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Item Planococcus spp.: behavior and monitoring in conilon coffee crops(Editora UFLA, 2021) Borghi, Edinei José Armani; Fornaciari, Gabriel; Vieira, Mayara Leite; Aguiar, Ronilda Lana; Holtz, Anderson Mathias; Verdin Filho, Abraão Carlos; Comério, Marcone; Andrade Júnior, Saul de; Carvalho, José Romário deThe damage caused by the citrus mealybug attack, Planococcus spp., on Coffea canephora crops is becoming more and more frequent, and may cause losses close to 100%. Knowledge of aspects related to pest behavior and adoption of methods to monitor crop incidence are important tools for integrated pest management. Thus, the objective was to study the behavior of citrus mealybug along the phenological stages of C. canephora and to propose methods to monitor its occurrence in the crops. The study was carried out in an area consisting of adult C. canephora plants of the variety Diamante ES 8112. The study was carried out using four sampling methods: 1) soil and root sampling with a probe; 2) opening small trenches; 3) plagiotropic branches and 4) weed sampling. Evaluations were carried out monthly by checking and confirming the presence of citrus mealybug in the sampled regions. The information obtained was used to describe the behavior of citrus mealybug and to analyze the applicability of the monitoring methods studied. In the absence of the reproductive phase, citrus mealybug lodges in the root system of coffee and weeds, in the region close to the colon, moving to the aerial part of the plants (rosettes) at the beginning of the flowering of the crop. Probe sampling was not efficient, while opening small trenches is a difficult procedure and causes damage to the root system of the coffee tree. The sampling of plagiotropic branches and weeds allows the monitoring of citrus mealybug duringall the phenological phases of C. canephora.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.