Vertical and temporal flight patterns of coffee berry borer (Coleoptera: Curculionidae) in Hawaii.

Coffee berry borer (Hypothenemus hampei Ferrari) (Coleoptera: Curculionidae) is the most damaging insect pest of coffee worldwide, causing significant losses in coffee yields and quality. Knowledge of vertical and temporal flight patterns in coffee berry borer could be used to optimize spray timing and precision targeting of areas within the coffee tree, which may be more susceptible. In the present study, we estimated the vertical distribution of coffee berry borer females using traps set at 1-m intervals up to 5 m in height. We also quantified coffee berry borer infestation in the low, mid, and high canopy and documented fruit availability. Temporal flight patterns were estimated using timer traps, and correlation analyses were conducted to determine the relationship between the timing of daily flight and weather variables. Across the 4 study sites, we observed that 77%-84% of the trap catch was at 1 m, 11%-20% was at 2 m, and 1%-4% was at 3-5 m in height. Fruit infestation was significantly higher in the low branches (35%) relative to the high branches (17%). Flight height remained the same year-round, regardless of fruit availability. Coffee berry borer flew in low numbers during the day and night but peaked from 12 to 4 PM. Daily flight was positively correlated with an increase in air temperature and wind speed and negatively correlated with relative humidity. Findings from this study suggest that pesticide sprays should target low- to mid-level branches at 1-2 m in height and aim to be conducted in the early afternoon when coffee berry borer are actively flying and most vulnerable to chemical controls.

Evaluation of macadamia felted coccid (Hemiptera: Eriococcidae) damage and cultivar susceptibility using imagery from a small unmanned aerial vehicle (sUAV), combined with ground truthing.

BACKGROUND: Macadamia felted coccid, Acanthococcus ioronsidei (Williams) (Hemiptera: Eriococcidae), is a significant pest of macadamia nut, Macadamia integrifolia Maiden & Betche (Protaceae), in Hawaii, and heavy infestations can kill branches, resulting in characteristic dead, copper-colored leaves. Small Unmanned Aerial Vehicles (sUAV) or 'drones,' combined with spatial data analysis, can provide growers with accurate and high-resolution detection of plant stress due to pest infestations. We investigated the feasibility of using RGB (red-green-blue) color images from sUAV to detect dieback caused by macadamia felted coccid infestation and compared sUAV estimates with ground-based damage estimates (ground truthing). RESULTS: Spatial analysis showed clustering of foliar damage that reflected cultivar susceptibility to macadamia felted coccid infestation, with cultivars 344 and 856 being susceptible, and cultivars 800 and 333 being tolerant. sUAV and ground-based estimates of foliar damage were similar for the cultivar 344, but ground-based assessments were higher than sUAV for cultivar 856, possibly due to the differences in canopy architecture and significant early dieback in the lower canopy. At foliar damage levels <10%, sUAV and ground truthing data were significantly positively correlated, suggesting sUAV may be useful in detecting early stages of macadamia felted coccid infestation. CONCLUSIONS: Cultivars showed varying susceptibility to macadamia felted coccid infestation and the foliage damage appeared in clusters. sUAV was able to detect the foliage damage under high and low infestation scenarios suggesting that it can be effectively used for the early detection of infestations. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Binomial Sequential Sampling Plans for Macadamia Felted Coccid (Hemiptera: Eriococcidae) Infesting Hawaii Macadamia Orchards.

The macadamia felted coccid, Eriococcus ironsidei (Williams) is an invasive pest of macadamia orchards that causes severe damage to the trees and reduces nut yield in Hawaii. Their feeding results in distortion and stunting of new growth, and with high population densities, dieback of entire branches may result. Ten macadamia orchards were sampled on the Hawaii island over a period of 18 mo to characterize the dispersion of the pest in the field, and to use those data as the basis for the development of a binomial sampling plan. Analyses based on Taylor's Power Law and Iwao's mean crowding index indicated an aggregated pattern of dispersion of the pest in the field. Curvilinear relationships between mean crawler density and the proportion of infested trees based on four tally thresholds (crawlers per unit area, T > 0, 5, 10, 20) were plotted. Results showed that a tally threshold T > 10 was the best predictor to model mean crawler density. Binomial sequential sampling plans based on three critical proportions of trees infested (0.40, 0.60, and 0.80) were developed. The adoption of a binomial sequential sampling plan for use in decision making can contribute to improve the management of E. ironsidei and optimize the number of samples needed to make decisions to control this pest.

Dispersion and Optimization of Sequential Sampling Plans for Coffee Berry Borer (Coleoptera: Curculionidae) Infestations in Hawaii.

Coffee berry borer, Hypothenemus hampei (Ferrari, Coleoptera: Curculionidae) is a serious pest of coffee in most coffee-growing areas of the world. This beetle was first detected in Big Island of Hawaii in 2010 and has since spread to other islands. Being an invasive pest that causes serious economic damage, efforts are in progress in Hawaii to develop an integrated approach to manage this pest. In this study, we sampled commercial coffee orchards from representative coffee-growing regions in the Big Island, Hawaii, to understand dispersion of the pest in the field and develop a reliable sampling plan based on the dispersion characteristics. Analysis of data collected from 12 commercial fields over three growing seasons suggests an aggregated pattern of dispersion of the pest in the field. Two fixed-precision sequential sampling plans based on berry cluster and branch as sample units were modeled and validated using Resampling for Validation of Sampling Plans software. The models suggest that infestation density can be estimated reliably for integrated pest management (IPM) practices with minimal sampling effort by sampling berry clusters or branches using sequential sampling plans. Sequential sampling plan based on berry cluster requires detection of fewer infested berries compared to branch sampling for a reliable estimation of mean density of infested berries and IPM decision making.

PCR-Based Gut Content Analysis to Detect Predation of Eriococcus ironsidei (Hemiptera: Eriococcidae) by Coccinellidae Species in Macadamia Nut Orchards in Hawaii.

Macadamia felted coccid, Eriococcus ironsidei (Williams) (Hemiptera: Eriococcidae) was first found infesting macadamia trees in the island of Hawaii in 2005. Macadamia felted coccid infests all above-ground parts of trees to feed and reproduce. Their feeding activity distorts and stunts new growth which causes yellow spotting on older leaves, and when population densities become high, branch dieback occurs. Different predatory beetles have been observed in macadamia nut trees infested by E. ironsidei, the most abundant were Halmus chalybeus, Curinus coeruleus, Scymnodes lividigaster, Rhyzobius forestieri, and Sticholotis ruficeps. To verify predation of E. ironsidei by these beetles, a molecular assay was developed utilizing species-specific primers to determine presence in gut content of predators. Using these primers for PCR analysis, wild predator beetles were screened for the presence of E. ironsidei DNA. Analysis of beetles collected from macadamia orchards revealed predation by H. chalybeus, C. coeruleus, S. lividigaster, R. forestieri, and S. ruficeps on E. ironsidei. This study demonstrates that these beetles may play an important role in controlling the population of E. ironsidei, and these predators may be useful as biocontrol agents for E. ironsidei.

Within-field spatial distribution of stink bug (Hemiptera: Pentatomidae)-induced boll injury in commercial cotton fields of the southeastern United States.

Spatial distribution of boll injury caused by stink bugs to developing cotton (Gossypium hirsutum L.) bolls was studied in five commercial fields (≍22 ha each) in 2011 and 2012 to understand variability in boll injury dynamics within fields. Cotton bolls and stink bugs were sampled weekly from a georeferenced grid of sampling points (one sample per 0.40 ha) in each field, but no samples were taken within 30 m of field edges. The inverse distance weighted interpolation, variogram analysis, and Moran's I were used to describe spatial variability of boll damage within the fields. Boll injury was found to be spatially associated at distances ranging from ≍75 to 275 m with an average distance ≍150 m. An exponential variogram model was selected as the best fitting model to describe the spatial association in four of the five fields. Moran's I indicated that spatial association was significant in three of the five fields. The spread of boll injury from stink bugs was gradual in most fields and always exceeded the treatment threshold during the fourth or fifth week of bloom. Capture of stink bugs using a sweep net was inefficient, strongly suggesting that quantifying boll injury is a better sampling method and predictor of stink bug activity when sampling all but the edges of the field. These data suggest that scouts need to sample boll injury from sample locations separated by at least 150 m to assure independence in the central part of large fields. Second, future researchers who plan to use parametric statistical methods could use a 150-m grid, as opposed to a denser grid that would require greater time and effort.

Influence of planting date on stink bug injury, yield, fiber quality, and economic returns in Georgia cotton.

Phytophagous stink bugs are economically important pests of annual and perennial crops in the southeastern United States. Because of insecticide resistance and risk of secondary pest outbreaks, there is interest in identifying cultural practices that could lead to reduced insecticide applications. The objective of this project was to assess the importance of cotton planting date on stink bug damage to cotton. Unsprayed cotton plots with biweekly planting dates were established at three locations in southern Georgia in each of two crop years. During the bloom cycle, stink bug-induced boll injury was estimated weekly in each plot. Plots were subsequently defoliated, mechanically harvested, and ginned to assess differences in fiber yield and quality attributable to stink bug injury. Results show that the rate of boll damage generally increased more rapidly through the bloom cycle for planting dates in June compared with May. Similarly, estimates of boll damage from June-planted cotton more frequently exceeded the stink bug treatment threshold compared with May-planted cotton. In 2011, mean lint yield and economic returns from May planting dates were significantly greater than June planting dates. In 2012, lint yield and economic returns were greater in plots established in early May compared with later planting dates. Estimates of HVI color + b, a measure of fiber yellowness, were lower in early May-planted cotton compared with June planting. These data show that growers need to be aware of increased stink bug damage potential when planting late.