Probably, the most important contributions to the use of the available simulation tools to support agricultural decision-making are CLIMAG (Climate Prediction and Agriculture) activities. The CLIMAG workshops were held in Geneva 1999 and 2005, sponsored by WMO and IRI (Sivakumar, 2001; Sivakumar and Hansen, 2007). The CLIMAG proceedings remain as significant guidelines for using such tools in future studies. However, the assessments of climate-impacts on agriculture made in the framework of CLIMAG were only specially-funding applications. The CLIMAG assessments did not yield to sustainable applications of the simulation tools in the targeted countries.
One of the most important successful applications of climate information, combined to crop-growth simulation models, have been made in Australia (Meinke et al, 2001; Hammer et al., 2001; Meinke et al., 2006). Rainfall and many other meteorological variables strongly depend on El Niño behaviour. Since ENSO occurrence can be forecasted several months in advance, this information can be used to support agricultural decision-making, through crop-growth modelling exercises (Hammer et al., 2001). A "participatory process" (Meinke et al., 2001) comprising researchers, stakeholders and extension facilities has been pointed out as a way to provide sustainable and sounder support to Australian agriculture.
Another important application of climate information and crop-growth models can be found in the South East Climate Consortium (SECC), as described by Hoogenboom (2007). Seasonal forecasts at the county level, using ENSO phases, are combined with DSSAT modelling results in order to provide estimations of final yields, water and fertilizer requirements and several other outputs very useful to farmers. The forecast and the whole system are only reliable in El Niño years, although current researches aim to enlarge the system reliability to other years (Baigorria, 2007).
El Niño signal is not very strong over Europe, which limits the applicability of ENSO-based forecasts. Marletto et al. (2005) showed that WOFOST simulations of winter wheat yields, based on the available seasonal forecasts, departed significantly from the recorded data. However, using spatial and temporal aggregated data, as usually done by JRC when providing recommendations to the EU Commission of Agriculture, might be not the right approach to capture the relationships between weather variables and crop growing.
Hansen et al. (2006) provided an insight view of current advances and challenges while translating climate forecasts into reliable agricultural decision-making. They describe several methods used up to now to spatially and temporal downscale the forecasts, recommending methods comparisons and evaluations at local scales. Likewise, Alexandrov (2007) provided an update revision of current state on applying climate scenarios and seasonal forecasts to support agricultural decision-making. Alexandrov (2007) points out that improved climate prediction techniques are growing faster and finding more applications; hence close contacts between climate forecasters, agrometeorologists, agricultural research and extension agencies in developing appropriate products for the user community are needed. Furthermore, feedbacks from end users are essential identifying the opportunities for agricultural applications (Alexandrov, 2007).
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