Empirical analysis and prediction of nitrate loading and crop yield for corn–soybean rotations

dc.contributor.author Karlen, Douglas
dc.contributor.author Malone, Robert
dc.contributor.author Ma, Liwang
dc.contributor.author Karlen, Douglas
dc.contributor.author Kanwar, Rameshwar
dc.contributor.author Meade, T.
dc.contributor.author Meek, D.
dc.contributor.author Kanwar, Ramesh
dc.contributor.author Hatfield, Jerry
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-14T16:19:28.000
dc.date.accessioned 2020-06-29T22:40:57Z
dc.date.available 2020-06-29T22:40:57Z
dc.date.embargo 2014-09-21
dc.date.issued 2007-07-15
dc.description.abstract <p>Nitrate nitrogen losses through subsurface drainage and crop yield are determined by multiple climatic and management variables. The combined and interactive effects of these variables, however, are poorly understood. Our objective is to predict crop yield, nitrate concentration, drainage volume, and nitrate loss in subsurface drainage from a corn (<em>Zea mays</em> L.) and soybean (<em>Glycine max</em> (L.) Merr.) rotation as a function of rainfall amount, soybean yield for the year before the corn–soybean sequence being evaluated, N source, N rate, and timing of N application in northeastern Iowa, U.S.A. Ten years of data (1994–2003) from a long-term study near Nashua, Iowa were used to develop multivariate polynomial regression equations describing these variables. The regression equations described over 87, 85, 94, 76, and 95% of variation in soybean yield, corn yield, subsurface drainage, nitrate concentration, and nitrate loss in subsurface drainage, respectively. A two-year rotation under average soil, average climatic conditions, and 125 kg N/ha application was predicted to loose 29, 37, 36, and 30 kg N/ha in subsurface drainage for early-spring swine manure, fall-applied swine manure, early-spring UAN fertilizer, and late-spring split UAN fertilizer (urea ammonium nitrate), respectively. Predicted corn yields were 10.0 and 9.7 Mg/ha for the swine manure and UAN sources applied at 125 kg N/ha. Timing of application (i.e., fall or spring) did not significantly affect corn yield. These results confirm other research suggesting that manure application can result in less nitrate leaching than UAN (e.g., 29 vs. 36 kg N/ha), and that spring application reduces nitrate leaching compared to fall application (e.g., 29 vs. 37 kg N/ha). The regression equations improve our understanding of nitrate leaching; offer a simple method to quantify potential N losses from Midwestern corn–soybean rotations under the climate, soil, and management conditions of the Nashua field experiment; and are a step toward development of easy to use N management tools.</p>
dc.description.comments <p>This article is from <em>Geoderma</em> 140 (2007): 223–234, doi:<a href="http://dx.doi.org/10.1016/j.geoderma.2007.04.007" target="_blank">10.1016/j.geoderma.2007.04.007</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/571/
dc.identifier.articleid 1866
dc.identifier.contextkey 6143549
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/571
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1352
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/571/2007_Malone_EmpiricalAnalysis.pdf|||Sat Jan 15 00:59:24 UTC 2022
dc.source.uri 10.1016/j.geoderma.2007.04.007
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Soil Science
dc.subject.disciplines Water Resource Management
dc.subject.keywords Multivariate polynomial regression
dc.subject.keywords Tile flow
dc.subject.keywords Subsurface drainage
dc.subject.keywords Drainage index
dc.subject.keywords Management effects
dc.title Empirical analysis and prediction of nitrate loading and crop yield for corn–soybean rotations
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication f36d4ee5-a0dc-46fc-9716-9cc7ad1e2871
relation.isAuthorOfPublication 5210e67e-b8da-4e17-be3f-843a09381196
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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