Technical efficiency and impact of improved farm inputs adoption on the yield of haricot bean producer in Hadiya zone, SNNP region, Ethiopia (original) (raw)

Abstract

Haricot bean is one of the most important food legumes of Ethiopia and it is considered as the main cash crop and the least expensive source of protein for the farmers. Low production and productivity, which are mainly associated with poor adoption and inefficient implementation of improved farm technologies, were among the major problems. Adoption and efficient utilization of improved farm inputs is one of the most promising ways to reduce food insecurity in study area. However, the adoption and implementation of these improved farm inputs is constrained by various factors. So, the aim of this study was to analyze the technical efficiency and impact of improved farm inputs adoption on the yield of haricot bean producers. A multi-stage sampling technique was employed to select 231 sample household heads and they were interviewed using structured interview schedule. Data analysis was done with the help of Stochastic Frontier Analysis; mainly Cobb-Douglas Production Function, logistic regression model were employed. The Stochastic Production Frontier result revealed that the allocated amount of land, labour, seed, chemical fertilizer and oxen were appeared to be positively and significantly influencing haricot bean production of both adopters and total sampled ABOUT THE AUTHOR Mr. Tamirat Beyene (Msc in Economic policy analysis) is a full time lecturer at Wolkite University for the last three years and yet now. Dr. Wondaferahu Mulugeta (PhD, Associate professor of Economics) is a full time professor at college of business and economics, Jimma University, Ethiopia. He has more than 10 years of teaching experience in total. His areas of research interest are macro issues like impact of monetary and fiscal policies analysis, on exchange rate instabilities etc. Mr. Tesfaye Melaku (Assistant professor of Economics, Msc in Economics) has worked more than 7 years at college of Business and Economics, Jimma University, Ethiopia. His research interests are program impact analysis, microfinance, poverty, and adoption of improved farm inputs. Further, Mr Tesfaye Melaku is a full member of Ethiopian economic association.

Figures (9)

Table 1. Selected districts and sample distribution where ni is the sample size from each selected district (i = Misrak badawacho, Meirab bada- wacho and Shashogo) woredas where, n is total sample size of the study which is the sum of the sample size of the three woredas, and Ni is total haricot bean farm household head in respective woredas, and N is the total population or haricot bean farm households of the three woredas combined (Table 1). This procedure was applied separately for each producer group. Similarly for the allocation of sample size for each kebele, the same procedure (PPS method) was applied. Then the total number of haricot bean producer households in the three woredas are 22,150 (Hadiya zone agricultural development offices, unprocessed data obtained through personal communication). The existing proportion of haricot bean producers of Misrak bada- wacho, Meirab badawacho and Shashogo woredas were estimated 37.7, 30.3 and 32 percent, respectively. Based on this proportion, sample respondents from participant and non- participants in improved seed; chemical fertilizer and fertilizer with improved seed for each woredas and kebelewas allocated. Finally, the participant and non-participant households were used for the analysis of all stated objectives. Numbers of sample respondents” distribution from each woredas are depicted as follow, Sample respondent distribution from each woredas are depicted as follow:

Table 1. Selected districts and sample distribution where ni is the sample size from each selected district (i = Misrak badawacho, Meirab bada- wacho and Shashogo) woredas where, n is total sample size of the study which is the sum of the sample size of the three woredas, and Ni is total haricot bean farm household head in respective woredas, and N is the total population or haricot bean farm households of the three woredas combined (Table 1). This procedure was applied separately for each producer group. Similarly for the allocation of sample size for each kebele, the same procedure (PPS method) was applied. Then the total number of haricot bean producer households in the three woredas are 22,150 (Hadiya zone agricultural development offices, unprocessed data obtained through personal communication). The existing proportion of haricot bean producers of Misrak bada- wacho, Meirab badawacho and Shashogo woredas were estimated 37.7, 30.3 and 32 percent, respectively. Based on this proportion, sample respondents from participant and non- participants in improved seed; chemical fertilizer and fertilizer with improved seed for each woredas and kebelewas allocated. Finally, the participant and non-participant households were used for the analysis of all stated objectives. Numbers of sample respondents” distribution from each woredas are depicted as follow, Sample respondent distribution from each woredas are depicted as follow:

Table 2. Definitions efficiency measurement variables Given the level of technical inefficiency derived from equation (7) and the above-specified Xvector inefficiency explanatory variables (Table 2), the coefficients of inefficiency variables was

Table 2. Definitions efficiency measurement variables Given the level of technical inefficiency derived from equation (7) and the above-specified Xvector inefficiency explanatory variables (Table 2), the coefficients of inefficiency variables was

source: Uwn Computation, 2UL6 Note: extension services refers: agriculture extension workers consultation and meet of farmers per month where as information access it means any information farmers may get from neighbours, friends or media regarding their farm inputs. Table 3. The maximum likelihood estimates of the logistic model

source: Uwn Computation, 2UL6 Note: extension services refers: agriculture extension workers consultation and meet of farmers per month where as information access it means any information farmers may get from neighbours, friends or media regarding their farm inputs. Table 3. The maximum likelihood estimates of the logistic model

Source: Own computation result, 2018 Table 4. Generalized likelihood ratio tests of hypothesis for the parameters of the SPF

Source: Own computation result, 2018 Table 4. Generalized likelihood ratio tests of hypothesis for the parameters of the SPF

*** *** Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 5. Maximum likelihood estimate of stochastic production frontier model (House holders

*** *** Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 5. Maximum likelihood estimate of stochastic production frontier model (House holders

Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 6. Maximum likelihood estimate of stochastic production frontier model (Adopters)

Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 6. Maximum likelihood estimate of stochastic production frontier model (Adopters)

oo Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 7. Maximum likelihood estimate of stochastic production frontier model (Non-Adopters

oo Significant at 10%, 5% and 1% level of significance Source: Model output, 2018 Table 7. Maximum likelihood estimate of stochastic production frontier model (Non-Adopters

Source: Model output, 2018. Table 8. Summary statistics of estimated technical efficiencies of sample households

Source: Model output, 2018. Table 8. Summary statistics of estimated technical efficiencies of sample households

™ ™* Significant at 10%, 5% and 1%, level of significance Source: Model output, 2018. Table 9. Maximum likelihood estimates of the factors determining technical inefficiency

™ ™* Significant at 10%, 5% and 1%, level of significance Source: Model output, 2018. Table 9. Maximum likelihood estimates of the factors determining technical inefficiency

Key takeaways

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  1. 85.4% of output variability is due to technical inefficiency, impacting food security in Hadiya zone.
  2. Average technical efficiency for haricot bean producers is 59.8%, indicating significant room for productivity improvement.
  3. Adoption of improved inputs is constrained by factors like market distance, access to extension services, and soil fertility perception.
  4. Significant variables influencing technical efficiency include livestock holdings, training access, and crop diversification.
  5. 1% increase in fertilizer leads to 0.2948% increase in haricot bean output for total sample households.

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