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

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.

Spatio-temporal distribution of Anthonomus grandis grandis Boh. in tropical cotton fields

BACKGROUND

Knowledge of the spatio-temporal distribution of pests is important for the development of accurate management approaches. The boll weevil, Anthonomus grandis grandis Boh., is a deleterious cotton, Gossypium hirsutum L., pest in the western hemisphere. The spread of boll weevils across cotton fields remains poorly understood. We assessed the dispersal pattern of adult weevils through cotton fields cultivated in a tropical area during dry and wet seasons using geostatistics for the number of adults and infested reproductive structures (buds, bolls and total).

RESULTS

Adult weevils and infested reproductive structures increased across both seasons despite the prevailing climatic variables. In both seasons, boll weevil adults and infested reproductive structures followed an aggregated distribution. The distances over which samples maintained spatial dependence varied from 0.7 to 43.4 m in the dry season and from 6.0 to 614.4 m in the wet season. Boll weevil infestations started at field borders and the infested reproductive structures (oviposition and/or feeding punctured) were greater than the adults regardless of cotton growth stage.

CONCLUSION

Sampling for boll weevils in cotton fields should start at the field borders and focus on total infested reproductive structures (buds + bolls) and as cotton plants develop, sampling should focus on the field as a whole. Distances among samples will vary from 6 to 470 m. Thus, despite the cotton phenological stage or growing season, monitoring of boll weevil should be done by sampling total infested reproductive structures with a minimum distance of 6 m among samples. © 2022 Society of Chemical Industry.

Sampling Optimization and Crop Interface Effects on Lygus lineolaris Populations in Southeastern USA Cotton

Tarnished plant bug, Lygus lineolaris (Hemiptera: Miridae), is an economically damaging pest in cotton production systems across the southern United States. We systematically scouted 120 commercial cotton fields across five southeastern states during susceptible growth stages in 2019 and 2020 to investigate sampling optimization and the effect of interface crop and landscape composition on L. lineolaris abundance. Variance component analysis determined field and within-field spatial scales, compared with agricultural district and state, accounted for more variation in L. lineolaris density using sweep net and drop cloth sampling. This result highlights the importance of field-level scouting efforts. Using within-field samples, a fixed-precision sampling plan determined 8 and 23 sampling units were needed to determine L. lineolaris population estimates with 0.25 precision for sweep net (100 sweeps per unit) and drop cloth (1.5 row-m per unit) sampling, respectively. A spatial Bayesian hierarchical model was developed to determine local landscape (<0.5 km from field edges) effects on L. lineolaris in cotton. The proportion of agricultural area and double-crop wheat and soybeans were positively associated with L. lineolaris density, and fields with more contiguous cotton areas negatively predicted L. lineolaris populations. These results will improve L. lineolaris monitoring programs and treatment management decisions in southeastern USA cotton.

Spatial Distribution of Euschistus heros (Hemiptera: Pentatomidae) in Cotton (Gossypium hirsutum Linnaeus)

Recently, the Neotropical brown stink bug, Euschistus heros (Pentatomidae: Hemiptera), has been detected in cotton in the Brazilian Cerrado. Several reliable surveys have been conducted on the spatial distribution of stink bugs in soybean; however, few studies have examined the behavior of this insect in cotton in Brazil. Thus, this study examined the spatial distribution of E. heros in cotton using aggregation indices based on the variance-to-mean ratio, Morisita index, exponent k, Poisson and negative binomial distributions. Inverse Distance Weighting interpolation maps were also used to represent insect density. The study was conducted in a 1.1 hectare experimental cotton field in which 64 equally-sized plots were sampled on a weekly basis. Our findings revealed that stink bugs presented an aggregate distribution in 2 sampling weeks based on aggregation indices, and this pattern was best described by a negative binomial distribution. Maps showing the density of pests can be an important tool for sampling pests in crops and control measures in early stages of infestation, when there is an edge effect on the distribution of E. heros, can be an efficient strategy in cotton.

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.

Spatial Distribution and Coexisting Patterns of Adults and Nymphs of Tibraca limbativentris (Hemiptera: Pentatomidae) in Paddy Rice Fields

The rice stem stink bug, Tibraca limbativentris Stål (Hemiptera: Pentatomidae), is a primary insect pest of paddy rice in South America. Knowledge of its spatial distribution can support sampling plans needed for timely decisions about pest control. This study aimed to investigate the spatial distribution of adults and nymphs of T. limbativentris and determine the spatial coexistence of these stages of development. Fifteen paddy rice fields were scouted once each season to estimate insect densities. Scouting was performed on regular grids with sampling points separated by ∼50 m. Moran's I and semivariograms were used to determine spatial distribution patterns. Spatial coexistence of nymphs and adults was explored via spatial point process. Here, adults and nymphs had typically contrasting spatial distribution patterns within the same field; however, the frequency of aggregation was not different between these developmental stages. Adults and nymphs were aggregated in seven fields and randomly distributed in the other eight fields. Uniform distribution of adults or nymphs was not observed. The study-wide semivariogram ranges were ∼40 m for adults and ∼55 m for nymphs. Nymphs and adults spatially coexisted on 67% of the fields. Coexisting patterns were classified using one of the following processes: stage-independent, bidirectional attractive, unidirectional attractive, bidirectional inhibiting, or unidirectional inhibiting. The information presented herein can be important for developing sampling plans for decision-making, implementing tactics for site-specific management, and monitoring areas free of T. limbativentris.