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The purpose of the current study is to provide a spatial climate-based model to measure the performance of apple tree via geographical weighted regression model. To this end, cognitive climatic factors were selected on 9-year spans (1996-2005) in line with statistical length of apple tree performance in the stations under study. Thereafter, these factors were entered into geographic information systems in the forms of positioning data layers. Spatial climate-based models were produced with the minimum variance of inflation factor by means of the application of climatic elements with the maximum spatial correlation as independent variables. The optimal model of performance was developed according to the statistical indexes of coefficient of determination, the lowest value of remains, and the lowest percentage of error. The output of the final model indicates that the most important factor in explaining the yield performance of apple tree is the average temperature of December whose role in the North and North West of our country is contrary to South and South East directions and it has a direct relationship with apple performance. Average minimum temperature in March has the second preference in the apple performance whose positioning role is negative in north, west and northeast of the country is direct and in the southern half of the country is opposite. Finally, the local coefficients of total sunshine hours was chosen as the third effective variable. The coefficient of determination (R2) of the model with maximum space explained.

Keywords: climate, Apple, Yield, geographically weighted regression, Iran

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