ANALYSIS OF CLIMATE CHANGE ADAPTATION IN AGRICULTURE BY PREDICTIVE MODELING FOR CROP RESILIENCE STRATEGIES

Abstract

This research focuses on climate change adaptation in agriculture using predictive modeling to enhance crop resilience. It addresses the global impact of climate change on agriculture and highlights the vulnerabilities of traditional farming methods. By utilizing historical climate data, soil quality indicators, and crop performance metrics, the study applies machine learning and statistical models to forecast agricultural outcomes under various climate scenarios. It evaluates different adaptation strategies and emphasizes the role of technologies such as precision agriculture, remote sensing, and data analytics. The research offers practical insights for policymakers, agronomists, and farmers to build climate-resilient systems and ensure global food security