Avaliou-se a ação de produtos fitossanitários usados em cafeeiros sobre pupas e adultos de Chrysoperla externa (Hagen, 1861) (Neuroptera: Chrysopidae). Os bioensaios foram conduzidos no Departamento de Entomologia da Universidade Federal de Lavras - UFLA, Lavras, MG, Brasil. Os tratamentos avaliados, em g i.a.L-1 de água, foram: 1- endosulfan (Thiodan 350 CE - 1,75), 2- chlorpyrifos (Lorsban 480 CE -1,2), 3- betacyfluthrin (Turbo 50 CE - 0,013), 4- enxofre (Kumulus 800 PM - 4,0), 5- azocyclotin (Peropal 250 PM - 0,31), 6- oxicloreto de cobre (Cuprogarb 500 PM - 5,0) e 7- Testemunha (água). As pulverizações foram realizadas diretamente sobre pupas e adultos do crisopídeo por meio de torre de Potter. As pupas foram colocadas em tubos de vidro e os adultos em gaiolas de PVC, e mantidos em sala climatizada a 25 ± 2°C, UR de 70 ± 10% e fotofase de 12 horas. O delineamento experimental foi inteiramente casualizado, com sete tratamentos e dez repetições, sendo cada parcela formada por quatro pupas ou um casal de C. externa. Os produtos foram distribuídos nas quatro classes de toxicidade conforme escala estabelecida pela IOBC. O chlorpyrifos mostrou-se levemente nocivo para pupas (classe 2, 30£E£79%), e os demais produtos foram inócuos (classe 1, E<30%). O endosulfan, enxofre, azocyclotin e oxicloreto de cobre foram inócuos em adultos, enquanto o betacyfluthrin foi moderadamente nocivo (classe 3, 80£E£99%) e o chlorpyrifos foi nocivo (classe 4, E>99%). Os produtos testados à base de endosulfan, enxofre, azocyclotin e oxicloreto de cobre podem ser recomendados em programas de manejo de pragas do cafeeiro em associação com C. externa, em função da baixa toxidade apresentada por esses compostos ao predador.
The effects of six pesticides applied to the coffee crop on eggs and their consequences on the subsequent de- velopmental stages of Chrysoperla externa (Neuroptera: Chrysopidae) were evaluated under laboratory conditions. The pesticides and water (control) were sprayed on eggs using a Potter´s tower. After spraying, forty eggs per treatment were individualized in glass tubes and maintained in a climatic chamber, in order to evaluate immature development of this predator. The treatments showed significant differences for egg viability and survival of first-instar larvae. Chlorpyrifos, sulphur and copper oxichlorate reduced the treated egg viability, whereas both sulphur and betacyfluthrin reduced the survival of first-instar larvae. Endosulphan and azociclotin reduced the daily oviposition of this green lacewing species. The harmless products (Class 1, E < 30%), can be recommended for use in integrated pest management programs in coffee crops, in order to preserve this predator.
A presente pesquisa foi realizada em dois talhões de 2,0 ha de cafeeiros sob plantio adensado (2,0 x 1,0m), cultivar Acaiá/IAC-474-19 : um sob cultivo orgânico e outro convencional, com cinco anos de idade em Santo Antônio do Amparo, MG. Estudou-se a dinâmica populacional do bicho-mineiro Leucoptera coffeella (Guérin-Mèneville & Perrottet, 1842) (Lepidoptera: Lyonetiidae) e seus inimigos naturais. Avaliaram-se a porcentagem de folhas minadas, a porcentagem de minas predadas por vespas, número de lagartas do bicho-mineiro vivas, número de pupas formadas/60 folhas coletadas na amostragem e a porcentagem de parasitismo total. Os dados referentes a cada avaliação e sistema de cultivo foram submetidos à análise de variância e ao teste de médias de Scott-Knott (p≤0,05). Observou-se que, em 1999, o cultivo de café orgânico apresentou a maior porcentagem de folhas minadas pelo bicho-mineiro (45,3%), de minas predadas por vespas (8,0%), maiores números de lagartas vivas e de pupas formadas, e maior porcentagem de parasitismo total (65,5%), em relação ao sistema convencional (11,2% de folhas minadas e 36,1% de parasitismo total). Esta situação se inverteu para a porcentagem de folhas minadas e número de lagartas vivas em 2000 e 2001. Não foram detectados parasitoides de ovos de bicho-mineiro em nenhum dos sistemas de cultivo. As espécies de parasitoídes mais abundantes foram Orgilus niger, Centistidea striata, Stiropius reticulatus (Hymenoptera: Braconidae) e Horismenus sp. (Hymenoptera: Eulophidae).
The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the “Coffee-Leaf-Miner Index” was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The “Coffee-Leaf-Miner Index” presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, “Coffee-Leaf-Miner Index” can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.
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