Arghajeet Saha
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Scientist / Professor
Also affiliated: United States Geological Survey (2025); Pennsylvania State University (2022–2023); University of Kansas (2025); University of Arkansas System (2025); Kyma Technologies (United States) (2025); Arizona State University (2023)
Faculty Researcher
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Arghajeet Saha's research addresses critical environmental and agricultural challenges, with a particular focus on phosphorus cycling and water quality management. His work investigates strategies for optimizing manure application timing and placement to improve water quality, as demonstrated in studies of agricultural landscapes. Saha also explores the potential for closing nutrient loops through eco-prospecting, eco-mining, and eco-refining of phosphorus, aiming to create a more circular phosphorus economy. His research utilizes advanced computational methods, including machine learning, for tasks such as identifying animal feeding operations and improving watershed modeling. Collaborating with colleagues at the University of Arkansas at Fayetteville, Saha has published on topics ranging from livestock data gaps to the potential of wastewater-derived fertilizers. His scholarship metrics include an h-index of 4 and 45 total citations across 13 publications.
Metrics
- h-index: 4
- Publications: 13
- Citations: 49
Selected Publications
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Soil smarts: A taxonomic approach to speed up watershed modeling without sacrificing water quality insights (2025)
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management (2025)
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Unlocking the phosphorus circularity potential of corn belt watersheds with biorefinery phosphorus recovery incentives (2025)
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Machine learning-based identification of animal feeding operations in the United States on a parcel-scale (2025)
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Unlocking the Phosphorus Circularity Potential of Corn Belt Watersheds with Biorefinery Phosphorus Recovery Incentives (2024)
Collaboration Network
Top Collaborators
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Unlocking the phosphorus circularity potential of corn belt watersheds with biorefinery phosphorus recovery incentives
- Unlocking the Phosphorus Circularity Potential of Corn Belt Watersheds with Biorefinery Phosphorus Recovery Incentives
- Unlocking the phosphorus circularity potential of corn belt watersheds with biorefinery phosphorus recovery incentives
- Unlocking the Phosphorus Circularity Potential of Corn Belt Watersheds with Biorefinery Phosphorus Recovery Incentives
- Unlocking the phosphorus circularity potential of corn belt watersheds with biorefinery phosphorus recovery incentives
- Unlocking the Phosphorus Circularity Potential of Corn Belt Watersheds with Biorefinery Phosphorus Recovery Incentives
- Unlocking the phosphorus circularity potential of corn belt watersheds with biorefinery phosphorus recovery incentives
- Unlocking the Phosphorus Circularity Potential of Corn Belt Watersheds with Biorefinery Phosphorus Recovery Incentives
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Soil smarts: A taxonomic approach to speed up watershed modeling without sacrificing water quality insights
- Soil smarts: A taxonomic approach to speed up watershed modeling without sacrificing water quality insights
- Soil smarts: A taxonomic approach to speed up watershed modeling without sacrificing water quality insights
- Soil smarts: A taxonomic approach to speed up watershed modeling without sacrificing water quality insights
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