Abstract
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.
Highlights
Brazilian coffee production in 2017 was 45 million bags, and a 29% increase in production is estimated for 2018, with a likely record of 58 million bags (CONAB, 2018)
The fit of the Poisson model (P-value=
The results showed that the assumption of a Poisson response (Figure 1) for the number of leaves in coffee seedlings over time is not confirmed, and the model shows an unsatisfactory fit
Summary
Brazilian coffee production in 2017 was 45 million bags, and a 29% increase in production is estimated for 2018, with a likely record of 58 million bags (CONAB, 2018). Given the importance of the coffee crop, farmers should maximize their knowledge about the causes and factors that contribute to improved productivity In this context, understanding the Approved 02.20.19 Returned CR-2018-0786.R1 by the author 03.14.1C9 iência. BACHIÃO et al (2018) assessed the number of leaves, leaf area, shoot and root dry matter, plant height, and stem diameter of four coffee cultivars using linear and polynomial regression models as a function of different fertilizer doses and observed the adequate fitness of these models. MARANA et al (2008) compared the effect of different fertilizer doses on coffee seedling growth and fit polynomial regression models to seedling height and root and stem and leaf dry matter data as a function of the doses and obtained satisfactory fits
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