Smallholder farmers’ contract farming as a response to climate change and variabilities in Ethiopia: Empirical analysis from Kofele and Adama districts of Oromia Regional State
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Date
2020-08
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Addis Ababa University
Abstract
This study aims to investigate CF as a response to climate change and climate viabilities in
Kofele and Adama Districts of Oromia Regional State. It specifically conducted to assess the
perception of SHFs’ on CF as climate change adaptation strategies (CCASs) and to assess the
impacts of CF on net-income. It also examined the factors affecting adoption of CCASs,
employed and explored the challenges and opportunities of CF in the study areas. This study
employed a concurrent or mixed research approach: quantitative and qualitative approach and
collected data from 368 households who were selected purposively and proportionately through
random sampling. The study relied on household survey, FGDs, KIIs, field observations, case
histories and secondary documents to collect data. The Likert Scale Measurement (LSM) was
used assess the perception of SHFs’ on CF as CCASs and employed Propensity Score Matching
(PSM) to assess the impacts of CF on net-income earned by SHFs’. Furthermore, the study
relied on the Multinomial Logit Model (MNL) to examine the factors of adoption of CCASs. The
Principal Component Analysis (PCA) was used to identify and examine the major challenges in
CF. Further, analysis of opportunities, challenges of CF were qualitatively described, and case
histories were used in the analysis. SPSS Version 20 Software, Stata Version 14 software,
Venizim Software 6.3 were used to enter and organize data. Moreover, the study organized and
analyzed metrological data through Microsoft Excel and used Arc GIS 10.1 for mapping the
study locations. For the perception studies, 46.2% (63) of farmers involved in Malt Barley at
Germama were belongs to average perception category followed by those 33.9% (46) and 19.9%
(27) categorized under better and poor perception category, respectively. Regarding SHFs
involved in Sugarcane outgrower, 81.5% (189) and 18.5% (43) were in poor to average
perception category, respectively. The Propensity Score Matching (PSM) and the average
treatment effect (ATT) techniques such as Nearest Neighbour Matching (NNM), Radius
Matching (RM), Kernel Matching (KM) and Stratification Matching (STM) revealed that the
sampled households’ heads that participated in CF arrangements experienced a decrease in netincome by 24.3%, 29.6%, 30.2% and 28.3% as measured by NNM, RM, KM and STM,
respectively. The overall impact of the treatment effect measurement on the net-income revealed
that the net-income of the participant SHFs CF faced a reduction of net-income by 28.1% on
average. The MNLM results revealed that age of household head, educational status of
household head, family size, livestock holding, access to credits, access to agricultural
technologies and metrological information significantly affects the adoption of various CCASs.
Moreover, the PCA results revealed that among all the variables in the five dimensions, lack of
storage facilities for agricultural produce, poor market linkages, and shortage of trainings and
other technical assistance, delay in financial services, poor pricing strategies and delay in
payments exhibited the factor loadings of 0.97, 0.92, 0.83, 0.82 and 0.72, respectively. It
explained the variation by 61.4% of the total variance in the 14 observed constructs. Therefore,
we suggested that the policy practitioners should design a policy and legal framework on CF
arrangement and scrutinize the overall CF processes. Consequently, the findings also implied
that addressing the major factors challenging CF and maximize those opportunities in CF is
essential. To this end, the concerned parties should work on strengthening the knowledge base
through education, field trainings, and proper implementation of agronomic practices and
ensuring sustainable livelihood.
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Keywords
Challenges, Contract farming, Climate change adaptation, Net income, LSM, Opportunities, PCA, PSM, MNL, SPSS, Stata.