Wheat blast is a major threat to wheat production in tropical and subtropical regions of Brazil, yet the influence of epidemiological processes and population genetic structure on epidemic development remains unclear. Therefore, this study aimed to investigate, across different temporal and spatial scales, the genetic dynamics of Pyricularia oryzae Triticum lineage populations and their potential role in shaping wheat head blast epidemics. To investigate temporal genetic dynamics, we analyzed 270 isolates collected from wheat and co-occurring grasses at different crop growth stages within a single field. To evaluate potential tissue specialization and regional genetic structuring, an additional 97 isolates from leaves and spikes were sampled in two distinct regions of Minas Gerais. The analyses revealed a predominantly clonal population structure, with identical multilocus genotypes occurring within the same field, across geographic regions, and between plant tissues. The absence of genetic differentiation between leaf- and spike-derived isolates indicates a lack of tissue specialization, suggesting that the same genotypes can infect multiple organs throughout the crop cycle. Moreover, the detection of Pyricularia oryzae Triticum-positive grasses both before and during wheat development highlights the potential contribution of grass-associated inoculum early in the epidemic. These findings support a scenario of low recombination, strong clonal expansion, and widespread dissemination of a few adapted genotypes, suggesting that early foliar infections and grass-associated inoculum may play a key role in initiating and sustaining wheat blast epidemics.

Disease progress curves (DPCs) are central to evaluating disease management strategies, including host plant resistance. Although widely used and often appropriate, scalar summaries such as the area under the disease progress curve (AUDPC) may obscure meaningful differences in epidemic timing and trajectory shape. Here, I introduce a curve-based framework for comparing plant disease epidemics that treats DPCs as epidemic phenotypes, enabling trajectory-based comparisons beyond conventional scalar summaries. Using a hierarchical generalized additive model, environment-adjusted mean epidemic curves were estimated for each treatment (corn hybrid) while accounting for repeated assessments and environmental heterogeneity. Similarity among hybrids was quantified using a functional distance defined over the epidemic time domain, and hierarchical clustering was used to identify epidemic phenotypes based on differences in curve shape. Applied to multi-environment field data (6 environments; 74 DPCs) for southern corn leaf blight in 13 hybrids, this approach identified distinct epidemic phenotypes that were not fully reflected by AUDPC-based comparisons, despite similar overall disease levels. In addition, a distance-based permutation test indicated that breeder-defined resistance classes (moderately resistant versus resistant), established independently of the curve analysis, were associated with systematic differences in epidemic trajectory shape across environments. By shifting emphasis from scalar summaries to curve-based epidemic representations, this framework provides a complementary tool for host resistance phenotyping and comparative epidemiology and establishes a foundation for hierarchical synthesis and trajectory-based inference across environments.

Target spot of soybean is strongly influenced by weather, but the major drivers of epidemics remain poorly understood. Using a dataset obtained in non-treated plots of 254 trials conducted across nine Brazilian states over 13 growing seasons, we applied a distributed lag nonlinear modeling to quantify delayed and nonlinear associations between weather and severity measured at the R6 growth stage. Daily and cumulative effects were evaluated across two epidemiologically-relevant pre‑assessment periods: an early-season period (41-85 days prior to R6), associated with epidemic onset, and a late-season period (0-40 days prior to R6), associated with epidemic development. Lag-response functions were incorporated into a beta mixed model with season as random effect. Maximum air temperature, accumulated precipitation, and vapour pressure deficit (VPD) exhibited period‑dependent associations with target spot severity. Increased levels of severity (>60%) were associated with temperatures above ~30 °C across periods. Precipitation showed linear cumulative effects during the late-season period, with increased severity (>70%) typically observed exceeding ~300 mm. In contrast, VPD displayed opposing period‑specific effects, with lower severity (<60%) at higher VPD during the earlier period and increased severity associated with values > ~1.3 kPa during the later period. A historical assessment of spatial and temporal variation in climatic favorability further revealed consistent shifts across planting periods, indicating lower climatic favorability for outbreaks under delayed soybean planting. The results indicate that more severe outbreaks may arise from the combined influence of elevated temperature, sustained moisture availability, and high atmospheric drying demand during the two critical phases of epidemic development.

Abstract Visual estimation of plant disease severity is widely used but inherently subjective, which results in variability among raters. Standard area diagrams (SADs) are commonly employed to improve accuracy and precision; however, the number of raters required to validate SAD performance is often arbitrarily defined. This study aimed to establish an empirical framework to determine the minimum number of raters needed to achieve both reliable agreement estimates and sufficient statistical power to detect improvements due to SAD use. Data from nine SAD validation studies across diverse plant disease pathosystems were analyzed. Agreement was quantified using Lin's concordance correlation coefficient (LCCC). Two complementary criteria were applied: a precision-based approach using the coefficient of variation (CV) of rater agreement, and a power-based approach to detect a predefined improvement (ΔLCCC = 0.10) between unaided and aided assessments. Results showed that the precision criterion required relatively few raters (pooled minimum = 7), whereas the power-based criterion was more demanding, with final recommendations ranging from 4 to 20 raters depending on disease complexity. Pathosystems with visually complex symptoms required larger sample sizes due to greater inter-rater variability. This framework provides evidence-based guidance to optimize experimental design, improving efficiency and reproducibility in SAD validation studies. An interactive web application was developed to support adaptive estimation of rater numbers.

Accurate estimation of coffee leaf rust (Hemileia vastatrix) severity remains challenging due to symptom heterogeneity, illumination variability and observer subjectivity, particularly under field conditions. We developed a segmentation pipeline combining zero-shot foundational models (SAM3) and fine-tuned deep learning (SAM2) for estimation of percentage severity and compared them with three other segmentation approaches: DeepLabV3+ (convolutional neural network model), ImageJ (colour thresholding) and the pliman R package (palette-based). A total of 1285 images were collected across several field plots, and 606 pixel-level rust lesion masks were curated for model fine-tuning and independent evaluation. A two-stage detection-segmentation workflow integrating YOLOv8 and SAM2 enabled robust leaf extraction from complex branch-level images, with detection performance consistently exceeding 0.98 across metrics. For lesion segmentation, at the pixel level, the zero-shot foundation model SAM3 achieved the highest disease segmentation performance (Dice approximate to 0.91; IoU approximate to 0.83), with balanced precision (approximate to 0.93) and recall (approximate to 0.94). DeepLabV3+ also performed well (Dice approximate to 0.86; IoU approximate to 0.75), whereas classical threshold-based approaches showed high recall but substantially lower precision, indicating frequent false positives and producing a systematic positive bias. SAM2 exhibited more conservative performance, yielding higher precision but reduced recall relative to SAM3. At the leaf level, the highest agreement between predicted and reference severity estimates was found for SAM3, followed by DeepLabV3+ and SAM2, while classical methods overestimated diseased area. Overall, SAM3 led to a reliable, scalable and consistent severity estimation under real-world field conditions.

Este protocolo descreve procedimentos metodológicos e analíticos padronizados para ensaios de campo destinados à avaliação da eficácia de fungicidas no controle de doenças foliares do milho, que frequentemente limitam a produtividade em sistemas de cultivo intensivos, especialmente na safrinha. Os ensaios são conduzidos por instituições públicas e privadas em regiões representativas dos biomas Mata Atlântica e Cerrado, sob infecção natural, com híbridos suscetíveis e delineamento em blocos casualizados. As aplicações de fungicidas ocorrem em estádios fenológicos críticos, e a severidade das principais manchas foliares e ferrugens é avaliada periodicamente com escalas diagramáticas. A severidade é sumarizada por AACPD, a produtividade é estimada nas parcelas úteis e as análises incluem abordagens tradicionais e metanalíticas. O protocolo visa garantir comparabilidade entre ensaios e subsidiar decisões técnicas sobre manejo químico do milho.

Wheat blast, caused by the Pyricularia oryzae Triticum lineage, is a major constraint to wheat expansion in the Brazilian Cerrado. Delayed sowing is often recommended for disease avoidance; however, late sowing can increase the risk of water deficit. This study quantifies the joint risks from head blast and water deficit in the Brazilian Cerrado to inform sowing-date recommendations. Using daily weather data from a 62-year period (1961 to 2023), crop phenology was simulated based on an accumulated growing degree day approach. Heading and maturity dates were estimated for six sowing dates defined at 10-day intervals across the sowing window, starting on 25 February. Epidemic probability was calculated using a logistic model, and water-deficit events were identified based on an accumulated effective precipitation threshold (150 mm) during crop growth. For each pixel within the potential wheat area in the Cerrado and for each sowing date, we empirically estimated the probability of at least one hazard, fitted smooth pixel-wise probability curves, and identified the optimal sowing date. A spatially smoothed 7-day bin classification was used to generate a practical sowing recommendation map. In addition, we assessed the effects of the El Niño-Southern Oscillation on epidemic probability. Results showed that mid- to late March sowings generally minimize joint risk. Spatial heterogeneity highlighted regions suitable for earlier sowing and potential expansion. El Niño/La Niña events amplified/suppressed epidemic probability, particularly for intermediate sowing dates for which sensitivity was greatest. These findings support region-specific, multi-hazard sowing recommendations and the incorporation of climate-variability signals into decision support for tropical rainfed wheat.

ABSTRACT Peanut smut, caused by Thecaphora frezzii , is an important constraint to peanut production in Argentina, but quantitative estimates of yield losses across environments remain limited. We quantified the relationship between disease incidence and kernel yield using 922 observations from 26 field studies conducted in Córdoba, Argentina, between 2021 and 2025. Study-specific incidence–yield relationships were analyzed using linear regression, random-effects meta-analysis, and linear mixed-effects models. Peanut smut incidence was consistently associated with yield reduction across studies. The estimated damage coefficient ranged from 24.2 to 28.7 kg ha⁻¹ per 1% increase in disease incidence, corresponding to a relative yield reduction of 0.74–0.87% of attainable yield. In contrast, attainable yield varied markedly among studies, ranging from 1,370 to 5,409 kg ha⁻¹. Although an exploratory segmented analysis suggested a breakpoint near 12% incidence, subsequent moderator analyses, study- specific regressions, and normalized response curves provided no evidence of a biologically meaningful change in the damage coefficient across incidence or yield classes. These results indicate that differences among environments were primarily associated with attainable yield rather than with changes in the magnitude of disease-associated yield loss. The resulting damage function provides a quantitative basis for yield-loss assessment and disease management in peanut.

Plant diseases occurring across wild and crop plants present modelling and management challenges. Wild plant and crop pathosystems differ in ecological structure, evolutionary dynamics and responsiveness to human intervention. At the interface, pathogens may spill over, spill back, persist or evolve, shaped by host diversity, dispersal processes and landscape connectivity. The potential importance of factors including pathogen dispersal, host life history and spatial configuration are examined through a qualitative comparison of case studies: Puccinia graminis, Phakopsora pachyrhizi, Xylella fastidiosa, Pyricularia oryzae Triticum lineage and Austropuccinia psidii. These examples illustrate how wild hosts may function as reservoirs, recombination partners or spillover targets, and how their role influences management efficacy and evolutionary risk. We explore the consequences of this wild-crop interface through two central questions: (i) how should plant diseases involving wild and cultivated pathosystems be managed, and (ii) what proportion of management effort should be allocated to each system? We show the principles underpinning answers to these questions via a conceptual framework based on a generic compartmental model incorporating asymmetric transmission and system-specific interventions, thereby accounting for key aspects of pathogen spread within and between wild host and crop populations. Finally, we identify critical data needs and modelling directions to better inform disease management on the wild plant-crop interface and argue for a more integrative approach bridging ecological and anthropogenic drivers of epidemics. This article is part of the theme issue 'Wild plant pathosystems'.

Soybean field trials were established in Alabama, Kentucky, and Tennessee in 2020 and 2021 to determine the effect of different strategies on management of foliar diseases. Cultivars Asgrow 53X0 (susceptible to target spot, caused by Corynespora cassiicola) and Asgrow 52X9 (moderately resistant to target spot) were planted using two different row spacings (38 and 76 cm). Foliar fungicides (pyraclostrobin or pydiflumetofen + difenoconazole) were applied at beginning pod development, and nontreated plots served as a control. Results from Kentucky trials showed lower target spot severity for Asgrow 52X9 compared with Asgrow 53X0. Both fungicide treatments significantly reduced target spot severity in the susceptible cultivar when compared with the nontreated control. Additionally, the severity of frogeye leaf spot (caused by Cercospora sojina) was evaluated, and significant effects of cultivars and fungicides were detected only in the 2020 crop season. In Tennessee, the main effect of cultivar was significant for target spot, and plots treated with pydiflumetofen + difenoconazole had significantly lower frogeye leaf spot severity compared with other treatments during the 2021 season. In Alabama, soybean rust (caused by Phakopsora pachyrhizi) was observed during both seasons, and fungicides significantly reduced disease severity compared with the nontreated control. The effect of row spacing was not significant for disease severity at any location, but the combination of cultivars and fungicides was found to be effective in protecting soybean plants against foliar diseases.Copyright (c) 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Fungicide efficacy guides, updated annually through the Crop Protection Network, inform fungicide selection for foliar and seedling diseases of corn (Zea mays) and soybean (Glycine max). These guides rank fungicides based on multistate field trials across the United States and Ontario, Canada. Trials were analyzed to validate these rankings by assessing the efficacy of fungicides under varying disease severities. Under high disease severity (>= 5%), fungicides with the best efficacy ratings significantly reduced gray leaf spot (GLS; caused by Cercospora zeae-maydis) and southern rust (SR; caused by Puccinia polysora) in corn when applied at the tasseling (VT) to silking (R1) growth stages and frogeye leaf spot (FLS; caused by Cercospora sojina) in soybean when applied at beginning pod (R3) growth stage. GLS severity was reduced by 8.6 to 8.8%, SR by 14.6 to 20.6%, and FLS by 15.3%. Corn yields were 420.4 kg/ha (6.7 bushels/acre) greater than the nontreated control, and yield response in 57.9 to 63.6% of the trials exceeded the economic breakeven point of 288.4 kg/ha (4.6 bushels/acre) for fungicide application. Soybean yields were 417.0 kg/ha (6.2 bushels/acre) greater than the nontreated control, with 83.3% of trials reaching the economic breakeven point of 134.5 kg/ha (2 bushels/acre). Under low disease severity (<5%), disease control and yield benefits diminished across all fungicide efficacy categories. These results validate the fungicide efficacy ratings as predictive tools for disease control and yield response, especially under high disease pressure, highlighting their importance for fungicide decisions in corn and soybean across the United States and Canada.

The increasing global demand for products derived from Eucalyptus spp. has stimulated its production in Brazil. However, productivity has declined in recent years due to multiple factors, with Ceratocystis wilt among the major causes. Conventional detection methods rely on visual assessment, histological sections, and/or molecular analyses—procedures that are time-consuming and impractical at scale. Proximal or remote sensing based on VIS–NIR–SWIR spectroscopy (400–2500 nm) has been proposed as non-destructive alternatives for characterizing plant biochemical and biophysical properties, yet its use for detecting Ceratocystis wilt in Eucalyptus spp. remains underexplored. Here, we evaluated whether leaf reflectance measurements in the VIS–NIR–SWIR, acquired with a proximal non-imaging sensor, can be used to detect the disease in asymptomatic cuttings (vegetatively propagated plants). For that a greenhouse experiment was established with two Eucalyptus clones, one susceptible and another resistant. Plants were visually assessed and tested via the “carrot bait” method for disease incidence, and spectral measurements collected four times between 12 and 60 hours after inoculation. Observations for inoculated plants were compared with those from non-inoculated references (total n = 77). Classification models trained with Partial Least Squares Discriminant Analysis (PLS-DA), Random Forest with Recursive Feature Elimination (RF+RFE), and Support Vector Machine with a Genetic Algorithm (SVM+GA) achieved balanced accuracy of 0.63 ± 0.11, 0.75 ± 0.11, and 0.75 ± 0.13, respectively. Features selected via RFE and GA, or identified as highly important in the PLS-DA, RF+RFE, and SVM+GA models, were mainly located within the visible, NIR, and particularly the SWIR regions. This distribution is consistent with absorption features associated with leaf water, cellulose, starch, and lignin (near 1100–1200 and 2300 nm), as well as proteins (near 1700, 2200, and 2300 nm). Spectra from the apical canopy layer generally provided better classification performance than from the basal or middle canopy sections. Despite the relatively small dataset and limited number of clones, our results demonstrate the potential of proximal spectroscopy for detecting Ceratocystis wilt in asymptomatic Eucalyptus plants.

Soybean rust (SBR), caused by the fungal pathogen Phakopsora pachyrhizi, severely threatens soybean production in Brazil, with outbreak dynamics tightly linked to climatic variability. This study evaluates how El Ni & ntilde;o Southern Oscillation (ENSO) phases modulate SBR severity and associated yield losses, employing a meta-analysis of 417 field trials across 73 Brazilian locations from 2005 to 2020. We quantified disease damage coefficients (yield loss per percentage point of disease severity) and the efficacy of management practices in protecting yields during warm (El Ni & ntilde;o), neutral and cold (La Ni & ntilde;a) ENSO phases. Results revealed that damage coefficients were significantly higher during El Ni & ntilde;o, causing disproportionately greater yield losses at equivalent disease severity levels compared to neutral and La Ni & ntilde;a phases. Notably, disease control scenarios provided the highest yield protection during El Ni & ntilde;o but were least effective during La Ni & ntilde;a. These findings highlight the critical role of ENSO-driven climatic variability in shaping SBR dynamics, influencing disease pressure and altering the effectiveness of management strategies. The study underscores the potential of integrating ENSO-based forecasts into pre-season disease outlooks to enhance resource allocation, optimise fungicide applications and improve profitability under varying climatic conditions.

Plant disease epidemiologists often work with datasets smaller than ideal for data-hungry machine-learning (ML) algorithms, thereby risking overfitting. We demonstrate how an interpretation-guided modeling approach, leveraging complex ML primarily for insight generation, can overcome this challenge, using white mold (caused by Sclerotinia sclerotiorum) in snap beans (Phaseolus vulgaris) as a case study. An observational dataset of white mold prevalence across 356 commercial snap bean fields in central and western New York State (2006 to 2008) was augmented by merging georeferenced observations with POLARIS soils data and engineered features from downscaled ERA5-Land environmental data. Functional data analysis identified weather periods associated with white mold risk, and random forests (RFs), used interpretatively, identified key predictors. Although RF models showed high apparent performance, they exhibited significant overfitting and poor calibration. Insights from RF interpretation (via SHapley Additive exPlanations analysis) guided the development of a simpler, four-predictor logistic regression model using restricted cubic splines. This simpler model was better calibrated and had acceptable discrimination (internally validated C statistic = 0.77). For smaller epidemiological datasets, our results advocate for using ML primarily as an interpretive tool to guide the development of simpler, less data-intensive, yet robust predictive models better suited for practical disease management decisions.

Maize white spot (MWS) is an important disease of maize in Brazil. This study examined the relationship between MWS severity and yield using Fisher's Z (Z) and the correlation coefficient (r). Meta-analytic and random-coefficients models were fitted to yield–disease data to estimate the regression intercept ( β_0 ) and slope ( β_1 ). The effects of moderator variables (yield class, severity class, and region) were also assessed in the random-coefficients model. Economic damage thresholds (EDTs) were then determined through simulation. A significant association was observed between white spot severity and maize yield (Z = 0.51; r = − 0.47), with both models indicating a negative relationship. In the meta-analytic model, β_0 and β_1 were estimated at 7,616.61 kg/ha and − 60.22, respectively; in the random-coefficients model, they were 7,555.61 kg/ha and − 57.53. No moderator significantly affected the slope (P > 0.06), but yield class had a significant effect on the intercept (P < 0.01). The MWS damage coefficient was − 0.80

Background: Fusarium wilt of banana (FWB), caused by Fusarium oxysporum f. sp. cubense (Foc) Tropical Race 4 (TR4), is one of the most severe threats to global banana production. The confirmed presence of Foc TR4 in neighboring countries (Colombia, Peru, and Venezuela) heightens the risk of its introduction into Brazil, with potentially serious socio-economic impacts. Methods: This study assessed the risk of Foc TR4 establishment and spread on 387 banana farms in the Vale do Ribeira region, S & atilde;o Paulo, using survey data on geographic (location), biosecurity, and phytosanitary categories. Each of 25 questions was assigned a different weight (0 to 5 points) defined by a panel of three experts based on the theoretical impact of each response in the respective category. A normalized risk index (0-1) (total points divided by maximum points) was calculated for each farm. Results: Results showed that most farms were located near roads, urban areas, and watercourses, and clustered within banana-producing zones, conditions that increase the risk of pathogen entry and spread. Biosafety adoption was critically low: 95% of farms lacked disinfection protocols, fencing, or visitor controls, and only 26% provided training on pests or diseases. Conventional propagation practices and the use of shared equipment further elevated phytosanitary risks. Although awareness of FWB symptoms was high, management of Foc Race 1 (R1) remained reactive, relying on replacement of R1-susceptible cultivars such as Prata (Pomme, AAB) and Ma & ccedil;& atilde; (Silk, AAB) with the R1-resistant but TR4-susceptible Cavendish (AAA). The overall risk ranged from 0.22 to 1.00 (mean = 0.63). When stratified by question category, the mean risk values were: location (0.70), biosecurity (0.66), and phytosanitary condition (0.40). Spatial analyses revealed high-risk clusters associated with proximity to highways, rivers, and urban areas. Conclusions: Based on these findings, mitigation strategies are proposed, including the implementation of biosecurity protocols, producer training, and regional contingency planning. This study provides technical and spatial evidence to support plant protection services in deploying preventive actions in one of Brazil's most important banana-producing regions.

Quantification of plant disease severity is key for plant pathology research, particularly in the evaluation of disease management strategies. Visual estimation of severity remains widely used, especially in field experiments. Training sessions and the use of standard area diagram sets (SADs) are known to enhance rater accuracy. In this study, we aimed to quantify and compare the benefits of these tools, either used alone or in combination, when visually assessing peanut late leaf spot severity. We designed and validated SADs to aid in disease severity estimation and also evaluated the training tool TraineR2, a web-based app that contains actual images of the disease with known severity. Our results show that both tools led to a significant improvement in rater accuracy after their use. For TraineR2, the gains in overall accuracy (ρc from 0.82 to 0.91) and precision (Pearson's r from 0.73 to 0.88) were slightly lower than those obtained with the SADs (ρc from 0.89 to 0.96 and Pearson's r from 0.85 to 0.95). When training and SADs were combined, the overall accuracy was 0.97, and Pearson's r was 0.96, values statistically similar to those achieved using SADs alone. Regarding inter-rater reliability, evaluated based on the intraclass correlation coefficient (ICC), using SADs and training together resulted in an ICC of 0.95, which was higher than using SADs alone (0.93) or training alone (0.84). Our study confirms the utility of combining training sessions and SADs for improving the accuracy of plant disease assessments.

Maize white spot (MWS) is one of the most damaging foliar diseases affecting maize production in Brazil, frequently leading to substantial yield losses. To assess how different fungicide chemistry influences disease control and yield response, we analyzed data from 87 independent field trials conducted over a nine-year period (2016-2024) across five Brazilian states and Distrito Federal. Eight fungicide treatments were included, each evaluated in at least 19 trials where disease severity was estimated. Two treatments involved solo active ingredients (MANCozeb and ChLORothalonil), while six were premix formulations (DIFenoconazole + PYDIflumetofen, PYRAclostrobin + EPOXiconazole, PYRAclostrobin + FLUXapyroxad, AZOxystrobin + TEBUconazole + MANCozeb, PYRAclostrobin + FLUXapyroxad + MEFEntrifluconazole, and TRiFloXystrobin + PROThioconazole + BIXafen). Percent control, estimated by back-transformation from a log-scale network meta-analysis, ranged from 53.2 % to 71.3 %. All treatments, except PYRA + EPOX and MANC, achieved mean efficacy values above 60 %. Yield responses from three sequential fungicide applications ranged from 694 to 1081 kg ha(-1), with the highest increases observed for DIF + PYDI, PYRA + FLUX + MEFE, and TRFX + PROT + BIX. These findings, derived from nearly a decade of field research, reinforce the importance of fungicide applications in reducing maize white spot severity and protecting yield. Moreover, they support current resistance management strategies that advocate for the combination of site-specific and multisite fungicides to optimize disease control and sustain long-term effectiveness.

Target spot, caused by Corynespora cassiicola, is an economically important disease of soybean, especially in Brazil. The performance of two commonly used fungicide premixes: a two-way (fluxapyroxad + pyraclostrobin) and a three-way (bixafen + prothioconazole + trifloxystrobin) premix for the control of target spot, have been tested over seven growing seasons (2016/2017 to 2022/2023) across six Brazilian states (MT, MS, DF, GO, TO, and PR). A network meta-analysis model was fitted to disease severity and yield data from 22 trials, and expanded to include harvest year as a continuous moderator. The overall disease control efficacy was higher (61.3