Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

Toshihiro Hasegawa*, Tao Li, Xinyou Yin, Yan Zhu, Kenneth Boote, Jeffrey Baker, Simone Bregaglio, Samuel Buis, Roberto Confalonieri, Job Fugice, Tamon Fumoto, Donald Gaydon, Soora Naresh Kumar, Tanguy Lafarge, Manuel Marcaida, Yuji Masutomi, Hiroshi Nakagawa, Philippe Oriol, Françoise Ruget, Upendra SinghLiang Tang, Fulu Tao, Hitomi Wakatsuki, Daniel Wallach, Yulong Wang, Lloyd Ted Wilson, Lianxin Yang, Yubin Yang, Hiroe Yoshida, Zhao Zhang, Jianguo Zhu

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Original languageEnglish
Article number14858
Number of pages13
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 1 Nov 2017

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