MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000200244 |
Resumo: | ABSTRACT The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than “one”: the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation. |
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MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTINGsoil textural classesArachis hypogaea L.principal components analysisABSTRACT The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than “one”: the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation.Associação Brasileira de Engenharia Agrícola2018-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000200244Engenharia Agrícola v.38 n.2 2018reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v38n2p244-250/2018info:eu-repo/semantics/openAccessNoronha,Rafael H. F.Zerbato,CristianoSilva,Rouverson P. daOrmond,Antonio T. S.Oliveira,Mailson F. deeng2018-05-29T00:00:00Zoai:scielo:S0100-69162018000200244Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2018-05-29T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
title |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
spellingShingle |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING Noronha,Rafael H. F. soil textural classes Arachis hypogaea L. principal components analysis |
title_short |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
title_full |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
title_fullStr |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
title_full_unstemmed |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
title_sort |
MULTIVARIATE ANALYSIS OF PEANUT MECHANIZED HARVESTING |
author |
Noronha,Rafael H. F. |
author_facet |
Noronha,Rafael H. F. Zerbato,Cristiano Silva,Rouverson P. da Ormond,Antonio T. S. Oliveira,Mailson F. de |
author_role |
author |
author2 |
Zerbato,Cristiano Silva,Rouverson P. da Ormond,Antonio T. S. Oliveira,Mailson F. de |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Noronha,Rafael H. F. Zerbato,Cristiano Silva,Rouverson P. da Ormond,Antonio T. S. Oliveira,Mailson F. de |
dc.subject.por.fl_str_mv |
soil textural classes Arachis hypogaea L. principal components analysis |
topic |
soil textural classes Arachis hypogaea L. principal components analysis |
description |
ABSTRACT The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than “one”: the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000200244 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000200244 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v38n2p244-250/2018 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.38 n.2 2018 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126273668251648 |