TY  - JOUR
A1  - Wang, Enli
A1  - He, Di
A1  - Wang, Jing
A1  - Lilley, Julianne M.
A1  - Christy, Brendan
A1  - Hoffmann, Munir P.
A1  - O'Leary, Garry
A1  - Hatfield, Jerry L.
A1  - Ledda, Luigi
A1  - Deligios, Paola A.
A1  - Grant, Brian
A1  - Jing, Qi
A1  - Nendel, Claas
A1  - Kage, Henning
A1  - Qian, Budong
A1  - Rezaei, Ehsan Eyshi
A1  - Smith, Ward
A1  - Weymann, Wiebke
A1  - Ewert, Frank
T1  - How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change?
JF  - Climatic change
N2  - To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5-5.7% per degrees C), but to increase with increasing CO2 concentration (4.6-8.3% per 100-ppm), rainfall (2.1-6.1% per 10% increase), and nitrogen rates (1.3-6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments.
KW  - AgMIP
KW  - Brassica napus L.
KW  - Model calibration
KW  - Model improvement;
KW  - Multimodel ensemble
KW  - Sensitivity analysis
Y1  - 2022
U6  - https://doi.org/10.1007/s10584-022-03375-2
SN  - 0165-0009
SN  - 1573-1480
VL  - 172
IS  - 1-2
PB  - Springer Nature
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Kothari, Kritika
A1  - Battisti, Rafael
A1  - Boote, Kenneth J.
A1  - Archontoulis, Sotirios
A1  - Confalone, Adriana
A1  - Constantin, Julie
A1  - Cuadra, Santiago
A1  - Debaeke, Philippe
A1  - Faye, Babacar
A1  - Grant, Brian
A1  - Hoogenboom, Gerrit
A1  - Jing, Qi
A1  - van der Laan, Michael
A1  - Macena da Silva, Fernando Antonio
A1  - Marin, Fabio R.
A1  - Nehbandani, Alireza
A1  - Nendel, Claas
A1  - Purcell, Larry C.
A1  - Qian, Budong
A1  - Ruane, Alex C.
A1  - Schoving, Celine
A1  - Silva, Evandro H. F. M.
A1  - Smith, Ward
A1  - Soltani, Afshin
A1  - Srivastava, Amit
A1  - Vieira, Nilson A.
A1  - Slone, Stacey
A1  - Salmeron, Montserrat
T1  - Are soybean models ready for climate change food impact assessments?
JF  - European journal of agronomy : the official journal of the European Society for Agronomy
N2  - An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.
KW  - Agricultural Model Inter-comparison and Improvement Project (AgMIP);
KW  - Model ensemble
KW  - Model calibration
KW  - Temperature
KW  - Atmospheric CO2
KW  - concentration
KW  - Legume model
Y1  - 2022
U6  - https://doi.org/10.1016/j.eja.2022.126482
SN  - 1161-0301
SN  - 1873-7331
VL  - 135
PB  - Elsevier
CY  - Amsterdam
ER  -