Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions

Abstract

© 2017 Elsevier Ltd Yield losses in field crops are most commonly predicted by using regression models that include either biotic or abiotic factors as predictor variables. Knowing that yield loss is a complex trait, the potential capability of regression models for predicting yield losses by using models containing both biotic and abiotic factors as predictors were estimated in this study. Biotic factors considered in regression models were: leaf rust, powdery mildew, septoria tritici blotch and tan spot occurrence on the varieties Barbee and Durumko known to have various degrees of susceptibility to obligate parasites and leaf blotch diseases. Among abiotic factors, monthly averages of temperature, relative humidity and total rainfall taken from November to June for growing seasons 2006–2013 were used as predictors. In 2014, yellow rust became the predominant pathogen over leaf rust, thus 2014 and 2015 were excluded from regression models and analyzed separately. Since a high correlation was found between abiotic and biotic factors, partial least squares regression, stepwise regression and best subsets regression were applied. Best subsets regression revealed that models consisted of both biotic and abiotic factors were more precise in estimating regression coefficients and predicting future responses. The potential yield loss predictions, conducted using these models, were regressed with actual yield losses, and high coefficients of determination (R2 = 79% for Barbee; and R2 = 63% for Durumko) were obtained. It was also evident that using more predictors in regression models does not necessarily mean that the model would have a higher potential in making yield loss predictions. This study confirms that the relationship between a disease scoring scale and yield loss is not straightforward and higher potentials for yield loss predictions were given due to the regression models using abiotic and biotic predictor variables.