| Records |
| Author |
Wallach, D.; Nissanka, S.P.; Karunaratne, A.S.; Weerakoon, W.M.W.; Thorburn, P.J.; Boote, K.J.; Jones, J.W. |
| Title |
Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice |
Type |
Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
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Issue |
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Pages |
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| Keywords |
Uncertainty; Phenology; Parameter uncertainty; Multi-model ensemble; Generalized least squares; Rice; Crop model; APSIM; DSSAT |
| Abstract |
We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2 °C, is estimated as 108 days2, with model error contributing 86 days2, followed by model structure uncertainty which contributes 15 days2 and parameter uncertainty which contributes 7 days2. We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures. |
| Address |
2016-09-13 |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1161-0301 |
ISBN |
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Medium |
Article |
| Area |
CropM |
Expedition |
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Conference |
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| Notes |
CropM; wos; ftnotmacsur; wsnotyet; |
Approved |
no |
Call Number  |
MA @ admin @ |
Serial |
4777 |
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| Author |
Maiorano, A.; Martre, P.; Asseng, S.; Ewert, F.; Müller, C.; Rötter, R.P.; Ruane, A.C.; Semenov, M.A.; Wallach, D.; Wang, E.; Alderman, P.D.; Kassie, B.T.; Biernath, C.; Basso, B.; Cammarano, D.; Challinor, A.J.; Doltra, J.; Dumont, B.; Rezaei, E.E.; Gayler, S.; Kersebaum, K.C.; Kimball, B.A.; Koehler, A.-K.; Liu, B.; O’Leary, G.J.; Olesen, J.E.; Ottman, M.J.; Priesack, E.; Reynolds, M.; Stratonovitch, P.; Streck, T.; Thorburn, P.J.; Waha, K.; Wall, G.W.; White, J.W.; Zhao, Z.; Zhu, Y. |
| Title |
Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles |
Type |
Journal Article |
| Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
202 |
Issue |
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Pages |
5-20 |
| Keywords |
Impact uncertainty; High temperature; Model improvement; Multi-model ensemble; Wheat crop model |
| Abstract |
To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. |
| Address |
2016-09-13 |
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Summary Language |
Newsletter July 2016 |
Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0378-4290 |
ISBN |
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Medium |
Article |
| Area |
CropM |
Expedition |
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Conference |
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| Notes |
CropMwp;wos; ft=macsur; wsnot_yet; |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4776 |
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| Author |
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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English |
Summary Language |
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Edition |
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| ISSN |
0936-577x |
ISBN |
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Article |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4754 |
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| Author |
Ewert, F.; van Bussel, L.G.J.; Zhao, G.; Hoffmann, H.; Gaiser, T.; Specka, X.; Nendel, C.; Kersebaum, K.-C.; Sosa, C.; Lewan, E.; Yeluripati, J.; Kuhnert, M.; Tao, F.; Rötter, R.P.; Constantin, J.; Raynal, H.; Wallach, D.; Teixeira, E.; Grosz, B.; Bach, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Kiese, R.; Haas, E.; Eckersten, H.; Trombi, G.; Bindi, M.; Klein, C.; Biernath, C.; Heinlein, F.; Priesack, E.; Cammarano, D.; Asseng, S.; Elliott, J.; Glotter, M.; Basso, B.; Baigorria, G.A.; Romero, C.C.; Moriondo, M. |
| Title |
Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments |
Type |
Book Chapter |
| Year |
2015 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
261-277 |
| Keywords |
CropM; |
| Abstract |
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| Address |
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Thesis |
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| Publisher |
Imperial College Press |
Place of Publication |
London |
Editor |
Rosenzweig, C.; Hillel, D. |
| Language |
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Summary Language |
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Original Title |
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Series Title |
Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts) |
Abbreviated Series Title |
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| Series Volume |
ICP Series on Climate Change Impacts, Adaptation, |
Series Issue |
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Edition |
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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 |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
2427 |
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| Author |
Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
| Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
Type |
Journal Article |
| Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
80 |
Issue |
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Pages |
100-112 |
| Keywords |
Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
| Abstract |
We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1364-8152 |
ISBN |
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Medium |
Article |
| 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 |
4724 |
| Permanent link to this record |
