Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods

Agricultural non-point source pollution (ANPSP) significantly affects worldwide water quality, soil integrity, and ecosystems. Primary factors are nutrient runoff, pesticide leaching, and inadequate livestock waste management. Nonetheless, a thorough assessment of ANPSP sources and efficient control techniques is still lacking. This research delineates the origins and present state of ANPSP, emphasizing its influence on agricultural practices, livestock, and rural waste management. It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. The research examines strategies designed to alleviate ANPSP, such as sustainable agricultural practices, fertilizer reduction, and waste management technology, highlighting the necessity for integrated, real-time monitoring systems. This report presents a comprehensive analysis of current tactics, finds significant gaps, and offers recommendations for enhancing both research and policy initiatives to tackle ANPSP and foster sustainable farming practices.

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Publication Details

Journal
Agriculture
Published
2025-02-28
DOI
https://doi.org/10.3390/agriculture15050531
Citations
35
Primary Topic
Water-Energy-Food Nexus Studies
Type
article
Field-Weighted Citation Impact
28.60

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Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods

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Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods

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article en
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Abstract

Agricultural non-point source pollution (ANPSP) significantly affects worldwide water quality, soil integrity, and ecosystems. Primary factors are nutrient runoff, pesticide leaching, and inadequate livestock waste management. Nonetheless, a thorough assessment of ANPSP sources and efficient control techniques is still lacking. This research delineates the origins and present state of ANPSP, emphasizing its influence on agricultural practices, livestock, and rural waste management. It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. The research examines strategies designed to alleviate ANPSP, such as sustainable agricultural practices, fertilizer reduction, and waste management technology, highlighting the necessity for integrated, real-time monitoring systems. This report presents a comprehensive analysis of current tactics, finds significant gaps, and offers recommendations for enhancing both research and policy initiatives to tackle ANPSP and foster sustainable farming practices.

AgricultureVol. 15(5)
Swansea University (GB), Henan Agricultural University (CN), McGill University (CA)
State Key Laboratory of Wheat and Maize Crop Science
Openalex Percentile: Top 0%
Water-Energy-Food Nexus Studies
28.60
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