Dr. Hans Edwin Winzeler

Research Associate

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. Edwin Winzeler received a bachelor's degree in Natural Resources and Environmental Sciences from the University of Illinois and a M.S. and PhD from Purdue University in Soil Science, Agronomy. As an undergraduate he studied soil variability and fertility of the 140+ year-old Morrow Plots at the University of Illinois research farm. His PhD involved the development of a mapping application of the soil climate classification system of the USDA to examine soil climate changes over time. Dr. Winzeler also conducted research on soil potassium availability in a suite of agricultural soils in southern Indiana. Additionally, he helped in the development of soil mapping tools for the Terrain Attribute Soil Mapping (TASM) effort of the USDA. Prior to joining the Dale Bumpers Small Farms Research Center, he worked for Pennsylvania State University at the Fruit Research Extension Center on problems in fruit tree horticulture and entomology. His research interests involve the use of geographical information systems (GIS) to model the variability of agricultural systems, with particular emphasis on soil moisture, terrain indices, and the development of dynamic soil information systems within artificial intelligence (AI) frameworks. Through his research Dr. Winzeler aims to increase the performance efficiency of small farms with limited resources by incorporating geospatial tools and AI in day-to-day operations.

• Projects

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• Publications

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  • 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.
    
  • Predicting fumonisins in Iowa corn: gradient boosting machine learning -(Peer Reviewed Journal)
    Branstad-Spates, E., Castano-Duque, L.M., Mosher, G., Hurburgh, Jr, C., Rajasekaran, K., Owens, P.R., Winzeler, H.E., Bowers, E. 2024. Predicting fumonisins in Iowa corn: gradient boosting machine learning. Cereal Chemistry. https://doi.org/10.1002/cche.10824.
    
  • Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial soil predictions and sampling strategies of alluvial soils-(Peer Reviewed Journal)
    
    
  • 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.
    
  • Influence of sample size, model selection, and land use on prediction accuracy of soil properties -(Peer Reviewed Journal)
    Safaee, S., Libohova, Z., Kladivko, E., Brown, A., Winzeler, H.E., Read, Q.D., Rahmani, S., Adhikari, K. 2024. Influence of sample size, model selection, and land use on prediction accuracy of soil properties. Geoderma Regional. https://doi.org/10.1016/j.geodrs.2024.e00766.
    
  • 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.
    
  • Gradient boosting machine learning model to predict aflatoxins in Iowa corn -(Peer Reviewed Journal)
    Branstad-Spates, E.H., Castano-Duque, L.M., Mosher, G.A., Hurburgh, Jr., C.R., Owens, P.R., Winzeler, H.E., Rajasekaran, K., Bowers, E.L. 2023. Gradient boosting machine learning model to predict aflatoxins in Iowa corn. Frontiers in Microbiology. 14. Article 1248772. https://doi.org/10.3389/fmicb.2023.1248772.
    
  • Identification and delineation of broad-base agricultural terraces in flat landscapes in Northeastern Oklahoma, USA -(Peer Reviewed Journal)
    Winzeler, H.E., Owens, P.R., Kharel, T.P., Ashworth, A.J., Libohova, Z. 2023. Identification and delineation of broad-base agricultural terraces in flat landscapes in Northeastern Oklahoma, USA. Land. 12(2):486. https://doi.org/10.3390/land12020486.
    
  • Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization -(Peer Reviewed Journal)
    Winzeler, H.E., Owens, P.R., Read, Q.D., Libohova, Z., Ashworth, A.J., Sauer, T.J. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land. 11(11):2018. https://doi.org/10.3390/land11112018.
    

• ARS News Articles

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