From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

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.

Meta-Analysis in Plant Pathology: Publication Patterns, Methodological Trends, and Potential Future Directions.

Meta-analysis (MA) is increasingly adopted in agricultural and ecological sciences, fields at the interface with plant pathology. Our review of the use of MA in the field since 1999 identified 79 original research papers. Fifty percent of these works were published in two American Phytopathological Society journals, emphasizing their central role in disseminating MA research. Analysis of the number of authors per study and the authorship network revealed a tightly knit field. The network shows a few "hub" authors or groups that significantly influence research directions, with a clear geographical clustering in North and South America. Most of the MA papers focused on the effects of disease control, where fungicide was the main product. The MA investigations often focus on response variables like yield and disease severity for which the analyzed effect sizes differ, but the log of the ratio of means and untransformed means are predominant. The study selection to be included in the MA often follows systematic review standards or when clear quantitative criteria are used for study inclusion. Yet, the inclusion of data from reports, newsletters, and research trials adds breadth. Frequentist rather than Bayesian inference is most common, and SAS and R are the preferred software. Our review underscores the escalating importance of MA in plant pathology, especially for determining the mean and variability of the effects of different control methods on disease and yield. Efforts should continue to improve the detailed reporting of MA methods used, data sharing, and reporting of measures of heterogeneity among studies.

Meta-Analytic Modeling of the Severity-Yield Relationships in Soybean Frogeye Leaf Spot Epidemics.

Frogeye leaf spot (FLS), caused by Cercospora sojina, is an important foliar disease affecting soybean in the United States. A meta-analytic approach including 39 fungicide trials conducted from 2012 to 2021 across eight states (Alabama, Arkansas, Illinois, Iowa, Kentucky, Louisiana, Mississippi, Tennessee) was used to assess the relationship between FLS severity and soybean yield. Correlation and regression analyses were performed separately to determine Fisher's transformation of correlation coefficients (Zr), intercept (ß0) and slope (ß1). Disease pressure (low severity, ≤34.5; high severity, >34.5%) and yield class (low, ≤3,352; high, >3,352 kg/ha) were included as categorical moderators. Pearson's [Formula: see text], obtained from back-transforming the [Formula: see text]r estimated by an overall random-effects model, showed a significant negative linear relationship between FLS severity and yield ([Formula: see text] = -0.60). The [Formula: see text]r was affected by disease pressure (P = 0.0003) but not by yield class (P = 0.8141). A random-coefficient model estimated a slope of -19 kg/ha for each percent severity for a mean attainable yield of 3,719.9 kg/ha. Based on the overall mean (95% CI) of the intercept and slope estimated by the random-coefficients model, the estimated overall relative damage coefficient was 0.51% (0.36 to 0.69), indicating that a percent increase in FLS severity reduced yield by 0.51%. The best model included yield class as a covariate, and population-average intercepts differed significantly between low (3,455.1 kg/ha) and high (3,842.7 kg/ha) yield classes. This highlights the potential impact of FLS on soybean yield if not managed and may help in disease management decisions.

Efficacy and Profitability of Fungicides for Managing Frogeye Leaf Spot on Soybean in the United States: A 10-Year Quantitative Summary.

Frogeye leaf spot (FLS), caused by Cercospora sojina, is an economically important disease of soybean in the United States. Data from 66 uniform fungicide trials (UFTs) conducted from 2012 to 2021 across eight states (Alabama, Arkansas, Illinois, Iowa, Kentucky, Louisiana, Mississippi, and Tennessee) were gathered and analyzed to determine the efficacy and profitability of the following fungicides applied at the beginning pod developmental stage (R3): azoxystrobin + difenoconazole (AZOX + DIFE), difenoconazole + pydiflumetofen (DIFE + PYDI), pyraclostrobin (PYRA), pyraclostrobin + fluxapyroxad + propiconazole (PYRA + FLUX + PROP), tetraconazole (TTRA), thiophanate-methyl (TMET), thiophanate-methyl + tebuconazole (TMET + TEBU), and trifloxystrobin + prothioconazole (TFLX + PROT). A network meta-analytic model was fitted to the log of the means of FLS severity data and to the nontransformed mean yield for each treatment, including the nontreated. The percent reduction in disease severity (%) and the yield response (kg/ha) relative to the nontreated was the lowest for PYRA (11%; 136 kg/ha) and the greatest for DIFE + PYDI (57%; 441 kg/ha). A significant decline in efficacy over time was detected for PYRA (18 percentage points [p.p.]), TTRA (27 p.p.), AZOX + DIFE (18 p.p.), and TMET + TEBU (19 p.p.) by using year as a continuous covariate in the model. Finally, probabilities of breaking even were the greatest (>65%) for the most effective fungicide DIFE + PYDI and the lowest (<55%) for PYRA. Results of this meta-analysis may be useful to support decisions when planning fungicide programs.

Linking Climate Variables to Large-Scale Spatial Pattern and Risk of Citrus Huanglongbing: A Hierarchical Bayesian Modeling Approach.

Huanglongbing (HLB) is one of the most important diseases affecting citriculture in the world. Knowledge of climatic factors linked to HLB risk at large spatial scales is limited. We gathered HLB presence and absence data from official surveys conducted in the state of Minas Gerais, Brazil, over 13 years. The total count of orange and mandarin orchards, and mean orchard area, normalized to a spatial grid of 60 cells (55 × 55 km), were derived from the same database. Monthly climate normals (1984 to 2013) of rainfall, mean temperature, and wind speed split into rainy (September to April) and dry (May to August) seasons (annual summary was retained) were obtained for each grid cell. Two hierarchical Bayesian modeling approaches were evaluated, both based on the integrated nested Laplace approximation method. The first, the climate covariates model (CC model), used orchard, climate, and the spatial effect as covariates. The second, principal components (PC model), used the first three components from a principal component analysis of all variables and the spatial effect as covariates. Both models showed an inverse relationship between posterior prevalence and grid cell mean temperature during the dry season. Annual wind speed, as well as annual and rainy season rainfall, contributed to HLB risk in the CC and PC models, respectively. A partial influence of neighboring regions on HLB risk was observed. The results should assist policymakers in defining regions at HLB risk and guide monitoring strategies to mitigate further spread of HLB in the state of Minas Gerais.

Aggressiveness and Mycotoxin Production by Fusarium meridionale Compared with F. graminearum on Maize Ears and Stalks in the Field.

Fusarium meridionale and F. graminearum both cause Gibberella ear rot (GER) and Gibberella stalk rot (GSR) of maize in Brazil, but the former is much more common. Recent work with two isolates of each from maize suggested this dominance could be caused by greater aggressiveness and competitiveness of F. meridionale on maize. We evaluated pathogenicity and toxigenicity of 16 isolates of F. graminearum and 24 isolates of F. meridionale recovered from both wheat and maize. Strains were individually inoculated into ears of four maize hybrids in field trials. GER severity varied significantly between isolates within each species. Although ranges overlapped, the average GER severity induced by F. meridionale (25.2%) was two times as high overall as that induced by F. graminearum (12.8%) for isolates obtained from maize but was similar for those isolated from wheat (19.9 and 21.4%, respectively). In contrast, severity of GSR was slightly higher for F. graminearum (22.2%) than for F. meridionale (19.8%), with no effect of the host of origin. Deoxynivalenol and its acetylated form 15ADON were the main mycotoxins produced by F. graminearum (7/16 strains), and nivalenol toxin was produced by F. meridionale (17/24 strains). Six isolates of F. graminearum and three of F. meridionale also produced zearalenone. Results confirmed that F. meridionale from maize is, on average, more aggressive on maize but also suggested greater complexity related to diversity among the isolates within each species and their interactions with different hybrids. Further studies involving other components of the disease cycle are needed to more fully explain observed patterns of host dominance.

Fusarium Head Blight of Small Grains in Pennsylvania: Unravelling Species Diversity, Toxin Types, Growth, and Triazole Sensitivity.

Fusarium graminearum is the main causal species of Fusarium head blight (FHB) globally. Recent changes in the trichothecene (toxin) types in the North American FHB pathogens support the need for continued surveillance. In this study, 461 isolates were obtained from symptomatic spikes of wheat, spelt, barley, and rye crops during 2018 and 2019. These were all identified to species and toxin types using molecular-based approaches. An additional set of 77 F. graminearum isolates obtained from overwintering crop residues during winter 2012 were molecularly identified to toxin types. A subset of 31 F. graminearum isolates (15 15-acetyl-deoxynivalenol [15ADON] and 16 3-acetyl-deoxynivalenol [3ADON]) were assessed for mycelial growth, macroconidia, perithecia, and ascospore production, and sensitivity to two triazoles. Ninety percent of isolates obtained from the symptomatic spikes (n = 418) belonged to F. graminearum, with four other species found at a lower frequency (n = 39). The F. graminearum isolates from symptomatic spikes were mainly of the 15ADON (95%), followed by 3ADON (4%), nivalenol (0.7%), and NX-2 (0.3%) toxin types. All F. graminearum isolates obtained from overwintering residue were of the 15ADON type. The toxin types could not be differentiated based on the multivariate analysis of growth and reproduction traits. All isolates were sensitive to tebuconazole and metconazole fungicides in vitro. This study confirms the dominance of F. graminearum and suggests ecological and environmental factors, to be further identified, that lead to similar composition of toxin types in the northern United States. Our results may be useful to assess the sustainability of FHB management practices and provide a baseline for future FHB surveys.

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.

Comparing the Fungicide Sensitivity of Sclerotinia sclerotiorum Using Mycelial Growth and Ascospore Germination Assays.

The infection of the floral tissues of snap bean and other crops by Sclerotinia sclerotiorum, the causative agent of white mold, is by ascospores. Irrespective of the fungicide mode of action being evaluated, in vitro fungicide sensitivity tests are conducted almost exclusively using mycelial growth assays. This is likely because of difficulties and time involved in sclerotial conditioning required to produce apothecia and ascospores. The objective of this research was to compare estimates of fungicide sensitivity between mycelial growth and ascospore germination assays for S. sclerotiorum. Sensitivity assays were conducted using serial doses of three fungicides commonly used to control white mold: boscalid, fluazinam, and thiophanate-methyl. A total of 27 isolates were evaluated in replicated trials conducted for each fungicide and assay type. The effective concentration to reduce mycelial growth or ascospore germination by 50% (EC50) was estimated for each isolate, fungicide, and assay type. The median EC50 values obtained from ascospore germination assays were 52.7, 10.0, and 2.7 times higher than those estimated from the mycelial growth for boscalid, fluazinam, and thiophanate-methyl, respectively. No significant correlation was found between EC50 values estimated by the two methods. These findings highlight differences that may be important in evaluating the sensitivity of S. sclerotiorum given the fungicide mode of action and how they will be used in the field.

The Dominance of Fusarium meridionale Over F. graminearum Causing Gibberella Ear Rot in Brazil May Be Due to Increased Aggressiveness and Competitiveness.

In Brazil, Gibberella ear rot (GER) of maize is caused mainly by Fusarium meridionale, whereas F. graminearum is a minor contributor. To test the hypothesis that F. meridionale is more aggressive than F. graminearum on maize, six experiments were conducted in the south (summer) and one in the central-south (winter), totaling seven conditions (year × location × hybrid). Treatments consisted of F. graminearum or F. meridionale (two isolates of each) inoculated once 4 days after silk, inoculated sequentially and alternately (F. graminearum → F. meridionale or F. meridionale → F. graminearum) 6 days apart, or (in the central-south) inoculated sequentially without alternating species (F. meridionale → F. meridionale or F. graminearum → F. graminearum). Overall, severity was two times greater in the south (37.0%), where summer temperatures were warmer (20 to 25°C) than in central-south. In the south, severity was greatest in F. meridionale treatments (67.8%); followed by F. meridionale → F. graminearum (41.1%), then F. graminearum → F. meridionale (19.4%), and lowest in F. graminearum (2.1%), suggesting an antagonistic relationship. In the central-south (15 to 20°C), severity was generally higher in the sequential nonalternating inoculation treatments (F. meridionale → F. meridionale or F. graminearum → F. graminearum) than when either species was inoculated only once. Only nivalenol (NIV) or deoxynivalenol was detected when F. meridionale or F. graminearum, respectively, was inoculated singly, or sequentially with no alternation. Both toxins were found in grains harvested from the F. meridionale → F. graminearum treatment, whereas only NIV was found in kernels from the F. graminearum → F. meridionale treatment, suggesting that F. meridionale was more competitive than F. graminearum in coinoculations. The dominance of F. meridionale as a cause of GER in Brazil may be due in part to its higher aggressiveness and competitiveness compared with F. graminearum.

Phylogenomic Analysis of a 55.1-kb 19-Gene Dataset Resolves a Monophyletic Fusarium that Includes the Fusarium solani Species Complex.

Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user's needs and established successful practice. In 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 in 2015 by one research group who proposed dividing the genus Fusarium into seven genera, including the FSSC described as members of the genus Neocosmospora, with subsequent justification in 2018 based on claims that the 2013 concept of Fusarium is polyphyletic. 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 genus 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 described as genus Neocosmospora were recombined in genus 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.

Sequential Post-Heading Applications for Controlling Wheat Blast: A 9-Year Summary of Fungicide Performance in Brazil.

Wheat blast, caused by Pyricularia oryzae Triticum lineage, is a major constraint to wheat production, mainly in the tropics of Brazil, where severe epidemics have been more frequent. We analyzed disease and wheat yield data from 42 uniform field trials conducted over 9 years (2012 to 2020) to assess whether the percent control and yield response were influenced by fungicide type, region (tropical or subtropical), and year. Six treatments were selected, all evaluated in at least 19 trials. Two fungicides were applied as solo active ingredients (MANCozeb, and TEBUconazole), and four were premixes (AZOXystrobin plus TEBU, TriFLoXystrobin plus PROThioconazole, TFLX plus TEBU, and PYRAclostrobin plus EPOXiconazole). Percent control, calculated from back-transforming estimates by a meta-analysis network model fitted to the log of the means, ranged from 43 to 58%, with all but PYRA plus EPOX showing efficacy >52% on average, not differing among them. The variation in both efficacy and yield response was explained by region, and all but TEBU performed better in the subtropics than in the tropics. Yield response from using three sequential sprays was approximately two times greater in the subtropics (319 to 532 kg/ha) than in the tropics (149 to 241.3 kg/ha). No significant decline in fungicide efficacy or yield response was observed in 9 years of study for any of the fungicides. These results reinforce the need to improve control by adopting an integrated management approach in the tropics given poorer performance and lower profitability, especially for the premixes, than in the subtropics.

Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust Control.

Soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rain-based action thresholds of disease severity values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of daily values of infection probability (DVIP6 and DVIP9) and with a calendar program, with regard to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during the 2012-13, 2014-15, and 2015-16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments, and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase in spray number, but the economic analysis showed no significant differences in the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into the management of SBR in soybean production fields in Brazil.

Nannochloropsis sp. and Spirulina sp. as a Source of Antifungal Compounds to Mitigate Contamination by Fusarium graminearum Species Complex.

Phenolic (free, conjugated and bound) and carotenoid extracts from microalgae Nannochloropsis sp. and Spirulina sp. were investigated regarding their potential to mitigate contamination by Fusarium complex fungal pathogens. Free phenolic acid extracts from both microalgae were the most efficient, promoting the lowest mycelial growth rates of 0.51 cm day- 1 (Spirulina sp.) and 0.78 cm day- 1 (Nannochloropsis sp.). An experiment involving natural free phenolic acid extracts and synthetic solutions was carried out based on the natural phenolic acid profile. The results revealed that the synthetic mixtures of phenolic acids from both microalgae were less efficient than the natural extracts at inhibiting fungal growth, indicating that no purification is required. The half-maximal effective concentration (EC50) values of 49.6 µg mL- 1 and 33.9 µg mL- 1 were determined for the Nannochloropsis and Spirulina phenolic acid extracts, respectively. The use of phenolic extracts represents a new perspective regarding the application of compounds produced by marine biotechnology to prevent Fusarium species contamination.

Evaluation of App-Embedded Disease Scales for Aiding Visual Severity Estimation of Cercospora Leaf Spot of Table Beet.

Two diagrammatic ordinal scales are available in the Estimate app (2017 version) for Cercospora leaf spot (CLS) severity on table beet: 10% linear (linear-based diagrammatic scale [LIN]) and logarithmic based (Horsfall-Barratt [HB]). These allow for estimating severity data of four types depending on the system used. A group of 30 raters assigned percentage severity on 30 photographs of diseased table beet leaves during five rounds first without an aid and then using each of the four rating systems in Estimate. In two, the perceived ordinal score of the HB or LIN scale was assigned where severity of the subject fit best. HB2 and LIN2 involved a second choice of unitary severity within the perceived score interval. There was large variation in unaided ability of raters to estimate severity: 13% were accurate (Lin's concordance correlation [LCC] > 0.9), 23% were inaccurate (LCC < 0.7), and the remaining had moderate accuracy. Larger disparities between assigned and actual ordinal scores (mostly overestimates) occurred using the LIN compared with the HB. The LIN2 produced the most accurate estimates (Lin's concordance correlation coefficient, ρc = 0.96; generalized bias parameter, Cb = 0.99; Pearson's correlation coefficient r = 0.95) and the greatest interrater reliability (overall concordance correlation coefficient and intraclass correlation coefficient > 0.93). The two-step process using the 10% linear scale is recommended for severity estimates of CLS in table beet.

Performance and Profitability of Fungicides for Managing Soybean White Mold: A 10-Year Summary of Cooperative Trials.

White mold, caused by Sclerotinia sclerotiorum, is a yield-limiting disease of soybean in Brazil. Uniform fungicide trials have been conducted annually since 2009. Data from 74 cooperative field trials conducted over a 10-year period were assembled. We selected five fungicides applied two times around flowering: dimoxystrobin plus boscalid (DIMO+BOSC), carbendazim plus procymidone (CARB+PROC), fluazinam (FLUZ), fluopyram (FLUO), and procymidone (PROC). For comparison, thiophanate-methyl (TMET) applied four times was also included as a low-cost treatment. Network models were fitted to the log of white mold incidence (percentages) and log of sclerotia mass data (grams/hectare) and to the nontransformed yield data (kilograms/hectare) for each treatment, including the untreated check. Back-transformation of the meta-analytic estimates indicated that the lowest and highest mean (95% confidence interval [CI]) percent reductions in incidence and sclerotia mass were 54.2 (49.3 to 58.7) and 51.6% (43.7 to 58.3) for TMET and 83.8 (79.1 to 87.5) and 87% (81.9 to 91.6) for CARB+PROC, respectively. The overall mean (95% CI) yield responses ranged from 323 kg/ha (247.4 to 400.3) for TMET to 626 kg/ha (521.7 to 731.7) for DIMO+BOSC, but the variance was significantly reduced by a binary variable (30% threshold) describing disease incidence in the untreated check. On average, an increment of 352 kg/ha was estimated for trials where the incidence was >30% compared with the low-disease scenario. Hence, the probability of breaking even on fungicide costs for the high-disease scenario was >65% for the more effective, but more expensive fungicide (FLUZ) than TMET. For the low-disease scenario, profitability was less likely and depended more on variations in fungicide cost and soybean price.

Fitness Traits of Deoxynivalenol and Nivalenol-Producing Fusarium graminearum Species Complex Strains from Wheat.

Fusarium graminearum of the 15-acetyl-deoxynivalenol (15-ADON) chemotype is the main cause of Fusarium head blight (FHB) of wheat in southern Brazil. However, 3-ADON and nivalenol (NIV) chemotypes have been found in other members of the species complex causing FHB in wheat. To improve our understanding of the pathogen biology and ecology, we assessed a range of fitness-related traits in a sample of 30 strains representatives of 15-ADON (F. graminearum), 3-ADON (F. cortaderiae and F. austroamericanum), and NIV (F. meridionale and F. cortaderiae). These included perithecia formation on three cereal-based substrates, mycelial growth at two suboptimal temperatures, sporulation and germination, pathogenicity toward a susceptible and a moderately resistant cultivar, and sensitivity to tebuconazole. The most important trait favoring F. graminearum was a two times higher sexual fertility (>40% perithecial production index [PPI]) than the other species (<30% PPI); PPI varied among substrates (maize > rice > wheat). In addition, sensitivity to tebuconazole appeared lower in F. graminearum, which had the only strain with effective fungicide concentration to reduce 50% of mycelial growth >1 ppm. In the pathogenicity assays, the deoxynivalenol producers were generally more aggressive (1.5 to 2× higher final severity) toward the two cultivars, with 3-ADON or 15-ADON leading to higher area under the severity curve than the NIV strains in the susceptible and moderately resistant cultivars, respectively. There was significant variation among strains of the same species with regards asexual fertility (mycelial growth, macroconidia production, and germination), which suggested a strain- rather than a species-specific difference. These results contribute new knowledge to improve our understanding of the pathogen-related traits that may explain the dominance of certain members of the species complex in specific wheat agroecosystems.

Meta-Analytic Modeling of the Decline in Performance of Fungicides for Managing Soybean Rust after a Decade of Use in Brazil.

An apparent decline of fungicide performance for the control of soybean rust in Brazil has been reported but the rate at which it has occurred has not been formally quantified. Control efficacy and yield response to three fungicides applied as single active ingredients (a.i.)-azoxystrobin (AZOX), cyproconazole (CYPR), and tebuconazole (TEBU)-and four applied as mixtures-AZOX+CYPR, picoxystrobin + CYPR, pyraclostrobin + epoxiconazole, and trifloxystrobin + prothioconazole (TRIF+PROT)-were summarized using network meta-analytic models fitted to mean severity and yield data from 250 trials (10-year period). The effect of year was tested on both variables in a meta-regression model. Overall control efficacy ranged from 56 to 84%; the three single-a.i. fungicides performed the poorest (56 to 62%). Yield increase for single-a.i. fungicides was as low as 30% but ranged from 47 to 65% for the premixes. Significant declines in both variables were detected for all fungicides except TRIF+PROT. For TEBU, control efficacy (yield response) declined the most: 78% (18%) to 54% (8%) from 2004-05 to 2013-14. The recent surge of resistant populations of Phakopsora pachyrhizi to both demethylation inhibitor and quinone outside inhibitor fungicides is likely the driving force behind a significant decline after 4 years of fungicide use.

Standard Area Diagrams for Aiding Severity Estimation: Scientometrics, Pathosystems, and Methodological Trends in the Last 25 Years.

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.