Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg

Dynamics of hybrid switching diffusions SIRS model

peer reviewedThe main aim of this paper is to study the effect of the environmental noises in the asymptotic properties of a stochastic version of the classical SIRS epidemic model. The model studied here include white noise and telegraph noise modeled by Markovian switching. We obtained conditions for extinction both in probability one and in pth moment. We also established the persistence of disease under different conditions on the intensities of noises, the parameters of the model and the stationary distribution of the Markov chain. The highlight point of our work is that our conditions are sufficient and almost necessary for extinction and persistence of the epidemic. The presented results are demonstrated by numerical simulations

Mathematical modelling of non-local spore dispersion of wind-borne pathogens causing fungal diseases

peer reviewedaudience: researcher, professional, student, popularization, otherTheoretical description of epidemics of plant diseases is an invaluable resource for their efficient management. Here we propose a mathematical model for describing the dispersal by wind of fungal pathogens in plant populations. The dispersal of pathogen spores was modelled using a non-local diffusion equation which took into account variations in wind velocity components and contained a threshold in the convolution kernel defining the non-local diffusion term. The model was analyzed and the epidemic levels and patterns of the plant disease were derived, based upon defined assumptions of the time and space variables (i.e., represented by continuous parameters), and the host population (i.e., fixed population size). Numerical applications were then performed using reported characteristic values for wheat leaf rust, stripe rust and stem rust

A lubricant boundary condition for a biological body lined by a thin heterogeneous biofilm

We study the asymptotic behavior of an incompressible viscous fluid flow in a biological body lined by a thin biological film with a cellular microstructure, varying thickness, and a heterogeneous viscosity regulated by a time random process. Letting the thickness of the film tend to zero, we derive an effective biological slip boundary condition on the boundary of the body. This law relates the tangential fluxes to the tangential velocities via a proportional coefficient corresponding to the energy of some local problem. This law describes the ability of the biological film to function as a lubricant reducing friction at the wall of the body. The tangential velocities are functions of the random trajectories of a finely concentrated biological particle

Employing weather-based disease model and machine learning techniques for optimal control of wheat stripe rust in Morocco

peer reviewedWheat stripe rust (WSR, caused by Puccinia striiformis Westend) is among the most important crop diseases causing a continuous threat to wheat production worldwide. In most seasons in temperate countries, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of P. striiformis, which are dispersed from distances of a few centimeters to thousands of kilometers, where they might reach a susceptible host plant. Weather-based systems, or weather-based systems combined with other disease or agronomic variables have been implemented in decision-support systems (DSS) to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss. Given WSR is becoming a major threat in wheat-producing regions in Morocco, a DSS integrating a disease risk model would help limiting potentially harmful side effects of fungicide applications while ensuring economic benefits. The main objective of this study is to develop a threshold-based weather model for predicting in-season WSR progress in selected wheat-producing regions (i.e., Sais, Gharb, Middle Atlas, Tadla, Zair, Zemmour, Pre-Rif, High Atlas and Oasis) in Morocco. The threshold-based weather modelling approach has been successfully applied for predicting WSR in Belgium and Luxembourg. Data collected during two consecutive crop seasons in 2010-2011 at the selected sites will be used to test the modelling approach in Morocco. Machine learning techniques including Random Forest, Multivariate Adaptive Regression Splines, and Naïve Bayes Algorithm will also be investigated to improve the model. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. As such, this study is a validation case study of the threshold-based weather modelling approach. Moreover, it explores the potential utility of coupling artificial intelligence algorithms with plant disease models in decision support systems as an effort to improve sustainable wheat production in Morocco

Weather conditions conducive to infection of winter wheat by Puccinia striiformis sp. tritici race ‘warrior’

Wheat stripe rust (WSR) (caused by Puccinia striiformis sp. tritici) continues to be a major threat in most wheat growing regions of the world, with potential to inflict regular yield losses when susceptible cultivars are grown and weather conditions are favourable. A recent isolated strain of P. striiformis sp. tritici, warrior, first identified in 2011 in Europe, is now virulent on adult plants of susceptible wheat cultivars across most of wheat growing regions, including Luxembourg. Daily weather conditions were monitored and related to development of WSR during the 2012-2014 period in Luxembourg. Favourable weather conditions were determined by (i) analysing Dennis model outputs generated through a Monte Carlo method, and (ii) identifying the best correlation between the frequencies of weather condition classes and the area under the disease progress curve on the uppermost three leaves (L1, L2, and L3; L1 being the flag leaf). Our results showed that combined weather conditions, including relative humidity >92% for ≥4 hours and air temperatures between 4°C and 16°C for ≥36 hours are necessary for WLR development, assuming inoculum is available. Furthermore, comparisons with reported WLR outbreaks in previous years showed that in recent years the disease is occurring at earlier stages in the growing season, suggesting a likely effect of climate change and/or climate variability

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A threshold-based weather model for predicting stripe rust infection in winter wheat

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999-2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity > 92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤ 0.1 mm (with the dekad having these conditions for 5-20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥ 0.90 and false alarm ratios were ≤ 0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications

Weather-Based Predictive Modeling of Wheat Stripe Rust Infection in Morocco

Predicting infections by Puccinia striiformis f. sp. tritici, with su cient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR) epidemics that might otherwise lead to yield loss. Despite the increasing threat of WSR to wheat production in Morocco, a model for predicting WSR infection events has yet to be developed. In this study, data collected during two consecutive cropping seasons in 2018–2019 in bread and durum wheat fields at nine representative sites (98 and 99 fields in 2018 and 2019, respectively) were used to develop a weather-based model for predicting infections by P. striiformis. Varying levels of WSR incidence and severity were observed according to the site, year, and wheat species. A combined e ect of relative humidity > 90%, rainfall 0.1 mm, and temperature ranging from 8 to 16 C for a minimum of 4 continuous hours (with the week having these conditions for 5% to 10% of the time) during March–May were optimum to the development of WSR epidemics. Using the weather-based model, WSR infections were satisfactorily predicted, with probabilities of detection 0.92, critical success index ranging from 0.68 to 0.87, and false alarm ratio ranging from 0.10 to 0.32. Our findings could serve as a basis for developing a decision support tool for guiding on-farm WSR disease management, which could help ensure a sustainable and environmentally friendly wheat production in Morocco