Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments
Hasegawa, Toshihiro ; Li, Tao ; Yin, Xinyou ; Zhu, Yan ; Boote, Kenneth ; Baker, Jeffrey ; Bregaglio, Simone ; Buis, Samuel ; Confalonieri, Roberto ; Fugice, Job ; Fumoto, Tamon ; Gaydon, Donald ; Kumar, Soora Naresh ; Lafarge, Tanguy ; Marcaida, Manuel ; Masutomi, Yuji ; Nakagawa, Hiroshi ; Oriol, Philippe ; Ruget, Françoise ; Singh, Upendra ; Tang, Liang ; Tao, Fulu ; Wakatsuki, Hitomi ; Wallach, Daniel ; Wang, Yulong ; Wilson, Lloyd Ted ; Yang, Lianxin ; Yang, Yubin ; Yoshida, Hiroe ; Zhang, Zhao ; Zhu, Jianguo - \ 2017
Scientific Reports 7 (2017). - ISSN 2045-2322 - 13 p.
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.
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 ; Buis, Samuel ; Fumoto, Tamon ; Gaydon, Donald ; Lafarge, Tanguy ; Marcaida, Manuel ; Nakagawa, Hiroshi ; Ruane, Alex C. ; Singh, Balwinder ; Singh, Upendra ; Tang, Liang ; Tao, Fulu ; Fugice, Job ; Yoshida, Hiroe ; Zhang, Zhao ; Wilson, Lloyd T. ; Baker, Jeff ; Yang, Yubin ; Masutomi, Yuji ; Wallach, Daniel ; Acutis, Marco ; Bouman, Bas - \ 2016
Environmental Modelling & Software 85 (2016). - ISSN 1364-8152 - p. 332 - 341.
Model classification - Model ensemble - Model parameterisation - Model structure - Rice - Uncertainty
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.
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
Makowski, D. ; Asseng, S. ; Ewert, F. ; Bassu, S. ; Durand, J.L. ; Li, G. ; Martre, P. ; Adam, M.Y.O. ; Aggarwal, P.K. ; Angulo, C. ; Baron, C. ; Basso, B. ; Bertuzzi, P. ; Biernath, C. ; Boogaard, H.L. ; Boote, K.J. ; Bouman, B. ; Bregaglio, S. ; Brisson, N. ; Buis, S. ; Cammarano, D. ; Challinor, A.J. ; Confalonieri, R. ; Conijn, J.G. ; Corbeels, M. ; Deryng, D. ; Sanctis, G. De; Doltra, J. ; Fumoto, T. ; Gayler, S. ; Gaydon, D. ; Goldberg, R. ; Grant, R.F. ; Grassini, P. ; Hatfield, J.L. ; Hasegawa, T. ; Heng, L. ; Hoek, S.B. ; Hooker, J. ; Hunt, L.A. ; Ingwersen, J. ; Izaurralde, C. ; Jongschaap, R.E.E. ; Jones, J.W. ; Kemanian, R.A. ; Kersebaum, K.C. ; Kim, S.H. ; Lizaso, J. ; Marcaida III, M. ; Müller, C. ; Nakagawa, H. ; Naresh Kumar, S. ; Nendel, C. ; O'Leary, G.J. ; Olesen, J.E. ; Oriol, P. ; Osborne, T.M. ; Palosuo, T. ; Pravia, M.V. ; Priesack, E. ; Ripoche, D. ; Rosenzweig, C. ; Ruane, A.C. ; Ruget, F. ; Sau, F. ; Semenov, M.A. ; Shcherbak, I. ; Singh, B. ; Soo, A.K. ; Steduto, P. ; Stöckle, C.O. ; Stratonovitch, P. ; Streck, T. ; Supit, I. ; Tang, L. ; Tao, F. ; Teixeira, E. ; Thorburn, P. ; Timlin, D. ; Travasso, M. ; Rötter, R.P. ; Waha, K. ; Wallach, D. ; White, J.W. ; Wilkens, P. ; Williams, J.R. ; Wolf, J. ; Ying, X. ; Yoshida, H. ; Zhang, Z. ; Zhu, Y. - \ 2015
Agricultural and Forest Meteorology 214-215 (2015). - ISSN 0168-1923 - p. 483 - 493.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
Li, T. ; Hasegawa, T. ; Yin, X. ; Zhu, Y. ; Boote, K. ; Adam, M. ; Bregaglio, S. ; Buis, S. ; Confalonieri, R. ; Fumoto, T. ; Gaydon, D. ; Marcaida III, M. ; Nakagawa, H. ; Oriol, P. ; Ruane, A.C. ; Ruget, F. ; Singh, B. ; Singh, U. ; Tang, L. ; Yoshida, H. ; Zhang, Z. ; Bouman, B. - \ 2015
Global Change Biology 21 (2015)3. - ISSN 1354-1013 - p. 1328 - 1341.
air co2 enrichment - high-temperature stress - elevated co2 - spikelet fertility - night temperature - carbon-dioxide - growth - sterility - face - productivity
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.
Specification of databases of SEAMLESS-IF
Andersen, E. ; Diepen, C.A. van; Hazeu, G.W. ; Elbersen, B.S. ; Terluin, I.J. ; Verhoog, D. ; Godeschalk, F.E. ; Genovese, G. ; Confàlonieri, R. ; Kyhn, M. - \ 2006
Wageningen : Wageningen University (Report / Seamless no. 20) - ISBN 9789085850472
The complete genome of the Crenarchaeote Sulfolobus solfataricus P2
She, Q. ; Singh, R.K. ; Confalonieri, F. ; Zivanovic, Y. ; Allard, G. ; Awayez, M.J. ; Chan-Weiher, C.C.Y. ; Clausen, I.G. ; Curtis, B.A. ; Moors, A. de; Heikamp-de Jong, I. ; Oost, J. van der - \ 2001
Proceedings of the National Academy of Sciences of the United States of America 95 (2001). - ISSN 0027-8424 - p. 7835 - 7840.
The genome of the crenarchaeon Sulfolobus solfataricus P2 contains 2,992,245 bp on a single chromosome and encodes 2,977 proteins and many RNAs. One-third of the encoded proteins have no detectable homologs in other sequenced genomes. Moreover, 40ppear to be archaeal-specific, and only 12nd 2.3re shared exclusively with bacteria and eukarya, respectively. The genome shows a high level of plasticity with 200 diverse insertion sequence elements, many putative nonautonomous mobile elements, and evidence of integrase-mediated insertion events. There are also long clusters of regularly spaced tandem repeats. Different transfer systems are used for the uptake of inorganic and organic solutes, and a wealth of intracellular and extracellular proteases, sugar, and sulfur metabolizing enzymes are encoded, as well as enzymes of the central metabolic pathways and motility proteins. The major metabolic electron carrier is not NADH as in bacteria and eukarya but probably ferredoxin. The essential components required for DNA replication, DNA repair and recombination, the cell cycle, transcriptional initiation and translation, but not DNA folding, show a strong eukaryal character with many archaeal-specific features. The results illustrate major differences between crenarchaea and euryarchaea, especially for their DNA replication mechanism and cell cycle processes and their translational apparatus
|A BAC library and paired-PCR approach to mapping and completing the genome sequence of Sulfolobus solfataricus P2 : an EC perspective
She, Q. ; Confalonieri, F. ; Zivanovic, Y. ; Medina, N. ; Billault, A. ; Awayez, M.J. ; Thi-Ghoc, H.P. ; Thi-Pham, B.T. ; Oost, J. van der; Duguet, M. ; Garrett, R.A. - \ 2000
Dna sequence 11 (2000). - ISSN 1042-5179 - p. 183 - 136.
Gene content and organization of a 281-kbp contig from the genome of the extremely thermophilic archaeon, Sulfolobus solfataricus P2
Charlebois, R. ; Confalonieri, F. ; Curtis, B. ; Doolittle, W.F. ; Duguet, M. ; Erauso, G. ; Faguy, D. ; Gaasterland, T. ; Garrett, R.A. ; Gordon, P. ; Kozera, C. ; Medina, N. ; Oost, J. van der; Peng, X. ; Ragan, M. ; She, Q. ; Singh, R.K. - \ 2000
Genome 43 (2000)1. - ISSN 0831-2796 - p. 116 - 136.
The sequence of a 281-kbp contig from the crenarchaeote Sulfolobus solfataricus P2 was determined and analysed. Notable features in this region include 29 ribosomal protein genes, 12 tRNA genes (four of which contain archaeal-type introns), operons encoding enzymes of histidine biosynthesis, pyrimidine biosynthesis, and arginine biosynthesis, an ATPase operon, numerous genes for enzymes of lipopolysaccharide biosynthesis, and six insertion sequences. The content and organization of this contig are compared with sequences from crenarchaeotes, euryarchaeotes, bacteria, and eukaryotes.