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Bellocchi, G., B.; Brilli, L.; Ferrise, R.; Dibari, C.; Bindi, M. |
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Title |
Model comparison and improvement: Links established with other consortia |
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2017 |
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FACCE MACSUR Reports |
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10 |
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XC1.3-D |
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XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org ) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org ). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls. |
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MA @ admin @ |
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4941 |
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Kersebaum, K.-C.; Wallor, E.; Ventrella, D.; Cammarano, D.; Choucheney, E.; Ewert, F.; Ferrise, R.; Gaiser, T.; Garofalo, P.; Giglio, L.; Giola, P.; Hoffmann, M.; Laan, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Mula, L.; Nendel, C.; Pohankova, E.; Roggero, P.P.; Trnka, M.; Trombi, G. |
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Title |
Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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C1.1-D2 |
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Site conditions and soil properties have a strong influence on impacts of climate change on crop production. 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 even 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. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables. (Main text to be published in a peer-reviewed journal) |
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CropM |
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MA @ admin @ |
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4951 |
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Leolini, L.; Moriondo, M.; Ferrise, R.; Bindi, M. |
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Title |
Relations between micrometeorological conditions and plant physiology |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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XC1.1-D2 |
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The changing climate and environmental conditions play a key role on plant physiology. In this context, crop simulation models represent a useful tool for investigating the main plant processes and provide a reliable estimation of crop productivity and quality. However, the most common crop models showed many limitations, with particular concern on the effect of some meteorological variables on plant processes during sensitive stages of development. Improving models by implementing the effect of such variables on crop processes may help to improve the accuracy of models, thus their usefulness. Here we focus on the analysis of the effect of high and low temperatures during flowering in grapevine. To this, the fruit-set index, developed for taking into account for the effect of temperature on setting the number of berries per cluster and the fruit-set percentage, was applied in a preliminary explorative study to assess the impact of different conditions during flowering at European scales. The sensitivity of the index allowed to identify the differential impact of temperature around flowering in different environment and for different varieties. Once meteorological variables are available at field or sub-field scale, the index can be used to provide information about the spatial variability of crop growth, thus allowing to identify the most appropriate interventions to improve productivity. |
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XC |
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MA @ admin @ |
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4975 |
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Hlavinka, P.; Olesen, J.E.; Kersebaum, K.-C.; Trnka, M.; Pohankova, E.; Stella, T.; Ferrise, R.; Moriondo, M.; Hoogenbom, G.; Shelia, V.; Nendel, C.; Wimmerová, M.; Topaj, A.; Medvedev, S.; Ventrella, D.; Ruiz-Ramos, M.; Rodríguez Sánchez, A.; Takáč, J.; Patil, R.H.; Öztürk, I.; Hoffmann, M.; Gobin, A.; Rötter, R.P. |
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Modelling long term effects of cropping and managements systems on soil organic matter, C/N dynamics and crop growth |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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C1.3-D |
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While simulation of cropping systems over a few years might reflect well the short term effects of management and cultivation, long term effects on soil properties and their consequences for crop growth and matter fluxes are not captured. Especially the effect on soil carbon sequestration/depletion is addressed by this task. Simulations of an ensemble of crop models are performed as transient runs over a period of 120 year using observed weather from three stations in Czech Republic (1961-2010) and transient long time climate change scenarios (2011-2080) from five GCM of the CMIP5 ensemble to assess the effect of different cropping and management systems on carbon sequestration, matter fluxes and crop production in an integrative way. Two cropping systems are regarded comprising two times winter wheat, silage maize, spring barley and oilseed rape. Crop rotations differ regarding their organic input from crop residues, nitrogen fertilization and implementation of catch crops. Models are applied for two soil types with different water holding capacity. Cultivation and nutrient management is adapted using management rules related to weather and soil conditions. Data of phenology and crop yield from the region of the regarded crops were provided to calibrate the models for crops of the rotations. Twelve models were calibrated in this first step. For the transient long term runs results of four models were submitted so far. Outputs are crop yields, nitrogen uptake, soil water and mineral nitrogen contents, as well as water and nitrogen fluxes to the atmosphere and groundwater. Changes in the carbon stocks and the consequences for nitrogen mineralisation, N fertilization and emissions also considered. |
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XC |
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MA @ admin @ |
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4976 |
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Leolini, L.; Moriondo, M.; De Cortazar-Atauri, I.; Ruiz-Ramos, M.; Nendel, C.; Roggero, P.P.; Spanna, F.; Ramos, M.C.; Costafreda-Aumedes, S.; Ferrise, R.; Bindi, M. |
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Modelling different cropping systems |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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C1.4-D |
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Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations. |
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CropM |
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MA @ admin @ |
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5033 |
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