Progresso temporal da brusone do trigo em função do inóculo primário, da aplicação de fungicida e da resistência dos genótipos

Gomes, Delineide Pereira; Rocha, Valterley Soares; Rocha, João Romero Amaral Santos de Carvalho; Souza, Moacil Alves de; Pereira, Olinto Liparini

doi:10.1590/0100-5405/187354

Cited by 10 publications

(1 citation statement)

References 21 publications

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“…The logistic model outperformed the Gompertz model when describing DPCs for W L B and W S B (10 out of 16 curves). These results agree with previous findings on W L B and W S B temporal progression, where the logistic model best described WB disease progression, indicating polycyclic‐like epidemics (Gomes et al, 2019; Gongora‐Canul et al, 2020; Mills et al, 2021). Furthermore, Fernández‐Campos et al (2020) modelled the W L B and W S B temporal dynamics in over 10 cultivars with different levels of resistance, describing a polycyclic‐like disease cycle characterized by the logistic model.…”

Section: Discussionsupporting

confidence: 92%

Multiscale temporal and spatiotemporal analysis of wheat blast infection‐like dynamics using vertical plant stratification, regression and Markov chain approaches

García‐Peña,

Ramírez‐Gil,

Góngora‐Canul

et al. 2023

Plant Pathology

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The spatiotemporal progress of wheat blast (WB) epidemics within the plant canopy remains poorly known due to complex pathogen–host–environment interactions. Although deterministic methods are popular and useful, robust stochastic methods, such as generalized additive models for location, scale and shape (GAMLSS) and probability matrix or Markov transition model (MTM), have seldom been used to analyse plant disease epidemics. Hence, both methods were employed to derive valuable insights into WB epidemiology at the vertical canopy level. We conducted experiments in three climatic zones in Bolivia, using three wheat cultivars, with disease data corresponding to different canopy positions (lower, L; middle, M; flag leaf, F; and spike, S). Using WB severity data (AUDPC, progress rate and maximum severity [Kmax]), we implemented the GAMLSS and MTM to test our hypothesis that WB is affected by host resistance, location and canopy level. Results showed that the AUDPC, progress rate and Kmax differed across sites, cultivars and canopy positions. Clearly, L and M canopies showed a lower progress rate than F and S. The disease showed an ascending movement from L and M canopies to F and S across locations and cultivars. However, descending transitions also occurred from M to L early or F to M canopy later in the season. Both ascending and descending movements can arise at a single state or several recurrent states, indicating indirect evidence of autoinfection within the canopy before and after spike emergence. Our findings contribute knowledge to improve monitoring and managing WB.

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Section: Discussionsupporting

confidence: 92%

Multiscale temporal and spatiotemporal analysis of wheat blast infection‐like dynamics using vertical plant stratification, regression and Markov chain approaches

García‐Peña,

Ramírez‐Gil,

Góngora‐Canul

et al. 2023

Plant Pathology

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Envirotypes applied to evaluate the adaptability and stability of wheat genotypes in the tropical region in Brazil

Casagrande,

Mezzomo,

de Sousa

et al. 2024

Euphytica

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

et al. 2021

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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), Okinawa1 (OK1) and Okinawa2 (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 x 10 grid (n = 100 clusters of spikes), beginning at head emergence (Feekes growth stage 10.5), for a total of 9 assessments at CC, 6 at OK1, and 6 at OK2. Spike blast increased over time for as many as 20-30 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, 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 rL 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 to 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%.

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Progresso temporal da brusone do trigo em função do inóculo primário, da aplicação de fungicida e da resistência dos genótipos

Cited by 10 publications

References 21 publications

Multiscale temporal and spatiotemporal analysis of wheat blast infection‐like dynamics using vertical plant stratification, regression and Markov chain approaches

Multiscale temporal and spatiotemporal analysis of wheat blast infection‐like dynamics using vertical plant stratification, regression and Markov chain approaches

Envirotypes applied to evaluate the adaptability and stability of wheat genotypes in the tropical region in Brazil

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

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