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 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.

Bibliographic Details

Main Authors:	Silva,Edilson Marcelino, Furtado,Thais Destefani Ribeiro, Fernandes,Jaqueline Gonçalves, Cirillo,Marcelo Ângelo, Muniz,Joel Augusto
Format:	Digital revista
Language:	English
Published:	Universidade Federal de Santa Maria 2019
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000400201
Tags:	Add Tag No Tags, Be the first to tag this record!