Performance and refinement of nitrogen fertilization tools
Data files
Aug 03, 2021 version files 11.70 MB
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1.Site_Characterization.xlsx
192.05 KB
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10.Weather.xlsx
1.08 MB
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11.Irrigation.xlsx
20.19 KB
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2.Soil_ECa_PlotAverage.xlsx
501.54 KB
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2.Soil_ECa_RawData.xlsx
6.61 MB
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3.SiteHistoryandManagement_SI.xlsx
51.35 KB
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3.SiteHistoryandManagement.xlsx
51.58 KB
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4.SoilN.xlsx
572.05 KB
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5.Potentially_MineralizableN.xlsx
97.96 KB
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6.Soil_Respiration.xlsx
57.37 KB
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7.RapidScan.xlsx
759.72 KB
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8.Yield_Plant_Measurements.xlsx
1.35 MB
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9.EONR_by_site-year.xlsx
31.40 KB
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Plot_Boundaries_Shapefiles.zip
293.97 KB
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PRNTreadme.txt
1.51 KB
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Supplemental_Descriptions.docx
20.46 KB
Abstract
Improving corn (Zea mays L.) N management is pertinent to economic and environmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014 to 2016 growing seasons with many soil, plant, and weather related measurements.
This dataset contains 49 site-years (2014–2016) of corn grain yield responses to added N fertilizer from eight U.S. Midwest states. Each site-year followed a standardized protocol which included the same 16 N rates, four replicates, two application timings (all at planting or the majority sidedressed; Table 1), N source (ammonium nitrate) from the same fertilizer production plant, site selection criteria (low and high productive soils within each state), data management, collaboration procedures, and explanatory variables. Additional background information and details about the standardized materials and methods, data management, and collaboration protocols are discussed in
Kitchen, N. R., Shanahan, J. F., Ransom, C. J., Bandura, C. J., Bean, G. M., Camberato, J. J., Carter, P. R., Clark, J. D., Ferguson, R. B., Fernández, F. G., Franzen, D. W., Laboski, C. A. M., Nafziger, E. D., Qing, Z., Sawyer, J. E., & Shafer, M. (2017). A public–industry partnership for enhancing corn nitrogen research and datasets: Project description, methodology, and outcomes. Agronomy Journal, 109(5), 2371–2388. https://doi.org/10.2134/agronj2017.04.0207
A summary of scientific findings and future use of this data can be found in
Ransom, C. J., Clark, J., Bean, G. Mac, Bandura, C., Shafer, M. E., Kitchen, N. R., Camberato, J. J., Carter, P. R., Ferguson, R. B., Fernández, F., Franzen, D. W., Laboski, C. A. M., Myers, D. B., Nafziger, E. D., Sawyer, J. E., & Shanahan, J. (2021). Data from a public–industry partnership for enhancing corn nitrogen research. Agronomy Journal, agj2.20812. https://doi.org/10.1002/agj2.20812
Major issues were noted within the dataset and separately in a summary document. Those interested in using these data for further analysis are advised to familiarize themselves with these issues and are encouraged to consider working with authors that have first-hand knowledge of the study. The most prominent issues are listed here:
- A 2015 Nebraska site showed a limited response to N which was believed to result from the previous soybean (Glycine max [L.] Merr) crop being severely damaged from hail, resulting in grain left on the soil—which was a source of mineralizable N.
- Intensive precipitation events caused ponding with substantial N lost from the 2015 Missouri sites and extensive anoxic plant stress from the anaerobic conditions in the root zone.
- An errant 45 kg N ha-1 was applied on all treatments on a 2016 North Dakota site. For some analyzes, adjustments taking this error into account were done (e.g., EONR).
- A high wind event late in the vegetative growth development stages resulted in lodging at one of the 2016 Iowa sites for all treatments, but especially affected treatments that received high N rates at planting.
Shapefiles of the plot boundaries with the corresponding treatments are available in the "Plot_Boundaries_Shapefiles.zip" folder. Files are organized by year and by state with each site-year a separate shapefile.
- Kitchen, Newell R. et al. (2017), A Public–Industry Partnership for Enhancing Corn Nitrogen Research and Datasets: Project Description, Methodology, and Outcomes, Agronomy Journal, Journal-article, https://doi.org/10.2134/agronj2017.04.0207
- Bean, G. M. et al. (2018), Active‐Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt, Agronomy Journal, Journal-article, https://doi.org/10.2134/agronj2018.03.0217
- Bean, G.M. et al. (2018), Improving an Active‐Optical Reflectance Sensor Algorithm Using Soil and Weather Information, Agronomy Journal, Journal-article, https://doi.org/10.2134/agronj2017.12.0733
- Bean, G. Mac et al. (2020), Relating four‐day soil respiration to corn nitrogen fertilizer needs across 49 U.S. Midwest fields, Soil Science Society of America Journal, Journal-article, https://doi.org/10.1002/saj2.20091
- Clark, Jason D. et al. (2020), Weather and soil in the US Midwest influence the effectiveness of single‐ and split‐nitrogen applications in corn production, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20446
- Clark, Jason D. et al. (2019), Predicting Economic Optimal Nitrogen Rate with the Anaerobic Potentially Mineralizable Nitrogen Test, Agronomy Journal, Journal-article, https://doi.org/10.2134/agronj2019.03.0224
- Clark, Jason D. et al. (2020), Adjusting corn nitrogen management by including a mineralizable‐nitrogen test with the preplant and presidedress nitrate tests, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20228
- Clark, Jason D. et al. (2020), Soil‐nitrogen, potentially mineralizable‐nitrogen, and field condition information marginally improves corn nitrogen management, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20335
- Clark, Jason D. et al. (2019), United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen, Soil Science Society of America Journal, Journal-article, https://doi.org/10.2136/sssaj2019.02.0047
- Clark, Jason D. et al. (2020), Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen, Soil Science Society of America Journal, Journal-article, https://doi.org/10.1002/saj2.20050
- Qin, Zhisheng et al. (2018), Application of Machine Learning Methodologies for Predicting Corn Economic Optimal Nitrogen Rate, Agronomy Journal, Journal-article, https://doi.org/10.2134/agronj2018.03.0222
- Ransom, Curtis J. et al. (2019), Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations, Computers and Electronics in Agriculture, Journal-article, https://doi.org/10.1016/j.compag.2019.104872
- Ransom, Curtis J. et al. (2020), Corn nitrogen rate recommendation tools’ performance across eight US midwest corn belt states, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20035
- Ransom, Curtis J. et al. (2021), Improving publicly available corn nitrogen rate recommendation tools with soil and weather measurements, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20627
- Yost, M.A. et al. (2018), Evaluation of the Haney Soil Health Tool for corn nitrogen recommendations across eight Midwest states, Journal of Soil and Water Conservation, Journal-article, https://doi.org/10.2489/jswc.73.5.587
- Ransom, Curtis J. et al. (2021), Data from a public–industry partnership for enhancing corn nitrogen research, Agronomy Journal, Journal-article, https://doi.org/10.1002/agj2.20812
