A global surveillance system for crop diseases

A Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2022

This scientometric study reviews the scientific literature and CABI distribution records published in 2022 to find evidence of major disease outbreaks and first reports of pathogens in new locations or on new hosts. This is the second time we have done this, and this study builds on our work documenting and analyzing reports from 2021. Pathogens with three or more articles identified in 2022 literature were Xylella fastidiosa, Bursaphelenchus xylophilus, Meloidogyne species complexes, 'Candidatus Liberibacter asiaticus', Raffaelea lauricola, Fusarium oxysporum formae specialis, and Puccinia graminis f. sp. tritici. Our review of CABI distribution records found 29 pathogens with confirmed first reports in 2022. Pathogens with four or more first reports were Meloidogyne species complexes, Pantoea ananatis, grapevine red globe virus, and Thekopsora minima. Analysis of the proportion of new distribution records from 2022 indicated that grapevine red globe virus, sweet potato chlorotic stunt virus, and 'Ca. Phytoplasma vitis' may have been actively spreading. As we saw last year, there was little overlap between the pathogens identified by reviewing scientific literature versus distribution records. We hypothesize that this lack of concordance is because of the unavoidable lag between first reports of the type reported in the CABI database of a pathogen in a new location and any subsequent major disease outbreaks being reported in the scientific literature, particularly because the latter depends on the journal policy on types of papers to be considered, whether the affected crop is major or minor, and whether the pathogen is of current scientific interest. Strikingly, too, there was also no overlap between species assessed to be actively spreading in this year's study and those identified last year. We hypothesize that this is because of inconsistencies in sampling coverage and effort over time and delays between the first arrival of a pathogen in a new location and its first report, particularly for certain classes of pathogens causing only minor or non-economically damaging symptoms, which may have been endemic for some time before being reported. In general, introduction of new pathogens and outbreaks of extant pathogens threaten food security and ecosystem services. Continued monitoring of these threats is essential to support phytosanitary measures intended to prevent pathogen introductions and management of threats within a country.

Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, whereas press stressors such as climate change leave more time for management adaptation but may ultimately cause broader challenges. Smart solutions for the effects of disasters can be deployed through digital agriculture and decision support systems supporting disaster preparedness and optimized humanitarian aid across scales. Here, we use the disaster plant pathology framework to synthesize the effects of disasters in plant pathology and outline solutions to maintain food security and plant health in catastrophic scenarios. We recommend actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Identification of two plastid transit peptides for construction of pollen-inactivation system in rice

Hybrid seed production technology (SPT) is achieved through the utilization of a recessive nuclear male-sterile mutant transformed with a transgenic cassette comprising three essential components: the wild-type gene to restore the fertility of the male-sterile mutant, an α-amylase gene to disrupt transgenic pollen grains, and red fluorescence protein gene DsRed to distinguish the transgenic seeds from the nontransgenic male sterile seeds. In rice, we establish the pollen disruption system by introducing an amyloplast targeting signal peptide (ASP) at the N-terminus of maize α-amylase protein ZM-AA1ΔSP (ZM-AA1 with the N-terminal signal peptide removed). The ASP facilitates the transport of ZM-AA1ΔSP protein into amyloplast where it degrades starch, resulting in disruption of the pollen fertility. To obtain such signal peptides for rice, we searched the rice proteins homologous to the defined wheat amyloplast proteins followed by protein-protein interaction network predictions and targeting signal peptides prediction. These analyses enabled the identification of four candidate ASPs in rice, which were designated as ASP1, ASP2, ASP3, and ASP4, respectively. ASP1 and ASP2, when linked with ZM-AA1ΔSP, exhibited the capability to disrupt transgenic pollen grains, whereas ASP3 and ASP4 did not produce this effect. Interestingly, the localization experiments showed that ASP3 and ASP4 were able to target the proteins into chloroplast. The ASP1 and ASP2 sequences provide valuable tools for genetic engineering of the rice male-sterile system, which will contribute to the hybrid rice breeding and production.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01471-y.

Infrared Thermography Monitoring of Durum and Common Wheat for Adaptability Assessing and Yield Performance Prediction

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p < 0.001 and p < 0.05), providing interesting information for their early prediction.

Genomic approaches to enhance adaptive plasticity to cope with soil constraints amidst climate change in wheat

Climate change is varying the availability of resources, soil physicochemical properties, and rainfall events, which collectively determines soil physical and chemical properties. Soil constraints-acidity (pH < 6), salinity (pH ≤ 8.5), sodicity, and dispersion (pH > 8.5)-are major causes of wheat yield loss in arid and semiarid cropping systems. To cope with changing environments, plants employ adaptive strategies such as phenotypic plasticity, a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies for constrained soils are complex, determined by key functional traits and genotype × environment × management interactions. The understanding of the molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies have identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms, and in silico gene expression profiles at various growth developmental stages. Our review focuses on favorable alleles for enhanced gene expression, quantitative trait loci, and epigenetic regulation of plant responses to soil constraints, including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles and functional characterization of variants, followed by gene validation using advanced genomic tools, and marker development for molecular breeding and genome editing. Moreover, the review highlights the progress of gene editing in wheat, multiplex gene editing, and novel alleles for smart control of gene expression. Application of these advanced genomic technologies to enhance plasticity traits along with soil management practices will be an effective tool to build yield, stability, and sustainability on constrained soils in the face of climate change.

Live tracking of a plant pathogen outbreak reveals rapid and successive, multidecade plasmid reduction

Quickly understanding the genomic changes that lead to pathogen emergence is necessary to launch mitigation efforts and reduce harm. In this study, we tracked in real time a 2022 bacterial plant disease outbreak in U.S. geraniums (Pelargonium × hortorum) caused by Xhp2022, a novel lineage of Xanthomonas hortorum. Genomes from 31 Xhp2022 isolates from seven states showed limited chromosomal variation and all contained a single plasmid (p93). Time tree and single nucleotide polymorphism whole-genome analysis estimated that Xhp2022 emerged within the last decade. The phylogenomic analysis determined that p93 resulted from the cointegration of three plasmids (p31, p45, and p66) found sporadically across isolates from previous outbreaks. Although p93 had a 49 kb nucleotide reduction, it retained putative fitness genes, which became predominant in the 2022 outbreak. Overall, we demonstrated, through rapid whole-genome sequencing and analysis, a recent, traceable event of genome reduction for niche adaptation typically observed over millennia in obligate and fastidious pathogens.IMPORTANCEThe geranium industry, valued at $4 million annually, faces an ongoing Xanthomonas hortorum pv. pelargonii (Xhp) pathogen outbreak. To track and describe the outbreak, we compared the genome structure across historical and globally distributed isolates. Our research revealed Xhp population has not had chromosome rearrangements since 1974 and has three distinct plasmids. In 2012, we found all three plasmids in individual Xhp isolates. However, in 2022, the three plasmids co-integrated into one plasmid named p93. p93 retained putative fitness genes but lost extraneous genomic material. Our findings show that the 2022 strain group of the bacterial plant pathogen Xanthomonas hortorum underwent a plasmid reduction. We also observed several Xanthomonas species from different years, hosts, and continents have similar plasmids to p93, possibly due to shared agricultural settings. We noticed parallels between genome efficiency and reduction that we see across millennia with obligate parasites with increased niche specificity.

IoT-based system of prevention and control for crop diseases and insect pests

Environmentally friendly technologies for the prevention and control of crop diseases and insect pests are important to reduce the use of chemical pesticides, improve the quality of agricultural products, protect the environment, and promote sustainable development of crop production. On the basis of Internet of Things (IoT) technology, we developed a prevention and control system for crop diseases and insect pests with two main components: a plant protection device (the hardware) and an information management system (the software). To be suitable for both facility- and field-based production scenarios, we incorporated two types of plant protection devices, utilizing ozone sterilization and light-trap technologies. The devices were equipped with various sensors to realize real-time collection and monitoring of data on the crop production environment. The information management system has an IoT-based architecture and includes a mobile device app to enable remote control of the plant protection devices for intelligent management of plant protection data. The system can achieve efficient management of large-scale equipment applications and multi-device collaborative work to prevent and control pests and diseases. The developed system has operated successfully for several years in China and has been applied to cucumber, tomato, rice, and other crops. We demonstrate the effectiveness and practicality of the system in a greenhouse facility and in the field.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Developing One Health surveillance systems

The health of humans, domestic and wild animals, plants, and the environment are inter-dependent. Global anthropogenic change is a key driver of disease emergence and spread and leads to biodiversity loss and ecosystem function degradation, which are themselves drivers of disease emergence. Pathogen spill-over events and subsequent disease outbreaks, including pandemics, in humans, animals and plants may arise when factors driving disease emergence and spread converge. One Health is an integrated approach that aims to sustainably balance and optimize human, animal and ecosystem health. Conventional disease surveillance has been siloed by sectors, with separate systems addressing the health of humans, domestic animals, cultivated plants, wildlife and the environment. One Health surveillance should include integrated surveillance for known and unknown pathogens, but combined with this more traditional disease-based surveillance, it also must include surveillance of drivers of disease emergence to improve prevention and mitigation of spill-over events. Here, we outline such an approach, including the characteristics and components required to overcome barriers and to optimize an integrated One Health surveillance system.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

Multi-locus genome-wide association studies reveal the genetic architecture of Fusarium head blight resistance in durum wheat

Durum wheat is more susceptible to Fusarium head blight (FHB) than other types or classes of wheat. The disease is one of the most devastating in wheat; it reduces yield and end-use quality and contaminates the grain with fungal mycotoxins such as deoxynivalenol (DON). A panel of 265 Canadian and European durum wheat cultivars, as well as breeding and experimental lines, were tested in artificially inoculated field environments (2019-2022, inclusive) and two greenhouse trials (2019 and 2020). The trials were assessed for FHB severity and incidence, visual rating index, Fusarium-damaged kernels, DON accumulation, anthesis or heading date, maturity date, and plant height. In addition, yellow pigment and protein content were analyzed for the 2020 field season. To capture loci underlying FHB resistance and related traits, GWAS was performed using single-locus and several multi-locus models, employing 13,504 SNPs. Thirty-one QTL significantly associated with one or more FHB-related traits were identified, of which nine were consistent across environments and associated with multiple FHB-related traits. Although many of the QTL were identified in regions previously reported to affect FHB, the QTL QFhb-3B.2, associated with FHB severity, incidence, and DON accumulation, appears to be novel. We developed KASP markers for six FHB-associated QTL that were consistently detected across multiple environments and validated them on the Global Durum Panel (GDP). Analysis of allelic diversity and the frequencies of these revealed that the lines in the GDP harbor between zero and six resistance alleles. This study provides a comprehensive assessment of the genetic basis of FHB resistance and DON accumulation in durum wheat. Accessions with multiple favorable alleles were identified and will be useful genetic resources to improve FHB resistance in durum breeding programs through marker-assisted recurrent selection and gene stacking.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Next-generation bulked segregant analysis for Breeding 4.0

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

What Can Be Learned by a Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2021?

A synoptic review of plant disease epidemics and outbreaks was made using two complementary approaches. The first approach involved reviewing scientific literature published in 2021, in which quantitative data related to new plant disease epidemics or outbreaks were obtained via surveys or similar methodologies. The second approach involved retrieving new records added in 2021 to the CABI Distribution Database, which contains over a million global geographic records of organisms from over 50,000 species. The literature review retrieved 186 articles, describing studies in 62 categories (pathogen species/species complexes) across more than 40 host species on six continents. Pathogen species with more than five articles were Bursaphelenchus xylophilus, 'Candidatus Liberibacter asiaticus', cassava mosaic viruses, citrus tristeza virus, Erwinia amylovora, Fusarium spp. complexes, F. oxysporum f. sp. cubense, Magnaporthe oryzae, maize lethal necrosis co-infecting viruses, Meloidogyne spp. complexes, Pseudomonas syringae pvs., Puccinia striiformis f. sp. tritici, Xylella fastidiosa, and Zymoseptoria tritici. Automated searches of the CABI Distribution Database identified 617 distribution records new in 2021 of 283 plant pathogens. A further manual review of these records confirmed 15 pathogens reported in new locations: apple hammerhead viroid, apple rubbery wood viruses, Aphelenchoides besseyi, Biscogniauxia mediterranea, 'Ca. Liberibacter asiaticus', citrus tristeza virus, Colletotrichum siamense, cucurbit chlorotic yellows virus, Erwinia rhapontici, Erysiphe corylacearum, F. oxysporum f. sp. cubense Tropical race 4, Globodera rostochiensis, Nothophoma quercina, potato spindle tuber viroid, and tomato brown rugose fruit virus. Of these, four pathogens had at least 25% of all records reported in 2021. We assessed two of these pathogens-tomato brown rugose fruit virus and cucurbit chlorotic yellows virus-to be actively emerging in/spreading to new locations. Although three important pathogens-'Ca. Liberibacter asiaticus', citrus tristeza virus, and F. oxysporum f. sp. cubense-were represented in the results of both our literature review and our interrogation of the CABI Distribution Database, in general, our dual approaches revealed distinct sets of plant disease outbreaks and new records, with little overlap. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Whole Genome Sequencing-Based Tracing of a 2022 Introduction and Outbreak of Xanthomonas hortorum pv. pelargonii

Globalization has made agricultural commodities more accessible, available, and affordable. However, their global movement increases the potential for invasion by pathogens and necessitates development and implementation of sensitive, rapid, and scalable surveillance methods. Here, we used 35 strains, isolated by multiple diagnostic laboratories, as a case study for using whole genome sequence data in a plant disease diagnostic setting. Twenty-seven of the strains were isolated in 2022 and identified as Xanthomonas hortorum pv. pelargonii. Eighteen of these strains originated from material sold by a plant breeding company that had notified clients following a release of infected geranium cuttings. Analyses of whole genome sequences revealed epidemiological links among the 27 strains from different growers that confirmed a common source of the outbreak and uncovered likely secondary spread events within facilities that housed plants originating from different plant breeding companies. Whole genome sequencing data were also analyzed to reveal how preparatory and analytical methods can impact conclusions on outbreaks of clonal pathogenic strains. The results demonstrate the potential power of using whole genome sequencing among a network of diagnostic labs and highlight how sharing such data can help shorten response times to mitigate outbreaks more expediently and precisely than standard methods.

Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis

The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties.

Double-stranded RNA (dsRNA) technology to control forest insect pests and fungal pathogens: challenges and opportunities

Climate change alters the seasonal synchronization between plants and respective pests plus pathogens. The geographical infiltration helps to shift their hosts, resulting in novel outbreaks that damage forests and ecology. Traditional management schemes are unable to control such outbreaks, therefore unconventional and competitive governance is needed to manage forest pests and pathogens. RNA interference (RNAi) mediated double-stranded RNA (dsRNA) treatment method can be implemented to protect forest trees. Exogenous dsRNA triggers the RNAi-mediated gene silencing of a vital gene, and suspends protein production, resulting in the death of targeted pathogens and pests. The dsRNA treatment method is successful for many crop insects and fungi, however, studies of dsRNA against forest pests and pathogens are depleting. Pesticides and fungicides based on dsRNA could be used to combat pathogens that caused outbreaks in different parts of the world. Although the dsRNA has proved its potential, the crucial dilemma and risks including species-specific gene selection, and dsRNA delivery methods cannot be overlooked. Here, we summarized the major fungi pathogens and insect pests that have caused outbreaks, their genomic information, and studies on dsRNA fungi-and pesticides. Current challenges and opportunities in dsRNA target decision, delivery using nanoparticles, direct applications, and a new method using mycorrhiza for forest tree protection are discussed. The importance of affordable next-generation sequencing to minimize the impact on non-target species is discussed. We suggest that collaborative research among forest genomics and pathology institutes could develop necessary dsRNA strategies to protect forest tree species.

Surveillance and invasive risk of the red imported fire ant, Solenopsis invicta Buren in China

BACKGROUND

The red imported fire ant, Solenopsis invicta Buren is one of the world's most successful and destructive invasive ant species. In mainland China, fast, monthly and annual pest reports on all pests have been established since 2010. The distribution of S. invicta resulting from climate change in China was predicted using MaxEnt modeling in combination with comprehensive surveillance data and 56 environmental factors.

RESULTS

The fast and monthly reports revealed that S. invicta had spread to new territories almost every year in this timeframe. The transportation of seedlings and deployment of turfgrass were the major artificial transmission pathways. Annual reports indicated that control efforts had effectively reduced its occurrence areas and degree of severity of infestations, and retrieved the economic loss caused by S. invicta. The MaxEnt model predicted that S. invicta would expand to 23 provinces in China as a result of climate change. Moisture variables were the key factors affecting the distribution of this pest.

CONCLUSION

Based on the theoretical reference framework of this research, China proposed the first-ever integrated tactics against a single pest, jointly involving nine ministries, which include clarifying responsibilities, cutting off transmission pathways, strengthening surveillance, declaring pest distributions and conducting preventive and control campaigns. Practical efforts and measures combating the devastation of S. invicta may shed light on its management and other invasive species worldwide. © 2022 Society of Chemical Industry.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Climate change and the potential distribution of the glassy-winged sharpshooter (Homalodisca vitripennis), an insect vector of Xylella fastidiosa

Biological invasions represent a major threat for biodiversity and agriculture. Despite efforts to restrict the spread of alien species, preventing their introduction remains the best strategy for an efficient control. In that context preparedness of phytosanitary authorities is very important and estimating the geographical range of alien species becomes a key information. The present study investigates the potential geographical range of the glassy-winged sharpshooter (Homalodisca vitripennis), a very efficient insect vector of Xylella fastidiosa, one of the most dangerous plant-pathogenic bacteria worldwide. We use species distribution modeling (SDM) to analyse the climate factors driving the insect distribution and we evaluate its potential distribution in its native range (USA) and in Europe according to current climate and different scenarios of climate change: 6 General Circulation Models (GCM), 4 shared socioeconomic pathways of gas emission and 4 time periods (2030, 2050, 2070, 2090). The first result is that the climate conditions of the European continent are suitable to the glassy-winged sharpshooter, in particular around the Mediterranean basin where X. fastidiosa is present. Projections according to future climate conditions indicate displacement of climatically suitable areas towards the north in both North America and Europe. Globally, suitable areas will decrease in North America and increase in Europe in the coming decades. SDM outputs vary according to the GCM considered and this variability indicated areas of uncertainty in the species potential range. Both potential distribution and its uncertainty associated to future climate projections are important information for improved preparedness of phytosanitary authorities.

Dual-branch collaborative learning network for crop disease identification

Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture's efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811.

Global dispersal and diversity of rust fungi in the context of plant health

Long-distance dispersal of plant pathogens at the continental scale may have strong implications on plant health, in particular when incursions result in spread of disease to new territories where the disease was previously absent or insignificant. These dispersions may be caused by airborne transmission of spores or accidental spread via human travel and trade. Recent surveillance efforts of cereal rust fungi have demonstrated that incursion of new strains with superior fitness into areas where the disease is already established may have similar implications on plant health. Since dispersal events are highly stochastic, irrespective of transmission mechanism, critical mitigation efforts include preparedness by coordinated pathogen surveillance activities, host crop diversification, and breeding for disease resistance with low vulnerability to sudden changes in the pathogen population.

Global predictions for the risk of establishment of Pierce's disease of grapevines

The vector-borne bacterium Xylella fastidiosa is responsible for Pierce's disease (PD), a lethal grapevine disease that originated in the Americas. The international plant trade is expanding the geographic range of this pathogen, posing a new threat to viticulture worldwide. To assess the potential incidence of PD, we have built a dynamic epidemiological model based on the response of 36 grapevine varieties to the pathogen in inoculation assays and on the vectors' distribution when this information is available. Key temperature-driven epidemiological processes, such as PD symptom development and recovery, are mechanistically modelled. Integrating into the model high-resolution spatiotemporal climatic data from 1981 onward and different infectivity (R₀) scenarios, we show how the main wine-producing areas thrive mostly in non-risk, transient, or epidemic-risk zones with potentially low growth rates in PD incidence. Epidemic-risk zones with moderate to high growth rates are currently marginal outside the US. However, a global expansion of epidemic-risk zones coupled with small increments in the disease growth rate is projected for 2050. Our study globally downscales the risk of PD establishment while highlighting the importance of considering climate variability, vector distribution, and an invasive criterion as factors to obtain better PD risk maps.

A nationwide pest risk analysis in the context of the ongoing Japanese beetle invasion in Continental Europe: The case of metropolitan France

The Japanese beetle, Popillia japonica, is native to Japan and became established in North America in the early twentieth century. The beetle was detected in Europe, first in Italy in 2014 and then in Switzerland in 2017. Metropolitan France is at the forefront of the Japanese beetle threat, due to its geographical proximity to the European populations established in the Piedmont, Lombardy and Ticino regions. An express pest risk analysis for metropolitan France was therefore conducted. The most likely pathways for entry include (i) natural dispersion, (ii) trades of plant products with adherent soil and (iii) hitchhiking behaviour, leading to a high probability of entry. The spread rate of P. japonica was also evaluated as high, resulting from natural spread as well as human activities. Given the absence of significant limiting factors, the potential impacts of P. japonica in France will likely be as important as in its current geographic distribution. Although several sources of uncertainty were highlighted throughout the evaluation, none of them has significant impact on the conclusions of the present express pest risk analysis. Measures to prevent entry, establishment and spread of P. japonica are recommended and include surveillance with pheromone traps and control via integrated pest management strategies. However, most efforts should be concentrated on eradication measures while P. japonica is still in the early stages of invasion.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Addressing biohazards to food security in primary production

This review addresses ways to prepare for and to mitigate effects of biohazards on primary production of crops and livestock. These biohazards can be natural or intentional introductions of pathogens, and they can cause major economic damage to farmers, the agricultural industry, society, and international trade. Agroterrorism is the intentional introduction of animal or plant pathogens into agricultural production systems with the intention to cause socioeconomic harm and generate public fear. Although few acts of agroterrorism are reported, the threat of agroterrorism in Europe is real. New concerns about threats arise from the rapid advancements in biotechnology and emerging technologies. FORSA, an analytical framework for risk and vulnerability analysis, was used to review how to prepare for and mitigate the possible effects of natural or intentional biohazards in agricultural production. Analyzing the effects of a biohazard event involves multiple scientific disciplines. A comprehensive analysis of biohazards therefore requires a systems approach. The preparedness and ability to manage events are strengthened by bolstered farm biosecurity, increased monitoring and laboratory capacity, improved inter-agency communication and resource allocation. The focus of this review is on Europe, but the insights gained have worldwide applications. The analytical framework used here is compared to other frameworks. With climate change, Covid-19 and the war in Ukraine, the supply chains are challenged, and we foresee increasing food prices associated with social tensions. Our food supply chain becomes more fragile with more unknowns, thereby increasing the needs for risk and vulnerability analyses, of which FORSA is one example.

Sustainable management of transboundary pests requires holistic and inclusive solutions

Globalization and changing climates are aggravating the occurrence and impacts of transboundary pests, and driving the emergence of new threats. Most of the low- and middle-income countries in Africa, Asia and Latin America are not fully prepared in terms of surveillance, diagnostics, and deployment of plant health solutions due to several factors: adequate investment is lacking; knowledge is inadequate; and connections from the local to global, and global to local are insufficient. Effectively countering the current and emerging threats to plant health requires a holistic approach that includes: 1) globally coordinated diagnostic and surveillance systems; 2) epidemiological modelling, risk assessment, forecasting and preparedness for proactive management and containment; and 3) implementation of context-sensitive, eco-friendly, gender-responsive and socially inclusive integrated disease and pest management approaches to reduce the impacts of devastating transboundary pests and diseases. Despite several success stories where major pests and diseases have been brought to control through integrated approaches, further multi-institutional and multi-disciplinary efforts are necessary. Plant health management requires stronger interface between the biophysical and social sciences, and empowerment of local communities. These reflections derive from the proceedings of a webinar on “Transboundary Disease and Pest Management,” organized by CGIAR (Consultative Group on International Agricultural Research) on March 3, 2021, in recognition of the United Nations designated International Year of Plant Health.

Modelling cassava production and pest management under biotic and abiotic constraints

We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

Optimal strategies to protect a sub-population at risk due to an established epidemic

Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a ‘high-value’ sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding ‘buffer region’ can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge.

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.

Pangenomic Analysis of Dickeya dianthicola Strains Related to the Outbreak of Blackleg and Soft Rot of Potato in the United States

Dickeya dianthicola has caused an outbreak of blackleg and soft rot of potato in the eastern half of the United States since 2015. To investigate genetic diversity of the pathogen, a comparative analysis was conducted on genomes of D. dianthicola strains. Whole genomes of 16 strains from the United States outbreak were assembled and compared with 16 previously sequenced genomes of D. dianthicola isolated from potato or carnation. Among the 32 strains, eight distinct clades were distinguished based on phylogenomic analysis. The outbreak strains were grouped into three clades, with the majority of the strains in clade I. Clade I strains were unique and homogeneous, suggesting a recent incursion of this strain into potato production from alternative hosts or environmental sources. The pangenome of the 32 strains contained 6,693 genes, 3,377 of which were core genes. By screening primary protein subunits associated with virulence from all U.S. strains, we found that many virulence-related gene clusters, such as plant cell wall degrading enzyme genes, flagellar and chemotaxis related genes, two-component regulatory genes, and type I/II/III secretion system genes, were highly conserved but that type IV and type VI secretion system genes varied. The clade I strains encoded two clusters of type IV secretion systems, whereas the clade II and III strains encoded only one cluster. Clade I and II strains encoded one more VgrG/PAAR spike protein than did clade III. Thus, we predicted that the presence of additional virulence-related genes may have enabled the unique clade I strain to become predominant in the U.S. outbreak.

Spatiotemporal Survey of Multiple Rice Diseases in Irrigated Areas Compared to Rainfed Lowlands in the Western Burkina Faso

Multiple constraints affect rice yields in West Africa. Among these constraints are viral, bacterial, and fungal pathogens. We aimed to describe the spatiotemporal patterns of occurrence and incidence of multiple rice diseases in farmers' fields in contrasting rice growing systems in the western Burkina Faso. For this purpose, we selected a set of three pairs of sites, each comprising an irrigated area and a neighboring rainfed lowland, and studied them over four consecutive years. We first performed interviews with the rice farmers to better characterize the management practices at the different sites. This study revealed that the transplanting of rice and the possibility of growing rice twice a year are restricted to irrigated areas, while other practices, such as the use of registered rice cultivars, fertilization, and pesticides, are not specific but differ between the two rice growing systems. Then, we performed symptom observations at these study sites to monitor the following four diseases: yellow mottle disease, Bacterial Leaf Streak (BLS), rice leaf blast, and brown spot. The infection rates were found to be higher in irrigated areas than in rainfed lowlands, both when analyzing all observed symptoms together (any of the four diseases) and when specifically considering each of the two diseases: BLS and rice leaf blast. Brown spot was particularly prevalent in all six study sites, while yellow mottle disease was particularly structured geographically. Various diseases were frequently found together in the same field (co-occurrence) or even on the same plant (coinfection), especially in irrigated areas.

Interactions between Brassica Biofumigants and Soil Microbiota: Causes and Impacts

Biofumigation is used to control soil-borne plant diseases, and it has paramount importance to reduce the cost of chemical fumigants. Information about the field control efficacies and impacts of Brassica-based biofumigation (BBF) on soil bacterial and fungal microbiota is scattered in the literature. Therefore, this review summarizes and discusses the nature and the underlying causes of soil bacterial and fungal community dynamics in response to BBF. In addition, the major factors influencing the interaction between a biofumigant and soil microbiota are discussed. The pros and cons of BBF to soil microbiota and the subsequent impacts on sustainable farming practices are also highlighted.

Drought imprints on crops can reduce yield loss: Nature's insights for food security

The Midwestern “Corn‐Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid‐ to late‐summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid‐ or late‐season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field‐grown crops and this response will protect crops against growth and yield losses from late‐season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high‐resolution daily weather data, satellite‐derived phenological metrics, field yield data, and ecosystem‐scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre‐primed crop species may provide improved resilience to future climate change.

Genomic Approaches to Plant-Pathogen Epidemiology and Diagnostics

Diseases have a significant cost to agriculture. Findings from analyses of whole-genome sequences show great promise for informing strategies to mitigate risks from diseases caused by phytopathogens. Genomic approaches can be used to dramatically shorten response times to outbreaks and inform disease management in novel ways. However, the use of these approaches requires expertise in working with big, complex data sets and an understanding of their pitfalls and limitations to infer well-supported conclusions. We suggest using an evolutionary framework to guide the use of genomic approaches in epidemiology and diagnostics of plant pathogens. We also describe steps that are necessary for realizing these as standard approaches in disease surveillance.

Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers' knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.

HTS-Based Diagnostics of Sugarcane Viruses: Seasonal Variation and Its Implications for Accurate Detection

Rapid global germplasm trade has increased concern about the spread of plant pathogens and pests across borders that could become established, affecting agriculture and environment systems. Viral pathogens are of particular concern due to their difficulty to control once established. A comprehensive diagnostic platform that accurately detects both known and unknown virus species, as well as unreported variants, is playing a pivotal role across plant germplasm quarantine programs. Here we propose the addition of high-throughput sequencing (HTS) from total RNA to the routine quarantine diagnostic workflow of sugarcane viruses. We evaluated the impact of sequencing depth needed for the HTS-based identification of seven regulated sugarcane RNA/DNA viruses across two different growing seasons (spring and fall). Our HTS analysis revealed that viral normalized read counts (RPKM) was up to 23-times higher in spring than in the fall season for six out of the seven viruses. Random read subsampling analyses suggested that the minimum number of reads required for reliable detection of RNA viruses was 0.5 million, with a viral genome coverage of at least 92%. Using an HTS-based total RNA metagenomics approach, we identified all targeted viruses independent of the time of the year, highlighting that higher sequencing depth is needed for the identification of DNA viruses.

Known and Unknown Transboundary Infectious Diseases as Hybrid Threats

The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.

Creation and judicious application of a wheat resistance gene atlas

Disease-resistance (R) gene cloning in wheat (Triticum aestivum) has been accelerated by the recent surge of genomic resources, facilitated by advances in sequencing technologies and bioinformatics. However, with the challenges of population growth and climate change, it is vital not only to clone and functionally characterize a few handfuls of R genes, but also to do so at a scale that would facilitate the breeding and deployment of crops that can recognize the wide range of pathogen effectors that threaten agroecosystems. Pathogen populations are continually changing, and breeders must have tools and resources available to rapidly respond to those changes if we are to safeguard our daily bread. To meet this challenge, we propose the creation of a wheat R-gene atlas by an international community of researchers and breeders. The atlas would consist of an online directory from which sources of resistance could be identified and deployed to achieve more durable resistance to the major wheat pathogens, such as wheat rusts, blotch diseases, powdery mildew, and wheat blast. We present a costed proposal detailing how the interacting molecular components governing disease resistance could be captured from both the host and the pathogen through biparental mapping, mutational genomics, and whole-genome association genetics. We explore options for the configuration and genotyping of diversity panels of hexaploid and tetraploid wheat, as well as their wild relatives and major pathogens, and discuss how the atlas could inform a dynamic, durable approach to R-gene deployment. Set against the current magnitude of wheat yield losses worldwide, recently estimated at 21%, this endeavor presents one route for bringing R genes from the lab to the field at a considerable speed and quantity.

A meta-analysis of projected global food demand and population at risk of hunger for the period 2010-2050

Quantified global scenarios and projections are used to assess long-term future global food security under a range of socio-economic and climate change scenarios. Here, we conducted a systematic literature review and meta-analysis to assess the range of future global food security projections to 2050. We reviewed 57 global food security projection and quantitative scenario studies that have been published in the past two decades and discussed the methods, underlying drivers, indicators and projections. Across five representative scenarios that span divergent but plausible socio-economic futures, the total global food demand is expected to increase by 35% to 56% between 2010 and 2050, while population at risk of hunger is expected to change by -91% to +8% over the same period. If climate change is taken into account, the ranges change slightly (+30% to +62% for total food demand and -91% to +30% for population at risk of hunger) but with no statistical differences overall. The results of our review can be used to benchmark new global food security projections and quantitative scenario studies and inform policy analysis and the public debate on the future of food.

The persistent threat of emerging plant disease pandemics to global food security

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Citizen science and niche modeling to track and forecast the expansion of the brown marmorated stinkbug Halyomorpha halys (Stål, 1855)

Halyomorpha halys (Stål, 1855), the Brown Marmorated StinkBug (BMSB) is a highly successful invasive species native to eastern Asia that managed to spread into North America and Europe in recent decades. We set up a citizen science survey to monitor BMSB expansion in France in 2012 and analyzed the data it yielded between 2012 and 2019 to examine the local expansion of the insect. These data were gathered with occurrences form various sources (GBIF, literature) to calibrate a species niche model and assess potential current BMSB range. We evaluated the potential changes to the BMSB range due to climate change by projecting the model according to 6 global circulation models (GCM) and the shared socio-economic pathways SSP245 in two time periods 2021-2040 and 2041-2060. Citizen science allowed to track BMSB expansion in France and provided information about its phenology and its habitat preferences. The model highlighted the potential for further range expansion in Europe and illustrated the impact of climate change. These results could help managing the current BMSB invasion and the framework of this survey could contribute to a better preparedness of phytosanitary authorities either for the BMSB or other invasive pests.

Genome-Wide Wheat 55K SNP-Based Mapping of Stripe Rust Resistance Loci in Wheat Cultivar Shaannong 33 and Their Alleles Frequencies in Current Chinese Wheat Cultivars and Breeding Lines

Wheat cultivar Shaannong 33 (SN33) has remained highly resistant to stripe rust in the field since its release in 2009. To unravel the genetic architecture of stripe rust resistance, seedlings of 161 recombinant inbred lines (RILs) from the cross Avocet S × SN33 were evaluated with two isolates (PST-Lab.1 and PST-Lab.2) of the stripe rust pathogen (Puccinia striiformis f. sp. tritici) in the greenhouse, and the RILs were evaluated in naturally or artificially inoculated field sites during two cropping seasons. The RILs and parents were genotyped with the wheat 55K single-nucleotide polymorphism array. Three genomic regions conferring seedling resistance were mapped on chromosomes 1DS, 2AS, and 3DS, and four consistent quantitative trait loci (QTL) for adult-plant resistance (APR) were detected on 1BL, 2AS, 3DL, and 6BS. The 2AS locus conferring all-stage resistance was identified as the resistant gene Yr17 located on 2NS translocation. The QTL identified on 1BL and 6BS likely correspond to Yr29 and Yr78, respectively. An APR QTL on 3DL explaining 5.8 to 12.2% of the phenotypic variation is likely to be new. Molecular marker detection assays with the 2NS segment (Yr17), Yr29, Yr78, and QYrsn.nwafu-3DL on a panel of 420 current Chinese wheat cultivars and breeding lines indicated that these genes were present in 11.4, 7.6, 14.8, and 7.4% of entries, respectively. The interactions among these genes and QTL were additive, suggesting their potential value in enhancing stripe rust resistance breeding materials as observed in the resistant parent. In addition, we also identified two leaf necrosis genes, Ne1 and Ne2; however, the F₁ plants from cross Avocet S × SN33 survived, indicating that SN33 probably has another allele of Ne1 which allows seed to be harvested.

A novel strategy for creating a new system of third-generation hybrid rice technology using a cytoplasmic sterility gene and a genic male-sterile gene

Heterosis utilization is the most effective way to improve rice yields. The cytoplasmic male-sterility (CMS) and photoperiod/thermosensitive genic male-sterility (PTGMS) systems have been widely used in rice production. However, the rate of resource utilization for the CMS system hybrid rice is low, and the hybrid seed production for the PTGMS system is affected by the environment. The technical limitations of these two breeding methods restrict the rapid development of hybrid rice. The advantages of the genic male-sterility (GMS) rice, such as stable sterility and free combination, can fill the gaps of the first two generations of hybrid rice technology. At present, the third-generation hybrid rice breeding technology is being used to realize the application of GMS materials in hybrid rice. This study aimed to use an artificial CMS gene as a pollen killer to create a smart sterile line for hybrid rice production. The clustered regularly interspaced short palindromic repeats/CRISPR-associated 9 (CRISPR/Cas9) technology was used to successfully obtain a CYP703A3-deficient male-sterile mutant containing no genetically modified component in the genetic background of indica 9311. Through young ear callus transformation, this mutant was transformed with three sets of element-linked expression vectors, including pollen fertility restoration gene CYP703A3, pollen-lethality gene orfH79 and selection marker gene DsRed2. The maintainer 9311-3B with stable inheritance was obtained, which could realize the batch breeding of GMS materials. Further, the sterile line 9311-3A and restorer lines were used for hybridization, and a batch of superior combinations of hybrid rice was obtained.

Central American and Caribbean population history of the Pseudocercospora fijiensis fungus responsible for the latest worldwide pandemics on banana

Among the emerging fungal diseases threatening food security, the Pseudocercospora fijiensis fungus causing black leaf streak disease of banana is one of the most marked examples of a recent worldwide pandemic on a major crop. We assessed how this pathogen spread throughout the latest invaded region, i.e. Central America and the Caribbean. We retraced its population history combining detailed monitoring information on disease outbreaks and population genetic analyses based on large-scale sampling of P. fijiensis isolates from 121 locations throughout the region. The results first suggested that sexual reproduction was not lost during the P. fijiensis expansion, even in the insular Caribbean context, and a high level of genotypic diversity was maintained in all the populations studied. The population genetic structure of P. fijiensis and historical data showed that two disease waves swept northward and southward in all banana-producing countries in the study area from an initial entry point in Honduras, probably mainly through gradual stepwise spore dispersal. Serial founder events accompanying the northern and southern waves led to the establishment of two different genetic groups. A different population structure was detected on the latest invaded islands (Martinique, Dominica and Guadeloupe), revealing multiple introductions and admixture events that may have been partly due to human activities. The results of this study highlight the need to step up surveillance to limit the spread of other known emerging diseases of banana spread mainly by humans, but also to curb gene flow between established pathogen populations which could increase their evolutionary potential.