Simulation of maize yield in current and changed climatic conditions: Addressing modelling uncertainties and the importance of bias correction in climate model simulations

European Journal of Agronomy

Volumn 37, Issue 1, 2012, Pages 83-95

  Ceglar, Andrej ^a   Kajfež Bogataj, Lučka ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Bias correction; Climate change; Ensembles; Impact; Slovenia; Statistical emulator; Uncertainty

Indexed keywords

AGRICULTURAL MODELING; ANNUAL VARIATION; ATMOSPHERIC CORRECTION; CLIMATE CHANGE; CROP PRODUCTION; CROP YIELD; DRY MATTER; ENSEMBLE FORECASTING; GROWING SEASON; GROWTH RATE; HIGH TEMPERATURE; MAIZE; PRECIPITATION INTENSITY; RAINFALL; RELATIVE HUMIDITY; UNCERTAINTY ANALYSIS; VARIANCE ANALYSIS;

SLOVENIA;

ZEA MAYS;

EID: 82155176178     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2011.11.005     Document Type: Article

References (49)

1. Abdi H. Partial least squares (PLS) regression. Encyclopedia of Social Sciences Research Methods 2003, 1-7. Sage, Thousands Oaks. M.L. Lewis-Beck, A. Bryman, T. Futing (Eds.).
2. Abdi H. Partial least square regression, projection on latent structure regression, PLS-Regression. Wiley interdisciplinary reviews. Computation. Stat. 2010, 2:97-106.
3. Baigorria G.A., Jones J.W., Shin D.W., Mishra A. Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Clim. Res. 2007, 34:211-222.
4. Baigorria G.A., Jones J.W., O'brien J.J. Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model. Agr. Forest Meteorol. 2008, 148:1353-1361.
5. Bergant, K., 2003. Projections of global climate simulations to local level and their use in agrometeorology. Doctoral Dissertation, Biotechnical Faculty, Ljubljana. 170 pp.
6. Boogard, H.L., van Diepen, C.A., Rotter, R.P., Cabrera, J.C.M.A., Laar, H.H.V., 1998. WOFOST 7.1: User Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 5.1. Wageningen, Alterra. 52 pp.
7. Ceglar, A., 2011. The use of dynamic crop model for simulation of plant growth and development for prediction of crop yield in changed climate conditions. Doctoral Dissertation, Biotechnical faculty, Ljubljana. pp. 185.
8. Ceglar A., Pogačar T., KajfeŽ-Bogataj L., črepinŠek Z. The simulation of phenological development in dynamic crop model: the Bayesian comparison of different methods. Agr. Forest Meteor. 2011, 151:101-115.
9. čergan Z. The morphology of the maize. The Maize 2008, 22-40. ZaloŽba Kmečki glas, Ljubljana. Z. čergan, V. Jejčič, M. Knapič, T. Modic, B. Moljk, T. Poje, A. Simončič, J. SuŠin, G. Urek, J. Verbič, B. VrŠčaj, M. Žerjav (Eds.).
10. De Konig G.H.J., van Diepen C.A. Crop Production Potential of Rural Areas within the European Communities. Potential, Water-Limited and Actual Crop Production 1992, Springer, Netherlands, 83 pp.
11. Deque M., Rowell D.P., Luthi D., Giorgi F., Christensen J.H., Rockel B., Jacob D., Kjellstrom E., de Castro M., van den Hurk B. An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Climatic Change 2007, 81:53-70.
12. Devillers J., Dore J.C., Tisseur M., Cluzeau S., Maurin G. Modelling the flight activity of Apis mellifera at the hive entrance. Comput. Electron. Agr. 2004, 42:87-109.
13. Diniz-Filho J.A.F., Bini L.M., Rangel T.F., Loyola R.D., Hof C., Nogueas-Bravo D., Araujo M.B. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 2009, 32:897-906.
14. Donat M., Leckebusch G.C., Wild S., Ulbrich U. European winter storm losses in a multi-model ensemble of GCM and RCM simulations. Geophys. Res. Lett. 2009, 11. pp. 7604-7604.
15. Dubrovsky M., Zalud Z., Stastna M. Sensitivity of CERES-maize yields to statistical structure of daily weather series. Climatic Change 2000, 46:447-472.
16. Eitzinger J., Trnka M., Hosch J., Dubrovsky M. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecol. Model. 2004, 171:223-246.
17. Goudriaan, J., Unsworth, M.H., 1990. Implications of increasing carbon dioxide and climate change for agricultural productivity and water resources. In: Impact of Carbon Dioxide, Trace Gases and Climate Change on Global Agriculture. ASA Spec. 53, 111-130.
18. Ines A.V.M., Hansen J.W. Bias correction of daily GCM rainfall for crop simulation studies. Agr. Forest Meteor. 2006, 138:44-53.
19. Ittersum M.V., Leffelaar P., Keulen H.V., Kropff M., Bastiaans L., Goudriaan J. On approaches and applications of Wageningen crop models. Eur. J. Agron. 2003, 18:201-234.
20. KajfeŽ-Bogataj L. Climate change and agriculture vulnerability. Acta Agr. Slov. 2005, 85(1):25-40.
21. KajfeŽ-Bogataj L., Pogačar T., Ceglar A., črepinŠek Z. Trends in agro-climate variables in Slovenia. Acta Agr. Slov. 2010, 95(1):97-109.
22. Kjellstrom E., Boberg F., Castro M., Christensen J.H., Nikulin G., Sanchez E. Daily and monthly temperature and precipitation statistics performance indicators for regional climate models. Climate Research 2010, 44:135-150.
23. Kvalheim O.M. Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots. J. Chemometr. 2010, 24:496-504.
24. Maroco J.P., Edwards G.E., Ku M.S.B. Photosynthetic acclimation of maize to growth under elevated levels of carbon dioxide. Planta 1999, 210:115-125.
25. Mearns L.O., Bogardi I., Giorgi F., Matyasovszky I., Palecki M. Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. J. Geophys. Res. 1999, 104(D6):6603-6621.
26. Nakicenovic N., Alcamo J., Davis G., de Vries B., Fenhann J., Gaffin S., Gregory K., Grübler A., Jung T.Y., Kram T., La Rovere E.L., Michaelis L., Mori S., Morita T., Pepper W., Pitcher H., Price L., Riahi K., Roehrl A., Rogner H.-H., Sankovski A., Schlesinger M., Shukla P., Smith S., Swart R., van Rooijen S., Victor N., Dadi Z. IPCC Special Report on Emissions Scenarios 2000, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 599 pp.
27. Nyugen H.T., Lee B.W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least squares regression. Eur. J. Agron. 2006, 24:349-356.
28. Perkins S.E., Pitman A.J., Holbrook N.J., McAneney J. Evaluation of the AR4 climate models simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J. Climate 2007, 20:4356-4376.
29. Piani C., Haerter J.O., Coppola E. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol. 2010, 99:187-192.
30. Pinheiro J.C., Bates D.M. Mixed-Effects Models in S and S-PLUS 2000, Springer, Wageningen, 528 pp.
31. Porter J.R., Semenov M.A. Crop responses to climatic variation. Philos. Trans. R. Soc. London B 2005, 360(1463):2021-2035.
32. 2 in maize is related to water status and dependent on leaf rank. Plant. Cell. Environ. 2011, 331:314-331.
33. Reaney S.M., Fowler H.J. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterisation error sources. BHS 10th National Hydrology Symposium, Exeter, 2008 2008, 387-394. Durham University, Durham.
34. Schneider S.H., Semenov S., Patwardhan A., Burton I., Magadza C.H.D., Oppenheimer M., Pittock A.B., Rahman A., Smith J.B., Suarez A., Yamin F. Assessing key vulnerabilities and the risk from climate change. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of working group 2 to the fourth assessment report of the intergovernmental panel on climate change 2007, 779-810. Cambridge University Press, Cambridge. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, C.E. Hanson (Eds.).
35. Segui P.Q., Ribes A., Martin E., Habets F., Boóe J. Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. J. Hydrol. 2009, 383:111-124.
36. Semenov M.A., Doblas-Reyes F.J. Utility of dynamical seasonal forecasts in predicting crop yield. Climate Research 2007, 60:71-81.
37. Sharma D., Gupta A.D., Babel M.S. Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin. Thailand. Hydrol. Earth. Syst. Sci. 2007, 11:1373-1390.
38. Takać, J., ŠiŠka, B., Nejedlik, P., 2009. Climate change impact on relations among evapotranspiration, water use efficiency and crop yields on Danubian lowland. In: Eitzinger, J. Climate Change and Adaptation Options in Agriculture. COST 734.
39. Tao F., Yokozawa M., Zhang Z. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization and uncertainties analysis. Agr. Forest Meteor. 2009, 149(5):831-850.
40. Van der Linden, P., Mitchell, J.F.B., 2009. ENSEMBLES: Climate Change and its Impacts: Summary of Research and Results from the ENSEMBLES Project. Exeter, Met. Office Hadley Centre. 160 pp.
41. Van Diepen C.A., Wolf J., van Keulen H., Rapoldt C. WOFOST: a simulation model of crop production. Soil Use Manage. 1989, 5:16-24.
42. Van Roosmalen L., Christensen J.H., Butts M.B., Jensen K.H., Refsgaard J.C. An intercomparison of regional climate model data for hydrological impact studies in Denmark. J. Hydrol. 2010, 380(3-4):406-419.
43. Viner D. A qualitative assessment of the sources of uncertainty in climate change impacts assessment studies. Climatic Change: Implications for the Hydrological Cycle and for Water Management 2002, 129-134. Springer, Netherlands. M. Beniston (Ed.).
44. Vučetić V. Modelling of maize production in Croatia: present and future climate. J. Agric. Sci. 2010, 149:145-157.
45. Wang E., Engel T. Simulation of phenological development of wheat crops. Agric. Syst. 1998, 58:1-24.
46. Weigel A.P., Liniger M.A., Appenzeller C. Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?. Q. J. R. Meteorol. Soc. 2008, 260:241-260.
47. Wilks D.S. Statistical Methods in the Atmospheric Sciences. International Geophysics Series 2006, Elsevier Academic, San Diego. second ed.
48. Wold S.M., Sjolstrom M., Erikkson L. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 2001, 58:109-130.
49. Žagar T., KajfeŽ-Bogataj L., črepinŠek Z. Change-point analysis of some climate variables in Slovenia. Acta Agric. Slov. 2006, 87(2):285-298.