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Constantin, J.; Raynal, H.; Casellas, E.; Hoffman, H.; Bindi, M.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Klatt, S.; Kuhnert, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Nendel, C.; Roggero, P.P.; Specka, X.; Trombi, G.; Villa, A.; Wang, E.; Weihermueller, L.; Yeluripati, J.; Zhao, Z.; Ewert, F.; Bergez, J.-E. |
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Title |
Management and spatial resolution effects on yield and water balance at regional scale in crop models |
Type |
Journal Article |
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Year |
2019 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
275 |
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Pages  |
184-195 |
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Keywords |
Drainage; Evapotranspiration; Aggregation; Decision rules; Scaling; winter-wheat yield; data aggregation; sowing dates; area index; input; data; carbon; growth; irrigation; productivity; assimilation |
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Abstract |
Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale. |
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2020-02-14 |
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English |
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0168-1923 |
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Article |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
5225 |
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Author |
Elliott, J.; Müller, C.; Deryng, D.; Chryssanthacopoulos, J.; Boote, K.J.; Büchner, M.; Foster, I.; Glotter, M.; Heinke, J.; Iizumi, T.; Izaurralde, R.C.; Mueller, N.D.; Ray, D.K.; Rosenzweig, C.; Ruane, A.C.; Sheffield, J. |
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Title |
The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0) |
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Journal Article |
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Year |
2015 |
Publication |
Geoscientific Model Development |
Abbreviated Journal |
Geosci. Model Dev. |
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Volume |
8 |
Issue |
2 |
Pages |
261-277 |
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Keywords |
land-surface model; climate-change; systems simulation; high-resolution; water; carbon; yield; agriculture; patterns; growth |
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Abstract |
We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12-15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record. |
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1991-9603 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4559 |
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Author |
Ma, S.; Lardy, R.; Graux, A.-I.; Ben Touhami, H.; Klumpp, K.; Martin, R.; Bellocchi, G. |
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Title |
Regional-scale analysis of carbon and water cycles on managed grassland systems |
Type |
Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
72 |
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Pages |
356-371 |
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Keywords |
carbon flux; eddy flux measurements; model evaluation; pasture simulation model (pasim); water balance; pasture simulation-model; nitrous-oxide emissions; primary productivity npp; comparing global-models; net ecosystem exchange; greenhouse-gas balance; climate-change; agricultural systems; co2 exchange; european grasslands |
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Abstract |
Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m(-2) yr(-1), which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m(-2) yr(-1) on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m(-2) yr(-1), respectively, in agreement with observed average GPP (1230 g C m(-2) yr(-1)) and RECO (1046 g C m(-2) yr(-1)). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was similar to 5-16 g C week(-1) across the study sites, while the goodness of fit (R-2) was similar to 0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415 mmyr(-1)) for all sites and years is close to the average value of the observed ET (451 mm yr(-1)) by flux towers (on a weekly basis, RMSE similar to 2-8 mm week(-1); R-2 = 0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. |
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2015-10-09 |
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1364-8152 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
Serial |
4695 |
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Author |
Grosz, B.; Dechow, R.; Gebbert, S.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Haas, E.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Tao, F.; Roetter, R.; Kassie, B.; Cammarano, D.; Asseng, S.; Weihermueller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
The implication of input data aggregation on up-scaling soil organic carbon changes |
Type |
Journal Article |
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Year |
2017 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
96 |
Issue |
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Pages |
361-377 |
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Keywords |
Biogeochemical model; Data aggregation; Up-scaling error; Soil organic carbon; DIFFERENT SPATIAL SCALES; NITROUS-OXIDE EMISSIONS; MODELING SYSTEM; DATA; RESOLUTION; CROP MODELS; CLIMATE; LONG; PRODUCTIVITY; CROPLANDS; DAYCENT |
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Abstract |
In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved. |
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2017-09-14 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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5176 |
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Moriondo, M.; Ferrise, R.; Trombi, G.; Brilli, L.; Dibari, C.; Bindi, M. |
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Title |
Modelling olive trees and grapevines in a changing climate |
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Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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72 |
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387-401 |
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Keywords |
tree crops; climate change; simulation models; crop yield; vitis-vinifera l.; air co2 enrichment; soil-water content; elevated co2; mediterranean basin; cropping systems; growth; yield; carbon; simulation |
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Abstract |
The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. (C) 2014 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
Serial |
4691 |
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