Integrated assessment of crop productivity based on the food supply forecasting

Abstract

Climate change scenarios suggest that long periods without rainfall will occur in the future often causing instability of the agricultural products market. The aim of the research was to build a model describing the amount of precipitation and droughts for forecasting crop yields in the future. In this study, the authors analysed a non-standard mixture of gamma and one point distributions as the model of rainfall. On the basis of the rainfall data, one can estimate the parameters of the distribution. The parameter estimators were constructed using the method of the maximum likelihood. The obtained rainfall data allow confirming the hypothesis of the adequacy of the proposed rainfall models. Long series of droughts allow one to determine the probabilities of adverse phenomena in agriculture. Based on the model, the yields of barley in the years 2030 and 2050 were forecasted which can be used for the assessment of other crops productivity. The results obtained with this approach can be used to predict decreases in agricultural production caused by the prospective rainfall shortages. This will enable decision makers to shape effective agricultural policies in order to learn how to balance the food supplies and demands through an appropriate management of the stored raw food materials and the import/export policies.

Keywords

• climate changes
• decision-making tools
• estimation of parameters
• forecasted outputs
• gamma distribution
• predicting yields