Big Data Analytics for Climate-Resilient Agriculture and Environmental Sustainability
DOI:
https://doi.org/10.64137/3108088X/IJAES-V2I3P101Keywords:
Big Data Analytics, Climate-Resilient Agriculture, Environmental Sustainability, Precision Agriculture, Climate Change, Internet of Things (Iot), Machine Learning, Artificial Intelligence, Predictive Analytics, Remote Sensing, Smart Farming, Sustainable Resource ManagementAbstract
Climate change is one of the most topical issues which affects agricultural yields and environmental sustainability. Nonlinear climate change between average temperature and annual precipitation has resulted in a perturbation of agricultural systems, reducing global food security and rural livelihoods through multiple mechanisms, including warming temperatures higher daily maximum temperatures, irregular rainfall pattern fluctuations from 1951 onwards with long dry episodes followed by intense short physical duress, recent flooding events causing soil degradation, which increases vulnerability and leads to pest infestations. Conventional approaches to agricultural management often rely on historical observations and human decision-making, which are ultimately inadequate for dealing with the challenges of unprecedented climatic variability. Big Data Analytics (BDA) has come as a mindset transformation in the way we can now collect, integrate, process and finally analyze huge heterogeneous datasets that are produced from satellite images, Internet of Things 02 IoT sensors on weather stations, unmanned aerial vehicles UAVs), soil monitoring systems or agricultural machinery. Big Data Analytics is one way of applying real-time decision-making and climate-resilient agricultural practices using advanced analytical techniques like machine learning, AI (artificial intelligence), predictive modelling and cloud-based modelling. This paper explores a detailed study on the Architecture, analytics & applications of Big Data Analytics for climate-resilient agriculture and environmental sustainability- covering modelling methodologies as well as its practical implementations in precision farming, crop yield prediction (CYP), irrigation optimization (IO), disease detection models/approaches (DDM/A) tools/methodologies; soil health assessment techniques soil health indicators sustainable resource management areas pragmatically. The research also presents an integrated Big Data Analytics framework that integrates multi-source data acquisition, distributed storage, intelligent data preprocessing, predictive analytics and a decision support mechanism for agricultural productivity under environment-damaging criteria. It presents mathematical models for data integration, predictive analysis and sustainability optimization. The framework enables timely and effective agricultural interventions, enhances water use efficiency in agriculture, reduces the overuse of fertilizers and pesticides especially at an uncertain scale due to climate change impact on crop yields under limited or semi-arid conditions with insufficient precipitation for irrigation -where we risk using too much fertilizer below optimum levels producing negative externalities such as pollution from decoupling soil-carbon cycling; it minimizes greenhouse gas emissions through enhancing composting practices outside traditional farming systems mitigating global warming contributions while supporting resilience development towards extreme weather events associated impacts. The paper also discusses the challenges for implementation, which include data heterogeneity, scalability and interoperability, along with cybersecurity as well as data privacy. Results show the potential of adopting Big Data Analytics along with climate-smart agricultural practices to increase efficiency and productivity in food production, improve efficiency in natural resources use, promote evidence-based policy formulation, as well as its contribution towards long-term environmental sustainability through resilient agricultural systems.
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