Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user's needs and established successful practice. Previously (Geiser et al. 2013; Phytopathology 103:400-408. 2013), the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani Species Complex (FSSC). Subsequently, this concept was challenged by one research group (Lombard et al. 2015 Studies in Mycology 80: 189-245) who proposed dividing Fusarium into seven genera, including the FSSC as the genus Neocosmospora, with subsequent justification based on claims that the Geiser et al. (2013) concept of Fusarium is polyphyletic (Sandoval-Denis et al. 2018; Persoonia 41:109-129). Here we test this claim, and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species recently described as Neocosmospora were recombined in Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural and practical taxonomic option available.

A multiyear survey of >200 wheat fields in Paraná (PR) and Rio Grande do Sul (RS) states was conducted to assess the extent and distribution of Fusarium graminearum species complex (FGSC) diversity in the southern Brazilian wheat agroecosystem. Five species and three trichothecene genotypes were found among 671 FGSC isolates from Fusarium head blight (FHB)-infected wheat heads: F. graminearum (83%) of the 15-acetyldeoxynivalenol (15-ADON) genotype, F. meridionale (12.8%) and F. asiaticum (0.4%) of the nivalenol (NIV) genotype, and F. cortaderiae (2.5%) and F. austroamericanum (0.9%) with either the NIV or the 3-ADON genotype. Regional differences in FGSC composition were observed, with F. meridionale and the NIV type being significantly (P<0.001) more prevalent in PR (>28%) than in RS (≤9%). Within RS, F. graminearum was overrepresented in fields below 600 m in elevation and in fields with higher levels of FHB incidence (P<0.05). Species composition was not significantly influenced by previous crop or the stage of grain development at sampling. Habitat-specific differences in FGSC composition were evaluated in three fields by characterizing a total of 189 isolates collected from corn stubble, air above the wheat canopy, and symptomatic wheat kernels. Significant differences in FGSC composition were observed among these habitats (P<0.001). Most strikingly, F. meridionale and F. cortaderiae of the NIV genotype accounted for the vast majority (>96%) of isolates from corn stubble, whereas F. graminearum with the 15-ADON genotype was dominant (>84%) among isolates from diseased wheat kernels. Potential differences in pathogenic fitness on wheat were also suggested by a greenhouse competitiveness assay in which F. graminearum was recovered at much higher frequency (>90%) than F. meridionale from four wheat varieties inoculated with an equal mixture of F. graminearum and F. meridionale isolates. Taken together, the data presented here suggest that FGSC composition and, consequently, the trichothecene contamination in wheat grown in southern Brazil is influenced by host adaptation and pathogenic fitness. Evidence that F. meridionale and F. cortaderiae with the NIV genotype are regionally significant contributors to FHB may have significant implications for food safety and the economics of cereal production.

Although Asian soybean rust occurs in a broad range of environmental conditions, the most explosive and severe epidemics have been reported in seasons with warm temperature and abundant moisture. Associations between weather and epidemics have been reported previously, but attempts to identify the major factors and model these relationships with field data have been limited to specific locations. Using data from 2002-03 to 2004-05 from 34 field experiments at 21 locations in Brazil that represented all major soybean production areas, we attempted to identify weather variables using a 1-month time window following disease detection to develop simple models to predict final disease severity. Four linear models were identified, and these models explained 85 to 93% of variation in disease severity. Temperature variables had lower correlation with disease severity compared with rainfall, and had minimal predictive value for final disease severity. A curvilinear relationship was observed between 1 month of accumulated rainfall and final disease severity, and a quadratic response model using this variable had the lowest prediction error. Linear response models using only rainfall or number of rainy days in the 1-month period tended to overestimate disease for severity <30%. The study highlights the importance of rainfall in influencing soybean rust epidemics in Brazil, as well as its potential use to provide quantitative risk assessments and seasonal forecasts for soybean rust, especially for regions where temperature is not a limiting factor for disease development.

The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

Standard area diagrams (SAD) have long been used as a tool to aid the estimation of plant disease severity, an essential variable in phytopathometry. Formal validation of SAD was not considered prior to the early 1990s, when considerable effort began to be invested developing SAD and assessing their value for improving accuracy of estimates of disease severity in many pathosystems. Peer-reviewed literature post-1990 was identified, selected, and cataloged in bibliographic software for further scrutiny and extraction of scientometric, pathosystem-related, and methodological-related data. In total, 105 studies (127 SAD) were found and authored by 327 researchers from 10 countries, mainly from Brazil. The six most prolific authors published at least seven studies. The scientific impact of a SAD article, based on annual citations after publication year, was affected by disease significance, the journal's impact factor, and methodological innovation. The reviewed SAD encompassed 48 crops and 103 unique diseases across a range of plant organs. Severity was quantified largely by image analysis software such as QUANT, APS-Assess, or a LI-COR leaf area meter. The most typical SAD comprised five to eight black-and-white drawings of leaf diagrams, with severity increasing nonlinearly. However, there was a trend toward using true-color photographs or stylized representations in a range of color combinations and more linear (equally spaced) increments of severity. A two-step SAD validation approach was used in 78 of 105 studies for which linear regression was the preferred method but a trend toward using Lin's correlation concordance analysis and hypothesis tests to detect the effect of SAD on accuracy was apparent. Reliability measures, when obtained, mainly considered variation among rather than within raters. The implications of the findings and knowledge gaps are discussed. A list of best practices for designing and implementing SAD and a website called SADBank for hosting SAD research data are proposed.