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: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018 http://hdl.handle.net/11449/179897 |
Resumo: | 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 harvestingArachis hypogaea L.Principal components analysisSoil textural classesThe 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.São Paulo State University - UNESPSão Paulo State University - UNESPUniversidade Estadual Paulista (Unesp)Noronha, Rafael H.F. [UNESP]Zerbato, Cristiano [UNESP]da Silva, Rouverson P. [UNESP]Ormond, Antonio T.S. [UNESP]de Oliveira, Mailson F. [UNESP]2018-12-11T17:37:12Z2018-12-11T17:37:12Z2018-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article244-250application/pdfhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018Engenharia Agricola, v. 38, n. 2, p. 244-250, 2018.1808-43890100-6916http://hdl.handle.net/11449/17989710.1590/1809-4430-Eng.Agric.v38n2p244-250/2018S0100-691620180002002442-s2.0-85047614963S0100-69162018000200244.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agricola0,305info:eu-repo/semantics/openAccess2024-06-06T15:18:44Zoai:repositorio.unesp.br:11449/179897Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-06T15:18:44Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)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. [UNESP] Arachis hypogaea L. Principal components analysis Soil textural classes |
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. [UNESP] |
author_facet |
Noronha, Rafael H.F. [UNESP] Zerbato, Cristiano [UNESP] da Silva, Rouverson P. [UNESP] Ormond, Antonio T.S. [UNESP] de Oliveira, Mailson F. [UNESP] |
author_role |
author |
author2 |
Zerbato, Cristiano [UNESP] da Silva, Rouverson P. [UNESP] Ormond, Antonio T.S. [UNESP] de Oliveira, Mailson F. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Noronha, Rafael H.F. [UNESP] Zerbato, Cristiano [UNESP] da Silva, Rouverson P. [UNESP] Ormond, Antonio T.S. [UNESP] de Oliveira, Mailson F. [UNESP] |
dc.subject.por.fl_str_mv |
Arachis hypogaea L. Principal components analysis Soil textural classes |
topic |
Arachis hypogaea L. Principal components analysis Soil textural classes |
description |
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 |
2018-12-11T17:37:12Z 2018-12-11T17:37:12Z 2018-03-01 |
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 |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018 Engenharia Agricola, v. 38, n. 2, p. 244-250, 2018. 1808-4389 0100-6916 http://hdl.handle.net/11449/179897 10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018 S0100-69162018000200244 2-s2.0-85047614963 S0100-69162018000200244.pdf |
url |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018 http://hdl.handle.net/11449/179897 |
identifier_str_mv |
Engenharia Agricola, v. 38, n. 2, p. 244-250, 2018. 1808-4389 0100-6916 10.1590/1809-4430-Eng.Agric.v38n2p244-250/2018 S0100-69162018000200244 2-s2.0-85047614963 S0100-69162018000200244.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engenharia Agricola 0,305 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
244-250 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803045531308523520 |