Infestation Levels Research Articles

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Due to its rapid reproduction rate and brief life cycle, the most well-known oil palm pest, Metisa plana (Lepidoptera: Psychidae), also known as the bagworm, can spread to epidemic proportions. The outbreak can significantly reduce oil palm yield by resulting in 40% crop losses and 10% to 13% leaf defoliation. A manual census was conducted to count the number of pests and determine the category of infestation; however, when covering a large area, it typically takes more time and labour. Therefore, this study used unmanned aerial vehicles (UAVs) as a quick way to detect the severity levels of infestation in oil palm plantations, including healthy (zero), low, mild, and severe infestation using DJI Inspire 2 with Micasense Altum-PT multispectral camera at an altitude of 70 m above ground. Three combinations were created from the most significant vegetation indices: NDVI and NDRE, NDVI and GNDVI, and NDRE and GNDVI. According to the results, the best combination in classifying healthy and low levels was found to be NDVI and GNDVI, with 100% F1 score. In addition, the combination of NDVI and NDRE was found to be the best combination in classifying mild and severe level. The most important vegetation index that could detect every level of infestation was NDVI. Furthermore, Weighted KNN become the best model that constantly gave the best performance in classifying all the infestation levels (F1 score > 99.70%) in all combinations. The suggested technique is crucial for the early phase of severity-level detection and saves time on the preparation and operation of the control measure.

Resident Hymenopteran Parasitoids with Potential Drosophilid Associations in Andean North Patagonia: Implications for the Biological Control of the Spotted Wing Drosophila.

The spotted wing drosophila, Drosophila suzukii Matsumura, is native to Southeast Asia and has become a serious pest of soft-skinned fruits worldwide. Several control methods are being tested worldwide as part of an integrated pest management approach. Biological control is a promising alternative tactic that can be used to manage D. suzukii populations, especially in unmanaged areas. This study aimed at exploring the diversity of resident hymenopteran parasitoids of drosophilids in northwestern Patagonia, where D. suzukii is considered an important pest. The survey also aimed to investigate possible associations between parasitoids and D. suzukii in several crops and non-crop fruits, and to determine D. suzukii fruits infestation levels. Fourteen sites with mainly berry crops were sampled biweekly using cider vinegar traps and collecting fresh fruits from a variety of crop and non-crop fruit plants. We identified five species of hymenopteran parasitoids, obtained from the baited traps, that have the potential to associate with D. suzukii: Leptopilina heterotoma Thomson, Ganaspis brasiliensis Ihering, Hexacola hexatoma Hartig (Figitidae), Pachycrepoideus vindemmiae Rondani (Pteromalidae), and Spalangia endius Walker (Spalangiidae). Leptopilina heterotoma is reported for the first time in Argentina. High numbers of D. suzukii adults were recovered from baited traps and field-collected fruits. However, no parasitoid emerged from D. suzukii pupae recovered from fruits, nor were any dead parasitoids recorded inside D. suzukii pupae. Overall infestation levels of D. suzukii on field-collected fruits did not differ significantly between species/varieties. The results are discussed with emphasis on the possible functionality and perspectives of using these species as biological control agents.

Detection of Varroa destructor Infestation of Honeybees Based on Segmentation and Object Detection Convolutional Neural Networks

Varroa destructor infestation is a major factor leading to the global decline of honeybee populations. Monitoring the level of Varroa mite infestation in order to take timely control measures is crucial for the protection of bee colonies. Machine vision systems can achieve non-invasive Varroa mite detection on bee colonies, but it is challenged by two factors: the complex dynamic scenes of honeybees and small-scale and limited data on Varroa destructor. We design a convolutional neural network integrated with machine vision to solve these problems. To address the first challenge, we separate the image of the honeybee from its surroundings using a segmentation network, and the object-detection network YOLOX detects Varroa mites within the segmented regions. This collaboration between segmentation and object detection allows for more precise detection and reduces false positives. To handle the second challenge, we add a Coordinate Attention (CA) mechanism in YOLOX to extract a more discriminative representation of Varroa destructor and improve the confidence loss function to alleviate the problem of class imbalance. The experimental results in the bee farm showed that the evaluation metrics of our model are better than other models. Our network’s detection value for the percentage of honeybees infested with Varroa mites is 1.13%, which is the closest to the true value of 1.19% among all the detection values.

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

Preferred Stage Of The Crop For Cotton Stem Weevil Infestation And Scanning Electron Microscopic View Of Healthy And Infested Cotton Stem

Cotton stem weevil, Pempherulus affinis Faust is the most destructive pest of the seedling stage of cotton in South India. Its habitat, inside the stem protects the pest from control measures and hence management of this pest is challenging. The pest occurred in the early stage of the crop, multiplied later, and continue the damage in grown-up crop until dislodging of galled plant and carry over to next season’s crop. Hence, the present study was carried out to identify and to generate information about the preferred stage of the crop for cotton stem weevil infestation to recommend timely management tactics as heavy yield loss occurs due to early infestation. Four staggered pot culture sowings at weekly intervals were done to attain four phonological stages of the crop viz.,15 Days After Sowing (DAS), 22 DAS, 29 DAS, and 36 DAS. Five pairs of weevils were released at each stage of the crop and the observation was made on the infestation level on different days after the release of the weevil. The mean percent infestation at different growth stages of the crop revealed that the highest percent infestation was found in fifteen days old crop (63.57). The present study revealed that the early stage (15 days old crop) are more prone to stem weevil infestation than compared to later stage.The contribution of this study is that the management tactics are advised to practice in early stage of the crop to reduce stem weevil infestation and to prevent yield loss.

Antiparasitic approaches and strategies in European aquaculture, with emphasis on Mediterranean marine finfish farming: Present scenarios and future visions

AbstractParasitic infections can be occasionally severe in the European marine aquaculture industry, including the Mediterranean region, as they can incur considerable financial losses. Due to the lack of commercial vaccines, therapeutic approaches seem the only measure to battle parasitic outbreaks. Integrated strategies and increased resilience of the hosts, may limit to some degree the level of infestation. Ectoparasitic therapy is traditionally based on baths, with few exceptions. Several antiparasitic compounds have been registered in European aquatic medicine to combat mainly salmon sea lice; however, few of them are readily used against Mediterranean fish parasites. Formalin and less commonly hydrogen peroxide baths are applied against ectoparasites in the Mediterranean region. Most of the registered anti‐lice antiparasitics have limited potential perhaps due to their adverse environmental impact. Future therapies against fish parasites will rely mainly on effective substances ensuring consumer, animal, and environmental welfare. Ideally, dietary antiparasitics such as praziquantel exhibiting mild environmental impact and high efficacy against a wide range of pathogens should be adopted. Moreover, combined strategies such as integrated pest management, involving various management practices with limited use of chemicals, should be a priority for specific parasitic outbreaks. The information presented in this review can guide future research and promote effective and prudent parasite control practices for Mediterranean‐farmed fish.

Epidemiology, factors influencing prevalence and level of varroosis infestation (Varroa destructor) in honeybee (Apis mellifera) colonies in different agroecologies of Southwest Ethiopia

Little information is available on the epidemiology of varroosis caused by Varroa mite, Varroa destructor infestation in Ethiopia, although it is a devastating honeybee disease that results in significant economic losses in beekeeping. Therefore, between October 2021 and October 2022, a cross-sectional study was carried out in different agroecology zones in Southwest Ethiopia to determine the prevalence and associated risk factors for varroosis, as well as the effects of this disease on honeybee colonies and honey production. A multivariate logistic regression analysis was performed to identify possible risk factors for the prevalence of V. destructor. A total of 384 adult honeybee and worker or drone brood samples were collected from honeybee colonies and examined using standard diagnostic techniques in the laboratory. The result shows that the prevalence of V. destructor was found to be 39.3% (95% CI 34.44–44.21) and 43.2% (38.27–48.18) in adult honeybees and brood, respectively. The major risk factors for the prevalence of V. destructor in the study areas included agroecology (OR = 5.2, 95% CI 1.75–14.85), type of hive (OR = 2.9, 95% CI 1.17–17.03), management system (OR = 4.3, 95% CI 1.23–14.70), and colony management (OR = 3.5, 95% CI 1.31–9.14). The lower level of colony infestation in adult bees and brood was measured as 1.97 ± 0.14 and 3.19 ± 0.25, respectively. Season, colony status, colony management, and agroecology were among the determinant factors of the level of varroa mite infestation in adult bees and brood. The results of the study demonstrated that honey production losses are largely attributable to V. destructor infestation. Therefore, it is critical to inform the community about the effects of V. destructor on honey production and develop and implement effective management strategies for this disease. In addition, further research should be done to identify and isolate additional factors that contribute to varroosis in honeybees in different regions.

Infestation and management of banana pseudostem borer, Odoiporus longicollis Oliver

The pseudostem borer, Odoiporus longicollis is a serious pest causing heavy losses to the banana growers. To manage the pest farmers rely on chemical insecticides even after bunch formation. Since the whole banana plant is edible in one way or the other for consumption, the usage of persistent insecticides will lead to residues. Therefore, studies were undertaken to assess the level of infestation by pseudostem borer in banana and to evaluate the botanical, neem formulation against banana pseudostem borer. Among the ten banana varieties screened for the infestation of pseudostem borer, the severity was maximum in Nendran (31.69%) followed by Red banana (19.07%). The pseudostem borer was recorded as a major pest in the banana varieties viz., Nendran, Red banana, Matti, and Palayamkottan (>10%) and a minor pest in the banana varieties viz., Robusta, Rasakathali, and Ney poovan (1-5%). The occurrence of O. longicollis was not observed in Karpooravalli, Monthan, and Peyan. According to the level of infestation, the banana varieties viz., Nendran and Red banana were found to be highly susceptible to O. longicollis and among them, nendran is best for trapping banana pseudostem borer and can be used as pseudostem traps. Application of azadirachtin 10000 ppm at 4 ml/l thrice at 5th, 6th, and 7th months after planting showed 55.57 and 59.35 per cent reduction of pseudostem borer over untreated check in nendran and red banana, respectively, and can be used for safe management of banana pseudostem borer.

Molecular adaptation and codon usage bias analysis of Heterodera glycines and Heterodera avenae in saffron (Crocus sativus) corms from Jammu and Kashmir

This study explores the prevalence, distribution, and codon usage bias of nematode species, primarily Heterodera glycines and Heterodera avenae, within saffron corms. Nematode analysis identified H. glycines and H. avenae as the predominant species. Geographical locations exhibited distinct cyst infestation levels, with H. avenae showing higher prevalence. The study reveals the preference of these nematodes for saffron corms, raising concerns for saffron production and suggesting the implementation of monitoring and management strategies. Additionally, the study analyzes the codon usage bias (CUB) of saffron and its associated nematode pathogens. Codon usage bias analysis revealed an AT bias in Crocus sativus and its pathogens. Codon Adaptation Index (CAI) values suggested pathogen adaptation to the host. Correlation analysis highlighted the interplay of nucleotide composition, mutational pressure, and natural selection in codon usage patterns. Amino acid frequency and codon context analyses provided insights into evolution and physiological functions. Furthermore, the study observed a mutual codon preference between the host and pathogens for certain amino acids, highlighting specific interactions at the molecular level. This study provides valuable information for understanding the molecular biology of plant-nematode interactions, and its findings can be useful for developing effective management strategies against these pests in saffron production and other crops such as crop rotation, use of resistant cultivars, and application of nematode-suppressive amendments.

Molecular detection of hemotropic Mycoplasma and Bartonella species in lice from sheep and goats of Mexico.

The knowledge of lice associated with small ruminants, especially sheep and goats, is scarce. In Mexico, there are historical reports of six species of chewing and sucking lice associated with Capra hircus and Ovis canadensis. However, the reports did not analyze the ecology of the infestations or the presence of potentially pathogenic bacteria. For this reason, the objectives of this study were i) to identify the species of lice associated with sheep and goats in three states of the Mexican Republic, ii) to characterize the infestations, and iii) to identify the presence of bacterial pathogens. From October 2019 to August 2021, six ranches with sheep and goats were sampled in the states of Hidalgo and Veracruz. Hosts were visually inspected, and lice were retrieved with forceps. The specimens were sexed and identified using morphological taxonomic keys. DNA extraction was performed individually, and a fragment of the cytochrome oxidase subunit 1 gene (COI) was amplified for the molecular identification of the specimens. Subsequently, Anaplasma, Bartonella, Ehrlichia, Mycoplasma, and Rickettsia were molecularly detected. Additionally, the infestations were characterized by calculating the prevalence and mean abundances. We collected 563 specimens of three species, Bovicola caprae, Bovicola ovis, and Linognathus africanus. The highest infestation levels were recorded for B. ovis (66.7%; 4.4) from Veracruz. Additionally, two Bartonella species were detected: Bartonella mellophagi in B. ovis and Bartonella capreoli in L. africanus. In contrast, Mycoplasma ovis was detected exclusively in one pool of B. ovis. This study provides new bacterial-ectoparasite associations and highlights the possible role of these neglected ectoparasites as vectors in the populations of sheep and goats from Mexico.

Assessing the Knowledge, Attitude and Practices of Farmer on the Biology and Management of Chinese Citrus Fly in Bagmati Province of Nepal

Chinese citrus fly is the major pest of sweet orange in citrus growing districts of Bagmati Province Nepal. Farmer’s production constrains and production problems such as Chinese citrus fly problem in sweet orange are not well documented and studied. This study was conducted in Sindhuli, Ramechhap, Dolakha and Kavre districts of Bagmati Province in 2021 with aim to understand the farmer’s perception, knowledge about CCF and their management practices using semi-structured questionnaire. Collected survey data was analyzed using the SPSS-20 and MS Excel. The survey results depicted that the intensity of Chinese citrus fly damage has been decreased in Sindhuli district with the infestation level of < 20% in sweet orange orchard. However, CCF population as well as their damage level was in increasing trend of > 50% in sweet orange orchard in Ramechhap, and > 80% in Dolakha and Kavre district. Chinese citrus fly loss has been recorded up to 25% in Mandarin orange in Dolakha and Kavre district. Maximum Chinese citrus fly loss was recorded in sweet orange orchard in October month. Farmers usefully managed the dropping fruits by collecting in plastic bags and burying in pits. Great fruit fly bait, has been used for the CCF management in three districts except in Kavre. Majority of farmers reported that chemical pesticides are less effective or sometime not-effective for this pest management. This study provides a preliminary information about sweet orange orchard management practices, general pest problems, Chinese citrus fly and future management strategy in survey districts.

Ornithodoros sonrai Soft Ticks and Associated Bacteria in Senegal.

The soft ticks, Ornithodoros sonrai, are known as vectors of the tick-borne relapsing fever caused by Borrelia spp. and have also been reported to carry other micro-organisms. The objective of this study was to collect and to identify O. sonrai ticks and to investigate the micro-organisms associated with them. In 2019, an investigation of burrows within human dwellings was conducted in 17 villages in the Niakhar area and in 15 villages in the Sine-Saloum area in the Fatick region of Senegal. Ticks collected from the burrows were identified morphologically and by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Micro-organism screening was performed by bacteria-specific qPCR and some identifications were made by standard PCR and gene sequencing. O. sonrai ticks were found in 100% (17/17) of the villages surveyed in the Niakhar area and in 66% (10/15) of the villages in the Sine-Saloum area. A total of 1275 soft tick specimens were collected from small mammal burrows. The ticks collected were morphologically identified as O. sonrai. About 20% (259/1275) of the specimens were also submitted to MALDI-TOF MS for identification. Among the resulting MS profiles, 87% (139/159) and 95% (95/100) were considered good quality specimens, preserved in alcohol and silica gel, respectively. All spectra of good quality were tested against our MALDI-TOF MS arthropod spectra database and identified as O. sonrai species, corroborating the morphological classification. The carriage of four micro-organisms was detected in the ticks with a high prevalence of Bartonella spp., Anaplasmataceae, and Borrelia spp. of 35, 28, and 26%, respectively, and low carriage of Coxiella burnetii (2%). This study highlights the level of tick infestation in domestic burrows, the inventory of pathogens associated with the O. sonrai tick, and the concern about the potential risk of tick involvement in the transmission of these pathogens in Senegal.

Influence of sowing dates on the population density of the fall armyworm <i>Spodoptera frugiperda</i> (JE. Smith) and its damage on maize plants in Chad

Fall armyworm, Spodoptera frugiperda, constitutes a major challenge for maize growers and scientists, as it threatens the food security of all these countries. In order to contribute to the sustainable management of the pest in Chad, this study aimed at assessing the effect of sowing dates on the level of maize infestation in the field. Three sowing periods for maize, chosen in connection with the onset of rains, were tested (the early sowing (July 2nd), the intermediate sowing (July 16th), and the late sowing (July 30th)) with a Fall armyworm- sensitive maize variety. The results indicated a significant effect of sowing dates on the population densities of the pest, with the early sowing hosting the highest density and infestation rate of the pest, compared to the intermediate and late sowing (P < 0.0001). Moreover, the results showed a significant impact of sowing dates on grain yield (P < 0.005), with significantly higher values in the intermediate sowing while, the early and late sowing recorded lower yields. These results indicated that the intermediate sowing should be recommended for this early maturing and fall armyworm-sensitive maize variety to limit population outbreaks of this pest, together with its adverse consequences on maize yield.

Efficiency of plant-based acaricide gels compared to fluvalinate-impregnated strips for control of Varroa destructor in honey bee colonies.

The Varroa mite is one of the most important pests of honey bee colonies. This study compares the efficiency of thymol-based acaricides with the tau-fluvalinate-based commercial treatment to control this mite species. Two experiments were conducted: (1) in the Fall, thymol-based gel was compared with a widely used acaricide, fluvalinate-impregnated strips (Apistan®), and (2) in the Spring, gels containing thymol-only, thymol + menthol + eucalyptus essential oil, and thymol + thyme essential oil were compared with fluvalinate-impregnated strips. In the first experiment, both treatments reduced Varroa mite infestation levels significantly after 70 days of their application compared to the control treatment. The results of the second experiment showed that thymol + thyme essential oil gel, thymol +menthol + eucalyptus essential oil gel, and Apistan strips had always higher efficiency on decreasing mite infestation levels of the selected colonies compared to the control treatment and thymol-only gel during their 7-week application. The results of this study demonstrate that plant-based acaricides for controlling Varroa mites in honey bee colonies have similar efficiency compared to the chemical control methods. Therefore, it is better to use these selected acaricides to reduce mite resistance to chemical control treatments in honey bee colonies.

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level.

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii.

Enhanced diversity of aquatic macroinvertebrate predators and biological pest control but reduced crop establishment in organic rice farming

Organic farming has been adopted as the main alternative to reduce the environmental impact of intensive agriculture by banning the use of synthetic inputs and bolstering key ecological functions such as biological pest control. Yet the potential cascading effects of farming management on the communities of natural enemies, the provisioning of pest control, and the ultimate impact on crop performance remain poorly explored. This is especially true for semi-aquatic crops such as rice (Oryza sativa, L) that largely depend on aquatic macroinvertebrate predators for biological pest control. Here we performed two consecutive landscape-scale field experiments by combining monthly surveys of macroinvertebrate aquatic predators with both pest predation and seed rice germination trials to assess differences between conventional and organic rice farming in all the above-mentioned issues. Specifically, we selected 6 organic and 6 conventionally managed rice paddies and evaluated differences in i) the diversity of aquatic macroinvertebrate predators (taxa richness and evenness), ii) the infestation level of a seed pest (chironomids larvae), iii) the efficiency of biological pest control provisioning and iv) the impact on germination of rice seeds. Communities of macroinvertebrate aquatic predators were more diverse (taxa richness) and abundant in organic than in conventional fields, yet community evenness was higher in conventionally-managed fields. Accordingly, we found that macroinvertebrate-mediated pest control was markedly more efficient in organic farms during the establishment crop stage. Pest infestation, however, remained also higher in organic farming, most likely because biological pest control in organic fields is not able to outcompete chemical control provided in conventional fields. Finally, the increased infestation level observed in organic fields ultimately resulted in reduced germination rates of rice seeds, thus limiting crop performance. Our results highlight the potential positive effects of pesticide banning in organic rice farming on aquatic macroinvertebrate communities and the consequently improvement of biological pest control provisioning. Yet conditions enhancing biodiversity may also favor pest infestation and ultimately increase crop damage. Management recommendations should therefore account for potential services and disservices associated to organic farming in order to avoid negative yield outcomes for rice farmers.

Coffee Berry Borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae): Activity and Infestation in the High Mountain and Blue Mountain Regions of Jamaica.

Jamaica produces coffee marketed as Blue Mountain and high mountain (grown outside the Blue Mountains). Since the discovery of the coffee berry borer (CBB; Hypothenemus hampei) in Jamaica in 1978, chemical control has traditionally been the primary approach used to protect the crop from the pest. However, in the last 20 years, there has been an effort to shift towards more sustainable management strategies. The study was conducted to determine CBB activity (trap catch) and field infestation on coffee farms in the high mountains and Blue Mountains of Jamaica, over a crop cycle. A total of 27,929 and 12,921 CBBs were captured at high mountain and Blue Mountain farms, respectively. Peak CBB activity occurred in April in the high mountain region (365 CBBs/trap/month) and February in the Blue Mountain region (129 CBBs/trap/month). The highest levels of infestation were in November (33%) and October (34%) in the high mountain region and Blue Mountain region, respectively. There was no significant difference in the patterns of CBB activity and infestation between the study locations, and neither were related to the temperature or relative humidity. However, there was a significant relationship with rainfall. These data suggest that the population dynamics of the CBB may involve complex interactions among weather conditions, berry development, and agronomic practices.

White Mango Scale (Aulacaspis tubercularis) infestation on mango: distinct scenarios of the pest status in Ethiopia and Kenya

Infestation of the White Mango Scale (WMS) (Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae) on mango was known in Kenya for a longer time, yet the severity of its impact has only been reported frequently in Ethiopia where the first record of the pest was only about a decade. This study investigated the WMS statuses in Kenya and Ethiopia. A total of four fields, two from each country, were addressed to determine the population abundances of the pest at different developmental stages. Five blocks, one at the center and the rest at each corner of the study orchards were set in Ethiopia. In Kenya, Randomized Complete Block Design (RCBD) was employed to determine the levels of susceptibility of different mango varieties to the pest. The abundance of WMS was significantly different (p < 0.05) between Arjo-Gudetu and Bako-Tibe sites in Ethiopia. Sessile mango scales in hundreds and eggs and crawlers each in thousands per leaf were recorded in Ethiopia. In contrast, very low WMS populations were recorded from the Kenyan orchards, with most leaves bearing no mango scale. However, the sessile stage populations observed were significantly different (p < 0.05) between Kimani-Sofia and Samuru-Kandara orchards. In each of these orchards, the levels of pest infestation varied significantly (p < 0.05) among the mango varieties, Kent and Vandyke being the most infested at Kimani-Sofia and Samuru-Kandara, respectively. In conclusion, these studies revealed the presence of a very high population abundance of mango scale in Ethiopia, whereas in Kenya it was very low. This variation may pertain mainly to the differences in mango production and pest management practices between the two countries. However, because these studies were conducted in different years in the two countries, cross-sectional investigations which include data from a considerable period may be required to better compare the levels of pest infestation in Kenya and Ethiopia.

Insights into varroa mite (Varroa destructor) infestation levels in local honeybee (Apis mellifera) colonies of Ethiopia

AbstractThe varroa mite (Varroa destructor) is the most economically important parasitic pest of honey bees (Apis mellifera) that causes global colony decline and loss. Since its first detection in Ethiopia in 2010, little is known about the current infestation levels and contributing factors to the mite's distribution across the country. In this study, we conducted a large‐scale assessment of varroa prevalence and infestation levels in managed colonies across three potential beekeeping regions of Ethiopia; Oromia, Amhara and Southern Nations, Nationalities and Peoples Region (SNNPR). A total of 360 samples from 39 apiaries were collected following standard protocols and the mite prevalence and infestation levels were determined using the established method of varroa research. Overall, 89% of sampled colonies were infested by the mite, with varying prevalence rates across the regions: 95.8% in Oromia, 85.2% in Amhara and 71.9% in SNNPR, indicating the widespread nature of the parasite throughout the country. The varroa infestation levels were lower in local hives (2.6 ± 5.9) than in frame hives (5.0 ± 0.9), and significantly higher in brood bees (5.6 ± 0.8) than in adult bees (1.93 ± 0.17) (p &lt; 0.001). Possible factors contributing to the varied mite prevalence and infestation levels in the country include differences in agro‐ecology, beekeeping practices, host age preference and the nature of bee hives. Therefore, our study provides insights into the current status of varroa prevalence and associated factors across regions in Ethiopia. Further investigation is required to explore the probable impact of the parasite on the health and productivity of local honey bees, in order to design future monitoring strategies.

Cold storage as part of a Varroa management strategy: effects on honey bee colony performance, mite levels and stress biomarkers

Placing honey bee colonies in cold storage has been proposed as a way to induce a pause in brood production as part of a Varroa mite treatment plan. Here, we exposed colonies to combinations of with or without an October cold storage period and with or without a subsequent miticide application. We then measured the effects of those treatments on colony-level variables (i.e. colony size, Varroa infestation level, survivorship and hive weight and temperature) and pooled individual-level variables that are associated with nutritional and stress responses. Colonies were assessed before and after cold storage, and again post winter, for a total duration of about 5 months, and the experiment was repeated. Brood levels were significantly lower after cold storage, and hive temperatures indicated that most or all brood had emerged after about two weeks in cold storage. However, Varroa levels at the end of the experiments in February were not significantly different among treatment groups. Colonies kept outside (not subjected to cold storage) and treated with a miticide had higher survivorship on average than any other treatment group, but no other group comparisons were significant, and long-term impact of cold storage on adult bee populations and on colony thermoregulation was low. The bee forage environment was also very different between the 2 years of the study, as rainfall and bee forage availability were much higher the second year. Colonies were over 2.5 times larger on average the second year compared to the first, both in terms of adult bee mass and brood area, and expression levels of nutrition and stress response genes were also significantly higher the second year. The results indicate that limited cold storage would likely have little long-term impact on most colony and individual measures of health, but for such a strategy to succeed levels of stressors, such as Varroa, may also need to be low.