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As the greatest water user in the world, the agricultural sector is vulnerable to changes in climate and water resource availability. Understanding the impact of these changes on crop yield is critical in order to achieve and maintain global food security. We analyze output from an ensemble of Agricultural Model Intercomparison and Improvement Project models to project the probability of rice, soybean, maize, and wheat yield failures across global and national breadbaskets through mid-century. The probability of crop yield failures is projected to be as much as 4.5 times higher by 2030 and up to 25 times higher by 2050 across global breadbaskets. Crop failures are projected to be more likely when effects of CO 2 fertilization are ignored. We utilize the open-source Aqueduct Water Risk Atlas to create a Water Scarcity Index composed of ten hydrological variables. The index reveals high water scarcity across crop breadbaskets in India, China, and the United States. If the ability to irrigate breadbaskets was eliminated due to water scarcity, the likelihood of crop failures would increase. Shifts in breadbaskets may cross national borders as crop yields will increase in Canada and decrease in the US as a response to a changing climate. Our analysis highlights top producing agricultural regions that have historically provided the global food system with large quantities of one or more major crops, but will face challenges in continuing to do so due to climate change and growing water scarcity.

Background Tomato gray leaf spot is a worldwide disease, especially in warm and humid areas. The continuous expansion of greenhouse tomato cultivation area and the frequent introduction of foreign varieties in recent years have increased the severity of the epidemic hazards of this disease in some tomato planting bases annually. This disease is a newly developed one. Thus, farmers generally lack prevention and control experience and measures in production; the disease is often misdiagnosed or not prevented and controlled timely; this condition results in tomato production reduction or crop failure, which causes severe economic losses to farmers. Therefore, tomato gray leaf spot disease should be identified in the early stage, which will be important in avoiding or reducing the economic loss caused by the disease. The advent of the era of big data has facilitated the use of machine learning method in disease identification. Therefore, deep learning method is proposed to realise the early recognition of tomato gray leaf spot. Tomato growers need to develop the app of image detection mobile terminal of tomato gray leaf spot disease to realise real-time detection of this disease. Results This study proposes an early recognition method of tomato leaf spot based on MobileNetv2-YOLOv3 model to achieve a good balance between the accuracy and real-time detection of tomato gray leaf spot. This method improves the accuracy of the regression box of tomato gray leaf spot recognition by introducing the GIoU bounding box regression loss function. A MobileNetv2-YOLOv3 lightweight network model, which uses MobileNetv2 as the backbone network of the model, is proposed to facilitate the migration to the mobile terminal. The pre-training method combining mixup training and transfer learning is used to improve the generalisation ability of the model. The images captured under four different conditions are statistically analysed. The recognition effect of the models is evaluated by the F1 score and the AP value, and the experiment is compared with Faster-RCNN and SSD models. Experimental results show that the recognition effect of the proposed model is significantly improved. In the test dataset of images captured under the background of sufficient light without leaf shelter, the F1 score and AP value are 94.13% and 92.53%, and the average IOU value is 89.92%. In all the test sets, the F1 score and AP value are 93.24% and 91.32%, and the average IOU value is 86.98%. The object detection speed can reach 246 frames/s on GPU, the extrapolation speed for a single 416 × 416 picture is 16.9 ms, the detection speed on CPU can reach 22 frames/s, the extrapolation speed is 80.9 ms and the memory occupied by the model is 28 MB. Conclusions The proposed recognition method has the advantages of low memory consumption, high recognition accuracy and fast recognition speed. This method is a new solution for the early prediction of tomato leaf spot and a new idea for the intelligent diagnosis of tomato leaf spot.

Aim Rice ( Oryza sativa L.) being the most important crop for human population in Asia region, accounts for 23% of the world’s caloric intake. Due to the changing climatic conditions, the agricultural crops are experiencing vagaries of the weather more frequently leading to yield losses and even crop failure. The objective of this study was to find out a suitable consortium of bacterial inoculants which can make the crop resilient to drought stress. Methods Bacterial isolates from different habitats were characterized for salt tolerance and multiple plant growth promoting traits. Four separate treatments were formulated, with two treatments having individual bacterial strain as PGPR and the rest two having consortia of three bacterial isolates as PGPR. High yielding variety MTU1010 was selected for pot experiments and treated with individual as well as consortium of isolates. Drought was imposed for 10 days to different batch of the rice crop (variety MTU1010) at two stages of crop growth i.e., pre-flowering and flowering stages. Results Results indicated amelioration of drought stress with higher biomass accumulation, increased grain yield and reversal of stress indicators in plants inoculated with PGPR. The antioxidant enzyme activity of SOD, CAT, and GPOX declined by 24%, 20.5% and 20% in plants treated with bacterial inoculum as compared to un-inoculated control. Conclusions This study indicates that the plant beneficial microorganisms can be used to induce systematic tolerance to rice plants under drought stress and furthermore, application of consortium of PGPR has better probability to improve the coping capacity of the plants exposed to stress conditions.

Maize production in Zambia is characterized by significant yield gaps attributed to nutrient management and climate change threatens to widen these gaps unless agronomic management is optimized. Insights in the impacts of climate change on maize yields and the potential to mitigate negative impacts by crop management are currently lacking for Zambia. Using five Global Circulation models and the WOFOST crop model, we assessed climate change impacts on maize yields at a 0.5° × 0.5° spatial resolution for RCP 4.5 and RCP 8.5 scenarios. Impacts were assessed for the near future (2035-2066) and far future (2065-2096) in comparison with a reference period (1971-2001). The surface temperature and warm days (above 30 °C) are projected to increase strongly in the southern and western regions. Precipitation is expected to decline, except in the northern regions, whereas the number of wet days declines everywhere, shortening the growing season. The risk of crop failure in western and southern regions increases due to dry spells and heat stress, while crops in the northern regions will be threatened by flooding or waterlogging due to heavy precipitation. The simulated decline in the water-limited and water- and nutrient-limited maize yields varied from 15 to 20% in the near future and from 20 to 40% in the far future, mainly due to the expected temperature increases. Optimizing management by adjusting planting dates and maize variety selection can counteract these impacts by 6-29%. The existing gaps between water-limited and nutrient-limited maize yields are substantially larger than the expected yield decline due to climate change. Improved nutrient management is therefore crucial to boost maize production in Zambia.

The world’s staple food crops, and other food crops that optimize human nutrition, suffer from global virus disease pandemics and epidemics that greatly diminish their yields and/or produce quality. This situation is becoming increasingly serious because of the human population’s growing food requirements and increasing difficulties in managing virus diseases effectively arising from global warming. This review provides historical and recent information about virus disease pandemics and major epidemics that originated within different world regions, spread to other continents, and now have very wide distributions. Because they threaten food security, all are cause for considerable concern for humanity. The pandemic disease examples described are six (maize lethal necrosis, rice tungro, sweet potato virus, banana bunchy top, citrus tristeza, plum pox). The major epidemic disease examples described are seven (wheat yellow dwarf, wheat streak mosaic, potato tuber necrotic ringspot, faba bean necrotic yellows, pepino mosaic, tomato brown rugose fruit, and cucumber green mottle mosaic). Most examples involve long-distance virus dispersal, albeit inadvertent, by international trade in seed or planting material. With every example, the factors responsible for its development, geographical distribution and global importance are explained. Finally, an overall explanation is given of how to manage global virus disease pandemics and epidemics effectively.

Rice blast is a serious fungal disease of rice (Oryza sativa L.) that is threatening global food security. It has been extensively studied due to the importance of rice production and consumption, and because of its vast distribution and destructiveness across the world. Rice blast, caused by Pyricularia oryzae Cavara 1892 (A), can infect aboveground tissues of rice plants at any growth stage and cause total crop failure. The pathogen produces lesions on leaves (leaf blast), leaf collars (collar blast), culms, culm nodes, panicle neck nodes (neck rot), and panicles (panicle blast), which vary in color and shape depending on varietal resistance, environmental conditions, and age. Understanding how rice blast is affected by environmental conditions at the cellular and genetic level will provide critical insight into incidence of the disease in future climates for effective decision-making and management. Integrative strategies are required for successful control of rice blast, including chemical use, biocontrol, selection of advanced breeding lines and cultivars with resistance genes, investigating genetic diversity and virulence of the pathogen, forecasting and mapping distribution of the disease and pathogen races, and examining the role of wild rice and weeds in rice blast epidemics. These tactics should be integrated with agronomic practices including the removal of crop residues to decrease pathogen survival, crop and land rotations, avoiding broadcast planting and double cropping, water management, and removal of yield-limiting factors for rice production. Such an approach, where chemical use is based on crop injury and estimated yield and economic losses, is fundamental for the sustainable control of rice blast to improve rice production for global food security.

Virus disease pandemics and epidemics that occur in the world’s staple food crops pose a major threat to global food security, especially in developing countries with tropical or subtropical climates. Moreover, this threat is escalating rapidly due to increasing difficulties in controlling virus diseases as climate change accelerates and the need to feed the burgeoning global population escalates. One of the main causes of these pandemics and epidemics is the introduction to a new continent of food crops domesticated elsewhere, and their subsequent invasion by damaging virus diseases they never encountered before. This review focusses on providing historical and up-to-date information about pandemics and major epidemics initiated by spillover of indigenous viruses from infected alternative hosts into introduced crops. This spillover requires new encounters at the managed and natural vegetation interface. The principal virus disease pandemic examples described are two (cassava mosaic, cassava brown streak) that threaten food security in sub-Saharan Africa (SSA), and one (tomato yellow leaf curl) doing so globally. A further example describes a virus disease pandemic threatening a major plantation crop producing a vital food export for West Africa (cacao swollen shoot). Also described are two examples of major virus disease epidemics that threaten SSA’s food security (rice yellow mottle, groundnut rosette). In addition, brief accounts are provided of two major maize virus disease epidemics (maize streak in SSA, maize rough dwarf in Mediterranean and Middle Eastern regions), a major rice disease epidemic (rice hoja blanca in the Americas), and damaging tomato tospovirus and begomovirus disease epidemics of tomato that impair food security in different world regions. For each pandemic or major epidemic, the factors involved in driving its initial emergence, and its subsequent increase in importance and geographical distribution, are explained. Finally, clarification is provided over what needs to be done globally to achieve effective management of severe virus disease pandemics and epidemics initiated by spillover events.

Drought is a complicated natural hazard that has far-reaching social and environmental impacts. In Ethiopia's diverse agro ecological zones, drought remains a severe challenge and problem. Livestock rising is one of the agricultural sub-sectors that provide income and livelihood to around one-third of African inhabitants and accounts for 30-50 percent of agricultural GDP. Pastoralists on the Ethiopia-Kenya-Somalia border endured extreme suffering, including the loss of nearly 80% of their cattle and huge migration out of drought-stricken areas. Drought can cause severe economic hardship and stress for farmers and local economies, like; lost productivity, population reduction, and the trauma of witnessing livestock, crops, soil, and native vegetation damage. Between 1990 and 2000, and 2001-2002/03, drought-related animal death rates in the Somali region increased by 60% and 80% of the entire cattle population, respectively. Drought has the greatest immediate effects on farmers, including depletion of water resources, crop failure, and an increase in food prices, ill health, livestock output losses and death, and a decline in livestock prices in the Borana zone. Drought adaptation and mitigation measures depending on geography and livestock system may improve the study's trajectory in the future if further review is done.

Rice production failure is a major threat to food security and supply chain resilience across India. In this paper, we examine future rice production failure risks across India by integrating down-scaled climate projections with machine learning models that capture complex crop-climate interactions. First, we identify key drivers of historical crop failures and demonstrate the critical role of monthly weather variability. We then use our historically-trained ML models to project future production failure risk across India, linking spatial changes in failure risk with projected future weather distributions and exploratory scenarios of changes in irrigation access. Our findings indicate that district-level risks of rice production failures in India are projected to increase (26% average) under future climate change across all shared socioeconomic pathways in the near (2025–2054) and far (2055–2084) future. Our analysis demonstrates that expanding irrigation access could play a vital role in mitigating these risks, with substantial risk reductions observed in high-vulnerability regions. These insights provide actionable information for policymakers aiming to enhance agricultural resilience, identifying priority areas where adaptive measures, particularly irrigation improvements, can most effectively reduce vulnerability to climate-driven production risks.

Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms.