Effect of Adoption of Improved Soybean Variety on Productivity of Farm Households in Benue State, Nigeria  

Obasoro O.A. , Iwinlade A. , Popoola O. , Adeoti A.I.
Department of Agricultural Economics, University of Ibadan, Nigeria
Author    Correspondence author
Legume Genomics and Genetics, 2015, Vol. 6, No. 5   doi: 10.5376/lgg.2015.06.0005
Received: 17 Sep., 2015    Accepted: 21 Oct., 2015    Published: 26 Oct., 2015
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This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Obasoro O.A.,Iwinlade A., Popoola O., and Adeoti A.I., 2015, Effect of Adoption of Improved Soybean Variety on Productivity of Farm Households in Benue State, Nigeria, Legume Genomics and Genetics, Vol.6, No.6, 1-9 (doi: 10.5376/lgg.2015.06.0006)

Abstract
The study examines the effect of adoption of improved soybean variety on Farmer’s productivity in Benue State, Nigeria. A multi-stage sampling technique was employed to select 267 respondents for the study. Data were analyzed using descriptive statistics, gross margin analysis and the Heckman selection model.
Result shows that most household heads were male with a mean age of 36 10 years and a mean household size of 5 3 members. The awareness characteristics revealed that the TGX 1987-10F soybean variety was the technology adopted in the study area. Majority of the farmers were aware (95.2%) of the improved soybean variety and mostly got their information from extension agents. The economic profitability of the production shows that the average cost incurred on labour was ₦177,091.29 per hectare and constituted the highest proportion (61%) of the total cost of production of the crop. The gross margin and the net revenue per hectare were ₦22,018.44k and ₦311,779.59k.
 
The Heckman model results revealed that the probability of adoption of the improved soybean variety is influenced by the age of the farmer, gender, dependency ratio and early maturity of the variety while the second stage revealed that the household size, farm experience, membership of cooperative, education and access to credit by these farmers influence the effect of adoption on yield.
 
It is therefore recommended that government should improve the education of rural farmers through adult education since education positively affects adoption of improved soybean and the yield of these farmers, set up and implement rural welfare schemes to make credit facilities readily available to farmers and strengthen extension service.
Keywords
Adoption; Improved soybean variety; Gross margin; Heckman model; Benue state

Introduction
The Agricultural sector is an important component of Nigerian economy with over 70% of the population engaged in agriculture and agricultural related activities. The sector is almost entirely dominated by small scale resource poor farmers living in rural areas, with farm holdings of 1~2 hectares, which are usually scattered over a wide area. The farms dominated by these small scale farmers are responsible for about 95% of the total production. In addition, small scale agriculture has in the time past suffered from limited access to credit facilities, modern technology farm input and inefficient use of resources (Izekor and Olomese, 2010). These poor outcomes were attributed to low productivity, poor agricultural produce price; hence poor farm income, inadequate infrastructure, limited access to credit and improved farm inputs (Babatunde et al., 2007). Hence, one way of tran -sforming agriculture is by exposing small-scale farmers to improved agricultural production technologies, such as high crop yielding seed varieties.

Soybean has been variously described as a “miracle bean” or a “golden bean” because it is a cheap, protein-rich grain. This is so because it contains 42.8% high quality protein, 22.8% edible vegetable oil, 33% carbonate and a good balance of amino acids (RMRDC, 2005). In addition, soybean oil is 85% unsaturated and cholesterol free when compared to other legumes and animal sources (IITA, 1998). Soybean can also contribute to the enhanced sustainability of intensified cropping systems by improving soil fertility through nitrogen fixation, permitting a longer duration of ground cover in the cropping sequence, and providing useful crop residues for animal feed.

Currently, Nigeria is Africa’s largest producer of soybean, producing about 437,000 metric tons and Benue State, producing about 175,000 metric tons, is Nigeria’s largest soybean producer (FMAWR, 2008). Following the development and introduction of improved varieties, many food recipes using soybean were found to be highly acceptable to Nigerians, including their incorporation into traditional local dishes (Osho and Dashiell, 1998). The rapid growth in the poultry sector and oil mill processors has increased demand for soybean in Nigeria. Soybean production is now increasing as more farmers are becoming aware of the potential of the crop as cash/food crop especially in the guinea savannah zone of Nigeria. It has also increased the income of small scale farmers as it is a cash crop for domestic and export markets. The market for soybean in Nigeria is growing very fast with opportunities for improving the income of farmers. In order to meet this demand, production acreages are increasing in key global areas. Doss et al., (2003) opined that adoption of improved technologies is an important means to increase the productivity of small holder agriculture in Africa, thereby fostering economic growth and improved wellbeing for millions of the poor households. Low adoption of improved agricultural production technologies that can increase farmers’ productivity is generally known to lead to reduced agricultural output.

Earlier studies (Obwoma, 2000; Ajibefun, 2006; Seyoum et al., 2006) opined that the low rate of adoption of improved agricultural technologies could be due to low expected benefits from the practice or could be due to other factors such as farmers’ characteristics or technology factors which may not encourage the adoption of technologies by farmers. Use of local crop varieties is also recognized as major impediments to the growth of African agriculture (Yates and Kiss, 1992; Valnauwe and Giller, 2006). This is evidenced by low and declining yield per hectare of major crops in Nigeria as revealed by NBS (2006). It is in recognition of this situation that, Ouma et al. (2006) suggested that the use of improved technologies will continue to be a critical input for improved farm productivity.

However, most of the new agricultural technologies have not fully achieved the desired goals (e.g., high rate of adoption (Faltermeier and Abdulai, 2009). This observation has, therefore, spawned numerous studies about agricultural technology adoption related issues in developing countries in recent years (Besley and Case, 1993; Doss and Morris, 2001;Mendola, 2007, Becerril and Abdulai, 2009). According to Sunding and Zilberman (2000), technological change has been a major factor shaping agriculture in the recent times to achieve significant increases in agricultural productivity. Technology adoption has also been identified as having direct effect on the farmer’s income, usually resulting from higher yields, higher prices, or both. Yield improving technologies usually involve bundling of improved seeds with appropriate fertilizer, pesticide and fungicide applications.

Over the years, the rural farmers depend on indigenous or local (Malayan) variety for production. Usage of this primitive variety by the rural farmers in the study area has not helped to improve agricultural yield. This variety is low yielding, biotic and abiotic stresses, susceptible to bacterial diseases and late maturing (Smith et al., 1995). It also exposes soybean to pod shattering during seasonal harmattan period. The utilization of soybean in local diets and local industries has increased the demand for the crop over the years in the state (Mary, 2010). However, small-scale farmers, who are the major producers of the crop, have not been able to significantly increase their production and productivity to meet these demands. This has been attributed to the low adoption of improved soybean variety by farmers.
 
Nigeria as a developing nation had long sought for the application of improved agricultural technologies through the launching of numerous projects and programmes. Notable ones are the Green Revolution (GR), Agricultural Development Programme (ADPs), and National Accelerated Food Production Projects (NAFPP), but with limited success. Despite all these efforts made by the Government to boost agricultural production with a view to improving the living standard of the rural farm families, these programmes have not translated into increased productivity and welfare of farmers.

Several studies have estimated the effect of adoption of soybean on incomeusing the logit and tobit regression models (Idrisa et al., 2009; Ojiako et al., 2007; Ouma et al., 2006). These studies failed to address the sample selection problems from the use of primary data. This study therefore employed the use of the heckman selection model to address the problem of selectivity bias. This study will generate useful information for development planners, policy makers and practitioners on ways to increase adoption of the improved soybean variety in order to meet the objectives of the Agricultural Transformation Agenda of the Federal Government of Nigeria.

Against this background, the paper identifies the factors affecting adoption and the effect of adoption of improved soybean variety on productivity of farmers in the study area.

The main objective of this study is to estimate the effect of adoption of improved soybean variety on productivity in Benue State, Nigeria.

Specific Objectives are to: (1) Estimate the profitability of use of the improved soybean variety; (2) Determine the determinants of adoption of improved soybean variety; (3) Determine the effect of adoption on income and yield.

1 Results and Discussion
1.1 Socio-economic characteristics of soybean farmers
Table 1 presents the socio-economic characteristics of soybean farmers. Majority (77.9%) of the household heads were male while female constituted only (22.1%). About 35.6% are within the economic active age of 20~39 years with mean age of 36±10years, implying that most of the respondents are in their active years, capable of making good production decisions and have potential for greater productivity. They are married (72%) and with average household size of 5±3 members which is fairly large, implying the existence of source of family labour. Sixty–three percent were literate having at least secondary education. This implies that most of the farmers are educated and will be able to take better decisions relating to adoption of innovation and application of agronomic practices to increase production. Majority (99.3%) had off farm income. Above half (58.1%) reported they own land. This will likely affect their choice of adoption of the technology. Majority (97%) had 1-2 hectares farm size with mean of 1hectare. Above half (56.6%) had above 7 years of farm experience with mean of 9years. Above half (83.9%) reported they had access to credit. Majority (82%) had access to extension service as Onu (2006) opined that farmers who had access to extension adopted improved farming technologies and had a higher productivity growth rate than those who had no access to extension services. About 58% belong to a cooperative as this avail the farmers the opportunity of not only obtaining credit and agricultural inputs but also information on how to improve his farming activities.


Table 1 Socioeconomic characteristics of soybean farmers (N=267) 


1.2 Awareness of improved soybean variety
Table 2 revealed TGX 1987-10F was the improved soybean variety adopted in the study area with majority of the farmers (95.2%) being aware of the variety while only 4.8% were not aware.

 
Table 2 Awareness of the improved soybean variety 

 
1.3 Source of awareness about the improved soybean variety
Table 3 presents the sources of awareness about improved soybean variety. About 42.55% got their information about improved soybean seed from extension agents followed by 19.15% from friends and neighbors, 25.53% through media (mainly radio), while the least (12.77%) got the information from Non-Governmental Organizations (NGOs). This implies that the extension agents are the main source of technical information to farmers.

 
Table 3 Sources of awareness about improved soybean variety 

 
1.4 Profitability of improved soybean variety
The result shows that the average cost incurred on labour was ₦177,091.29 per hectare and constituted the highest proportion (61%) of the total cost of production of the crop. This is in line with the work of Ani et al. (2010) who found that average cost incurred on hired labour constituted the highest proportion of the average total costs of production of leguminous crops in Benue State. It is therefore implies that labour to a large extent determines the viability and profitability of soybean production as shown in table 4. The gross margin and the net revenue per hectare are ₦22,018.44k and ₦311,779.59k. However, based on this finding, production of improved soybean variety is profitable. The average rate of return per naira invested was 0.07 implying that for every one naira invested in soybean production, there is a profit of 7 kobo.

 
Table 4 Profitability analysis of soybean production/ hectare 


1.5 Determinants of adoption of improved soybean variety on yield
The factors that determine the adoption of improved soybean variety and its effect on yield of the farmers shown in table 5. This involves a two-stage process with the first stage being the probit model which identifies the factors that affect adoption of soybean is presented using the Heckman two-stage model as of adoption on yield. The diagnostic statistics from the estimation revealed that the rho (0.2581) indicates absence of correlation between the error term and the quantity of soybean produced. The lambda is significant and shows that there is selectivity bias in the sample. The correction of this bias implies that the covariates that condition the soybean yield operate conditional on the probability to adopt the improved variety.

 
Table 5 Determinant of adoption of improved soybean variety and its effect on yield 


Four variables significantly explained the probability of adoption and they include age of the farmers, gender, dependency ratio and early maturity of the variety.

The age of the farmer was significant at 5% and negatively related with the probability of adoption of improved soybean variety. This implies that the probability of adopting improved soybean variety decreases as the farmer gets older. In support of this finding, Caswell et al. (2001) opined that the effect of age of farmers on adoption cannot be pre-determined because older farmers are sometimes considered to be risk-averse and thus less willing to try new innovations than younger farmers. On the contrary, Tjornhom (1995) considers older farmers as experienced and, therefore, in a better position to make sound judgment regarding the adoption of new technologies, suggesting that older farmers will be quick to adopt improved technologies that offer better returns than younger and inexperience farmers

The gender of the farmer was significant at 1% and positively related to the probability of adoption of improved soybean variety implying that being a male farmer increases the likelihood of adoption of the improved soybean variety.

Dependency ratio was significant at 5% and increases the probability of adoption.

Knowledge on the early maturing characteristic of the soybean variety increases the probability of adoption. This implies that if farmers are aware that the soybean variety introduced is early maturing, it will increase the likelihood of adoption of such variety. This is expected as early maturity gives the crop an advantage, especially in the study area which is prone to drought which gives the farmers an advantage of preventing pod shattering during the harmattan period as opined by Sanginga et al. (1999).

1.6 Effect of adoption on soybean yield
Five variables significantly explained the effect of adoption on yield. They are household size, farm experience, cooperative membership, education and access to credit.

The household size was significant and positively related to the yield of soybean at 1% level. The implication of this finding is that as the number of persons in the household increases there is the tendency that yields of farmer’s increase. This follows that as the number of household increases, the cost of hiring labour reduces and the yield increases.

Education showed significant and positive relationship with the yield of soybean at 5%, implying that as level of education increases yield also increases. Education increases exposure to useful information such as increase in yield which will assist farmer in decision making (Waller et al., 1998). Caswell et al. (2001) opined that education is thought to create a favorable mental attitude for the acceptance of new practices especially of information-intensive and management-intensive practices.

Farming experience had a significant negative relationship with yield of soybean. This means that the yield of the soybean farmer reduces with the age of the farmer. This is so because more experienced farmers who are expected to have better skills and access to new information about the improved technology are working with uncertain production inputs which as a result affects their yield (Shiyani et al., 2002).

Belonging to a cooperative society was positively related with the soybean yield. This implies that farmers who belong to cooperative societies have more access to information and resources that could help improve yield. Consistent with this finding are the studies of Ajammy and Olayemi (2001) and Fleming et al. (2004).

The results revealed that access to credit increases the soybean yield. This is because farmers with access to credit are able to cultivate large hectares of land and purchase inputs for soybean cultivation which leads to increased yield.

2 Conclusion and Recommendation
The study highlighted the awareness rate, sources of awareness, profitability, determinants of adoption and effect of adoption on soybean yield in Benue state. It was discovered that soybean production on the average was profitable. It was revealed that age of the farmer, gender, dependency ratio and early maturity of the variety influence the probability of adoption of improved soybean variety. Also, factors that determine the effect of adoption on soybean yield were household size, education, farming experience, membership of cooperative and access to credit.

It is therefore recommended that farmers should be exposed to adult education programs that will change the attitude and orientation of the farmers towards adoption of innovation and modernized agricultural practices so as to improve farmer’s productivity. Farmers should be encouraged to form cooperatives or join existing ones by government and non-governmental organizations to enhance their access to improved seeds and inputs. Extension service should be strengthened so as to expose farmers to modern farming techniques and improved technologies.

3 Methodology
3.1 Study area

The study was conducted in Benue State, Nigeria. The state is popularly referred to as the “Food Basket of the Nation” on the basis that agriculture is the main economic activity, Benue state has an estimated population of 2.8 million people; made up of 413,159 farm families, majority of whom are rural dwellers and are directly involved in subsistence agriculture characterized by small farm holdings with an average farm size of 1.5-2.0 ha (NPC 1996). Soybean is mainly produced in the Northern and Eastern Zones of the State.

3.2 Sampling procedure and sample size
A multi-stage sampling procedure was employed in selecting farmers for the study. The first stage involved the random selection of 5 local governments out of 23 local governments in the state. The second stage involved the random selection of one ward from the local government areas selected. The third stage involved the purposive selection of 6 villages from the selected ward. The fourth stage involved the random selection of 10 soybean farmers to give a total of 300 respondents. However, due to incomplete response, only 267 questionnaires were used for the analysis.

3.3 Type of data and data collection
Primary data was collected through the use of structured questionnaires administered to the selected farmers in the study area. Data were collected on the socioeconomic characteristics (age, gender, education, etc), awareness of seed variety. Data on production input cost such as cost of seed, cost of fertilizer, cost of agrochemicals and operational inputs cost such as cost of land preparation, cost of weeding, cost of planting, cost of spraying, cost of fertilizer application and transportation cost were collected.
 
3.4 Analytical technique
Descriptive statistics was used to describe the socio- economic characteristics of soybean farmers.
 
3.4.1 Gross margin analysis
The gross margin analysis was used to estimate the profitability of the improved soybean production. Following Olukosi and Erhabor (2005), it is given as:

GM = GI – TVC (N/ha)    (1)
GI   = TVP = TPP. Py (N/ha)  (2)
GM = TPP. Py – TVC (N/ha)   (3)
Where, 
GM= the gross margin
Py = the price of a unit product of soybean
TVC = the total variable cost of inputs
TVP = Revenue from soybean production

Inputs considered were: production inputs such as cost of seed, cost of fertilizer, cost of agrochemicals; operational inputs which are cost of land preparation, weeding, planting, spraying and fertilizer application) and transportation cost.

3.4.2 The heckman two-step model
Following the work of Shephard et al. (2010), the heckman procedure is a relatively simple procedure for correcting sample selectivity bias and was used to examine the determinants of adoption and effect of adoption on productivity of soybean farmers.

It consists of two steps.
 
 (4)
Where y_i^(* )= quantity of soybean produced. These quantities are observed only for those farmers that adopt.

β1i= parameters to be estimated
ε1i = error term
Xi=vector of explanatory variables
X1=Gender of the farmers (male =1, 0 otherwise)
X2= Age of the farmers head in years
X3=Farm size in hectare
X4= Household’s size in number
X5= Farm experience in years
X6= Education in years
X7= Cooperative membership (1 if a member, 0 otherwise)
X =Access to credit in naira (1 if farmer has access to credit, 0 otherwise)
X9= Early maturity (1 if seed matures early, 0 otherwise)
X10= Distance to market in kilometers
X11 =Access to extension agent (Yes =1, 0 otherwise)
X12= Dependency ratio
X13= Pattern of Land ownership (Yes =1, 0 otherwise).
First a selection equation is estimated using a probit model. This model predicts the probability that a farmer adopt or does not adopt, and the inverse Mills ratio is obtained. 
  (5)

                        
Where h_i is farmer’s adoption of improved soybean variety.

 (6)
where σ_12is the covariance between the two error terms, the termis the inverse mill’s ratio called the Heckman’s lambda. The second step of the model as developed by Heckman J.J, (1979) is the OLS estimation corrected by the inclusion of Heckman’s lambda among the regressors and is indicated as follow:

 (7)
Then the OLS regression equation including the inverse Mills ratio (λ) as a regressor is estimated for the quantity of soybean yield produced.

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