Modelos de crescimento na cultura de Crotalária juncea
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/13555 |
Resumo: | The objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of morphological and productive traits of sunn hemp in two sowing season, October the 22th 2014 and December the 3th 2014. Two uniformity were performed. The seeds were between rows 0.5m with rows and density of 20 plants per row meter of floor area of 52m x 50m (2.600m2). The evaluations began on October the 29th 2014 and December the 16th 2014, totaling 94 and 76 evaluation days for seasons 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for season 1 from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: plant height (PH), number of leaves (NL), stem diameter (SD), root length (RL), fresh matter leaf (FML), fresh matter stem (FMS), fresh matter root (FMR), fresh matter shoot (FMSH), the fresh matter total (FMT), mass of dry matter leaf (DML), dry matter stem (DMS), dry matter root (DMR), dry matter shoot (DMSH), and total dry matter (DMT). The residual assumptions of the characters studied were verified by test Shapiro-Wilk, test Breusch-Pagan and test Durbin-Watson. To model was used Gompertz equation: and the Logistic model was used the equation: . The inflection point (pi), maximum acceleration point (pam), maximum deceleration point (pdm) and asymptotic deceleration point (pda) for the Gompertz and Logistic models were calculated. For both models the confidence interval (CI) was calculated. The quality setting of the Gompertz and Logistic models was verified by the determination coefficient (R²), the Akaike information criteria(AIC), residual standard deviation (RSD), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean prediction error (MPE). The calculations were performed with the help of SOLVER of Microsoft Office Excel® application and software statistic R. The Gompertz model when compared between the sowing dates by means of the CI of the parameters, for the morphological and 9 productive characters differ. Same result was found for the Logistic model. The Gompertz and Logistic models are suitable for adjusting the morphological and productive traits of the sunn hemp. |
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2018-06-26T18:47:24Z2018-06-26T18:47:24Z2017-07-19http://repositorio.ufsm.br/handle/1/13555The objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of morphological and productive traits of sunn hemp in two sowing season, October the 22th 2014 and December the 3th 2014. Two uniformity were performed. The seeds were between rows 0.5m with rows and density of 20 plants per row meter of floor area of 52m x 50m (2.600m2). The evaluations began on October the 29th 2014 and December the 16th 2014, totaling 94 and 76 evaluation days for seasons 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for season 1 from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: plant height (PH), number of leaves (NL), stem diameter (SD), root length (RL), fresh matter leaf (FML), fresh matter stem (FMS), fresh matter root (FMR), fresh matter shoot (FMSH), the fresh matter total (FMT), mass of dry matter leaf (DML), dry matter stem (DMS), dry matter root (DMR), dry matter shoot (DMSH), and total dry matter (DMT). The residual assumptions of the characters studied were verified by test Shapiro-Wilk, test Breusch-Pagan and test Durbin-Watson. To model was used Gompertz equation: and the Logistic model was used the equation: . The inflection point (pi), maximum acceleration point (pam), maximum deceleration point (pdm) and asymptotic deceleration point (pda) for the Gompertz and Logistic models were calculated. For both models the confidence interval (CI) was calculated. The quality setting of the Gompertz and Logistic models was verified by the determination coefficient (R²), the Akaike information criteria(AIC), residual standard deviation (RSD), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean prediction error (MPE). The calculations were performed with the help of SOLVER of Microsoft Office Excel® application and software statistic R. The Gompertz model when compared between the sowing dates by means of the CI of the parameters, for the morphological and 9 productive characters differ. Same result was found for the Logistic model. The Gompertz and Logistic models are suitable for adjusting the morphological and productive traits of the sunn hemp.O objetivo deste trabalho foi ajustar os modelos não lineares, Gompertz e Logístico, na descrição dos caracteres morfológicos e produtivos de crotalária juncea em duas épocas de semeadura, 22/10/2014 e 03/12/2014. Foram realizados dois ensaios de uniformidade. A semeadura foi em fileiras com 0,5m entre fileiras e com densidade de 20 plantas por metro de fileira em área útil de 52m x 50m (2.600m2). O início das avaliações foi em 29/10/2014 e 16/12/2014, totalizando 94 e 76 dias de avaliação para as épocas 1 e 2, respectivamente. Após a emergência das plântulas de crotalária juncea, para a época 1 a partir dos 7 dias após a semeadura, e para época 2 a partir dos 13 dias após a semeadura, em cada dia, foram coletadas, aleatoriamente, quatro plantas. Foram avaliados os caracteres: altura de planta (AP), número de folhas (NF), diâmetro de caule (DC), comprimento de raiz (CR), massa de matéria verde de folha (MVF), massa de matéria verde de caule (MVC), massa de matéria verde de raiz (MVR), massa de matéria verde de parte aérea (MVPA), massa de matéria verde total (MVT), massa de matéria seca de folha (MSF), massa de matéria seca de caule (MSC), massa de matéria seca de raiz (MSR), massa de matéria seca de parte aérea (MSPA), e massa de matéria seca total (MST). Foram verificados os pressupostos dos resíduos dos caracteres, por meio dos seguintes testes: teste de Shapiro-Wilk, teste de Breusch-Pagan e teste de Durbin-Watson. Para o modelo de Gompertz foi utilizada a equação:, e para o modelo Logístico foi utilizada a equação. Também foi calculado o ponto de inflexão (pi), ponto de aceleração máxima (pam), ponto de desaceleração máxima (pdm) e ponto de desaceleração assintótica (p1) para os modelos Gompertz e Logístico. Para ambos os modelos, realizou-se o cálculo do intervalo de confiança (IC) para os parâmetros a, b e c. A qualidade do ajuste dos modelos de Gompertz e Logístico foi verificada pelo coeficiente de determinação (R²), o critério de informação de Akaike (AIC), desvio padrão residual (DPR), desvio médio absoluto (DMA), erro percentual médio absoluto (MAPE) e erro de predição médio (EPM). Os cálculos foram realizados com o auxílio do aplicativo SOLVER do Microsoft Office Excel® e o software estatístico R. O modelo de Gompertz quando comparado entre as épocas de semeadura por meio do IC dos parâmetros, para os caracteres morfológicos e produtivos diferem. Mesmo resultado foi encontrado para o modelo Logístico. Os modelos de Gompertz e Logístico são adequados para o ajuste dos caracteres morfológicos e produtivos para a cultura de crotalária juncea.Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em AgronomiaUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessCaracteres morfológicos e produtivosÉpocas de semeaduraModelos não linearesPlanejamento experimentalPlanta de coberturaCover cropsExperimental designNon – linear modelsMorphological and productive charactersSowing datesCNPQ::CIENCIAS AGRARIAS::AGRONOMIAModelos de crescimento na cultura de Crotalária junceaGrowth models in culture of Crotalária junceainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisCargnelutti Filho, Albertohttp://lattes.cnpq.br/0233728865094243Boligon, Alexandra Augustihttp://lattes.cnpq.br/7812195778307072Lorentz, Leandro Homrichhttp://lattes.cnpq.br/3133075693356442Rigão, Maria Helenahttp://lattes.cnpq.br/2192780629442651Nunes, Ubirajara Russihttp://lattes.cnpq.br/8937125213120268http://lattes.cnpq.br/5251846633924132Bem, Cláudia Marques de500100000009600a106a3de-c40f-40f6-80de-397e8002690ee1c0c6a4-1b65-4f93-bfe5-4e3e18bebeb50c4f9283-0821-47d6-b489-33a1264d386ebfac3973-1ea7-46d8-948b-902a730d3ac51f6d237c-bb0c-40e1-9c44-fb0ac53b808e3880ac5b-aa46-46cd-887f-cfea74516fc5reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGAGRONOMIA_2017_BEM_CLAUDIA.pdfTES_PPGAGRONOMIA_2017_BEM_CLAUDIA.pdfTese de Doutoradoapplication/pdf3800142http://repositorio.ufsm.br/bitstream/1/13555/2/TES_PPGAGRONOMIA_2017_BEM_CLAUDIA.pdf0e4aebd9ca489a2eab48b3a1aff65160MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Modelos de crescimento na cultura de Crotalária juncea |
dc.title.alternative.eng.fl_str_mv |
Growth models in culture of Crotalária juncea |
title |
Modelos de crescimento na cultura de Crotalária juncea |
spellingShingle |
Modelos de crescimento na cultura de Crotalária juncea Bem, Cláudia Marques de Caracteres morfológicos e produtivos Épocas de semeadura Modelos não lineares Planejamento experimental Planta de cobertura Cover crops Experimental design Non – linear models Morphological and productive characters Sowing dates CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Modelos de crescimento na cultura de Crotalária juncea |
title_full |
Modelos de crescimento na cultura de Crotalária juncea |
title_fullStr |
Modelos de crescimento na cultura de Crotalária juncea |
title_full_unstemmed |
Modelos de crescimento na cultura de Crotalária juncea |
title_sort |
Modelos de crescimento na cultura de Crotalária juncea |
author |
Bem, Cláudia Marques de |
author_facet |
Bem, Cláudia Marques de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Cargnelutti Filho, Alberto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0233728865094243 |
dc.contributor.referee1.fl_str_mv |
Boligon, Alexandra Augusti |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7812195778307072 |
dc.contributor.referee2.fl_str_mv |
Lorentz, Leandro Homrich |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/3133075693356442 |
dc.contributor.referee3.fl_str_mv |
Rigão, Maria Helena |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/2192780629442651 |
dc.contributor.referee4.fl_str_mv |
Nunes, Ubirajara Russi |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/8937125213120268 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5251846633924132 |
dc.contributor.author.fl_str_mv |
Bem, Cláudia Marques de |
contributor_str_mv |
Cargnelutti Filho, Alberto Boligon, Alexandra Augusti Lorentz, Leandro Homrich Rigão, Maria Helena Nunes, Ubirajara Russi |
dc.subject.por.fl_str_mv |
Caracteres morfológicos e produtivos Épocas de semeadura Modelos não lineares Planejamento experimental Planta de cobertura |
topic |
Caracteres morfológicos e produtivos Épocas de semeadura Modelos não lineares Planejamento experimental Planta de cobertura Cover crops Experimental design Non – linear models Morphological and productive characters Sowing dates CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Cover crops Experimental design Non – linear models Morphological and productive characters Sowing dates |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of morphological and productive traits of sunn hemp in two sowing season, October the 22th 2014 and December the 3th 2014. Two uniformity were performed. The seeds were between rows 0.5m with rows and density of 20 plants per row meter of floor area of 52m x 50m (2.600m2). The evaluations began on October the 29th 2014 and December the 16th 2014, totaling 94 and 76 evaluation days for seasons 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for season 1 from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: plant height (PH), number of leaves (NL), stem diameter (SD), root length (RL), fresh matter leaf (FML), fresh matter stem (FMS), fresh matter root (FMR), fresh matter shoot (FMSH), the fresh matter total (FMT), mass of dry matter leaf (DML), dry matter stem (DMS), dry matter root (DMR), dry matter shoot (DMSH), and total dry matter (DMT). The residual assumptions of the characters studied were verified by test Shapiro-Wilk, test Breusch-Pagan and test Durbin-Watson. To model was used Gompertz equation: and the Logistic model was used the equation: . The inflection point (pi), maximum acceleration point (pam), maximum deceleration point (pdm) and asymptotic deceleration point (pda) for the Gompertz and Logistic models were calculated. For both models the confidence interval (CI) was calculated. The quality setting of the Gompertz and Logistic models was verified by the determination coefficient (R²), the Akaike information criteria(AIC), residual standard deviation (RSD), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean prediction error (MPE). The calculations were performed with the help of SOLVER of Microsoft Office Excel® application and software statistic R. The Gompertz model when compared between the sowing dates by means of the CI of the parameters, for the morphological and 9 productive characters differ. Same result was found for the Logistic model. The Gompertz and Logistic models are suitable for adjusting the morphological and productive traits of the sunn hemp. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-07-19 |
dc.date.accessioned.fl_str_mv |
2018-06-26T18:47:24Z |
dc.date.available.fl_str_mv |
2018-06-26T18:47:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/13555 |
url |
http://repositorio.ufsm.br/handle/1/13555 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500100000009 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
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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.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Agronomia |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
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UFSM |
reponame_str |
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Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
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