Model intercomparison for calibrated models

The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments.Within the first Step (1a2a) data sets (comprising a total of 301 crop growth seasons) for 5 locations in Europe were distributed to 15 interested modeller groups.For this step only minimal information for calibration were provided to the modellers. In total 15 modelling teams sent their “uncalibrated” results as single-year calculations and/or continuous calculations of rotation depending on the capability of the model. Results have been evaluated and the paper submitted (European Journal of Agronomy).Now, within the 2nd step (1b2b) modellers were provided with more information on the crop for the calibration of models. Again, results of calibrated runs were collected.6 models were capable to run the rotations as continuous runs and another set of 6 models provided single year simulations.A first overview of the improvement of predictions due to calibration has been produced. Result files have been uploaded to the web platform for CropM results at Aarhus University (Work package C2 – data management)

Strategien für die Landwirtschaft im Klimawandel: eine Modellstudie

Das dynamische, prozess-basierte Simulati-onsmodell MONICA wurde eingesetzt, um die Entwicklung des Bewässerungs- und des Stickstoffbedarfs von Winterweizen, Wintergerste und Silomais in einem angenommenen Klimaszenario (A1B) zu berechnen. Der Bewässerungsbedarf wurde mit einer automatischen Bewässe-rungsfunktion in Simulationen auf einem Sandstandort (531 mm durchschnittlicher Jahresniederschlag 1951 – 2003), der Stickstoffbedarf zusätzlich auf einem Lössstandort ermittelt (876 mm). Es ergaben sich ein signifikant erhöhtes Ertragsniveau bei Mais und Weizen unter Bewässerung im Zeitraum um das Jahr 2070 im Vergleich zur nicht bewässerten Kultur, jedoch eine kaum ertragssteigernde Wirkung bei Gerste. Der Stickstoffbedarf steigt in der Simulation um etwa 20 kg N ha–1 bei Weizen, bleibt bei Gerste und Mais jedoch auf heutigem Niveau

Probabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean

Effective adaptation of agricultural systems to climate change has to: Consider local specificities; provide sound and practical information and deal with the uncertainty We present a methodology for assessing different aspects of adaptation. Our study case is adaptation of winter wheat in the Mediterranean

Modelling plant disease and pest effects on crop performances.

Modelling the effects of plant diseases and pests on crop performance, starting with crop yield, is an important new challenge MACSUR wants to address. We have established a small "Pest and Disease" group within MACSUR, where we address this question, with particular emphasis on wheat and grapevine. In the case of wheat, a reference data set from Denmark is being used as a key reference set for wheat - septoria tritici blotch - leaf rust interaction. In a first step an ensemble of seven wheat growth models of different complexity implement defined mechanisms for damages through pest and diseases using field data of a "pest-free" treatment for crop model calibration and idealised (temporal) patterns of injuries represented by simplified disease progress curves. In a second step field data of non-protected field plots are provided together with disease severity data to test simulations of real disease effects on crop yield loss against observed data. In parallel, we collected information on available data for pest and disease impacts by a questionnaire to evaluate their suitability for crop growth as well as for pest and disease modelling. We shall report our results in this exercise, and outline the approach we envision to (i) continue this work on wheat, and (ii) expand it to other crops such as grapevine

Comparing the site sensitivity of crop models using spatially variable field data from precision agriculture.

Impacts of climate change on crop production depend strongly on the site conditions and properties. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy were provided. Results of twelve models are compared against measured state variables analyzing their site response and consistency across crop and soil variables

Optimization of Relief Classification for Different Levels of Generalisation

Relief plays an important role in the spatial and temporal distribution of soil water and matter transport processes. Each landscape can be segmented into different landform elements based on a digital elevation model. Each of these landforms contains characteristic properties in terms of energy and material balance. Several algorithms are available to classify landscapes at different scales. However, lack of knowledge exists concerning the applicability of relief parameters for landscape stratification for different generalisation levels of underlying data. The objective of this study was to develop a method for agricultural landscapes to classify landform elements across series of elevation datasets with different spatial resolutions. A non-linear parameter optimization algorithm was coupled with a relief classification scheme to optimize four classification parameters with regard to environmentally sensitive landforms: shoulder and footslope. Input data sets were based on a LIDAR scan and topographic maps. The magnitude of the optimized relief parameters decreased with decreasing map scale from 1:10000 to 1:100000 or increasing contour line interval. The main conclusion is that if one set of classification rules for a specific landscape was determined for a high-resolution dataset at a small subset, it could be applied for larger areas even if only coarser digital elevation model information were available.JRC.H.6-Spatial data infrastructure