Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
dARK ID: | ark:/26339/001300000m4zr |
Texto Completo: | http://repositorio.ufsm.br/handle/1/19460 |
Resumo: | Brazil has world prominence in soy production, farmers seek new technologies to improve their profitability. Precision Farming is an essential tool for detecting problems within the property and assisting in decision making. In the field we observed many plots that the soybean plant population gets very stained even if sowing at a fixed rate and this can often affect productivity. The objective of this work was to evaluate the variability of soybean plant population in different productive environments and its impact on crop maps. The work was carried out in a commercial field of 35.05 hectares and the soybean cultivar NS 5959 was used. Sowing was carried out at a variable rate, with a variation of only 5% according to the productive potential of the field, precisely to evaluate the behavior. of the final plant population. At stage R6 a 0.25 ha sample grid was generated, ie one plot every 50 meters, each plot was evaluated 6.75 m² and the final plant population was counted. This field was harvested and the harvest map was generated, soon after the soil was collected with georeferenced samples with a 2 ha grid. After several cycles of georeferenced soil sampling and variable rate interventions, the studied area presented a good nutrient balance, thus meeting the demands of the soybean crop that was implanted. Saturation of Bases, Calcium, Magnesium, Potassium, Copper and Manganese were negatively correlated, while Phosphorus, Boron and Sulfur were positively correlated with the soybean study. The population variability of final plants presented a amplitude of 26.16% and a CV of 4.95%, showing that the environment had an influence on the loss of viable seeds in the soil. With the principal component analysis it was possible to identify the measures responsible for the largest variations among the results, reducing the volume from 18 initial attributes to 5, directing the study. Excess plant population caused the cultivar to become bedridden and reduced soybean yield. With a population of 31 plants.m-2 the productivity was 3984.4 kg.ha1 , when the population increased to 36 plants.m-2 the productivity was 3785.7 kg.ha-1 and in the maximum population, of 39 plants.m-2 yield was 3625.2 kg.ha-1 , which treatment differed statistically with the first treatment described, causing variability in the harvest map. It is a difference of 359.2 kg.ha-1 , in the average price of the year in the amount of R$ 65.00 the bag is a value of R$ 389.13/ha difference. This shows the insecurity of the farmer at the time of sowing, which for fear of seed quality, climate and sowing condition end up putting more seeds than it should, resulting in increased cost and decreased productivity. |
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Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheitaRelation of population of soybean Glicine max L.) plants by productive environments defined by the harvest mapAgricultura de precisãoPopulação de plantasMapa de colheitaPrecision agriculturePlant populationHarvest mapCNPQ::CIENCIAS AGRARIAS::AGRONOMIABrazil has world prominence in soy production, farmers seek new technologies to improve their profitability. Precision Farming is an essential tool for detecting problems within the property and assisting in decision making. In the field we observed many plots that the soybean plant population gets very stained even if sowing at a fixed rate and this can often affect productivity. The objective of this work was to evaluate the variability of soybean plant population in different productive environments and its impact on crop maps. The work was carried out in a commercial field of 35.05 hectares and the soybean cultivar NS 5959 was used. Sowing was carried out at a variable rate, with a variation of only 5% according to the productive potential of the field, precisely to evaluate the behavior. of the final plant population. At stage R6 a 0.25 ha sample grid was generated, ie one plot every 50 meters, each plot was evaluated 6.75 m² and the final plant population was counted. This field was harvested and the harvest map was generated, soon after the soil was collected with georeferenced samples with a 2 ha grid. After several cycles of georeferenced soil sampling and variable rate interventions, the studied area presented a good nutrient balance, thus meeting the demands of the soybean crop that was implanted. Saturation of Bases, Calcium, Magnesium, Potassium, Copper and Manganese were negatively correlated, while Phosphorus, Boron and Sulfur were positively correlated with the soybean study. The population variability of final plants presented a amplitude of 26.16% and a CV of 4.95%, showing that the environment had an influence on the loss of viable seeds in the soil. With the principal component analysis it was possible to identify the measures responsible for the largest variations among the results, reducing the volume from 18 initial attributes to 5, directing the study. Excess plant population caused the cultivar to become bedridden and reduced soybean yield. With a population of 31 plants.m-2 the productivity was 3984.4 kg.ha1 , when the population increased to 36 plants.m-2 the productivity was 3785.7 kg.ha-1 and in the maximum population, of 39 plants.m-2 yield was 3625.2 kg.ha-1 , which treatment differed statistically with the first treatment described, causing variability in the harvest map. It is a difference of 359.2 kg.ha-1 , in the average price of the year in the amount of R$ 65.00 the bag is a value of R$ 389.13/ha difference. This shows the insecurity of the farmer at the time of sowing, which for fear of seed quality, climate and sowing condition end up putting more seeds than it should, resulting in increased cost and decreased productivity.O Brasil tem destaque mundial na produção da soja, os agricultores buscam novas tecnologias para melhorar sua rentabilidade. A Agricultura de Precisão é uma ferramenta essencial para detectar problemas dentro da propriedade e ajudar no momento de tomadas de decisão. No campo observamos muitos talhões que a população de plantas da soja fica bastante manchado mesmo fazendo a semeadura em taxa fixa e muitas vezes isso pode afetar a produtividade. O objetivo do trabalho foi avaliar a variabilidade da população de plantas da soja em diferentes ambientes produtivos definidos pelo mapa de colheita. O trabalho foi realizado em uma lavoura comercial de 35,05 hectares e foi utilizado a cultivar de soja NS 5959, a semeadura foi realizada em taxa variável, com uma variação de somente 5% conforme o potencial produtivo do talhão, justamente para avaliar o comportamento da população final de plantas. No estádio R6 foi gerado um grid amostral de 0,25 ha, ou seja, uma parcela a cada 50 metros, cada parcela foi avaliada 6,75 m² e contado a população final de plantas. Foi colhido este talhão e gerado o mapa de colheita, logo após coletado o solo com amostras georeferenciadas com um grid de 2 ha. Após vários ciclos de amostragem georeferenciada de solo e intervenções em taxa variável a área estudada apresentou um bom equilíbrio de nutrientes, suprindo assim as demandas da cultura da soja que estava implantada. A Saturação de Bases, Cálcio, Magnésio, Potássio, Cobre e Manganês tiveram correlação negativa, enquanto o Fósforo, Boro e Enxofre tiveram correlação positiva com a produtividade da soja. A variabilidade de população de plantas finais apresentou uma amplitude de 26,16% e um CV de 4,95%, mostrando que o ambiente teve uma influência na perda de sementes viáveis no solo. Com a análise de componentes principais foi possível identificar as medidas responsáveis pelas maiores variações entre os resultados, reduzindo o volume de 18 atributos iniciais para 5, direcionando o estudo. O excesso de população de plantas causou acamamento da cultivar e reduziu a produtividade de soja. Com a população de 31 plantas.m-2 a produtividade foi de 3984,4 kg.ha-1 , quando a população aumentou para 36 plantas.m-2 a produtividade foi de 3785,7 kg.ha-1 e na máxima população, de 39 plantas.m-2 a produtividade foi de 3625,2 kg.ha-1 , tratamento este que diferiu estatisticamente com o primeiro tratamento descrito, causando perda de produtividade com excesso de plantas e gerando variabilidade no mapa de colheita. É uma diferença de 359,2 kg.ha-1 , na cotação média do ano no valor de R$65,00 a saca é um valor de R$389,13/ha de diferença. Isso mostra a insegurança do agricultor no momento da semeadura, que por medo da qualidade da semente, clima e condição de semeadura acabam colocando mais sementes do que deveria, acarretando aumento do custo e diminuindo a produtividade.Universidade Federal de Santa MariaBrasilTecnologia em Agricultura de PrecisãoUFSMPrograma de Pós-Graduação em Agricultura de PrecisãoColégio Politécnico da UFSMAmado, Telmo Jorge Carneirohttp://lattes.cnpq.br/8591926237097756Amaral, Lúcio de Paulahttp://lattes.cnpq.br/6612592358172016Acosta, José Alan de Almeidahttp://lattes.cnpq.br/0137668094045807Rosa, David Peres dahttp://lattes.cnpq.br/2756916660743293Pesini, Felipe2020-02-03T11:33:54Z2020-02-03T11:33:54Z2019-08-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/19460ark:/26339/001300000m4zrporAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2021-12-29T14:16:59Zoai:repositorio.ufsm.br:1/19460Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2021-12-29T14:16:59Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita Relation of population of soybean Glicine max L.) plants by productive environments defined by the harvest map |
title |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
spellingShingle |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita Pesini, Felipe Agricultura de precisão População de plantas Mapa de colheita Precision agriculture Plant population Harvest map CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
title_full |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
title_fullStr |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
title_full_unstemmed |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
title_sort |
Relação da população de plantas de soja (Glicine max L.) por ambientes produtivos definidos pelo mapa de colheita |
author |
Pesini, Felipe |
author_facet |
Pesini, Felipe |
author_role |
author |
dc.contributor.none.fl_str_mv |
Amado, Telmo Jorge Carneiro http://lattes.cnpq.br/8591926237097756 Amaral, Lúcio de Paula http://lattes.cnpq.br/6612592358172016 Acosta, José Alan de Almeida http://lattes.cnpq.br/0137668094045807 Rosa, David Peres da http://lattes.cnpq.br/2756916660743293 |
dc.contributor.author.fl_str_mv |
Pesini, Felipe |
dc.subject.por.fl_str_mv |
Agricultura de precisão População de plantas Mapa de colheita Precision agriculture Plant population Harvest map CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
topic |
Agricultura de precisão População de plantas Mapa de colheita Precision agriculture Plant population Harvest map CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Brazil has world prominence in soy production, farmers seek new technologies to improve their profitability. Precision Farming is an essential tool for detecting problems within the property and assisting in decision making. In the field we observed many plots that the soybean plant population gets very stained even if sowing at a fixed rate and this can often affect productivity. The objective of this work was to evaluate the variability of soybean plant population in different productive environments and its impact on crop maps. The work was carried out in a commercial field of 35.05 hectares and the soybean cultivar NS 5959 was used. Sowing was carried out at a variable rate, with a variation of only 5% according to the productive potential of the field, precisely to evaluate the behavior. of the final plant population. At stage R6 a 0.25 ha sample grid was generated, ie one plot every 50 meters, each plot was evaluated 6.75 m² and the final plant population was counted. This field was harvested and the harvest map was generated, soon after the soil was collected with georeferenced samples with a 2 ha grid. After several cycles of georeferenced soil sampling and variable rate interventions, the studied area presented a good nutrient balance, thus meeting the demands of the soybean crop that was implanted. Saturation of Bases, Calcium, Magnesium, Potassium, Copper and Manganese were negatively correlated, while Phosphorus, Boron and Sulfur were positively correlated with the soybean study. The population variability of final plants presented a amplitude of 26.16% and a CV of 4.95%, showing that the environment had an influence on the loss of viable seeds in the soil. With the principal component analysis it was possible to identify the measures responsible for the largest variations among the results, reducing the volume from 18 initial attributes to 5, directing the study. Excess plant population caused the cultivar to become bedridden and reduced soybean yield. With a population of 31 plants.m-2 the productivity was 3984.4 kg.ha1 , when the population increased to 36 plants.m-2 the productivity was 3785.7 kg.ha-1 and in the maximum population, of 39 plants.m-2 yield was 3625.2 kg.ha-1 , which treatment differed statistically with the first treatment described, causing variability in the harvest map. It is a difference of 359.2 kg.ha-1 , in the average price of the year in the amount of R$ 65.00 the bag is a value of R$ 389.13/ha difference. This shows the insecurity of the farmer at the time of sowing, which for fear of seed quality, climate and sowing condition end up putting more seeds than it should, resulting in increased cost and decreased productivity. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-08-27 2020-02-03T11:33:54Z 2020-02-03T11:33:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/19460 |
dc.identifier.dark.fl_str_mv |
ark:/26339/001300000m4zr |
url |
http://repositorio.ufsm.br/handle/1/19460 |
identifier_str_mv |
ark:/26339/001300000m4zr |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Tecnologia em Agricultura de Precisão UFSM Programa de Pós-Graduação em Agricultura de Precisão Colégio Politécnico da UFSM |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Tecnologia em Agricultura de Precisão UFSM Programa de Pós-Graduação em Agricultura de Precisão Colégio Politécnico da UFSM |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
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1815172360001552384 |