PJB-2019-867
Investigating wheat yield and climate parameters regression model based on Akaike information criteria
Sardar Sarfaraz, Syed Shahid Shoukat and Tariq Masood Ali Khan
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
To Cite this article:
Sarfaraz, S., S.S. Shoukat and T.M.A. Khan. 2021. Investigating wheat yield and climate parameters regression model based on Akaike information criteria. Pak. J. Bot., 53(4): DOI: http://dx.doi.org/10.30848/PJB2021-4(26)
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