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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
25/10/2019 |
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Data da última atualização: |
24/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PEIXOTO, D. S.; SILVA, B. M.; SILVA, S. H. G.; KARLEN, D. L.; MOREIRA, S. G.; SILVA, A. A. P. da; RESENDE, A. V. de; NORTON, L. D.; CURI, N. |
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Afiliação: |
Devison Souza Peixoto, Universidade Federal de Lavras; Bruno Montoani Silva, Universidade Federal de Lavras; Sérgio Henrique Godinho Silva, Universidade Federal de Lavras; Douglas L. Karlen, USDA; Silvino Guimarães Moreira, Universidade Federal de Lavras; Alessandro Alvarenga Pereira da Silva, Universidade Federal de Lavas; ALVARO VILELA DE RESENDE, CNPMS; Lloyd Darrell Norton, Purdue University; Nilton Curi, Universidade Federal de Lavras. |
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Título: |
Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Agrosystems, Geosciences & Environment, v. 2, article 180035, 2019. |
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DOI: |
10.2134/age2018.09.0035 |
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Idioma: |
Inglês |
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Conteúdo: |
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. MenosSoil 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 soyb... Mostrar Tudo |
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Palavras-Chave: |
Random Forest. |
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Thesagro: |
Compactação do Solo; Física do Solo; Plantio Direto; Rendimento. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/203695/1/Diagnosing-Ameliorating.pdf
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Marc: |
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024 7 $a10.2134/age2018.09.0035$2DOI
100 1 $aPEIXOTO, D. S.
245 $aDiagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils.$h[electronic resource]
260 $c2019
520 $aSoil 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.
650 $aCompactação do Solo
650 $aFísica do Solo
650 $aPlantio Direto
650 $aRendimento
653 $aRandom Forest
700 1 $aSILVA, B. M.
700 1 $aSILVA, S. H. G.
700 1 $aKARLEN, D. L.
700 1 $aMOREIRA, S. G.
700 1 $aSILVA, A. A. P. da
700 1 $aRESENDE, A. V. de
700 1 $aNORTON, L. D.
700 1 $aCURI, N.
773 $tAgrosystems, Geosciences & Environment$gv. 2, article 180035, 2019.
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Embrapa Milho e Sorgo (CNPMS) |
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| 1. |  | PEIXOTO, D. S.; SILVA, B. M.; SILVA, S. H. G.; KARLEN, D. L.; MOREIRA, S. G.; SILVA, A. A. P. da; RESENDE, A. V. de; NORTON, L. D.; CURI, N. Diagnosing, ameliorating, and monitoring soil compaction in no-till brazilian soils. Agrosystems, Geosciences & Environment, v. 2, article 180035, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 5 |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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