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The state and contributions of extension services to agricultural productivity

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The state and contributions of
extension services to agricultural productivity

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The state and contributions of extension services to agricultural productivity

  1. 1. ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE 1 The state and contributions of extension services to agricultural productivity Guush Berhane, Catherine Ragasa, Gashaw T. Abate, Thomas W. Assefa IFPRI ESSP EDRI Seminar, Mar 30, 2018 Addis Ababa
  2. 2. Presentation outline • Background – provide a brief overview • Conceptualization of agricultural knowledge systems • State of Ethiopia’s agricultural knowledge systems: • Extension • Research • Research – extension linkages • Agricultural productivity growth in recent years (yield, TFP, and intensification) • Empirical results: extension – technology adoption – productivity links? • Summary and implications
  3. 3. 1. Background • Sustained increase in agricultural productivity (& TFP) - prerequisite for economic transformation! • Where ever agriculture made substantial contributions to the economy, it did so following major investments by public and private sectors, and major institutional changes (Tsakok, 2011). • Public investments to improve the supply of public goods and services that give farmers the incentive to invest. • Critical among these is the provision of “an effective technology-transfer system” – effective agriculture extensions system where research and extension messages reach the majority of farmers. • Success requires decades of public investments in these services so that generations of farmers are motivated and able to increase farm productivity.
  4. 4. 1. Background • Extension services can serve as crucial vehicles of change through 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 & advisory services and delivery of modern inputs. • Ethiopia is among few African countries that heavily invested in its agriculture sector, mainly on its massive extension and input delivery system (under ADLI, PASDEP, GTPs,). • Ethiopia’s extension system is primarily tasked to deliver extension information, provide advisory services, and demonstrate new knowledge - serve as knowledge transfer vehicle. But also, on the side, serve as input delivery channel. • To what extent is Ethiopia’s investment in agriculture – particularly in its extension system - linked to recent productivity growth? • Evidence is limited regarding the extent to which such publicly provided extension system can have productivity increasing effects in Africa.
  5. 5. Research Questions • This paper contributes to filling this knowledge gap by studying the link between - extension services, adoption of modern inputs and practices, and productivity. • First, we look at what determines access to extension services. • 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)
  6. 6. Agricultural knowledge systems (Rivera et al 2005) Towards an integrated and synced knowledge system thinking: • Research & development: • Products (hybrids, varieties, Germplasm, Vaccines, kits for diagnostics, eqpt …) • Extension (technology transfer centers): • Services (consultancy, training, demonstration, Germplasm exchange, crop mgt systems, crop adaptation processes,) • Support systems: • Technical (Input delivery systems, plant and animal transformation, quality control, soil testing & diagnostics, Gene prospection & diversity identification, pest mgt, fingerprinting, agroecological zoning, traceability and certification, …) • Education: • Continuous capacity building (support the system with continued technical flow, but highly synched to local conditions and problems) 2. Conceptualization: agricultural knowledge systems RESEARCH SUPPORT SYSTEM EDUCATION EXTENSION (TT Centers) FARMERS
  7. 7. 3.1. Extension 3. State of agricultural knowledge systems in Ethiopia
  8. 8. • In principle, Ethiopia’s investment in agriculture has focused on the provision of advisory and training services; in practice, emphasis is largely on input delivery & persuasion on adoption. • Characteristically, • A public extension structure that spans from the federal ministry to the regions and down to the kebeles involving frontline extension agents (lengthy work process) • It operates within a complex & inflexible public bureaucratic structures – limited innovation. • Functions in a widely dispersed geography, heterogenous livelihoods. Regardless, the services remain largely standardized. • Implementation - begun by setting up - 25 Agricultural Technical, Vocational, Education Training (ATVET) centers around the country • Substantial progress has been made since the official government document envisioning this system came out in 2002. 3.1. Extension
  9. 9. Highest in terms of farmer-extension agent ratio: More than 65,000 DAs, (one DA per 476 (or, 21 DAs per 10,000) farmers) 21 16 6 4 3 2 0 5 10 15 20 25 Ethiopia China Indonesia Tanzania Nigeria India More than 15,000 FTCs (one in each kebele), 7,000 SMS (woreda), 4,000 Supervisors (regional offices) Source: Davis et al. (2010). Note: for Ethiopia, figures in 2016/2017 show a higher ratio, 43 DA-to- 10,000 farmer ratio.
  10. 10. Close to 11 million holders having access (about 80% of farmers); Close to 4 million ha under the “extension package” But what is access? What is “under extension package”? - 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2012/13 2013/14 advisory (number of holders) Holders (under ext-pkg) Hectares (under ext-pkg) 0 10 20 30 40 50 60 70 80 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 %offarmerswithadvisoryservices Source: CSA Ag Sample Survey
  11. 11. • 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 inflexible delivery system itself - innovation is limited, not effective, … • Moreover, DAs spend substantial amount of their time on promoting and channeling fertilizer and improved seeds to farmers; but also so many other, often, unrelated tasks! • DAs lack additional training, no injection of new knowledge, and no or very limited linkages with knowledge centers. • FTCs are under resourced, dysfunctional, or even non-existent in some kebeles 3.1. Extension
  12. 12. Number of DAs graduated from agricultural TVET collages declined significantly 0 2000 4000 6000 8000 10000 12000 14000 16000 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Plant Science Animal Science Natural Science Animal Health Cooperatives Total
  13. 13. Technologies promoted were not necessarily demand driven Technology/practices % of DAs promoted the technology during 2015/16 Was the topic (technology) requested by farmers? (%, 1=Yes) Land preparation 98.6 57.0 Seed selection 97.0 60.0 Row planting 98.0 53.0 Fertilizer application 98.2 57.4 Crop management 97.2 58.4 Post-harvest handling 96.0 57.4 Natural resource conservation 96.4 49.2 Climate smart practices 85.2 53.3 Market linkages 75.5 57.7 Source: Digital Green DA Survey (2016).
  14. 14. No wonder farmer satisfaction levels are not that high Source: AGP Survey (2011, 2013), two rounds 2011 2013 The service provided by the DAs was satisfactory? Strongly agree 62.5 58.7 Agree 35.5 39.6 Disagree 1.1 1.3 Strongly disagree 0.7 0.4 Observation 7381 7381 Relates to services provided in the last 12 months (in terms of individual visits and demonstrations in group)
  15. 15. A typical week of a DA in Ethiopia – DAs are overburdened Source: EEA/IFPRI DA Survey, 2009 WORK LOAD! DA’s are extremely busy and overloaded, including work they are not supposed to do! - credit, - supervising other projects, - distribute inputs, - collecting data
  16. 16. 3.2. Research
  17. 17. The research system landscape There are 62 Federal and Regional Agricultural Research Centers (excluding university research institutes) Well-spread across the various agro-ecologies of the country
  18. 18. 2,768.5 (FTE, full time equivalent) researchers, in 2014 Number of agricultural researchers has been increasing, mostly gov’t at gov’t research institutes
  19. 19. 7.5 FTEs per 100,000 farmers, in 2014 0.24% as a share of AgGDP, in 2014 Number of agricultural researchers per 100,000 farmers has increased but spending as share of AgGDP has declined
  20. 20. 0.2 1.2 2.3 2.4 2.9 3.4 5.1 5.8 6.7 6.9 6.9 7.7 10.3 12.5 13.1 14.5 15.3 15.6 21.3 22.8 26.9 28.1 28.2 35.2 36.5 37.9 38.8 39.6 43.4 45.9 48.5 51.3 82.1 103.9 127.3 152.5 197.4 274.1 417.4 433.5 0.0 100.0 200.0 300.0 400.0 500.0 Guinea-Bissau Gabon Cabo Verde Lesotho Eritrea Central African Rep. Gambia, The Congo, Rep. Liberia Swaziland Togo Guinea Madagascar Chad Burundi Niger Sierra Leone Mauritania Botswana Benin Zambia Malawi Mozambique Mauritius Congo, Dem. Rep. Mali Namibia Rwanda Zimbabwe Cameroon Burkina Faso Senegal Cote d'Ivoire Tanzania Ethiopia Uganda Ghana Kenya South Africa Nigeria Public research spending by country, 2014 (million constant 2011 PPP dollars) 0.0 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.8 0.8 0.9 0.9 1.0 1.0 1.0 1.0 1.1 1.4 2.8 2.9 3.1 5.9 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Guinea-Bissau Chad Gabon Madagascar Central African Rep. Togo Nigeria Niger Ethiopia Sierra Leone Tanzania Guinea Eritrea Cameroon Congo, Dem. Rep. Mali Mozambique Congo, Rep. Burundi Mauritania Zambia Liberia Benin Malawi Cote d'Ivoire Rwanda Kenya Gambia, The Swaziland Lesotho Cabo Verde Uganda Ghana Burkina Faso Senegal Zimbabwe South Africa Botswana Namibia Mauritius Ag R & D spending as share of AgGDP, (%), 2014
  21. 21. -1.3 -1.1 -0.8 -0.5 -0.4 0.3 0.6 1.0 1.1 1.2 1.2 1.4 1.5 1.5 1.6 3.3 3.5 4.0 4.5 4.5 4.7 4.8 5.1 5.3 5.7 5.8 5.9 8.0 8.1 9.3 10.1 10.5 11.4 13.5 13.9 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 Malawi Cabo Verde Swaziland Gabon Congo, Rep. South Africa Zambia Mali Ghana Guinea Mauritius Madagascar Mozambique Lesotho Kenya Namibia Benin Senegal Gambia, The Rwanda Tanzania Togo Congo, Dem. Rep. Zimbabwe Botswana Nigeria Cote d'Ivoire Burundi Burkina Faso Uganda Mauritania Niger Chad Ethiopia Sierra Leone Growth rate (%) of # researchers, 2010 - 2014 0.0 7.2 10.6 10.8 11.2 11.2 12.1 13.1 13.7 13.8 14.0 14.1 14.1 14.2 15.3 16.6 17.2 17.6 19.4 20.0 22.1 22.5 23.7 23.8 32.8 34.1 35.9 36.4 36.7 40.1 40.8 40.8 46.3 46.6 48.1 51.6 52.5 71.3 71.7 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 Guinea-Bissau Ethiopia Liberia Eritrea Mozambique Cabo Verde Lesotho Gambia, The Sierra Leone Namibia Central African Rep. Mauritius Zambia Rwanda Guinea Congo, Dem. Rep. Mauritania Zimbabwe Chad Burundi Gabon Botswana Nigeria Tanzania Malawi Uganda Kenya Congo, Rep. Togo Niger Ghana Cameroon Madagascar Swaziland Benin Mali Burkina Faso Cote d'Ivoire Senegal Share of researchers with PhD to total (FTEs), %, 2014
  22. 22. 3.3. Research – Extension Links
  23. 23. Period Program/Event Objectives/Highlights What happened? 1952- 1965 Integrating education, research and extension Formal research and extension service was started in 1952 (Agricultural and Technical School at Jimma and the College of Agriculture and Mechanical Arts) The system was modeled after the US land grant university system. 1966 Establishment of the Institute of Agricultural Research (IAR) Research was divorced from education and extension without setting a mechanism for coordination of research and extension. A linear research-extension-farmer model was adopted. 1974- 1977 Extension Project Implementation Department of MoA of IAR Joint IAR/EPID program was mainly initiated for agricultural technology package testing and formulation of research recommendations Program discontinued in 1977 due to budget problems and reinitiated in 1980/81, but was not successful due to various reasons. 1980s Farming System Research (FSR) research-extension linkage Conducted multidisciplinary surveys and focus on providing feedback to researchers on the characteristics of technologies, information on farmers’ problems, formulating recommendations appropriate to smallholder farmers, and generating useful recommendations for policymakers Follow the FSR model, but the program was found to be expensive and time consuming and phased out as project funds run out. 1985/86 Research-Extension Liaison Committee (RELC) was established under Research– Extension Division (RED) of IAR The RELC was established at zonal and national levels: review and approve research proposals and extension recommendations, identify training needs for SMSs, and oversee research-extension and farmer linkage; and national level to give overall policy direction. RELC was largely ad hoc—i.e. it didn’t have any legal status, which affected its decision-making power and institutionalizing accountability among members. Late 1990s Research-Extension-Farmers Linkage Advisory Council (REFAC) REFAC was organized at national, regional and research center levels but run by EARO. REFAC provided overall guidance to research and extension programs, and oversight of the linkage between the two activities. It was mainly funded by the World Bank. REFAC did not produce strong linkage as expected, mainly due to lack of clarity on actor’s responsibility. 2008/09 Agriculture Development Partners Linkage Advisory Council (ADPLAC) ADPLAC was organized at national, regional, zonal, and woreda levels. Mainly focused on evaluating targets. Like REFAC, it was mainly funded by the World Bank. ADPLAC - a first attempt to institutionalize the linkages through allocation of regular finance and accountable institutional setup within the MoA, but still an ad-hoc. Historical evolution of research-extension-farmer linkages in Ethiopia: all ad hoc, unsystematic, and project funded
  24. 24. 5. Ag. productivity growth
  25. 25. 25 Cereal yield (quintals/ha) has increased substantially but from a low base 14.2 19.4 34.3 8.5 11.5 15.8 0 5 10 15 20 25 30 35 40 Qt/ha Maize Wheat Barley Teff Annual average growth rate (levels, Qt/ha): Maize: 23.4; Wheat:17.5; Barely: 13.9; Teff: 11.6
  26. 26. 26 Maize and wheat yield levels (mt/ha) and growth rates, selected countries, 2004-2013 Period China Egypt Ethiopia Kenya USA Maize 2004 5.1 7.9 1.6 1.9 10.1 2013 6.2 7.2 3.2 1.6 10.0 Annual average growth (%) 2.3 -1.0 6.2 -1.8 -0.1 Wheat 2004 4.3 6.6 1.5 2.5 2.9 2013 5.1 6.7 2.4 3.0 3.2 Annual average growth (%) 2.1 0.2 5.4 2.5 1.0 • Substantial growth rate in maize and wheat yield but started from a low base; • Yield-level still one of lowest, … way to go!
  27. 27. 28 Growth in area cultivated and yield 3.7 1.2 -2 0 2 4 6 8 10 12 Percent Area Yield • Growth in crop output driven by cultivated area expansion and yield.
  28. 28. 6. Results: Links between extension, adoption, and productivity
  29. 29. • Data, • Several data sources: AGP, IFPRI/EEA, Digital Green, CSA, … • Regression: use AGP - a unique and large panel (2011 and 2013) dataset covering the most important agricultural potential zones of Ethiopia • Methodologically, • 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 facing the incidental parameter problem 6.1. Data and methodology
  30. 30. 6.2. Access to extension is not wealth and gender neutral: Literate, wealthy, male farmers, are significantly more likely to have access to extension than illiterate, poor women farmers Advised Advised abt. fertilizer Advised abt. Land Prep. 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 Note: Access to extension equations estimated based on CRE approach, logit model
  31. 31. 6.5. Adoption of chem. fertilizers is associated with extension, but not improved seeds 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 Mass media: Household gets information about production methods and technologies from radio (=1) 0.011 0.077 -0.037 0.089 Zonal Dummies Yes Yes Observation 21088 21423 Note: Adoption of fertilizer and improved seed equations, estimated based on CRE approach
  32. 32. 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 Mass media: Household gets information about production methods and technologies from radio (=1) 0.264 ** 0.104 0.165 0.104 Zonal Dummies Yes Yes Observation 10487 10487 Note: Adoption of farmers’ advices on new production methods, estimated based on CRE approach 6.6. Adoption of (basic) new production methods is associated with extension
  33. 33. 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 Mass media: Household gets information about production methods and technologies from radio (=1) 0.247 ** 0.1 0.053 0.234 Zonal Dummies Yes Yes Observation 21090 17141 Note: Adoption of row plating and irrigation equations, estimated based on CRE approach 6.7. Adoption of row planting is associated with extension, but not irrigation
  34. 34. 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) 0.000 0.017 0.015 0.032 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) 0.010 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) 0.010 0.013 0.015 0.02 Controlled for fertilizers, improved seeds, irrigation, herbicides All significant Some significant 6.8. Extension is not associated with productivity once we control for the effect of extension on modern input adoption
  35. 35. • We find strong 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. • We also find positive and significant association between extension and productivity but disappears when we control for the effect of adoption on productivity. • Thus, we don’t find any direct effect of agricultural advisory services on productivity. • But, we find that advisory services enhance productivity through improving adoption of agricultural technologies like fertilizer and row planting. • Regarding access: extension is not wealth and gender neutral • Robustness checks: we also run (other than CRE) Multivariate Probit (MVP) for joint adoption of fertilizers and improved seeds; Estimated OLS, FE models, … results remained consistent. 7. Summary
  36. 36. • These results are plausible, given Ethiopia’s AES system is geared towards conveying these inputs to farmers and has limited capability of conveying critical knowledge-based support to farmers. • These results are broadly consistent with earlier studies by Krishnan and Patnam (2014), which used a panel dataset and show that the impact of DA extension wears off over time as farmers are learning more from other farmers after some time. • Adoption of these fundamental inputs has been instrumental to recent productivity increases and will continue to be important insofar as Ethiopia’s agriculture production system starts from a rather low base. • To that extent, the essential role that DAs place in channeling inputs to farmers will remain critical. However, further gains in agricultural productivity would have to come through significant improvement of the existing input supply-led extension system, upgrading it to one that is knowledge-driven and addresses some of the complex problems farmers face. 7. Summary
  37. 37. • These findings indicate three key constraints that play against the greater contributions of AES to productivity growth and agricultural transformation. • First, with limited institutional innovations and poor coordination with research centers - hence the limited injection of new knowledge into the system - the DAs are left with little leverage to convince lead and other farmers. • Second, the fact that DAs are overburdened by activities beyond their regular mandates provides little time for them to search for additional knowledge and information. While the current system can be commended for being one of the highest in terms of DA-to-farmer ratio, it is overly standardized (one- size-fits-all) and lacks the flexibility to adapt to local conditions. • Third, the efficacy of FTCs is also constrained because they are generally under-resourced and scattered, with little focus and scale. While evidence suggests that there has been a substantial increase in the number of farm households reached by the system, these constraints negate sustaining future gains. • It is unlikely, therefore, that the increased farmers’ access to the system, as it is now, can be translated into productivity gains. Institutional innovations require the channeling of new knowledge to extension agents, with a strong link between extension, knowledge systems and other support systems. 8. Implications
  38. 38. Thank you for your time!

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