V. Steven Green
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor Soil and Water Conservation
Also affiliated: Agricultural Research Service (2006–2007); United States Department of Agriculture (2005–2006); State Street (United States) (2005–2006); Purdue University West Lafayette (2000); University of Arkansas System (2022–2026); Beltsville Agricultural Research Center (2004–2007); National Soil Erosion Research Laboratory (2004)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
V. Steven Green's research focuses on the application of remote sensing technologies and data analysis to agricultural and conservation practices. He investigates methods for identifying crop nutrient deficiencies, such as nitrogen levels in maize, and for detecting variations in crop yield using drone imagery and deep learning algorithms. His work also examines the methodological frameworks for using remote sensing to generate data on cover crops, including their adoption patterns and identification during winter months.
His publications explore trends in remote sensing for conservation agriculture and the short-term effects of cover crops and tillage on soil physical properties. Green also has an interest in educational assessment, as evidenced by his work analyzing competency assessments in soil fertility courses. He has collaborated with researchers from the University of Arkansas at Fayetteville on multiple publications.
Metrics
- h-index: 14
- Publications: 23
- Citations: 1,632
Selected Publications
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Managing yield and soil trace gas fluxes in rice through furrow irrigation and sulfur‐enriched urea (2026)
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Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain (2025)
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Analysis of Competency Assessments Used in an Upper-Level Soil Fertility Course (2025)
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Short-Term Effects of Cover Crops and Tillage Management on Soil Physical Properties on Silt Loam Soil (2024)
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Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation (2023)
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An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture (2023)
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Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning (2022)
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Determining nitrogen deficiencies for maize using various remote sensing indices (2022)
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Characterizing Organic Carbon Storage in Experimental Agricultural Ditch Systems in Northeast Arkansas (2019)
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Predicted harvest time effects on switchgrass moisture content, nutrient concentration, yield, and profitability (2017)
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Earthworm Preference Bioassays to Evaluate Land Management Practices (2016)
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Ethanol fermentation of energy beets by self-flocculating and non-flocculating yeasts (2014)
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Eastern gamagrass as an alternative cellulosic feedstock for bioethanol production (2011)
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Soil quality: An essential component of environmental sustainability (2008)3 citations OpenAlex
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Evaluation of a Polyacrylamide Soil Additive to Reduce Agricultural-Associated Contamination (2008)
Collaboration Network
Top Collaborators
- Determining nitrogen deficiencies for maize using various remote sensing indices
- An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
- Satellite Remote Sensing Reveals Voluntary Cover-Crop Adoption and Crop-Rotation Hotspots in the Mississippi Alluvial Plain
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
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