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dc.contributor.advisorChairperson, Graduate Committee: Bruce D. Maxwell.en
dc.contributor.authorWagner, Nicole Catherineen
dc.date.accessioned2013-06-25T18:38:49Z
dc.date.available2013-06-25T18:38:49Z
dc.date.issued2004en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/2486en
dc.description.abstractThe specific goal of this thesis was the development of a five-variable dryland wheat yield prediction model for the optimal localized variable-rate management of fertilizer and herbicide considering varying levels of available water and weed infestation. The motivation for this work was to increase on-farm net return and reduce off-target chemical effects. The five most influential predictor variables of wheat yield were investigated: wheat density, wild oat density, nitrogen fertilizer rate, herbicide rate, and water level Previously collected field data sets that included dryland wheat yield as the dependent variable and at least one of the predictors were investigated for linear and nonlinear trends. The best-fit nonlinear yield model to the combined field data set included crop and wild oat density, and growing season precipitation; nitrogen and herbicide rates were not significant factors in this model. These results illustrated the large amount of unexplored variation in wheat yield, and the lack of ecological first principles upon which farmers base input management decisions, especially when weed infestation causes competition for limited nitrogen and water. To take an initial step towards elucidating the biological mechanisms of wheatwild oat competition with varying combinatorial levels of resources, a five-variable greenhouse experiment was conducted. The best-fitting yield model to the greenhouse data set was a nonlinear equation including all five variables. This predictive model was used to demonstrate how such an equation would help farmers make localized variable rate input decisions within a decision support framework. Monte Carlo simulation was used to produce net return prediction probabilities for site-specific variable-rate management, low level input management, and high level input management of nitrogen and herbicide based on the two sources of parameter estimates-field data and greenhouse data. The variable rate scenario resulted in larger net returns over the broadcast management scenarios in at least 48%, and at most 66%, of the simulations. This initial exploration provided considerable support for future on-farm experiments and yield prediction modeling. In addition, it established a first principle model to be parameterized for use in different dryland spring wheat growing regions.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.subject.lcshCrop yieldsen
dc.subject.lcshPrecision farmingen
dc.titleWheat yield prediction modeling for localized optimization of fertilizer and herbicide applicationen
dc.typeDissertationen
dc.rights.holderCopyright Nicole Catherine Wagner 2004en
thesis.catalog.ckey1147922en
thesis.degree.committeemembersMembers, Graduate Committee: Lisa Rew; Daniel Goodman; Jim Robison-Cox; Mike Gilpinen
thesis.degree.departmentLand Resources & Environmental Sciences.en
thesis.degree.genreDissertationen
thesis.degree.namePhDen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage239en
mus.relation.departmentLand Resources & Environmental Sciences.en_US


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