Introduction

Agricultural systems are largely dependent on weather and climate, then management and planning decisions are made in condition of risk or uncertainty due to the high level of complexity of the agricultural systems. Despite the important advances in technology over the last decades, many production factors are not well defined and they are outside of the farmer control (Orlandini and Cappugi 2001). tte lack of precise information increases the level of uncertainty in farm management. To overcome these problems, farmers increased the level of energy and chemical inputs above the necessary requirements with the aim of decreasing the impacts of the variability of agricultural systems. Unfortunately, the consequence of this strategy was the increasing of environmental impact and production costs without obtaining the expected goal (Travis et al. 1992). A solution to interrupt this negative trend is to substitute expensive and pollutant chemical and energy inputs with elaborated information of high quality. In this way it is possible to decrease the risk of the uncertainties of decision making and thus to minimise the application of excessive inputs and increase the potential income (Maracchi 2001).

tterefore, the monitoring of environmental variables and the elaboration of information represent a necessary support for decision making both for long-term and short-term management of agricultural activities. Information can hardly be used as a raw datum, but it needs to be analysed, processed and organised according to the final operational use. A new approach to agriculture seeks to increase the application of agrometeorological information for the development of models for the assessment of the quality of agricultural products, estimation and monitoring of yields, environmental protection and cultural rural heritage conservation. Agriculture needs agrometeorological models to minimise environmental costs of its activity and to determine short and long term consequences (reduction of soil fertility).

Agrometeorological models are basically formal expressions of biological, physical and chemical functions fed with environmental and climatic forcing variables (Table 26.1). Models are often the only tools available to study the behaviour of complex systems, and they offer unique insights to understand the frequent nonlinear interactions among processes in soil-plant systems.

In the last few years an increasing interest in this subject was observed and a high number of computer applications for agrometeorological purposes was developed.

Table 26.1. Examples of agrometeorological forcing variables and their effect in epidemiological models.

Variable

Effect

Temperature

Phenological development

Solar radiation

Biomass assimilation and growth

High temperature

Rate of infection

Higher threshold of development and survival

Low temperature

Spore and insect conservation

Lower threshold of development and survival

Leaf wetness

Inoculation Survival of organism

Precipitation

Dispersion of spore and insect Survival of organism

Relative humidity

Presence of saturation conditions Survival of organism

Wind

Dispersion of spore and insect Modification of temperature and humidity

Modelling can find useful applications in many fields: plant growth and development, crop yield quality and quantity estimation, water balance, plant protection against pests, diseases, weed and weather hazards, climatic changes, generation of weather data, spatial and temporal interpolation or extrapolation, soil erosion and conservation, etc. (Orlandini 1996).

Moreover, concerning a more complex and articulated context, models can be included for setting up Decision Support Systems (DSS) and Early Warning Systems (EWS). In this case, models are usually integrated with other technologies such as remote sensing, geographic information systems, and numerical weather models. Information elaborated is used not only for regulating the agricultural and land management activities but also, in more critical cases such as in developing countries or in particularlyvulnerable areas, to manage food security concerns.

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