Forecasting pre-harvest rice (Oryza sativa) yield: A regression analysis of meteorological factors and climate change impacts on food security

In order to arrive at the findings, different statistical models have been developed as a result to examine how climate change may affect rice yield at various phases of the crop as well as it has been attempted to forecast its output for Karnal district. Time series data on rice yield for the past 37 years on crop and weather variables have been used in the Karnal district of Haryana from 1985–1986 through 2021–22. The relationship between rice (Oryza sativa L.) crop and various models was investigated. A boost in yield can be obtained by creating fresh weather indices from weekly data. The model takes various weather variables into account. It was discovered that the best models (models 1, 2, and 7, 8) for assessing the impact of specific weather variables were linear functions across weekly data, meteorological factors, and adjusted crop production for the trend impact are the independent variables. A forecast model was also built and the findings revealed that forecasting at the 15th week of the crop period or one and a half months before harvest was found reliable.