Dr. Joshua Blackstock

Physical Scientist

United States Department of Agriculture

Agricultural Research Service

Dale Bumpers Small Farms Research Center

6883 South State Highway 23

Booneville AR 72927

(479)675-3834

     Dr. Joshua Blackstock received a bachelor's degree in Geology and a Minor in Environmental Geology from the University of Arkansas-Little Rock, a Masters with First Class Honours from the University of Canterbury in Geological Sciences, and a PhD from the University of Arkansas-Fayetteville in Geosciences. As an undergraduate, Dr. Blackstock studied land use and water quality changes within watersheds of the Buffalo National River. During his graduate work, his Masters involved the development and application of novel water identification techniques towards tracing moisture sources of surface water and groundwater that comprise water resources of the South Island, New Zealand. His PhD focused on determination of sources, transport, and fate of water solutes in low and high temperature surface waters both in the US and New Zealand. As part of this research, Dr. Blackstock also developed a direct measurement system for measuring dissolved carbon dioxide, the CO2-LAMP. Continuing from this research, several applications and further adaptions of the CO2-LAMP have been used at a range of localities worldwide. Additionally, Dr. Blackstock recently developed an in-situ, high-temperature dissolved gas measurement system for a NASA-funded project monitoring hot springs systems near small farms in central Costa Rica.

     Prior to joining the Dale Bumpers Small Farms Research Center, he worked for the University of Arkansas Department of Geosciences investigating how groundwater affects surface water inundation and flood duration in agricultural areas of southeast Missouri. His research interests involve the use of water chemistry, physical monitoring of water features, and geographical information systems (GIS) to model the variability of water quality, with particular emphasis on the determining the sources of carbon, nutrients, and pollutants to both water and airways. Through his research Dr. Blackstock aims to increase the knowledge and efficiency of water quantity, water quality, and environmental management on small farms.

• Projects

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• Publications

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  • 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 -(Peer Reviewed Journal)
    Blackstock, J.M., Owens, P.R., Moore Jr, P.A., Torbenson, M.A., Ashworth, A.J., Anderson, K.R., Burgess-Conforti, J., Delhom, C.D. 2024. 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. Journal of Hydrology: Regional Studies. https://doi.org/10.1016/j.ejrh.2024.101992.
    
  • Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model -(Peer Reviewed Journal)
    Libohova, Z., Mancini, M., Winzeler, H.E., Read, Q.D., Sun, N., Beaudette, D., Williams, C., Blackstock, J.M., Silva, S., Curi, N., Adhikari, K., Ashworth, A.J., Minai, J., Owens, P.R. 2024. Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model. Geoderma Regional. https://doi.org/10.1016/j.geodrs.2024.e00863.
    
  • Vegetation masking of remote sensing data aids machine learning for soil fertility prediction -(Peer Reviewed Journal)
    Winzeler, H.E., Mancini, M., Blackstock, J.M., Libohova, Z., Owens, P.R., Ashworth, A.J., Miller, D., Silva, H.G. 2024. Vegetation masking of remote sensing data aids machine learning for soil fertility prediction. Remote Sensing. https://doi.org/10.3390/rs16173297.
    
  • Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils -(Peer Reviewed Journal)
    Mancini, M., Winzeler, H.E., Blackstock, J.M., Owens, P.R., Miller, D.M., Silva, S.H., Ashworth, A.J. 2024. Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial predictions and sampling strategies of alluvial soils. Geoderma. https://doi.org/10.1016/j.geoderma.2024.116919.
    
  • Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping -(Peer Reviewed Journal)
    Owens, P.R., Mancini, M., Winzeler, H.E., Read, Q.D., Sun, N., Blackstock, J.M., Libohova, Z. 2024. Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping. Geoderma. https://doi.org/10.1016/j.geoderma.2024.116911.
    
  • Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors -(Peer Reviewed Journal)
    Adhikari, K., Mancini, M., Libohova, Z., Blackstock, J.M., Winzeler, H.E., Smith, D.R., Owens, P.R., Silva, S.H., Curi, N.C. 2024. Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors. Science of the Total Environment. 919. Article 170972. https://doi.org/10.1016/j.scitotenv.2024.170972.
    
  • Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning -(Peer Reviewed Journal)
    Castano-Duque, L.M., Winzeler, H.E., Blackstock, J.M., Cheng, L., Vergopolan, N., Focker, M., Barnett, K., Owens, P.R., Van Der Fels-Klerx, I., Vaughan, M.M., Rajasekaran, K. 2023. Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning. Frontiers in Microbiology. 14. Article 1283127. https://doi.org/10.3389/fmicb.2023.1283127.
    

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