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The concept of pre-harvesting of crop yield using nonlinear growth models and detrended yield for developing yield forecast model is rarely employed in forecasting. A novel approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Weather indices based regression models were developed using weather indices as independent variables whiledetrended yield (residuals) was considered as dependent variable. The approach provided reliable yield forecastabout two months before harvest.

A crop forecast is a statement of the most likely magnitude of yield or production of a crop. It is made on the basis of known facts on a given date and it assumes that the weather conditions and damages during the remainder of the growing season will be about the same as the average of previous year. The present paper deals with use of non-linear regression analysis for developing wheat yield forecast model for Allahabad district (India). A novel statistical approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Time series yield data of 40 years (1970-2010) and weather data for the year 1970-71 to 2009-10 have been utilized. The models have been used to forecast yield in the subsequent three years 2008-09 to 2009-10 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

A study was carried out to forecast the yield of the wheat crop for five districts of Uttar Pradesh namely Lucknow, Kanpur, Banda, Jhansi and Faizabad. The daily weather data on variables such as maximum temperature, rainfall, minimum temperature, and relative humidity were arranged week wise from sowing to harvesting and the relations between the weather variables and yield was worked out using statistical tools like correlation and regression. The yield has been detrended by obtaining the parameter estimates of the model and subsequently the detrended yield was used to forecast the yield of the crop using ARIMA model. The proposed method of obtaining pre-harvest forecasting of yield of crops was compared with the traditional approaches of forecasting and the proposed method was evaluated in terms of criteria's such as goodness of fit of the model. It was observed that in all the districts the proposed model performed better as compared to the traditional method both in terms of goodness of fit as well as forecasting performance. Thus it can be concluded that the proposed approach is better and more suitable as compared to the traditional approach for forecasting the wheat yield in the five districts of Uttar Pradesh.