Planejamento experimental e descrição da produção de ervilha
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/24061 |
Resumo: | The pea is a legume cultivated annually, its grain has great nutritional value, being an important source of nutrients for human consumption. Thus, due to the importance of this vegetable garden, several researches are carried out. However, information for conducting experiments with high experimental precision is scarce for culture, as well as information on the application of nonlinear regression models to describe their production. In this sense, this work aims to evaluate the cause-and-effect relationships between the variables of pea production and verify if they follow the same trend between harvests and growing seasons, estimate the sample size, plot size, the number of repetitions and model the production cycle of the pea crop. Uniformity tests were conducted in the field in the years 2016, 2017 and 2018 in the experimental area of the Departament of Plant Science of the Federal University of Santa Maria - UFSM, in the municipality of Santa Maria - RS. The cultivar used was the Pea Grain 40. The characters of mass and total number of pods, length of pods, numbers and mass of grains per pod were measured. The relationships between the variables were estimated by Pearson's linear correlations and, later, the direct and indirect effects were unfolded by the trail analysis. Canonical correlation analysis was also carried out between the group of pod variables and grain variables. The plot size, sample size and number of repetitions were estimated, and the logistic nonlinear model was adjusted to characterize the production. The results show that pea production is affected by environmental conditions, however, it presented the same trend in the relationships between variables, in different harvests and growing seasons. The pod mass and grain number variables are the variables with the highest cause and effect relationships on the grain mass and can be used for the indirect selection of more productive plants. Plants with a lower pod mass provide pods with fewer grains and less grain mass. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a semi-amplitude of the confidence interval of 20% of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in the randomized block design and in the incomplete block design with up to 100 treatments for significant differences of 35 % between treatment averages. By adjusting the logistic model, it was found that season 1 was the most productive, with maximum increases in production in a shorter period (592.5 °C days-1 to produce 119.52g), causing a high production peak in relation to the other periods analyzed. |
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Planejamento experimental e descrição da produção de ervilhaExperimental planning and description of pea productionPisum sativumAnálise de trilhaCorrelação canônicaTamanho de parcelaTamanho de amostraNúmero de repetiçõesModelos não linearesModelo logísticoPath analysisCanonical correlationPlot sizeSample sizeNumber of repetitionsNon-linear modelsLogistic modelCNPQ::CIENCIAS AGRARIAS::AGRONOMIAThe pea is a legume cultivated annually, its grain has great nutritional value, being an important source of nutrients for human consumption. Thus, due to the importance of this vegetable garden, several researches are carried out. However, information for conducting experiments with high experimental precision is scarce for culture, as well as information on the application of nonlinear regression models to describe their production. In this sense, this work aims to evaluate the cause-and-effect relationships between the variables of pea production and verify if they follow the same trend between harvests and growing seasons, estimate the sample size, plot size, the number of repetitions and model the production cycle of the pea crop. Uniformity tests were conducted in the field in the years 2016, 2017 and 2018 in the experimental area of the Departament of Plant Science of the Federal University of Santa Maria - UFSM, in the municipality of Santa Maria - RS. The cultivar used was the Pea Grain 40. The characters of mass and total number of pods, length of pods, numbers and mass of grains per pod were measured. The relationships between the variables were estimated by Pearson's linear correlations and, later, the direct and indirect effects were unfolded by the trail analysis. Canonical correlation analysis was also carried out between the group of pod variables and grain variables. The plot size, sample size and number of repetitions were estimated, and the logistic nonlinear model was adjusted to characterize the production. The results show that pea production is affected by environmental conditions, however, it presented the same trend in the relationships between variables, in different harvests and growing seasons. The pod mass and grain number variables are the variables with the highest cause and effect relationships on the grain mass and can be used for the indirect selection of more productive plants. Plants with a lower pod mass provide pods with fewer grains and less grain mass. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a semi-amplitude of the confidence interval of 20% of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in the randomized block design and in the incomplete block design with up to 100 treatments for significant differences of 35 % between treatment averages. By adjusting the logistic model, it was found that season 1 was the most productive, with maximum increases in production in a shorter period (592.5 °C days-1 to produce 119.52g), causing a high production peak in relation to the other periods analyzed.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESA ervilha é uma leguminosa de cultivo anual, seu grão apresenta grande valor nutricional, sendo uma importante fonte de nutrientes para a alimentação humana. Assim, devido à importância desta olerícola várias pesquisas são realizadas. No entanto, informações para a condução de experimentos com elevada precisão experimental são escassos para a cultura, bem como informações de aplicação de modelos de regressão não linear para a descrição da sua produção. Neste sentido, este trabalho tem como objetivo avaliar as relações de causa e efeito entre as variáveis de produção de ervilha e verificar se elas seguem a mesma tendência entre as colheitas e épocas de cultivo, estimar o tamanho de amostra, tamanho de parcela, o número de repetições e modelar o ciclo da produção da cultura da ervilha. Os ensaios de uniformidade foram conduzidos a campo nos anos de 2016, 2017 e 2018 na área experimental do Departamento de Fitotecnia da Universidade Federal de Santa Maria – UFSM, no município de Santa Maria – RS. A cultivar utilizada foi a Ervilha Grão 40. Foram mensurados os caracteres de massa e número total de vagens, comprimento das vagens, números e massa de grãos por vagens. As relações entre as variáveis foram estimadas pelas correlações lineares de Pearson e, posteriormente, desdobrou-se os efeitos diretos e indiretos pela análise de trilha. Realizou-se ainda análise de correlações canônica entre o grupo de variáveis de vagem e variáveis de grão. O tamanho de parcela, o tamanho de amostra e número de repetições foram estimados, e ajustou-se o modelo não linear logístico para caracterizar a produção. Os resultados mostram que a produção de ervilha sofre interferência das condições ambientais, porém, apresentou a mesma tendência nas relações entre as variáveis, nas diferentes colheitas e épocas de cultivo. As variáveis massa de vagens e números de grãos são as variáveis com maiores relações de causa e efeito sobre a massa de grãos e podem ser utilizadas para a seleção indireta de plantas mais produtivas. Plantas com menor massa de vagens proporcionam vagens com menor número de grãos e menor massa de grãos. O tamanho de parcela para avaliar o número de vagens por planta e massa de vagens por planta para a cultura da ervilha é de oito e nove plantas, respectivamente. O tamanho de amostra para a avaliar o número de vagens por planta e massa de vagens por planta é de oito plantas na direção da linha com uma semi-amplitude do intervalo de confiança de 20% da média. Para as variáveis número de vagens por plantas e massa de vagens por planta de ervilha são necessários 10 e 12 repetições, respectivamente, para avaliar até 20 tratamentos no delineamento de blocos ao acaso e no delineamento blocos incompletos com até 100 tratamentos para diferenças significativas de 35% entre médias de tratamentos. Pelo ajuste do modelo logístico, verificou-se que a época 1 foi a mais produtiva, apresentando incrementos máximos na produção em menor período (592,5 °C dias-1 para produzir 119,52g), ocasionando um pico de produção elevado em relação as outras épocas analisadas.Universidade Federal de Santa MariaBrasilAgronomiaUFSMPrograma de Pós-Graduação em AgronomiaCentro de Ciências RuraisLúcio, Alessandro Dal'Colhttp://lattes.cnpq.br/0972869223145503Toebe, MarcosHaesbaert, Fernando MachadoTartaglia, Francieli de Lima2022-04-14T18:40:23Z2022-04-14T18:40:23Z2021-02-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/24061porAttribution-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:UFSM2022-07-04T13:40:18Zoai:repositorio.ufsm.br:1/24061Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-07-04T13:40:18Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Planejamento experimental e descrição da produção de ervilha Experimental planning and description of pea production |
title |
Planejamento experimental e descrição da produção de ervilha |
spellingShingle |
Planejamento experimental e descrição da produção de ervilha Tartaglia, Francieli de Lima Pisum sativum Análise de trilha Correlação canônica Tamanho de parcela Tamanho de amostra Número de repetições Modelos não lineares Modelo logístico Path analysis Canonical correlation Plot size Sample size Number of repetitions Non-linear models Logistic model CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Planejamento experimental e descrição da produção de ervilha |
title_full |
Planejamento experimental e descrição da produção de ervilha |
title_fullStr |
Planejamento experimental e descrição da produção de ervilha |
title_full_unstemmed |
Planejamento experimental e descrição da produção de ervilha |
title_sort |
Planejamento experimental e descrição da produção de ervilha |
author |
Tartaglia, Francieli de Lima |
author_facet |
Tartaglia, Francieli de Lima |
author_role |
author |
dc.contributor.none.fl_str_mv |
Lúcio, Alessandro Dal'Col http://lattes.cnpq.br/0972869223145503 Toebe, Marcos Haesbaert, Fernando Machado |
dc.contributor.author.fl_str_mv |
Tartaglia, Francieli de Lima |
dc.subject.por.fl_str_mv |
Pisum sativum Análise de trilha Correlação canônica Tamanho de parcela Tamanho de amostra Número de repetições Modelos não lineares Modelo logístico Path analysis Canonical correlation Plot size Sample size Number of repetitions Non-linear models Logistic model CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
topic |
Pisum sativum Análise de trilha Correlação canônica Tamanho de parcela Tamanho de amostra Número de repetições Modelos não lineares Modelo logístico Path analysis Canonical correlation Plot size Sample size Number of repetitions Non-linear models Logistic model CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The pea is a legume cultivated annually, its grain has great nutritional value, being an important source of nutrients for human consumption. Thus, due to the importance of this vegetable garden, several researches are carried out. However, information for conducting experiments with high experimental precision is scarce for culture, as well as information on the application of nonlinear regression models to describe their production. In this sense, this work aims to evaluate the cause-and-effect relationships between the variables of pea production and verify if they follow the same trend between harvests and growing seasons, estimate the sample size, plot size, the number of repetitions and model the production cycle of the pea crop. Uniformity tests were conducted in the field in the years 2016, 2017 and 2018 in the experimental area of the Departament of Plant Science of the Federal University of Santa Maria - UFSM, in the municipality of Santa Maria - RS. The cultivar used was the Pea Grain 40. The characters of mass and total number of pods, length of pods, numbers and mass of grains per pod were measured. The relationships between the variables were estimated by Pearson's linear correlations and, later, the direct and indirect effects were unfolded by the trail analysis. Canonical correlation analysis was also carried out between the group of pod variables and grain variables. The plot size, sample size and number of repetitions were estimated, and the logistic nonlinear model was adjusted to characterize the production. The results show that pea production is affected by environmental conditions, however, it presented the same trend in the relationships between variables, in different harvests and growing seasons. The pod mass and grain number variables are the variables with the highest cause and effect relationships on the grain mass and can be used for the indirect selection of more productive plants. Plants with a lower pod mass provide pods with fewer grains and less grain mass. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a semi-amplitude of the confidence interval of 20% of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in the randomized block design and in the incomplete block design with up to 100 treatments for significant differences of 35 % between treatment averages. By adjusting the logistic model, it was found that season 1 was the most productive, with maximum increases in production in a shorter period (592.5 °C days-1 to produce 119.52g), causing a high production peak in relation to the other periods analyzed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-19 2022-04-14T18:40:23Z 2022-04-14T18:40:23Z |
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/1/24061 |
url |
http://repositorio.ufsm.br/handle/1/24061 |
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 Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Agronomia UFSM Programa de Pós-Graduação em Agronomia Centro de Ciências Rurais |
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 |
_version_ |
1805922054144262144 |