DEFINING MANAGEMENT ZONES BASED ON SOIL ATTRIBUTES AND SOYBEAN PRODUCTIVITY
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
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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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 |
_version_ |
1797674026084073472 |