Wheat Mite Research Articles

Effect of Different Fertilization Strategies on Infestation of Brown Wheat Mite and Wheat Productivity

The brown wheat mite, Petrobia tritici, poses a significant threat to wheat fields. While fertilizers can increase crop productivity, imbalanced application may exacerbate plant susceptibility to pests. This study aimed to evaluate the impact of various NPK fertilization programs on P. tritici infestations over two consecutive cropping seasons. The results revealed significant differences in mite infestation among the treatment groups (p < 0.001). The lowest populations (1.1 and 3.0 mites/leaf) were observed in the treatments sprayed with phosphoric acid (at 0.75 and 1.00 cm/L), where the infestation appeared approximately 120 days after sowing; in contrast, it appeared early at 75 days in the other treatments. Conversely, treatments lacking potassium fertilizer presented the greatest degree of mite injury levels (49.5–57.7 mites/leaf). Although these treatments provided moderate leaf nutrition and crop yield, the highest nutritional content and total yield (10.49 and 9.71 1 t/ha for the two years) were observed in the treatment that received 224:70:100 kg fad−1 commercial fertilizers (=178:25:114 kg ha−1 NPK units) as soil fertilization, which was followed by the treatment with a foliar application of phosphoric acid (1.00 cm/L) with a total yield of 9.34 and 8.53 1 t/ha for the two years. In this treatment, the P. tritici density was moderately high at 9.40 and 6.32 mites/leaf over the two years, respectively. The consistency of P. tritici density and total yield ranking across both years indicated reliable estimates of the impact of fertilization. This study suggests that potassium sulfate application is crucial for reducing P. tritici density and that foliar phosphoric acid application instead of soil application reduces the number of P. tritici and delays its occurrence.

First report of brown wheat mite, Petrobia sp. on wheat in Gujarat

First report of brown wheat mite, Petrobia sp. on wheat in Gujarat

Spider Mites Detection in Wheat Field Based on an Improved RetinaNet

As a daily staple food of more than one third of the world’s population, wheat is one of the main food crops in the world. The increase in wheat production will help meet the current global food security needs. In the process of wheat growth, diseases and insect pests have great influence on the yield, which leads to a significant decline. Wheat spider mites are the most harmful to wheat because they are too small to be found. Therefore, how to use deep learning to identify small pests is a hot spot in modern intelligent agriculture research. In this paper, we propose an improved RetinaNet model and train it on our own dataset of wheat spider mites. Firstly, the wheat spider mites dataset is expanded from 1959 to 9215 by using two different angles and image segmentation methods. Secondly, the wheat spider mite feature detection head is added to improve the identification of small targets. Thirdly, the feature pyramid in FPN is further optimized, and the high-resolution feature maps are fully utilized to fuse the regression information of shallow feature maps and the semantic information of deep feature maps. Finally, the anchor generation strategy is optimized according to the amount of mites. Experimental results on the newly established wheat mite dataset validated our proposed model, yielding 81.7% mAP, which is superior to other advanced object detection methods in detecting wheat spider mites.

Insecticide Evaluations for Control of Brown Wheat Mite in Winter Wheat, 2022

Insecticide Evaluations for Control of Brown Wheat Mite in Winter Wheat, 2022

Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning

To solve the problem of lacking scientific guidance in aerial pesticide application, this study introduced an adaptive spraying decision system (ASDS) for the unmanned aerial vehicle (UAV) spraying to guide the operators of plant protection UAVs to set reasonable spraying parameters under complicated environment. The minimum applied volume rate, proper spraying velocity, spraying height, and initial droplet size were recommended by the ASDS. The key factor of the decision system is the decision model of reinforcement learning based on the actor-critic neural network. In specific, the field experimental data were used to train the critic and actor networks, which made the model adaptive to optimize the output of spraying parameters. Compared with the conventional spraying parameters, the spraying parameters recommended by the ASDS had a positive impact on wheat parcels. The decision results of the ASDS showed that the spraying volume rate was lower in the blocks with a small leaf area index. In addition, the spraying volume rate for the whole parcel was reduced by 14%. After UAV spraying, the uniformity of the droplet deposition in the ASDS parcel was better than that in the conventional parcel. Moreover, the penetrability of the droplets and the control efficacy for the brown wheat mite Petrobia latens (Muller) were similar in the two parcels. The ASDS can recommend the optimal spraying parameters to minimize pesticide application. Keywords: unmanned aerial vehicle, spraying parameters, decision, reinforcement learning, droplet deposition DOI: 10.25165/j.ijabe.20221504.6929 Citation: Hao Z Y, Li X Z, Meng C, Yang W, Li M Z. Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning. Int J Agric & Biol Eng, 2022; 15(4): 16–26.

Wheat yield losses from pests and pathogens in China

Wheat is a main food source for more than 40% of the world’s population, while pests and pathogens are widely recognized as a significant obstacle to food security. Assessing the impact of pests and pathogens on yield losses is therefore critical to maintaining global food supply. However, quantitative estimation of wheat yield losses is notoriously challenging. Here, we report on an experiment-based quantitative assessment of wheat yield losses in China caused by pests and pathogens from 2000 to 2018. We found that wheat yield losses caused by pests and pathogens in six main wheat planting regions (the Yellow and Huai River valleys, the middle and lower reaches of the Yangtze River, the Southwest China, the Loess Plateau, the Northeast China and the Xinjiang province) were 16.29%, 7.46%, 11.71%, 12.64%, 6.54% and 4.84%, respectively, of the regional yield. The proportions of yield losses caused by aphids, wheat mites, scab and sharp eyespot have been on the rise, while those of underground pests have declined. Wheat powdery mildews were shown to have increased in the Xinjiang province but declined in the Northeast China. Future breeding research should focus on varieties resistant to rising group like aphids, wheat mites, scab, sharp eyespot and powdery mildews. The greatest yield loss occurred in the Yellow and Huai River valleys which was also the place with the highest wheat yield. More efforts are needed to provide management measures on wheat health management in the Yellow and Huai River valleys. Our results provide an accurate assessment of wheat yield losses in main wheat production regions of China and help devise region-specific agricultural management strategies to secure food supply to an increasingly aging population.

Recognition and counting of wheat mites in wheat fields by a three-step deep learning method

The wheat mite always causes major damage in wheat plants and results in significant yield losses. Therefore, detecting wheat mites can provide important information, such as pest population dynamics and integrated pest management by monitoring wheat mite populations. However, the automatic classification and counting of wheat mites from images taken from crop fields are more difficult than those obtained under laboratory conditions, due to complicated background in crop fields, light instability and small wheat mites in images. Furthermore, the manual identification of wheat mites is very time-consuming and complex. Deep learning technique provides an efficiently automated way for address the issue. This paper proposes a three-step deep learning method to identify and count wheat mites from digital images. First, original large images are separated into smaller images as datasets. Then, the small images are labeled and then enlarged so that each of them can be located in corresponding position of original image. Second, one CNN takes an image (of any size) as input and outputs a set of feature maps for the image. Afterwards, the extracted feature maps are input to Region Proposal Network (RPN), which may be most likely the areas of wheat mites and output a set of rectangular objective proposals, each with an object score. Then one 256-d vector is generated from the obtained proposals by the other CNN. The vector is input into two fully connected layers, a box-regression layer and a box-classification layer, which output the probability scores of the position information and the population of wheat mites, respectively. Moreover, the superposition of the results for the small images is taken as the number of wheat mites for each original image. By using different backbone deep learning networks, ZFnet with five layers and VGG16 with sixteen layers achieved the accuracies of 94.6% and 96.4%, respectively.

Impact of integrated pest management (IPM) module on major insect-pests of wheat and their natural enemies in North-western plains of India

In order to sustain the wheat production in North-western plains of India, a study was conducted to test the integrated pest management (IPM) module with commonly used farmer’s practices for the management of insect pests during 2017-18 and 2018-19. It was observed that aphid population was significantly lower in IPM plot (2.48-2.60 aphids/tiller) as compared to farmer’s practices (13.93-16.46 aphids/tiller) at peak period of their activity. At the same time, the population of coccinellids predators was significantly higher in IPM plots (6.80 beetle/m2) as compared to non-IPM plots (1.26 beetles/m2) during 2017-18. The termite damage ranged from 2.48-2.96 per cent in farmer's practices while it was only 0.20-0.80 per cent in IPM fields. Similarly, greater pink stem borer (PSB) damage was recorded in farmer's practices (0.96-2.48 per cent) as compared to IPM fields (0.46-0.68 per cent). The incidence of brown wheat mite (BWM) was recorded on maturing wheat and it was 2-5 fold higher in farmer's practice (11.73-31.53 mites/10 cm2) as compared to IPM plots (4.20- 5.00 mites/10 cm2). Approximately 9 per cent increase in grain yield resulted in additional income of Rs. 9291/ha in IPM plot. Seed treatment, yellow sticky trap based monitoring of aphids and need based application of insecticide resulted in better control of insect. Doi.org/10.25174/2582-2675/2020/100185

Temperature Limits for the Brown Wheat Mite, in Colorado.

Brown wheat mites, Petrobia latens (Müller 1776, Acari: Tetranychidae), are sporadic yet economically damaging pests of winter cereals. In Colorado, their life history is closely tied to the development of winter wheat, where they are present in the field from crop planting in late September through harvest in early June. In order to withstand winter months, these mites are able to survive cold temperatures. However, the mechanisms of cold hardening and their temperature limits are unknown. This research documents the seasonal supercooling points of the brown wheat mite. Their seasonal average supercooling point stayed consistent throughout the year, never varying more than a degree from the overall average supercooling point of -17°C. The greatest variation in supercooling point was seen in the spring, during which supercooling point temperatures ranged from -9.2 to -25.5°C. We also documented the upper and lower lethal temperatures for the brown wheat mite. When comparing small nymphs to large nymph and adult stages, small nymphs were slightly more cold tolerant (lethal temperature estimates required to kill 99% of the population [LT99] were -30.8 and -30.6°C, respectively), but less heat tolerant (LT99 was 50 and 56°C, respectively).

An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field

In agriculture, pest always causes the major damage in fields and results in significant crop yield losses. Currently, manual pest classification and counting are very time-consuming and many subjective factors can affect the population counting accuracy. In addition, the existing pest localization and recognition methods based on Convolutional Neural Network (CNN) are not satisfactory for practical pest prevention in fields because of pests’ different scales and attitudes. In order to address these problems, an effective data augmentation strategy for CNN-based method is proposed in this paper. In training phase, we adopt data augmentation through rotating images by various degrees followed by cropping into different grids. In this way, we could obtain a large number of extra multi-scale examples that could be adopted to train a multi-scale pest detection model. In terms of test phase, we utilize the test time augmentation (TTA) strategy that separately inferences input images with various resolutions using the trained multi-scale model. Finally, we fuse these detection results from different image scales by non-maximum suppression (NMS) for the final result. Experimental results on wheat sawfly, wheat aphid, wheat mite and rice planthopper in our domain specific dataset, show that our proposed data augmentation strategy achieves the pest detection performance of 81.4% mean Average Precision (mAP), which improves 11.63%, 7.93%,4.73% compared to three state-of-the-art approaches.

BIODIVERSITY OF SOIL MITES ASSOCIATED WITH WHEAT AND SOYBEAN CROPS IN SHARKEIA AND BEHEIRA GOVERNORATES

biodiversity of different soil mite associated with both wheat and soybean crops surveyed in Sharkeia and Beheira governorates during two successive seasons 2016 and 2017. Fifty-seven mite species belonging to 41 genera under 22 families belonging to four sub-orders; Mesostigmata, Prostigmata, Cryptostigmata and Astigmata were identified. The total numbers of the collected mite groups recorded 69, 295, 533 and 602 individuals for Astigmata, Mesostigmata, Cryptostigmata and Prostigmata in Sharkeia, while the number of mites recorded 104, 378, 282 and 405 individuals for the previous groups in Beheira, respectively. Soil mites associated with wheat crop were higher numbers than soil mites of soybean. The oribatid and mesostigmatid mite species exhibited the higher number of soil mites in soil wheat followed by prostigmatid and astigmatid, while the low number of mites were recorded in soil cultivated by soybean plants.

Multiple Diseases, Insect Pests Resistant Genotypes and Their Utilization in Breeding for Resistance in Wheat and Triticale

A total of 347 numbers of genotypes of Triticum aestivum , 66 of T. durum , 8 of T. dicoccum and 18 of triticale possessing resistance against multiple diseases and insect pests resistance were taken for the study. These were identified resistant to biotic stresses through rigorous evaluation under artificially inoculated conditions at hot spot locations in six agro ecological zones of India during 1998-99 till 2011-12 cropping seasons. The diseases and insect pests targeted were three rusts (stem, leaf and stripe rust), Karnal bunt, flag smut, leaf blight, powdery mildew, loose smut, brown wheat mite, root aphid, foliar aphid and shoot fly. The seeds of these genotypes along with passport data on disease resistance were shared with breeders at 30 main wheat and triticale breeding centres in India. The percentage of centres utilized these genotypes for hybridization and resistance breeding ranged from 4.0-63.6%. The per cent utilization was maximum in case of T. aestivum followed by T. durum, T. dicoccum . The most preferred genotypes of T. aestivum were, ‘DBW 18’, ‘DBW 32’, ‘GW 276’, ‘HD 2851’, ‘HD 2964’, ‘HD 2997’, ‘HP 1872’, ‘HW 2044’, ‘HW 3027’, ‘HPW 42’, ‘HPW 155’, ‘HPW 237’, ‘HPW 285’, ‘HUW 620’, ‘HS 295’, ‘HS 318’, ‘HS 345’, ‘HS 375’, ‘HS 420’, ‘HS 468’, ‘HS 493’, ‘HW 2045’, ‘HW 3083’, ‘HW 5037’, ‘HW 5208’, ‘K 9351’, ‘K 9441’, ‘MACS 295’, ‘MACS 2959’, ‘NW 3087’, ‘NW 1012’, ‘PBW 373’, ‘PBW 475’, ‘PBW 521’, ‘PBW 550’, ‘PBW 554’, ‘PBW 573’, ‘PBW 589’, ‘Raj 3765’, ‘Raj 4012’, ‘RAJ 4120’, ‘RAJ 4131’, ‘UP 2425’, ‘UP 2719’, ‘VL 801’, ‘VL 868’ and ‘VL 907’. In case of T. durum, 66 genotypes were utilized by 4.0-36% centres and most liked were, ‘AKDW 4155’, ‘CDW 04’, ‘GW 1139’, ‘HD 4696’, ‘HI 8498’, ‘MACS 3313’, ‘PDW 267’ and ‘PDW 312’. Likewise, out of eight genotypes of T. dicoccum , ‘MACS 2956’ (32.0%) and ‘MACS 2971’ (15.0%) were used most. Utilization of 18 numbers of triticale genotypes was 4.0-14.8% only. It may be due to lesser preference of triticale as compared to bread and durum wheat for breeding. The major preference of breeders was on genotypes having resistance against three or two rusts depending on agro climatic zones. The genotypes having Lr34 gene which is known for providing resistance to leaf rust were also found to be resistant to other diseases and aphids. The incorporation of resistance genes from resistant genotypes shared in breeding programme has helped greatly in management of diseases and insect pests using host resistance and avoided or reduced application of costly fungicides on wheat and producing pesticide free wheat in India. The deployment of resistant cultivars strategically in areas of prevalence of diseases and insect pests helped to further curtail losses in yield and quality of wheat and outbreak of rusts and other diseases thus ensure food self sufficiency as well as export of surplus wheat in future. These genotypes will be good source for molecular studies related to multiple disease and insect pests.

Studies on the wheat curl mite, Aceria tulipae Keifer (Eriophyidae), in Egypt

Field experiments were carried out with the goal of estimating the population fluctuations of the wheat curl mite, Aceria tulipae Keifer, and its control. These experiments were conducted at an abandoned wheat field in Egypt during a growing season in 2012 in Cairo Governorate. Also, studies on life history aspects and reproductive parameters of the same species in the laboratory at 25 1 °C and 60 15% R.H. were carried out. There were population fluctuations of the wheat curl mite, A. tulipae, when affected by temperature increase during the experimental season. The results showed that the population fluctuation continued at a moderate level until the end of February and then started to increase until it reached to three peaks from the mid of March to the mid of April when the temperature degrees were at 17–24 °C. A. tulipae removes cell contents of the leaves which generally become yellow. The obtained data indicated that the total developmental time of the immature stages was 8.90 ± 0. 74 and 9.58 ± 0.88 days for male and female, respectively, and the female fecundity was 22.8 eggs; 1.17 eggs/♀/day. Intrinsic rate of increase (r m) was found to be 0.153 individuals per female per day and the population multiplied 12.54 times in a generation time of 19.20 days under the given conditions. Three acaricides: Abamectin, Antimite and Sulphur were selected to control the wheat mite A. tulipae. The results of control pest experiments indicated that the Abamectin was superior in reducing the wheat mite A. tulipae (79% reduction) compared to Sulphur and Antimite which had approximately equal reductions (67 and 65% reductions, respectively). The study ended with the conclusion that the population of the wheat mite A. tulipae was significantly highest at the interval from the mid of March to the mid of April. The wheat mite A. tulipae is considered to be disastrous as its intrinsic rate of increase (r m) is high and the best control of this species at this interval was attained with Abamectin.

Temperature Preference and Respiration of Acaridid Mites

The thermal preferences in a grain mass and respiration at various temperatures in mites (Acari: Acarididae) of medical and economical importance [Acarus siro (L. 1758), Dermatophagoides farinae Hughes 1961, Lepidoglyphus destructor (Schrank 1871), and Tyrophagus putrescentiae (Schrank 1781)] were studied under laboratory conditions. Based on the distribution of mites in wheat, Triticum aestivum L., grain along a thermal gradient from 10 to 40 degrees C, L. destructor, D. farinae, and A. siro were classified as eurythermic and T. putrescentiae as stenothermic. The lowest preferred temperature was found for D. farinae (28 degrees C), followed by A. siro (28.5 degrees C), L. destructor (29.5 degrees C), and T. putrescentiae (31.5 degrees C). The relationship between the respiration rate and the temperature was similar for all four mite species. The highest respiration was found in the range from 31 to 33 degrees C. This is approximately 2 degrees C higher than the preferred temperature of these species. The lower temperature threshold of respiration ranged from 1 to 5 degrees C and the upper threshold ranged from 45 to 48 degrees C. Acclimatization of A. siro to temperature regimes of 5, 15, and 35 degrees C resulted in thermal preferences between 9 and 12 degrees C, 9 and 20 degrees C, and 28 and 35 degrees C, respectively. The respiration rate of acclimatized specimens increased with the temperature, reaching a maximum at 29.0 degrees C for mites acclimatized at 5 and 15 degrees C and a maximum at 33.7 degrees C for those acclimatized at 30 degrees C.

Effectiveness of carbon dioxide in compressed gas or solid formulation for the control of insects and mites in stored wheat and barley

Carbon dioxide can be used as an effective stored-grain fumigant in relatively air-tight bins. Carbon dioxide was added to wheat (Triticum aestivum) as a compressed gas and to barley (Hordeum vulgare) as a solid (dry ice) in 322-kg grain bulks. Wheat was stored at temperature decreasing from 18 to 10°C over a 12-wk period. Bins were left open, sealed without C02 added, or with C02 supplemented at 25,34, and 46% levels. Barley was stored at temperature decreasing from 25 to 20°C over an 8-wk period. Bins were left open, sealed without C02 added, or with C02 treatments of 23, 29, and 34%. The wheat and barley moisture content were 14.5-16.3% and 14.5-16.1%, respectively. Oxygen levels in the wheat reflected air displacement with C02, but lower O2 levels in the barley reflected a combination of air displacement by C02 and consumption of O2 by respiring grain and microorganisms at the warmer temperatures. The insects Cryptolestes ferrugineus and Tribolium castaneum were controlled in 2 wk at 34% C02 and 15% O2 at temperature decreasing from 18 to 10°C, or 29% C02 and 3% O2 at temperature decreasing from 25 to 20°C. The mites Tarsonemus granarius, Lepidoglyphus destructor, and Aeroglyphus robustus were killed in less than 2 wk at these C02 levels. Seed germination and microflora were unaffected by all gaseous environments.

Three-dimensional distribution and sampling indices of insects and mites in horizontally-stored wheat

Studies were conducted in three flat storerooms in Southern Greece, filled with approx. 45 tons of wheat each, stored in 1.5 m deep bulks, in order to examine the seasonal abundance of insect and mite species, in relation to their horizontal and vertical spatial distribution into the bulked grain. For this purpose, wheat samples were taken from June 2000 until March 2001, at 10-day intervals, by using a partitioned grain trier. The surface of the grain bulk was divided into three sampling zones, the central zone (the central part of the bulk), the edge zone (close to the walls) and the corners (the corners of the store rooms). The trier samples were examined separately for the upper, medial and lower 0.5 m of the bulk. Significant differences were recorded for wheat moisture content among zones (p<0.0001) and among depths (p=0.0012). On the contrary, no significant differences were noted for temperature among zones (p=0.2281) and depths (p=0.3049). In general, the moisture content was higher in the upper 0.5 m of the bulk, compared to the other two depths. Temperature decreased during the sampling period and this reduction was more vigorous from October and on. Eight insect species and 25 mite taxa were found during the sampling period. The most abundant insect species were Plodia interpunctella Hubner (Lepidoptera: Pyralidae), Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae) and Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae). The most abundant mite species were Acaropsis sollers Rohdendorf (Prostigmata: Cheyletidae), Tyrophagus putrescentiae (Schrank) (Astigmata: Acaridae) and Glycyphagus domesticus (De Geer) (Astigmata: Glycyphagidae). For the majority of the most abundant insect and mite species, the highest population densities were recorded during autumn. More individuals were found at the corners and the central zone, compared to the edge zone. In addition, significantly more individuals were found in the upper 0.5 m of the bulk (p<0.05), compared to the other two depths, with the exception of A. sollers which was equally distributed in the upper 1 m. All the aforementioned species of insects and mites showed an aggregated spatial pattern, as indicated by Taylor's Power Law estimates. The use of Taylor's parameters for the estimation of accuracies and sample sizes for the most abundant species indicated that these parameters were notably varied among species.

Associated-food-hazards: storage fungi and mites in poppy, mustard, lettuce and wheat

Storage fungi and mites frequently cause injury of crops and contamination of crop agro-products (= “sensitive food ingredients”) by allergens and toxins. This may have serious practical consequences since currently the food safety is one of the most important priorities of EU-agricultural policy. However, the risk of occurrence of biotic-hazard in various agricultural product and food ingredients is not equal since they differ in their sensitivity to infestation/contamination by various fungi- and mite-hazards. Therefore, the goal of our study was to identify and review the fungi-hazards connected with occurrence of 5 key-species of mite-hazards, in 4 kinds of “sensitive food ingredients” that include poppy, mustards, lettuce and wheat grain. Different numbers of fungal-hazards (wheat: 44, poppy: 37, mustard: 13, lettuce: 31) were isolated from the tested 4 kinds of crop agro-product. This indicates that their sensitivity to mite-associated fungal infestation/contamination increases in the following order: mustard, lettuce, poppy, and wheat. Mite-hazards differ in their vector-capacity of various fungi-hazards. Generally, predatory mites (i.e. Cheyletus spp.) represent lower risk than fungivorous and herbivorous species of mites (i.e. Acarus siro, Tyrophagus putrescentiae, Lepidoglyphus destructor, Caloglyphus rhizoglyphoides) in terms of vectoring fungi hazards. Many of the mites and fungi hazards rarely occurred independently. We therefore propose that (i) such pest-hazard-systems (i.e. fungi-mite-hazard-systems) should be called “associated-hazards” (ii) the new and specific approaches to risk assessment of “associated hazards” should be developed and implemented into practice.

First Report of Barley yellow streak mosaic virus-Infected Barley in Alaska.

Barley yellow streak mosaic virus (BaYSMV) was first described and reported in Montana and Alberta, Canada, more than 17 years ago (1). Since then, it has been detected in two other locations: Pocatello Valley, ID (3), and across the border in Utah. BaYSMV has now been found in the Alaskan interior. In July 1999, dry-land barley (Hordeum vulgare L.) growing in University of Alaska-Fairbanks experimental plots exhibited symptoms similar to those described for BaYSMV, including parallel chlorotic streaks and leaf banding. Mechanical inoculation of Nicotiana benthamiana with diseased barley sap produced systemic mosaic symptoms. As previously reported for BaYSMV sap-transmission tests (2), parallel inoculations to barley plants yielded no symptoms. Electron microscopy of leaf dips and minipurifications of infected N. benthamiana revealed long filamentous particles that matched the size and shape reported for BaYSMV (1). Ultrathin sections of diseased barley and N. benthamiana leaves displayed characteristic virus particles. BaYSMV was confirmed by immuno-sorbent electron microscopy assays (4) and western blot analysis with polyclonal antiserum. Long filamentous BaYSMV particles appeared only on grids coated with BaYSMV antiserum and exposed to diseased N. benthamiana sap. Total protein extracts from diseased barley tissue and inoculated N. benthamiana, as well as with protein extracted from partially purified preparations, were applied to a 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis minigel and stained with Coomassie blue. Diseased samples, but not healthy controls, contained a protein of ≈33 kDa that was within the size range of a previously described protein from partially purified BaYSMV particles (2). Western blot analysis with an Immuno-Blot alkaline phosphatase assay system (Bio-Rad Laboratories, Hercules, CA) confirmed that the protein reacted with polyclonal BaYSMV. This is the first serological documentation of a BaYSMV-specific protein and that the ≈33-kDa protein is the main antigen recognized by the BaYSMV polyclonal antiserum. Based on virus particle shape and size, symptomology, mechanical transmission host range, and serology, we conclude that BaYSMV is associated with the barley disease observed. Barley yellow streak mosaic virus disease outbreaks are associated with recurring drought and are accompanied by infestations of the brown wheat mite vector, Petrobia latens Müller (1), so it is not surprising that this report coincides with abnormally dry conditions occurring throughout the 1990s in the interior of Alaska.

Detection of mite infestation in wheat by electronic nose with transient flow sampling

A total of 71 wheat samples of 70 g each, to which were added 0–70 flour mites (Acarus siro L), were scanned, 1–2 weeks after preparation, by a conducting polymer-type electronic nose with transient flow sampling. The device uses an array of non-selective sensors to produce a digital fingerprint (odour profile) relating to the volatile composition of the sample. The results demonstrate the ability of the device to detect the mites in wheat at concentrations of relevance to the cereal trade, of the order of just 1000 A siro kg−1 (around 0.001% by weight). One week from preparation, discrimination between samples with no added mites and samples with 70 added mites achieved a classification accuracy of over 83%. Evidence is presented which suggests the known mite-produced volatile undecane as the origin of the electronic nose response. © Crown copyright 1999

Factors Influencing the Outcome of Barley Yellow Streak Mosaic Virus-Brown Wheat Mite-Barley Interactions

Barley yellow streak mosaic virus (BaYSMV), the cause of disease in barley, is transmitted in nature only by the brown wheat mite (Petrobia latens). Greenhouse and growth chamber experiments were conducted to gain insight into what underlies the reported association between severe BaYSMV-induced disease outbreaks and large mite populations with warm and dry conditions. Experiments were also done to determine the efficiency of the mite as a vector for BaYSMV. The presence of BaYSMV antigen in diseased plants and viruliferous mites was confirmed by enzyme-linked immunosorbent assay (ELISA). Evidence was found for a critical temperature threshold between 21 and 26 degrees C for efficient expression of BaYSMV-induced symptoms in barley. The influence of barley host plants stressed by drought on disease incidence was neutral, positive, and negative at 21, 26, and 30 degrees C, respectively. Applying water periodically to mite egg deposition substrates on the soil surface appeared to reduce the number of eggs deposited. Mite counts were generally higher on BaYSMV-infected barley than on healthy barley. Preadult nonviruliferous mites readily acquired BaYSMV from infected host plants. Adult mites efficiently inoculated the virus into barley plants, and preadults were also able to inoculate the virus. Mite populations were able to expand at temperatures too low to support all but very low incidences of BaYSMV-induced disease in barley. There was also indirect evidence for transovarial passage of BaYSMV to red nondiapausal eggs within the mite vector.