Optimizing site-specific nitrogen fertilizer management based on maximized profit and minimized pollution

dc.contributor.advisorChairperson, Graduate Committee: Bruce D. Maxwell and Stephanie A. Ewing (co-chair)en
dc.contributor.authorHegedus, Paul Briggsen
dc.contributor.otherThis is a manuscript style paper that includes co-authored chapters.en
dc.date.accessioned2022-10-17T21:36:35Z
dc.date.available2022-10-17T21:36:35Z
dc.date.issued2022en
dc.description.abstractApplication of nitrogen fertilizers beyond crop needs contributes to nitrate pollution and soil acidification. Excess nitrogen applications are most prevalent when synthetic fertilizers are applied at uniform rates across fields. Precision agroecology harnesses the tools and technology of variable rate precision agriculture, a common but underutilized management strategy, to make ecologically conscious decisions about field management that promote economic and environmental sustainability. On-farm precision experimentation provides the basis for making data driven ecological management decisions through the field-specific assessment of crop responses. This dissertation work used on-farm experimentation with variable nitrogen fertilizer rates, combined with intensive data collection and data science, to address the main objective of this dissertation: development and evaluation of optimized nitrogen fertilizer management on a subfield scale, based on maximization of farmer net-returns and nitrogen use efficiency. The response of winter wheat yield and grain protein concentration to rates of nitrogen fertilizer application varied among fields, and across time, which influenced the model form used to characterize the relationships of grain yield and quality to fertilizer within a field. Machine learning approaches, such as random forest regression, tended to provide the lowest degree of error when forecasting future crop responses. Machine learning also demonstrated its utility for use in agronomic applications, as a support vector regression model provided the most accurate predictions of nitrogen use efficiency on a subfield scale. Crop response and nitrogen use efficiency models were integrated into a decision-making framework for optimized site-specific based nitrogen fertilizer management based on between maximized profits and minimized potential of nitrogen loss. Simulations of optimized site-specific nitrogen fertilizer management compared to farmer's status quo management showed a 100% probability across all fields tested that that mean net-return from the site-specific approaches were more profitable than applications of farmer selected nitrogen fertilizer rates. However, even while considering minimization of the potential for nitrogen loss when identifying optimum nitrogen fertilizer rates, there was field specific variation in the probability that site-specific, compared to farmer selected, nitrogen fertilizer management reduced the total amount of nitrogen applied across a field.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/16982en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.rights.holderCopyright 2022 by Paul Briggs Hegedusen
dc.subject.lcshFarm managementen
dc.subject.lcshNitrogen fertilizersen
dc.subject.lcshPrecision farmingen
dc.subject.lcshMachine learningen
dc.subject.lcshPollutionen
dc.titleOptimizing site-specific nitrogen fertilizer management based on maximized profit and minimized pollutionen
dc.typeDissertationen
mus.data.thumbpage50en
thesis.degree.committeemembersMembers, Graduate Committee: Patrick M. Carr; Robert A. Payn; Eric A. Sprolesen
thesis.degree.departmentLand Resources & Environmental Sciences.en
thesis.degree.genreDissertationen
thesis.degree.namePhDen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage370en

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