The role of big data in sustainable agriculture: advancing environmental sustainability in precision farming systems

Sustainable agriculture requires production systems that balance food security, environmental integrity, and economic viability. This narrative review examines how big data analytics, when integrated with precision agriculture technologies, can contribute to environmentally sustainable farming systems. Literature published between 2005 and 2025 was identified through Google Scholar, Scopus, Web of Science, IEEE Xplore, and ScienceDirect using targeted keywords related to big data analytics, IoT, and sustainable agriculture. The review synthesizes evidence on applications including site-specific nutrient management, precision irrigation, yield prediction, disease surveillance, and climate risk monitoring. Findings indicate that big data analytics can reduce input use (water, fertilizers, and pesticides) by 15%–40% while maintaining or improving yields, thereby supporting climate change mitigation and resource efficiency. However, adoption remains uneven due to high costs, data governance concerns, infrastructure limitations, and exclusion of smallholder farmers. The review highlights pathways for inclusive implementation through low-cost sensing, cooperative data platforms, public extension services, and supportive policy frameworks. By critically evaluating both opportunities and constraints, this study demonstrates that big data analytics can support sustainable agriculture only when technological innovation is aligned with equity-oriented governance and farmer capacity building.