Honey bees (Apis mellifera) are essential for their products—honey, royal jelly, pollen, propolis and beeswax. They are also indispensable because they support ecosystems with their pollination services. However, the production and functions of honey bees are hindered by the arthropod pest Varroa destructor, which attacks bees through its feeding activities. Efforts to control varroa mites have been made through the development of various synthetic pesticide groups, but have had limited success because the mites developed resistance and some of these pesticides are harmful to bees. Branded pesticides are rarely used in Pakistan, as beekeepers utilize acaricides from unknown sources. There is a need to create awareness of available naturally occurring acaricides that may serve as an alternative to synthetic acaricides. Although some naturally occurring compounds are considered toxic to the environment, the soft acaricides oxalic acid, thymol, and formic acid 65% are usually safe for honey bee colonies and beekeepers, when handled appropriately. The current study investigated the effectiveness of formic acid (10, 15, and 20 mL/hive), oxalic acid (4.2, 3.2, and 2.1%/hive), and thymol (6, 4, and 2 g/hive) in controlling mite infestation. The results indicated that all treatments significantly reduced the mite population (p < 0.05). The average efficacies of oxalic acid at 3.2% (94.84% ± 0.34) and 4.2% (92.68% ± 0.37) were significantly higher than those of the other treatments. The lowest efficacy was recorded in formic acid 65% at 10 mL (54.13%). Overall, the results indicated that soft acaricides—such as oxalic acid at 3.2% and 4.2% concentrations—are very effective at controlling varroa mites and can be used in broodless conditions without side effects.

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Rice (Oryza sativa L.) is one of the highly consumed cereal grain crops in Pakistan. In September 2017, leaf samples of cultivar Basmati-385 showing brown to dark brown spots (5 to 9 mm in diameter...

Plant diseases are major causes of crop yield losses and exert a financial burden via expenditure on disease control. The magnitude of these burdens depends on biological, environmental and management factors, but this variation is poorly understood. Here we model the effects of weather on potential yield losses due to fungal plant pathogens (the biotic yield gap, Y _gb ) using experimental trials of fungicide-treated and untreated cereal crops in the UK, and project future Y _gb under climate change. We find that Y _gb varies between 10 and 20 % of fungicide-treated yields depending on crop, and increases under warmer winter and wetter spring conditions. Y _gb will increase for winter wheat and winter barley under climate change, while declining for spring crops because drier summers offset the effects of warmer winters. Potential disease impacts are comparable in magnitude to the effects of suboptimal weather and crop varieties.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, causes substantial yield losses in soybean. However, relationships between soybean yield and components of disease progress, including time of disease onset, are poorly understood. Individual soybean plants (2018) and quadrats (2016 to 2018) were monitored in commercial fields and experimental plots in Iowa to quantify the impact of SDS foliar symptom onset on final SDS intensity, soybean yield components, and yield. The date when SDS foliar symptoms were first detected (onset time) and progress of SDS incidence and severity were recorded weekly. Individual soybean plants and quadrats were harvested at the end of each season. Beta-regression showed that date of SDS onset had a consistent and stable effect on final disease intensity both at individual plant and quadrat levels. The slope of the relationship between date of SDS onset and final SDS severity was common across all field sites and years. Weighted linear regression revealed that SDS onset explained 60 to 83% of the variation in number of pods, number of seeds, and total seed weight in individual plants, and 94 to 97% of the variation in seed yield in quadrats. Soybean yield damage functions (slopes) indicated that for each day SDS onset was delayed, soybean yield increased by 30.5 to 31.3 kg/ha. This new quantitative information improves understanding of the impact of SDS on final disease intensity and soybean yield. Further experiments are needed to determine how this relationship is affected by site-specific factors.