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!