Potato Late Blight Management in the Toluca Valley: Field Validation of SimCast Modified for Cultivars with High Field Resistance

Abstract

Management of potato late blight in the highland tropics is very costly and remains difficult. Reducing the impact of late blight through the use of resistant cultivars in combination with a fungicide forecasting system could lower the number of costly fungicide applications. Previously, we evaluated the fungicide advisory SimCast for use with Mexican cultivars of high levels of field resistance for use in the central highlands of Mexico and found that it predicted too many fungicide applications for cultivars of moderate to high levels of resistance. We adapted SimCast for cultivars with higher levels of resistance, and field validations conducted in 1999 and 2000 show that SimCast resulted in good disease control on cultivars ranging from susceptible to highly resistant. The number of fungicide applications forecast for cultivars with moderate to high levels of resistance was reduced. Precipitation was the environmental variable responsible for most of the forecasts made. Our work demonstrates that a predictive system can be portable from the temperate climate to a tropical highland climate. A user-friendly decision support system consisting of just a rain-gauge and the exclusive use of SimCast's fungicide units could be a valuable and affordable tool in managing potato late blight in the highland tropics.

A metamodeling framework for extending the application domain of process-based ecological models

“…The form of SimCast (Grü nwald et al 2002) suggested a simple linear model would not be sufficient. We developed and tested GAMs to model the relationship between aggregated weather data and aggregated 'true' blight units (Table 1).…”

“…3) (Fig. 3) that might reasonably be expected based on the structure of SimCast (Grü nwald et al 2002). Once the metamodel forms were selected, metamodels for resistant genotypes were also constructed using these forms of GAM for mm Daily resistant and mm Monthly resistant (Fig.…”

“…This is particularly true for process-based models that capture, for example, the short generation times of microbial and arthropod populations and communities (Fry et al 1983, 2004, De Wolf et al 2002, Grü nwald et al 2002, Scherm and van Bruggen 1994. Even for plants and animals with long generation times, many ecophysiological processes occur at short time scales (e.g., an extreme frost event; Hijmans et al 2003).…”

“…Fry et al (1983) developed SimCast, a predictive late blight model designed for analysis of economic timing of pesticide applications, which included the effect of potato genetic resistance to disease. Grü nwald et al (2002) further refined the SimCast model for potato genotypes with moderate to high disease resistance and demonstrated that it performed well in a tropical highland climate, even though it was originally developed in a temperate climate. The SimCast algorithm is based on counting the hours during a day (from noon to noon the next day because the period of high relative humidity occurs overnight) when relative humidity (RH) is above 90% (a proxy for the presence of leaf surface moisture (Kim et al 2010)).…”

Development of Predictive Model Based on Epidemiological Factors for the Management of Potato Late Blight Disease

“…Disease forecasting models demand a permanent need of validation due to continuous modifications in the model (Andrade-Piedra et al, 2005). The method of validation is to test the model fitness within its region of application and consistent with its future aspects (Grünwald et al, 2002). A disease predictive model may reduce uncertainty about the management decisions by providing a quantitative description of disease potential.…”