Probabilistic modelling of the dependence between rainfed crops and drought hazard

Extreme weather events, such as droughts, have been increasingly affecting the agricultural sector causing several socio-economic consequences. The growing economy requires improved assessments of drought-related impacts in agriculture, particularly under a climate that is getting drier and warmer. This work proposes a probabilistic model which intends to contribute to the agricultural drought risk management in rainfed cropping systems. Our methodology is based on a bivariate copula-approach using Elliptical and Archimedean copulas, which application is quite recent in agrometeorological studies. In this work we use copulas to model joint probability distributions describing the amount of dependence between drought conditions and crop anomalies. Afterwards, we use the established copula models to simulate pairs of yield anomalies and drought hazard, preserving their dependence structure, to further estimate the probability of crop-loss. In the first step, we analyse the probability of crop-loss without distinguishing the class of drought, and in a second step we compare the probability of crop-loss under drought and non-drought conditions. The results indicate that, in general, Archimedean copulas provide the best statistical fits of the joint probability distributions, suggesting a dependence among extreme values of rainfed cereal yield anomalies and drought indicators. Moreover, the estimated conditional probabilities suggest that the likelihood of crop-loss under dry conditions is higher than under non-drought conditions. From an operational point of view, the results aim to contribute to the decision-making process in agricultural practices.