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

Abstract

Predicting infections by Puccinia striiformis f. sp. tritici, with sufficient 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 effect 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.

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

“…The second concentrates on the change in spectrum when crops are infected with diseases, describing crop stress by spectral features extracted from various sensors. Because the appropriate temperature, rainfall, and relative humidity around wheat are the main factors for the reproduction of the pathogen [7], a method to judge the severity of disease in the field by combining weather forecast data with microclimate measurements has been developed [8][9][10]. Besides this, the change in crop structure and pigment in the process of the highly specific disease will result in a change in reflectance.…”

Puccinia Spore Concentrations in Relation to Weather Factors and Phenological Development of a Wheat Crop in Northwestern Spain

“…The severity of rust epidemics depends on the timing of infection by the primary inoculum, plant resistance, and the climatic conditions [13]. Various disease models with different levels of complexity and data requirements have been developed worldwide to predict rust progression in wheat [10,[13][14][15][16][17]. These models are site-specific, due to the climatic variability that affects spore dispersal and deposition.…”

“…Previous studies on the detection of P. conducted in the air of wheat-growing areas utilized volumetric collectors and non-viable identification methodologies [5,[11][12][13]. By combining the aerobiological study of rust incidence and severity data with meteorological data, models for rust forecasting have been developed in Canada, the United States, Mexico, Argentina, India, Ethiopia, and Morocco [10,[13][14][15][16][17]. Phenological studies should also be considered to define regularities in the crop's growth in relation to its environment and enable the application of disease control measures at the appropriate time [18].…”

Deciphering field resistance to powdery mildew and yellow rust among popular cultivars of wheat and set of differential lines

“…Mean severity of YR was highest at Kukumseri and the lowest at Malan. The maximum and minimum temperature of 23.60ºC and 13.4ºC and RH 45.08% at Kukumseri and Palampur (2017-18) i.e., 22.50ºC and 9.67ºC and RH of 51.78% were highly favorable for the development of PM and YR as the diseases require low temperature for development (Singh and Pannu 2014;EI Jarroudi et al 2020) (Table 1). The inconsistency in disease severity at different locations might be due to evolution in the pathotypes of the pathogens in NHZ, variability among the genotypes, or both (Aggarwal et al 2018;Vikas et al 2020).…”