Dare to be resilient: the key to future pesticide-free orchards?

Considering the urgent need for more sustainable fruit tree production, it is high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this end, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance, and recovery traits, disease resilience appears as a cornerstone to cope with the multiple pest and disease challenges over an orchard's lifetime. Here, we describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting phenotypic and genotypic components of resilience. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on ecophysiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate fine scale temporal monitoring of tree health, allowing identification of resilient genotypes with the calculation of specific resilience indicators. Although resilience could be considered as a 'black box' trait, we demonstrate how it could become a realistic breeding goal.

Advances and knowledge gaps on climate change impacts on honey bees and beekeeping: A systematic review.

The Western honey bee Apis mellifera is a managed species that provides diverse hive products and contributing to wild plant pollination, as well as being a critical component of crop pollination systems worldwide. High mortality rates have been reported in different continents attributed to different factors, including pesticides, pests, diseases, and lack of floral resources. Furthermore, climate change has been identified as a potential driver negatively impacting pollinators, but it is still unclear how it could affect honey bee populations. In this context, we carried out a systematic review to synthesize the effects of climate change on honey bees and beekeeping activities. A total of 90 articles were identified, providing insight into potential impacts (negative, neutral, and positive) on honey bees and beekeeping. Interest in climate change's impact on honey bees has increased in the last decade, with studies mainly focusing on honey bee individuals, using empirical and experimental approaches, and performed at short-spatial (<10 km) and temporal (<5 years) scales. Moreover, environmental analyses were mainly based on short-term data (weather) and concentrated on only a few countries. Environmental variables such as temperature, precipitation, and wind were widely studied and had generalized negative effects on different biological and ecological aspects of honey bees. Food reserves, plant-pollinator networks, mortality, gene expression, and metabolism were negatively impacted. Knowledge gaps included a lack of studies at the apiary and beekeeper level, a limited number of predictive and perception studies, poor representation of large-spatial and mid-term scales, a lack of climate analysis, and a poor understanding of the potential impacts of pests and diseases. Finally, climate change's impacts on global beekeeping are still an emergent issue. This is mainly due to their diverse effects on honey bees and the potential necessity of implementing adaptation measures to sustain this activity under complex environmental scenarios.

La abeja occidental Apis mellifera es una especie manejada que proporciona diversos productos de la colmena y servicios de polinización, los cuales son cruciales para plantas silvestres y cultivos en todo el mundo. En distintos continentes se han registrado altas tasas de mortalidad, las cuales son atribuidas a diversos factores, como el uso de pesticidas, plagas, enfermedades y falta de recursos florales. Además, el cambio climático ha sido identificado como un potencial factor que afecta negativamente a los polinizadores, pero aún no está claro cómo podría afectar a las poblaciones de abejas melíferas. En este contexto, realizamos una revisión sistemática de la literatura disponible para sintetizar los efectos del cambio climático en las abejas melíferas y las actividades apícolas. En total, se identificaron 90 artículos que proporcionaron información sobre los posibles efectos (negativos, neutros y positivos) en las abejas melíferas y la apicultura. El interés por el impacto del cambio climático en las abejas melíferas ha aumentado en la última década, con estudios centrados principalmente en individuos de abejas melíferas, utilizando enfoques empíricos y experimentales y realizados a escalas espaciales (<10 km) y temporales (<5 años) cortas. Además, los análisis ambientales fueron basaron principalmente en datos a corto plazo (meteorológicos) y se concentraron sólo en algunos países. Variables ambientales como la temperatura, las precipitaciones y el viento fueron ampliamente estudiadas y tuvieron efectos negativos generalizados sobre distintos aspectos biológicos y ecológicos de las abejas melíferas. Además, las reservas alimenticias, las interacciones planta-polinizador, la mortalidad, la expresión génica y el metabolismo se vieron afectados negativamente. Entre los vacios de conocimiento cabe mencionar la falta de estudios a nivel de colmenar y apicultor, la escasez de estudios de predicción y percepción, la escasa representación de las grandes escalas espaciales y a mediano plazo, el déficit de análisis climáticos y la escasa comprensión de los impactos potenciales de plagas y enfermedades. Por último, las repercusiones del cambio climático en la apicultura mundial siguen siendo un tema emergente, que debe estudiarse en los distintos países. Esto se debe principalmente a sus diversos efectos sobre las abejas melíferas y a la necesidad potencial de aplicar medidas de adaptación para mantener esta actividad crucial en escenarios medioambientales complejos.

Research on a Method for Identification of Peanut Pests and Diseases Based on a Lightweight LSCDNet Model.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

[Development of green pesticides from traditional Chinese medicines based on artificial intelligence and compatibility of traditional Chinese medicines].

The food security of China as a big agricultural country is attracting increasing attention. With the progress in the traditional Chinese medicine industry, Chinese medicinal materials and their preparations have been gradually developed as agents for disease prevention and with antimicrobial and insecticidal functions in agriculture. Promoting pesticide innovation by interdisciplinary integration has become the trend in pesticide research globally. Considering the increasingly important roles of green pesticides from traditional Chinese medicines and artificial intelligence in pest target prediction, this paper proposed an innovative green control strategy in line with the concepts of ecological sustainable development and food security protection. CiteSpace was used for visual analysis of the publications. The results showed that artificial intelligence had been extensively applied in the pesticide field in recent years. This paper explores the application and development of biopesticides for the first time, with focus on the plant-derived pesticides. The thought of traditional Chinese medicine compatibility can be employed to creat a new promosing field: pesticides from traditional Chinese medicine. Moreover, artificial intelligence can be employed to build the formulation system of pesticides from traditional Chinese medicines and the target prediction system of diseases and pests. This study provides new ideas for the future development and market application of biopesticides, aiming to provide more healthy and safe agricultural products for human beings, promote the innovation and development of green pesticides in China, and protect the sustainable development of the environment and ecosystem. This may be the research hotspot and competition point for the green development of the pesticide industry chain in the future.

Performance of a hermetic device and neem (Azadirachta indica) in storing wheat seed: Evidence from participatory household trials in central Bangladesh.

Smallholder farmers in Bangladesh often use low-density polyethylene (LDPE) bags contained within woven polypropylene bags to store wheat seed during the summer monsoon that precedes winter season planting. High humidity and temperature during this period can encourage increased seed moisture and pests, thereby lowering seed quality. Following a farm household survey conducted to inform trial design, eighty farmers were engaged in an action research process in which they participated in designing and conducting trials comparing traditional and alternative seed storage methods over 30 weeks. Factorial treatments included comparison of hermetic SuperGrainbags® (Premium RZ) against LDPE bags, both with and without the addition of dried neem tree leaves (Azadirachta indica). SuperGrainbags® were more effective in maintaining seed moisture at acceptable levels close to pre-storage conditions than LDPE bags. Both seed germination and seedling coleoptile length were significantly greater in hermetic than LDPE bags. Neem had no effect on seed moisture, germination, or coleoptile length. SuperGrainbags® were also more effective in abating seed damage during storage, although inclusion of neem within LDPE bags also had significant damage. Quantification of seed predating insects and diseases suggested that SuperGrainbags® also suppressed Coleopteran pests and blackspot, the latter indicative of Fusarium graminearum. Conversely, where farmers used LDPE bags, neem also had an additional though limited pest suppressive effect. Post-storage treatment scoring by farmers revealed a strong preference for SuperGrainbags® and no preference differences for or against neem. This study demonstrates a process by which farmers can be involved in the participatory co-design and testing of alternative wheat storage options, and stresses the need to develop SuperGrainbag® supply chains so hermetic storage can be made widely available.

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles.

With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 °C and 16 °C, and high rainfall decreases the spread of wheat powdery mildew.

Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks.

Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have achieved good results. METHODS: First, this papaer used the common Aprioro algorithm to find the association rules between weather factors and the occurrence of cotton pests. Then, in this paper, the problem of predicting the occurrence of pests and diseases is formulated as time series prediction, and an LSTM-based method was developed to solve the problem. RESULTS: The association analysis reveals that moderate temperature, humid air, low wind spreed and rain fall in autumn and winter are more likely to occur cotton pests and diseases. The discovery was then used to predict the occurrence of pests and diseases. Experimental results showed that LSTM performs well on the prediction of occurrence of pests and diseases in cotton fields, and yields the Area Under the Curve (AUC) of 0.97. CONCLUSION: Suitable temperature, humidity, low rainfall, low wind speed, suitable sunshine time and low evaporation are more likely to cause cotton pests and diseases. Based on these associations as well as historical weather and pest records, LSTM network is a good predictor for future pest and disease occurrences. Moreover, compared to the traditional machine learning models (i.e., SVM and Random Forest), the LSTM network performs the best.

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network.

Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.

Closing the global ozone yield gap: Quantification and cobenefits for multistress tolerance.

Increasing both crop productivity and the tolerance of crops to abiotic and biotic stresses is a major challenge for global food security in our rapidly changing climate. For the first time, we show how the spatial variation and severity of tropospheric ozone effects on yield compare with effects of other stresses on a global scale, and discuss mitigating actions against the negative effects of ozone. We show that the sensitivity to ozone declines in the order soybean > wheat > maize > rice, with genotypic variation in response being most pronounced for soybean and rice. Based on stomatal uptake, we estimate that ozone (mean of 2010-2012) reduces global yield annually by 12.4%, 7.1%, 4.4% and 6.1% for soybean, wheat, rice and maize, respectively (the "ozone yield gaps"), adding up to 227 Tg of lost yield. Our modelling shows that the highest ozone-induced production losses for soybean are in North and South America whilst for wheat they are in India and China, for rice in parts of India, Bangladesh, China and Indonesia, and for maize in China and the United States. Crucially, we also show that the same areas are often also at risk of high losses from pests and diseases, heat stress and to a lesser extent aridity and nutrient stress. In a solution-focussed analysis of these results, we provide a crop ideotype with tolerance of multiple stresses (including ozone) and describe how ozone effects could be included in crop breeding programmes. We also discuss altered crop management approaches that could be applied to reduce ozone impacts in the shorter term. Given the severity of ozone effects on staple food crops in areas of the world that are also challenged by other stresses, we recommend increased attention to the benefits that could be gained from addressing the ozone yield gap.

Payment for multiple forest benefits alters the effect of tree disease on optimal forest rotation length.

Forests deliver multiple benefits both to their owners and to wider society. However, a wave of forest pests and pathogens is threatening this worldwide. In this paper we examine the effect of disease on the optimal rotation length of a single-aged, single rotation forest when a payment for non-timber benefits, which is offered to private forest owners to partly internalise the social values of forest management, is included. Using a generalisable bioeconomic framework we show how this payment counteracts the negative economic effect of disease by increasing the optimal rotation length, and under some restrictive conditions, even makes it optimal to never harvest the forest. The analysis shows a range of complex interactions between factors including the rate of spread of infection and the impact of disease on the value of harvested timber and non-timber benefits. A key result is that the effect of disease on the optimal rotation length is dependent on whether the disease affects the timber benefit only compared to when it affects both timber and non-timber benefits. Our framework can be extended to incorporate multiple ecosystem services delivered by forests and details of how disease can affect their production, thus facilitating a wide range of applications.

Identification of cotton pest and disease based on CFNet- VoV-GCSP -LSKNet-YOLOv8s: a new era of precision agriculture.

Introduction: The study addresses challenges in detecting cotton leaf pests and diseases under natural conditions. Traditional methods face difficulties in this context, highlighting the need for improved identification techniques. Methods: The proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s. This model is an enhancement of YOLOv8s and includes several key modifications: (1) CFNet Module. Replaces all C2F modules in the backbone network to improve multi-scale object feature fusion. (2) VoV-GCSP Module. Replaces C2F modules in the YOLOv8s head, balancing model accuracy with reduced computational load. (3) LSKNet Attention Mechanism. Integrated into the small object layers of both the backbone and head to enhance detection of small objects. (4) XIoU Loss Function. Introduced to improve the model's convergence performance. Results: The proposed method achieves high performance metrics: Precision (P), 89.9%. Recall Rate (R), 90.7%. Mean Average Precision (mAP@0.5), 93.7%. The model has a memory footprint of 23.3MB and a detection time of 8.01ms. When compared with other models like YOLO v5s, YOLOX, YOLO v7, Faster R-CNN, YOLOv8n, YOLOv7-tiny, CenterNet, EfficientDet, and YOLOv8s, it shows an average accuracy improvement ranging from 1.2% to 21.8%. Discussion: The study demonstrates that the CFNet-VoV-GCSP-LSKNet-YOLOv8s model can effectively identify cotton pests and diseases in complex environments. This method provides a valuable technical resource for the identification and control of cotton pests and diseases, indicating significant improvements over existing methods.

Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching accuracy between anchors and ground truths. In addition, two residual units are added to the second residual block of the original YOLOv3 network to obtain more information about the location of the underlying small targets, and one ResNet unit is used in the feature pyramid network structure to replace two DBL(Conv+BN+LeakyReLU) structures to enhance the reuse of pest features. Experiment results show that the mAP and mRecall of our proposed method are improved by 6.3% and 4.61%, respectively, compared with the original YOLOv3. The proposed method outperforms other state-of-the-art methods (SSD, Faster-rcnn, and YOLOv4), indicating that the proposed method achieves the best detection performance, which can provide an effective model for the realization of intelligent monitoring of maize pests.

Origin, Evolution, Breeding, and Omics of Chayote, an Important Cucurbitaceae Vegetable Crop.

Chayote (Sechium edule), a member of the Cucurbitaceae family, is cultivated throughout tropical and subtropical regions of the world and utilized in pharmaceutical, cosmetic and food industries because it is an excellent source of minerals, dietary fibers, protein, vitamins, carotenoids, polysaccharides, phenolic and flavonoid compounds, and other nutrients. Chayote extracts process various medicinal properties, such as anti-cardiovascular, antidiabetic, antiobesity, antiulcer, and anticancer properties. With the rapid advancements of molecular biology and sequencing technology, studies on chayote have been carried out. Research advances, including molecular makers, breeding, genomic research, chemical composition, and pests and diseases, regarding chayote are reviewed in this paper. Future exploration and application trends are briefly described. This review provides a reference for basic and applied research on chayote, an important Cucurbitaceae vegetable crop.

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field.

The accurate classification of crop pests and diseases is essential for their prevention and control. However, datasets of pest and disease images collected in the field usually exhibit long-tailed distributions with heavy category imbalance, posing great challenges for a deep recognition and classification model. This paper proposes a novel convolutional rebalancing network to classify rice pests and diseases from image datasets collected in the field. To improve the classification performance, the proposed network includes a convolutional rebalancing module, an image augmentation module, and a feature fusion module. In the convolutional rebalancing module, instance-balanced sampling is used to extract features of the images in the rice pest and disease dataset, while reversed sampling is used to improve feature extraction of the categories with fewer images in the dataset. Building on the convolutional rebalancing module, we design an image augmentation module to augment the training data effectively. To further enhance the classification performance, a feature fusion module fuses the image features learned by the convolutional rebalancing module and ensures that the feature extraction of the imbalanced dataset is more comprehensive. Extensive experiments in the large-scale imbalanced dataset of rice pests and diseases (18,391 images), publicly available plant image datasets (Flavia, Swedish Leaf, and UCI Leaf) and pest image datasets (SMALL and IP102) verify the robustness of the proposed network, and the results demonstrate its superior performance over state-of-the-art methods, with an accuracy of 97.58% on rice pest and disease image dataset. We conclude that the proposed network can provide an important tool for the intelligent control of rice pests and diseases in the field.

Importance of Potato as a Crop and Practical Approaches to Potato Breeding.

Potatoes (Solanum tuberosum L. subsp. tuberosum and andigena) and seven other related species, which are cultivated today, have become the most important non-cereal crop in the world. It is grown on a significant scale in 130 countries, with a gross production value of 63.6 billion US dollars in 2016, with the yearly potato production of 368 million tons in 2018. Today potato is grown for food, animal feed, industrial uses, and seed tuber production, depending on the region, country development, and historical reasons. The food production is both for fresh ware markets and for processing into crisps, french fries, canned potatoes, flakes, etc. More than 10,000 potato varieties have been grown worldwide to date, many of which are still grown. Despite such a large number of varieties, there is still a need for new varieties. Classical breeding of new potato varieties in many programs around the world has changed little in decades and differs mainly in terms of scope and technologies used. Until the turn of the millennium, it was based primarily on empirical experience and selection of individual phenotypic traits. The great genetic diversity that exists in potato and its wild relatives is both an opportunity and a challenge to introduce traits that do not currently exist in the potato gene pool into modern potato varieties. Molecular marker technology development has reached the point where published markers for use in commercial breeding are available. Markers can be used during the whole selection process, with an even more important role of molecular breeding in pre-breeding programs and creation of the most appropriate parental lines.

Mitigation of climate change and environmental hazards in plants: Potential role of the beneficial metalloid silicon.

In the last decades, the concentration of atmospheric CO2 and the average temperature have been increasing, and this trend is expected to become more severe in the near future. Additionally, environmental stresses including drought, salinity, UV-radiation, heavy metals, and toxic elements exposure represent a threat for ecosystems and agriculture. Climate and environmental changes negatively affect plant growth, biomass and yield production, and also enhance plant susceptibility to pests and diseases. Silicon (Si), as a beneficial element for plants, is involved in plant tolerance and/or resistance to various abiotic and biotic stresses. The beneficial role of Si has been shown in various plant species and its accumulation relies on the root's uptake capacity. However, Si uptake in plants depends on many biogeochemical factors that may be substantially altered in the future, affecting its functional role in plant protection. At present, it is not clear whether Si accumulation in plants will be positively or negatively affected by changing climate and environmental conditions. In this review, we focused on Si interaction with the most important factors of global change and environmental hazards in plants, discussing the potential role of its application as an alleviation strategy for climate and environmental hazards based on current knowledge.

Photosynthesis Inhibiting Effects of Pesticides on Sweet Pepper Leaves.

Although a large number of pesticides of different compositions are regularly used in agriculture, the impact of pesticides on the physiology of field crops is not well understood. Pesticides can produce negative effects on crop physiology-especially on photosynthesis-leading to a potential decrease in both the growth and the yield of crops. To investigate these potential effects in greenhouse sweet peppers, the effect of 20 insecticides and 2 fungicides (each sprayed with a wetting agent) on the photosynthesis of sweet pepper leaves was analyzed. Among these pesticides, nine caused significant reductions in photosynthetic activity. The effects were observed in distinctive ways-either as a transitory drop of the photosynthetic-rate values, which was observed at two hours after the treatment and was found to have recovered after 24 h, or as a sustained reduction of these values, which remained substantial over a number of days. The results of this study suggest that the production of a crop may substantially benefit when the frequent use of pesticides can be substituted with alternative pest control methods (e.g., biological control). Our results advocate further investigation of the potential impact of pesticides, either alone or in combination, on the photosynthesis of crop plants.

Climate change and plant virus epidemiology.

Changes in global climate driven by anthropogenic activities, especially the burning of fossil fuels and deforestation, have been progressively increasing and are projected to intensify. Increasing concentrations of atmospheric carbon dioxide and temperature will have significant consequences for future food production, quality, distribution and security. The epidemiology of plant viruses will be altered in the future as a result of climate change. Elevated atmospheric carbon dioxide, increased temperature, changes to water availability and more frequent extreme weather events will have direct and indirect effects on plant viruses through changes in hosts and vectors. Predicted climatic changes will affect the distribution and survival of plant viruses and their vectors, which are expected to increase in many geographic regions. Furthermore, climate change can affect the virulence and pathogenicity of plant viruses, consequently increasing the frequency and scale of disease outbreaks. Thus, greater understanding of plant virus epidemiology is needed to better anticipate challenges ahead and to develop effective and robust control strategies that will aid in securing global food production for the future.