Records |
Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
Title |
Estimating model prediction error: Should you treat predictions as fixed or random |
Type |
Journal Article |
Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
Volume |
84 |
Issue |
|
Pages |
529-539 |
Keywords |
Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty |
Abstract |
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation. |
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
English |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Article |
Area |
|
Expedition |
|
Conference |
|
Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4773 |
Permanent link to this record |
|
|
|
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 |
|
Issue |
|
Pages |
|
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 |
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
Language |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
1161-0301 |
ISBN |
|
Medium |
Article |
Area |
CropM |
Expedition |
|
Conference |
|
Notes |
CropM; wos; ftnotmacsur; wsnotyet; |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4777 |
Permanent link to this record |
|
|
|
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 |
|
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 |
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
Language |
Summary Language |
Newsletter July 2016 |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
0378-4290 |
ISBN |
|
Medium |
Article |
Area |
CropM |
Expedition |
|
Conference |
|
Notes |
CropMwp;wos; ft=macsur; wsnot_yet; |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4776 |
Permanent link to this record |
|
|
|
Author |
Ewert, F.; Boote, K.J.; Rötter, R.P.; Thorburn, P.; Nendel, C. (eds) |
Title |
Crop modelling for agriculture and food security under global change. Abstracts. International Crop Modelling Symposium iCROPM2016, 15-17 March 2016, Berlin, Germany |
Type |
Book Whole |
Year |
2016 |
Publication |
|
Abbreviated Journal |
|
Volume |
|
Issue |
|
Pages |
|
Keywords |
CropM; MACSUR_ACK |
Abstract |
|
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
Berlin |
Editor |
Ewert, F.; Boote, K.J.; Rötter, R.P.; Thorburn, P.; Nendel, C. |
Language |
|
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
|
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
|
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
2428 |
Permanent link to this record |
|
|
|
Author |
Boote, K.J.; Porter, C.; Jones, J.W.; Thorburn, P.J.; Kersebaum, K.C.; Hoogenboom, G.; White, J.W.; Hatfield, J.L. |
Title |
Sentinel site data for crop model improvement – definition and characterization |
Type |
Conference Article |
Year |
2015 |
Publication |
|
Abbreviated Journal |
|
Volume |
Advances in Agricultural Systems Modeling (7) |
Issue |
|
Pages |
|
Keywords |
CropM; |
Abstract |
|
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
ASA, CSSA, and SSSA |
Place of Publication |
Madison, WI |
Editor |
Hatfield, J.L.; Fleisher, D. |
Language |
|
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
|
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
|
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
2338 |
Permanent link to this record |