Pest Management Decisions Research Articles

Detection and Early Warning of Duponchelia fovealis Zeller (Lepidoptera: Crambidae) Using an Automatic Monitoring System

In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation.

An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

A framework for evaluating the value of agricultural pest management decision support systems

Disease management decision support systems (DSS) are typically prediction algorithms that help farmers assess the risk of an epidemic, to guide whether, and to what extent, fungicide treatment is needed. However, there is frequently little information presented to quantify the value of using the DSS, i.e. the likely increased profit or reduced impact to the environment, and the risks of failing to control the pest. Validation of DSS is often limited to a small number of sites and seasons, as extensive field testing is prohibitively expensive. It would therefore be beneficial to have a method to estimate the value of a DSS using existing data sets gathered for other purposes.We present a theoretical framework for evaluating the value of DSS, and then describe how this can be applied in practice using four case studies of contrasting DSS under different data constraints. The four case studies include DSS that guide (i) the total dose of pesticide applied; (ii) the number of sprays required; (iii) the timing of the first fungicide application in a spray programme; and (iv) infection risk alerts. We demonstrate how our theoretical framework can be used to evaluate DSS, using existing field and literature data to infer the benefits and risks associated with their use. The limitations of using existing data are explored.

Integrated Pest Management Decision Support System (IPM-DSS) a tool to support management of tree diseases in Kenya

Tree diseases have been noted as one of the major causes of abnormalities and malformations on perennial plants. Tree disease research in Kenya has resulted in a list of diseases identified to cause significant losses on forest plantations. The need for management of such diseases have been on the increase. The Decision Support System (DSS) was envisioned to help stakeholders make decisions to manage tree diseases on farm and in tree plantations. Information was collected across Kenya on diseases affecting seed, seedlings, plantations, natural forests and post-harvest products in sawmills. The data was entered into excel sheets and organized for database administration on MySQL software. Over 20 major fungal species were identified and their biology, distribution and management determined. This information was further fed into a query system to allow different groups of stakeholders to access the information for diagnosis and control of tree diseases. Results showed that major tree diseases in Kenya were caused by fungi belonging to the families; Botryosphaeriaceae, Nectriaceae, and Amphisphaeriaceae. The data and information availed through this query system is being used in trend analysis for proactive control and management of tree diseases in Kenya. It will also be used for farmer advisory services through closest match selection of symptom appearance based on the tree species. The query system is an innovation that will also be used for Citizen Science Data Capture to aid Kenya Forestry Research Institute in detecting emerging diseases in real-time for phytosanitary measures. The identification of commonly occurring pathogens in commercial forestry species in the country has led to research towards their control and tree improvement for resistance to tree diseases. Future plantation establishment should be based on resistant species and high-quality germplasm for maximum survival and improved yields. Key words: Advisory services, Botryosphaeriaceae, citizen science, Decision Support System, Integrated Pest Management, tree diseases.

E-Pest Surveillance for Assessment of Seasonal Occurrence of Orange Pests in Maharashtra

Monitoring of incidence and intensity of pests over the crop season is crucial for effective pest management. ICT (Information and communication technology) not only enables the assimilation of pest databases over space and time, but also aids in the rapid processing of data to make pest management decisions using the available knowledge base and essential inputs that can be mobilised and adopted for plant protection on wide area basis. This study generated valuable data on the population dynamics of orange pests over the seasonal months from 2012 to 2022 with the aid of applied information and communication technology tools. The majority of the pests under current study showed their seasonal activity throughout the monitoring period. The study on the seasonal incidence revealed bark eating caterpillar Inderbela sp. having their peak infestation during September and October whereas January to March were observed as prime months for fruit sucking moths Othreis sps to reach their peak prevalence and damage. The citrus leaf miner Phyllocnistis citrella population dynamics showed two peaks in August and September. The data generated in this study would help in the forecast of the pests and helpful in devising an effective IPM strategy.

Phenology and sampling crawlers of rhodesgrass mealybug (Hemiptera: Pseudococcidae) in Georgia golf course putting greens.

Rhodesgrass mealybug, Antonina graminis Maskell (Hemiptera: Pseudococcidae), is an emerging pest of turfgrass in Georgia golf course putting greens. Because the feeding damage of A. graminis severely affects the aesthetics of the putting surface, it is necessary to understand the phenology of A. graminis on putting greens. To develop management strategies, the temporal emergence of crawlers is determined; however, a sampling tool for A. graminis crawlers on putting greens has not been developed. Thus, the objectives were to determine (i) the phenology of A. graminis and turfgrass quality and (ii) the best trap types for sampling crawlers on the putting greens in Georgia. From 2019 to 2022, 10-20 turfgrass plugs were sampled from the putting greens at biweekly intervals from the spring to fall. The numbers of crawlers, sessile nymphs, and adults of A. graminis were quantified from these plug samples. To determine the best trap types for sampling crawlers, 6 trap types were evaluated on the putting greens in 2021 and 2022. In the spring, the A. graminis densities remained low until June or July, then all stages of A. graminis increased. In the late fall and winter, A. graminis densities declined and remained low. The turfgrass quality improved temporally from April to June but progressively declined from the mid-to-late summer to fall. Significantly greater numbers of crawlers were sampled in the paper-folded sticky card method than in the turfgrass plug method. Thus, sticky traps could be used to sample crawlers for pest management decision.

Blue LED trap and commercial lure improve western flower thrips (Frankliniella occidentalis) monitoring in cucumber crops

Blue sticky traps contribute substantially to monitoring the western flower thrips, Frankliniella occidentalis Pergande (Thysanoptera: Thripidae), in greenhouses. Although sticky traps can detect the initial presence of thrips reliably, an estimation of the actual thrips density in the crop by counting number of thrips on the traps is often not accurate. To overcome this issue, we compared blue sticky traps and newly developed sticky LED-enlightened traps in combination with the commercial thrips kairomone Lurem-TR under commercial growing conditions. Therefore, an experiment was conducted in cucumber, Cucumis sativus L. (Cucurbitaceae), crop stands in greenhouse cabins investigating the correlation between thrips caught on (LED) traps and the thrips density in the crop for an accurate and reliable thrips monitoring. Additionally, experiments aiming to understand underlying mechanisms of thrips orientation towards traps in different scenarios were conducted under controlled conditions. Results show that thrips catches on sticky LED enlightened coloured traps correlated strongly positive with number of thrips in the crop, especially at low thrips population densities. Adding Lurem to this trap type further improved accuracy of the correlation in the greenhouse cabin experiment. Moreover, LED traps with and without Lurem were more attractive towards thrips in small follow-up experiments compared to standard blue sticky traps. The results are discussed in the context of general orientation of thrips and its behaviour towards visual and olfactory cues when considering different scenarios. Our study shows the successful integration of blue LEDs into an existing trapping system and underlines the advantages compared with standard sticky plates. In conclusion, sticky LED enlightened coloured traps have a potential as an improved thrips monitoring device that might improve pest management decisions.

Physalis virginiana as a Wild Field Host of Bactericera cockerelli (Hemiptera: Triozidae) and 'Candidatus Liberibacter solanacearum'.

The potato/tomato psyllid, Bactericera cockerelli (Šulc), is among the most important pests of solanaceous crops as a vector of the pathogen 'Candidatus Liberibacter solanacearum' (Lso). Lso-infected psyllids often arrive in crop fields from various wild species of Solanaceae and Convolvulaceae, especially those that provide early-season hosts for the vector. Physalis species are perennial plants within the family Solanaceae with often broad geographical distributions that overlap those of B. cockerelli, yet the status of many Physalis species as hosts for B. cockerelli or Lso remains unknown. Our objective was to determine whether wild Physalis species that occur in the potato-growing region of Galeana, Nuevo León, Mexico, host B. cockerelli populations and whether they also are susceptible to Lso. Sampling was carried out in the potato-growing zone of Galeana, Nuevo León, Mexico, where unidentified Physalis spp. are common. In March to October 2021, a wild plant identified as Physalis virginiana was observed; eggs, nymphs, and adults of B. cockerelli were observed on these plants throughout the growing season, and nymphs completed development on these plants under laboratory conditions. Lso also was detected in 22 of the 93 (23.7%) wild P. virginiana plants using conventional PCR, while 13.3% of B. cockerelli adults that emerged from P. virginiana cuttings harbored the pathogen. This is the first report that P. virginiana is a host for B. cockerelli and for Lso. These results suggest that P. virginiana is a likely source of Lso-infected psyllids colonizing solanaceous crops in northeastern Mexico. The importance of P. virginiana and other wild hosts on the population dynamics of the vector and pathogen should be investigated to assist in pest management decision-making.

IPM messaging: who, what, how?—Insights from a survey of farmers and agronomists in Scotland

Abstract Enhanced awareness and knowledge among arable (field crop) farmers and agronomists are pivotal for making informed integrated pest management (IPM) decisions. In this study, we had 3 primary objectives: (i) identifying the key factors influencing the adoption of IPM, (ii) gathering insights into the currently perceived pest threats, and (iii) identifying any necessary intervention tools or methods for modifying the flow of information. In 2021, a telephone survey involving 267 arable and arable/livestock farmers and 26 agronomists was conducted in Scotland. The survey was designed to gather information on the perceived invertebrate pest, disease, and weed threats, the extent of IPM implementation, and the sources of information consulted. Several factors influenced the uptake of IPM, including farm type, location, agronomist engagement, farmer’s age, education, and membership in schemes promoting IPM. Notably, there were disparities in perceptions of the most significant pest threats between arable farmers and agronomists. This points to a division of decision-making responsibilities and, consequently, varying information needs. Agronomists typically require technical information related to pesticide effectiveness and disease management, whereas arable farmers seek guidance on cultural approaches for managing weeds and invertebrate pests. Furthermore, preferences for information sources also differed, with agronomists showing a preference for social media as a source of IPM information, while farmers favored the farming press. Ultimately, a deeper understanding of IPM practices equips farmers to actively participate in IPM discussions with their agronomists. This enables the collaborative development of an effective IPM strategy and, in turn, increases the adoption of IPM practices.

Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (Helicoverpa armigera) Larvae

The cotton bollworm (Helicoverpa armigera, Lepidoptera: Noctuidae) poses significant risks to maize. Changes in the maize plant, such as its phenology, influence the short-distance movement and oviposition of cotton bollworm adults and, thus, the distribution of the subsequent larval damage. We aim to provide an overview of future approaches to the surveillance of maize ear damage by cotton bollworm larvae based on remote sensing. We focus on finding a near-optimal combination of Landsat 8 or Sentinel-2 spectral bands, vegetation indices, and maize phenology to achieve the best predictions. The study areas were 21 sweet and grain maze fields in Hungary in 2017, 2020, and 2021. Correlations among the percentage of damage and the time series of satellite images were explored. Based on our results, Sentinel-2 satellite imagery is suggested for damage surveillance, as 82% of all the extremes of the correlation coefficients were stronger, and this satellite provided 20–64% more cloud-free images. We identified that the maturity groups of maize are an essential factor in cotton bollworm surveillance. No correlations were found before canopy closure (BBCH 18). Visible bands were the most suitable for damage surveillance in mid–late grain maize (|rmedian| = 0.49–0.51), while the SWIR bands, NDWI, NDVI, and PSRI were suitable in mid–late grain maize fields (|rmedian| = 0.25–0.49) and sweet maize fields (|rmedian| = 0.24–0.41). Our findings aim to support prediction tools for cotton bollworm damage, providing information for the pest management decisions of advisors and farmers.

A high-throughput plate method for nucleic acid extraction from beet leafhopper (Hemiptera: Cicadellidae) and potato psyllid (Hemiptera: Triozidae) for pathogen detection.

Plant pathogens that are transmitted by insect vectors cause considerable damage to crops when pests or pathogens are not detected early in the season and populations are not controlled. Knowledge of pathogen prevalence in insect pest populations can aid growers in their insect pest management decisions but requires the timely dissemination of results. This process requires that specimen capture, identification, nucleic acid extraction, and molecular detection of a pathogen(s) occur alongside a platform for sharing results. The potato psyllid (Bactericera cockerelli, Sulc; Hemiptera: Triozidae) and beet leafhopper (Circulifer tenellus, Baker; Hemiptera: Cicadellidae) transmit pathogens to potato and other vegetable or seed crops each season in the northwestern United States. While the potato psyllid has been tested for pathogen occurrence for the past decade, testing of the beet leafhopper is a new endeavor and substantially increases the specimen number that must be tested by our laboratories each season. To aid in the rapid processing of individual insect specimens, we optimized and validated a new high-throughput 96-well plate nucleic acid extraction method for use in place of a standard 1.5-ml single-tube extraction method. Processing efficiency, in terms of total specimens processed over a 2-day period, improved 2.5-fold, and the cost associated with processing a single sample was nearly cut in half with this newly developed plate nucleic acid extraction method. Overall, this method has proven to be an excellent tool for the rapid testing of large numbers of small, individual insect vectors to enable timely dissemination of data on pathogen prevalence to growers.

Farm2Fork through the lens of community ecology: concepts and applications in postharvest storage

The environment in which postharvest crops are processed and stored is a dynamic ecosystem influenced not only by environmental factors such as temperature and humidity, but also by biotic influences such as humans and insects. Abiotic influences such as variation in landscapes of warehouses, processing facilities, storage and shipping containers, and urban and agricultural settings can also drive changes in ecosystem processes for insects living in a postharvest system. Principles of community ecology can help to tease apart broad interactions among the environment including succession, interactions with conspecifics that lead to competition and niche partitioning, behavioral ecology variation, and physiology and developmental changes. Focusing on these concepts for integrated pest management (IPM) for stored product insect pests can help pest managers to better predict risk thresholds and develop targeted approaches for treatments. Typically, pest management decisions focus on single species without regards to the interactions with other species. Without consideration of the entire ecosystem, targeted treatments for one species can have both direct and indirect impacts on other species that may have equally detrimental effects on stored products. Current knowledge of stored product ecosystems lags behind what is known for field pest ecosystems, and hinders our ability to design effective control strategies for the whole system. Here, we present a review of work on stored product insect pests using a community ecology lens. We analyze how the current state of the knowledge regarding species interactions and variation and incorporating factors such as movement, species interactions, energy transfer models in succession, behavior, and effects of climate change in ecological modeling can be used to better develop and implement more effective postharvest IPM. Implementing these concepts will significantly improve management of these insects and can help reduce time and cost associated with managing and treating insect infestations.

Smallholder farmers’ knowledge, attitudes and practices towards biological control of papaya mealybug in Kenya

BackgroundFarmer perceptions are highly important in influencing on-farm pest management decision-making. Biological control is extremely sustainable in the smallholder production context, but in Sub Saharan Africa (SSA) few attempts using this pest control method for arthropod pests have been successful, with one of the key reasons cited as poor involvement of farming communities and extension in the dissemination of information. Although farmers’ knowledge and attitudes are hugely important for the successful implementation of biological control, they are often disregarded. Papaya mealybug (Paracoccus marginatus) (PMB) has rapidly spread and established in suitable areas across Kenya becoming a serious pest. The objective of this study is to determine smallholder farmers’ knowledge, attitudes and practices towards biological control; farmers’ willingness to reduce their chemical pesticide use; and levels of support for a classical biological control initiative for PMB management.MethodsHousehold surveys were conducted covering 383 farming households (148 women) in four papaya producing counties in Kenya alongside key informant interviews with eight extension agents and thirty agro-dealers, and eight focus group discussions.ResultsAlthough some farmers demonstrated awareness of the concept of biological control they lacked knowledge, experience and technical support from extension or agro-dealers. Reasons for not using biological control included inadequate awareness and concerns over efficacy and safety. Farmers expressed high levels of interest and willingness to support biological control, and were willing to reduce their chemical pesticide use to help conserve, and support the establishment of natural enemies. County, perception of biological as safe, training in IPM and gender were all highly significant factors determining farmers willingness to support biological control.ConclusionsPreviously, poor attention has been paid to farmer perceptions and participation in biological control, which has resulted in limited success in developing countries. With high levels of interest and willingness to support biological control, the next step is to engage with farming communities impacted by PMB. By building awareness and capacity, and developing a management plan with farmers that will support the release and establishment of the biological control agent, Acerophagus papayae, long-term, sustainable control of PMB in Kenya is possible.

Perspectives of Certified Crop Advisers on Pesticide Resistance Management

AbstractPesticide resistance presents serious agronomic, environmental, and socio‐economic threats that continue to grow despite significant research and outreach initiatives. Existing research on producers’ pest management decisions and behaviors suggests that they face a variety of barriers and limitations to best management practices. While we know an increasing amount about producers’ perspectives on, and management of, pesticide resistance, there is limited understanding of how their advisers think about and deal with the issue. A recent survey sent to U.S.‐based CCAs provides some insight into these questions. Earn 0.5 CEUs in Integrated Pest Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.

Assessment of the major insect pests associated with kola nuts in Ghana

ABSTRACT Cola nitida (Kola) is an important cash crop cultivated in the forest areas of West Africa. The crop is attacked by a lot of insect pests, chief among them are the kola seed weevil, Balanogastris kolae, and some Sophrorhinus pest species. Not much is known about the pest status of fruit fly species on the crop, especially Bactrocera invadens and Ceratitis colae which is known to feed exclusively on the crop. This study seeks to assess the incidence, population dynamics and damage caused by insect pests on kola in the middle belt of Ghana to inform pest management decisions. The major insect pests identified on kola in the study include the kola seed weevils Balanogastris kolae, Sophrorhinus spp and fruit fly species Bactrocera invadens, Ceratitis colae and other Ceratitis spp. There was a significant locational effect on the number of fruit fly species trapped. The kola nut weevils (Balanogastris kolae and the Sophrorhinus species) remain the most important kola pests accounting for 81.4% of the sampled damaged pods. Information obtained from the population dynamics of these kola insect pests could aid in the development of an integrated strategy for managing these pests on the crop.

Predicting the Occurrence and Risk Damage Caused by the Two-Spotted Spider Mite Tetranychus urticae (Koch) in Dry Beans (Phaseolus vulgaris L.) Combining Rate and Heat Summation Models for Digital Decisions Support

In this work, we use developmental rate models to predict egg laying activity and succession of generations of T. urticae populations under field conditions in the Prespa lakes region in Northern Greece. Moreover, the developmental rate model predictions are related to accumulated heat summations to be compared with actual bean damage and to generate pest-specific degree-day risk thresholds. The oviposition was predicted to start at 57.7 DD, while the first peak in egg laying was estimated to be at 141.8 DD. The second and third peak in egg production were predicted to occur at 321.1 and 470.5 DD, respectively. At the degree-day risk threshold, half development of the first summer generation was estimated at 187 DD and 234 DDm while for the second, it was estimated at 505 DD and 547 DD for 2021 and 2022, respectively. According to the model predictions, no significant differences were observed in the mean generation time (total egg to adult development) of T. urticae between the two observation years (t = 0.01, df = 15, p = 0.992). The total generation time was estimated at 249.3 (±7.7) and 249.2 (±6.7), for 2021 and 2022, respectively. The current models will contribute towards predictions of the seasonal occurrence and oviposition of T. urticae to be used in pest management decision-making. Moreover, the development of population model is a prerequisite for the buildup and implementation of smart plant protection solutions.

Using targeted sequencing and TaqMan approaches to detect acaricide (bifenthrin, bifenazate, and etoxazole) resistance associated SNPs in Tetranychus urticae collected from peppermint fields and hop yards.

Tetranychus urticae (Koch) is an economically important pest of many agricultural commodities world-wide. Multiple acaricides, including bifenazate, bifenthrin, and extoxazole, are currently registered to control T. urticae. However, populations of T. urticae in many different growing regions have developed acaricide resistance through multiple mechanisms. Within T. urticae, single nucleotide polymorphisms (SNPs) have been documented in different genes which are associated with acaricide resistance phenotypes. The detection of these mutations through TaqMan qPCR has been suggested as a practical, quick, and reliable tool to inform agricultural producers of acaricide resistance phenotypes present within their fields and have potential utility for making appropriate acaricide application and integrated pest management decisions. Within this investigation we examined the use of a TaqMan qPCR-based approach to determine genotypes which have been previously associated with acaricide resistance in field-collected populations of T. urticae from peppermint fields and hop yards in the Pacific Northwest of the United States and confirmed the results with a multiplex targeted sequencing. The results suggest that a TaqMan qPCR approach accurately genotypes T. urticae populations for SNPs that have been linked to Bifenazate, Bifenthrin, and Etoxazole resistance. The results also demonstrated that different populations of mites in Washington and Idaho displayed varying frequencies of the examined SNPs. While we were able to detect the SNPs associated with the examined acaricides, the mutation G126S was not an appropriate or accurate indicator for bifenazate resistance.

A real-time decision-making tool based on dynamic thresholds for Phthorimaea absoluta management in greenhouse tomato

The onset of digital information and computing power brings the opportunity to optimize the use of agricultural inputs and favors the transit to an information-based agriculture. The tomato leafminer (TLM), Phthorimaea absoluta, is a major pest of greenhouse tomatoes, and its control relies heavily on chemical pesticides. This study presents a decision-making system for TLM that dynamically adjusts decision thresholds throughout the crop cycle. Through a controlled experiment, we obtained pest damage curves for three crop phenological stages, which were used to derive a set of non-linear functions that estimates a decision threshold expressed as the maximum tolerable number of larvae/plant for any given crop age. We show that produce losses due to TLM are far from constant across the crop cycle, and that such variation can be modeled as a function of the effect of TLM on yield, cumulative control costs, potential yield, and produce prices. The decision threshold function was linked to a previously validated sampling plan, which was adjusted to work in serial bouts in frequencies that vary according to sampling results and pest growth rates. The decision-making system is presented as a web-based app that delivers a management recommendation, the timing for the next sampling bout, and an indication of how close the population is from the threshold. This study presents a step forward towards a modern application of the economic injury level concept by adapting pest management decision criteria to the changing ecological and economic context of each production system.

Effect of infestation with Callosobruchus maculatus on the GC/MS chemical constituents and minerals of cowpea seeds

• The effect of cooking and insect infestation on cowpea seeds was studied. • We found volatiles characteristic of raw, infested and infested cooked beans. • GC-MS is used to determine these volatiles characteristic. • These volatile compounds can be used to detect infested cowpea. • Mineral content was also modified by infestation and cooking. Poor storage of cowpea seeds causes the infestation of store pests, and consequently, the quality of the seeds decreases in quantity and quality. Therefore, continuous detection of the infestation has become necessary. Cowpea is one of the most economically essential legumes in different world areas. Cowpea is severely damaged in the store by the cowpea storage pest, Callosobruchus maculatus (Fab.), and early detection of insect infestation is essential for implementing timely and effective pest management decisions to protect them. The effect of insect infestation with Callosobruchus maculatus on the chemical composition of cowpea seeds ( Vigna unguiculata ) and mineral content was studied. The volatile compounds were analyzed and identified by gas chromatography-mass spectrometry (GC–MS) for the raw (non-infested), cooked, infested, and cooked cowpea seeds and the adult insect of C. maculatus . GC–MS analysis results showed that six volatile compounds were identified from raw cowpea seeds and only three from cooked ones. (9,12- Octadecadienoic acid, methyl ester) was identified in cooked seeds but not detected in raw seeds. Eight volatile compounds were detected only in infested seeds with C. maculatus alone, and these compounds can be used for detecting C. maculatus in cowpea seeds. Two specific compounds, such as Stigmasterol, and Beta-Sitosterol, were detected in infested cooked cowpea seeds. In cowpea seeds, infestation caused a reduction in the concentration of Aluminum (Al) and Iron (Fe), while Manganese (Mn) increased either after or before the cooking. Without infestation, the cooking process alone decreased Al, Fe, and Mn concentrations.

Wind Tunnel and Field Evaluation of Trapping Efficiency of Semiochemical Baited Camera-Traps for Capturing Codling Moth (Lepidoptera: Tortricidae).

Sex pheromone baited monitoring traps are a critical tool for integrated pest management decisions against many insects, particularly codling moths (Cydia pomonella L.). The addition of cameras for remote monitoring has the potential to enhance the usefulness of these important tools. However, changes in trap design could potentially alter plume structure and trapping efficiency of these new traps. Here we look at several trap configurations designed to optimize the capture of codling moths in traps equipped with cameras. We found that, in both wind tunnel and field trials, camera equipped triangle traps and camera equipped rectangle traps (both V1 and V2) caught codling moths equivalent to a standard 'delta' style trap. While catch was unaffected, altering our rectangular trap opening from 4 to 8 cm (V1 and V2, respectively) decreased frequency of moths contacting the front of trap and increased the frequency of moths flying directly into the trap. We show that these novel camera equipped semiochemical-baited traps catch equivalent to the industry standard white delta trap.