Forecasting models

Modelling is a tool for developing early warning systems and reducing the application of chemicals. Forecasting models need to be valid and to predict actual field observations adequately. With climate change, the challenge is to take account of the variability in disease epidemiology. Disease forecasting systems using non-linear responses to temperature and leaf wetness offer more potential for representing these effects (Bourgeois et al., 2004). However, although modelling is becoming more sophisticated, the main concern for these studies on the impact of climate change on crop production is to include the changed pest dynamics and intensity (insects, plant pathogens and weeds) that are generally ignored under climate change (Scherm, 2004). Savary et al. (2006) have reviewed the types of crop loss knowledge and various models integrating environment, disease and losses. The ultimate objective is to contribute to decisions on whether or not to apply a pesticide and minimize economic losses. With the development of new tools such as geographic information systems (GIS) and remote sensing, access through the Internet to site-specific weather information without sensors could offer new possibilities for forecasting conditions that favour a disease or pest (Magarey et al., 2001). The Integrated Pest Management - Pest Information Platform for Extension and Education (IpmPIPE) site illustrates the effectiveness of Internet-based tools to monitor and manage new disease outbreaks such as that of soybean rust in the USA (USDA, 2009). Research on the relationship between leaf area and relative yield is expected to lead to the development of a yield-loss prediction model specific to the impact of soybean rust (Kumudini et al., 2008; University of Kentucky, 2009). The 'Rustmapper' system is another example using the Internet that allows the risk of dispersal of the wheat stem rust pathogen by tracking unusual climatic events (winds, rainfall) to be assessed (Hodson et al., 2009). Similarly the Desert Locust Information Service (DLIS) based at the Food and Agriculture Organization of the United Nations (FAO) headquarters in Rome sends early alerts and forecasts for each country on desert locust plagues; it generates maps showing where solitary and gregarious hoppers and adults are observed (FAO, 2009). The locust forecasting system is based on a network of surveillance, remote sensing, meteorological information and

GIS analysis. The impact of climate change is also under investigation.

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