How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change?

Enli Wang (), Di He (), Jing Wang (), Julianne M. Lilley, Brendan Christy, Munir P. Hoffmann, Garry O’Leary, Jerry L. Hatfield, Luigi Ledda, Paola A. Deligios, Brian Grant, Qi Jing, Claas Nendel, Henning Kage, Budong Qian, Ehsan Eyshi Rezaei, Ward Smith, Wiebke Weymann and Frank Ewert
Additional contact information
Enli Wang: CSIRO Agriculture and Food
Di He: CSIRO Agriculture and Food
Jing Wang: China Agricultural University
Julianne M. Lilley: CSIRO Agriculture and Food
Brendan Christy: Department of Jobs, Precincts and Regions Victoria
Munir P. Hoffmann: University of Goettingen
Garry O’Leary: Department of Jobs, Precincts and Regions Victoria
Jerry L. Hatfield: USDA-ARS
Luigi Ledda: Polytechnic University of Marche
Paola A. Deligios: University of Sassari
Brian Grant: Agriculture and Agri-Food Canada
Qi Jing: Agriculture and Agri-Food Canada
Claas Nendel: Leibniz Centre for Agricultural Landscape Research (ZALF)
Henning Kage: Kiel University
Budong Qian: Agriculture and Agri-Food Canada
Ehsan Eyshi Rezaei: University of Bonn
Ward Smith: Agriculture and Agri-Food Canada
Wiebke Weymann: Kiel University
Frank Ewert: University of Bonn

Climatic Change, 2022, vol. 172, issue 1, No 20, 22 pages

Abstract: Abstract 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 °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.

Keywords: AgMIP; Brassica napus L.; Model calibration; Model improvement; Multi-model ensemble; Sensitivity analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10584-022-03375-2

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