Productivity of soybean in management zones with application of different sowing densities
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
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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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 |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1749140553040134144 |