Author(s):

Ankita Negi, Rajeev Ranjan and Ajay Kumar

Abstract: The stepwise regression equation method was used to assess the effect of weather parameters on productivity of soybean crop in Udham Singh Nagar district of Uttarakhand, India. The analysis was done for ten years (2007 to 2016) considering different growth stages. SPSS showed both positive and negative effect of weather variables in different months of the year. The yield and weather elements were considered as dependent and independent variables, respectively. Three regression equation models were developed to see the correctness of the model. Model 1, Model 2 and Model 3 showed R values as 0.71, 0.95 and 0.98, respectively. Model 3 showed highest accuracy. The observed and predicted yield was then compared to make model more accurate for work. The comparison of weather factors with observed soybean yield showed that increasing yield was obtained in bright sunshine hour and maximum temperature whereas minimum temperature of week decreased crop yield in all ten years. The result of SPSS version 16 showed that bright sunshine hours (BSS) of 1st week of July was the most important factor to influence crop productivity followed by average minimum temperature of 4th week of October and average maximum temperature of 1st week of November, respectively.

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