Records |
Author |
Yin, X.; Kersebaum, K.-C.; Beaudoin, N.; Constantin, J.; Chen, F.; Louarn, G.; Manevski, K.; Hoffmann, M.; Kollas, C.; Armas-Herrera, C.M.; Baby, S.; Bindi, M.; Dibari, C.; Ferchaud, F.; Ferrise, R.; de Cortazar-Atauri, I.G.; Launay, M.; Mary, B.; Moriondo, M.; Öztürk, I.; Ruget, F.; Sharif, B.; Wachter-Ripoche, D.; Olesen, J.E. |
Title |
Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models |
Type |
Journal Article |
Year |
2020 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
Volume |
|
Issue |
|
Pages |
107863 |
Keywords |
multi-model ensemble; crop rotations; catch crops; N cycling; N export |
Abstract |
Modelling N transformations within cropping systems is crucial for N management optimization in order to increase N use efficiency and reduce N losses. Such modelling remains challenging because of the complexity of N cycling in soil–plant systems. In the current study, the uncertainties of six widely used process-based models (PBMs), including APSIM, CROPSYST, DAISY, FASSET, HERMES and STICS, were tested in simulating different N managements (catch crops (CC) and different N fertilizer rates) in 12-year rotations in Western Europe. Winter wheat, sugar beet and pea were the main crops, and radish was the main CC in the tested systems. Our results showed that PBMs simulated yield, aboveground biomass, N export and N uptake well with low RMSE values, except for sugar beet, which was generally less well parameterized. Moreover, PBMs provided more accurate crop simulations (i.e. N export and N uptake) compared to simulations of soil (N mineralization and soil mineral N (SMN)) and environmental variables (N leaching). The use of multi-model ensemble mean or median of four PBMs significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15% for yield, aboveground biomass, N export and N uptake. Multi-model ensemble also significantly reduced the MAPE for net N mineralization and annual N leaching to around 15%, while it was larger than 20% for SMN. Generally, PBMs well simulated the CC effects on N fluxes, i.e. increasing N mineralization and reducing N leaching in both short-term and long-term, and all PBMs correctly predicted the effects of the reduced N rate on all measured variables in the study. The uncertainties of multi-model ensemble for N mineralization, SMN and N leaching were larger, mainly because these variables are influenced by plant-soil interactions and subject to cumulative long-term effects in crop rotations, which makes them more difficult to simulate. Large differences existed between individual PBMs due to the differences in formalisms for describing N processes in soil–plant systems, the skills of modelers and the model calibration level. In addition, the model performance also depended on the simulated variables, for instance, HERMES and FASSET performed better for yield and crop biomass, APSIM, DAISY and STICS performed better for N export and N uptake, STICS provided best simulation for SMN and N leaching among the six individual PBMs in the study, but all PBMs met difficulties to well predict either average or variance of soil N mineralization. Our results showed that better calibration for soil N variables is needed to improve model predictions of N cycling in order to optimize N management in crop rotations. |
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
|
Summary Language |
|
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 |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
5235 |
Permanent link to this record |
|
|
|
Author |
Cammarano, D.; Rötter, R.P.; Asseng, S.; Ewert, F.; Wallach, D.; Martre, P.; Hatfield, J.L.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Kersebaum, K.C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Heng, L.; Hooker, J.E.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Müller, C.; Kumar, S.N.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Priesack, E.; Ripoche, D.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
Title |
Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2 |
Type |
Journal Article |
Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
Volume |
198 |
Issue |
|
Pages |
80-92 |
Keywords |
Multi-model simulation; Transpiration efficiency; Water use; Uncertainty; Sensitivity |
Abstract |
Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand. |
Address |
2016-10-31 |
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 |
0378-4290 |
ISBN |
|
Medium |
Article |
Area |
|
Expedition |
|
Conference |
|
Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4786 |
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 |