DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY

Detalhes bibliográficos
Autor(a) principal: Ramos, Fabricio Tomaz
Data de Publicação: 2017
Outros Autores: Santos, Raul Teruel, Campelo Júnior, José Holanda, Maia, João Carlos de Souza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Caatinga
Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/5765
Resumo: Demarcating soil management zones can be useful, for instance, delimiting homogeneous areas and selecting attributes that are generally correlated with plant productivity, but doing so involves several different steps. The objective of this study was to identify the chemical and physical attributes of soil and soybean plants that explain crop productivity, in addition to suggesting and testing a methodological procedure for defining soil management zones. The procedure consisted of six steps: sample collection, data filtering, variable selection, interpolation, grouping, and evaluation of management zones. The samples were collected in an experimental area of 12.5 ha cultivated with soybean during the 2013/14 crop in Dystrophic Red Latosol, in Mato Grosso, Brazil. A total of 117 pairs of plant and soil samples were collected. Student’s t-test was used (α = 0.02) to verify that the number of samples was adequate for correlation analysis. Results showed that only the P and Mn content in the grains explained (based on R2 values) the variation in soybean grain productivity the area. Based on the interpolation of these contents by ordinary kriging, the fuzzy C-means algorithm was used to separate them into groups by similarity. Division into two groups was the best option, which could be differentiated by Mann–Whitney test (P < 0.05), resulting in a map with 10 management zones.
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spelling DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITYDEFINIÇÃO DE ZONAS DE MANEJOS A PARTIR DE ATRIBUTOS DO SOLO E PRODUTIVIDADE DE SOJAGlycine max L.. Plantio direto. Agricultura de precisão.Glycine max L.. Direct seeding. Precision agriculture.Demarcating soil management zones can be useful, for instance, delimiting homogeneous areas and selecting attributes that are generally correlated with plant productivity, but doing so involves several different steps. The objective of this study was to identify the chemical and physical attributes of soil and soybean plants that explain crop productivity, in addition to suggesting and testing a methodological procedure for defining soil management zones. The procedure consisted of six steps: sample collection, data filtering, variable selection, interpolation, grouping, and evaluation of management zones. The samples were collected in an experimental area of 12.5 ha cultivated with soybean during the 2013/14 crop in Dystrophic Red Latosol, in Mato Grosso, Brazil. A total of 117 pairs of plant and soil samples were collected. Student’s t-test was used (α = 0.02) to verify that the number of samples was adequate for correlation analysis. Results showed that only the P and Mn content in the grains explained (based on R2 values) the variation in soybean grain productivity the area. Based on the interpolation of these contents by ordinary kriging, the fuzzy C-means algorithm was used to separate them into groups by similarity. Division into two groups was the best option, which could be differentiated by Mann–Whitney test (P < 0.05), resulting in a map with 10 management zones.Zonas de manejo do solo são usadas, por exemplo, para delimitar áreas homogêneas, selecionando atributos que no geral correlacionam com a produtividade das plantas, mas, defini-las requer diferentes etapas. Objetivou-se neste trabalho identificar atributos químicos e físicos do solo e de plantas de soja que explicaram a produtividade de grãos da cultura e, também, sugerir e testar um procedimento metodológico para definir zonas de manejo do solo. O procedimento consistiu de seis etapas: coleta de amostras, filtragem dos dados, seleção das variáveis, interpolação, agrupamento e avaliação das zonas de manejo. As amostras foram coletadas em uma área experimental de 12,5 ha, cultivada com soja na safra 2013/14, em um Latossolo Vermelho Distrófico, em Mato Grosso, onde foram coletados 117 pares de amostras de plantas e de solo. Utilizando-se o teste de Student (α = 0,02), verificou-se que o número de amostras foi adequado para a análise de correlação. Entretanto, apenas os teores de P e Mn dos grãos explicaram (R2) a variação da produtividade de grãos de soja na área. Com base na interpolação destes teores por krigagem ordinária utilizou-se o algoritmo fuzzy C-means para separá-los em grupos por similaridade, em que a divisão em 2 grupos foi a melhor opção, que diferiram pelo teste de Mann-Whitney (P < 0,05), resultando em um mapa com 10 zonas de manejo.Universidade Federal Rural do Semi-Árido2017-01-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufersa.edu.br/caatinga/article/view/576510.1590/1983-21252017v30n218rcREVISTA CAATINGA; Vol. 30 No. 2 (2017); 427-436Revista Caatinga; v. 30 n. 2 (2017); 427-4361983-21250100-316Xreponame:Revista Caatingainstname:Universidade Federal Rural do Semi-Árido (UFERSA)instacron:UFERSAenghttps://periodicos.ufersa.edu.br/caatinga/article/view/5765/pdfRamos, Fabricio TomazSantos, Raul TeruelCampelo Júnior, José HolandaMaia, João Carlos de Souzainfo:eu-repo/semantics/openAccess2023-07-20T10:56:15Zoai:ojs.periodicos.ufersa.edu.br:article/5765Revistahttps://periodicos.ufersa.edu.br/index.php/caatinga/indexPUBhttps://periodicos.ufersa.edu.br/index.php/caatinga/oaipatricio@ufersa.edu.br|| caatinga@ufersa.edu.br1983-21250100-316Xopendoar:2024-04-29T09:46:24.580760Revista Caatinga - Universidade Federal Rural do Semi-Árido (UFERSA)true
dc.title.none.fl_str_mv DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
DEFINIÇÃO DE ZONAS DE MANEJOS A PARTIR DE ATRIBUTOS DO SOLO E PRODUTIVIDADE DE SOJA
title DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
spellingShingle DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
Ramos, Fabricio Tomaz
Glycine max L.. Plantio direto. Agricultura de precisão.
Glycine max L.. Direct seeding. Precision agriculture.
title_short DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
title_full DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
title_fullStr DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
title_full_unstemmed DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
title_sort DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
author Ramos, Fabricio Tomaz
author_facet Ramos, Fabricio Tomaz
Santos, Raul Teruel
Campelo Júnior, José Holanda
Maia, João Carlos de Souza
author_role author
author2 Santos, Raul Teruel
Campelo Júnior, José Holanda
Maia, João Carlos de Souza
author2_role author
author
author
dc.contributor.author.fl_str_mv Ramos, Fabricio Tomaz
Santos, Raul Teruel
Campelo Júnior, José Holanda
Maia, João Carlos de Souza
dc.subject.por.fl_str_mv Glycine max L.. Plantio direto. Agricultura de precisão.
Glycine max L.. Direct seeding. Precision agriculture.
topic Glycine max L.. Plantio direto. Agricultura de precisão.
Glycine max L.. Direct seeding. Precision agriculture.
description Demarcating soil management zones can be useful, for instance, delimiting homogeneous areas and selecting attributes that are generally correlated with plant productivity, but doing so involves several different steps. The objective of this study was to identify the chemical and physical attributes of soil and soybean plants that explain crop productivity, in addition to suggesting and testing a methodological procedure for defining soil management zones. The procedure consisted of six steps: sample collection, data filtering, variable selection, interpolation, grouping, and evaluation of management zones. The samples were collected in an experimental area of 12.5 ha cultivated with soybean during the 2013/14 crop in Dystrophic Red Latosol, in Mato Grosso, Brazil. A total of 117 pairs of plant and soil samples were collected. Student’s t-test was used (α = 0.02) to verify that the number of samples was adequate for correlation analysis. Results showed that only the P and Mn content in the grains explained (based on R2 values) the variation in soybean grain productivity the area. Based on the interpolation of these contents by ordinary kriging, the fuzzy C-means algorithm was used to separate them into groups by similarity. Division into two groups was the best option, which could be differentiated by Mann–Whitney test (P < 0.05), resulting in a map with 10 management zones.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-24
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufersa.edu.br/caatinga/article/view/5765
10.1590/1983-21252017v30n218rc
url https://periodicos.ufersa.edu.br/caatinga/article/view/5765
identifier_str_mv 10.1590/1983-21252017v30n218rc
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.ufersa.edu.br/caatinga/article/view/5765/pdf
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal Rural do Semi-Árido
publisher.none.fl_str_mv Universidade Federal Rural do Semi-Árido
dc.source.none.fl_str_mv REVISTA CAATINGA; Vol. 30 No. 2 (2017); 427-436
Revista Caatinga; v. 30 n. 2 (2017); 427-436
1983-2125
0100-316X
reponame:Revista Caatinga
instname:Universidade Federal Rural do Semi-Árido (UFERSA)
instacron:UFERSA
instname_str Universidade Federal Rural do Semi-Árido (UFERSA)
instacron_str UFERSA
institution UFERSA
reponame_str Revista Caatinga
collection Revista Caatinga
repository.name.fl_str_mv Revista Caatinga - Universidade Federal Rural do Semi-Árido (UFERSA)
repository.mail.fl_str_mv patricio@ufersa.edu.br|| caatinga@ufersa.edu.br
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