Assessing Crop Yield and Risk: A New Method for Calculating Insurance Based on Rainfall

The aim of this paper is to explore a new method for data analysis that could be used for insurance calculations. In many agricultural nations rainfall per year and per annual quarter are good indications of the productive capacity in farmland. Essentially, there is a curvilinear relationship between rain and crop yield. The goldilocks zone varies by region and product, however, every farmer and minister of agriculture fears drought and/or flood. A Copula Quantile Regression (CQR) approach provides a novel approach to estimate the dependence of a function of the yield with respect to climate factors. This is then combined with quantile regression for nonlinear optics. This approach utilizes “Big Data” modeling and analytics to draw upon the wealth of information contained in the RICA databases. This study assesses variables such as the share of land covered by a sprinkler system, altitude, fragmentation of land, production intensity, rain, and temperature. It was found that this method provides a simpler and more flexible approach to analyze complex ecological, geological, economic, and sociological factors that impact business and commerce through risk management, strategic planning, and insurance.