| Records |
| Author |
Cantelaube, P.; Jayet, P. |
| Title |
Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level |
Type |
Journal Article |
| Year |
2012 |
Publication |
Land Use Policy |
Abbreviated Journal |
Land Use Policy |
Volume  |
29 |
Issue |
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Pages |
35-44 |
| Keywords |
Downscaling; Land use; Spatial statistics; Farm-groups; Farm Accountancy Data Network; FADN |
| Abstract |
There is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate both spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on farm-groups. These entities are defined according to variables such as altitude, economic size and type of farming (referring to land uses). European farm-groups are provided through the Farm Accountancy Data Network (FADN) as statistical information delivered at regional level. The aim of the study is to map locally farm-group probabilities within each region. The mapping of the farm-groups is done in two steps: (1) by mapping locally the co-variables associated to the farm-groups, i.e. altitude and land uses; (2) by using regional FADN data as a priori knowledge for transforming land uses and altitude information into farm-groups location probabilities within each region. The downscaling process focuses on the land use mapping since land use data are originally point information located every 18 km. Interpolation of land use data is done at 100 m by using co-variables like land cover, altitude, climate and soil data which are continuous layers usually provided at fine resolution. Once the farm-groups are mapped, European Policy and global changes scenarios are run through an agro-economic model for assessing environmental impacts locally. |
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TradeM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4582 |
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| Author |
Toscano, P.; Ranieri, R.; Matese, A.; Vaccari, F.P.; Gioli, B.; Zaldei, A.; Silvestri, M.; Ronchi, C.; La Cava, P.; Porter, J.R.; Miglietta, F. |
| Title |
Durum wheat modeling: The Delphi system, 11 years of observations in Italy |
Type |
Journal Article |
| Year |
2012 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
43 |
Issue |
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Pages |
108-118 |
| Keywords |
durum wheat; crop modeling; yield forecasting; calibration; scenarios; decision-support-system; crop simulation-model; ceres-wheat; mediterranean environment; winter-wheat; scaling-up; variability; quality; growth; water |
| Abstract |
► Delphi system, based on AFRCWHEAT2 model, for durum wheat forecast. ► AFRCWHEAT2 model was calibrated and validated for three years. ► A scenario approach was applied to simulation of durum wheat yield. ► Operational mode for eleven years in rainfed and water limiting conditions. ► Accurate forecast as an useful planning tool. Crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. The aim of this work was to test the predictive capacity of the Delphi system, calibrated and determined for each pedoclimatic factor affecting durum wheat during phenological development. at regional scale. We present an innovative system capable of predicting spatial yield variation and temporal yield fluctuation in long-term analysis, that are the main purposes of regional crop simulation study. The Delphi system was applied to simulate growth and yield of durum wheat in the major Italian supply basins (Basilicata, Capitanata, Marche, Tuscany). The model was validated and evaluated for three years (1995-1997) at 11 experimental fields and then used in operational mode for eleven years (1999-2009), showing an excellent/good accuracy in predicting grain yield even before maturity for a wide range of growing conditions in the Mediterranean climate, governed by different annual weather patterns. The results were evaluated on the basis of regression and normalized root mean squared error with known crop yield statistics at regional level. (c) 2012 Elsevier B.V. All rights reserved. |
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1161-0301 |
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CropM, ftnotmacsur |
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MA @ admin @ |
Serial |
4596 |
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Semenov, M.A.; Pilkington-Bennett, S.; Calanca, P. |
| Title |
Validation of ELPIS 1980-2010 baseline scenarios using the observed European Climate Assessment data set |
Type |
Journal Article |
| Year |
2013 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
57 |
Issue |
1 |
Pages |
1-9 |
| Keywords |
climate change; impact assessment; downscaling; lars-wg; stochastic weather generators; diverse canadian climates; lars-wg; aafc-wg; radiation; impacts |
| Abstract |
Local-scale daily climate scenarios are required for assessment of climate change impacts. ELPIS is a repository of local-scale climate scenarios for Europe, which are based on the LARS-WG weather generator and future projections from 2 multi-model ensembles, CMIP3 and EU-ENSEMBLES. In ELPIS, the site parameters for the 1980-2010 baseline scenarios were estimated by LARS-WG using daily weather from the European Crop Growth Monitoring System (CGMS) used in many European agricultural assessment studies. The objective of this paper was to compare ELPIS baseline scenarios with observed daily weather obtained independently from the European Climate Assessment (ECA) data set. Several statistical tests were used to compare distributions of climatic variables derived from ECA-observed daily weather and ELPIS-generated baseline scenarios. About 30% of selected sites have a difference in altitude of > 50 m compared with the CGMS grid-cell altitude that was selected to represent agricultural land within a grid-cell. Differences in altitude can explain significant Kolmogorov-Smirnov test (KS-test) results for distribution of daily temperature and in t-tests for temperature monthly means, because of the well-known negative correlation between temperature and elevation. For daily precipitation, the KS-test showed little difference between generated and observed data; however, the more sensitive t-test showed significant results for the sites where altitude differences were large. Approximately 11% of sites showed small positive or negative bias in monthly solar radiation, although 86% sites showed > 3 significant t-test results for monthly means. These results can be explained by differences in conversion of sunshine hours to solar radiation used in CGMS and LARS-WG. We conclude that, considering the limitations above, ELPIS baseline scenarios are suitable for agricultural impact assessments in Europe. |
| Address |
2016-10-31 |
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English |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
Serial |
4812 |
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| Author |
Bindi, M.; Palosuo, T.; Trnka, M.; Semenov, M.A. |
| Title |
Modelling climate change impacts on crop production for food security INTRODUCTION |
Type |
Journal Article |
| Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
65 |
Issue |
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Pages |
3-5 |
| Keywords |
Crop production; Climate change impact and adaptation assessments; Upscaling; Model ensembles |
| Abstract |
Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector. |
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2016-10-31 |
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0936-577x |
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Editorial Material |
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CropM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4785 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
| Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
Type |
Journal Article |
| Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
65 |
Issue |
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Pages |
141-157 |
| Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
| Abstract |
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
Approved |
no |
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MA @ admin @ |
Serial |
4754 |
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