A DYNAMIC-MODEL FOR WATER AND NITROGEN LIMITED GROWTH IN SPRING WHEAT TO PREDICT YIELD AND QUALITY

Annual variation of bread wheat (Triticum aestivum L.) yield and quality has caused problems for agronomic policy in northern regions. Yield prediction methods based on visual assessment of crop may be inaccurate as they are not based on quantitative data. The aim of this study was to develop a simple dynamic model, based on daily climatological data, enabling prediction of crop growth, and changes in crop yield, and grain protein concentration and starch quality. The model was built using field data collected in 1972-88. Spring wheat cultivars included in the study were Kadett and Ruso. The calibration of growth and Hagberg falling number (used as a measure of starch quality) sub-models resulted in a highly significant positive correlation between measured and calculated values. The calibration of nitrogen sub-models failed, however, with poor correlation between measured and calculated values. The model was tested against independent field data collected during 1989-90, and results correlated with calibration results. The yield predictions based on independent field data were accurate, and the same as or similar to field trial results. However, the independent data revealed flaws in soil-water and Hagberg falling number sub-models.