Prediction of winter wheat cultivar performance in Germany: At national,regional and location scale

Winter wheat cultivar recommendation is usually based on the cultivar performance observed in post-registration trials. In Germany, official recommendations are based on state cultivar trials, which are conducted individually by the federal states, usually over a period of three years. In each predefined winter wheat cultivation region a subset of registered cultivars is tested. The recommendation in a particular region is mainly based on the yields from trials on several locations in this region. Practically, the farmer's interest is a prediction of the yielding ability of cultivars on his own farm in the following growing season. This prediction can be made based on data from different scales, and with one year or multiple-year data. Here, we evaluated the prediction ability with the data from national, regional and location scales per se, and tried to find the optimal information source (scale and number of years) to predict the relative yield of a specific cultivar for a specific location. For this purpose, data from the country wide value testing trials from 1991 to 2001 carried out by the Federal Office of Plant Varieties (Bundessortenamt) were used. Winter wheat cultivation regions were adopted according to the German convention which gives the chance of further dividing the data into regional subgroups. The results of the analyses indicate that for a given location, the two years regional data have the highest predictive power for superior cultivars. Two years’ data from that specific location give the highest predictive power for intermediate and inferior cultivars. In general, the predictive power of single year data is much lower than of two years data. The results confirm the merit of the definition of different cultivation regions. By proper definition of regions, the multiple year data collected within the region have high predictive power for the cultivar performance for the locations within that region.