Promising agricultural technologies: What
evidence do we need on the profitability of
new technologies?
Bekele Shiferaw
CIMMYT
Evidence Summit Agenda (Detailed Draft)
June 1-2, 2011 – Washington, DC
Outline
● Current challenges in estimating profitability of new technologies
● Some evidence on the profitability of maize and other crop varieties in Africa
● Need for broader view of profitability – need to look at ‘adoptability’
● Profitability and farmer variety adoption
● How we can generate reliable evidence
– Profitability/adoptability
–Adoption constraints
CGIAR Technologies for Africa
Maize
● High yielding varieties
– Hybrids
– Open pollinated
● Drought tolerant maize
(DTM) varieties
● Quality protein maize
(QPM)
● Insect resistant maize
Other cereals (wheat, rice,
sorghum, pearl millet, etc):
● High yielding
● Pest and disease resistant
Legumes (pigeonpea, groundnut,
beans, chickpea, etc)
● High yielding
● Disease and pest resistant
Roots and tubers (potatoes,
cassava, etc)
Livestock and agroforestry
Current challenges in estimating profitability of new technologies
● Yield and cost estimation
dependent on cross-
sectional small sample
and recall surveys
● No actual measurement of
land area, yield and input
use
● Poor representation –
agro-ecology, markets etc
● Poor data on risk and
inter-seasonal yield
variation
● Panel surveys with
repeated visits
● Actual measurement of
land area, yield and key
inputs (e.g. fertilizer)
● Representative sampling
to capture variation in
agroecology, markets etc
● Capture effect of weather
and growing conditions
What we have on the on-farm yield and profitability of new DT varieties in Africa - Maize
Maize yield (tons/ha) and variance for different varieties
1.2
0.9
1.7
1.1
1.2
1.0
1.3
1.2
-0.5
0.0
0.5
1.0
1.5
2.0
Improved
(N=390)
Local
(N=521)
Improved
(N=764)
Local
(N=937)
Improved
(N=402)
Local
(N=490)
Improved
(N=475)
Local
(N=731)
Kenya Tanzania Malawi Uganda
Maize - Returns to family labor and land
Net margins for maize by variety in selected ESA countries
(US$/ha)
217
143
312
187
239
177
85 57
-700
-500
-300
-100
100
300
500
Improved
(N=390)
Local
(N=521)
Improved
(N=738)
Local
(N=934)
Improved
(N=402)
Local
(N=491)
Improved
(N=475)
Local
(N=731)
Kenya Tanzania Malawi Uganda
What we have on the on-farm yield and profitability of new varieties in Africa - Wheat
Yield of different wheat varieties in
Ethiopia (tons/ha)
2.72.4
-0.5
0.5
1.5
2.5
3.5
4.5
5.5
Improved (N=347) Local (N=638)
Pigeonpea - Returns to family labor and land
Yields of pigeonpea by variety in selected
countries (tons/ha)
0.20.2
0.4
0.6
0.20.2
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Improved
(N=262)
Local
(N=183)
Improved
(N=322)
Local
(N=1113)
Improved
(N=579)
Local
(N=363)
Kenya Tanzania Malawi
Net margins of pigeonpea by variety in selected
countries (US$/ha)
2831
150200
5164
-200
-100
0
100
200
300
400
Improved
(N=262)
Local
(N=183)
Improved
(N=316)
Local
(N=1106)
Improved
(N=579)
Local
(N=363)
Kenya Tanzania Malawi
Groundnut - yields
Yields of groundnut by variety in selected ESA countries
(ton/ha)
0.450.43
0.81
0.330.35 0.33
0.87
0.64
-0.7
-0.2
0.3
0.8
1.3
Improved
(N=314)
Local
(N=299)
Improved
(N=20)
Local
(N=210)
Improved
(N=323)
Local
(N=502)
Improved
(N=1428)
Local
(N=1002)
Kenya Tanzania Malawi Uganda
Broader view of ‘profitability’
● Adoptability of the technology is a function of market value plus other factors valued by farmers
● Other non-market traits may include:
– Risk (stress tolerance and stability of yield)
– Taste and nutrition
– Grain color
– Storage pests and storability
– Value of stover for feeding livestock
– Soil fertility restoration (e.g. legumes)
Profitability per se does not explain adoption: Seed supply, credit, information, etc
Adoption constrains
82
59
18
6 8 10
56
34
23
44
0
10
20
30
40
50
60
70
80
90
Demand (+) Adopted Lack seed
supply
Lack credit Information Profitability
Want to adopt Not know
technology
Do not want
to adopt
Pe
rce
nt fa
rme
rs
Groundnut (Uganda) Pigeonpea (Tanz)
How we can generate reliable evidence on adoption constraints for new technologies?
● Regional on-farm trials for promising varieties– Randomized control trial design
– Using local varieties as checks
– Representative systems (agroecology, etc)
– Actual measurement of yields, crop area, inputs, etc
● Technology adoption and impact studies– Panel data (plot, household, village)
– Experimental (RCTs, natural experiments) and quasi-experimental designs
– Representative and large scale