Adapting pest management strategies to changing climates for the redlegged earth mite, Halotydeus destructor.

As climate change continues to modify temperature and rainfall patterns, risks from pests and diseases may vary as shifting temperature and moisture conditions affect the life history, activity, and distribution of invertebrates and diseases. The potential consequences of changing climate on pest management strategies must be understood for control measures to adapt to new environmental conditions. The redlegged earth mite (RLEM; Halotydeus destructor [Tucker]) is a major economic pest that attacks pastures and grain crops across southern Australia and is typically controlled by pesticides. TIMERITE® is a management strategy that relies on estimating the optimal timing (the TIMERITE® date) for effective chemical control of RLEM populations in spring. In this study, we assessed the efficacy of control at the TIMERITE® date from 1990 to 2020 across southern Australia using a simulation approach that incorporates historical climatic data and field experimental data on life history, seasonal abundance, and population level pesticide responses. We demonstrate that moisture and temperature conditions affect the life history of RLEM and that changes in the past three decades have gradually diminished the efficacy of the TIMERITE® strategy. Furthermore, we show that by incorporating improved climatic data into predictions and shifting the timing of control to earlier in the year, control outcomes can be improved and are more stable across changing climates. This research emphasises the importance of accounting for dynamic environmental responses when developing and implementing pest management strategies to ensure their long-term effectiveness. Suggested modifications to estimating the TIMERITE® date will help farmers maintain RLEM control outcomes amidst increasingly variable climatic conditions.

Bird Boxes and Sparrow Traps: The Technological Regulation of Avian Life in the United States.

Only a few decades after its introduction to the United States in the mid-nineteenth century, the house sparrow was considered a pest that drove away native birds. Its downfall is representative of a story familiar to scholars of animals and technology who have studied the methods used to control or exclude unwanted species from both rural and urban areas. The case of the house sparrow, however, differs in a crucial respect: the birds made their homes in bird boxes, built technologies designed to attract avian species and bring them closer to humans. This article documents how bird boxes were used as tools to regulate avian life in the late nineteenth and early twentieth centuries in the United States and argues that they should be seen as a technology that mediates and regulates our relationship with nature by promoting or controlling certain aspects of living organisms.

Ethnobotanical insights on the management of plant pests and diseases by smallholder farmers in Mpumalanga Province of South Africa.

BACKGROUND: Pests and diseases are a major contributor to yield losses in sub-Saharan Africa, prompting smallholder farmers to seek cost-effective, accessible and ecologically friendly alternatives for crop protection. This study explored the management of pests and diseases affecting crops across eight selected villages in Ehlanzeni District, Mpumalanga Province, South Africa. METHODS: A total of 120 smallholder farmers were purposefully selected utilising the snowball technique. Information on the management of plant pests and diseases was collected through interviews and focus group discussions using semi-structured interview schedules. Ethnobotanical indices, including relative frequency of citation (RFC), use-value (UV) and informant consensus factor (Fic), were used to quantify and rank the plants used for crop protection in the study area. RESULTS: Twenty-three plant species (16 naturalised exotics and seven indigenous plants) belonging to 16 families were used for managing pests (vertebrates and invertebrates) and diseases (fungal and bacterial related) affecting crops in the study area. The dominant (100%) crops cultivated by the participants were Allium cepa L., Mangifera indica L., Solanum lycopersicum L. and Zea mays L. The RFC value ranged from 0.08 to 0.83 and the three most popular plants for crop protection were Capsium annuum L. (0.83), A. cepa (0.63) and Dichrostachys cinerea (L.) Wight & Arn. (0.43). In terms of the UV, the five most promising plants used as biocontrol were Tulbaghia violacea (0.13), A. cepa (0.12), C. annuum L. (0.09), Solanum campylacanthum Hochst. Ex A.Rich.(0.09) and Pinus pinaster (0.08). Based on the Fic, four categories were established and dominated by fungal diseases (0.64). Furthermore, T. violacea and A. cepa were the most often mentioned plants used against fungal conditions. Other categories cited were bacterial diseases (0.3), invertebrate pests (0.11) and vertebrate pests (0.14), an indication that smallholder farmers had limited agreement or common knowledge about the plants used for their management. The preparation methods included maceration (38%), decoction (38%) and burning (24%). Foliar application (67%) and soil drenching (33%) were used for administering plant extracts during the management of crop pests and diseases. CONCLUSION: The study highlights the importance of botanicals and associated indigenous knowledge among smallholder farmers in Mpumalanga Province, South Africa. It is pertinent to explore the valorisation of these botanicals by generating empirical data on their biological efficacies and phytochemical profiles.

[Effect of ground cage use on Oncomelania hupensis spread].

OBJECTIVE: To examine the effect of ground cage use on Oncomelania hupensis spread, so as to provide insights into precision snail control. METHODS: Twenty ground cages that were frequently used to capture rice field eels were purchased, including 11 packaging tape-made cages, 7 plastic cages and 2 nylon rope-made cages. The eel-capturing activity was mimicked, and 20 ground cages were assigned in settings with relatively high (1.00 snail/0.1 m2 and higher) and low snail densities (< 1.00 snail/0.1 m2) in Xindai Township, Pinghu City, Zhejiang Province during the period from 15 : 00 to 8 : 00 of the following day on April 13, 26 and 28. The numbers of snails carried by different types of ground cages were compared in settings with different types of snail densities using the rank-sum test. RESULTS: A total of 11 cage-times were assigned in settings with a high snail density, and a total of 77 snails were captured, with a mean number of 7 snails in each cage-time and 2.15 snails in 0.1 m2 ground cage. The mean numbers of snails carried by packaging tape-made and plastic cages were 2.47 snails/0.1 m2 cage and 0.37 snails/0.1 m2 cage, respectively. A total of 24 cage-times were assigned in settings with a low snail density, and a total of 8 snails were captured, with a mean number of 0.33 snails in each cage-time and 0.09 snails in 0.1 m2 ground cage. The mean numbers of snails carried by packaging tape-made cages were 0.12 snails/0.1 m2 cage; however, no snails were carried by plastic or nylon rope-made cages. The number of snails carried by ground cages was higher in settings with a high snail density than in settings with a low snail density (Z = -4.019, P < 0.01), and the number of snails carried by packaging tape-made cages was higher in settings with a high snail density than in settings with a low snail density (Z = -4.086, P < 0.01). No significant differences were found in the numbers of snails carried by different types of ground cages. CONCLUSIONS: The use of ground cage in snail habitats is a contributor to snail spread.

Fabrication and application of carrier-free and carrier-based nanopesticides in pest management.

Pesticides are widely used for pest control to promote sustained and stable growth of agricultural production. However, indiscriminate pesticide usage poses a great threat to environmental and human health. In recent years, nanotechnology has shown the ability to increase the performance of conventional pesticides and has great potential for improving adhesion to crop foliage, solubility, stability, targeted delivery, and so forth. This review discusses two types of nanopesticides, namely, carrier-free nanopesticides and carrier-based nanopesticides, that can precisely release necessary and sufficient amounts of active ingredients. At first, the basic characterization and preparation methods of these two distinct types of nanopesticides are briefly summarized. Subsequently, current applications and future perspectives on scientific examples and strategies for promoting the usage efficacy and reducing the environmental risks of these nanopesticides were also described. Overall, nanopesticides can promote higher crop yields and lay the foundation for sustainable agriculture and global food security.

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer.

Agricultural pests and diseases pose major losses to agricultural productivity, leading to significant economic losses and food safety risks. However, accurately identifying and controlling these pests is still very challenging due to the scarcity of labeling data for agricultural pests and the wide variety of pest species with different morphologies. To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin Transformer for basic feature extraction. Subsequently, we designed the SCF-FPN module to enhance the basic features to extract richer pest features. Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. Finally, to further improve our detection results, we incorporated non-maximum suppression (Soft NMS) and Cascade R-CNN's cascade structure into the optimization process to ensure more accurate and reliable prediction results. In a detection task involving 28 pest species, our algorithm achieves 92.5%, 91.8%, and 93.7% precision in terms of accuracy, recall, and mean average precision (mAP), respectively, which is an improvement of 12.1%, 5.4%, and 7.6% compared to the original baseline model. The results demonstrate that our method can accurately identify and localize farmland pests, which can help improve farmland's ecological environment.

Landscape features support natural pest control and farm income when pesticide application is reduced.

Future trajectories of agricultural productivity need to incorporate environmental targets, including the reduction of pesticides use. Landscape features supporting natural pest control (LF-NPC) offer a nature-based solution that can serve as a partial substitute for synthetic pesticides, thereby supporting future productivity levels. Here, we introduce a novel approach to quantify the contribution of LF-NPC to agricultural yields and its associated economic value to crop production in a broad-scale context. Using the European Union as case study, we combine granular farm-level data, a spatially explicit map of LF-NPC potential, and a regional agro-economic supply and market model. The results reveal that farms located in areas characterized by higher LF-NPC potential experience lower productivity losses in a context of reduced synthetic pesticides use. Our analysis suggests that LF-NPC reduces yield gaps on average by four percentage points, and increases income by a similar magnitude. These results highlight the significance of LF-NPC for agricultural production and income, and provide a valuable reference point for farmers and policymakers aiming to successfully invest in landscape features to achieve pesticides reduction targets.

Immobilization of Bacillus thuringiensis Cry1Ac in metal-organic frameworks through biomimetic mineralization for sustainable pest management.

Traditional chemical pesticide dosage forms and crude application methods have resulted in low pesticide utilization, increased environmental pollution, and the development of resistance. Compared to traditional pesticides, nanopesticides enhance the efficiency of pesticide utilization and reduce the quantity required, thereby decreasing environmental pollution. Herein, Cry1Ac insecticidal crystal protein from Bacillus thuringiensis Subsp. Kurstaki HD-73 was encapsulated in a metal-organic framework (zeolite imidazolate framework-8, ZIF-8) through biomimetic mineralization to obtain Cry1Ac@ZIF-8 nanopesticides. The Cry1Ac@ZIF-8 nanopesticides exhibited a dodecahedral porous structure, and the introduction of Cry1Ac did not affect the intrinsic crystal structure of ZIF-8. The indoor toxicity analysis revealed that the toxicity of Cry1Ac towards Ostrinia furnacalis (Guenée), Helicoverpa armigera Hubner, and Spodoptera litura Fabricius was not affected by ZIF-8 encapsulation. Surprisingly, Cry1Ac@ZIF-8 still exhibited excellent pest management efficacy even after exposure to heat, UV irradiation, and long-term storage. More importantly, the encapsulation of ZIF-8 significantly enhanced the internal absorption performance of Cry1Ac in maize leaves and extended its persistence period. Thus, ZIF-8 could potentially serve as a promising carrier for the preparation of nanopesticides with enhanced applicability, stability, and persistence period, providing a powerful strategy to improve the application of Cry1Ac in future agricultural pest management.

Enhancing arthropod occurrence in wheat cropping systems: the role of non-chemical pest management and nitrogen optimization.

This study analyzes arthropod biomass and abundance to track the changes in arthropod occurrence in relation to pesticide use in three winter wheat cropping systems managed at different intensities (organic, conventional, and hybrid). Arthropod occurrence was surveyed using three collection tools: sweeping nets, eclector traps, and yellow traps. Sampling was conducted over three years from 2020 to 2022 with 588 samples collected. The wet weight of the captured organisms was determined and arthropod abundance calculated. The application of a NOcsPS (no chemical-synthetic pesticides) strategy, a new hybrid cultivation method realized with optimized use of nitrogen fertilizers but without chemical-synthetic pesticides, showed a higher arthropod occurrence and performed more convincingly regarding produced arthropod biomass and abundance than the other cropping variants. The results also demonstrate a dependence of the obtained insect indices on the collection method. Although arthropod biomass and abundance correlated for all collection methods, the combination of various methods as well as multiple procedures of sample analysis gives a more realistic and comprehensive view of the impact of the wheat cultivation systems on the arthropod fauna than one-factor analyses.

Effective pest management approaches can mitigate honey bee (Apis mellifera) colony winter loss across a range of weather conditions in small-scale, stationary apiaries.

Managed honey bee (Apis mellifera L.) colonies in North America and Europe have experienced high losses in recent years, which have been linked to weather conditions, lack of quality forage, and high parasite loads, particularly the obligate brood parasite, Varroa destructor. These factors may interact at various scales to have compounding effects on honey bee health, but few studies have been able to simultaneously investigate the effects of weather conditions, landscape factors, and management of parasites. We analyzed a dataset of 3,210 survey responses from beekeepers in Pennsylvania from 2017 to 2022 and combined these with remotely sensed weather variables and novel datasets about seasonal forage availability into a Random Forest model to investigate drivers of winter loss. We found that beekeepers who used treatment against Varroa had higher colony survival than those who did not treat. Moreover, beekeepers who used multiple types of Varroa treatment had higher colony survival rates than those who used 1 type of treatment. Our models found weather conditions are strongly associated with survival, but multiple-treatment type colonies had higher survival across a broader range of climate conditions. These findings suggest that the integrated pest management approach of combining treatment types can potentially buffer managed honey bee colonies from adverse weather conditions.

From economic threshold to economic injury level: Modeling the residual effect and delayed response of pesticide application.

Integrated Pest Management (IPM) poses a challenge in determining the optimal timing of pesticide sprays to ensure that pest populations remain below the Economic Injury Level (EIL), due to the long-term residual effects of many pesticides and the delayed responses of pest populations to pesticide sprays. To address this issue, a specific pesticide kill-rate function is incorporated into a deterministic exponential growth model and a subsequent stochastic model. The findings suggest the existence of an optimal pesticide spraying cycle that can periodically control pests below the EIL. The results regarding stochasticity indicate that random fluctuations promote pest extinction and ensure that the pest population, under the optimal cycle, does not exceed the EIL on average, even with a finite number of IPM strategies. All those confirm that the modeling approach can accurately reveal the intrinsic relationship between the two key indicators Economic Threshold and EIL in the IPM strategy, and further realize the precise characterization of the residual effect and delayed response of pesticide application.

Emerging trends to replace pesticides with nanomaterials: Recent experiences and future perspectives for ecofriendly environment.

Despite the widespread usage to safeguard crops and manage pests, pesticides have detrimental effects on the environment and human health. The necessity to find sustainable agricultural techniques and meet the growing demand for food production has spurred the quest for pesticide substitutes other than traditional ones. The unique qualities of nanotechnology, including its high surface area-to-volume ratio, controlled release, and better stability, have made it a promising choice for pest management. Over the past ten years, there has been a noticeable growth in the usage of nanomaterials for pest management; however, concerns about their possible effects on the environment and human health have also surfaced. The purpose of this review paper is to give a broad overview of the worldwide trends and environmental effects of using nanomaterials in place of pesticides. The various types of nanomaterials, their characteristics, and their possible application in crop protection are covered. The limits of the current regulatory frameworks for nanomaterials in agriculture are further highlighted in this review. Additionally, it describes how standard testing procedures must be followed to assess the effects of nanomaterials on the environment and human health before their commercialization. In order to establish sustainable and secure nanotechnology-based pest control techniques, the review concludes by highlighting the significance of taking into account the possible hazards and benefits of nanomaterials for pest management and the necessity of an integrated approach. It also emphasizes the importance of more investigation into the behavior and environmental fate of nanomaterials to guarantee their safe and efficient application in agriculture.

Copper-based grape pest management has impacted wine aroma.

Despite the high energetic cost of the reduction of sulfate to H2S, required for the synthesis of sulfur-containing amino acids, some wine Saccharomyces cerevisiae strains have been reported to produce excessive amounts of H2S during alcoholic fermentation, which is detrimental to wine quality. Surprisingly, in the presence of sulfite, used as a preservative, wine strains produce more H2S than wild (oak) or wine velum (flor) isolates during fermentation. Since copper resistance caused by the amplification of the sulfur rich protein Cup1p is a specific adaptation trait of wine strains, we analyzed the link between copper resistance mechanism, sulfur metabolism and H2S production. We show that a higher content of copper in the must increases the production of H2S, and that SO2 increases the resistance to copper. Using a set of 51 strains we observed a positive and then negative relation between the number of copies of CUP1 and H2S production during fermentation. This complex pattern could be mimicked using a multicopy plasmid carrying CUP1, confirming the relation between copper resistance and H2S production. The massive use of copper for vine sanitary management has led to the selection of resistant strains at the cost of a metabolic tradeoff: the overproduction of H2S, resulting in a decrease in wine quality.

Acute toxicity effects of pesticides on beneficial organisms - Dispelling myths for a more sustainable use of chemicals in agricultural environments.

Agricultural practitioners, researchers and policymakers are increasingly advocating for integrated pest management (IPM) to reduce pesticide use while preserving crop productivity and profitability. Using selective pesticides, putatively designed to act on pests while minimising impacts on off-target organisms, is one such option - yet evidence of whether these chemicals control pests without adversely affecting natural enemies and other beneficial species (henceforth beneficials) remains scarce. At present, the selection of pesticides compatible with IPM often considers a single (or a limited number of) widely distributed beneficial species, without considering undesired effects on co-occurring beneficials. In this study, we conducted standardised laboratory bioassays to assess the acute toxicity effects of 20 chemicals on 15 beneficial species at multiple exposure timepoints, with the specific aims to: (1) identify common and diverging patterns in acute toxicity responses of tested beneficials; (2) determine if the effect of pesticides on beetles, wasps and mites is consistent across species within these groups; and (3) assess the impact of mortality assessment timepoints on International Organisation for Biological Control (IOBC) toxicity classifications. Our work demonstrates that in most cases, chemical toxicities cannot be generalised across a range of beneficial insects and mites providing biological control, a finding that was found even when comparing impacts among closely related species of beetles, wasps and mites. Additionally, we show that toxicity impacts increase with exposure length, pointing to limitations of IOBC protocols. This work challenges the notion that chemical toxicities can be adequately tested on a limited number of 'representative' species; instead, it highlights the need for careful consideration and testing on a range of regionally and seasonally relevant beneficial species.

EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

BACKGROUND: The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy. RESULTS: Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model. CONCLUSION: This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.

Segmentation and detection of crop pests using novel U-Net with hybrid deep learning mechanism.

OBJECTIVE: In India, agriculture is the backbone of economic sectors because of the increasing demand for agricultural products. However, agricultural production has been affected due to the presence of pests in crops. Several methods were developed to solve the crop pest detection issue, but they failed to achieve better results. Therefore, the proposed study used a new hybrid deep learning mechanism for segmenting and detecting pests in crops. METHOD: Image collection, pre-processing, segmentation, and detection are the steps involved in the proposed study. There are three steps involved in pre-processing: image rescaling, equalized joint histogram based contrast enhancement (Eq-JH-CE), and bendlet transform based De-noising (BT-D). Next, the pre-processed images are segmented using the DenseNet-77 UNet model. In this section, the complexity of the conventional UNet model is mitigated by hybridizing it with the DenseNet-77 model. Once the segmentation is done with an improved model, the crop pests are detected and classified by proposing a novel Convolutional Slice-Attention based Gated Recurrent Unit (CS-AGRU) model. The proposed model is the combination of a convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). In order to achieve better accuracy outcomes, the proposed study hybridized these models due to their great efficiency. Also, the slice attention mechanism is applied over the proposed model for fetching relevant feature information and thereby enhancing the computational efficiency. So, pests in the crop are finally detected using the proposed method. RESULT: The Python programming language is utilized for implementation. The proposed approach shows a better accuracy range of 99.52%, IoU of 99.1%, precision of 98.88%, recall of 99.53%, F1-score of 99.35%, and FNR of 0.011 compared to existing techniques. DISCUSSION: Identifying and classifying pests helps farmers anticipate potential threats to their crops. By knowing which pests are prevalent in their region or are likely to infest certain crops, farmers can implement preventive measures to protect their crops, such as planting pest-resistant varieties, using crop rotation, or deploying traps and barriers. © 2024 Society of Chemical Industry.

Biosafety aspects of RNAi-based pests control.

While the overuse of classical chemical pesticides has had a detrimental impact on the environment and human health, the discovery of RNA interference (RNAi) offered the opportunity to develop new and sustainable approaches for pest management. RNAi is a naturally occurring regulation and defense mechanism that can be exploited to effectively protect crops by silencing key genes affecting the growth, development, behavior or fecundity of pests. However, as with all technologies, there is a range of potential risks and challenges associated with the application of RNAi, such as dsRNA stability, the potential for off-target effects, the safety of non-target organisms, and other application challenges. A better understanding of the molecular mechanisms involved in RNAi and in-depth discussion and analysis of these associated safety risks, is required to limit or mitigate potential adverse effects. © 2024 Society of Chemical Industry.

A theoretical framework to improve the adoption of green Integrated Pest Management tactics.

Sustainable agriculture relies on implementing effective, eco-friendly crop protection strategies. However, the adoption of these green tactics by growers is limited by their high costs resulting from the insufficient integration of various components of Integrated Pest Management (IPM). In response, we propose a framework within IPM termed Multi-Dimensional Management of Multiple Pests (3MP). Within this framework, a spatial dimension considers the interactive effects of soil-crop-pest-natural enemy networks on pest prevalence, while a time dimension addresses pest interactions over the crop season. The 3MP framework aims to bolster the adoption of green IPM tactics, thereby extending environmental benefits beyond crop protection.

Seasonal efficacy and risks from a sodium nitrite toxic bait for wild pigs.

BACKGROUND: Wild pigs (Sus scrofa) are an invasive and destructive species throughout many regions of the world. A sodium nitrite (SN) toxic bait is currently used in Australia and being developed for use in the US and other countries to combat the increasing populations of wild pigs. In the US, efforts to modify the Australian SN-toxic bait and baiting strategy have focused on reducing issues with non-target animals accessing the SN-toxic bait spilled outside of bait stations by wild pigs. We tested and compared modifications for efficacy (with wild pigs) and hazards (with non-targets) in north-central Texas, US during summer (July 2021) and winter (March 2023) seasons. RESULTS: During both seasons we found that visitation to the bait sites declined 94-99% after deploying the SN-toxic bait, and we found a total of 106 dead wild pigs, indicating considerable lethality for the local population. Prior to deploying the SN-toxic bait, Global Positioning System (GPS)-collared wild pigs were more likely to cease visiting bait sites during summer when foraging resources were abundant. Farrowing decreased visitation to bait sites during the winter. We observed no dead non-targets during summer; winter results showed an average of 5.2 dead migrating birds per bait site (primarily Dark-eye juncos [Junco hyemalis]) from consuming SN-toxic bait spilled by wild pigs. The presence and winter-foraging behaviors of migrating birds appeared to increase hazards for those species. CONCLUSION: The current formulation of SN-toxic bait was effective at removing wild pigs during both seasons, however it is clear that different baiting strategies may be required in winter when migrating birds are present. Baiting wild pigs prior to farrowing during the winter, and during drier summers, may further improve efficacy of the bait. Reducing hazards to non-targets could be achieved by refining the SN-toxic bait or modifying bait stations to decrease the potential for spillage, decreasing environmental persistence if spilled, or decreasing attractiveness to migrating birds. © 2024 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.

Diverse genetic basis of Vip3Aa resistance in five independent field-derived strains of Helicoverpa zea in the US.

BACKGROUND: Practical resistance of Helicoverpa zea to Cry proteins has become widespread in the US, making Vip3Aa the only effective Bacillus thuringiensis (Bt) protein for controlling this pest. Understanding the genetic basis of Vip3Aa resistance in H. zea is essential in sustaining the long-term efficacy of Vip3Aa. The objectives of this study were to characterize the inheritance of Vip3Aa resistance in four distinct field-derived H. zea strains (M1-RR, AC4-RR, R2-RR and R15-RR), and to test for shared genetic basis among these strains and a previously characterized Texas resistant strain (LT#70-RR). RESULTS: Maternal effects and sex linkage were absent, and the effective dominance level (DML) was 0.0 across Vip3Aa39 concentrations ranging from 1.0 to 31.6 µg cm-2, in all H. zea resistant strains. Mendelian monogenic model tests indicated that Vip3Aa resistance in each of the four strains was controlled by a single gene. However, interstrain complementation tests indicated that three distinct genetic loci are involved in Vip3Aa resistance in the five resistant H. zea strains: one shared by M1-RR and LT#70-RR; another shared by R2-RR and R15-RR; and a distinct one for AC4-RR. CONCLUSION: Results of this study indicate that Vip3Aa resistance in all H. zea strains was controlled by a single, recessive and autosomal gene. However, there were three distinct genetic loci associated with Vip3Aa resistance in the five resistant H. zea strains. The information generated from this study is valuable for exploring mechanisms of Vip3Aa resistance, monitoring the evolution of Vip3Aa resistance, and devising effective strategies for managing Vip3Aa resistance in H. zea. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.