Managing a Destructive, Episodic Crop Disease: A National Survey of Wheat and Barley Growers' Experience With Fusarium Head Blight

Into the Trees: Random Forests for Predicting Fusarium Head Blight Epidemics of Wheat in the United States

Constructing models that accurately predict Fusarium head blight (FHB) epidemics and are also amenable to large-scale deployment is a challenging task. In the United States, the emphasis has been on simple logistic regression (LR) models, which are easy to implement but may suffer from lower accuracies when compared with more complicated, harder-to-deploy (over large geographies) model frameworks such as functional or boosted regressions. This article examined the plausibility of random forests (RFs) for the binary prediction of FHB epidemics as a possible mediation between model simplicity and complexity without sacrificing accuracy. A minimalist set of predictors was also desirable rather than having the RF model use all 90 candidate variables as predictors. The input predictor set was filtered with the aid of three RF variable selection algorithms (Boruta, varSelRF, and VSURF), using resampling techniques to quantify the variability and stability of selected variable sets. Post-selection filtering produced 58 competitive RF models with no more than 14 predictors each. One variable representing temperature stability in the 20 days before anthesis was the most frequently selected predictor. This was a departure from the prominence of relative humidity-based variables previously reported in LR models for FHB. The RF models had overall superior predictive performance over the LR models and may be suitable candidates for use by the Fusarium Head Blight Prediction Center.

Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management

Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.

Fusarium graminearum Species Complex: A Bibliographic Analysis and Web-Accessible Database for Global Mapping of Species and Trichothecene Toxin Chemotypes

Fusarium graminearum is ranked among the five most destructive fungal pathogens that affect agroecosystems. It causes floral diseases in small grain cereals including wheat, barley, and oats, as well as maize and rice. We conducted a systematic review of peer-reviewed studies reporting species within the F. graminearum species complex (FGSC) and created two main data tables. The first contained summarized data from the articles including bibliographic, geographic, methodological (ID methods), host of origin and species, while the second data table contains information about the described strains such as publication, isolate code(s), host/substrate, year of isolation, geographical coordinates, species and trichothecene genotype. Analyses of the bibliographic data obtained from 123 publications from 2000 to 2021 by 498 unique authors and published in 40 journals are summarized. We describe the frequency of species and chemotypes for 16,274 strains for which geographical information was available, either provided as raw data or extracted from the publications, and sampled across six continents and 32 countries. The database and interactive interface are publicly available, allowing for searches, summarization, and mapping of strains according to several criteria including article, country, host, species and trichothecene genotype. The database will be updated as new articles are published and should be useful for guiding future surveys and exploring factors associated with species distribution such as climate and land use. Authors are encouraged to submit data at the strain level to the database, which is accessible at https://fgsc.netlify.app.

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.

Can natural gene drives be part of future fungal pathogen control strategies in plants?

Globally, fungal pathogens cause enormous crop losses and current control practices are not always effective, economical or environmentally sustainable. Tools enabling genetic management of wild pathogen populations could potentially solve many problems associated with plant diseases. A natural gene drive from a heterologous species can be used in the globally important cereal pathogen Fusarium graminearum to remove pathogenic traits from contained populations of the fungus. The gene drive element became fixed in a freely crossing population in only three generations. Repeat-induced point mutation (RIP), a natural genome defence mechanism in fungi that causes C to T mutations during meiosis in highly similar sequences, may be useful to recall the gene drive following release, should a failsafe mechanism be required. We propose that gene drive technology is a potential tool to control plant pathogens once its efficacy is demonstrated under natural settings.

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.