| Records |
| Author |
Ginaldi, F.; Bindi, M.; Marta, A.D.; Ferrise, R.; Orlandini, S.; Danuso, F. |
| Title |
Interoperability of agronomic long term experiment databases and crop model intercomparison: the Italian experience |
Type |
Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
| Volume |
77 |
Issue |
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Pages  |
209-222 |
| Keywords |
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| Abstract |
• ICFAR-DB organises and stores data from 16 Italian long term agronomic experiments. • ICFAR-DB fulfils interoperability using system dynamics ontology and AgMIP nomenclature. • ICFAR information management system moves closer data to model and vice versa. The IC-FAR national project (Linking long term observatories with crop system modelling for better understanding of climate change impact, and adaptation strategies for Italian cropping systems) initiated in 2013 with the primary aim of implementing data from 16 long term Italian agronomic experiments in a common, interoperable structure. The building of a common database (DB) structure demands a harmonization process aimed at standardising concepts, language and data in order to make them clear, and has to produce a well-documented and easily available tool for the whole scientific community. The Agricultural Model Intercomparison and Improvement Project (AgMIP) has made a great effort in this sense, improving the vocabulary developed by the International Consortium for Agricultural Systems Applications (ICASA) and defining harmonization procedures. Nowadays, these ones have also to be addressed to facilitate the extraction of input files for crop model simulations. Substantially, two alternative directions can be pursued: adapting data to models, building a standard storage structure and using translators that convert DB information to model input files; or adapting models to data, using the same storage structure for feeding modelling solutions constituted by combining model components, re-implemented in the same model platform. The ICFAR information management system simplifies data entry, improves model input extraction (implementing System Dynamics ontology), and satisfies both the paradigms. This has required the development of different software tools: ICFAR-DB for data entry and storage; a model input extractor for feeding the crop models (MoLInEx); SEMoLa platform for building modelling solutions and performing via scripts the model intercomparison. The use of the standard AgMIP/ICASA nomenclature in the ICFAR-DB and the opportunity to create files with MoLInex for feeding AgMIP model translators allow full system interoperability. |
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Edition |
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| ISSN |
1161-0301 |
ISBN |
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article |
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Expedition |
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Conference |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4972 |
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| Author |
Fronzek, S.; Pirttioja, N.; Carter, T.R.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.-F.; Dumont, B.; Ewert, F.; Ferrise, R.; Francois, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.C.; Kollas, C.; Krzyszczaki, J.; Lorite, I.J.; Minet, J.; Ines Minguez, M.; Montesino, M.; Moriondo, M.; Mueller, C.; Nendel, C.; Ozturk, I.; Perego, A.; Rodriguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rotter, R.P. |
| Title |
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change |
Type |
Journal Article |
| Year |
2018 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
| Volume |
159 |
Issue |
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Pages |
209-224 |
| Keywords |
Classification; Climate change; Crop model; Ensemble; Sensitivity analysis; Wheat; Climate-Change; Crop Models; Probabilistic Assessment; Simulating; Impacts; British Catchments; Uncertainty; Europe; Productivity; Calibration; Adaptation |
| Abstract |
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9 degrees C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities. |
| Address |
2018-01-25 |
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English |
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| ISSN |
0308-521x |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5186 |
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| Author |
Mäkinen, H.; Kaseva, J.; Trnka, M.; Balek, J.; Kersebaum, K.C.; Nendel, C.; Gobin, A.; Olesen, J.E.; Bindi, M.; Ferrise, R.; Moriondo, M.; Rodriguez, A.; Ruiz-Ramos, M.; Takáč, J.; Bezák, P.; Ventrella, D.; Ruget, F.; Capellades, G.; Kahiluoto, H. |
| Title |
Sensitivity of European wheat to extreme weather |
Type |
Journal Article |
| Year |
2018 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
222 |
Issue |
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Pages |
209-217 |
| Keywords |
European wheat; Cultivar; Weather; Extreme; Climate change; Yield response; High-Temperature; Heat-Stress; Use Efficiency; Growth-Stages; Winter-Wheat; Yield; Crop; Barley; Tolerance |
| Abstract |
The frequency and intensity of extreme weather is increasing concomitant with changes in the global climate change. Although wheat is the most important food crop in Europe, there is currently no comprehensive empirical information available regarding the sensitivity of European wheat to extreme weather. In this study, we assessed the sensitivity of European wheat yields to extreme weather related to phenology (sowing, heading) in cultivar trials across Europe (latitudes 37.21 degrees to 61.34 degrees and longitudes- 6.02 degrees to 26.24 degrees) during the period 1991-2014. All the observed agro-climatic extremes (>= 31 degrees C, >= 35 degrees C, or drought around heading; >= 35 degrees C from heading to maturity; excessive rainfall; heavy rainfall and low global radiation) led to marked yield penalties in a selected set of European cultivars, whereas few cultivars were found to with no yield penalty in such conditions. There were no European wheat cultivars that responded positively (+ 10%) to drought after sowing, or frost during winter (- 15 degrees C and – 20 degrees C). Positive responses to extremes were often shown by cultivars associated with specific regions, such as good performance under high temperatures by southern-origin cultivars. Consequently, a major future breeding challenge will be to evaluate the potential of combining such cultivar properties with other properties required under different growing conditions with, for example, long day conditions at higher latitudes, when the intensity and frequency of extremes rapidly increase. |
| Address |
2018-06-05 |
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English |
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Series Issue |
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Edition |
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| ISSN |
0378-4290 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5200 |
| Permanent link to this record |
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| Author |
Wolf, J.; Ouattara, K.; Supit, I. |
| Title |
Sowing rules for estimating rainfed yield potential of sorghum and maize in Burkina Faso |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
214-215 |
Issue |
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Pages |
208-218 |
| Keywords |
crop modelling; maize; sorghum; sowing; WOFOST; yield potential; semiarid west-africa; pearl-millet cultivation; soil organic-matter; climate-change; planting dates; crop model; variability; water; adaptation; tillage |
| Abstract |
To reduce the dependence on local expert knowledge, which is important for large-scale crop modelling studies, we analyzed sowing dates and rules for maize (Zea mays L.) and sorghum (Sorghum bicolor (L)) at three locations in Burkina Faso with strongly decreasing rainfall amounts from south to north. We tested in total 22 methods to derive optimal sowing dates that result in highest water-limited yields and lowest yield variation in a reproducible and objective way. The WOFOST crop growth simulation model was used. We found that sowing dates that are based on local expert knowledge, may work quite well for Burkina Faso and for West Africa in general. However, when no a priori information is available, maize should be sown between Julian days 160 and 200, with application of the following criteria: (a) cumulative rainfall in the sowing window is >= 3 cm or available soil moisture content is >2 cm in the moderately dry central part of Burkina Faso, (b) cumulative rainfall in this period is >= 2 cm or available soil moisture content is >1 cm in the more humid regions in the southern part of Burkina Faso. Sorghum should also be sown between Julian days 160 and 200 with application of the following criteria: (a) in the dry northern part of Burkina Faso the long duration sorghum variety should be sown when cumulative rainfall is >2 cm in the sowing window, and the short duration sorghum variety should be sown later when cumulative rainfall is >= 3 cm, (b) in central Burkina Faso sowing should start when cumulative rainfall in this period is >= 2 cm or when available soil moisture content is >1 cm. Sowing date rules are shown to be generally crop and location specific and are not generic for West Africa. However, the required precision of the sowing rules appears to rapidly decrease with increasing duration and intensity of the rainy season. Sowing delay as a result of, for example, labour constraints, has a disastrous effect on rainfed maize and sorghum yields, particularly in the northern part of West Africa with low rainfall. Optimization of sowing dates can also be done by simulating crop yields in a time window of two months around a predefined sowing date. Using these optimized dates appears to result in a good estimate of the maximal mean rainfed yield level. (C) 2015 Elsevier B.V. All rights reserved. |
| Address |
2015-10-12 |
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English |
Summary Language |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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Medium |
Article |
| Area |
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Conference |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4702 |
| Permanent link to this record |
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| Author |
Teixeira, E.I.; Fischer, G.; van Velthuizen, H.; Walter, C.; Ewert, F. |
| Title |
Global hot-spots of heat stress on agricultural crops due to climate change |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
206-215 |
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Series Issue |
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Edition |
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0168-1923 |
ISBN |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4929 |
| Permanent link to this record |
