Ciência Rural

URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10366

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2014 - 20192

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Autor: search.filters.author.Muniz, Joel AugustoAssunto: search.filters.subject.Sementes e mudas

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Leaf count overdispersion in coffee seedlings
    (Universidade Federal de Santa Maria, 2019) Silva, Edilson Marcelino; Furtado, Thais Destefani Ribeiro; Fernandes, Jaqueline Gonçalves; Cirillo, Marcelo Ângelo; Muniz, Joel Augusto
    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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    Fitting nonlinear autoregressive models to describe coffee seed germination
    (Universidade Federal de Santa Maria, 2014-11) Sousa, Iábita Fabiana; Kunzle Neto, Johan Eugen; Muniz, Joel Augusto; Guimarães, Renato Mendes; Savian, Taciana Villela; Muniz, Fabiana Rezende
    Cumulative germination of coffee has a longitudinal behavior mathematically characterized by a sigmoidal model. In the seed germination evaluation, the study of the germination curve may contribute to better understanding of this process. The aim of this study was to evaluate the goodness of fi t of Logistic and Gompertz models, with independent and fi rst-order autoregressive errors structure, AR (1), in the description of coffee (Coffea arabica L.) line Catuai vermelho IAC 99 germination, at fi ve different potential germination. The data used were from an experiment conducted in 2011 at the Seed Analysis Laboratory of the Federal University of Lavras. The Logistic and Gompertz nonlinear models were appropriately adjusted to the percentage germination data. The Gompertz model with fi rst-order autoregressive errors structure was the best to describe the germination process.