An Agricultural Drought Risk-Assessment Model for Corn and Soybeans

An agricultural drought risk-assessment model was developed for Nebraska, USA, for corn and soybeans on the basis of variables derived from the standardized precipitation index and crop-specific drought index using multivariate techniques. This model can be used to assess real-time agricultural drought risk for specific crops at critical times before and during the growing season by retaining previous, and adding current, weather information as the crops pass through the various development stages. This model will be helpful to decision makers, ranging from agricultural producers to policy makers and from local to national levels. The results of the model validation using three different datasets show that the risk-assessment accuracy improves as the crop develops. At the end of April, before corn is planted, the average assessment accuracy rate of drought risks on final yield is 60%. At the beginning of July, when corn is at the vegetative stage, the average assessment accuracy rate reaches 76%. In late July, when corn is at the ovule stage, the rate increases to 85%. The rates are 89% in the second half of August and the end of September, when corn is at the reproduction and ripening stages respectively. The model assessment accuracy for soybeans is lower than that for corn at the same growth stages because weather has less impact on soybeans than on corn. A reliable assessment, with 80% assessment accuracy rate, begins at mid-August, when soybeans are at pod formation stage. In early September and October, when soybeans are at pod fill and ripening stages respectively, the model is able to assess risks on soybean yield with 83% and 81% accuracy rates respectively. W U , H U B B A R D , A N D W I L H I T E , I N T E R N A T I O N A L J O U R N A L O F C L I M A T O L O G Y 2 4 (2 0 0 4 ) 2