EFEITO DA COR DE ARMADILHAS ADESIVAS PARA MONITORAMENTO DE Thaumastocoris peregrinus CARPINTERO & DELLAPÉ (HEMIPTERA: THAUMASTOCORIDAE) NO CAMPO

The bronze bug Thaumastocoris peregrinus is native from Australia and has been detected in Brazil since 2008, causing damages in eucalyptus plantations. This insect feeds on the phloem-sap, preferentially on the oldest leaves, that evolves into chlorotic spots and later in bronze spots. In high infestations, plant defoliation may occur. Although the feeding damage results in losses, the efforts for monitoring are still scarce. Therefore, by using adhesive traps colored differently we sought to determine which color would be the best for monitoring this insect in eucalyptus plantations. White and yellow traps are efficient for field monitoring and should be installed in the top of the trees located in the edge of the plots. O percevejo-bronzeado-do-eucalipto Thaumastocoris peregrinus é originário da Austrália e foi detectado no Brasil em 2008, causando danos em plantações de eucalipto. O percevejo suga a seiva, principalmente das folhas mais velhas, onde se formam pontos cloróticos que, posteriormente, tornam-se manchas bronzeadas. Em casos extremos pode ocorrer desfolhação. Os trabalhos sobre o monitoramento do inseto ainda são escassos perante os registros de danos em plantações de eucalipto. Diante disso, com o uso de diferentes cores de armadilhas adesivas, buscou-se determinar qual seria a melhor coloração para uso no monitoramento de Thaumastocoris peregrinus em plantios de eucalipto. As armadilhas de cor branca e amarela são eficientes para monitoramento de infestações no campo e devem ser instaladas próximo à copa das plantas e na borda dos talhões

Biological parameters, life table and thermal requirements of Thaumastocoris peregrinus (Heteroptera: Thaumastocoridae) at different temperatures.

Temperature affects the development, population dynamics, reproduction and population size of insects. Thaumastocoris peregrinus Carpintero et Dellape (Heteroptera: Thaumastocoridae) is a eucalyptus pest. The objective of this study was to determine biological and life table parameters of T. peregrinus on Eucalyptus benthamii at five temperatures (18 °C; 22 °C; 25 °C; 27 °C and 30 °C) with a relative humidity (RH) of 70 ± 10% and photoperiod of 12 hours. The duration of each instar and the longevity of this insect were inversely proportional to the temperature, regardless of sex. The nymph stage of T. peregrinus was 36.4 days at 18 °C and 16.1 days at 30 °C. The pre-oviposition period was 5.1 days at 30 °C and 13.1 days at 18 °C and that of oviposition was 7.6 days at 30 °C and 51.2 days at 22 °C. The generation time (T) of T. peregrinus was 27.11 days at 22 °C and 8.22 days at 30 °C. Lower temperatures reduced the development and increased the life stage duration of T. peregrinus. Optimum temperatures for T. peregrinus development and reproduction were 18 and 25 °C, respectively

Airborne Hyperspectral Data Application in Stress Detection of Blueberry Fields and Ash Trees

Water management and irrigation practices are persistent challenges for many agricultural systems. Changing seasonal and weather patterns impose a greater need for understanding crop deficiencies and excesses (e.g. water, sunlight, nutrients) for optimal growth while allocating proper resources for prompt response. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Early detection of stress in agricultural fields can prompt management responses to mitigate detrimental conditions including drought and disease. Remote sensing has provided timely and reliable information covering large spatial extents, while novel applications in hyperspectral data and imaging spectroscopy have shown potential in early stress detection. We assess airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries to accurately detect water content. Airborne scans of spectral data were collected three times throughout the 2019 summer in Deblois, Maine. Data were collected over two adjacent fields, one irrigated and one nonirrigated. Ground sampled data were collected in tandem to the UAV collection. The ground sampled data over the irrigated and non-irrigated fields guided digital sampling from the imagery to act as training for our models. Using methods in machine learning and statistical analysis, we related hyperspectral reflectance measurements to different water potential levels in blueberry plant leaves to decipher vegetation signals both spatially and temporally through utilizing the capacity of imaging spectroscopy. Models were developed to determine irrigation status and water potential. Seven models were assessed in this study with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated water potential levels. Our global water potential model had an R2 of 0.62. Models for the water potential predictions were verified with a validation dataset. Forest insect and disease pests have a significant impact on the well-being of individual trees and forest stands, affecting ecosystem processes and potentially human health. Dispersing through 35 states within only 17 years (USDA, 2020), the effect of emerald ash borer (Agrilus Planipennis Fairmaire) (EAB) in the United States has been particularly severe and devastating. Early detection of stress in forests can prompt management responses to mitigate detrimental conditions including drought and disease as well as pest outbreaks. Remote sensing has provided timely and reliable information covering large spatial extents, while novel applications in hyperspectral data and imaging spectroscopy have shown potential in early stress detection. We build on previous work by assessing airborne spectral data, and health classifications of EAB infested ash trees in aims to accurately detect stress. Airborne scans of spectral data were collected within three days in late July 2019 over three sites in southern New Hampshire. Ground sampled data were collected in November 2019 and include sampled ash classified on a scale of 1-5 (1=healthy, no major branch morality, 5=dead). The ground sampled data of different health classifications guided digital sampling from the imagery to act as training and validation for our models. Using methods in machine learning and statistical analysis, we related reflectance measurements to different classifications of ash tree health to understand tree stress signals while utilizing the capacity of remote sensing. Models were developed to classify health in ash trees impacted by EAB. The first entailed a shadow classifier, followed by one for health. Eighteen cube images contained ground sampled data and were processed with the two models, then further buffered. Pixel classification for each buffer sample was calculated. The health classifier model was used on a validation test set and had an prediction accuracy of 76.1%

A statistical approach for modelling forest structural attributes using multispectral remote sensing data within a commercial forest plantation.

Master of Science in Geography. University of KwaZulu-Natal, Durban 2017.Abstract available in PDF file