A new mechanistic model of weather-dependent Septoria tritici blotch disease risk

Abstract

We present a new mechanistic model for predicting Septoria tritici blotch (STB) disease, parameterized with experimentally derived data for temperature- and wetness-dependent germination, growth and death of the causal agent, Zymoseptoria tritici . The output of this model (A) was compared with observed disease data for UK wheat over the period 2002–2016. In addition, we compared the output of a second model (B), in which experimentally derived parameters were replaced by a modified version of a published Z. tritici thermal performance equation, with the same observed disease data. Neither model predicted observed annual disease, but model A was able to differentiate UK regions with differing average disease risks over the entire period. The greatest limitations of both models are: broad spatial resolution of the climate data, and lack of host parameters. Model B is further limited by its lack of explicitly defined pathogen death, leading to a cumulative overestimation of disease over the course of the growing season. Comparison of models A and B demonstrates the importance of accounting for the temperature-dependency of pathogen processes important in the initiation and progression of disease. However, effective modelling of STB will probably require similar experimentally derived parameters for host and environmental factors, completing the disease triangle. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Monitoring of Natural Occurrence and Severity of Leaf and Glume Blotch Diseases of Winter Wheat and Winter Triticale Incited by Necrotrophic Fungi Parastagonospora spp. and Zymoseptoria tritici

“…However, when considering worldwide distribution, geographical latitude had an impact on the incidence of Z. tritici [3]. In other studies, geographical coordinates were useful features in the modelling of the onset of necrotrophic fungal diseases [52,53,55]. In the present study, 12 meteorological factors correlated linearly with the Z. tritici incidence, seven significant factors were identified from January to June and 5 factors were associated with the early-season months from July through December (Table 4).…”

“…On the contrary, a positive correlation of Z. tritici occurrence with air temperature factors such as the average maximum temperature, the monthly average temperature and the average minimum temperature was observed in February, June, August and September (r values from 0.232 to 0.262). In the studies of Chaloner et al [55], temperature-and wetness-dependent germination was one of the most important factors associated with the growth rate of Z. tritici mycelium. The results of these studies were used to build a Z. tritici risk assessment model [55].…”

The septoria leaf blotch of wheat in Central Kazakhstan: prognosis, evaluation and monitoring with remotely sensed data

“…Those parts of the wheat field, where the SLBS values are significantly higher than the average value for the given field, must attract the immediate attention of the farmer and, most likely, will require fungicide injection as a focus of developing an infection. The modeling of potential wheat disease development and disease severity is the subject of current studies (Chaloner et al,2019;Beest et al, 2009;Savary et al, 2015). There is a number of issues associated with predictive disease models (Minchinton et al, 2008):…”

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…Bebber [65] uses hourly microclimate data to describe the invasion of black sigatoka disease in banana plants. Chaloner et al [66] discuss the challenges of resolving the spatio-temporal scales of climate data with host data for septoria leaf blotch disease of wheat. We hope that these illuminating examples will lead to a more widespread interest in plant disease modelling among other epidemiological modellers.…”

“…[58,59]). Important themes include interactions between different pathogens [51] or different strains of the same pathogen [63], pathogen evolution to escape interventions [95], and the impact of weather or climate on the dynamics of epidemics in human and animal populations [75,96] as well as plant populations [64][65][66].…”