Big Data Analytics in Precision Agriculture: Enhancing Decision-Making for Climate-Resilient Farming

Abstract

Climate change is increasingly disrupting agricultural productivity through extreme weather events, soil degradation, and water scarcity, requiring innovative approaches to farming decision-making. Big data analytics has emerged as a key driver of precision agriculture by integrating data from sensors, satellites, climate models, and farm machinery to optimize resource utilization and improve productivity. This study explores the role of big data analytics in enhancing decision-making for climate-resilient farming systems. The paper highlights how data-driven models support predictive irrigation, crop health monitoring, yield forecasting, and risk management under changing climatic conditions. Furthermore, it examines technological challenges such as data integration, infrastructure limitations, and digital literacy barriers among farmers. The analysis demonstrates that big data-enabled precision agriculture significantly improves sustainability by reducing resource waste while enhancing resilience against climate variability. The study concludes with recommendations for policy support, technological investment, and capacity-building strategies to promote data-driven agricultural transformation in developing countries.