Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils

Detalhes bibliográficos
Autor(a) principal: Peixoto, Devison Souza
Data de Publicação: 2019
Outros Autores: Silva, Bruno Montoani, Karlen, Douglas L., Moreira, Silvino Guimarães, Silva, Alessandro Alvarenga Pereira da, Resende, Álvaro Vilela de, Norton, Lloyd Darrell, Curi, Nilton
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/39566
Resumo: Soil compaction can significantly reduce crop yield. Our objective was to identify the most sensitive soil physical property and process indicators related to crop yield using a Random Forest algorithm (RFA). This machine-learning, decision-making tool was used with field-scale data from five soil management treatments designed to ameliorate compaction in no-tillage (NT) fields. The treatments were: T1, NT for 10 yr (control); T2, NT with surface application of 3.6 Mg ha-1 of agricultural gypsum; T3, NT with subsoiling plus 1.44 Mg ha-1 of highly reactive limestone applied to a depth of 0.60 m; T4, NT planting following chisel plowing at a depth of 0.26 m; and T5, NT with subsoiling to a depth of 0.60 m plus 1.44 Mg ha-1 of surface-applied, highly reactive limestone. Fifteen soil physical properties and processes related to growth and yield of soybean [Glycine max (L.) Merr.] were measured. Mechanical intervention, specifically subsoiling, improved soil physical properties and increased soybean yield cultivated following occasional tillage. The RFA ranked penetration resistance (PR), air capacity, macroporosity, relative field capacity, and the Dexter-S index as the most sensitive soil physical indicators affecting soybean yield. Those indicators were also sensitive to changes in soil structure due to subsoiling. We conclude that the RFA was an effective tool for screening indicators and that those chosen can be effective for monitoring soil compaction and its effect on soybean yield. Penetration resistance may be used to guide on-farm decision-making regarding when and how NT soil compaction should be addressed
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spelling Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soilsSoil compaction can significantly reduce crop yield. Our objective was to identify the most sensitive soil physical property and process indicators related to crop yield using a Random Forest algorithm (RFA). This machine-learning, decision-making tool was used with field-scale data from five soil management treatments designed to ameliorate compaction in no-tillage (NT) fields. The treatments were: T1, NT for 10 yr (control); T2, NT with surface application of 3.6 Mg ha-1 of agricultural gypsum; T3, NT with subsoiling plus 1.44 Mg ha-1 of highly reactive limestone applied to a depth of 0.60 m; T4, NT planting following chisel plowing at a depth of 0.26 m; and T5, NT with subsoiling to a depth of 0.60 m plus 1.44 Mg ha-1 of surface-applied, highly reactive limestone. Fifteen soil physical properties and processes related to growth and yield of soybean [Glycine max (L.) Merr.] were measured. Mechanical intervention, specifically subsoiling, improved soil physical properties and increased soybean yield cultivated following occasional tillage. The RFA ranked penetration resistance (PR), air capacity, macroporosity, relative field capacity, and the Dexter-S index as the most sensitive soil physical indicators affecting soybean yield. Those indicators were also sensitive to changes in soil structure due to subsoiling. We conclude that the RFA was an effective tool for screening indicators and that those chosen can be effective for monitoring soil compaction and its effect on soybean yield. Penetration resistance may be used to guide on-farm decision-making regarding when and how NT soil compaction should be addressedWiley2020-03-31T12:04:43Z2020-03-31T12:04:43Z2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfPEIXOTO, D. S. et al. Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils. Agrosystems, Geosciences & Environment, [S.l.], v. 2, n. 1, p. 1-14, 2019.http://repositorio.ufla.br/jspui/handle/1/39566Agrosystems, Geosciences & Environmentreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessPeixoto, Devison SouzaSilva, Bruno MontoaniKarlen, Douglas L.Moreira, Silvino GuimarãesSilva, Alessandro Alvarenga Pereira daResende, Álvaro Vilela deNorton, Lloyd DarrellCuri, Niltoneng2020-03-31T12:04:44Zoai:localhost:1/39566Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2020-03-31T12:04:44Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
title Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
spellingShingle Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
Peixoto, Devison Souza
title_short Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
title_full Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
title_fullStr Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
title_full_unstemmed Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
title_sort Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils
author Peixoto, Devison Souza
author_facet Peixoto, Devison Souza
Silva, Bruno Montoani
Karlen, Douglas L.
Moreira, Silvino Guimarães
Silva, Alessandro Alvarenga Pereira da
Resende, Álvaro Vilela de
Norton, Lloyd Darrell
Curi, Nilton
author_role author
author2 Silva, Bruno Montoani
Karlen, Douglas L.
Moreira, Silvino Guimarães
Silva, Alessandro Alvarenga Pereira da
Resende, Álvaro Vilela de
Norton, Lloyd Darrell
Curi, Nilton
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Peixoto, Devison Souza
Silva, Bruno Montoani
Karlen, Douglas L.
Moreira, Silvino Guimarães
Silva, Alessandro Alvarenga Pereira da
Resende, Álvaro Vilela de
Norton, Lloyd Darrell
Curi, Nilton
description Soil compaction can significantly reduce crop yield. Our objective was to identify the most sensitive soil physical property and process indicators related to crop yield using a Random Forest algorithm (RFA). This machine-learning, decision-making tool was used with field-scale data from five soil management treatments designed to ameliorate compaction in no-tillage (NT) fields. The treatments were: T1, NT for 10 yr (control); T2, NT with surface application of 3.6 Mg ha-1 of agricultural gypsum; T3, NT with subsoiling plus 1.44 Mg ha-1 of highly reactive limestone applied to a depth of 0.60 m; T4, NT planting following chisel plowing at a depth of 0.26 m; and T5, NT with subsoiling to a depth of 0.60 m plus 1.44 Mg ha-1 of surface-applied, highly reactive limestone. Fifteen soil physical properties and processes related to growth and yield of soybean [Glycine max (L.) Merr.] were measured. Mechanical intervention, specifically subsoiling, improved soil physical properties and increased soybean yield cultivated following occasional tillage. The RFA ranked penetration resistance (PR), air capacity, macroporosity, relative field capacity, and the Dexter-S index as the most sensitive soil physical indicators affecting soybean yield. Those indicators were also sensitive to changes in soil structure due to subsoiling. We conclude that the RFA was an effective tool for screening indicators and that those chosen can be effective for monitoring soil compaction and its effect on soybean yield. Penetration resistance may be used to guide on-farm decision-making regarding when and how NT soil compaction should be addressed
publishDate 2019
dc.date.none.fl_str_mv 2019
2020-03-31T12:04:43Z
2020-03-31T12:04:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv PEIXOTO, D. S. et al. Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils. Agrosystems, Geosciences & Environment, [S.l.], v. 2, n. 1, p. 1-14, 2019.
http://repositorio.ufla.br/jspui/handle/1/39566
identifier_str_mv PEIXOTO, D. S. et al. Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils. Agrosystems, Geosciences & Environment, [S.l.], v. 2, n. 1, p. 1-14, 2019.
url http://repositorio.ufla.br/jspui/handle/1/39566
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Agrosystems, Geosciences & Environment
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
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institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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