The state and contributions of extension services to agricultural productivity

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. 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. 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. 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. 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. 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. 3.1. Extension
3. State of agricultural knowledge systems in Ethiopia

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. 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. 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. • 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. 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. 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. 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. 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. 3.2. Research

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. 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. 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. 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. -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. 3.3. Research – Extension Links

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. 5. Ag. productivity growth

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
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. 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. 6. Results:
Links between extension, adoption, and productivity

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. 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. 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. 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. 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. 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. • 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. • 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. • 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. Thank you for your time!