Among many other options, seasonal weather forecasts and the use of cultivars that are better adapted to local climate and climate variability have been discussed as two potential supporting measures to assist farmer adaptation to climatic variability and change. In this article, we eval-uate the potential benefit of combining these two measures, i.e., choosing specific crop varieties based on seasonal forecasts - focusing on the Central Rift Valley in Ethiopia. We base our value of information analysis on the available records of field trial data for publicly released crop varieties. We find that experimental evidence must be extended and improved in order to provide reliable evidence of yield performance differences between crop varieties, which is an essential prereq-uisite for exploiting forecast information. Classification of cumulative seasonal rainfall based on the modified Rainfall Anomaly Index provides a sharper distinction than using the standard tercile-based approach employed in the Ethiopian seasonal forecast communication. Even with a fairly optimistic interpretation of the evidence with respect to exploitable yield differences, we find only modest benefits of seasonal forecasts at realistic forecast accuracy for the region. Given the empirical limitations when assembling long-run yield data, the presented results have to be understood as a first approximation. Apart from sufficiently accurate forecasts, success of forecast-based cultivar choice will depend on (i) more reliable evidence on performance differ-ences between crop varieties under different weather conditions, and (ii) changes in the current seed breeding and distribution system in developing countries, because the full potential of high-accuracy seasonal forecast information could only be tapped, if forecast-matching cultivars are being made available to farmers in time.
Kayamo, SE; Troost, C; Yismaw, H; Berger, T
The financial value of seasonal forecast-based cultivar choice: Assessing the evidence in the Central Rift Valley of Ethiopia
Climate Risk Management