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Investigating wheat yield and climate parameters regression model based on Akaike information criteria
Abstract
Wheat is a staple food of Pakistan and a central commodity of world food security. Wheat yield production is likely to be affected adversely (or positively at some places) in a changing climate scenario and ever-increasing demand due to burgeoning world population and may lead to a growing food security issue because of changing climate. This study investigated the co-variability of wheat yield production in Pakistan with the principal climate parameters, precipitation and temperature, through a linear regression method by adopting the Akaike Information Criteria (AIC)-based best model selection strategy, for given data over 51-year period. Employing the AIC technique on twenty different combinations of seasonal aggregates of rainfall, seasonal mean temperature, seasonal minimum and maximum temperatures, the investigation revealed that the model containing a combination of seasonal-minimum temperature and seasonal-mean temperature is the best model for wheat yield production followed by 7 equally adequate models with different combinations of climate parameters from the data. Hence, seasonal-averaged minimum and mean temperatures proved to be the best-fit regressors deduced by the AIC-based criterion
