Productivity of soybean in management zones with application of different sowing densities

Detalhes bibliográficos
Autor(a) principal: Camicia,Rafaela Greici da Motta
Data de Publicação: 2018
Outros Autores: Maggi,Marcio Furlan, Souza,Eduardo Godoy de, Bazzi,Claudio Leones, Konopatzki,Evandro André, Michelon,Gabriela Karoline, Pinheiro,José Bruno Santos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018001200201
Resumo: ABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.
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spelling Productivity of soybean in management zones with application of different sowing densitiesprecision agriculturegrain productionmanagement zonesABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.Universidade Federal de Santa Maria2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018001200201Ciência Rural v.48 n.12 2018reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20180532info:eu-repo/semantics/openAccessCamicia,Rafaela Greici da MottaMaggi,Marcio FurlanSouza,Eduardo Godoy deBazzi,Claudio LeonesKonopatzki,Evandro AndréMichelon,Gabriela KarolinePinheiro,José Bruno Santoseng2018-12-04T00:00:00ZRevista
dc.title.none.fl_str_mv Productivity of soybean in management zones with application of different sowing densities
title Productivity of soybean in management zones with application of different sowing densities
spellingShingle Productivity of soybean in management zones with application of different sowing densities
Camicia,Rafaela Greici da Motta
precision agriculture
grain production
management zones
title_short Productivity of soybean in management zones with application of different sowing densities
title_full Productivity of soybean in management zones with application of different sowing densities
title_fullStr Productivity of soybean in management zones with application of different sowing densities
title_full_unstemmed Productivity of soybean in management zones with application of different sowing densities
title_sort Productivity of soybean in management zones with application of different sowing densities
author Camicia,Rafaela Greici da Motta
author_facet Camicia,Rafaela Greici da Motta
Maggi,Marcio Furlan
Souza,Eduardo Godoy de
Bazzi,Claudio Leones
Konopatzki,Evandro André
Michelon,Gabriela Karoline
Pinheiro,José Bruno Santos
author_role author
author2 Maggi,Marcio Furlan
Souza,Eduardo Godoy de
Bazzi,Claudio Leones
Konopatzki,Evandro André
Michelon,Gabriela Karoline
Pinheiro,José Bruno Santos
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Camicia,Rafaela Greici da Motta
Maggi,Marcio Furlan
Souza,Eduardo Godoy de
Bazzi,Claudio Leones
Konopatzki,Evandro André
Michelon,Gabriela Karoline
Pinheiro,José Bruno Santos
dc.subject.por.fl_str_mv precision agriculture
grain production
management zones
topic precision agriculture
grain production
management zones
description ABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
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=S0103-84782018001200201
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018001200201
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20180532
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 Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.48 n.12 2018
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
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repository.mail.fl_str_mv
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