Statistical Analysis of Crop-Weather Regression Model for Forecasting Production Impact of Aus Rice in Bangladesh

This study is an endeavor to evaluate statistically predicts a weather-crop yield-forecasting model to generate early crop production estimates. The weather-crop yield-forecasting model was applied to estimate prospective production of Aus rice in Jessore and Rajshahi districts of Bangladesh. This model is the relationship between the crop yield and input weather parameters influencing the crop yield (Aus rice). The model evaluated using a multiple regression and ridge regression techniques. Ridge regression simulation study is used to perform sensitivity analysis. The agro climatic variables and others non-climatic variables were used in the study. The climatic data (1980-2000) were collected and transformed to the model specified from Bangladesh Meteorological Department (BMD) and yield data (1980-2000) were collected from various issues Bangladesh Bureau of Statistics (BBS) and Department of Agricultural Extension (DAE). Thus, compared to the actual production of local variety (LCV), the predicted production of local variety (LCV) Aus was lower in Jessore and Rajshahi districts; the predicted production of high yielding variety (HYV) Aus was lower in Jessore district but same in Rajshahi district; and the predicted production of total (All) Aus was lower in Jessore and Rajshahi districts. However, the predicted production was closer to actual production in both Jessore and Rajshahi districts. The results showed that the yields varied from stage to stage of crop growth. That is climate factor is playing an important role for yield and production level of the Aus crop. This study suggests that global warming might affect not only food grain production but also the distribution and species composition of grasslands. Forecasting of food grain production reliable comprehensive and timely information on the food situation and similar assessment of the prospects may contribute to the government policies. Finding of this study will serve as a basis for making decision by the higher authorities, in the procurement of food grains internally, whenever, harvest is good. Also, it will allow them to maintain a stable price by distribution of food grains from its store if prospects of poor harvests are likely to occur in order to mitigate the suffering of the rural poor.