Corn Background

Publications

  • Hans Edwin Winzeler, 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, 2023. URL: https://www.mdpi.com/2073-445X/12/2/486, doi:10.3390/land12020486.
  • Hans Edwin Winzeler, 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.
  • Nan Li, 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.
  • Giorgio Morales 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.
  • Paul Hegedus, Stephanie Ewing, Claim Jones, and Bruce Maxwell. Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency. Nutrient Cycling in Agroecosystems, Preprint:1–29, March 2023.
  • Paul Hegedus and Bruce Maxwell. Rationale for field-specific on-farm precision experimentation. Agriculture, Ecosystems & Environment, 338:14, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0167880922002377, doi:10.1016/j.agee.2022.108088.
  • P. Paccioretti, 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.
  • P.W. Queiroz, R.K. Perrin, L.E. Fulginiti, and D.S. Bullock. N Expected Value of Sample Information (ESVI) Approach for Estimating the Payoff from a Variable Rate Technology. Journal of Agricultural and Resource Economics, 2023.
  • B. Marks 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.
  • and B.W. Brorsen Poursina, D. Nearly Optimal Assigned Location Design for Linear Model with Random Spatial Coefficients. In Southern Agricultural Economics Association. 2022.
  • 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.
  • and B.W. Brorsen Poursina, D. Site-Specific Nitrogen Recommendation: Using Bayesian Kriging Method with Different Correlation Matrices. In 2021 Annual Meeting of the Agricultural and Applied Economics Association. Austin, TX, 2021. URL: https://ageconsearch.umn.edu/record/312653?ln=en.
  • Giorgio Morales, John Sheppard, Bryan Scherrer, and Joseph Shaw. Reduced-cost hyperspectral convolutional neural networks. Journal of Applied Remote Sensing, September 2020.
  • Amy Peerlinck and John Sheppard. Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms. In Metaheuristics. Springer, 2023.
  • Giorgio Morales and John Sheppard. Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat. In SPIE Future Sensing Technologies 2021. 2021. doi:10.1117/12.2612209.
  • Giorgio Morales, John Sheppard, Riley Logan, and Joseph Shaw. Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection. Remote Sensing, September 2021. doi:10.3390/rs13183649.
  • Md Asaduzzaman Noor, John Sheppard, and Sean Yaw. Mixing Grain to Improve Profitability in Winter Wheat using Evolutionary Algorithms. SN Computer Science, February 2022. doi:10.1007/s42979-022-01062-8.
  • Giorgio Morales, John Sheppard, Paul Hegedus, and Bruce Maxwell. Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing. Sensors, January 2023. doi:10.3390/s23010489.
  • Paul Hegedus, Bruce Maxwell, and Taro Mieno. Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on‑farm precision experimentation. Precision Agriculture, 2022. URL: https://link.springer.com/article/10.1007/s11119-022-09968-2, doi:10.1007/s11119-022-09968-2.
  • D.S. Bullock. Modern Approaches to Estimating Site-specific Profit-maximizing Nitrogen Application Strategies. January 2022.
  • D.S. Bullock. 'Modern Approaches to Estimating Site-specific Profit-maximizing Nitrogen Application Strategies' (*Invited Speaker). January 2022.
  • Tibbs and Boerngen. Examining the perceptions of precision agriculture technologies and on-farm precision experimentation. November 2022.
  • Amy Peerlinck, John Sheppard, and Bruce Maxwell. Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision Agriculture. In Proceedings of the 14th International Conference on Precision Agriculture. June 2018.
  • 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.
  • Giorgio Morales, John Sheppard, Amy Peerlinck, Paul Hegedus, and Bruce Maxwell. Generation of Site-specific Nitrogen Response Curves for Winter Wheat using Deep Learning. 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.
  • John Sheppard. The Ethics of Precision Agriculture. July 2020.
  • Amy Peerlinck and John Sheppard. Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem. In 2022 IEEE Congress on Evolutionary Computation (CEC). July 2022.
  • G.S.W. Hoselton and M.A. Boerngen. Farmers' awareness of and concerns about nutrient loss. Journal of Soil and Water Conservation, 75(5):387–391, 2021. doi:10.2489/jswc.2021.00124.
  • Haiying Tao and David S. Bullock. Using Digital Agriculture Technologies to Improve Nitrogen Management and Wheat Yield. Cereal Foods World, 2019. URL: https://cerealsgrains.org/publications/cfw/2019/November-December/Pages/CFW-64-6-0068.aspx (visited on 2022-07-08), doi:10.1094/CFW-64-6-0068.
  • Rodrigo Gonçalves Trevisan, Luciano Shozo Shiratsuchi, David S. Bullock, and Nicolas Federico Martin. Improving Yield Mapping Accuracy Using Remote Sensing. preprint, BIOLOGY, January 2019. URL: http://www.preprints.org/manuscript/201901.0287/v1 (visited on 2022-07-08), doi:10.20944/preprints201901.0287.v1.
  • Haiying Tao, 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.
  • Divina Gracia P. Rodriguez, David S. Bullock, and Maria A. Boerngen. The Origins, Implications, and Consequences of Yield‐Based Nitrogen Fertilizer Management. Agronomy Journal, 111(2):725–735, March 2019. URL: https://onlinelibrary.wiley.com/doi/10.2134/agronj2018.07.0479 (visited on 2022-07-08), doi:10.2134/agronj2018.07.0479.
  • David S. Bullock, Taro Mieno, and Jaeseok Hwang. The value of conducting on-farm field trials using precision agriculture technology: a theory and simulations. Precision Agriculture, 21(5):1027–1044, October 2020. URL: http://link.springer.com/10.1007/s11119-019-09706-1 (visited on 2022-07-08), doi:10.1007/s11119-019-09706-1.
  • R. G. Trevisan, D. S. Bullock, and N. F. Martin. Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation. Precision Agriculture, 22(2):342–363, April 2021. URL: https://link.springer.com/10.1007/s11119-020-09720-8 (visited on 2022-07-08), doi:10.1007/s11119-020-09720-8.
  • German Mandrini, David S. Bullock, and Nicolas F. Martin. Modeling the economic and environmental effects of corn nitrogen management strategies in Illinois. Field Crops Research, 261:108000, February 2021. URL: https://linkinghub.elsevier.com/retrieve/pii/S0378429020312843 (visited on 2022-07-08), doi:10.1016/j.fcr.2020.108000.
  • Grant Gardner, Taro Mieno, and David S. Bullock. An economic evaluation of site-specific input application Rx maps: evaluation framework and case study. Precision Agriculture, 22(4):1304–1316, August 2021. URL: https://link.springer.com/10.1007/s11119-021-09785-z (visited on 2022-07-08), doi:10.1007/s11119-021-09785-z.
  • P. Paccioretti, C. Bruno, F. Gianinni Kurina, M. Córdoba, D. S. Bullock, and M. Balzarini. Statistical models of yield in on‐farm precision experimentation. Agronomy Journal, 113(6):4916–4929, November 2021. URL: https://onlinelibrary.wiley.com/doi/10.1002/agj2.20833 (visited on 2022-07-08), doi:10.1002/agj2.20833.
  • Shunkei Kakimoto, 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, 199:107164, August 2022. URL: https://linkinghub.elsevier.com/retrieve/pii/S0168169922004811 (visited on 2022-07-08), doi: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. In 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. In 2019 International Joint Conference on Neural Networks (IJCNN), 1–8. July 2019. ISSN: 2161-4407. doi:10.1109/IJCNN.2019.8851976.
  • Maseko, S., Van Der Laan, M., Tesfamariam, E., Delport, M., & Otterman, H. (2024). Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management. European Journal of Agronomy, 157, 127193. https://doi.org/10.1016/j.eja.2024.127193

  • Pires, C. B., Krupek, F. S., Carmona, G. I., Ortez, O. A., Thompson, L., Quinn, D. J., Reis, A. F. B., Werle, R., Kovács, P., Singh, M. P., Hutchinson, J. M. S., Diaz, D. R., Rice, C. W., & Ciampitti, I. A. (2024). Perspective of US farmers on collaborative on‐farm agronomic research. Agronomy Journalhttps://doi.org/10.1002/agj2.21560

  • Tibbs, R. G., & Boerngen, M. A. (2024). Discovering farmers’ views of on-farm precision experimentationAgricultural & Environmental Letters9, e20130. https://doi.org/10.1002/ael2.20130