In order to reduce the vulnerability of wheat production to drought, a calibrated and validated CERES Wheat crop simulation model was used to predict wheat yields on major soil textural groups using historical weather data at Swift Current, Saskatoon and Melfort. Yields were predicted using a run-out technique which involved the use of actual weather data to the prediction date and historical weather data from 1960 to 1990 for the remainder of the growing season. Yield predictions were made at five Julian dates during the crop calendar and these dates coincided with crop emergence, terminal spikelet initiation, end of the vegetative growth, heading and start of grain filling. Three sample years were used as case studies to test the applicability of the run-out method in making yield predictions. Sample base years were those with the lowest, medium and highest yields between 1960 and 1990 and these were selected from ranked yield values using quartiles. Test years were termed base years and weather files that were joined with the test years were run-out years. Each base year had 30 run-out years (1960-1990) and the mean of each run-out year was compared with the observed yield at the end of the season. Run-out yields for each base year were summarised as simple probability distributions so that yields exceeding certain values could be selected. Run-out yields at five prediction dates were found to be in close agreement with observed yields at the end of the growing season. To account for the variability in yields that can be found between places within the same climatic zone, simulated yields were re-classified by soil type and water stress level. These modifiers (soil type and water stress level) showed that chances of getting high yields diminish from Melfort to Swift Current at all prediction points due to the high variability of yield factors. Yield predictions that were made as above suggested that if historical weather records are combined with available weather data during the growing season, a good indication of yields can be obtained ahead of the harvest time and this could allow producers and those in the agri-business to decide on alternative actions of minimizing losses when prospects of getting a good yield are poor.

Das Wurzelwachstum von Winterweizen wurde erfasst und modelliert, um teilflächenspezifisches Boden- und Düngemanagement zu verbessern. Die Variation von Wurzellängendichten im Feld wurde über zwei Vegetationsperioden hinweg an zwei unterschiedlichen Standorten in Ostdeutschland untersucht. Zur Auswertungserleichterung der hohen Anzahl an Wurzelproben, wurde eine halbautomatische Methode zur Bildanalyse von Wurzeln entwickelt. Der Einfluss von Änderungen bezüglich Bodenwasserstatus und Bodendichte bzw. Durchdringungswiderstand auf das Wurzelwachstum wurde untersucht. Die erhobenen Felddaten dienten gleichzeitig dazu, die Bodenwasser- und Wurzelwachstumsberechnung des Modells CERES-Wheat zu validieren. Das Modell simulierte die unterschiedlichen Bodeneigenschaften sowie die Wurzellängendichten und Bodenwassergehalte nur unzureichend. Der Effekt von Änderungen der Niederschlagsmengen auf die Simulationen von Wurzellängendichten und Bodenwassergehalten wurde anhand einer Unsicherheitsanalyse getestet und war extrem gering. Des Weiteren wurde eine Methode für praktische Zwecke entwickelt, mit der die Generierung von räumlich hoch aufgelösten Bodeninformationen unter Verwendung limitierter Eingangsdaten möglich ist. Die Modellkalkulationen basieren auf der Dempster-Shafer-Theorie. Anhand von multitemporal und multimodal erfassten Bodenleitfähigkeitsdaten, die Eingangsdaten für den Modellansatz sind, wurden Bodentypen und Texturklassen bestimmt. Das Modell generiert eine digitale Bodenkarte, die flächenhafte Informationen über Bodentypen und Bodeneigenschaften enthält. Die Validation der Bodenkarte mit zusätzlich erhobenen Bodeninformationen ergab gute bis sehr gute Ergebnisse. / Winter wheat root growth was measured and modelled to improve site-specific soil and fertilizer management in commercial wheat fields. Field variations in root length densities were analysed at two contrasting sites in East-Germany during two vegetation seasons. A semi-automated root analysing method was developed to facilitate analyses of large numbers of samples. Influences of variations in soil water states, bulk densities and penetration resistances on spatial distributions of roots were quantified. Differences in soil characteristics were large between the two sites and affected root growth considerably. The same field data was used for validating the soil moisture and root growth calculations of the widely applied growth model CERES-Wheat. Simulations of root length densities, soil physical properties and soil water contents were inadequate. The effects of changes of rainfall variabilities on simulated root length densities and soil water contents were tested by uncertainty analysis but were negligible low. A methodology for generating soil information for practical management purposes at a high degree of spatial resolution using limited input information was developed. The corresponding model calculations were carried out based on the Dempster and Shafer theorem. Soil types and texture classes were determined with multimodally and multitemporally captured data of soil electrical conductivities which are required input data of the new model approach. The model generates a digital map with extensive information of spatial variations in soil properties. The validation of the generated soil map with soil data from independent measurements yielded close correlation between measured and calculated values.