An efficient method for screening rice breeding lines against races of Magnaporthe oryzae.

Rice blast, caused by Magnaporthe oryzae, is the most destructive rice disease worldwide. The disease symptoms are usually expressed on the leaf and panicle. The leaf disease intensity in controlled environmental conditions is frequently quantified using a 0-5 scale, where 0 represents the absence of symptoms and 5 represents large eyespot lesions. However, this scale restricts the qualitative classification of the varieties into intermediate resistant and susceptible categories. Here we develop a 0-6 scale for blast disease that allows proper assignment of rice breeding lines and varieties into six resistance levels (highly resistant, resistant, moderate resistant, moderate susceptible, susceptible, and highly susceptible). We evaluated 41 common rice varieties against four major blast races (IB1, IB17, IB49, and IE1-K). Varieties carrying the Pi-ta gene were either highly resistant, resistant, or moderate resistant to IB17. The IE1-K race was able to break Pi-ta-mediate resistance of the rice varieties. The Pi-z gene conferred resistance to the IB17 and IE1-K races. The varieties M201, Cheniere, and Frontier were highly susceptible (score 6; 100% disease) to the race IE1-K. Moreover, varieties that were resistant or susceptible to all four blast races also showed similar levels of resistance/susceptibility to blast disease in the field. Taken together, our data proved that the 0-6 blast scale can efficiently determine the resistance levels of rice varieties against major blast races. This robust method will assist rice breeding programs to incorporate durable resistance against major and emerging blast races.

Phyllachora species infecting maize and other grass species in the Americas represents a complex of closely related species.

The genus Phyllachora contains numerous obligate fungal parasites that produce raised, melanized structures called stromata on their plant hosts referred to as tar spot. Members of this genus are known to infect many grass species but generally do not cause significant damage or defoliation, with the exception of P. maydis which has emerged as an important pathogen of maize throughout the Americas, but the origin of this pathogen remains unknown. To date, species designations for Phyllachora have been based on host associations and morphology, and most species are assumed to be host specific. We assessed the sequence diversity of 186 single stroma isolates collected from 16 hosts representing 15 countries. Samples included both herbarium and contemporary strains that covered a temporal range from 1905 to 2019. These 186 isolates were grouped into five distinct species with strong bootstrap support. We found three closely related, but genetically distinct groups of Phyllachora are capable of infecting maize in the United States, we refer to these as the P. maydis species complex. Based on herbarium specimens, we hypothesize that these three groups in the P. maydis species complex originated from Central America, Mexico, and the Caribbean. Although two of these groups were only found on maize, the third and largest group contained contemporary strains found on maize and other grass hosts, as well as herbarium specimens from maize and other grasses that include 10 species of Phyllachora. The herbarium specimens were previously identified based on morphology and host association. This work represents the first attempt at molecular characterization of Phyllachora species infecting grass hosts and indicates some Phyllachora species can infect a broad range of host species and there may be significant synonymy in the Phyllachora genus.

Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol.

Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm. The presence and severity of GER predicted DON risk with an accuracy of 0.81 and 0.87, respectively. Temperature, relative humidity, surface wetness, and rainfall were used to generate 37 weather-based predictor variables summarized over each of six 15-day windows relative to maize silking (R1). With these variables, least absolute shrinkage and selection operator (LASSO) followed by all-subsets variable selection and logistic regression with 10-fold cross-validation were used to build single-window weather-based models, from which 11 with one or two predictors were selected based on performance metrics and simplicity. LASSO logistic regression was also used to build more complex multiwindow models with up to 22 predictors. The performance of the best single-window models was comparable to that of the best multiwindow models, with accuracy ranging from 0.81 to 0.83 for the former and 0.83 to 0.87 for the latter group of models. These results indicated that the risk of DON ≥1 ppm can be accurately predicted with simple models built using temperature- and moisture-based predictors from a single window. These models will be the foundation for developing tools to predict the risk of DON contamination of maize grain.

A Quantitative Synthesis of the Efficacy and Profitability of Conventional and Biological Fungicides for Botrytis Fruit Rot Management on Strawberry in Florida.

Botrytis fruit rot (BFR) is a major disease that affects strawberry production in Florida and worldwide. BFR management relies on frequent fungicide applications. A meta-analysis was conducted on the outcomes from nine field trials to evaluate the efficacy and profitability of conventional and biological fungicides compared with a nontreated control (NTC). All trials were conducted in Florida between the 2005/06 and 2016/17 growing seasons. Fungicide treatments were applied weekly, and plots were harvested twice a week for yield and BFR incidence quantification. Treatments were grouped into four categories: NTC, multisite only (Thiram), Standard (captan alternated with fludioxonil + cyprodinil), and Bacillus. Following primary analyses, a random effects network meta-analytical model was fitted to estimate the mean yield and BFR incidence responses for each treatment group and to compare means between pairs of groups. The Thiram and the Standard treatment groups increased yield by 378.8 and 502.2 kg/ha/week, respectively, compared with the NTC. The yield difference between Bacillus and NTC was not statistically significant. Besides increasing yield, Thiram and Standard also reduced BFR incidence by approximately 10% compared with the NTC. The mean yield responses and among-study variability from the meta-analysis were used to estimate the probability of a given yield response in a new future trial. The Standard and Thiram treatment groups showed higher estimated probabilities of increasing yield and resulting in a profitable return on application investments than the Bacillus group of treatments. The results from this study provide growers with information that will aid their decision-making process regarding BFR management.

Effects of Pre- and Postanthesis Applications of Demethylation Inhibitor Fungicides on Fusarium Head Blight and Deoxynivalenol in Spring and Winter Wheat.

Anthesis is generally recommended as the optimum growth stage for applying a foliar fungicide to manage Fusarium head blight (FHB) and the Fusarium-associated toxin deoxynivalenol (DON) in wheat. However, because it is not always possible to treat fields at anthesis, studies were conducted to evaluate pre- and postanthesis treatment options for managing FHB and DON in spring and winter wheat. Network meta-analytical models were fitted to data from 19 years of fungicide trials, and log response ratio ([Formula: see text]) and approximate percent control ([Formula: see text]) relative to a nontreated check were estimated as measures of the effects of six treatments on FHB index (IND: mean percentage of diseased spikelets per spike) and DON. The evaluated treatments consisted of either Caramba (metconazole) applied early (at heading [CE]), at anthesis (CA), or late (5 to 7 days after anthesis; CL), or Prosaro (prothioconazole + tebuconazole) applied at the same three times and referred to as PE, PA, and PL, respectively. All treatments reduced mean IND and DON relative to the nontreated check, but the magnitude of the effect varied with timing and wheat type. CA and PA resulted in the highest [Formula: see text] values for IND, 52.2 and 51.5%, respectively, compared with 45.9% for CL, 41.3% for PL, and less than 33% for CE and PE. Anthesis and postanthesis treatments reduced mean IND by 14.9 to 29.7% relative to preanthesis treatments. The estimated effect size was also statistically significant for comparisons between CA and CL and PA and PL; CA reduced IND by 11.7% relative to CL, whereas PA reduced the disease by 17.4% relative to PL. Differences in efficacy against IND between pairs of prothioconazole + tebuconazole and metconazole treatments applied at the same timing (CE versus PE, CA versus PA, and CL versus PL) were not statistically significant. However, CA and CL outperformed PA and PL by 7 and 12.8%, respectively, in terms of efficacy against DON. All application programs had comparable efficacy against IND between spring and winter wheat types, but efficacy against DON was 10 to 16% greater for spring than winter wheat for applications made at or after anthesis. All programs led to an increase in mean grain yield and test weight relative to the nontreated check.

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.

Meta-analysis of the relationship between crop yield and soybean rust severity.

Meta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k=231) and regression (k=210) analysis for the Y-S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher's transformation of the Pearson's correlation coefficient (Zr) and the intercept (ß0) and slope (ß1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (70%, moderate=>40 and ≤70%, and low=≤40% S the check treatment), and growing season. The overall mean for r- (back-transformed Z-r) was -0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in Z-r. Stronger associations (r-=-0.87 and -0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP>70%) and earliest rust onset (DOT0.73 pp/%(-1)) were estimated for studies with DOT70%; the latter possibly due to high fungicide efficacy when DP is low, thus leading to higher yield differences between fungicide-protected and nontreated plots. The critical-point meta-analytic models can provide general estimates of yield loss based on a composite measure of disease severity. They can also be useful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development.