Author: admin

United States Department of Agriculture, National Agricultural Statistics Service. Quick stats: NASS statistical data. https://quickstats.nass.usda.gov/ (Accessed 16 October 2024) (2023).

Cassman, K. G. & Dobermann, A. Nitrogen and the future of agriculture: 20 years on: this article belongs to Ambio’s 50th anniversary collection. theme: solutions-oriented research. Ambio 51, 17–24 (2022).

Google Scholar 

Donner, S. & Kucharik, C. Corn-based ethanol production compromises goal of reducing nitrogen export by the Mississippi River. Proc. Natl. Acad. Sci. USA 105, 4513–4518 (2008).

Google Scholar 

Ribaudo, M. Reducing agriculture’s nitrogen footprint: are new policy approaches needed?. Amber Waves: The Economics of Food, Farming, Natural Resources, and Rural America. Technical Report, 34–39 (2011).

Greer, K. D. & Pittelkow, C. M. Linking nitrogen losses with crop productivity in maize agroecosystems. Front. Sustain. Food Syst. 2, 29 (2018).

Google Scholar 

Li, A. et al. A case study of environmental benefits of sensor-based nitrogen application in corn. J. Environ. Qual. 45, 675–683 (2016).

Google Scholar 

Galloway, J. N. & Cowling, E. B. Reactive nitrogen and the world: 200 years of change. Ambio 31, 64–71 (2002).

Google Scholar 

Kim, S. & Dale, B. E. Effects of nitrogen fertilizer application on greenhouse gas emissions and economics of corn production. Environ. Sci. Technol. 42, 6028–6033 (2008).

Google Scholar 

Ravishankara, A., Daniel, J. S. & Portmann, R. W. Nitrous oxide (N₂O): the dominant ozone-depleting substance emitted in the 21st century. Science 326, 123–125 (2009).

Google Scholar 

Zhang, X. et al. Quantifying nitrous oxide fluxes on multiple spatial scales in the Upper Midwest, USA. Int. J. Biometeorol. 59, 299–310 (2014).

Google Scholar 

Gardner, J. B. & Drinkwater, L. E. The fate of nitrogen in grain cropping systems: a meta-analysis of ¹⁵N field experiments. Ecol. Appl. 19, 2167–2184 (2009).

Google Scholar 

Millar, N., Robertson, G. P., Grace, P. R., Gehl, R. J. & Hoben, J. P. Nitrogen fertilizer management for nitrous oxide (N₂O) mitigation in intensive corn (maize) production: an emissions reduction protocol for US Midwest agriculture. Mitig. Adapt. Strateg. Glob. change 15, 185–204 (2010).

Google Scholar 

Robertson, G. P. et al. Nitrogen–climate interactions in U.S. agriculture. Biogeochemistry 114, 41–70 (2013).

Google Scholar 

Davidson, E. A., Suddick, E. C., Rice, C. W. & Prokopy, L. S. More food, low pollution (mo fo lo po): a grand challenge for the 21st century. J. Environ. Qual. 44, 305–311 (2015).

Google Scholar 

Khanna, M., Gramig, B. M., DeLucia, E. H., Cai, X. & Kumar, P. Harnessing emerging technologies to reduce Gulf hypoxia. Nat. Sustain. 2, 889–891 (2019).

Kanter, D. R., Del Grosso, S., Scheer, C., Pelster, D. E. & Galloway, J. N. Why future nitrogen research needs the social sciences. Curr. Opin. Environ. Sustain. 47, 54–60 (2020).

Google Scholar 

Zhang, X. et al. Nitrogen management during decarbonization. Nat. Rev. Earth Environ. 5, 717–731 (2024).

Heady, E. O., Pesek, J. T. & Brown, W. G. Crop response surfaces and economic optima in fertilizer use. Iowa State Coll. J. Sci. 29, 653–665 (1955).

Google Scholar 

Cerrato, M. & Blackmer, A. Comparison of models for describing; corn yield response to nitrogen fertilizer. Agron. J. 82, 138–143 (1990).

Google Scholar 

Bullock, D. G. & Bullock, D. S. Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: a comparison. Agron. J. 86, 191–195 (1994).

Google Scholar 

McSwiney, C. P. & Robertson, G. P. Nonlinear response of N₂O flux to incremental fertilizer addition in a continuous maize (Zea mays L.) cropping system. Glob. Change Biol. 11, 1712–1719 (2005).

Google Scholar 

Hoben, J., Gehl, R., Millar, N., Grace, P. & Robertson, G. Nonlinear nitrous oxide (N₂O) response to nitrogen fertilizer in on-farm corn crops of the US Midwest. Glob. Change Biol. 17, 1140–1152 (2011).

Google Scholar 

Zhang, X., Mauzerall, D. L., Davidson, E. A., Kanter, D. R. & Cai, R. The economic and environmental consequences of implementing nitrogen-efficient technologies and management practices in agriculture. J. Environ. Qual. 44, 312–324 (2015).

Google Scholar 

Mandrini, G., Pittelkow, C. M., Archontoulis, S. V., Mieno, T. & Martin, N. F. Understanding differences between static and dynamic nitrogen fertilizer tools using simulation modeling. Agric. Syst. 194, 103275 (2021).

Google Scholar 

Banger, K., Nasielski, J., Janovicek, K., Sulik, J. & Deen, B. Potential farm-level economic impact of incorporating environmental costs into nitrogen decision making: a case study in Canadian corn production. Front. Sustain. Food Syst. 4, 96 (2020).

Correndo, A. A. et al. Unraveling uncertainty drivers of the maize yield response to nitrogen: a Bayesian and machine learning approach. Agric. For. Meteorol. 311, 108668 (2021).

Google Scholar 

Morris, T. F. et al. Strengths and limitations of nitrogen rate recommendations for corn and opportunities for improvement. Agron. J. 110, 1–37 (2018).

Google Scholar 

Ransom, C. J. et al. Corn nitrogen rate recommendation tools’ performance across eight US Midwest corn belt states. Agron. J. 112, 470–492 (2020).

Google Scholar 

Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).

Google Scholar 

Chai, Y., Pannell, D. J. & Pardey, P. G. Nudging farmers to reduce water pollution from nitrogen fertilizer. Food Policy 120, 102525 (2023).

Google Scholar 

Houser, M. Farmer motivations for excess nitrogen use in the US Corn Belt. Case Stud. Environ. 6, 1688823 (2022).

Google Scholar 

Sellars, S. C., Schnitkey, G. D. & Gentry, L. F. Do Illinois farmers follow university-based nitrogen recommendations? In Proc. 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304617. https://ageconsearch.umn.edu/record/304617?ln=en&v=pdf (Agricultural and Applied Economics Association, 2020).

Sheriff, G. Efficient waste? Why farmers over-apply nutrients and the implications for policy design. Rev. Agric. Econ. 27, 542–557 (2005).

Google Scholar 

Stuart, D., Schewe, R. & McDermott, M. Reducing nitrogen fertilizer application as a climate change mitigation strategy: understanding farmer decision-making and potential barriers to change in the U.S. Land Use Policy 36, 210–218 (2014).

Google Scholar 

Osmond, D. L., Hoag, D. L., Luloff, A. E., Meals, D. W. & Neas, K. Farmers’ use of nutrient management: lessons from watershed case studies. J. Environ. Qual. 44, 382–390 (2015).

Google Scholar 

Rajsic, P., Weersink, A. & Gandorfer, M. Risk and nitrogen application levels. Can. J. Agric. Econ./Rev. can.’agroecon. 57, 223–239 (2009).

Google Scholar 

Mitchell, P. D. Nutrient best management practice insurance and farmer perceptions of adoption risk. J. Agric. Appl. Econ. 36, 657–673 (2004).

Google Scholar 

Pannell, D. J. Economic perspectives on nitrogen in farming systems: managing trade-offs between production, risk and the environment. Soil Res. 55, 473–478 (2017).

Google Scholar 

Tevenart, C. & Brunette, M. Role of farmers’ risk and ambiguity preferences on fertilization decisions: an experiment. Sustainability 13, 9802 (2021).

Google Scholar 

Stuart, D., Denny, R. C., Houser, M., Reimer, A. P. & Marquart-Pyatt, S. Farmer selection of sources of information for nitrogen management in the US Midwest: implications for environmental programs. Land Use Policy 70, 289–297 (2018).

Google Scholar 

Mitchell, P. D. & Hennessy, D. A. Factors determining best management practice adoption incentives and the impact of green insurance. In Risk management and the environment: Agriculture in perspective 52–66 (Dordrecht: Springer Netherlands, 2003).

Coble, K. H., Hanson, T., Miller, J. C. & Shaik, S. Agricultural insurance as an environmental policy tool. J. Agric. Appl. Econ. 35, 391–405 (2003).

Google Scholar 

Metcalfe, T., Bosch, D. J., Pease, J. W., Alley, M. M. & Phillips, S. B. Yield reserve program costs in the Virginia Coastal Plain. Agric. Resour. Econ. Rev. 36, 197–212 (2007).

Google Scholar 

Harris, L. M. & Swinton, S. M. Using BMP insurance to improve farm management. (Michigan State University, 2012).

Thorburn, P. J. et al. Innovative economic levers: a system for underwriting risk of practice change in cane-farming. (Reef and Rainforest Research Centre, 2020).

McLellan, E. L. et al. The nitrogen balancing act: tracking the environmental performance of food production. Bioscience 68, 194–203 (2018).

Google Scholar 

Zhang, X. et al. Quantification of global and national nitrogen budgets for crop production. Nat. Food 2, 529–540 (2021).

Google Scholar 

Gray Betts, C., Hicks, D., Reader, M. & Wilson, P. Nitrogen balance is a predictor of farm business performance in the English Farm Business Survey. Front. Sustain. Food Syst. 7, 1106196 (2023).

Google Scholar 

Paut, R. et al. On-farm assessment of an innovative dynamic fertilization method to improve nitrogen recovery in winter wheat. Nutr. Cycl. Agroecosyst. 129, 475–490 (2024).

Google Scholar 

Shahadha, S. S., Wendroth, O. & Ding, D. Nitrogen and rainfall effects on crop growth—experimental results and scenario analyses. Water 13, 2219 (2021).

Google Scholar 

McKay Fletcher, D. et al. Projected increases in precipitation are expected to reduce nitrogen use efficiency and alter optimal fertilization timings in agriculture in the south east of England. ACS Es&t Eng. 2, 1414–1424 (2022).

Google Scholar 

Govindasamy, P. et al. Nitrogen use efficiency-“a key to enhance crop productivity under a changing climate. Front. Plant Sci. 14, 1121073 (2023).

Google Scholar 

Northrup, D. L., Basso, B., Wang, M. Q., Morgan, C. L. & Benfey, P. N. Novel technologies for emission reduction complement conservation agriculture to achieve negative emissions from row-crop production. Proc. Natl. Acad. Sci. USA 118, e2022666118 (2021).

Google Scholar 

Menegat, S., Ledo, A. & Tirado, R. Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture. Sci. Rep. 12, 14490 (2022).

Google Scholar 

Sela, S. et al. Dynamic model improves agronomic and environmental outcomes for maize nitrogen management over static approach. J. Environ. Qual. 46, 311–319 (2017).

Google Scholar 

Puntel, L. A. et al. A systems modeling approach to forecast corn economic optimum nitrogen rate. Front. Plant Sci. 9, 436 (2018).

Google Scholar 

Scharf, P. C., Brouder, S. M. & Hoeft, R. G. Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north-central USA. Agron. J. 98, 655–665 (2006).

Google Scholar 

Schmidt, J. P., Dellinger, A. E. & Beegle, D. B. Nitrogen recommendations for corn: an on-the-go sensor compared with current recommendation methods. Agron. J. 101, 916–924 (2009).

Google Scholar 

Ciampitti, I. A. et al. Does the critical n dilution curve for maize crop vary across genotype x environment x management scenarios?—A Bayesian analysis. Eur. J. Agron. 123, 126202 (2021).

Google Scholar 

Mandrini, G., Bullock, D. S. & Martin, N. F. Modeling the economic and environmental effects of corn nitrogen management strategies in Illinois. Field Crops Res. 261, 108000 (2021).

Google Scholar 

Chunjiang, Z. et al. Evaluation of variable-rate nitrogen recommendation of winter wheat based on SPAD chlorophyll meter measurement. N. Z. J. Agric. Res. 50, 735–741 (2007).

Google Scholar 

Dumont, B., Basso, B., Bodson, B., Destain, J.-P. & Destain, M.-F. Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium. Environ. Model. Softw. 79, 184–196 (2016).

Google Scholar 

Kablan, L. A. et al. Variability in corn yield response to nitrogen fertilizer in eastern Canada. Agron. J. 109, 2231–2242 (2017).

Google Scholar 

Vergara, O., Zuba, G., Doggett, T. & Seaquist, J. Modeling the potential impact of catastrophic weather on crop insurance industry portfolio losses. Am. J. Agric. Econ. 90, 1256–1262 (2008).

Google Scholar 

Kunreuther, H. Correlated risks. In Encyclopedia of quantitative risk analysis and assessment (eds E. L. Melnick & B. S. Everitt), 374–375 (John Wiley & Sons, 2008).

Hernández-Rojas, L. F. et al. The role of data-driven methodologies in weather index insurance. Appl. Sci. 13, 4785 (2023).

Google Scholar 

Roy, E. D., Wagner, C. R. H. & Niles, M. T. Hot spots of opportunity for improved cropland nitrogen management across the United States. Environ. Res. Lett. 16, 035004 (2021).

Google Scholar 

Van Grinsven, H. J. et al. Costs and benefits of nitrogen for Europe and implications for mitigation. Environ. Sci. Technol. 47, 3571–3579 (2013).

Google Scholar 

Rosas, F., Babcock, B. A. & Hayes, D. J. Nitrous oxide emission reductions from cutting excessive nitrogen fertilizer applications. Clim. Change 132, 353–367 (2015).

Google Scholar 

Blandford, D. & Hassapoyannes, K. The role of agriculture in global GHG mitigation. OECD Food, Agriculture and Fisheries Papers No. 112, https://doi.org/10.1787/da017ae2-en (OECD Publishing, Paris, 2018).

Gao, Y. & Cabrera Serrenho, A. Greenhouse gas emissions from nitrogen fertilizers could be reduced by up to one-fifth of current levels by 2050 with combined interventions. Nat. Food 4, 170–178 (2023).

Google Scholar 

Jain, A. K. Greenhouse gas emissions from nitrogen fertilizers. Nat. Food 4, 139–140 (2023).

Google Scholar 

Illinois-EPA. Illinois nutrient loss reduction strategy. Illinois Environmental Protection Agency. https://epa.illinois.gov/topics/water-quality/watershed-management/excess-nutrients/nutrient-loss-reduction-strategy.html (Accessed 28 November 2024) (2015).

Pannell, D. J. Flat earth economics: the far-reaching consequences of flat payoff functions in economic decision making. Rev. Agric. Econ. 28, 553–566 (2006).

Google Scholar 

Pannell, D. J. et al. Understanding and promoting adoption of conservation practices by rural landholders. Aust. J. Exp. Agric. 46, 1407–1424 (2006).

Google Scholar 

SriRamaratnam, S., Bessler, D. A., Rister, M. E., Matocha, J. E. & Novak, J. Fertilization under uncertainty: an analysis based on producer yield expectations. Am. J. Agric. Econ. 69, 349–357 (1987).

Google Scholar 

Engel, S., Pagiola, S. & Wunder, S. Designing payments for environmental services in theory and practice: an overview of the issues. Ecol. Econ. 65, 663–674 (2008).

Google Scholar 

Dionne, K. Y. & Horowitz, J. The political effects of agricultural subsidies in Africa: Evidence from Malawi. World Dev. 87, 215–226 (2016).

Google Scholar 

Reyes-García, V. et al. The costs of subsidies and externalities of economic activities driving nature decline. Ambio 54, 1128–1141 (2025).

Campbell, B. M. et al. Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems. Curr. Opin. Environ. Sustain. 34, 13–20 (2018).

Google Scholar 

Lyle, G., Bryan, B. & Ostendorf, B. Post-processing methods to eliminate erroneous grain yield measurements: review and directions for future development. Precis. Agric. 15, 377–402 (2014).

Google Scholar 

Arslan, S. & Colvin, T. S. Grain yield mapping: yield sensing, yield reconstruction, and errors. Precis. Agric. 3, 135–154 (2002).

Google Scholar 

Trevisan, R., Bullock, D. & Martin, N. Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation. Precis. Agric. 22, 342–363 (2021).

Google Scholar 

Alesso, C. A. & Martin, N. F. Spatial and temporal variability of corn response to nitrogen and seed rates. Agron. J. 116, 897–916 (2024).

Google Scholar 

Lemaire, G. & Ciampitti, I. Crop mass and N status as prerequisite covariables for unraveling nitrogen use efficiency across genotype-by-environment-by-management scenarios: a review. Plants 9, 1309 (2020).

Google Scholar 

Wang, X. et al. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. Eur. J. Agron. 123, 126193 (2021).

Google Scholar 

Ciampitti, I. A., Briat, J.-F., Gastal, F. & Lemaire, G. Redefining crop breeding strategy for effective use of nitrogen in cropping systems. Commun. Biol. 5, 823 (2022).

Google Scholar 

Shao, H. et al. Evaluating critical nitrogen dilution curves for assessing maize nitrogen status across the US Midwest. Agronomy 13, 1948 (2023).

Google Scholar 

Preza-Fontes, G., Nafziger, E. D., Christianson, L. E. & Pittelkow, C. M. Relationship of in-season soil nitrogen concentration with corn yield and potential nitrogen losses. Soil Sci. Soc. Am. J. 84, 1296–1306 (2020).

Google Scholar 

Kyveryga, P. & Blackmer, T. On-farm evaluations to calibrate tools for estimating late-season nitrogen status of corn. Agron. J. 104, 1284–1294 (2012).

Google Scholar 

Just, R. E., Calvin, L. & Quiggin, J. Adverse selection in crop insurance: actuarial and asymmetric information incentives. Am. J. Agric. Econ. 81, 834–849 (1999).

Google Scholar 

Tremblay, N. et al. Corn response to nitrogen is influenced by soil texture and weather. Agron. J. 104, 1658–1671 (2012).

Google Scholar 

Lory, J. & Scharf, P. Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agron. J. 95, 994–999 (2003).

Google Scholar 

Meisinger, J. Evaluating plant-available nitrogen in soil-crop systems. in Nitrogen in Crop Production (ed. Hauck, R. D.) 389–416 (1984).

Zhu, Q., Schmidt, J., Lin, H. & Sripada, R. Hydropedological processes and their implications for nitrogen availability to corn. Geoderma 154, 111–122 (2009).

Google Scholar 

Agflex. Final Report on the BMP Challenge Program (American Farmland Trust, 2014).

Finger, R., Swinton, S. M., El Benni, N. & Walter, A. Precision farming at the nexus of agricultural production and the environment. Annu. Rev. Resour. Econ. 11, 313–335 (2019).

Google Scholar 

Campbell, S. Insuring best management practices. J. Soil Water Conserv. 58, 116A–117A (2003).

Google Scholar 

Mandrini, G., Archontoulis, S. V., Pittelkow, C. M., Mieno, T. & Martin, N. F. Simulated dataset of corn response to nitrogen over thousands of fields and multiple years in Illinois. Data Brief 40, 107753 (2022).

Google Scholar 

Mandrini, G., Pittelkow, C. M., Archontoulis, S., Kanter, D. & Martin, N. F. Exploring trade-offs between profit, yield, and the environmental footprint of potential nitrogen fertilizer regulations in the US Midwest. Front. Plant Sci. 13, 852116 (2022).

Google Scholar 

Liu, M., Khanna, M. & Atallah, S. S. Policy instruments to promote the adoption of sustainable nitrogen management practices. Paper presented at the 2024 Agricultural and Applied Economics Association (AAEA) Annual Meeting. https://doi.org/10.22004/ag.econ.344024 (New Orleans, LA, 2024).

Thornton, P. E. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2 Technical Report (Oak Ridge National Laboratory (ORNL), 2014).

Natural Resources Conservation Service, USDA. Soil Survey Geographic (SSURGO) Database for Illinois. (2018).

Thorburn, P. J., Biggs, J. S., Collins, K. & Probert, M. Using the APSIM model to estimate nitrous oxide emissions from diverse Australian sugarcane production systems. Agric. Ecosyst. Environ. 136, 343–350 (2010).

Google Scholar 

Del Grosso, S. et al. General model for N₂O and N₂ gas emissions from soils due to denitrification. Glob. Biogeochem. cycles 14, 1045–1060 (2000).

Google Scholar 

Li, Y. et al. A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain. Ecol. Model. 203, 395–423 (2007).

Google Scholar 

Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021—The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2023).

Liu, X., Elgowainy, A. & Wang, M. Life cycle energy use and greenhouse gas emissions of ammonia production from renewable resources and industrial by-products. Green. Chem. 22, 5751–5761 (2020).

Google Scholar 

Hood, C. & Kidder, G. Fertilizers and energy. Fact Sheet EES-58, November (1992).

Basso, B., Shuai, G., Zhang, J. & Robertson, G. P. Yield stability analysis reveals sources of large-scale nitrogen loss from the US Midwest. Sci. Rep. 9, 1–9 (2019).

Google Scholar 

Puntel, L. A., Pagani, A. & Archontoulis, S. V. Development of a nitrogen recommendation tool for corn considering static and dynamic variables. Eur. J. Agron. 105, 189–199 (2019).

Google Scholar 

Nafziger, E., Sawyer, J., Laboski, C. & Franzen, D. The MRTN approach to making nitrogen rate recommendations: background and implementation. Crops Soils 55, 4–11 (2022).

Google Scholar 

Baum, M. E. et al. The optimum nitrogen fertilizer rate for maize in the US Midwest is increasing. Nat. Commun. 16, 404 (2025).

Tenorio, F. A. et al. Luck versus skill: is nitrogen balance in irrigated maize fields driven by persistent or random factors?. Environ. Sci. Technol. 55, 749–756 (2020).