enjoyed increased incomes from livestock production, increased crop production, and reduced labour especially for herding cattle from adoption of agroforestry practices (FAO 2005). Improved soil fertility through production of leguminous and other agroforestry trees is another benefit. In Malawi and Zambia, planting shrubs in fallow for two years and rotating with maize has improved maize yields compared with planting continuous unfertilized maize (Franzel et al 2002). Timber and firewood as well as environmental services such as wind breaks, carbon sequestration and biodiversity among others are more benefits that can be obtained from agroforestry practices (FAO 2005).  

Given the immense agricultural and environmental potential of agroforestry, it is no wonder that it is being promoted for adoption among farmers in most developing countries especially in sub-Sahara Africa where productivity is low and more marginal lands are increasingly being brought under cultivation with increasing demand. Agroforestry technologies were introduced in Zimbabwe in 1987, with efforts to diagnose farming system constraints and to design agro-forestry interventions through initiatives by the Ministry of Agriculture and other organizations. Examples of the introduced technologies are alley cropping, woodlot, and windbreaks, fruit trees on cropland.


Adoption of agroforestry can lead to an improved crop and livestock production because agro-forestry practices are less costly, more affordable since inputs for fodder and soil amendments are readily available to small holder farmers (Parwada et al 2010). However, technology transfer and adoption have not been very easy in the country because of several existing barriers, which have not yet been fully overcome. There is consensus in literature that sustainable land management practices such as agroforestry practices are feasible and technically sound (Ajayi et al 2008; Sileshi et al 2008), but the level of uptake of the technologies by farmers has been very low particularly in low income regions of the world or attained only a modest success in other regions (Antle and Diagana 2003; Mercer 2004). Tripp 1993 cited illiteracy, in-adequate credit facilities, non-availability of farm inputs and socio-cultural factors as contributing factors to low levels of adoption. Ajayi et al 2008 reasoned that low levels of uptake of sustainable land management practices could be due to use of moral persuasion approach (farmer sensitization, farmer training  and field demonstrations) and wielding the stick approach (regulations, land use enforcements and instructions) without creating incentives for adoption of technologies.


Trials and demonstration farms were established in some rural communities of Zimbabwe such as Muzarabani, Mutoko, Mt Darwin, Sadziwa in Manicaland, and Masvingo in a bid to test and promote agroforestry adoption. The problem is that despite the benefits of agro-forestry to smallholder farmers, there has been low and uneven adoption rate of agroforestry in Zimbabwe’s smallholder areas. It is therefore important to understand the reasons behind this so as to influence both government and policy makers in the allocation of scarce resources to facilitate improvement in adaptation of agroforestry technologies in rural communities. Therefore, the objectives of the study were to (i) study economic implications of agroforestry practices and to (ii) determine major factors influencing adoption of agroforestry technologies and (iii) inform policy actions on courses of action that could be taken to improve adoption of technologies.

Materials and Methods

Description of the Study Area

The study was carried out in Chikwaka communal lands, of Mashonaland East Province. The study area is located about 54 km away from the capital city Harare, along Mutoko road. The area is in natural region ll of rainfall and it receives good rains of about 700mm per annum on average. Maize, sweet potatoes and groundnuts are the main crops grown by the farmers in the area while cattle and goats are the main livestock species.  

Sampling procedure

Fifty five farming households were selected for interview using a purposive sampling method based on the list of agroforestry adopters and non-agroforestry adopters obtained from the extension officer. The sample was composed of 30 adopters of agroforestry with fodder trees and 25 non-adopters of agroforestry.  This design enabled firstly the comparisons of the economic status of farmers who adopted and those who did not adopt fodder agroforestry technologies and secondly the association between socio-economic factors and adoption of agroforestry technologies. 

Data collection

A standard structured questionnaire was used to collect data, the questionnaire contained information on general socio-economic characteristics, agricultural practices related to agroforestry, factors affecting integration of agroforestry in the community and lastly economic status of farmers. The local language “Shona” was used to conduct interviews and the responses were recorded in English. This study considered various agroforestry practices that included fodder trees (Leucaena, Cajanas cajan, Sesbania sesban and fruit trees) such as improved fallow, orchards and nutritional gardens.  A farmer was considered an adopter if they were practicing at least one of these practices and non adopters were not into any agroforestry practices. 

Data analysis                                                                             

Descriptive statistics were used to clean and summarize the data into meaningful forms and the student t test was used to evaluate the impact of adoption of agro-forestry technologies on economic (farm income, assets, livestock and maize output) status of farmers. Logistic regression model was used to determine the relationship between adoption of fodder agroforestry practices and socio-economic factors of the farmer. The logistic model contained the following variables;

Dependent variable Z = adoption of agroforestry =1, or non adoption of agroforestry = 0

X1= Age of household head (years)

X2= Years in education (years spent at school)

X3= Money constraint

X4= Household size

X5 = Land Size (acres)

X6= Accessibility of seedlings

U= represents other factors that are likely to affect adoption of agro-forestry technologies.


Adoption of agroforestry technologies

About 55% of the interviewed farmers were adopters of fodder agroforestry practices. Adopters of fodder agroforestry practices were into improved fallow with fodder crops such as Leucaena and Sesbania sesban integrated on farm (crop) land (30%), orchards (65%) and nutritional gardens (10%).  

Economic implications of adoption of agroforestry practices


Adopters of agroforestry practices exceeded non-adopters in crop production,  numbers of ruminant livestock and income (Table 1).

Table 1.  Summary statistics of variables used in assessment of economic implications of adoption of agro-forestry technologies.


Adopters n=30 

Non adopters n=25

Significance level

Maize output (kg)




Number of cattle




Number of goats




Number of ploughs




Number of scotch cart




Farm income/year in US dollars




*10%, **5%, ***1%
Note. Please note that the Zimbabwean economy dollarized in 2009.

Determinants of adoption of agroforestry practices compared between adopters and non adopters

Adopters were younger, more educated and had greater land sizes than non adopters (Table 2). 

Table 2. Comparison of socio-economic factors affecting adoption of agro-forestry technologies in Zimbabwe


Adopters n=30

Non adopters n-=25

Significance level

Age (years)




Years in Education




Money constraint




Arable land area (acres)




Size of household




Accessibility of seedlings




 *10%, **5%, ***1%)

Determinants of adoption of agroforestry practices

 Age, years spent in education, and land size were factors likely to affect adoption of agroforestry. Money constraint, size of household and accessibility of seedlings did not influence adoption of agroforestry in Goromonzi. The results from the regression analysis (Table 3) showed that 82% of the variation in adoption of agroforestry practices was accounted for by the variables in the model.

Table 3. Logistic regression model for determinants of adoption of agroforestry technologies in Zimbabwe


Coefficient (B)





Years in Education



Money constraint



Land size



Size of household



Accessibility of seedlings






Model Chi square =34.6



R2 = 0.82


Economic implications of adoption of agroforestry practices


There were differences in livestock ownership, assets, output and income between adopters and non adopter of agroforestry practices. Ownership of livestock (cattle and goats) was positively skewed towards adopters indicating that adopters owned more livestock than non adopters and the adoption behavior in this regard could be attributable to the need for supplementary feeding of animals with fodder trees especially during dry seasons. Furthermore, besides driving the need to adopt agroforestry practices, livestock ownership is also an indicator of wealth status together with physical assets and income. The wealth status of farmers is an important factor that affects their risk taking behavior, according to informed literature, the probability that the farmer will invest in new technology increases with an increase in wealth status (Jera and Ajayi 2008). Another possibility could be that adopters use fodder banks to feed their cattle hence they find it easy to supplement the feed during periods of low nutrition from the veld thus stimulating growth of livestock among agroforestry practitioners than in non practitioners who could not afford supplementary feeding.  Thus there is an iterative interrelationship between adoption of fodder related agroforestry practices and livestock production.


Ownership of physical assets (ploughs and scotch carts), farm income and maize output was higher among adopters than non adopters. These factors are all indicators of wealth status of farmers as stated earlier on and they are interlinked. The interrelationships between livestock ownership and farm production and productivity are explained by the fact that livestock are an input into farm production given the manure and draught power provided by livestock into crop farming, while crop provide animal feed such as stover in smallholder farming sectors. Adoption of agroforestry practices can lead to an improved crop and livestock production because agroforestry practices improves soil fertility for crops while providing supplementary feeding for animals among other benefits (FAO 2005).  Thus maize output is higher among adopters than non adopters due to soil and environmental management benefits associated with agroforestry practices. Higher farm output in terms of crops, vegetables and livestock can translate into higher incomes as excess production beyond subsistence requirements are sold. This has been proved in this study given the higher farm incomes obtained by agroforestry practitioners than non practitioners. Besides crops and animal sales income, agroforestry has resulted in diversification of farmer’s income through sale of firewood in forests deprived areas. Caveness and Kurtz (1993) stated that agroforestry systems on cropland brought significant economic benefits to the farmer. Such benefits include diversified farm incomes due to improved and sustained productivity among other income activities. The higher incomes generated can now enable farmers to purchase more farm assets such as ploughs and scotch carts, an investment in agricultural production and productivity and the cycle continues with higher societal goals attained. Place et al (2003) observed that various agroforestry technologies adopted have increased farm yields, raised household incomes, and improved food security and the ability to mitigate vulnerable situations. Research conducted by Boatang (2008) found that a greater proportion of households (97%) had improved food security after adopting agroforestry. This was partly due to the fact that most farmers used money accruing from the sales of tree crops/products in purchasing food items to supplement food in the household.


Factors affecting adoption of agroforestry technologies


Socio-economic factors such as age, years in education, and land size influenced adoption of agroforestry technologies at 5%, 10% and 10% level respectively. The likelihood to adopt was inversely related to age, meaning that an increase in age would decrease the chances of a farmer to adopt agroforestry practices. This outcome is in line with previous studies that have shown that younger households are more likely to adopt agroforestry technologies (Alavalapati et al 1995). In 2008, Jera and Ajayi also concluded that an increase in age would result in lack of receptivity towards new technologies by farmers. The reluctance by older farmers to adopt new technologies can also be explained by fear of risk attached to new technologies that increases with age. It is also important to note that as age increase, people tend to prefer less labor demanding activities and thus shy away from practices such as agroforestry that demands much labor especially at establishment and harvesting stages.

The number of years in education had a positive influence that was significant at 10%. This implied that the more educated the head of household, the more the likelihood that the household will adopt. This is in contrast to the findings by Jera and Ajayi (2008), where they found out that farmer’s level of education was not a significant determinant of adoption of technology. However, the findings of the study agreed with Boateng (2008), where the high level of literacy rate would result in increase of technical efficiency and decreased conservationism among farmers. This would also contribute to the acceptance of agroforestry innovations in Chikwaka.

Land size had a positive influence on adoption of agroforestry practices. As the land size increases, the model suggests that adoption of agroforestry technologies also increases. This could be because farmers with extra land are likely to use it for experimenting new technologies. This is in tandem with what Jera and Ajayi (2008) found that land size has a positive effect on adoption because farmers with more cultivatable land are more likely to set aside a piece of land for fodder trees without impacting much negatively on land available to grow  food crops or disturbing household food security.



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Received 4 September 2012; Accepted 22 October 2012; Published 6 November 2012

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