Author(s)

Ismoth Zerine, Md Mainul Islam^*

Affiliation(s)

College of Graduate and Professional Studies, Trine University, USA.

Corresponding Author

Md Mainul Islam

ABSTRACT

Climate change, resource depletion, soil degradation, and rising global food demand are some of the issues facing the U.S. agriculture industry at this pivotal moment.  Predictive analytics combined with sustainable agricultural methods provides a revolutionary way to solve these problems by enhancing output, effectiveness, and environmental sustainability. With the help of machine learning, artificial intelligence (AI), and big data, predictive analytics helps farmers forecast yields, identify plant diseases, improve crop management, and make informed decisions.  Predictive models can increase resilience against climate unpredictability, decrease waste, and optimize resource allocation by evaluating both historical and current data on weather patterns, soil health, and crop performance.  Technologies for precision agriculture, such as automated irrigation systems, Internet of Things (IoT)-enabled gadgets, and remote sensing, improve data-driven decision-making even further and guarantee effective use of herbicides, fertilizers, and water. By maintaining soil health, enhancing biodiversity, and lowering the carbon footprint of farming operations, sustainable farming techniques include regenerative agriculture, crop rotation, conservation tillage, agroforestry, and cover crops enhance predictive analytics.  Organic additions and no-till farming are two methods that improve soil organic matter, boosting long-term sustainability and production.  Drip irrigation and intelligent water management are two examples of water-efficient irrigation techniques that are essential for preserving crop yields while conserving water resources. Through case studies and technology advancements that have effectively increased production and decreased environmental impact, this paper investigates the synergy between predictive analytics and sustainable farming.  U.S. farmers can attain more productivity, greater profitability, and a more robust agricultural system by implementing these cutting-edge techniques. The findings underscore the importance of integrating technology with ecological stewardship to ensure food security and sustainability in the face of global agricultural challenges.

KEYWORDS

Predictive analytics; Sustainable farming; Precision agriculture; Machine learning; Artificial intelligence; Big data; Regenerative agriculture; Climate resilience; Soil conservation; Smart irrigation; Crop management; Environmental sustainability

CITE THIS PAPER

Ismoth Zerine, Md Mainul Islam. Enhancing U.S. agricultural productivity through predictive analytics and sustainable farming practices. World Journal of Agriculture and Forestry Sciences. 2025, 3(1): 26-35. DOI: https://doi.org/10.61784/wjafs3018.

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