Impact of cowpea breeding and storage research in Cameroon - IITA

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420 Table 2. Summary comparison of combined and separate estimation, ex-post and sensitivity analysis. Combined Separate Difference Relative NPV IRR IRR difference IRR NPV (%) Annual NPV (%) Annual NPV (%) (%) Annual Baseline values 100 469 4.74 6072 103 214 4.75 6 238 2745 2.73 0.01 166 Sensitivity analysis η 1 = –0.4 88 131 4.68 5326 92 525 4.70 5592 4394 4.99 0.02 266 η 2 = –10.00 628 261 0.08 –37 970 –588 763 0.44 –35 582 39 498 6.29 0.36 2387 q s = 0.4 1 456 093 10.20 88 000 1 461 715 10.22 88 340 5623 0.39 0.01 340 r = 0.25 102 198 4.75 6,176 104 870 4.76 6 338 2672 2.61 0.01 161 Supply elasticity = 0.2 866 055 7.70 52 341 875 404 7.73 52 906 9349 1.08 0.03 565 Adoption plateau = 0.25 349 294 5.83 21 110 358 461 5.87 21 664 9167 2.62 0.04 554 Two holes/grain –626 172 0.32 –37 843 –625 126 0.33 –37 780 1046 0.17 0.01 63 P2 higher 113 552 4.80 6863 115 743 4.82 6995 2191 1.93 0.01 132 SPEAPA avg † –110 673 3.62 -6689 –107 677 3.64 –6508 2996 2.71 0.02 181 DEAPA avg ‡ –656 382 –0.07 –39 669 –654 508 –0.06 –39 556 1874 0.29 0.02 113 Cowpea postharvest and socioeconomic studies † SPEAPA average yield, which is higher than DEAPA average yield. ‡ with DEAPA average yield.
Impact of cowpea breeding and storage research in Cameroon At some relatively extreme values of the parameter ranges the IRR drops below the cost of capital and the NPV is negative. If second period demand is very elastic (η 2 = –10) or if it assumed that two holes constitute a loss, the IRR is less than the opportunity cost of capital and the NPV is negative. The difference between separate and combined estimates is largest when the second period demand elasticity is large (η 2 = –10), but even then it is still less than a one percentage point difference in the IRR. The NPV is also negative when either the SPEAPA or DEAPA average area and yield are used. Given the information on elasticities, partitioning of the economic surplus into producer and consumer surplus is not particularly reliable, but it can provide some insight into potential distributional effects. Producer surplus in the separate estimate of storage impacts is negative every year after the postharvest technologies are introduced. In effect, reducing storage loss substitutes “saved” grain for produced grain. As defined in the model, producers only gain by producing and hence they lose when less total grain is needed. Of course cowpea producers are also cowpea consumers who gain every year. The profits for storage are built into the storage cost calculation. Because of the high demand elasticity in the first period, most of the impact due to yield gains from the new varieties goes to producers. In the combined estimation, producer surplus is positive after the new varieties are introduced because the production benefit from higher yield outweighs the producer surplus declines from reduced storage loss. Conclusions From a methodological perspective we have shown that the interaction of production and storage technology can have important consequences for impact assessment. Higher yields and reduced storage losses are two ways to have more usable product. The sum of their separate benefits is often greater than the combined estimate of benefits. The bias introduced by separate estimation is greatest when the demand for the product in period two is elastic and when the storage loss reduction is large. Combined estimation should be used when the bias is likely to be large, but a simpler separate estimate is still useful for other situations. For the Cameroon cowpea example, separate estimation had little effect on impact estimates. With conservative estimates of cowpea area and new technology adoption, both combined and separate analyses estimate the baseline IRR at about 5% and the NPV at about US$200 000. With a real opportunity cost of capital of about 4%, the Cameroon project is about breakeven from just the adoption in Cameroon alone. The real net gains from the research are coming from the extension of cowpea storage technologies into other areas of West Africa. As with any empirical impact assessment, the Cameroon example has numerous limitations. The cowpea area, production, yield, and price data provide a general indication of conditions in northern Cameroon, but they are not perfect. In particular, the data on trends in area and production were hard to interpret. The postharvest adoption estimates were based mainly on interviews with key informants. The new variety adoption estimates were made with the assumption that they would follow a path similar to those of BR1 and BR2. The analysis assumed linear supply and demand throughout. Changes in grain quality due to the new varieties are not included in benefit calculations. The focus was on economic impact. Environmental and human health benefits from the reduction in use of storage chemicals were not assessed. 421
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Impact of cowpea breeding and storage research in Cameroon - IITA

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