Priority-Setting for
Agricultural Biotechnology
in West Africa
USAID/EGAT
March 9, 2005
William A. Masters
Purdue University
The economic gains from new technology
are proportional to output before adoption (PxQ)
times the probability of cost reduction (“K”)
Figure 1. Economic impact assessment in one picture
S
S’
S”
Price D
Variables and data sources
J (output gain)
P
K ΔQ
(cost reduction)
Field data
J
Yield change×adoption rate
I
Input change per unit
I
(input change)
Q
Market data
P,Q National ag. stats.
Q’
Economic parameters
K Supply elasticity (=1 to omit)
ΔQ Demand elasticity (=0?)
Quantity
Strategic targeting can be much
improved through concordance…
Table 1. Concordance and the allocation of R&D investment
in Mozambique (1990s)
Share of
Share of
Research
Agricultural
research
intensity
GDP expenditure
ratio
Cassava
44
15
0.3
Maize
16
12
0.7
Pulses
9
5
0.5
Peanuts
7
5
0.6
Sorghum
6
10
1.6
Rice
4
4
1.0
Cotton
2
15
6.4
Cashew
2
7
3.7
Sweet potato
1
14
14.2
Source: Uaiene, Rafael, 2002. “Priority setting and resource allocation in the
National Agronomic Research Institute, Mozambique” (Dec. 2002).
Strategic targeting aims for large problems
that are being missed by other investors
Figure 2. Prevalence
of stunting in SubSaharan Africa
(latest available,
includes subnational data)
Source: Redrawn from data compiled by the FAO’s
Poverty and Food Security Mapping Project, using the most
recent Demographic and Health Survey (DHS) data from
ORC Macro, Multiple Indicator Cluster Survey (MICS) data
from UNICEF, WHO survey data and national government
estimates.
Note: Data shown are the percentage of children aged 0-5
whose height for age is at least two standard deviations
below the NCHS standard for their age.
The biggest needs are in cereals,
cassava, and oilcrops
Fig. 3. Share of food production by crop, 1961-2002
Source: Calculated from data in FAOStat (2005), reproduced in Annex 1.
Cereals and oilcrops are especially
important for food quality
Fig. 3. Share of protein output by crop, 1961-2002
Source: Calculated from data in FAOStat (2005), reproduced in Annex 1.
There are huge catch-up opportunities
for Africa to do what Asia did
5
W.Afr.Excl.Nigeria
Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
Rest of the World
4
3
2
Source: Figures 5-10 calculated from FAOStat (2005) data
2000
1995
1990
1985
1980
1975
1970
0
1965
1
1960
Average yield of all cereals (mt/ha) .
Figure 5. Average yield of all cereals by region, 1961-2004
The catch-up opportunities
are large in maize
Figure 6. Average yield of maize by region, 1961-2004
W.Afr.Excl.Nigeria
Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
2.0
1.5
2000
1995
1990
1985
1980
1975
1970
0.5
1965
1.0
1960
Average yield of maize (mt/ha) .
2.5
…but catch-up opportunities
are big in small grains also!
Figure 7. Average yield of millet by region, 1961-2004
W.Afr.Excl.Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
2000
1995
1990
1985
1980
1975
1970
0
1965
1
1960
Average yield of millet (mt/ha) .
2
There are huge catch-up
opportunities in cassava
Figure 9. Average yield of cassava by region, 1961-2004
W.Afr.Excl.Nigeria
Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
Rest of the World
25
20
15
10
2000
1995
1990
1985
1980
1975
1970
0
1965
5
1960
Average yield of cassava (mt/ha) .
30
Figure 10. Average yield of other root crops
by region, 1961-2004
20
W.Afr.Excl.Nigeria
Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
15
10
2000
1995
1990
1985
1980
1975
1970
0
1965
5
1960
Average yield of other roots and tubers (mt/ha) .
and also catch-up opportunities
in other root crops
Africa has already done
relatively well in cotton
3
W.Afr.Excl.Nigeria
Nigeria
Central Africa
RestOfSub-Sah.Afr.
South Asia
Rest of the World
2
2000
1995
1990
1985
1980
1975
1970
0
1965
1
1960
Average yield of seed cotton (mt/ha) .
Figure 8. Average yield of seed cotton by region, 1961-2004
Africa’s lag is mainly driven by the
relatively low level of R&D spending
Figure 11. Public agricultural R&D per unit of agricultural
land, 1971-91 (1985 PPP dollars per hectare)
.
There is huge variation but no growth
in R&D expenditure across the region
Figure 12. Agricultural R&D intensity
in West and Central Africa, 1971-2001
Agricultural R&D Intensity in West and Central Africa, 1971-2001
10
Cape Verde
Senegal
Mali
Cote d'Ivoire
Mauritania
Guinea
Ghana
Togo
Burkina Fas
Niger
Nigeria
1
00
0
00
2
8
99
6
99
99
2
99
4
99
0
8
98
6
98
98
2
98
4
98
0
8
97
97
4
97
6
0
97
0
97
2
Public R&D (1993 US$/per ha of arable land)
100
From Priority-Setting to Capacity Building
Can build on experience of seven Sahel regional
workshops (1994-2002)
 all participants use common spreadsheet methods
• formulas derived directly from graphical model
• using each kind of data in sequence for intermediate results
• with “open architecture” to facilitate adaptation
 participants have access to small grants
• to implement priority-setting exercises
• to report their results at follow-on workshops
Strategic Targeting for Economic Gains
Results and methods are well-tested across West Africa