Leaf count overdispersion in coffee seedlings

Detalhes bibliográficos
Autor(a) principal: Silva,Edilson Marcelino
Data de Publicação: 2019
Outros Autores: Furtado,Thais Destefani Ribeiro, Fernandes,Jaqueline Gonçalves, Cirillo,Marcelo Ângelo, Muniz,Joel Augusto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201
Resumo: ABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.
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spelling Leaf count overdispersion in coffee seedlingsPoisson modelnegative binomial modelexponential familygeneralized linear modelABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.Universidade Federal de Santa Maria2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201Ciência Rural v.49 n.4 2019reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20180786info:eu-repo/semantics/openAccessSilva,Edilson MarcelinoFurtado,Thais Destefani RibeiroFernandes,Jaqueline GonçalvesCirillo,Marcelo ÂngeloMuniz,Joel Augustoeng2019-04-03T00:00:00ZRevista
dc.title.none.fl_str_mv Leaf count overdispersion in coffee seedlings
title Leaf count overdispersion in coffee seedlings
spellingShingle Leaf count overdispersion in coffee seedlings
Silva,Edilson Marcelino
Poisson model
negative binomial model
exponential family
generalized linear model
title_short Leaf count overdispersion in coffee seedlings
title_full Leaf count overdispersion in coffee seedlings
title_fullStr Leaf count overdispersion in coffee seedlings
title_full_unstemmed Leaf count overdispersion in coffee seedlings
title_sort Leaf count overdispersion in coffee seedlings
author Silva,Edilson Marcelino
author_facet Silva,Edilson Marcelino
Furtado,Thais Destefani Ribeiro
Fernandes,Jaqueline Gonçalves
Cirillo,Marcelo Ângelo
Muniz,Joel Augusto
author_role author
author2 Furtado,Thais Destefani Ribeiro
Fernandes,Jaqueline Gonçalves
Cirillo,Marcelo Ângelo
Muniz,Joel Augusto
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Silva,Edilson Marcelino
Furtado,Thais Destefani Ribeiro
Fernandes,Jaqueline Gonçalves
Cirillo,Marcelo Ângelo
Muniz,Joel Augusto
dc.subject.por.fl_str_mv Poisson model
negative binomial model
exponential family
generalized linear model
topic Poisson model
negative binomial model
exponential family
generalized linear model
description ABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20180786
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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.49 n.4 2019
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
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