Priority-Setting for Agricultural Biotechnology in West Africa USAID/EGAT March 9, 2005 William A. Masters Purdue University

DSS’S” Price Quantity J (output gain) I (input change) QQ’ K (cost reduction) Variables and data sources Market data P,Q National ag. stats. Field data J Yield change×adoption rate I Input change per unit Economic parameters K Supply elasticity (=1 to omit) ΔQ Demand elasticity (=0?) ΔQΔQ P 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

Table 1. Concordance and the allocation of R&D investment in Mozambique (1990s) Share of Agricultural GDP Share of research expenditure Research intensity ratio Cassava Maize Pulses950.5 Peanuts750.6 Sorghum Rice441.0 Cotton Cashew273.7 Sweet potato Source: Uaiene, Rafael, “Priority setting and resource allocation in the National Agronomic Research Institute, Mozambique” (Dec. 2002). Strategic targeting can be much improved through concordance…

Figure 2. Prevalence of stunting in Sub- Saharan Africa (latest available, includes sub- national data) Strategic targeting aims for large problems that are being missed by other investors

Fig. 3. Share of food production by crop, Source: Calculated from data in FAOStat (2005), reproduced in Annex 1. The biggest needs are in cereals, cassava, and oilcrops

Source: Calculated from data in FAOStat (2005), reproduced in Annex 1. Fig. 3. Share of protein output by crop, Cereals and oilcrops are especially important for food quality

Figure 5. Average yield of all cereals by region, Source: Figures 5-10 calculated from FAOStat (2005) data There are huge catch-up opportunities for Africa to do what Asia did

Figure 6. Average yield of maize by region, The catch-up opportunities are large in maize

Figure 7. Average yield of millet by region, …but catch-up opportunities are big in small grains also!

Figure 9. Average yield of cassava by region, There are huge catch-up opportunities in cassava

Figure 10. Average yield of other root crops by region, and also catch-up opportunities in other root crops

Figure 8. Average yield of seed cotton by region, Africa has already done relatively well in cotton

Figure 11. Public agricultural R&D per unit of agricultural land, (1985 PPP dollars per hectare) Africa’s lag is mainly driven by the relatively low level of R&D spending

Figure 12. Agricultural R&D intensity in West and Central Africa, There is huge variation but no growth in R&D expenditure across the region

Can build on experience of seven Sahel regional workshops ( )  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 From Priority-Setting to Capacity Building

Results and methods are well-tested across West Africa Strategic Targeting for Economic Gains