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

Confalonieri, Roberto; Bregaglio, Simone; Adam, Myriam; Ruget, Françoise; Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth J.; Buis, Samuel; Fumoto, Tamon; Gaydon, Donald S.; Lafarge, Tanguy; Marcaida, Manuel; Nakagawa, Hiroshi; Ruane, Alex C.; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Fugice, Job; Yoshida, Hiroyuki; Zhang, Zhao; Wilson, L. T.; Baker, Jeff; Yang, Yingzhen; Masutomi, Yuji; Wallach, Daniel; Acutis, Marco; Bouman, B.A.M.

doi:10.1016/j.envsoft.2016.09.007

Cited by 19 publications

(19 citation statements)

References 42 publications

(51 reference statements)

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“…Biomass is accumulated from the photosynthate and further transferred into crop yield by harvest index. This model has been widely used to simulate the growth and productivity of rice in previous studies Li et al, 2015;Confalonieri et al, 2016;Wang et al, 2016).…”

Section: Mcwla-rice Model and Its Parameterizationsmentioning

confidence: 99%

Future extreme temperature and its impact on rice yield in China

Zhang

Chen

Wang

et al. 2017

Intl Journal of Climatology

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Rice in China is increasingly suffered from extreme temperature stress (ETS) with ongoing climate change. It is projected that ETS would increase notably across the world in the future. However, the spatio-temporal change of ETS in main rice planting areas in China is still unclear; and the future yield loss caused by ETS (YL ETS ) has seldom been investigated quantitatively. In this study, we first investigated the spatio-temporal change of ETS across China under 20 climate change scenarios consisting of five global climate models and four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5). Then, using a process-based crop model (MCWLA-Rice), its 30 sets of model parameters and the 20 climate change scenarios, we conducted a super-ensemble assessment to investigate the YL ETS over 2020-2049, relative to the baseline period , across China. The results showed that, an increased heat ETS and a decreased cold ETS would be expected for most areas. As a result, a large spatial variability of yield loss would be expected in the future, including severely cold stress for region I (northeastern China, single rice) and region IV (southern China, early rice), but severely heat stress for region III (the middle and lower reaches of Yangtze River, single rice) and region IV (southern China, late rice). Comparing yield loss from both ETS, a decreased change in yield loss would be mainly expected in region I (northeastern China, single rice), while an increased change for region III (the middle and lower reaches of Yangtze River, single rice) and IV (southern China, late rice), with less change for region II (southwestern China, single rice) and IV (southern China, early rice). Finally, some adaptation measures were proposed for the ETS-sensitive areas. Our findings are useful to develop effective policies to cope with climate risk and relieve ETS disasters.

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Section: Mcwla-rice Model and Its Parameterizationsmentioning

confidence: 99%

Future extreme temperature and its impact on rice yield in China

Zhang

Chen

Wang

et al. 2017

Intl Journal of Climatology

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“…Some crop models simulate instantaneous leaf photosynthesis by use of the biochemical model of Farquhar, von Caemmerer & Berry (FvCB) 9 . Such diversity in model algorithms, combined with inconsistency in model parameterization procedures, can create a large range of uncertainties in model projections 10 – 13 .…”

Section: Introductionmentioning

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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“…Further universal (global) or more particular (local) Nc concentrations for rice plant and organs should be developed for more precise N prediction and management. Different N routines, algorithms, parameters and results of different rice models should be further compared by multi-model inter-comparison studies (Li et al ., 2015; Confalonieri et al ., 2016 a ; Yin et al ., 2017). The calibration method used in the current study is to try the parameters in their physiological ranges to find the best parameter set manually, which also could be regarded as a ‘trial and error’ method, as many crop models are used.…”

Section: Discussionmentioning

confidence: 99%

Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

et al. 2018

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Plant nitrogen (N) links with many physiological progresses of crop growth and yield formation. Accurate simulation is key to predict crop growth and yield correctly. The aim of the current study was to improve the estimation of N uptake and translocation processes in the whole rice plant as well as within plant organs in the RiceGrow model by using plant and organ maximum, critical and minimum N dilution curves. The maximum and critical N (Nc) demand (obtained from the maximum and critical curves) of shoot and root and Nc demand of organs (leaf, stem and panicle) are calculated by N concentration and biomass. Nitrogen distribution among organs is computed differently pre- and post-anthesis. Pre-anthesis distribution is determined by maximum N demand with no priority among organs. In post-anthesis distribution, panicle demands are met first and then the remaining N is allocated to other organs without priority. The amount of plant N uptake depends on plant N demand and N supplied by the soil. Calibration and validation of the established model were performed on field experiments conducted in China and the Philippines with varied N rates and N split applications; results showed that this improved model can simulate the processes of N uptake and translocation well.

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

Cited by 19 publications

References 42 publications

Future extreme temperature and its impact on rice yield in China

Future extreme temperature and its impact on rice yield in China

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

Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

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