Early warning systems for assessing agrometeorological risks

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Use of improved climate and weather information and forecasts along with efficient early warning systems would contribute to the preparedness for extreme weather events. New technologies have brought about an accelerated increase in our knowledge of the climate system. Today the accuracy of forecasts of large-scale weather patterns for seven days in advance is the same as those for two days in advance only 25 years ago. tte accuracy of tropical cyclone track forecasts and the timeliness of warnings have been steadily improving in the past few years. When properly communicated and absorbed, early warnings may empower farmers and communities threatened by natural hazards to prepare themselves in sufficient time and in an appropriate manner so as to minimize the risk of the impending hazard. Technologically oriented early warning, integrated with field data on crop and livestock conditions, price movements, human welfare etc. is for example crucial for tracking drought, its onset, its impact and farmers response to it. Primary policy decision makers, resource generators, and relief and mitigation workers need information about early warning of onset of drought events, estimation of area, intensity and duration, long term and short term plans for coping with droughts etc. It is a challenge to have this information operationally provided.

A definition of warning stages (e.g. normal, alert, alarm, emergency as is prevalent for cyclones in India) should be generated by the early warning system to trigger government and other responses, tte effective warning system should have meteorological/agricultural information, production estimates, price trends of food and feed, availability of drinking water and household vulnerability, so that a variety of indices related to production, exchange and consumption could be addressed. tte challenge is that information on the spatial extent and duration of risk events, time of occurrence with reference to crop calendar and severity of the events could operationally help in the preparations of coping strategies.

Several approaches are employed to estimate the impact of weather conditions on plant diseases. One can provide predictions based on previously established empirical relations between the population density of pathogens, vegetation status and climatic variables. But, faced with the complexity of the problem, it is much more efficient to use epidemiological models: epidemiological development is described in the form of a functional model where each biological process is linked to climatic parameters, ttese models when coupled to crop simulation models may provide a reasonable forecast on likely infestation. However, the establishment of these models necessitates the acquisition of various observed data and knowledge acquired by experimenting on the disease. At the present time, very few of these models are available and it is a great challenge that progress is made in this direction.

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