Quantifying the Effects of Temperature and Relative Humidity on the Development of Wheat Blast Incited by the Lolium Pathotype of Magnaporthe oryzae

Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

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.

Night warming at the vegetative stage improves pre-anthesis photosynthesis and plant productivity involved in grain yield of winter wheat

We conducted pot experiments during the 2018-2020 growing seasons to study the effects of night warming at different growth stages of wheat on the photosynthetic performance; accumulation, transportation, and distribution of dry matter; and grain yield of winter wheat. Night warming at all the different growth stages resulted in an elevation of wheat yield by increasing the 1000-grain weight and the number of grains per ear. Night warming during the period from jointing to booting stage resulted in the greatest increase in wheat yield. It also increased the amount of overall dry matter and transferrable amount of dry matter in plants and increased the distribution of dry matter to grains to increase grain weight. Night warming treatments at three different growth stages enhanced pre-anthesis photosynthetic capacity by increasing flag leaf net photosynthetic rate, chlorophyll content, and photochemical efficiency of winter wheat at the early stage of grain filling, especially in the night warming treatment from jointing to booting stage. Night warming not only increased the stomatal density and stomatal index of wheat leaves but also increased stomatal conductance and transpiration rate in the early stage of grain filling, thus being conducive to the smooth progress of photosynthesis. In conclusion, night warming treatment at different growth stages increased the photosynthesis of flag leaves at the early stage of grain filling, and promoted the accumulation of dry matter in plants after anthesis, which was conducive to the grain yield of winter wheat.

Effector Genes in Magnaporthe oryzae Triticum as Potential Targets for Incorporating Blast Resistance in Wheat

Wheat blast (WB), caused by Magnaporthe oryzae Triticum pathotype, recently emerged as a destructive disease that threatens global wheat production. Because few sources of genetic resistance have been identified in wheat, genetic transformation of wheat with rice blast resistance genes could expand resistance to WB. We evaluated the presence/absence of homologs of rice blast effector genes in Triticum isolates with the aim of identifying avirulence genes in field populations whose cognate rice resistance genes could potentially confer resistance to WB. We also assessed presence of the wheat pathogen AVR-Rmg8 gene and identified new alleles. A total of 102 isolates collected in Brazil, Bolivia, and Paraguay from 1986 to 2018 were evaluated by PCR using 21 pairs of gene-specific primers. Effector gene composition was highly variable, with homologs to AvrPiz-t, AVR-Pi9, AVR-Pi54, and ACE1 showing the highest amplification frequencies (>94%). We identified Triticum isolates with a functional AvrPiz-t homolog that triggers Piz-t-mediated resistance in the rice pathosystem and produced transgenic wheat plants expressing the rice Piz-t gene. Seedlings and heads of the transgenic lines were challenged with isolate T25 carrying functional AvrPiz-t. Although slight decreases in the percentage of diseased spikelets and leaf area infected were observed in two transgenic lines, our results indicated that Piz-t did not confer useful WB resistance. Monitoring of avirulence genes in populations is fundamental to identifying effective resistance genes for incorporation into wheat by conventional breeding or transgenesis. Based on avirulence gene distributions, rice resistance genes Pi9 and Pi54 might be candidates for future studies.

Comparing the Temporal Development of Wheat Spike Blast Epidemics in a Region of Bolivia Where the Disease Is Endemic

Epidemics of wheat blast, caused the Triticum pathotype of Magnaporthe oryzae, were studied in the Santa Cruz del la Sierra region of Bolivia to quantify and compare the temporal dynamics of the disease under different growing conditions. Six plots of a susceptible wheat cultivar were planted at Cuatro Cañadas (CC), Okinawa 1 (OK1), and Okinawa 2 (OK2) in 2015. Spike blast incidence (INC) and severity (SEV) and leaf blast severity (LEAF) were quantified in each plot at regular intervals on a 10 × 10 grid (n = 100 clusters of spikes), beginning at head emergence (Feekes growth stage 10.5), for a total of nine assessments at CC, six at OK1, and six at OK2. Spike blast increased over time for 20 to 30 days before approaching a mean INC of 100% and a mean SEV of 60 to 75%. The logistic model was the most appropriate for describing the temporal dynamics of spike blast. The highest absolute rates of disease increase occurred earliest at OK1 and latest at OK2, and in all cases it coincided with major rain events. Estimated y₀ values (initial blast intensity) were significantly (P < 0.05) higher at OK1 than at CC or OK2, whereas r_L values (the logistic rate parameter) were significantly higher at OK2 than at CC or OK1. It took about 10 fewer days for SEV to reach 10, 15, or 20% at OK1 compared with OK2 and CC. Based on survival analyses, the survivor functions for time to 10, 15 and 20% SEV (ts) were significantly different between OK1 and the other locations, with the probabilities of SEV reaching the thresholds being highest at OK1. LEAF at 21 days after Feekes 10.5 had a significant effect on ts at OK1. For every 5% increase in LEAF, the chance of SEV reaching the thresholds by day 21 increased by 30 to 55%.

Stability of Hybrid Maize Reaction to Gibberella Ear Rot and Deoxynivalenol Contamination of Grain

Trials were conducted to quantify the stability (or lack of G × E interaction) of 15 maize hybrids to Gibberella ear rot (GER; caused by Fusarium graminearum) and deoxynivalenol (DON) contamination of grain across 30 Ohio environments (3 years × 10 locations). In each environment, one plot of each hybrid was planted and 10 ears per plot were inoculated via the silk channel. GER severity (proportion of ear area diseased) and DON contamination of grain (ppm) were quantified. Multiple rank-based methods, including Kendall's concordance coefficient (W) and Piepho's U, were used to quantify hybrid stability. The results found insufficient evidence to suggest crossover G × E interaction of ranks, with W greater than zero for GER (W = 0.28) and DON (W = 0.26), and U not statistically significant for either variable (P > 0.20). Linear mixed models (LMMs) were also used to quantify hybrid stability, accounting for crossover or noncrossover G × E interaction of transformed observed data. Based on information criteria and likelihood ratio tests for GER and DON response variables, the models with more complex variance-covariance structures-heterogeneous compound symmetry and factor-analytic-provided a better fit than the model with the simpler compound symmetry structure, indicating that one or more hybrids differed in stability. Overall, hybrids were stable based on rank-based methods, which indicated a lack of crossover G × E interaction, but the LMMs identified a few hybrids that were sensitive to environment. Resistant hybrids were generally more stable than susceptible hybrids.