Ciência Rural
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10366
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Item Different nitrogen levels on vegetative growth and yield of conilon coffee (Coffea canephora)(Universidade Federal de Santa Maria, 2022-05-11) Busato, Camilo; Reis, Edvaldo Fialho dos; Oliveira, Marcos Góes; Garcia, Giovanni de Oliveira; Busato, Cristiani Campos Martins; Partelli, Fábio LuizThe determination of nitrogen in plants by techniques that allow a fast diagnosis, based on plant growth characteristics, can be a useful tool for the nutritional management of coffee plants. Thus, this study evaluated growth and yield characteristics of irrigated conilon coffee in response to different nitrogen levels, resulting in the determination of the minimum N levels required to achieve the maximum yield, here called critical levels. The experiment was carried out in Colatina, Espirito Santo, Brazil, on plantations of conilon coffee, clonal variety Emcapa 8111, genotype 02. Six nitrogen levels were applied (0, 110, 220, 440, 880 and 1320 kg N ha-1) and the response in growth and yield characteristics periodically evaluated. There was a positive effect of the increasing N levels on yield, in that the N levels that provided 95% of the maximum yield (137.4 bags ha-1 and 108.5 bags ha-1) in the 2012/2013 and 2013/2014 growing seasons, respectively, were 420.7 and 543.1 kg N ha-1. There was also a positive effect of N levels on the growth characteristics and nitrogen contents, indicating their use as tools for a rapid nutritional diagnosis, with a view to optimizing the nitrogen management in Conilon coffee.Item Bayesian modeling of the coffee tree growth curve(Universidade Federal de Santa Maria, 2022-03-14) Pereira, Adriele Aparecida; Silva, Edilson Marcelino; Fernandes, Tales Jesus; Morais, Augusto Ramalho de; Sáfadi, Thelma; Muniz, Joel AugustoWhen modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.