Match tier
Confirmed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-06
J
Joshua Blackstock
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Also affiliated: United States Geological Survey (2019); Agricultural Research Service (2023–2026); United States Department of Agriculture (2023–2026); Dale Bumpers Small Farms Research Center (2023–2026)
6 h-index
251 pubs
242 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Joshua Blackstock's research focuses on environmental monitoring and the study of water resources, particularly groundwater in Arkansas. His work investigates spatial prediction, model uncertainty, and the factors influencing environmental conditions, as demonstrated by his research on heavy metal concentrations in soils across the USA. Blackstock has also examined the characteristics of groundwater resources in Arkansas, including their protection and management. His publications include studies on the dynamic geospatial modeling of mycotoxin contamination in crops and the development of low-cost monitoring platforms for atmospheric and soil CO2. His research network includes collaborations on isotope studies of moisture sources and groundwater flow paths in other regions.
Metrics
- h-index: 6
- Publications: 251
- Citations: 242
Selected Publications
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Evaluating groundwater–surface water interactions at selected streams in the Mississippi River Valley alluvial aquifer, USA, and comparison to regional potentiometric surfaces (2025)
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Surface water irrigation reservoirs improve groundwater recovery in a heavily stressed aquifer (2025)
Collaboration Network
Top Collaborators
Phillip Ray Owens
Dale Bumpers Small Farms Research Center (US)
6 shared publications
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Spatiotemporal patterns of pH related to streamflow variability, drought conditions, and bedrock lithology in acid sensitive streams within a humid, subtropical catchment: Mulberry River, Arkansas, USA
- Surface water irrigation reservoirs improve groundwater recovery in a heavily stressed aquifer
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Showing 5 of 6 shared publications
Matthew David Covington
University of Arkansas at Fayetteville
3 shared publications
In database
- Monitoring Atmospheric, Soil, and Dissolved CO2 Using a Low-Cost, Arduino Monitoring Platform (CO2-LAMP): Theory, Fabrication, and Operation
- Englacial Drainage Drives Positive Feedback Depression Growth on the Debris‐Covered Ngozumpa Glacier, Nepal
- LOW-COST MONITORING OF CO2 CONCENTRATIONS AND FLUXES WITHIN THE CRITICAL ZONE: INSIGHTS FROM THE SPRINGFIELD PLATEAU AQUIFER, NORTHWEST ARKANSAS
Marcelo Mancini
Universidade Federal de Lavras (BR)
3 shared publications
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Hans Edwin Winzeler
Agricultural Research Service (US)
3 shared publications
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Sérgio Henrique Godinho Silva
Universidade Federal de Lavras (BR)
3 shared publications
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Amanda Joy Ashworth
Agricultural Research Service
3 shared publications
In database
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Joseph M. Myre
University of St. Thomas - Minnesota (US)
2 shared publications
- Monitoring Atmospheric, Soil, and Dissolved CO2 Using a Low-Cost, Arduino Monitoring Platform (CO2-LAMP): Theory, Fabrication, and Operation
- LOW-COST MONITORING OF CO2 CONCENTRATIONS AND FLUXES WITHIN THE CRITICAL ZONE: INSIGHTS FROM THE SPRINGFIELD PLATEAU AQUIFER, NORTHWEST ARKANSAS
Phillip D. Hays
2 shared publications
In database
- LOW-COST MONITORING OF CO2 CONCENTRATIONS AND FLUXES WITHIN THE CRITICAL ZONE: INSIGHTS FROM THE SPRINGFIELD PLATEAU AQUIFER, NORTHWEST ARKANSAS
- GROUNDWATER-LEVEL CHANGE AND GROUNDWATER-SURFACE EXCHANGE IN THE NORTHWESTERN MISSISSIPPI RIVER ALLUVIAL AQUIFER IMPOSED BY SEASONAL PUMPING AND CLIMATE CHANGE
Matija Perne
Jožef Stefan Institute (SI)
2 shared publications
- Monitoring Atmospheric, Soil, and Dissolved CO2 Using a Low-Cost, Arduino Monitoring Platform (CO2-LAMP): Theory, Fabrication, and Operation
- LOW-COST MONITORING OF CO2 CONCENTRATIONS AND FLUXES WITHIN THE CRITICAL ZONE: INSIGHTS FROM THE SPRINGFIELD PLATEAU AQUIFER, NORTHWEST ARKANSAS
Max C. A. Torbenson
Johannes Gutenberg University Mainz (DE)
2 shared publications
- Spatiotemporal patterns of pH related to streamflow variability, drought conditions, and bedrock lithology in acid sensitive streams within a humid, subtropical catchment: Mulberry River, Arkansas, USA
- Pre‐instrumental perspectives on Arkansas River cross‐watershed flow variability
David M. Miller
2 shared publications
- Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
Zamir Libohova
Agricultural Research Service (US)
2 shared publications
- Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model
Oladipo S. Obembe
Arkansas State University
2 shared publications
In database
- Surface water irrigation reservoirs improve groundwater recovery in a heavily stressed aquifer
- Evaluating groundwater–surface water interactions at selected streams in the Mississippi River Valley alluvial aquifer, USA, and comparison to regional potentiometric surfaces
Aaron M. Shew
University of Arkansas at Fayetteville
2 shared publications
In database
- Surface water irrigation reservoirs improve groundwater recovery in a heavily stressed aquifer
- Evaluating groundwater–surface water interactions at selected streams in the Mississippi River Valley alluvial aquifer, USA, and comparison to regional potentiometric surfaces
Christopher D. Delhom
Agricultural Research Service (US)
2 shared publications
- Surface water irrigation reservoirs improve groundwater recovery in a heavily stressed aquifer
- Evaluating groundwater–surface water interactions at selected streams in the Mississippi River Valley alluvial aquifer, USA, and comparison to regional potentiometric surfaces
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