Publications

• Winzeler, Hans Edwin, Phillip R. Owens, Tulsi Kharel, Amanda Ashworth, and Zamir Libohova. “Identification and Delineation of Broad-Base Agricultural Terraces in Flat Landscapes in Northeastern Oklahoma, USA.” Land 12, no. 2 (February 16, 2023): 486. https://doi.org/10.3390/land12020486.

   

   

• Winzeler, Hans Edwin, Phillip R. Owens, Quentin D. Read, Zamir Libohova, Amanda Ashworth, and Tom Sauer. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land, 2022. URL: https://www.mdpi.com/2073-445X/11/11/2018, doi:10.3390/land11112018.

• Li, Nan, David Bullock, Carrie Butts‐Wilmsmeyer, Laura Gentry, Greg Goodwin, Jaeyeong Han, Nathan Kleczweski, Nicolas F. Martín, Patricia Paulausky, Pete Pistorius, Nicholas Seiter, Nathan Schroeder, and Andrew J. Margenot. Distinct soil health indicators are associated with variation in maize yield and tile drain nitrate losses. Soil Science Society of America Journal, pages saj2.20586, September 2023. URL: https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20586 (visited on 2023-10-05), doi:10.1002/saj2.20586.

• Morales, Giorgio and John Sheppard. Counterfactual explanations of neural network-generated response curves. In 2023 International Joint Conference on Neural Networks (IJCNN), volume, 01-08. 2023. doi:10.1109/IJCNN54540.2023.10191746.

• Paul Hegedus, Bruce Maxwell, John Sheppard, Sasha Loewen, Hannah Duff, Giorgio Morales-Luna, and Amy Peerlinck. Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis. Agriculture, 13(524):1–20, February 2023. doi:https://doi.org/10.3390/agriculture13030524.

• Hegedus, Paul B., Stephanie A. Ewing, Clain Jones, and Bruce D. Maxwell. “Using Spatially Variable Nitrogen Application and Crop Responses to Evaluate Crop Nitrogen Use Efficiency.” Nutrient Cycling in Agroecosystems 126, no. 1 (March 18, 2023): 1–20. https://doi.org/10.1007/s10705-023-10263-3.

   

   

• Hegedus, Paul B., and Bruce D. Maxwell. “Rationale for Field-Specific on-Farm Precision Experimentation.” Agriculture, Ecosystems & Environment 338, no. 14 (October 2022): 108088. https://doi.org/10.1016/j.agee.2022.108088.

   

   

• Paccioretti P., M. Córdoba, C. Bruno, F.G. Kurina, D.S. Bullock, and M. Balzarini. Statistical Models of Yield in On-farm Experimentation. Agronomy Journal, 113(6):4916–4929, 2021. URL: https://doi.org/10.1002/agj2.20833, doi:10.1002/agj2.20833.

• Queiroz, P.W., R.K. Perrin, L.E. Fulginiti, and D.S. Bullock. “An Expected Value of Sample Information (ESVI) Approach for Estimating the Payoff from a Variable Rate Technology.” Journal of Agricultural and Resource Economics. 48(1) (January 2023): 723-735. doi: 10.22004/ag.econ.320680.

   

• Marks, B. and M.A. Boerngen. A farming community's perspective on nutrient loss reduction. Agricultural & Environmental Letters, 2019. URL: https://www.jswconline.org/content/76/5/387, doi:10.2134/ael2019.02.0004.

• Poursina, Davood, Wade Brorsen, and Dayton M. Lambert. “Nearly DS-Optimal Assigned Location Design for a Linear Model with Spatially Varying Coefficients.” Spatial Statistics 53 (March 2023): 100727. https://doi.org/10.1016/j.spasta.2023.100727.

   

   

• and D. Poursina Brorsen, B.W. Where to Put Treatments for On-Farm Experimentation. In Proceedings of the International Conference on Precision Agriculture. Minneapolis, MN, 2022.

• Poursina, Davood, and Wade Brorsen. “ 2021 Annual Meeting of the Agricultural and Applied Economics Association.” Austin, Texas, 2021. 10.22004/ag.econ.312653

   

   

   

• Morales, Giorgio, John Sheppard, Bryan Scherrer, and Joseph Shaw. “Reduced-Cost Hyperspectral Convolutional Neural Networks.” Journal of Applied Remote Sensing 14, no. 03 (September 29, 2020). https://doi.org/10.1117/1.jrs.14.036519.

• Peerlinck, Amy, and John Sheppard. “Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms.” Metaheuristics. MIC 2022. Lecture Notes in Computer Science, 13838 (February 2023): 391–405. https://doi.org/10.1007/978-3-031-26504-4_28.

   

   

• Morales, Giorgio, and John W. Sheppard. “Two-Dimensional Deep Regression for Early Yield Prediction of Winter Wheat.” SPIE Future Sensing Technologies 2021, November 14, 2021. https://doi.org/10.1117/12.2612209.

   

   

• Morales, Giorgio, John W. Sheppard, Riley D. Logan, and Joseph A. Shaw. “Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection.” Remote Sensing 13, no. 18 (September 13, 2021): 3649. https://doi.org/10.3390/rs13183649.

   

   

• Noor, Md Asaduzzaman, John W. Sheppard, and Sean Yaw. “Mixing Grain to Improve Profitability in Winter Wheat Using Evolutionary Algorithms.” SN Computer Science 3, no. 2 (February 24, 2022). https://doi.org/10.1007/s42979-022-01062-8.

   

   

• Morales, Giorgio, John W. Sheppard, Paul B. Hegedus, and Bruce D. Maxwell. “Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing.” Sensors 23, no. 1 (January 2, 2023): 489. https://doi.org/10.3390/s23010489.

   

   

• Hegedus, Paul B., Bruce D. Maxwell, and Taro Mieno. “Assessing Performance of Empirical Models for Forecasting Crop Responses to Variable Fertilizer Rates Using On-Farm Precision Experimentation.” Precision Agriculture 24, no. 2 (October 19, 2022): 677–704. https://doi.org/10.1007/s11119-022-09968-2.

   

   

• Bullock, David S. “Modern Approaches to Estimating Site-Specific Profit-Maximizing Nitrogen Application Strategies.” Data-Intensive Farm Management, January 2022. difm.farm.

   

   

• Tibbs, Reagen. “Examining F Examining Farmers’ P Armers’ Perceptions of Pr Ceptions of Precision Agricultur Ecision Agriculture Technologies and Their Interest in Conducting on-Farm Precision Experimentation ,” 2023.

  Theses and Dissertations. 1873. https://doi.org/10.30707/ETD2023.20240124055108482898.999976

   

• Amy Peerlinck, Giorgio Morales, John Sheppard, Paul Hegedus, and Bruce Maxwell. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat Production. In Proceedings of the 15th International Conference on Precision Agriculture. (July 2022)

   

   

• Bruce Maxwell, Paul Hegedus, Sasha Loewen, Hannah Duff, John Sheppard, Amy Peerlinck, Giorgio Morales, and Anton Bekkerman. Decision Support From On-Field Precision Experiments. In Proceedings of the 15th International Conference on Precision Agriculture. (July 2022)

• A. Peerlinck and J. Sheppard, "Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem," 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, (2022) pp. 1-8, doi: 10.1109/CEC55065.2022.9870377.

• Hoselton, George, and Maria Boerngen. “Awareness of Nutrient Loss among Illinois Corn Farmers.” Journal of Soil and Water Conservation 75, no. 5 (February 2021): 387–91. https://doi.org/10.2489/jswc.2021.00124.

   

   

• Tao, Haiying, and David Bullock. “Using Digital Agriculture Technologies to Improve Nitrogen Management and Wheat Yield.” Cereal Foods World, 2019. https://doi.org/10.1094/cfw-64-6-0068.

   

   

• Trevisan, Rodrigo Gonçalves, Luciano Shiratsuchi, David S. Bullock, and Nicolas Federico Martin. “Improving Yield Mapping Accuracy Using Remote Sensing.” Biology, January 29, 2019. https://doi.org/10.20944/preprints201901.0287.v1.

   

   

• Tao, Haiying, Jan Boll, Thomas F. Morris, David S. Bullock, and Bruce D. Maxwell. Farmers’ networks for farmer-centric collaborative research and extension. Crops & Soils, 52(5):40–46, September 2019. URL: http://doi.wiley.com/10.2134/cs2019.52.0503 (visited on 2022-07-08), doi:10.2134/cs2019.52.0503.

• Rodriguez, Divina Gracia, David S. Bullock, and Maria A. Boerngen. “The Origins, Implications, and Consequences of Yield‐based Nitrogen Fertilizer Management.” Agronomy Journal 111, no. 2 (March 2019): 725–35. https://doi.org/10.2134/agronj2018.07.0479.

   

   

• Bullock, David S., Taro Mieno, and Jaeseok Hwang. “The Value of Conducting On-Farm Field Trials Using Precision Agriculture Technology: A Theory and Simulations.” Precision Agriculture 21, no. 5 (January 4, 2020): 1027–44. https://doi.org/10.1007/s11119-019-09706-1.

   

   

• Trevisan, Rodrigo G., David Bullock, and Nicolas Martin. “Spatial Variability of Crop Responses to Agronomic Inputs in On-Farm Precision Experimentation.” Precision Agriculture 22, no. 2 (April 2021): 342–63. https://doi.org/10.1007/s11119-020-09720-8.

   

   

• Mandrini, German, David S. Bullock, and Nicolas F. Martin. “Modeling the Economic and Environmental Effects of Corn Nitrogen Management Strategies in Illinois.” Field Crops Research 261 (February 2021): 108000. https://doi.org/10.1016/j.fcr.2020.108000.

   

   

• Gardner, Grant, Taro Mieno, and David S. Bullock. “An Economic Evaluation of Site-Specific Input Application RX Maps: Evaluation Framework and Case Study.” Precision Agriculture 22, no. 4 (August 2021): 1304–16. https://doi.org/10.1007/s11119-021-09785-z.

• Paccioretti, Pablo, Cecilia Bruno, Franca Gianinni Kurina, Mariano Córdoba, David Bullock, and Monica Balzarini. “Statistical Models of Yield in On‐farm Precision Experimentation.” Agronomy Journal 113, no. 6 (November 24, 2021): 4916–29. https://doi.org/10.1002/agj2.20833.

   

   

• Kakimoto, Shunkei, Taro Mieno, Takashi S.T. Tanaka, and David S Bullock. “Causal Forest Approach for Site-Specific Input Management via on-Farm Precision Experimentation.” Computers and Electronics in Agriculture 199 (August 2022): 107164. https://doi.org/10.1016/j.compag.2022.107164.

• Amy Peerlinck, John Sheppard, Julie Pastorino, and Bruce Maxwell. Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm. 2019 IEEE Congress on Evolutionary Computation (CEC), 1838–1845. (June 2019) doi:10.1109/CEC.2019.8790267.

• Amy Peerlinck, John Sheppard, and Jacob Senecal. AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture. 2019 International Joint Conference on Neural Networks (IJCNN), 1–8. (July 2019). ISSN: 2161-4407. doi:10.1109/IJCNN.2019.8851976.

• Maseko, S, M Van Der Laan, E Tesfamariam, M Delport, and H Otterman. “EditoriaEvaluating Machine Learning Models and Identifying Key Factors Influencing Spatial Maize Yield Predictions in Data Intensive Farm Management.l Board.” European Journal of Agronomy 157, no. 127193 (April 2024): 127046. https://doi.org/https://doi.org/10.1016/j.eja.2024.127193.

   

   

• Pires, Carlos B., Fernanda S. Krupek, Gabriela I. Carmona, Osler A. Ortez, Laura Thompson, Daniel J. Quinn, Andre F. Reis, et al. “Perspective of Us Farmers on Collaborative on‐Farm Agronomic Research.” Agronomy Journal 116, no. 3 (March 21, 2024): 1590–1602. https://doi.org/10.1002/agj2.21560.

   

   

• Tibbs, Reagen G., and Maria A. Boerngen. “Discovering Farmers’ Views of on‐Farm Precision Experimentation.” Agricultural & Environmental Letters, e20130, 9, no. 1 (May 28, 2024). https://doi.org/10.1002/ael2.20130.

   

   

   

• Bullock, D.S.  “Conducting On-farm Precision Experimentation with U of I Extension and the Data-Intensive Farm Management Project".  U of I Extension Ewing Field Day.  July 24, 2025.

• Mieno, T., X. Li, and D.S. Bullock. “Bias in Economic Evaluation of Variable Rate Application based on Geographically Weighted Regression Models with Mis-specified Functional Form.” Journal of the Agricultural and Applied Economics Association (2024): 135-151.

• Morales, Giorgio, and John Sheppard. “Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones.” arXiv, March 15, 2024. https://doi.org/10.48550/arXiv.2403.10730.

• Tanaka, T.S.T., G.B.M. Heuvelink, T. Mieno, and D.S. Bullock. “Can Machine Learning Models Provide Accurate Fertilizer Recommendations?” Precision Agriculture (2024): 1-18. DOI : 10.1007/s11119-024-10136-x.

• Li, X., T. Mieno, and D.S. Bullock. “The Economic Performances of Different Trial Designs in On-Farm Precision Experimentation: A Monte Carlo Evaluation.” Precision Agriculture, 24(6) (December 2023): 2500-2521. https://doi.org/10.1007/s11119-023-10050-8.

• Li, N., D.S. Bullock, D.S., C. Butts-Wilmseyer, L. Gentry, G. Goodwin, J. Han, N. Kleczweski, N., N.F. Martin, P. Paulausky, P. Pistorius, N.J. Sieter, N.E. Schroeder, and A.J. Margenot. “Distinct Soil Health Indicators Are Associated with On-farm Variation in Maize Yield and Tile Drain Nitrate Loses across Contrasting Nitrogen Application in Central Illinois.” Soil Science Society of America Journal 87(6) (August 2023): 1332-1347.

• Kleczweski, Nathan, et al. “Distinct Soil Health Indicators Are Associated with Variation in Maize Yield and Tile Drain Nitrate Losses.” Soil Science Society of America Journal, September 21, 2023, saj2.20586. https://doi.org/10.1002/saj2.20586.

• Morales, Giorgio, and John Sheppard. “Counterfactual Explanations of Neural Network-Generated Response Curves,” 2023. https://doi.org/10.1109/IJCNN54540.2023.10191746.

• Boerngen, M.A., and J.W. Rickard. “Assessment and Perception of Student Farm Background in an Introductory Agriculture Course.” Natural Sciences Education 49 (May 2022): e20013. https://doi.org/10.1002/nse2.20013.

• Hegedus, Paul, and Bruce Maxwell. “Constraint of Data Availability on the Predictive Ability of Crop Response Models Developed from On-Farm Experimentation,” 2022. https://www.ispag.org/proceedings/?action=abstract&id=8533&title=Constraint+of+Data+Availability+on+the+Predictive+Ability+of+Crop+Response+Models+Developed+from+On-farm+Experimentation.

• Kakimoto, Shunkei, Taro Mieno, Takashi S.T. Tanaka, and David S Bullock. “Causal Forest Approach for Site-Specific Input Management via on-Farm Precision Experimentation.” Computers and Electronics in Agriculture 199 (2022): 107164. https://doi.org/10.1016/j.compag.2022.107164.

• Lacoste, M., S. Cook, M. McNee, D. Gale, J. Ingram, V. Bellon-Maurel, T. MacMillan, et al. “On-Farm Experimentation to Transform Global Agriculture.” Nature Food 3, no. 1 (2022): 11–18. https://doi.org/10.1038/s43016-021-00424-4.

• Peerlinck, Amy, and John Sheppard. “Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms.” Springer, 2022.

• Du, Q, T Mieno, and D.S. Bullock. “Economically Optimal Nitrogen Side-Dressing Based on Vegetation Indices from Satellite Images Through On-Farm Experiments.” Precision Agriculture, August 2021.

• Friedrichesen, C.N., S. Hagen-Zakarison, M.L. Friesen, C.R. McFarland, H. Tao, and J.D. Wulfhorst. “Soil Health and Well-Being: Redefining Soil Health Based upon a Plurality of Values.” Soil Security 2 (2021): 100004.

• Hegedus, P.B. “MSU EAL Costech 4010 Data Report Generator for Carbon and Nitrogen Samples.,” January 22, 2021. https://paulhegedus.shinyapps.io/msucostech_report_app/.

• Hegedus, P.B. OFPE: An R Package for Automating Data Management, Analysis, and Experimental Design of On-Farm Precision Experiments (version v1.7.23.), 2021. https://github.com/paulhegedus/OFPE.git.

• Hegedus, P.B. SampleBuilder: An R Package for Creating Field Sampling Designs. (version Published.), 2021. https://github.com/paulhegedus/SampleBuilder.git.

• Logan, Riley, Bryan Scherrer, Jacob Senecal, Niel Walton, Amy Peerlinck, John Sheppard, and Joseph Shaw. “Assessing Produce Ripeness Using Hyperspectral Imaging and Machine Learning.” Journal of Applied Remote Sensing 15, no. 3 (2021): 034505. https://doi.org/10.1117/1.JRS.15.034505.

• Paccioretti, P., M. Cordoba, C. Bruno, F.G. Kurina, D.S. Bullock, and M. Balzarini. “Statistical Modeling for On-Farm Experimentation with Precision Agricultural Technology.” Agronomy Journal, July 2021.

• Barbosa, A.O, N. Hovakimyan, and N.F Martin. “Risk-Averse Optimization of Crop Inputs Using a Deep Ensemble of Convolutional Neural Networks.” Computers and Electronics in Agriculture., 2020. https://doi.org/10.1016/j.compag.2020.105785.

• Barbosa, A.O, N Trevisan, N Hovakimyan, and N.F Martin. “Modeling Yield Response to Crop Management Using Convolutional Neural Networks.” Computesr and Electronics in Agriculture 170, no. 105197 (March 2020).

• Dahal, S., E. Phillippi, L. Longchamps, R. Khosla, and A. Andales. “Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US.” Agronomy 10, no. 10 (2020): 1533. https://doi.org/10.3390/agronomy10101533.

• Crago, C., and J Paudel. “Agricultural Adaptation to Climate Change: Implications for Fertilizer Use and Water Quality in the United States.,” Precision Agriculture (2020)

• Paudel, Jayash, and Christine L. Crago. “Environmental Externalities from Agriculture: Evidence from Water Quality in the United States.” American Journal of Agricultural Economics 103, no. 1 (September 7, 2020): 185–210. https://doi.org/10.1111/ajae.12130.

• Queiroz, P.W., R.K. Perrin, L.E. Fulginiti, and D.S. Bullock. “An Expected Value of Sample Information (ESVI) Approach for Estimating the Payoff from a Variable Rate Technology.” American Journal of Agricultural Economics Submitted (December 2020).