Genetic strategies for improving crop yields.

The current trajectory for crop yields is insufficient to nourish the world's population by 20501. Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests.

The Nuances of Plant Disease Severity Estimation Using Quantitative Ordinal Scales-Lessons Learned over Four Decades.

We revisit the foundations of the Horsfall-Barratt (HB) scale, a widely cited and applied plant disease visual assessment tool introduced in 1945, a full 37 years prior to T. T. Hebert's 1982 critique that raised concerns regarding the scale's rationale, particularly its reliance on the Weber-Fechner law and visual perception assumptions. Although use of the HB scale and similar ordinal scales persists, comprehensive studies have revealed that direct visual estimation using percentage scales often proves more accurate and reliable. Challenges remain, such as biases due to estimator subjectivity and the potential for misclassification. The logarithmic assumptions of the HB scale have been debunked, and the importance of choosing appropriate interval sizes and numbers of classes in developing ordinal scales is emphasized. Analyzing ordinal scale data appropriately is crucial, and recent advances offer promising methods that reduce type II error rates. The closely related disease severity index is noted to have its shortcomings and potential for misuse. The letter underscores the need for continuous refinement and critical evaluation of disease assessment methodologies.

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.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

Performance of Outbreak Management Plans for Emerging Plant Diseases: The Case of Almond Leaf Scorch Caused by Xylella fastidiosa in Mainland Spain.

Outbreak response to quarantine pathogens and pests in the European Union (EU) is regulated by the EU Plant Health Law, but the performance of outbreak management plans in terms of their effectiveness and efficiency has been quantified only to a limited extent. As a case study, the disease dynamics of almond leaf scorch, caused by Xylella fastidiosa, in the affected area of Alicante, Spain, were approximated using an individual-based spatial epidemiological model. The emergence of this outbreak was dated based on phylogenetic studies, and official surveys were used to delimit the current extent of the disease. Different survey strategies and disease control measures were compared to determine their effectiveness and efficiency for outbreak management in relation to a baseline scenario without interventions. One-step and two-step survey approaches were compared with different confidence levels, buffer zone sizes, and eradication radii, including those set by the EU legislation for X. fastidiosa. The effect of disease control interventions was also considered by decreasing the transmission rate in the buffer zone. All outbreak management plans reduced the number of infected trees (effectiveness), but large differences were observed in the number of susceptible trees not eradicated (efficiency). The two-step survey approach, high confidence level, and the reduction in the transmission rate increased the efficiency. Only the outbreak management plans with the two-step survey approach removed infected trees completely, but they required greater survey efforts. Although control measures reduced disease spread, surveillance was the key factor in the effectiveness and efficiency of the outbreak management plans. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Mitigating Emerging and Reemerging Diseases of Fruit and Vegetable Crops in a Changing Climate.

Fruit and vegetable crops are important sources of nutrition and income globally. Producing these high-value crops requires significant investment of often scarce resources, and, therefore, the risks associated with climate change and accompanying disease pressures are especially important. Climate change influences the occurrence and pressure of plant diseases, enabling new pathogens to emerge and old enemies to reemerge. Specific environmental changes attributed to climate change, particularly temperature fluctuations and intense rainfall events, greatly alter fruit and vegetable disease incidence and severity. In turn, fruit and vegetable microbiomes, and subsequently overall plant health, are also affected by climate change. Changing disease pressures cause growers and researchers to reassess disease management and climate change adaptation strategies. Approaches such as climate smart integrated pest management, smart sprayer technology, protected culture cultivation, advanced diagnostics, and new soilborne disease management strategies are providing new tools for specialty crops growers. Researchers and educators need to work closely with growers to establish fruit and vegetable production systems that are resilient and responsive to changing climates. This review explores the effects of climate change on specialty food crops, pathogens, insect vectors, and pathosystems, as well as adaptations needed to ensure optimal plant health and environmental and economic sustainability.

Dynamical analysis of a stochastic maize streak virus epidemic model with logarithmic Ornstein-Uhlenbeck process.

To describe the transmission dynamics of maize streak virus infection, in the paper, we first formulate a stochastic maize streak virus infection model, in which the stochastic fluctuations are depicted by a logarithmic Ornstein-Uhlenbeck process. This approach is reasonable to simulate the random impacts of main parameters both from the biological significance and the mathematical perspective. Then we investigate the detailed dynamics of the stochastic system, including the existence and uniqueness of the global solution, the existence of a stationary distribution, the exponential extinction of the infected maize and infected leafhopper vector. Especially, by solving the five-dimensional algebraic equations corresponding to the stochastic system, we obtain the specific expression of the probability density function near the quasi-endemic equilibrium of the stochastic system, which provides valuable insights into the stationary distribution. Finally, the model is discretized using the Milstein higher-order numerical method to illustrate our theoretical results numerically. Our findings provide a groundwork for better methods of preventing the spread of this type of virus.

Economical-epidemiological analysis of the coffee trees rust pandemic.

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution spatiotemporal economical-epidemiological model, extending the Susceptible-Infected-Removed model, that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

Peanut Smut in Argentina: An Analysis of the Disease, Advances, and Challenges.

Peanut (Arachis hypogaea L.) is a globally high-value food crop, with Argentina ranking third in global peanut exports. However, Argentine peanut production faces a severe threat from a fungal disease, peanut smut, caused by Thecaphora frezzii. This disease is particularly prevalent in the Córdoba Province, where recent surveys have documented a gradual increase in the prevalence and incidence of peanut smut, becoming a significant challenge to peanut production. First identified in Brazil in the 1960s in wild peanut and later in Argentina in 1995 in commercial peanut fields, the disease has rapidly spread owing to its distinctive pathogen characteristics, including the lack of visible symptoms on aerial plant parts, spore spread, and survival, and with a lack of proactive efforts to develop and apply management strategies. This results in the gradual accumulation of teliospores of T. frezzii in soil, further exacerbating the problem in subsequent growing seasons by increasing the intensity of the disease and driving a reduction in crop yield and quality. This review summarizes recent research on peanut smut, focusing on disease assessment, molecular characterization, diagnosis and detection, epidemiology, host range and environmental conditions, and the latest advancements in management approaches, including fungicide spraying, breeding programs, cultural management, and biological control, aimed to enhance understanding and support effective disease management strategies in peanut production systems.

Weather-Based Logistic Regression Models for Predicting Wheat Head Blast Epidemics.

Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics, with each being classified as either outbreak (≥20% head blast incidence) or nonoutbreak. Daily weather variables were collected from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, with each comprising two 7-day intervals (before and after WHD), which combined with weather-based variables resulted in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using least absolute shrinkage and selection operator (LASSO) and sequentially best subset analyses. The models were validated using the leave-one-out cross-validation (LOOCV) technique, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and area under the curve (AUC) from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in preheading date, as well as postheading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.

Survey of Heterodera glycines Population Abundance and Virulence Phenotypes During 2021 to 2022 in Heilongjiang Province.

Soybean cyst nematode (SCN), Heterodera glycines, poses a significant threat to global soybean production. Heilongjiang, the largest soybean-producing province in China, contributes more than 40% to the country's total yield. This province has much longer history of SCN infestation. To assess the current situation in Heilongjiang, we conducted a survey to determine the SCN population density and virulence phenotypes during 2021 to 2022 and compared the data with a previous study in 2015. A total of 377 soil samples from 48 counties representing 11 major soybean-planting regions were collected. The prevalence of SCN increased from 55.4% in 2015 to 59% in the current survey. The population densities ranged from 80 to 26,700 eggs and juveniles per 100 cm3 of soil. Virulence phenotypes were evaluated for 60 representative SCN populations using the H. glycines (HG) type test, revealing nine different HG types. The most common virulence phenotypes were HG types 7 and 0, accounting for 56.7 and 20% of all SCN populations, respectively. The prevalence of populations with a female index (FI) greater than 10% on PI 548316 increased from 64.5% in 2015 to 71.7%. However, the FI on the commonly used resistance sources PI 548402 (Peking) and PI 437654 remained low at 3.3%. These findings highlight the increasing prevalence and changing virulence phenotypes of SCN in Heilongjiang. They also emphasize the importance of rotating soybean varieties with different resistance sources and urgently identifying new sources of resistance to combat SCN.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

When a Palearctic bacterium meets a Nearctic insect vector: Genetic and ecological insights into the emergence of the grapevine Flavescence dorée epidemics in Europe.

Flavescence dorée (FD) is a European quarantine grapevine disease transmitted by the Deltocephalinae leafhopper Scaphoideus titanus. Whereas this vector had been introduced from North America, the possible European origin of FD phytoplasma needed to be challenged and correlated with ecological and genetic drivers of FD emergence. For that purpose, a survey of genetic diversity of these phytoplasmas in grapevines, S. titanus, black alders, alder leafhoppers and clematis were conducted in five European countries. Out of 132 map genotypes, only 11 were associated to FD outbreaks, three were detected in clematis, whereas 127 were detected in alder trees, alder leafhoppers or in grapevines out of FD outbreaks. Most of the alder trees were found infected, including 8% with FD genotypes M6, M38 and M50, also present in alders neighboring FD-free vineyards and vineyard-free areas. The Macropsinae Oncopsis alni could transmit genotypes unable to achieve transmission by S. titanus, while the Deltocephalinae Allygus spp. and Orientus ishidae transmitted M38 and M50 that proved to be compatible with S. titanus. Variability of vmpA and vmpB adhesin-like genes clearly discriminated 3 genetic clusters. Cluster Vmp-I grouped genotypes only transmitted by O. alni, while clusters Vmp-II and -III grouped genotypes transmitted by Deltocephalinae leafhoppers. Interestingly, adhesin repeated domains evolved independently in cluster Vmp-I, whereas in clusters Vmp-II and-III showed recent duplications. Latex beads coated with various ratio of VmpA of clusters II and I, showed that cluster II VmpA promoted enhanced adhesion to the Deltocephalinae Euscelidius variegatus epithelial cells and were better retained in both E. variegatus and S. titanus midguts. Our data demonstrate that most FD phytoplasmas are endemic to European alders. Their emergence as grapevine epidemic pathogens appeared restricted to some genetic variants pre-existing in alders, whose compatibility to S. titanus correlates with different vmp gene sequences and VmpA binding properties.

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models.

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission.

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vector's own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding-as well as potential effects of infection on vector population density-on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics.

Biomass losses resulting from insect and disease invasions in US forests.

Worldwide, forests are increasingly affected by nonnative insects and diseases, some of which cause substantial tree mortality. Forests in the United States have been invaded by a particularly large number (>450) of tree-feeding pest species. While information exists about the ecological impacts of certain pests, region-wide assessments of the composite ecosystem impacts of all species are limited. Here we analyze 92,978 forest plots distributed across the conterminous United States to estimate biomass loss associated with elevated mortality rates caused by the 15 most damaging nonnative forest pests. We find that these species combined caused an additional (i.e., above background levels) tree mortality rate of 5.53 TgC per year. Compensation, in the form of increased growth and recruitment of nonhost species, was not detectable when measured across entire invaded ranges but does occur several decades following pest invasions. In addition, 41.1% of the total live forest biomass in the conterminous United States is at risk of future loss from these 15 pests. These results indicate that forest pest invasions, driven primarily by globalization, represent a huge risk to US forests and have significant impacts on carbon dynamics.

The spread of a wild plant pathogen is driven by the road network.

Spatial analyses of pathogen occurrence in their natural surroundings entail unique opportunities for assessing in vivo drivers of disease epidemiology. Such studies are however confronted by the complexity of the landscape driving epidemic spread and disease persistence. Since relevant information on how the landscape influences epidemiological dynamics is rarely available, simple spatial models of spread are often used. In the current study we demonstrate both how more complex transmission pathways could be incorpoted to epidemiological analyses and how this can offer novel insights into understanding disease spread across the landscape. Our study is focused on Podosphaera plantaginis, a powdery mildew pathogen that transmits from one host plant to another by wind-dispersed spores. Its host populations often reside next to roads and thus we hypothesize that the road network influences the epidemiology of P. plantaginis. To analyse the impact of roads on the transmission dynamics, we consider a spatial dataset on the presence-absence records on the pathogen collected from a fragmented landscape of host populations. Using both mechanistic transmission modeling and statistical modeling with road-network summary statistics as predictors, we conclude the evident role of the road network in the progression of the epidemics: a phenomena which is manifested both in the enhanced transmission along the roads and in infections typically occurring at the central hub locations of the road network. We also demonstrate how the road network affects the spread of the pathogen using simulations. Jointly our results highlight how human alteration of natural landscapes may increase disease spread.

Computational models to improve surveillance for cassava brown streak disease and minimize yield loss.

Cassava brown streak disease (CBSD) is a rapidly spreading viral disease that affects a major food security crop in sub-Saharan Africa. Currently, there are several proposed management interventions to minimize loss in infected fields. Field-scale data comparing the effectiveness of these interventions individually and in combination are limited and expensive to collect. Using a stochastic epidemiological model for the spread and management of CBSD in individual fields, we simulate the effectiveness of a range of management interventions. Specifically we compare the removal of diseased plants by roguing, preferential selection of planting material, deployment of virus-free 'clean seed' and pesticide on crop yield and disease status of individual fields with varying levels of whitefly density crops under low and high disease pressure. We examine management interventions for sustainable production of planting material in clean seed systems and how to improve survey protocols to identify the presence of CBSD in a field or quantify the within-field prevalence of CBSD. We also propose guidelines for practical, actionable recommendations for the deployment of management strategies in regions of sub-Saharan Africa under different disease and whitefly pressure.