The state of Ethiopia’s agricultural extension system and effects on modern input use and...
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ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE
The state of Ethiopia’s agricultural extension system and effects on modern input use and productivity
By Thomas WolduWith Guush Berhane and Fanaye Taddese
Ethiopian Economics Association (EEA) 14th International Conference on the Ethiopian Economy
July21-24, 2016Addis Ababa
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1. Introduction• Agricultural productivity and rural incomes - key for economic growth and
transformation in poor agrarian economies
• Modern agricultural technologies, as in the Asian Green revolution, is critical
• Public investments on agricultural extension services are justified on the grounds of high, long-term, returns • Extension services - serve as crucial vehicles - linking agricultural knowledge centers
to farmers, as well as conveying modern inputs
• Recently, - enormous interest to investing significant portion of national budgets on agriculture; mainly on extension services and delivery of modern inputs • Ethiopia is one - few African countries - invested > 10 percent of GDP in agriculture
set by the Comprehensive Africa Agriculture Development Program commitment
1. Introduction• Mixed evidences – if these services achieved intended goals –mainly in terms of
key knowledge transfers and productivity increases• Due to - lack of suitable observational data to identify impacts; or - the complexities
associated with the nature of extension services preventing experimental studies
• Generally, focus of extension services - has primarily been the delivery of modern inputs than conveying critical knowledge and skills needed
• This paper contributes to filling this knowledge gap by studying the link between -extension services, adoption of modern inputs, and productivity • First, we tried to see what determines access to extension• Second, we provide evidence - direct (through conveying new knowledge) and indirect
(through promoting modern inputs) effects of extension on productivity. • Third, we also provide evidence on the effect of extension through farmer to farmer
interaction mechanism (recently started in Ethiopia)
• Ethiopia’s investment in agriculture has focused on the provision of ‘advisory and training services’ • A public extension structure that spans from the federal ministry to the regions and
down to the kebelles through frontline extension agents
• Implementation -begun by setting up - 25 Agricultural Technical, Vocational, Education Training (ATVET) centers around the country • Which by 2010 trained close to 45,000 DAs, specializing in crop, livestock and natural
resources
• These DAs were deployed to 8,489 Farmer Training Centers (FTCs) established throughout the country - one of the largest DA to farmer ratio in the world
2. State of the extension system
• There is also a plan to further expand the service by employing more and more development agents during the 2nd GTP
• Substantial progress has been made since the official government document envisioning these current extension system came out in 2002
• Despite the progress made along this line, owing to its scale, the extension system has faced many challenges;• Related to the quality of service delivered and the delivery system itself
• In practice, the DAs spend substantial amount of their time on promoting and channeling fertilizer and improved seeds to farmers
2. State of the extension system
• Data, • A unique and large panel (2011 and 2013) dataset covering the most important
agricultural potential zones of Ethiopia
• Methodologically,
• We estimated the following three models;• Access to extension = f(HH, Farm, Community)
• Adoption of MT= f(Ext, HH, Farm, Community )
• Productivity = f(Ext, MT, HH, Farm, Community)
• We mainly used CRE, the Correlated Random Effects, approach - exploits the panel nature of the data to remove selection bias due to time-invariant heterogeneities
• CRE does what FE model can do with an additional attraction of allowing us to do the estimation without having to enter into incidental parameter problem
3. Data and methodology
4. ResultsAccess to extension
Advised Advised abt. fertilizer Advised abt. Land Pr. & Pl.
Explanatory Variables Coff. SE. Coff. SE. Coff. SE.
Household head is literate (=1) 0.354 *** 0.084 0.354 *** 0.084 0.319 *** 0.082
Household head is male (=1) 0.266 0.191 0.414 ** 0.194 0.33 * 0.189
Wealth quantile 2 0.186 ** 0.073 0.222 *** 0.076 0.224 *** 0.074
Wealth quantile 3 0.177 ** 0.084 0.25 *** 0.087 0.236 *** 0.085
Wealth quantile 4 0.437 *** 0.101 0.563 *** 0.104 0.563 *** 0.102
Wealth quantile 5 0.54 *** 0.123 0.609 *** 0.126 0.603 *** 0.123
Cultivated land size in hectare 0.205 *** 0.063 0.252 *** 0.064 0.228 *** 0.063
Cultivated land size in hectare squared -0.02 ** 0.008 -0.023 *** 0.008 -0.02 ** 0.008
Year dummy Yes Yes Yes
Zonal Dummies Yes Yes Yes
Constant -224.121 ** 87.899 -205.246 ** 89.674 -253.098 *** 87.267
N 19607 19584 19909
Access to extension equations estimated based on CRE approach, logit model
• In summary; literate, wealthy male farmers are significantly more likely to have access to extension than illiterate, poor women farmers.
4. ResultsExtension vs input use
Fertilizer Improved seed
Coff. SE. Coff. SE.
Farmer advices
Household gets advice on when and how to use fertilizer (=1) 0.236 *** 0.064
DA's advice
Household gets advice on how to use fertilizer (=1) 0.573 *** 0.067
Household gets advice and assistance to use improved seed (=1) 0.014 0.134
Household believes DA's do their best to help farmers(=1) -0.065 0.063 0.03 0.078
Zonal Dummies Yes Yes
Observation 21088 21423
Adoption of fertilizer and improved seed equations, estimated based on CRE approach
4. ResultsExtension vs input use
New production methods Planted new crop
Coff. SE. Coff. SE.
Farmer advices
Household gets advice on planting and harvesting (=1) 0.674 ** 0.264
Household advised to plant new crop (=1) 1.522 *** 0.115
Farmers' advice is not from neighbors (=1) 0.289 ** 0.137 0.248 ** 0.114
Farmers' advice is from neighboring plots (=1) 0.092 0.107 0.25 ** 0.099
DA's advice
Household gets advice on planting (=1) 0.238 ** 0.1 0.148 0.099
Household believes DA's do their best to help farmers 0.051 0.096 -0.167 * 0.096
Zonal Dummies Yes Yes
Observation 10487 10487
Adoption of farmers’ advices on new production methods, estimated based on CRE approach
4. ResultsExtension vs input use
Row planting Irrigation
Coff. SE. Coff. SE.
DA's advice
Household gets advice on planting (=1) 0.268 *** 0.088
Household believes DA's do their best to help farmers (=1) 0.026 0.082 0.224 0.22
Zonal Dummies Yes Yes
Observation 21090 17141
Adoption of row planting and irrigation equations, estimated based on CRE approach
• In summary, there exists positive and significant association between extension (both from farmers and development agents) and adoption of modern technologies like fertilizer and row planting but not with improved seed and irrigation .
4. ResultsExtension vs productivity
Explanatory Variables All sample Young farmers
Coff. SE. Coff. SE.
DAs’ advice:
Household gets advice on land preparation or planting (=1) 0.008 0.016 0.003 0.03
Household gets advice on how to use fertilizer (=1) 0.005 0.017 0.013 0.03
Household gets advice and assistance to use improved seed (=1) 0 0.017 0.015 0.032
Household believes DA's do their best to help farmers 0.011 0.009 0.039 ** 0.016
Farmers' advice:
Household advised to plant new crop (=1) -0.006 0.013 -0.019 0.023
Household gets advice on planting and harvesting (=1) -0.006 0.031 0.011 0.059
Household gets advice on when and how to use fertilizer (=1) 0.01 0.017 0.015 0.03
Farmers' advice is from neighbors (=1) 0.009 0.017 0.018 0.03
Farmers' advice is from neighboring plots (=1) 0.01 0.013 0.015 0.022
All sample Young farmers
Coff. SE. Coff. SE.
New agricultural technologies and modern inputs:
Household used fertilizer (=1) 0.031 ** 0.013 0.048 ** 0.025
Household used improved seed (=1) 0.023 * 0.014 0.055 ** 0.024
Household implemented row planting (=1) 0.038 ** 0.018 0.006 0.033
Household used irrigation (=1) 0.061 * 0.036 0.055 0.067
Amount of pesticide used in liters 0 0 0.015 *** 0.004
Amount of herbicide used in cubic meters ('000 of litters) 0.259 ** 0.111 0.001 0.193
Constant -15.448 14.584 -32.729
Observation 19203 5937
Adjusted R2 0.21 0.218
4. ResultsInput use vs productivity
• Agricultural advisory services (both from farmers and development agents) are found - significantly associated with adoption of modern technologies
• We don’t find any direct effect of agricultural advisory services on productivity.
• But, we found that advisory services enhance productivity through improving adoption of agricultural technologies
• Why? Because; • First, in practice, the DAs spend substantial amount of their time on promoting and
channeling fertilizer and improved seeds to farmers• Second, DAs are not injected with new techniques, skills and technologies dynamically
and continuously, the system is not knowledge base
• The extension system should be dynamically supported by evidence based knowledge and continuously injected with new techniques, skills and technologies
• DAs should be directed in transferring knowledge from research centers to farmers
5. Conclusion and policy implications
• Literate, male and wealthy farmers are significantly more likely to have access to extension than illiterate, women and poor farmers• First, education is the key to reach more farmers through the extension system in
place.
• The current expansion in education is the right path. Creating literate farmers is the base to disseminate any knowledge acquired through the extension system
• Second, the extension system should be made Gender sensitive
• Third, wealth is found to determine access to extension because;
• DAs know wealthy farmers take risk and adopt new modern technologies
• The poor needs to be supported by some kind of insurance mechanism so that they take risk in adopting modern agricultural technologies
• Wealthy farmers have the cash to spend on modern inputs.
• Efficient credit system should be in place, so that poor, credit constrained, farmers can buy these modern inputs and repay their loan after harvest
5. Conclusion and policy implications
Thank you for your time!