Simulation of the effects of genotype and N availability on rice growth and yield response to an elevated atmospheric CO2 concentration

Yoshida, Hiroyuki; Horie, Takeshi; Nakazono, Kou; Ohno, Hiroyuki; Nakagawa, Hiroshi

doi:10.1016/j.fcr.2011.07.016

Cited by 11 publications

(16 citation statements)

References 24 publications

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“…15). 26) This agreed well with the measurements in FACE. 73,76,78) The large number of spikelet per unit area is also an indispensable character for high yielding rice under ambient [CO 2 ].…”

Section: Adaptation Of Asian Rice Culture To Elevated [Co2] and Globasupporting

confidence: 85%

“…73,76,78) The large number of spikelet per unit area is also an indispensable character for high yielding rice under ambient [CO 2 ]. The analysis by GEMRICE also showed that rice genotypes having higher response to [CO 2 ] have higher nitrogen (N) use efficiency in spikelet production, 26) as spikelet number produced per unit area was proportional to plant N content at the late stage of spikelet differentiation as shown in Fig. 8.…”

Section: Adaptation Of Asian Rice Culture To Elevated [Co2] and Globamentioning

confidence: 94%

“…15–17) Using SIMRIW, thus developed, we predicted the effects of doubled [CO 2 ] and projected global warming on the production of irrigated rice in Japan 18–21) and Asia. 22,23) Analyses by SIMRIW and its succeeding model GEMRICE (Genotype by Environment Interaction Simulation Model for Rice Growth and Yield) 24) revealed that higher tolerance to heat-induced spikelet sterility at flowering stage 21,23) and higher yielding potential 25,26) under elevated [CO 2 ] are required for adaptive rice genotypes to projected future environments, and also to meet the increasing rice demand in Asia. We further investigated on the mechanisms and traits determining the heat tolerance of rice at flowering stage 27–34) and the yield potential, 25,26,35–38) and explored for genetic resources for the traits, which were necessary for the development of adaptive rice cultivars to projected global warming.…”

Section: Introductionmentioning

confidence: 99%

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Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Horie

2019

Proc. Jpn. Acad., Ser. B

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Since the projection of global warming emerged in 1980s with the potential of laying enormous impacts on agriculture and food security of the world, we have conducted experimental and modeling studies for clarifying its effects on rice production in Asia and for developing adaptive rice production technologies. On the basis of measurement of rice responses to climate and carbon dioxide concentration ([CO2]), the dynamic process model named SIMRIW was developed to predict global warming effects on irrigated rice. The model predicted differential regional effects of the projected global warming by doubling [CO2] on the rice yield over Asia, and indicated that high tolerance to heat-induced spikelet sterility and high yield potential under elevated [CO2] are the two important characteristics required for rice genotypes adaptive to global warming environment. Further, genetic traits associated with these characteristics and their genetic resources for breeding adaptive genotypes were identified from diverse rice germplasms. This article reviews our initiative studies in the light of the recent studies, and points out further research that is needed for better understanding and overcoming of this unprecedentedly large problem.

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Section: Adaptation Of Asian Rice Culture To Elevated [Co2] and Globasupporting

confidence: 85%

Section: Adaptation Of Asian Rice Culture To Elevated [Co2] and Globamentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Horie

2019

Proc. Jpn. Acad., Ser. B

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“…GECROS ● ● ● Gs, Gm, Tr ● ● ● 52 5. GEMRICE ● ● ● phenology, spikelet sterility ● ● ● 53 – 56 6. H/H ● ● ● Gs ● ● 20 , 51 , 57 7.…”

Section: Resultsmentioning

confidence: 99%

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

Hasegawa

Yin

et al. 2017

Sci Rep

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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.

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“…Major updates primarily consist of the incorporation of new approaches for specific sub-domains (GEMRICE, Yoshida et al, 2011;WARM, Confalonieri et al, 2012;STICS;Bergez et al, 2014) or extension to encompass processes not included in previous versions (APSIM-Oryza, Gaydon et al, 2012a). Other improvements are ongoing for many models, in part due to activities performed within the AgMIP Rice Team.…”

Section: Rice Modelsmentioning

confidence: 99%

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

Confalonieri

Bregaglio

Adam

et al. 2016

Environmental Modelling & Software

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Keywords:Model classification Model parameterisation Model ensemble Model structure Rice Uncertainty a b s t r a c t For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

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Simulation of the effects of genotype and N availability on rice growth and yield response to an elevated atmospheric CO2 concentration

Cited by 11 publications

References 24 publications

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

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

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

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