Predictive Mathematical Modelling For Major Food And Non-Food Grain Crop Yields In India

Abstract

Abstract: In this paper, we will discuss a comprehensive approach to agricultural prediction in India, encompassing both food and non-food grain crops, using advanced mathematical models. The main objective of this research is to develop accurate models that consider several aspects such as weather, soil, irrigation methods, and agricultural technology, to predict crop yields. The procedure involves gathering extensive quantities of data from various regions of India about agronomic practices, meteorological patterns, soil characteristics, and crop production. To identify connections and patterns in this data, advanced statistical and machine learning techniques are employed. The models are designed to possess adaptability, allowing for adjustments in response to the arrival of new data or changes in circumstances. The primary objective is to enhance the predictability of crop yields, a crucial aspect of agricultural policymaking, supply chain management, and food security planning. The objective of this project is to enhance market price stability, improve farming techniques for farmers, and aid government and non-government groups in allocating resources by accurately estimating the yields of both food and non-food grain crops. The predictive models' results indicate a significant correlation between the identified parameters and agricultural productivity. The models demonstrate exceptional predictive accuracy, making them potentially valuable for real-time monitoring and decision-making in agriculture. The research's contributions to agricultural economics and planning have resulted in sustainable agricultural practices and improved food security. Additionally, it serves as a valuable decision-making tool for agronomists, farmers, and politicians in India.