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 to 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 to 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.

Brown rust disease control in winter wheat: I. Exploring an approach for disease progression based on night weather conditions

An empirical approach for simulating the infection and progress of leaf rust (caused by Puccinia triticina) during stem elongation on winter wheat was analysed for the 2000 to 2006 growing seasons. The approach was elaborated based on night weather conditions (i.e., air temperature, relative humidity and rainfall) and leaf rust occurrences. Data from three consecutive cropping seasons (2000-2002) at four representative sites of the Grand-Duchy of Luxembourg were used in the set-up phase. The capability to correctly simulate the occurrence expression of P. triticina infections on the upper leaf layers was then assessed over the 2003-2006 period. Our study revealed that the development of leaf rust required a period of at least 12 consecutive hours with air temperatures ranging between 8 and 16 °C, a relative humidity greater than 60 % (optimal values being 12-16 °C and up to 80 % for air temperatures and relative humidity, respectively) and rainfall less than 1 mm. Moreover, leaf rust occurrences and infections were satisfactorily simulated. The false alarm ratio was ranged from 0.06 to 0.20 in all the study sites. The probability of detection and critical success index for WLR infection were also close to 1 (perfect score).

Brown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall) and a mechanistic model for leaf emergence and development simulation (i.e., PROCULTURE) was tested in order to schedule fungicide time spray for controlling leaf rust progress in wheat fields. Experiments including a single fungicide treatment based upon the DSS along with double and triple treatment were carried out over the 2007-2009 cropping seasons in four representative Luxembourgish wheat field locations. The study showed that the WLR occurrences and severities differed according to the site, cultivar, and year. We also found out that the single fungicide treatment based on the DSS allowed a good protection of the three upper leaves of susceptible cultivars in fields with predominant WLR occurrences. The harvested grain yield was not significantly different from that of the double and triple fungicide-treated plots (P < 0.05). Such results could serve as basis or be coupled to cost-effective and environmentally friendly crop management systems in operational context.

Site-Specific Septoria Leaf Blotch Risk Assessment in Winter Wheat Using Weather-Radar Rainfall Estimates

The Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on simulated infection rates when using rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar- and gauge-derived data, the simulated probability of detection (POD) of infection events was high (0.83 on average), and the simulated false alarm ratio (FAR) of infection events was not negligible (0.24 on average). For most stations, simulation-observed FAR decreased to 0 and simulation-observed POD increased (0.85 on average) when the model outputs for both datasets were compared against visual observations of disease symptoms. An analysis of 148 infection events over 3 years at four locations showed no significant difference in the number of infection events of simulations using either dataset, indicating that, for a given location, radar estimates were as reliable as rain gauges for predicting infection events. Radar also provided better estimates of rainfall occurrence over a continuous space than weather station networks. The high spatial resolution provides radar with an important advantage that could significantly improve existing warning systems.

Assessing the Accuracy of Simulation Model for Septoria Leaf Blotch Disease Progress on Winter Wheat

A mechanistic model, PROCULTURE, for assessing the development of each of the last five leaf layers and the progress of Septoria leaf blotch, caused by Septoria tritici (teleomorph Mycosphaerella graminicola), has been applied on susceptible and weakly susceptible winter wheat (Triticum aestivum) cultivars in two locations (Everlange and Reuland) in Luxembourg over a 3-year period (2000 to 2002). A double performance assessment of PROCULTURE was conducted in this study. First, the capability of PROCULTURE to correctly simulate S. tritici incidence was checked. Second, the model's ability to accurately estimate disease severity was assessed on the basis of the difference between simulated and observed levels of disease development at each leaf layer. The model accurately predicted disease occurrence in the 2000 and 2002 seasons, on susceptible and semi-susceptible cultivars, with a probability of detection (POD) exceeding 0.90. However, in 2001, even though the POD never fell below 0.90, the false alarm ratio (FAR) was too high to consider the simulations satisfactory. Concerning the evaluation of disease severity modeling, statistical tests revealed accurate simulations performed by PROCULTURE for susceptible cultivars in 2000 and 2002. By contrast, for weakly susceptible cultivars, the model overestimated disease severity, especially for the upper leaves, for the same period.