Leaf count overdispersion in coffee seedlings

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 p...

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Main Authors: Edilson Marcelino Silva, Thais Destefani Ribeiro Furtado, Jaqueline Gonçalves Fernandes, Marcelo Ângelo Cirillo, Joel Augusto Muniz
Format: Article
Language:English
Published: Universidade Federal de Santa Maria 2019-04-01
Series:Ciência Rural
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201&lng=en&tlng=en
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spelling doaj-61352fddbe87414e92655b5b5d74b3432020-11-25T00:28:08ZengUniversidade Federal de Santa MariaCiência Rural1678-45962019-04-0149410.1590/0103-8478cr20180786S0103-84782019000400201Leaf count overdispersion in coffee seedlingsEdilson Marcelino SilvaThais Destefani Ribeiro FurtadoJaqueline Gonçalves FernandesMarcelo Ângelo CirilloJoel Augusto MunizABSTRACT: 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201&lng=en&tlng=enPoisson modelnegative binomial modelexponential familygeneralized linear model
collection DOAJ
language English
format Article
sources DOAJ
author Edilson Marcelino Silva
Thais Destefani Ribeiro Furtado
Jaqueline Gonçalves Fernandes
Marcelo Ângelo Cirillo
Joel Augusto Muniz
spellingShingle Edilson Marcelino Silva
Thais Destefani Ribeiro Furtado
Jaqueline Gonçalves Fernandes
Marcelo Ângelo Cirillo
Joel Augusto Muniz
Leaf count overdispersion in coffee seedlings
Ciência Rural
Poisson model
negative binomial model
exponential family
generalized linear model
author_facet Edilson Marcelino Silva
Thais Destefani Ribeiro Furtado
Jaqueline Gonçalves Fernandes
Marcelo Ângelo Cirillo
Joel Augusto Muniz
author_sort Edilson Marcelino Silva
title Leaf count overdispersion in coffee seedlings
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
publisher Universidade Federal de Santa Maria
series Ciência Rural
issn 1678-4596
publishDate 2019-04-01
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.
topic Poisson model
negative binomial model
exponential family
generalized linear model
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201&lng=en&tlng=en
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AT marceloangelocirillo leafcountoverdispersionincoffeeseedlings
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