The main objective of this study is to identify factors affecting adoption of high yielding wheat varieties in Mao-Komo district of Benishangul-Gumuz, Ethiopia.
Adoption of high yielding wheat varieties is one of the measures presumed to enhance wheat yield in Ethiopia. However, there are several socio-economic and institutional factors that limit the adoption of high yielding wheat varieties. Wheat is one of the major cereals of choice in Ethiopia, dominating food habits and dietary practices, and is known to be a major source of energy and protein in the country. The utilization of wheat has increased due to the growing urbanization and the expansion of agro-industries used as raw material, and also considered to attain food security in Ethiopia. It is also used for traditional foods and the straw is used for animal feed and thatching of roofs. To feed the rapidly growing population and meet the high demand of wheat in the country, it needs to increase the production and yield of wheat. However, increasing yield requires successful adoption of improved agricultural technologies.
The present study uses cross-sectional data collected from sample of 174 farm households selected through two-stage stratified random sampling techniques. Descriptive statistics and econometric models are used to analyze the data. Probit model is employed for adoption analyze of high yielding wheat varieties. The probit model result depicts that land holding size, tropical livestock unit, access to agricultural information, frequency of extension contacts, off-farm income, and perception of farmers toward attributes of high yielding wheat varieties affect the likelihood of adoption of high yielding wheat varieties positively and significantly. But sex of household heads and affiliation to organizations has negative and significant effects on the likelihood of adoption of high yielding wheat varieties. The findings suggest that the government and stakeholders should need to focus on improving farm land and livestock productivity, strengthening frequency of extension visits, encouraging participation in off-farm activities, creating reliable information and awareness towards farmers’ perceptions in the area. Finally, further support of high yielding wheat varieties adoption should be given due attention for smallholders.
DETERMINANTS OF HIGH YIELDING WHEAT VARIETIES ADOPTION BY SMALL-HOLDER FARMERS IN ETHIOPIA
ABSTRACT
Adoption of high yielding wheat varieties is one of the measures presumed to enhance wheat yield in Ethiopia. However, there are several socioeconomic and institutional factors that limit the adoption of high yielding wheat varieties. The main objective of this study was to identify factors affecting adoption of high yielding wheat varieties in Mao-Komo district of Benishangul-Gumuz, Ethiopia. The study used cross-sectional data collected from sample of 174 farm households selected through two-stage stratified random sampling techniques. Descriptive statistics and econometric models were used to analyze the data. Probit model was employed for adoption analyze of high yielding wheat varieties. The probit model result depicted that land holding size, tropical livestock unit, access to agricultural information, frequency of extension contacts, off- farm income, and perception of farmers toward attributes of high yielding wheat varieties affected the likelihood of adoption of high yielding wheat varieties positively and significantly. But, sex of household heads, and affiliation to organizations had negative and significant effect on the likelihood of adoption of high yielding wheat varieties. The findings suggest that the government and stakeholders should need to focus on improving farm land and livestock productivity, strengthening frequency of ext ension visits, encouraging participation in off-farm activities, creating reliable information and awareness towards farmers’ perceptions in the area. Finally, further support of high yielding wheat varieties adoption should be given due attention for smallholders.
Key words: Adoption, High yielding wheat varieties, Smallholder, Binary probit
1. INTRODUCTION
The economic development of Ethiopia is highly dependent on the performance of its agricultural sector since it is the main economic pillar of economic growth of the country. Agriculture contributes 42 % of the GDP of the country and about 85 % of the population gains their livelihood directly or indirectly from agricultural production (CSA, 2015).
Wheat (Triticum aestivum L.) is one of the major food and cash crops for smallholders in Ethiopia. It is important cereal crop with annual production of about 4.23 million tons and cultivated on an area of 1.66 million hectares (CSA, 2015). According to the CSA report, it occupies about 24.02 % of the total cereal area in the country and contribute the grain production about 15.65 %. However, its national averag e yield is about 25.43 quintals per hectare. This is low yield compared to global average of 40 quintals per hectare (FAO, 2009). The low yield has made Ethiopia unable to meet the high demand and the country is net importer of wheat (Rashid, 2010).
Wheat is one of the major cereals of choice in Ethiopia, dominating food habits and dietary practices, and is known to be a major source of energy and protein in the country (Hailu, 2003).The utilization of wheat has increased due to the growing of urbanization and the expansion of agro-industries used as raw material, and also considered to attain food security in Ethiopia. It is also used for traditional foods and the straw is used for animal feed and thatching of roofs (Katherine, 2013).
To feed the rapidly growing population and meet the high demand of wheat in the country, it needs to increase the production and yield of wheat. However, increasing yield requires successful adoption of improved agricultural technologies (Dorosh and Rashid, 2013). Low yield due to low adoption of improved agricultural technologies is believed to be the main factor that prevented agricultural production from coping with the rapid population growth in Ethiopia. For this reason technological change is commonly considered as one of the major options leading to successful productivity growth in agriculture. The objective of this study was to assess the adoption of high yielding wheat varieties by smallholder farmers.
2. Empirical Studies on Adoption of Agricultural Technologies
A number of empirical studies have been conducted by different people and institutions on farmers’ adoption behavior both outside and inside Ethiopia using econometric models. The results of various empirical studies confirmed that adoption of a new technology offers opportunities for increasing productivity and production.
Assefa and Gezahegn (2010), Solomon et al. (2011) and Hassen et al. (2012) found that age of household head, educational status, livestock holding, non-farm income, sex, and information access plays important factors in affecting the decision of farmers to adopt improved technology. Mahadi et al. (2012) studied factors affecting adoption of improved sorghum varieties in Somali Region of Ethiopia. They have fou nd out that more educated farmers are more likely to adopt improved sorghum varieties in the study area.
Degye (2013) conducted study on agricultural technology adoption, diversification and commercialization for enhancing food security in Eastern and Central Ethiopia by using multivariate probit revealed that adoption of high yielding crop variety was influenced by land allocated, agricultural income, distance to research institution, and the farming system. It also was reported significantly and negativ ely affected by other exogenous shocks. Degnet and Mekibib (2013) found that membership to farmer cooperatives has a strong positive effect on adoption of chemical fertilizer.
The study by Bekele et al. (2014) on adoption of improved wheat varieties and impact on household food security in Ethiopia, indicated that wheat technology adoption has generated a significant positive impact on food security and these results provide strong evidence for the positive impact of adoption of modern agricultural technologies for a major food staple on alleviating food insecurity in rural Ethiopia.
Leake and Adam (2015) conducted study on factors influencing allocation of land for improved wheat variety by smallholder farmers in Adwa district. They pointed out adopters had high family labor, high number of tropical livestock unit, large land size, high frequency of extension contact, access to credit, access to education, access to nearest to main road and market as compared to non-adopters. They also indicated that education level of household head, family size, tropical livestock, distance from main road and nearest market, access to credit service, extension contact and perception of household toward cost of the technology have to be significantly affecting factors adoption of improved wheat variety.
The study conducted by Sisay (2016) on agricultural technology adoption, crop diversification and efficiency of maize- dominated farming system in Jimma Zone of South-Western Ethiopia by using Tobit model indicated that age, family size, level of education, family education, ownership of mobile phone, extension services, cooperative membership, livestock holding and land holding size have positively and significantly influenced probability of improved maize variety and/or chemical fertilizer adoption in maize farming while, distance of development center from residence has a significant negative effect.
3. MATERIALS AND METHODS
3.1. Description of Study Area
The study was conducted in Mao-Komo Special district of Benishangul-Gumuz Region located in the Western part of Ethiopia and stretches along the Sudanese border found around 661 km away from Addis Ababa. Mao-Komo Special district is one of the 20 districts found in Benishangul-Gumuz Region, its capital, Tongo, located 112 km away from Assosa town, the capital city of the region. It is bordered by Oromia Regional state in the East, Sudan in the West, Assosa Zone in the North and Gambela Region in the South. The altitude of the district ranges from 950-1960 m.a.s.l. The temperature of the district ranges from 17.5-32 oC. The rainfall is uni-modal which starts in the month of April and ends in mid-October. The annual rainfall ranges from 900-1800 mm with mean annual rainfall is 1316 mm, mostly received between May and September with the highest in July and August. The du ration is about 6 to 7 months with good amount of rainfall distribution.
Having an area of about 2100 Km2 and population of about 42,050 (CSA, 2007), the district has 7848 households with 7185 and 663 male and female headed households, respectively. The district is mainly characterized by two agro- ecologies; namely, "Kolla" and "Woina Dega" respectively. From these, 5 Kebeles are the most wheat producers in the area. Farming is the predominant occupation of the people in the area since it is the main economic stay of the district. Maize, sorghum, wheat, and finger millet are the dominant cereal crops produced for consumption. Coffee, sesame, nigger seed and teff are produced for income generation in the district. Cattle, small ruminant, donkey, poultry and honey bee are the most important livestock species. The district has potential and favorable environmental and socio-economic conditions that would suitable to wheat production.
3.2. Methods of Data Collection and Sampling Procedures
The data for this study were collected from both primary and secondary sources on a wide variety of variables. The primary data were collected through individual interviews of selected respondents and the survey was administered using semi-structured questionnaires within individual interview. To complement the primary data, secondary data were obtained from different unpublished and archival sources such as article s/literatures, official reports , CSA report data, and personal communications.
This study defines the survey population at two levels, namely at the rural kebele level and at the farm household level. A two-stage stratified random sampling method was employed to draw representative sample respondents to increase homogeneity within adoption stratum and heterogeneity between strata. In the first stage, rural kebele administrations were stratified into two categories as potential and less potential wheat growers. Accordingly, three potential wheat producing kebeles were randomly selected.
In the second stage, members of each kebele were stratified into two groups based on their adoption status of high yielding wheat varieties. Then, a total of 174 farmers were randomly sampled (87 from each group) taking into account probability proportional to size of households in each kebele for both groups . Finally, the survey was administered and data were collected and analyzed on 174 respondents.
3.4. Methods of Data Analysis
In the study of adoption of high yielding wheat varieties through descriptive and econometric methods of data analysis were used to assess the relationship between explanatory and dependent variable. Adoption of high yielding wheat varieties was evaluated by statistical tools and econometric models for concluding the socio-economic, institutional, and environmental factors that hinder the adoption by smallholder farmers in the study area.
Descriptive statistics
Descriptive statistics were utilized to assess the socio-economic characteristics of the sample respondents for adoption of high yielding wheat varieties in the study area. These information was considered to augment the econometric analysis results. The descriptive analysis tools such as t-test and chi-square were employed to assess the relationship among the variables of interest that statistically to compare users and non-users respondents of high yielding wheat varieties.
Econometric models
In this study, the econometric analysis that employed was binary probit model for adoption of high yielding wheat varieties.
Probability of Adoption
A household level adoption study considers the decision made by the household head to include new or improved variety in usual farming practice. The decision made to adopt or otherwise depend on different factors. Farmers’ decision to adopt high yielding wheat varieties is assumed to be the product of a complex preference comparison made by a farm household. To adopt or not to adopt high yielding wheat varieties is often a discrete choice. Discrete choice models have widely been used in estimating models that involve discrete economic decision -making processes (Guerrem and Moon, 2004).
The dependent variable which is normally used with these models is dichotomous in nature, taking the values 1 or 0, a qualitative variable which is incorporated into the regression model as dummy variable. In this case the value 1 indicates a farmer who adopts the high yielding wheat varieties while the value 0 indicates the farmer who does not adopt. A number of studies conducted on technology adoption of smallholder farmers indicate that for such type, the most commonly used models are the logit and probit models. The binary model, a logistic distribution function, and the probit model, a normal distribution function, is used in estimating the probability of technology adoption (Pindyck and Rubinfeld, 1981; Feder et al., 1985; CIMMYT, 1993; Greene and Zhang, 2003). Such models have been widely used in different adoption studies not only to help in assessing the effects of various factors that influence the adoption of a given technology, but also to provide the predicted probabilities of adoption (Asfaw et al., 1997). The estimating model that emerges from normal CDF is popularly known as the Probit model (Gujarati, 1995).
Therefore, the study utilized the probit model to analyze likelihood of adoption of smallholder farmers because it is an appropriate econometric model for the binary dependent variable and the error term is assumed to be normally distributed. Often, probit model is imperative when an individual is to choose one from two alternative choices, in this case, either to adopt or not to adopt high yielding wheat varieties. An individual makes a decision to adopt high yielding varieties of wheat if the utility associated with that adoption choice is higher than the utility associated with decision not to adopt. Hence, in this model there is a latent or unobservable variable that takes all the values in (-∞, +∞).
In this study the classical probit model was employed to analyze the adoption decision behaviour of the respondents. To motivate the Probit model it can be assumed that the decision of a household (in this case an adopter) to adopt the high yielding wheat varieties (Yi=1) or not (Yi=0) depends on an unobserv able utility index (also known as a latent variable), that is determined by one or more explanatory variables, Xi, in such a way that the larger the value of the latent variable, the greater the probability of a household to adopt the high yielding wheat v arieties.
The Probit model is specified as:
Where yi = 𝑋𝑖 𝛽𝑖+𝜀𝑖 + where i= 1, 2, 3…..n (1)
Where: is a dummy variable indicating the probability of adoption and related as: Yᵢ = 1 if Yᵢ > 0, otherwise Yᵢ= 0
Xᵢ - is household characteristics of variables that determining farmers adoption in the probit model
𝑖- is unknown parameter to be estimated in the probit regression model
4. RESULTS AND DISCUSSION
The study presents the descriptive results explaining smallholder farmers’ probability of adoption of high yielding wheat varieties by smallholder farmers through the statistical analysis of descriptive tools and empirical results of econometric analysis.
4.1. Descriptive Results
Descriptive statistics such as mean, minimum and maximum values, range and standard deviations were used to describe the socio-economic and institutional characteristics of the households under considered in the study of high yielding wheat varieties adoption. For this study, the data was collected from both adopters and non -adopters of high yielding wheat varieties that consists of 50 % each of two group. Table 1 & 2 below, depicts the statistical t/x2 -test comparison of variables expected to determine adoption of high yielding wheat varieties of sample households.
Table 1. Descriptive Statistics for Some selected Continues Variables
Abbildung in dieser Leseprobe nicht enthalten
*, **, and *** indicates significant at 10 %, 5 % and 1 % significance levels, respectively. Source: Own survey (2015)
The descriptive results revealed that adopters of high yielding wheat varieties were significantly different from non - adopters in many cases such as farm land holding size, family size, livestock ownership, frequency of extension visit, educational level, and perceptions’ of farmers toward high yielding wheat varieties on certain attributes. On the other hand, adopters did not make significant difference in terms of distance from market center, distance to main road, farming experiences, access to credit services, sex of household head, off/non -farm income activities, and participation in local level organization with compared to non -adopters (Table 1).
Table 2. Descriptive Statistics for Some selected Discrete/ Dummy Variables
Abbildung in dieser Leseprobe nicht enthalten
*, **, and *** indicates significant at 10 %, 5 % and 1 % significance levels, respectively.
Source: Own survey (2015)
Mostly, peoples living in same environment share a common understanding of various circumstances and from more or less similar perception about certain situation. However, the degree of perception varies from individual to individual due to different factors. Adopters have more experienced advantages of high yielding wheat varieties with attributes like higher yield per hectare, short duration to maturity, better market price, and resistance to pests and diseases. The chi-square test indicated that there is systematic relationship between perception of respondents about high yielding wheat varieties and the two categories of sample household groups that adopters and non -adopters were statistically significant (Table 2).
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- Arbeit zitieren
- Regasa Wake (Autor:in), 2018, The Determinants of High Yielding Wheat Varieties Adoption by Small-Holder Farmers in Ethiopia, München, GRIN Verlag, https://www.grin.com/document/446948
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