{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84060"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84060","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Using Principal Component Regression to Predict Soil Nitrogen Supply Potential from Electromagnetic Induction","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Haines, Walter"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Oware, Erasmus","Geology"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:30Z","date_published":"2022-06-21T15:47:30Z","updated_at":"2026-07-27T19:05:30Z","subjects":["geophysics","agriculture"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84060","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Oware, Erasmus","Geology"]},{"key":"dc:creator","label":"Author","values":["Haines, Walter"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:30Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["geophysics","agriculture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84060"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Detailed nitrogen (N) management is crucial for viable and cost-effective corn production. SurplusN can negatively impact air and water quality while inadequate amounts of N for crop growth will have adverse effects on corn yield and quality. Determining the correct amount of N to apply requires knowledge of the soil N supply potential (sNsp). The use of electromagnetic induction(EMI) in precision agriculture has become increasingly popular because it noninvasively provides spatially continuous soil characterization. We hypothesize that the potential of soil organic matter(SOM) to mineralize into usable N can be predicted from EMI soil characterization. Thus, by incorporating EMI's spatially continuous soil characterization in a multivariate linear regression model, we propose that EMI can be utilized to predict the soils potential to release N. We employed Principal Component Regression (PCR) to predict high-resolution sNspfrom EMI. Specifically, we developed a sNsp predictive model from collocated EMI and sNspsparse measurements and subsequently applied the model to predict high-resolution sNsp from high-resolution EMI measurements. PCR is a particularly appealing nonlinear, multivariate regression technique for our purpose due to its dimensionality reduction capability and its capacity to create new uncorrelated variables. Using uncorrelated predictor variables in regression analysis eliminates correlations in the variables (multicollinearity), which improves the predictive accuracy of the model. We illustrated the performance of the strategy on EMI data and soil samples collected on a regular grid from a 60-acre corn field in Western New York. We demonstrated the potential to estimate high-resolution sNsp from EMI measurements to improve N fertilizer recommendations, for profitable corn production in an environmentally friendly manner.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using Principal Component Regression to Predict Soil Nitrogen Supply Potential from Electromagnetic Induction"]}]}],"canonical_facts":{"dc:contributor":["Oware, Erasmus","Geology"],"dc:creator":["Haines, Walter"],"dc:date":["2022-06-21T15:47:30Z","2020"],"dc:description":["M.S.","Detailed nitrogen (N) management is crucial for viable and cost-effective corn production. SurplusN can negatively impact air and water quality while inadequate amounts of N for crop growth will have adverse effects on corn yield and quality. Determining the correct amount of N to apply requires knowledge of the soil N supply potential (sNsp). The use of electromagnetic induction(EMI) in precision agriculture has become increasingly popular because it noninvasively provides spatially continuous soil characterization. We hypothesize that the potential of soil organic matter(SOM) to mineralize into usable N can be predicted from EMI soil characterization. Thus, by incorporating EMI's spatially continuous soil characterization in a multivariate linear regression model, we propose that EMI can be utilized to predict the soils potential to release N. We employed Principal Component Regression (PCR) to predict high-resolution sNspfrom EMI. Specifically, we developed a sNsp predictive model from collocated EMI and sNspsparse measurements and subsequently applied the model to predict high-resolution sNsp from high-resolution EMI measurements. PCR is a particularly appealing nonlinear, multivariate regression technique for our purpose due to its dimensionality reduction capability and its capacity to create new uncorrelated variables. Using uncorrelated predictor variables in regression analysis eliminates correlations in the variables (multicollinearity), which improves the predictive accuracy of the model. We illustrated the performance of the strategy on EMI data and soil samples collected on a regular grid from a 60-acre corn field in Western New York. We demonstrated the potential to estimate high-resolution sNsp from EMI measurements to improve N fertilizer recommendations, for profitable corn production in an environmentally friendly manner.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84060"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["geophysics","agriculture"],"dc:title":["Using Principal Component Regression to Predict Soil Nitrogen Supply Potential from Electromagnetic Induction"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:30Z"}