Crop health and its global impacts on the components of food security

Multi-environment GWAS uncovers markers associated to biotic stress response and genotype-by-environment interactions in stone fruit trees

While breeding for improved immunity is essential to achieve sustainable fruit production, it also requires to account for genotype-by-environment interactions (G × E), which still represent a major challenge. To tackle this issue, we conducted a comprehensive study to identify genetic markers with main and environment-specific effects on pest and disease response in peach (Prunus persica) and apricot (Prunus armeniaca). Leveraging multienvironment trials (MET), we assessed the genetic architecture of resistance and tolerance to seven major pests and diseases through visual scoring of symptoms in naturally infected core collections, repeated within and between years and sites. We applied a series of genome-wide association models (GWAS) to both maximum of symptom severity and kinetic disease progression. These analyses lead to the identification of environment-shared quantitative trait loci (QTLs), environment-specific QTLs, and interactive QTLs with antagonist or differential effects across environments. We mapped 60 high-confidence QTLs encompassing a total of 87 candidate genes involved in both basal and host-specific responses, mostly consisting of the Leucine-Rich Repeat Containing Receptors (LRR-CRs) gene family. The most promising disease resistance candidate genes were found for peach leaf curl on LG4 and for apricot and peach rust on LG2 and LG4. These findings underscore the critical role of G × E in shaping the phenotypic response to biotic pressure, especially for blossom blight. Last, models including dominance effects revealed 123 specific QTLs, emphasizing the significance of non-additive genetic effects, therefore warranting further investigation. These insights will support the development of marker-assisted selection to improve the immunity of Prunus varieties in diverse environmental conditions.

Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios

Introduction

Climate change forms one of the most dangerous problems that disturb the earth today. It not only devastates the environment but also affects the biodiversity of living organisms, including fungi. Macrophomina phaseolina (Tassi) Goid. is one of the most pervasive and destructive soil-borne fungus that threatens food security, so predicting its current and future distribution will aid in following its emergence in new regions and taking precautionary measures to control it.

Methods

Throughout this work, there are about 324 records of M. phaseolina were used to model its global prevalence using 19 environmental covariates under several climate change scenarios for analysis. Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.

Results

Based on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. Two representative concentration pathways (RCPs) 2.6 and 8.5 of global climate model (GCM) code MG, were used to forecast the global spreading of the fungus in 2050 and 2070. The area under curve (AUC) and true skill statistics (TSS) were assigned to evaluate the resulted models with values equal to 0.902 ± 0.009 and 0.8, respectively. These values indicated a satisfactory significant correlation between the models and the ecology of the fungus. Two-dimensional niche analysis illustrated that the fungus could adapt to a wide range of temperatures (9 °C to 28 °C), and its annual rainfall ranges from 0 mm to 2000 mm. In the future, Africa will become the low habitat suitability for the fungus while Europe will become a good place for its distribution.

Discussion

The MaxEnt model is potentially useful for predicting the future distribution of M. phaseolina under changing climate, but the results need further intensive evaluation including more ecological parameters other than bioclimatological data.

Eco-smart biocontrol strategies utilizing potent microbes for sustainable management of phytopathogenic diseases

Plants have an impact on the economy because they are used in the food and medical industries. Plants are a source of macro- and micronutrients for the health of humans and animals; however, the rise in microbial diseases has put plant health and yield at risk. Because there are insufficient controls, microbial infections annually impact approximately 25 % of the world's plant crops. Alternative strategies, such as biocontrol, are required to fight these illnesses. This review discusses the potential uses of recently discovered microorganisms because they are safe, effective, and unlikely to cause drug resistance. They have no negative effects on soil microbiology or the environment because they are environmentally benign. Biological control enhances indigenous microbiomes by reducing bacterial wilt, brown blotch, fire blight, and crown gall. More research is required to make these biocontrol agents more stable, effective, and less toxic before they can be used in commercial settings.

Regional Variability of Chestnut (Castanea sativa) Tolerance Toward Blight Disease

Since its introduction into Europe in the first half of the 20th century, Cryphonectria parasitica has been gradually spreading across the natural range of the sweet chestnut (Castanea sativa Mill.), infecting the trees and causing lethal bark cankers. Serendipitously, a hyperparasitic Cryphonectria hypovirus 1 (CHV1), which attenuates C. parasitica virulence in combination with more tolerant European chestnut species, was able to ward off the worst effect of the disease. In North America, unfortunately, the native Castanea dentata is now functionally extinct since it occurs only as root sprouts in eastern deciduous forests where it was once dominant. In our work, we investigated changes in C. parasitica populations over time and the regional variability in chestnut populations' tolerance toward the blight disease. While vegetative compatibility (vc) type diversity and prevalence of hypovirulence remained similar as in previous studies, in the Buje population, unlike in previous studies, we were unable to find any hypovirulent fungal strains. The most common vegetative compatibility types (vc types) were EU-1, EU-2 and EU-12. However, several rare EU-types were found, including one previously unreported: EU-46. By inoculating several C. parasitica strains on tree stems from several chestnut populations, we observed that the induced lesion size was affected by the type of inoculum (CHV1-free or CHV1-infected), genotype-related individual chestnut stem and chestnut stem population of origin-related variability. The largest lesions were induced by CHV1-free fungal isolate DOB-G: 20.13 cm2 (95% C.I. 18.10-22.15) and the smallest by CHV1-infected L14/EP713: 2.49 cm2 (95% C.I. 1.59-3.39). Surprisingly, the size of the lesions induced by other CHV1-infected strains fell somewhere in between these extremes. The size of induced lesions was dependent on the population of origin as well and ranged from 11.60 cm2 (95% C.I. 9.87-13.33) for stems from the Moslavačka gora population to 17.75 cm2 (95% C.I. 15.63-19.87) for stems from Ozalj.

Sustainability in Global Agri-Food Supply Chains: Insights from a Comprehensive Literature Review and the ABCDE Framework

The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 relevant articles from the Web of Science core database spanning January 2009 to July 2023. Initially, bibliometric analysis and knowledge mapping reveal the annual progression of research on the sustainability of global agricultural produce supply chains, the collaborative networks among research institutions and authors, and the geographic distribution of research activities worldwide, successfully pinpointing the current research focal points. Subsequently, the ABCDE framework, constructed from the quantitative findings, helps us identify and comprehend the antecedents, barriers and challenges, impacts, and driving forces affecting the sustainability of these supply chains. The study identifies globalization and technological advancement as the primary forces shaping the sustainability of agricultural produce supply chains, despite them also posing challenges such as resource constraints and environmental pressures. Moreover, the application of innovative technologies, the optimization of organizational models, and active stakeholder engagement are key to propelling supply chains toward more sustainable development, exerting a profound impact on society, the environment, and the economy. In conclusion, this study suggests future research directions. The integrated methodology presented offers new perspectives and deep insights into the complexities of sustainable global agricultural produce supply chains, demonstrating its potential to foster knowledge innovation and practical applications, providing valuable insights for academic research and policy formulation in this domain.

Metabonomic analysis to identify exometabolome changes underlying antifungal and growth promotion mechanisms of endophytic Actinobacterium Streptomyces albidoflavus for sustainable agriculture practice

In recent years, there has been an increasing focus on microbial ecology and its possible impact on agricultural production, owing to its eco-friendly nature and sustainable use. The current study employs metabolomics technologies and bioinformatics approaches to identify changes in the exometabolome of Streptomyces albidoflavus B24. This research aims to shed light on the mechanisms and metabolites responsible for the antifungal and growth promotion strategies, with potential applications in sustainable agriculture. Metabolomic analysis was conducted using Q Exactive UPLC-MS/MS. Our findings indicate that a total of 3,840 metabolites were identified, with 137 metabolites exhibiting significant differences divided into 61 up and 75 downregulated metabolites based on VIP >1, |FC| >1, and p < 0.01. The interaction of S. albidoflavus B24 monoculture with the co-culture demonstrated a stronger correlation coefficient. The Principal Component Analysis (PCA) demonstrates that PCA1 accounted for 23.36%, while PCA2 accounted for 20.28% distinction. OPLS-DA score plots indicate significant separation among different groups representing (t1) 24% as the predicted component (to1) depicts 14% as the orthogonal component. According to the findings of this comprehensive study, crude extracts from S. albidoflavus demonstrated varying abilities to impede phytopathogen growth and enhance root and shoot length in tested plants. Through untargeted metabolomics, we discovered numerous potential molecules with antagonistic activity against fungal phytopathogens among the top 10 significant metabolites with the highest absolute log2FC values. These include Tetrangulol, 4-Hydroxybenzaldehyde, and Cyclohexane. Additionally, we identified plant growth-regulating metabolites such as N-Succinyl-L-glutamate, Nicotinic acid, L-Aspartate, and Indole-3-acetamide. The KEGG pathway analysis has highlighted these compounds as potential sources of antimicrobial properties. The inhibitory effect of S. albidoflavus crude extracts on pathogen growth is primarily attributed to the presence of specific gene clusters responsible for producing cyclic peptides such as ansamycins, porphyrin, alkaloid derivatives, and neomycin. Overall, it is apparent that crude extracts from S. albidoflavus exhibited varying abilities to inhibit the growth of three phytopathogens and enhancement in both root and shoot length of tested plants. This research enhances our understanding of how secondary metabolites contribute to growth promotion and biocontrol, supporting ecosystem sustainability and resilience while boosting productivity in sustainable agriculture.

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

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

Harnessing the potential of mutation breeding, CRISPR genome editing, and beyond for sustainable agriculture

By 2050, the global population is projected to exceed 9.5 billion, posing a formidable challenge to ensure food security worldwide. To address this pressing issue, mutation breeding in horticultural crops, utilizing physical or chemical methods, has emerged as a promising biotechnological strategy. However, the efficacy of these mutagens can be influenced by various factors, including biological and environmental variables, as well as targeted plant materials. This review highlights the global challenges related to food security and explores the potential of mutation breeding as an indispensable biotechnological tool in overcoming food insecurity. This review also covers the emergence of CRISPR-Cas9, a breakthrough technology offering precise genome editing for the development of high-yield, stress-tolerant crops. Together, mutation breeding and CRISPR can potentially address future food demands. This review focuses into these biotechnological advancements, emphasizing their combined potential to fortify global food security in the face of a booming population.

Revolutionizing nematode management to achieve global food security goals - An overview

Nematodes are soil-dwelling organisms that inflict substantial damage to crops, resulting in significant declines in agricultural productivity. Consequently, they are recognized as one of the primary contributors to global crop damage, with profound implications for food security. Nematology research assumes a pivotal role in tackling this issue and safeguarding food security. The pursuit of nematology research focused on mitigating nematode-induced crop damage and promoting sustainable agriculture represents a fundamental strategy for enhancing food security. Investment in nematology research is crucial to advance food security objectives by identifying and managing nematode species, developing novel technologies, comprehending nematode ecology, and strengthening the capabilities of researchers and farmers. This endeavor constitutes an indispensable step toward addressing one of the most pressing challenges in achieving global food security and promoting sustainable agricultural practices. Primarily, research endeavors facilitate the identification of nematode species responsible for crop damage, leading to the development of effective management strategies. These strategies encompass the utilization of resistant crop varieties, implementation of cultural practices, biological control, and chemical interventions. Secondly, research efforts contribute to the development of innovative technologies aimed at managing nematode populations, such as gene editing techniques that confer resistance to nematode infestations in crops. Additionally, the exploration of beneficial microbes, such as certain fungi and bacteria, as potential biocontrol agents against nematodes, holds promise. The study of nematode ecology represents a foundational research domain that fosters a deeper comprehension of nematode biology and ecological interactions. This knowledge is instrumental in devising precise and efficacious management strategies.

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

Subcritical water extraction of Equisetum arvense biomass withdraws cell wall fractions that trigger plant immune responses and disease resistance

Plant cell walls are complex structures mainly made up of carbohydrate and phenolic polymers. In addition to their structural roles, cell walls function as external barriers against pathogens and are also reservoirs of glycan structures that can be perceived by plant receptors, activating Pattern-Triggered Immunity (PTI). Since these PTI-active glycans are usually released upon plant cell wall degradation, they are classified as Damage Associated Molecular Patterns (DAMPs). Identification of DAMPs imply their extraction from plant cell walls by using multistep methodologies and hazardous chemicals. Subcritical water extraction (SWE) has been shown to be an environmentally sustainable alternative and a simplified methodology for the generation of glycan-enriched fractions from different cell wall sources, since it only involves the use of water. Starting from Equisetum arvense cell walls, we have explored two different SWE sequential extractions (isothermal at 160 ºC and using a ramp of temperature from 100 to 160 ºC) to obtain glycans-enriched fractions, and we have compared them with those generated with a standard chemical-based wall extraction. We obtained SWE fractions enriched in pectins that triggered PTI hallmarks in Arabidopsis thaliana such as calcium influxes, reactive oxygen species production, phosphorylation of mitogen activated protein kinases and overexpression of immune-related genes. Notably, application of selected SWE fractions to pepper plants enhanced their disease resistance against the fungal pathogen Sclerotinia sclerotiorum. These data support the potential of SWE technology in extracting PTI-active fractions from plant cell wall biomass containing DAMPs and the use of SWE fractions in sustainable crop production.

Secretome Analysis for a New Strain of the Blackleg Fungus Plenodomus lingam Reveals Candidate Proteins for Effectors and Virulence Factors

The fungal secretome is the main interface for interactions between the pathogen and its host. It includes the most important virulence factors and effector proteins. We integrated different bioinformatic approaches and used the newly drafted genome data of P. lingam isolate CAN1 (blackleg of rapeseed fungus) to predict the secretion of 217 proteins, including many cell-wall-degrading enzymes. All secretory proteins were identified; 85 were classified as CAZyme families and 25 were classified as protease families. Moreover, 49 putative effectors were predicted and identified, where 39 of them possessed at least one conserved domain. Some pectin-degrading enzymes were noticeable as a clustering group according to STRING web analysis. The secretome of P. lingam CAN1 was compared to the other two blackleg fungal species (P. lingam JN3 and P. biglobosus CA1) secretomes and their CAZymes and effectors were identified. Orthologue analysis found that P. lingam CAN1 shared 14 CAZy effectors with other related species. The Pathogen-Host Interaction database (PHI base) classified the effector proteins in several categories where most proteins were assigned as reduced virulence and two of them termed as hypervirulence. Nowadays, in silico approaches can solve many ambiguous issues about the mechanism of pathogenicity between fungi and plant host with well-designed bioinformatics tools.

Identifying patterning behavior in a plant infestation of insect pests

In this study, we developed a mechanistic model formulated as a system of reaction-diffusion equations (RDE) to explore the spatiotemporal dynamics of a theoretical pest with a tillering host plant in a controlled rectangular plant field. Local perturbation analysis, a recently developed method of analysis for wave propagation, was utilized to determine patterning regimes resulting from the local and global behaviors of the slow and fast diffusing components of the RDE system, respectively. Turing analysis was done to show that the RDE system does not exhibit Turing patterns. With bug mortality as the bifurcation parameter, regions with oscillations and stable coexistence of the pest and tillers were identified. Numerical simulations illustrate the patterning regimes in 1D and 2D settings. The oscillations suggest that recurrences in pest infestation is possible. Moreover, simulations showed that patterns produced in the model are strongly influenced by the pests' homogeneous dynamics inside the controlled environment.

Mechanisms underlying food insecurity in the aftermath of climate-related shocks: a systematic review

Food insecurity is prevalent, affecting 1·2 billion people globally in 2021. However, the effects of food insecurity are unequally distributed across populations and climate-related shocks threaten to exacerbate food insecurity and associated health consequences. The mechanisms underlying this exacerbation at the household level are largely unknown. We aimed to synthesise the available evidence on the mechanisms connecting extreme climate events to household-level food insecurity and highlight the research gaps that must be addressed to inform better food security and health policy. For this systematic review, a comprehensive literature search was done by a medical librarian in February, 2021 for articles about food security and climate-related shocks. Relevant publications were identified by searching the following databases with a combination of standardised index terms and keywords: MEDLINE, Embase, CINAHL, GreenFILE, Environment Complete, Web of Science Core Collection, and Global Health. Searches were limited to human studies published in English. Included studies measured food security outcomes using indicators developed by the UN Food and Agricultural Organization (ie, consumption patterns, livelihood change, malnutrition, and mortality) and explained the mechanism behind the household-level or population-level food insecurity. Purely theoretical, modelling, and review studies were excluded. Quality assessment was conducted using the appropriate Joanna Briggs Institute Critical Appraisal Tool. Data were analysed using thematic analysis of the categories of mechanism (interpreted using internationally accepted frameworks), risk and resilience factors, and author policy recommendations. We found a paucity of data with only 18 studies meeting criteria for inclusion out of 337 studies identified for full-text review. All the studies that were included in our analysis showed worse food security outcomes after climate-related shocks. Food availability was the most common mechanism cited (17 studies), although most studies addressed at least one additional mechanism (15 studies). Studies were of mixed methodologies with nuanced discussions of risk and resilience factors, and of policy recommendations. This systematic review shows that there is an incomplete assessment of food security at the household and community level after climate-related shocks in the literature and finds that food availability is the primary mechanism studied. The low number of studies on this topic limits subgroup analysis and generalisability; however, the good quality of the studies allows for important policy recommendations around improving resilience to climate shocks and suggestions for future research including the need for a more granular understanding of mechanisms and feasible adaptation solutions.

Mapping the global distribution of invasive pest Drosophila suzukii and parasitoid Leptopilina japonica: implications for biological control

Insect pest invasions cause significant damage to crop yields, and the resultant economic losses are truly alarming. Climate change and trade liberalization have opened new ways of pest invasions. Given the consumer preference towards organic agricultural products and environment-friendly nature of natural pest control strategies, biological control is considered to be one of the potential options for managing invasive insect pests. Drosophila suzukii (Drosophilidae) is an extremely damaging fruit pest, demanding development of effective and sustainable biological control strategies. In this study, we assessed the potential of the parasitoid Leptopilina japonica (Figitidae) as a biocontrol agent for D. suzukii using ecological niche modeling approaches. We developed global-scale models for both pest and parasitoid to identify four components necessary to derive a niche based, target oriented prioritization approach to plan biological control programs for D. suzukii: (i) potential distribution of pest D. suzukii, (ii) potential distribution of parasitoid L. japonica, (iii) the degree of overlap in potential distributions of pest and parasitoid, and (iv) biocontrol potential of this system for each country. Overlapping suitable areas of pest and parasitoid were identified at two different thresholds and at the most desirable threshold (E = 5%), potential for L. japonica mediated biocontrol management existed in 125 countries covering 1.87 × 10⁷ km², and at the maximum permitted threshold (E = 10%), land coverage was reduced to 1.44 × 10⁷ km² in 121 countries. Fly pest distributional information as a predictor variable was not found to be improving parasitoid model performance, and globally, only in half of the countries, >50% biocontrol coverage was estimated. We therefore suggest that niche specificities of both pest and parasitoid must be included in site-specific release planning of L. japonica for effective biocontrol management aimed at D. suzukii. This study can be extended to design cost-effective pre-assessment strategies for implementing any biological control management program.

The C2H2 Zinc Finger Protein MaNCP1 Contributes to Conidiation through Governing the Nitrate Assimilation Pathway in the Entomopathogenic Fungus Metarhizium acridum

Zinc finger proteins are an important class of multifunctional regulators. Here, the roles of a C2H2 zinc finger protein MaNCP1 (Metarhizium acridum nitrate-related conidiation pattern shift regulatory factor 1) in nitrogen utilization and conidiation were explored in the entomopathogenic fungus M. acridum. The results showed that MaNCP1-disruption mutant (ΔMaNCP1) impaired the ability to utilize nitrate, ammonium and glutamine and reduced the expression of nitrate assimilation-related genes, suggesting that MaNCP1 was involved in governing nitrogen utilization. In addition, the conidial yield of the ΔMaNCP1 strain, cultured on the microcycle conidiation medium (SYA), was significantly decreased, which could be restored or even enhanced than that of the WT strain through increasing the nitrate content in SYA medium. Further study showed that MaAreA, a core regulator in the nitrogen catabolism repression (NCR) pathway, was a downstream target gene of MaNCP1. Screening the differential expression genes between WT and ΔMaNCP1 strains revealed that the conidial yield of M. acridum regulated by nitrate might be related to NCR pathway on SYA medium. It could be concluded that MaNCP1 contributes to the nitrate assimilation and conidiation, which will provide further insights into the relationship between the nitrogen utilization and conidiation in fungi.

Whole genome sequences reveal the Xanthomonas perforans population is shaped by the tomato production system

Modern agricultural practices increase the potential for plant pathogen spread, while the advent of affordable whole genome sequencing enables in-depth studies of pathogen movement. Population genomic studies may decipher pathogen movement and population structure as a result of complex agricultural production systems. We used whole genome sequences of 281 Xanthomonas perforans strains collected within one tomato production season across Florida and southern Georgia fields to test for population genetic structure associated with tomato production system variables. We identified six clusters of X. perforans from core gene SNPs that corresponded with phylogenetic lineages. Using whole genome SNPs, we found genetic structure among farms, transplant facilities, cultivars, seed producers, grower operations, regions, and counties. Overall, grower operations that produced their own transplants were associated with genetically distinct and less diverse populations of strains compared to grower operations that received transplants from multiple sources. The degree of genetic differentiation among components of Florida's tomato production system varied between clusters, suggesting differential dispersal of the strains, such as through seed or contaminated transplants versus local movement within farms. Overall, we showed that the genetic variation of a bacterial plant pathogen is shaped by the structure of the plant production system.

Enhanced stripe rust resistance obtained by combining Yr30 with a widely dispersed, consistent QTL on chromosome arm 4BL

YrFDC12 and PbcFDC, co-segregated in chromosome 4BL, and significantly interacted with Yr30/Pbc1 to enhance stripe rust resistance and to promote pseudo-black chaff development. Cultivars with durable resistance are the most popular means to control wheat stripe rust. Durable resistance can be achieved by stacking multiple adult plant resistance (APR) genes that individually have relatively small effect. Chinese wheat cultivars Ruihua 520 (RH520) and Fengdecun 12 (FDC12) confer partial APR to stripe rust across environments. One hundred and seventy recombinant inbred lines from the cross RH520 × FDC12 were used to determine the genetic basis of resistance and identify genomic regions associated with stripe rust resistance. Genotyping was carried out using 55 K SNP array, and eight quantitative trait loci (QTL) were detected on chromosome arms 2AL, 2DS, 3BS, 4BL, 5BL (2), and 7BL (2) by inclusive composite interval mapping. Only QYr.nwafu-3BS from RH520 and QYr.nwafu-4BL.2 (named YrFDC12 for convenience) from FDC12 were consistent across the four testing environments. QYr.nwafu-3BS is likely the pleiotropic resistance gene Sr2/Yr30. YrFDC12 was mapped in a 2.1-cM interval corresponding to 12 Mb and flanked by SNP markers AX-111121224 and AX-89518393. Lines harboring both Yr30 and YrFDC12 displayed higher resistance than the parents and expressed pseudo-black chaff (PBC) controlled by loci Pbc1 and PbcFDC12, which co-segregated with Yr30 and YrFDC12, respectively. Both marker-based and pedigree-based kinship analyses revealed that YrFDC12 was inherited from founder parent Zhou 8425B. Fifty-four other wheat cultivars shared the YrFDC12 haplotype. These results suggest an effective pyramiding strategy to acquire highly effective, durable stripe rust resistance in breeding.

Genetic differentiation of Xylella fastidiosa following the introduction into Taiwan

The economically important plant pathogen Xylella fastidiosa has been reported in multiple regions of the globe during the last two decades, threatening a growing list of plants. Particularly, X. fastidiosa subspecies fastidiosa causes Pierce's disease (PD) of grapevines, which is a problem in the USA, Spain, and Taiwan. In this work, we studied PD-causing subsp. fastidiosa populations and compared the genome sequences of 33 isolates found in Central Taiwan with 171 isolates from the USA and two from Spain. Phylogenetic relationships, haplotype networks, and genetic diversity analyses confirmed that subsp. fastidiosa was recently introduced into Taiwan from the Southeast USA (i.e. the PD-I lineage). Recent core-genome recombination events were detected among introduced subsp. fastidiosa isolates in Taiwan and contributed to the development of genetic diversity. The genetic diversity observed includes contributions through recombination from unknown donors, suggesting that higher genetic diversity exists in the region. Nevertheless, no recombination event was detected between X. fastidiosa subsp. fastidiosa and the endemic sister species Xylella taiwanensis, which is the causative agent of pear leaf scorch disease. In summary, this study improved our understanding of the genetic diversity of an important plant pathogenic bacterium after its invasion to a new region.

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

Opportunities for Improved Potato Late Blight Management in the Republic of Ireland: Field Evaluation of the Modified Irish Rules Crop Disease Risk Prediction Model

Potato late blight remains the most significant disease threat of potato cultivation globally, often requiring expensive, time-consuming, and environmentally unfriendly approaches to disease management. The goal of this research was to evaluate whether an estimation of potato late blight risk based on environmental factors can be reliably used to adjust the standard potato late blight management practices and the role of cultivar resistance under growing conditions and contemporary Phytophthora infestans populations in the Republic of Ireland. The modified Irish Rules model made it possible to reduce fungicide usage by 58.7% on average, compared with current standard practices used by growers and without adversely compromising disease control and yield, with similar results achieved by the half-dose program. Host resistance levels were found to be correlated with a delay in the initiation of the epidemics, final foliar disease levels, and reduction of fungicide usage. Disease levels on the highly resistant cultivars remained low, and a clear selection pattern toward the P. infestans genotypes EU_13_A2 and EU_6_A1 was observed. An increase in the frequency of strains belonging to genotypes EU_13_A2 and EU_6_A1 was also observed to occur in the latter part of the trial growing seasons. Because of the increasingly dynamic nature of the population structure, associated with the continued evolution of the P. infestans population and the arrival of EU_36_A2 in the Republic of Ireland, routine population monitoring is necessary to ensure that potato late blight control strategies remain effective.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

LC-MS/MS-based profiling of bioactive metabolites of endophytic bacteria from Cannabis sativa and their anti-Phytophthora activity

Protection of crop plants from phytopathogens through endophytic bacteria is a newly emerged area of biocontrol. In this study, endophytic bacteria were isolated from the rhizosphere of Cannabis sativa. Based on initial antimicrobial screening, three (03) bacteria Serratia marcescens MOSEL-w2, Enterobacter cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 were selected. Antimicrobial assays of these selected bacteria against Phytophthora parasitica revealed that E. cloacae MOSEL-w7 and Paenibacillus sp. MOSEL-w13 possessed strong activity against P. parasitica. All these bacterial extracts showed strong inhibition against P. parasitica at different concentrations (4-400 µg mL^-1). P. parasitica hyphae treated with ethyl acetate extract of E. cloacae MOSEL-w7 resulted in severe growth abnormalities compared to control. The extracts were further evaluated for in vivo detached-leaf assay against P. parasitica on the wild type tobacco. Application of 1% ethyl acetate bacterial extract of S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus sp. MOSEL-w13 reduced P. parasitica induced lesion sizes and lesion frequencies by 60-80%. HPLC based fractions of each extract also showed bioactivity against P. parasitica. A total of 24 compounds were found in the S. marcescens MOSEL-w2, 15 compounds in E. cloacae MOSEL-w7 and 20 compounds found in Paenibacillus sp. MOSEL-w13. LC-MS/MS analyses showed different bioactive compounds in the bacterial extracts such as Cotinine (alkylpyrrolidine), L-tryptophan, L-lysine, L-Dopa, and L-ornithine. These results suggest that S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 are a source of bioactive metabolites and could be used in combination with other biocontrol agents, with other modes of action for controlling diseases caused by Phytophthora in crops. They could be a clue for the broad-spectrum biopesticides for agriculturally significant crops.

Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava

Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.

Plant health and its effects on food safety and security in a One Health framework: four case studies

Although healthy plants are vital to human and animal health, plant health is often overlooked in the One Health literature. Plants provide over 80% of the food consumed by humans and are the primary source of nutrition for livestock. However, plant diseases and pests often threaten the availability and safety of plants for human and animal consumption. Global yield losses of important staple crops can range up to 30% and hundreds of billions of dollars in lost food production. To demonstrate the complex interrelationships between plants and public health, we present four case studies on plant health issues directly tied to food safety and/or security, and how a One Health approach influences the perception and mitigation of these issues. Plant pathogens affect food availability and consequently food security through reductions in yield and plant mortality as shown through the first case study of banana Xanthomonas wilt in East and Central Africa. Case studies 2, 3 and 4 highlight ways in which the safety of plant-based foods can also be compromised. Case study 2 describes the role of mycotoxin-producing plant-colonizing fungi in human and animal disease and examines lessons learned from outbreaks of aflatoxicosis in Kenya. Plants may also serve as vectors of human pathogens as seen in case study 3, with an example of Escherichia coli (E. coli) contamination of lettuce in North America. Finally, case study 4 focuses on the use of pesticides in Suriname, a complex issue intimately tied to food security though protection of crops from diseases and pests, while also a food safety issue through misuse. These cases from around the world in low to high income countries point to the need for interdisciplinary teams to solve complex plant health problems. Through these case studies, we examine challenges and opportunities moving forward for mitigating negative public health consequences and ensuring health equity. Advances in surveillance technology and functional and streamlined workflow, from data collection, analyses, risk assessment, reporting, and information sharing are needed to improve the response to emergence and spread of plant-related pathogens and pests. Our case studies point to the importance of collaboration in responses to plant health issues that may become public health emergencies and the value of the One Health approach in ensuring food safety and food security for the global population.

Institutional approaches for plant health provision as a collective action problem

The provision of plant health has public good attributes when nobody can be excluded from enjoying its benefits and individual benefits do not reduce the ability of others to also benefit. These attributes increase risk of free-riding on plant health services provided by others, giving rise to a collective action problem when trying to ensure plant health in a region threatened by an emerging plant disease. This problem has traditionally been addressed by government intervention, but top-down approaches to plant health are often insufficient and are increasingly combined with bottom-up approaches that promote self-organization by affected individuals. The challenge is how to design plant health institutions that effectively deal with the spatial and temporal dynamics of plant diseases, while staying aligned with the preferences, values and needs of affected societies. Here, we illustrate how Ostrom’s design principles for collective action can be used to guide the incorporation of bottom-up approaches to plant health governance in order to improve institutional fit. Using the ongoing epidemic of huanglongbing (HLB) as a case study, we examine existing institutions designed to ensure citrus health under HLB in Brazil, Mexico, the United States and Argentina, and discuss potential implications of Ostrom’s design principles for the collective provision of plant health under HLB and other plant diseases that are threatening food security worldwide. The discussion leads to an outline for the interdisciplinary research agenda that would be needed to establish the link between institutional approaches and plant health outcomes in the context of global food security.

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

Modeling the Impact of Crop Diseases on Global Food Security

Plant pathology must contribute to improving food security in a safe operating space, which is shrinking as a result of declining natural resources, climate change, and the growing world population. This review analyzes the position of plant pathology in a nexus of relationships, which is mapped and where the coupled dynamics of crop growth, disease, and yield losses are modeled. We derive a hierarchy of pathogens, whereby pathogens reducing radiation interception (RI), radiation use efficiency (RUE), and harvest index increasingly impact crop yields in the approximate proportions: 1:4.5:4,700. Since the dawn of agriculture, plant breeding has targeted the harvest index as a main objective for domesticated plants. Surprisingly, the literature suggests that pathogens that reduce yields by directly damaging harvestable plant tissues have received much less attention than those that reduce RI or RUE. Ecological disease management needs to target diverse production situations and therefore must consider variation in attainable yields; this can be achieved through the reengineering of agrosystems to incorporate built-in dynamic diversity of genes, plants, and crop stands.

Mapping disruption and resilience mechanisms in food systems

This opinion article results from a collective analysis by the Editorial Board of Food Security. It is motivated by the ongoing covid-19 global epidemic, but expands to a broader view on the crises that disrupt food systems and threaten food security, locally to globally. Beyond the public health crisis it is causing, the current global pandemic is impacting food systems, locally and globally. Crises such as the present one can, and do, affect the stability of food production. One of the worst fears is the impacts that crises could have on the potential to produce food, that is, on the primary production of food itself, for example, if material and non-material infrastructure on which agriculture depends were to be damaged, weakened, or fall in disarray. Looking beyond the present, and not minimising its importance, the covid-19 crisis may turn out to be the trigger for overdue fundamental transformations of agriculture and the global food system. This is because the global food system does not work well today: the number of hungry people in the world has increased substantially, with the World Food Programme warning of the possibility of a "hunger pandemic". Food also must be nutritious, yet unhealthy diets are a leading cause of death. Deepening crises impoverish the poorest, disrupt food systems, and expand "food deserts". A focus on healthy diets for all is all the more relevant when everyone's immune system must react to infection during a global pandemic. There is also accumulating and compelling evidence that the global food system is pushing the Earth system beyond the boundaries of sustainability. In the past twenty years, the growing demand for food has increasingly been met through the destruction of Earth's natural environment, and much less through progress in agricultural productivity generated by scientific research, as was the case during the two previous decades. There is an urgent need to reduce the environmental footprint of the global food system: if its performances are not improved rapidly, the food system could itself be one main cause for food crises in the near future. The article concludes with a series of recommendations intended for policy makers and science leaders to improve the resilience of the food system, global to local, and in the short, medium and long term.

Spatial-temporal variability of rainfall and mean air temperature for the state of Bahia, Brazil

The aim of this study was to evaluate spatial behavior and temporal stability of rainfall and mean air temperature in the state of Bahia, using historical series from 1975 to 2011 and 1961 to 2009, respectively. The analyses were performed considering the accumulated variables of each month of the historical series. The accumulated monthly totals were divided by the number of years of observation, obtaining the monthly average values ​​of rainfall and air temperature for each measurement point. The data were submitted to descriptive statistical analysis and linear correlation studies. Geostatistical analysis was used to verify the existence and quantify spatial dependence between the values of the studied variables. In addition, the maps were submitted to algebra operations, calculating the spatial difference between months for each of the variables. For that purpose, the difference between one month and its subsequent period was calculated in order to establish the behavior of the variables over time. Climatic variables showed a close relationship between each other, demonstrating their spatial and temporal variation, which is mainly dependent on the seasons of the year. The rainfall and mean air temperature variables showed stable spatial behavior and high temporal stability between subsequent months.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Measuring the nutritional cost of insect infestation of stored maize and cowpea

Our understanding and prevention of postharvest losses are critical if we are to feed a growing global population. Insect infestation-related losses of stored commodities are typically considered only in terms of quantitative, physical weight loss. Insect infestation affects the nutritional value and some nutritional components are impacted more severely than others. We infested maize and cowpea grain with commonly occurring stored product insect pests, and mapped infestation levels against nutritional composition over a 4-to-6 month storage period to analyse how insect infestation relates to different macro- and micro-nutrient contents. Insect infestation decreased the carbohydrate content of the stored grains, causing a relative increase in the proportion of protein and fibre in the remaining grain, and moisture content also increased. Sitophilus zeamais preferentially fed in the floury endosperm of maize, resulting in more carbohydrate loss relative to protein loss. Conversely, Prostephanus truncatus consumed the germ and endosperm, disproportionately reducing the fat, protein, iron and zinc grain contents. Nutrients are distributed more homogenously within cowpea than in maize grains, but Callosobruchus maculatus infestation increased the relative protein, fat, iron and zinc to carbohydrate ratios. This indicates how the nutrient content of insect-infested stored grain depends upon the grain type, the infesting insect, and the infestation level. Insect infestation therefore has consequences for human nutrition beyond those of grain weight loss. Using data collected on the changing nutritional composition of grain over time, with and without insect infestation, we modelled the associations between infestation and nutritional quality to predict estimated nutritional losses that could be associated with consumption of insect-infested stored maize and cowpea.

Many unreported crop pests and pathogens are probably already present

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

Many unreported crop pests and pathogens are probably already present

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

The global burden of pathogens and pests on major food crops

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.