Feasibility study of rainfed barley annual yield prediction based on different drought indices

Background and Objectives: At the arid and semi-arid climates which the rainfed farming has a high degree of importance, it is essential to evaluate both the effective factors on the rainfed crop yield and its predicting as well. In this respect, it is unavoidable to have a special consideration for some tolerable crops such as barley. The aim of the current study was to assess the possibility of rainfed barley annual yield prediction using some drought indices at a semi-arid climate.
Materials and Methods: By calculating SPEI, EDDI and SPI drought indices for four growth stages of rainfed barley including sowing-emerge, emerge-tillering, tillering-stem and stem-flowering at the Sararoud-Kermanshah station, a number of 12 time series of these indices were extracted during 2000-2015 period. The cross-tabulation was used to evaluating the overall relationship between drought indices and rainfed barley annual yield and to modeling rainfed barley annual yield based on drought indices, the best subset-based multiple linear regression model and Principal Component Regression (PCR) were applied at two different stages. The modelling procedure performed in two overall cases including considering a unique drought index and considering a combination of three different drought indices cases.
Results: the results of cross-tabulation technique showed an appropriate relationship between rainfed barley annual yield and drought indices. Therefore, a potential is available to use drought indices to predict rainfed barley annual yield. Based on the results of considering a unique drought index case, the highest (63.6%) and lowest (54.1%) values of coefficient of determination between rainfed barley annual yield and drought indices were for SPEI and EDDI indices, respectively and a value between them (62.4%) for SPI. The values of these indices were appeared at the model during the sowing-emerge and tillering-stem stages for SPEI and SPI and sowing-emerge and stem-flowering stages for EDDI. By considering the combination of three different drought indices case, the results revealed that the best multiple linear regression model is obtained by presence of SPEI (during tillering-stem and stem-flowering stages) and EDDI (during sowing-emerge and tillering-stem stages) indices in the model with a good coefficient of determination (R2= 78.7% and R2adj= 69.2%). However, the high value of Variance Inflation Factor (VIF) revealed that it is necessary to solve this issue by considering the Principal Component Regression (PCR) model. By applying PCR model to predict rainfed barley annual yield, the coefficient of determination for the PCR (78.2%) showed a negligible decrease compared to the multiple regression model. However, the adjusted coefficient of determination properly improved to 71.7%. By considering the PCR model as the final model of predicting rainfed barley annual yield, the cross-validation results of this model led to obtaining R2=58.5% and RMSE=572.3kg/hec (equal to 22% of the mean of annual yield).
Conclusion: The overall results of this research showed that applying different drought indices could lead to increasing the explained variance of rainfed barley annual yield. The overall results of this research showed that the occurrence of drought during the emerge-tillering stage does not have a considerable impact on the rainfed barley annual yield. With respect to the higher role of the tillering-stem stage in the regression models, this stage was detected as the most important effective period on the rainfed barley annual yield. Therefore, among different growth stages, occurring drought in the tillering-stem period is expected to lead to a lesser amount of annual yield.