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Item Association between the artificial aging test and the natural storage of coffee seeds(Associação Brasileira de Tecnologia de Sementes - ABRATES, 2018) Fantazzini, Tatiana Botelho; Rosa, Sttela Dellyzete Veiga Franco da; Pereira, Cristiane Carvalho; Pereira, Diego de Sousa; Cirillo, Marcelo Ângelo; Ossani, Paulo CésarThe accelerated aging test is recognized as an efficient method for evaluating the vigor of seed lots and for estimating their storage potential. Thus, this work aimed to evaluate the association between artificial aging and natural storage of coffee seeds, through the correlation factor analysis. Seeds of four cultivars of Coffea arabica L. (Catuaí Amarelo, Arara, Catiguá, and Mundo Novo) and one of Coffea canephora Pierre (Apoatã) were used. Part of the newly-harvested seeds were aged in a growth chamber under controlled temperature and relative humidity conditions (42 ºC and 100% RH) for periods of 0, 4, 6, 8, and 10 days. The other part of the seeds was stored in tri-wall paper packaging for a period of 2, 4, and 6 months in a non-climate-controlled environment. Artificial aging allows predictions on the storage potential of coffee seeds, although the artificial aging periods depend on the cultivars.Item 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 AugustoCoffee 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.Item Bayesian sequential procedure to estimate the viability of seeds Coffea arabica L. in tetrazolium test(Escola Superior de Agricultura "Luiz de Queiroz", 2019-05) Brighenti, Carla Regina Guimarães; Cirillo, Marcelo Ângelo; Costa, André Luís Alves; Rosa, Sttela Dellyzete Veiga Franco da; Guimarães, Renato MendesTetrazolium tests use conventional sampling techniques in which a sample has a o Zootecnia, Av. Visconde do Rio Preto, s/n — 36301-360 — fixed size. These tests may be improved by sequential sampling, which does not work with fixed- s São João Del-Rei, MG — Brasil. size samples. When data obtained from an experiment are analyzed sequentially the analysis can OM 2Universidade Federal de Lavras -— Depto. de Estatística, C.P. be terminated when a particular decision has been made, and thus, there is no need to pre-es- "O 3037 - 37200-000 - Lavras, MG - Brasil. tablish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient = Embrapa Café, PqEB, s/n - 70770-901 - Brasília, DF — knowledge about coffee production in the area to construct a prior distribution. Therefore, we Brasil. used the Bayesian sequential approach to estimate the percentage of viable coffee seeds sub- os “Universidade Federal de Lavras — Depto. de Agricultura. mitted to tetrazolium testing, and we incorporated priors with information from other analyses — *Corresponding author of crops from previous years. We used the Beta prior distribution and, using data obtained from Ss sample lots of Coffea arabica, determined its hyperparameters with a histogram and O'Hagan's O Edited by: Marcin Kozak methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a = basis for deciding whether or not we should continue the sampling process. The results confirm e Received April 13, 2017 that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average Fi; Accepted January 06, 2018 percentage of viability obtained with the conventional frequentist method was 88 %, whereas that v obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method E required, on average, only 89 samples to reach this value while the traditional estimation method O needed as many as 200 samples.