Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield

Journal of Advances in Modeling Earth Systems

Volumn 8, Issue 3, 2016, Pages 1358-1375

  Xiong, Wei ^a,b   Skalský, Rastislav ^c,d   Porter, Cheryl H ^b   Balkovič, Juraj ^c,e   Jones, James W ^b   Yang, Di ^a  

^a INSTITUTE OF PLANT PROTECTION   (China)

^b J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

^c INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^d Research Institute for Animal Production Nitra   (Slovakia)

^e Faculty of Natural Sciences   (Slovakia)

Author keywords

calibration; climate change impact; EPIC

Indexed keywords

BIOLOGY; CALIBRATION; CLIMATE MODELS; CROPS; DECISION MAKING; ENVIRONMENTAL PROTECTION; INPUT OUTPUT PROGRAMS; UNCERTAINTY ANALYSIS;

ADAPTATION STRATEGIES; AGRICULTURAL PRODUCTIONS; CLIMATE CHANGE IMPACT; CLIMATE SCENARIOS; ENVIRONMENTAL POLICY INTEGRATED CLIMATE MODELS; EPIC; MODEL CALIBRATION; SOURCES OF UNCERTAINTY;

CLIMATE CHANGE;

CALIBRATION; CLIMATE CHANGE; CLIMATE EFFECT; CLIMATE MODELING; CROP PRODUCTION; CROP YIELD; DATA SET; ENVIRONMENTAL POLICY; GLOBAL CHANGE; MAIZE; PHENOLOGY; UNCERTAINTY ANALYSIS; YIELD RESPONSE;

ZEA MAYS;

EID: 84983486825     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2016MS000625     Document Type: Article

References (48)

1. Angulo, C., R. Rötter, R. Lock, A. Enders, S. Fronzek, and F. Ewert (2013), Implication of crop calibration strategies for assessing regional impacts of climate change in Europe, Agric. For. Meteorol., 170, 32–40.
2. Angulo, C., T. Gaiser, R. Rötter, C. Børgesen, P. Hlavinka, M. Trnka, and F. Ewert (2014), ‘Fingerprints’ of four crop models as affected by soil input data aggregation, Eur. J. Agron., 61, 35–48.
3. Asseng S., et al. (2013), Uncertainty in simulating wheat yields under climate change, Nat. Clim. Change, 3, 827–832.
4. Balkovič, J., M. van der Velde, E. Schmid, R. Skalský, N. Khabarov, N. Obersteriner, B. Sturmer, and W. Xiong (2013), Pan-European crop modeling with EPIC: Implementation, up-scaling and regional crop yield validation, Agr. Syst., 120, 61–75.
5. Balkovič, J., M. van der Velde, R. Skalský, W. Xiong, C. Folberth, N. Khabarov, A. Smirnov, N.D. Müeller, and M. Obersteiner (2014), Global wheat production potentials and management flexibility under the representative concentration pathways, Global Planet. Change, 122, 107–121.
6. Bassu, S., et al. (2014), How to various maize crop models vary in their responses to climate change factors? Global Change Biol., 20, 2301–2320.
7. Batjes, H. N. (1995), A homogenized soil data file for global environmental research: A subset of FAO. ISRIC and NRCS profiles (version 1.0), Working Paper and Preprint 95/10b, International Soil Ref. and Inform. Cent., Wageningen, Netherlands.
8. Boogaart, H. L., C. A. Van Diepen, R. P. Rotter, J. M. Cabrera, and H. H. Van Laar (1998), Useŕs Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5, technical document, 141 p., DLO Winand Staring Cent., Wageningen, Netherlands.
9. Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes (2004), Design and optimisation of a large-area process-based model for annual crops, Agr. For. Meteor., 124(1-2), 99–120.
10. Challinor, A. J., J. Watson, D. B. Lobell, S. M. Howden, D. R. Smith, and N. Chhetri (2014), A meta-analysis of crop yield under climate change and adaptation, Nat Clim. Change, 4(4), 287–291.
11. Collins, W., N. Bellouin, M. Doutriaux-Boucher, N. Gedney, T. Hinton, C.D. Jones, S. Liddicoat, and J. Martin (2008), Evaluation of the HadGEM2 model, Met Off. Hadley Cent. Tech. Note HCTN 74, Exeter, U. K. [Available at http://www.metoffice.gov.uk/media/pdf/8/7/HCTN_74.pdf, accessed 11 Oct 2013.]
12. Deng, J., J. Ran, Z. Wang, Z. Fan, G. Wang, M. Ji, J. Liu, T. Wang, J. Liu, and J.H. Brown (2012), Models and tests of optimal density and maximal yield for crop plants, Proc. Natl. Acad. Sci. U. S. A., 109, 15,823–15,828.
13. Deryng, D., W. J. Sacks, C. C. Barford, and N. Ramankutty (2011), Simulating the effects of climate and agricultural management practices on global crop yield, Global Biogeochem. Cycles, 25, GB2006, doi:10.1029/2009GB003765.
14. Deryng, D., D. Conway, N. Ramankutty, J. Price, and R. Warren (2014), Global crop yield response to extreme heat stress under multiple climate change futures, Environ. Res. Lett., 9, 041001.
15. Elliott, J., D. Kelly, J. Chryssanthacopoulos, M. Glotter, K. Jhunjhnuwala, N. Best, M. Wilde, and I. Foster (2014), The parallel system for integrating impact models and sectors (pSIMS), Environ. Model. Software, 62, 509–516.
16. Elliott, J., et al. (2015), The global gridded crop model intercomparison: Data and modeling protocols for Phase I (v1.0), Geosci. Model Dev., 8, 261–277.
17. Ewert, F., et al. (2014), Crop modelling for integrated assessment of risk to food production from climate change, Environ. Model. Software, 72, 287–303.
18. Ewert, F., M. K. van Ittersum, T. Heckeler, O. Therond, I. Bezlepkina, and E. Andersen (2011), Scale changes and model linking methods for integrated assessment of agri-environmental systems, Agric. Ecosyst. Environ., 142, 6–17.
19. FAO, (1990), Soil Units of the Soil Map of the World, FAO-UNESCO-ISRIC, Rome.
20. Farr, T.G., et al. (2007), The shuttle radar topography mission, Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183.
21. Folberth, C., T. Gaiser, K. C. Abbaspour, R. Schulin, and H. Yang (2012), Regionalization of a large-scale crop growth model for sub-Saharan African: Model setup, evaluation, and estimation of maize yields, Agric. Ecosyst. Environ., 151(1), 21–33.
22. Hempel, S., K. Frieler, L. Warszawski, J. Schewe, and F. Piontek (2013), A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dyn. Discuss., 4, 49–92.
23. Jones, J. W., G. Hoogenboom, C. H. Porter, K. J. Boote, W. D. Batchelor, L. A. Hunt, P. W. Wilkens, U. Singh, A. J. Gijsman, and J. T. Ritchie (2003) The DSSAT cropping system model, Eur. J. Agron., 18, 235–265.
24. Keating, B. A., et al. (2003) An overview of APSIM, a model designed for farming systems simulation, Eur. J. Agron., 18, 267–288.
25. Li, T., et al. (2014) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions, Global Change Biol., 21, 1328–1341.
26. Liu, J. (2009), A GIS-based tool for modeling large-scale crop-water relations, Environ. Model. Software, 23(3), 411–422.
27. Liu, J. G., J. R. Williams, A. J. B. Zehnder, and H. Yang (2007), GEPIC: Modelling wheat yield and crop water productivity with high resolution on a global scale, Agric. Syst., 94, 478–493.
28. Liu, M., B. He, A. Lu, L. Zhou, and J. Wu (2014), Parameters sensitivity analysis for a crop growth model applied to winter wheat in the Huanghuaihai Plain in China, Geosci. Model Dev. Discuss., 7, 3867–3888.
29. Lychuk, T. E., R. C. Izaurralde, R. L. Hill, W. B. McGill, and J. R. Williams (2014), Biochar as a global change adaptation: Predicting biochar impacts on crop productivity and soil quality for a tropical soil with the Environmental Policy Integrated Climate (EPIC) model, Mitig. Adapt. Strat. Global Change, 20, 1437–1458.
30. Monfreda, C., N. Ramankutty, and J. A. Foley (2008), Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in year 2000, Global Biogeochem. Cycles, 22, GB1022, doi:10.1029/2007GB002947.
31. Müeller, N. D., J. S. Gerber, M. Johnston, D. K. Ray, N. Ramankutty, and J. A. Foley (2012), Closing yield gaps through nutrient and water management, Nature, 490, 254–257.
32. Rosenzweig, C., et al. (2013), The agricultural model intercomparison and improvement project (AgMIP): Protocols and pilot studies, Agric. For. Meteorol., 170, 166–182.
33. st century in a global gridded crop model intercomparison, Proc. Natl. Acad. Sci. U. S. A., 111, 3268–3273.
34. Sacks, W. J., D. Derying, J. A. Foley, and N. Ramankutty (2010), Crop planting dates: An analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620.
35. Schaap, M. G., and W. Bouten (1996), Modelling water retention curves of sandy soils using neural networks, Water Resour. Res., 32, 3033–3040.
36. Sheffield, J., G. Goteti, and E. F. Wood (2006), Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling, J Clim., 19, 3088–3111.
37. Tao, F., and Z. Zhang (2011), Impacts of climate change as a function of global mean temperature: Maize productivity and water use in China, Clim. Change, 105, 409–432.
38. Tatsumi, K., Y. Yamashiki, V. S. Roverto, K. Takara, Y. Matsuoka, I. Takahashi, K. Maruyama, and N. Kawahara (2011), Estimation of potential changes in cereals productions under climate change scenarios, Hydrol. Processes, 25(17), 2715–2725.
39. Van Wart, J., K. C. Kersebaum, S. Peng, M. Milner, and K. G. Cassman (2013), Estimating crop yield potential at regional to national scales, Field Crops Res., 143, 34–43.
40. Waha, K., L. G. J. Van Bussel, C. Muller, and A. Bondeau (2012), Climate-driven simulation of global crop sowing dates, Global Ecol. Biogeogr., 21(2), 247–259.
41. Wang, X., X. He, J. R. Williams, R. C. Izaurralde, and J. D. Atwood (2005), Sensitivity and uncertainty analysis of crop yields and soil organic carbon simulated with EPIC, Trans. ASAE, 48(3), 1041–1054.
42. Wang, X., R. D. Harmel, J. R. Williams, and W. L. Harman (2006), Evaluation of EPIC for assessing crop yield, runoff, sediment and nutrient losses from watersheds with poultry litter fertilization, Trans. ASABE, 49(1), 47–59.
43. Williams, J. R. (1995), The EPIC model, in In Computer Models of Watershed Hydrology, edited by V. P. Singh, pp. 909–1000, Water Resour. Publ., Highlands Ranch, Colo.
44. Xiong, W., I. Holman, D. Conway, E. Lin, and Y. Li (2008), A crop model cross calibration for use in regional climate impacts studies, Ecol. Modell., 213, 365–380.
45. Xiong, W., J. Balkovic, M. van der Velde, Z. Zhang, R.C. Izaurralde, R. Skalsky, E. Lin, N. Mueller, and M. Obersteiner (2014), A calibration procedure to improve global rice yield simulations with EPIC, Ecol. Modell., 273, 128–139.
46. You, L., and S. Wood (2006), An entropy approach to spatial disaggregation of agricultural production, Agric. Syst., 90, 329–347.
47. You, L., S. Crespo, Z. Guo, J. Koo, W. Ojo, K. Sebastian, M. T. Tenorio, S. Wood, and U. Wood-Sichra (2009), Spatial Production Allocation Model (SPAM) 2000, version 3 Release 2, IFPRI, Washington D. C. [Available at http://MapSPAM.info.]
48. Zhang, X., R. C. Izaurralde, D. H. Manowitz, R. Sahajpal, T. O. West, A. M. Thomson, M. Xu, K. Zhao, S. D. LeDuc, and J. R. Williams (2015), Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data, Environ. Modell. Software, 63, 199–216.