Hypothenemus hampei is the main coffee pest in the Amazon and worldwide. Although coffee cultivation exists in the State of Amazonas, there is no information regarding the behavior of this species population in this region of Brazil. Since the Amazon region is a favorable environment for H. hampei, the present study aimed to investigate this insect population in the following cultivation systems implanted in the municipality of Humaitá, Southern Amazonas: i) coffee in native forest (CNF), ii) coffee intercropped with cupuaçu and açaí (native Brazilian fruits) (CCA) and iii) coffee in monoculture (MC). Also, the climatic and phenological parameters of the plant were used in relation with the population dynamics of the insect. For that, ten baited traps were installed in each field for the monthly sampling of the H. hampei population from July/2018 to June/2019. The highest population abundance of the insect was observed during the coffee flowering season, mainly in monoculture crops, when compared to the other intercropped fields. The total mean value of the insect population observed in the three environments evaluated was highest in August/2018, with a recorded value of 1749. The increase in rainfall indexes occurred simultaneously with a decrease in the insect population; this coincides with the end of the flowering season (October/2018, 161.8 mm) and the beginning of grain formation (November/2018, 234.8 mm). The lowest abundance of the insect observed in the coffee fields in native forest, with an abundance of 83 (CNF), indicates that the local biodiversity disfavors the population size of the pest in the environment. These results suggest that phenological phases of conilon coffee and the influence of climatic conditions can result in changes in the population dynamics of the H. hampei. Coffee agroforestry systems are more promising to maintain this population at low levels.

The present study aimed to evaluate the effect of alternative products on the control of coffee brown-eye spot and coffee rust. The experiment was carried out in two conilon coffee plantations, in the following agroforestry system models: a) continuous system (SM-1) and b) mixed system (SC-2). The following treatments were used: i) aqueous plant extract of Himatanthus sucuuba (2%) - SUC, ii) Bordeaux mixture (2%) - CB and iii) control (water). Four monthly samplings of the temporal progress of the diseases were carried out, during the period from October/2019 to January/2020, evaluating the symptoms in three leaves per plant, one from each of the upper, middle and lower thirds of the plant. Incidence percentages were determined according to the number of leaves with symptoms in relation to the total number of leaves sampled. In order to estimate severity, diagrammatic scales were used for each disease evaluated. The obtained data were submitted to routine statistical analysis in R programming with a significance of 5%. Interaction effects were not significant according to the ANOVA test. Significant differences were observed among the treatments studied for the two diseases with the following results: percent reduction of rust incidence: (SUC - 50%, CB - 80% and TEST - 95%) and percent reduction of brown-eye spot: (SUC - 30%, CB - 60 % and TEST - 80%). SUC treatment was more effective in reducing disease severity with the following results: (SUC - 0.26%, CB - 0.40% and TEST - 0.83%) and (SUC - 0.35%, CB - 0.41% and TEST - 0.99%) for rust and brown-eye spot diseases, respectively. The treatment (SUC) proved to be a promising alternative product for the management of brown-eye spot and rust diseases.

No abstract

Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição 4.0 Internacional (CC BY 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.