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ARS Home » Plains Area » Lubbock, Texas » Cropping Systems Research Laboratory » Wind Erosion and Water Conservation Research » Research » Publications at this Location » Publication #326949
Title: Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevaton

Author
item LI, TAO - International Rice Research Institute
item YIN, XINYOU - Wageningen Agricultural University
item HASEGAWA, TOSHIHIRO - National Institute For Agro-Environmental Sciences
item BOOTE, KENNETH - University Of Florida
item ZHU, YAN - National Institute For Agro-Environmental Sciences
item ADAM, MYRIAM - Cirad, France
item Baker, Jeffrey
item BOUMAN, BAS - International Rice Research Institute
item BREGAGLIO, SIMONE - University Of Milan
item BUIS, SAMUEL - Inra, Génétique Animale Et Biologie Intégrative , Jouy-En-josas, France
item COFALONIERI, ROBERT - University Of Milan
item FUGICE, JOB - International Fertilizer Development Center (IFDC)
item FUMOTO, TAMON - Wageningen Agricultural University
item GAYDON, DONALD - Csiro European Laboratory
item KUMAR, SOORA - Indian Agricultural Research Institute
item LAFARGE, TANGUY - Cirad, France
item MARCAIDA, MANUEL - International Rice Research Institute
item MASUTOMI, YUJI - Ibaraki University
item NAKAGAWA, HIROSHI - National Agriculture And Food Research Organization (NARO), Agricultrual Research Center
item PEQUENO, DIEGO - University Of Florida
item RUANE, ALEX - Nasa Goddard Institute For Space Studies
item RUGET, FRANCOISE - Inra, Génétique Animale Et Biologie Intégrative , Jouy-En-josas, France
item SINGH, UPENDRA - International Fertilizer Development Center (IFDC)
item TAO, FULU - Chinese Academy Of Sciences
item WALLACH, DANIEL - Inra, Génétique Animale Et Biologie Intégrative , Jouy-En-josas, France
item WILSON, LLOYD - Texas A&M Agrilife
item YANG, YUBIN - Texas A&M Agrilife
item ZHANG, ZHAO - Beijing Normal University
item ZHU, JIANGUO - Chinese Academy Of Sciences

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: 2/23/2016
Publication Date: 2/2/2016
Citation: Li, T., Yin, X., Hasegawa, T., Boote, K., Zhu, Y., Adam, M., Baker, J.T., Bouman, B., Bregaglio, S., Buis, S., Cofalonieri, R., Fugice, J., Fumoto, T., Gaydon, D., Kumar, S.N., Lafarge, T., Marcaida, M., Masutomi, Y., Nakagawa, H., Pequeno, D., Ruane, A.C., Ruget, F., Singh, U., Tao, F., Wallach, D., Wilson, L., Yang, Y., Zhang, Z., Zhu, J. 2016. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevaton. [abstract]. Meeting Abstract. 21: 1328-1341.

Interpretive Summary: Increased CO2 concentration and air temperature are two very important variables associated with global warming and climate change. Assessing the putative impacts of these factors on rice production is crucial for global food security due to rice being the staple food for more than half of the world population. Rice crop models are useful for predicting rice productivity under climate change. However, model predictions have uncertainties arisen due to the inaccurate inputs and the varying capabilities of models to capture yield performance. A series of modeling activities were implemented by the AgMIP Rice Team (consisting of 16 rice models currently) to improve the model capability for reducing the uncertainties of model prediction.

Technical Abstract: Materials and Methods The simulation exercise and model improvement were implemented in phase-wise. In the first modelling activities, the model sensitivities were evaluated to given CO2 concentrations varying from 360 to 720 'mol mol-1 at an interval of 90 'mol mol-1 and air temperature increments of 0, 3, 6 and 9 oC (Li et al. 2015). In the second phase, in order to improve model response to CO2 elevation, rice models were tested against Free-Air CO2 Enrichment (FACE) measurements and individual model groups conducted essential modifications on the quantification of model response. The models were firstly calibrated with the data under ambient CO2 concentration and were then tested against the evaluated CO2 FACE data. Further simulation exercises and model modifications were undertaken to improve response to CO2 and temperature elevation using data from chamber experiments. Results and Discussion The quantified enhancement of rice grain yield varied from 2% to 38% when the CO2 increased from 360 to 540 'mol mol-1, and 4 to 68% if it was doubled from 360 to 720 'mol mol-1. Model predictions of grain yield changes significantly varied from +68% to -75% with 3 oC temperature increase, and from +30% to -98% with 6 oC increase, although the averages of all model predictions showed a 20% and 40% decreases with 3 and 6 oC increase which is close to literature reports. The large variations among models are due to fundamental differences in model algorithms that describe CO2 fertilization and temperature effects on plant development, biomass accumulation and yield formation (Confalonieri et al., 2016, under review). Models differed in simulated yield enhancement ranging from 1% to 19% with ~200 'mol mol-1 CO2 elevation after models were calibrated to ambient CO2 condition in FACE experiments. Calibration reduced model-to-model variation, and the average grain yield enhancement over all model estimations agreed with field measurements from FACE experiments conducted at two field sites. The results of simulation exercises with chamber experiments show the models captured the CO2 fertilization and temperature effects on above-ground biomass with low variation among models, but less agreement among models on predicted CO2 effects on grain yield. Many models overestimated the grain yield gains per unit CO2 elevation on higher CO2 conditions. Most models also underestimated the grain yield decline due to increased air temperature, which indicates a need to improve model functions related to grain-set and grain growth at elevated temperatures.