Rice yield prediction using statistical regression models in the selected districts of Maharashtra

Abstract

Climate plays an important role in production of rice. Adequate quantum and timely receipt of rainfall influence the yield of rice. Harvesting of rice in the state of Maharashtra is done by September–October. Here, attempt has been made to develop agrometeorological model to forecast the yield of rice. Long-term data from 1981to 2014 on rice yields from Economics and Statistics Department of Maharashtra, Pune, and weather data since 1981–2014 archived from India Meteorological Department (IMD), Pune, were used to develop agrometeorological model which is also known as statistical regression model by using modified Hendrik and Scholl method. Statistical regression model between climate indices and of rice was developed, and the model was validated using crop and climate data for the years 2011, 2012 and 2013. The model demonstrated that climate indices–based agrometeorological model is able to forecast the yield of rice. The FASAL methodology takes into account factors such as rainfall, temperature, solar radiation, and soil moisture to estimate crop yields. Satellite imagery provides information on vegetation health, crop growth, and land use patterns, which are used to assess the condition of the rice crop. Weather data, including rainfall and temperature, are collected from meteorological stations across the country. Ground-based observations, such as crop cutting experiments and field surveys, are also conducted to validate and calibrate the model. Different climate indices derived based on maximum temperature, minimum temperature, rainfall, morning and evening relative humidity, and their combinations have positive correlation with yield of rice. Models developed for forecasting of rice in Maharashtra for selected districts have good coefficients of determination and could predict the yield of rice closer to observed yield during the years 2011, 2012, and 2013.