An ensemble approach to the representation of subgrid-scale heterogeneity of crop phenology and yield in coarse-resolution large-area crop models

Journal of Agricultural Meteorology

Volumn 69, Issue 4, 2013, Pages 243-254

  Iizumi, Toshichika ^a   Sakurai, Gen ^a   Yokozawa, Masayuki ^a  

^a NATIONAL INSTITUTE FOR AGRO ENVIRONMENTAL SCIENCES   (Japan)

Author keywords

Calibration; Large area crop model; Markov Chain Monte Carlo method; Soil and Water Assessment Tool (SWAT); Spatial heterogeneity

Indexed keywords

EID: 84902169695     PISSN: 00218588     EISSN: 18810136     Source Type: Journal    
DOI: 10.2480/agrmet.69.4.2     Document Type: Article

References (32)

1. Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze-Campen, H., Muller, C., Reichstein, M., and Smith, B., 2007: Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol., 13, 679-706
2. Challinor, A.J., Wheeler, T.R., Craufurd, P.Q., Slin-go, J.M., and Grimes, D.I.F., 2004: Design and optimization of a large-area process-based model for annual crops. Agric. For. Meteorol., 124, 99-120.
3. 2 Metabolism.(ed. by Ludden, P.W., and Burris, J.E.), Elsevier Amsterdam, pp.379–387.
4. United Nations Food and Agriculture Organization (FAO), 2010. FAOSTAT. Available at http://faostat.fao.org/default.aspx (Accessed on May 10,2010).
5. Ganguly, S., Schull, M.A., Samanta, A., Shabanov, N.V., Milesi, C., Nemani, R.R., Knyazikhin, Y., and Myneni, R.B., 2008: Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory. Remote Sens. Environ., 112, 4333-4343.
6. Gelman, A., and Rubin, D.B., 1992: Inference from iterative simulation using multiple sequences. Stat. Sci., 7, 457-511
7. Hall, F.G., Brown de Colstoun, E., Collatz, G.J., Lan-dis, D., Dirmeyer, P., Betts, A., Huffman, G.J., Bounoua, L., and Meeson, B., 2006: ISLSCP Initiative II global data sets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies. J. Geophys. Res., 111, D22S01, doi:10.1029/2006JD007366.
8. Hastings, W.K., 1970: Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
9. Howell, T.A., Evett, S.R., Tolk, J.A., Schneider, A.D., and Steiner, J.L., 1996: Evapotranspiration of corn: Southern high plains. In Evapotranspiration and Irrigation Scheduling, Proc. Intl. Conference, (ed. by Camp, C.R., Sadler, E.J., and Yoder, R.E.). Am. Soc. Agric. Eng., San Antonio, TX, pp.158166.
10. Iizumi, T., Yokozawa, M., and Nishimori, M., 2009: Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agric. For. Meteorol., 149, 333-348.
11. Iizumi, T., Yokozawa, M., Sakurai, G., and Nishimori, M., 2010: Simulating large-scale crop yield by using perturbed-parameter ensemble method. Amer. Geophys. Union, Fall Meeting 2010, abstract #GC23C-0921.
12. Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J.J., Fiorino, M., and Potter, G.L., 2002: NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteorol Soc, 83, 1631-1643.
13. 2/contents.htm (Accessed on May 10, 2010).
14. Ko, J., Piccinni, G., and Steglich, E., 2009: Using EPIC model to manage irrigated cotton and maize. Agric. Water Manage, 96, 1323-1331.
15. Makowski, D., Wallach, D., and Tremblay, M., 2002: Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods. Agronomie, 22, 191-203.
16. Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P.C., Ebisuzaki, W., Dusan, J., Woollen, J., Rogers, E., Berbery, E.H., Ek, M.B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W., 2006: North American regional reanalysis. Bull. Amer. Meteorol. Soc., 87, 343-360.
17. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E., 1953: Equation of state calculations by fast computing machines. J. Chem. Phys, 21, 1087-1092.
18. Monfreda, C., Ramankutty, N., and Foley, J.A., 2008: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles, 22, GB1022, doi:10.1029/2007GB002947.
19. Murphy, J.M., Booth, B.B.B., Collins, M., Harris, G.R., Sexton, D.M.H., and Webb, M.J., 2007: A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Phil. Trans. R Soc. A, 365, 1993-2028.
20. Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R., and King, K.W., 2005: Soil and Water Assessment Tool Theoretical Documentation (Version 2005). Grassland Soil and Water Research Laboratory, Agricultural Research Service, United States Department of Agriculture (USDA), Temple, TX. U.S.A., 476 pp.
21. Roth, G.W., and Yocum, J.O., 1997: Use of hybrid growing degree days ratings for corn in the northeastern USA. J. Prod. Agric., 10, 283-288.
22. Sacks, W.J., and Kucharik, C.J., 2011: Crop management and phenology trends in the US Corn Belt: Impacts on yields, evapotranspiration and energy balance. Agric. For. Meteorol, 151, 882-894.
23. Siebert, S., Doll, P., Hoogeveen, J., Faures, J.-M., Frenken, K., and Feick, S., 2005: Development and validation of the global map of irrigation areas. Hydro! Earth Syst. Sci, 9, 535-547.
24. Srinivasan, R., Zhang, X., and Arnold, J., 2010: SWAT Ungauged: Hydrological budget and crop yield predictions in the upper Mississippi River Basin. Trans. Amer. Soc. Agric. Biol. Eng., 53, 1533-1546.
25. Steduto, P., and Hsiao, T.C., 1998: Maize canopies under two soil water regimes: II. Seasonal trends of evapotranspiration, carbon dioxide assimilation and canopy conductance, and as related to leaf area index. Agric. For. Meteorol, 89, 185-200.
26. Stewart, D.W., Dwyer L.M., and Carrigan, L.L., 1998: Phenological temperature response of maize. Agron. J, 90, 73-79.
27. Stockle, C.O., and Kiniry, J.R., 1990: Variability in crop radiation-use efficiency associated with vapor-pressure deficit. Field Crops Res., 25, 171-181.
28. Stockle, C.O., Williams, J.R., Rosenberg, N.J., and Jones, C.A., 1992: A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part I-Modification of the EPIC model for climate change analysis. Agric. Syst, 38, 225-238.
29. U.S. Department of Agriculture (USDA), 2010: Crop Production Statistics Information System. Available at http://apy.dacnet.nic.in/ (Accessed on December 13, 2010).
30. van Bussel, L.G.J., Ewert, F., and Leffelaar, P.A., 2011: Effects of data aggregation on simulations of crop phenology, Agric. Ecosyst. & Environ., 142, 75-84.
31. van den Hoof, C., Hanert, E., and Vidale, P.L., 2011: Simulating dynamic crop growth with an adapted land surface model-JULES-SUCROS: Model development and validation. Agric. For. Meteorol., 151, 137-153.
32. Williams, W.A., Loomis, R.S., Duncan, W.G., Dorvrat, A., and Nunez, A.F., 1968: Canopy architecture at various population densities and the growth and grain yield of corn. Crop Sci., 8, 303-308.