If mean temperature increases, in accordance with Intergovernmental Panel on Climate Change (IPCC) projections and adaptations and/or genetic modifications are not considered, suitable areas for farming corn (Zea mays) and coffee (Coffea arabica) will decrease in the state of Sao Paulo (Brazil). Further, increases in precipitation will not be enough to ameliorate the impacts associated with increases in mean temperatures. Suitability for grain production will decrease more rapidly in regions with sandy soils than in regions with clay or medium soils, as the temperature increases. The projected increase in mean temperature of up to 5.8°C would decrease the suitability for grain production drastically in spite of soil texture. Besides the reduction of suitable areas for coffee production, the projections suggest that changes will be more enhanced in the southeast of the state, especially in higher elevation regions, where farming practice, soils, and infrastructure are unsuitable for the economic production of coffee. In both cases, no compensatory increase in suitable areas for production is likely under current IPCC scenarios.

Atmospheric pollution models are generally evaluated using measures of the difference between predicted and observed concentrations. These statistics can be inadequate as they tend to be sensitive to small errors in the timing of peak events that are generally of little consequence from a health point of view. This paper presents a technique for evaluating atmospheric carbon monoxide models based on the statistical properties of the uptake of carbon monoxide, rather than relying on traditional model evaluation measures. A simple semi-empirical atmospheric carbon monoxide model is used as an example to illustrate the approach.

A forecasting system for fungal infection of turfgrass using weather-based empirical indices (the ‘Fidanza’ and ‘Schumann’ models) was developed and evaluated for its ability to predict the occurrence of brown patch (Rhizoctonia blight) infection episodes at an experimental site in southeastern USA. Disease observations took place at the Turfgrass Field Laboratory in Raleigh, North Carolina between 8 June and 17 August 2003. Three meteorological data sources were used to generate disease risk indices using the empirical models: an on-site observing station, an observing station at a nearby airport, and the US National Weather Service's operational Eta weather forecast model. Visual observations of brown patch activity were conducted in the field and used to evaluate the accuracy of the disease prediction models. Results indicate that the Fidanza and Schumann models correctly predicted brown patch activity on 48% and 30% of the days on which disease occurred, respectively. A diagnosis of the model performance of these disease indices was undertaken. Results are dependent on occurrence of high temperatures and rainfall and independent of the source of the meteorological information (on-site, airport and the Eta model); therefore, regional meteorological information can be effectively applied to develop turfgrass disease forecasting systems. Ongoing efforts are directed towards developing new disease indices and modifying existing indices before an operational disease forecasting system can be implemented.

Fruit growing in Slovenia has become one of the more important agricultural production branches over the last two decades. Due to climate variability over a relatively small area of 20,000 km2, irrigation practices are not only important but also indispensable agrotechnical measures for obtaining permanent, high quality crop yields in the northeastern and southwestern parts of the country. Hence, accurate estimation of water consumption by agricultural crops with the inclusion of metorological monitoring is essential in the field of water management. As a decision support tool, the Slovenian Agrometeorological Information System (SAgMIS) developed by Agrometeorological Department in the Environmental Agency of the Republic of Slovenia (EARS) and introduced 10 years ago has improved significally over the last two years. Transfer of actual and forecasted agrometeorological data to the end users has been quick and accurate, with the last version of mobile data dissemination in testing use. A case study of water use by irrigated peach trees in the plantation orchards of lower Vipava valley was undertaken from 1996 to 2004. A variety of irrigation systems with diverse irrigation water applications was introduced for the purposes of studying irrigation water benefits derived from accurate weather data and forecasts. The results showed that important savings in the amount of irrigation water used, on an annual average of 20%, could be achieved with accurate and timely irrigation forecasts during the vegetation periods.

Research was performed in Poggio Casciano Estate (Chianti area, central Italy) with the aim of defining a general approach to analyse the spatial variability of temperature at the microscale. Hourly data were collected from a network of 27 temperature stations covering an area of about 120 ha and determination coefficients r between station pairs on the basis of different geo-topographical factors were calculated. The data were analysed in order to investigate trends describing the spatial distribution of temperature inside the study area. The results pointed out a strong effect of some topographical condition on the distribution of thermal patterns, in particular altitude and the distance from valley bottoms. The results are discussed in order to formulate a general approach for the characterisation of climatic conditions at small scale.