AS  Vol.6 No.8 , August 2015
A Dual Ensemble Agroclimate Modelling Procedure to Assess Climate Change Impacts on Sugarcane Production in Australia
ABSTRACT
Climate is a key driver of sugarcane production and all its by-products. Consequently, it is important to understand how climate change will influence sugarcane crop productivity. Ensembles from a crop model and climate projections form part of the dual ensemble methodology to assess climate change impacts on sugarcane productivity for three major sugarcane-growing regions in Australia—Burdekin, Mackay and New South Wales (NSW). Different parameterisations of a crop model injected with climate outputs from eleven statistically downscaled general circulation models (GCM) were used to estimate regionally averaged sugarcane yields for the base period 1971 to 2000. The forward stagewise algorithm selected crop model parameterisations that best explained the observed yields. Leave-one-out cross validation assessed the predictive capability of the equally weighted crop ensemble members characterised by the selected crop model parameterizations. A Monte Carlo permutation testing procedure was employed to measure the significance of the predictive correlations. The predictive correlations between historical yields and simulated historical yields for the Burdekin, Mackay and NSW were 0.69 (p = 0.030), 0.83 (p < 0.001) and 0.70 (p = 0.034), respectively. Simulations were run based on GCM projections for 2046 to 2065 for a low (B1) and a high (A2) emission scenario, with and without elevated CO2 levels. We found it was plausible for industry to consider an increase in yields to all three regions under the B1 scenario and highly plausible for NSW under the A2 scenario. Higher CO2 levels resulted in lower demand of water for the crop, particularly in the Burdekin region and suggested that industry could expand into regions currently considered as marginal owing to the benefits of increased transpiration efficiency that are associated with increased CO2. Although this study favoured neutral or positive impacts on sugarcane production, industry should not overlook negative impacts when developing a risk management framework in response to a changing climate.

Cite this paper
Everingham, Y. , Inman-Bamber, G. , Sexton, J. and Stokes, C. (2015) A Dual Ensemble Agroclimate Modelling Procedure to Assess Climate Change Impacts on Sugarcane Production in Australia. Agricultural Sciences, 6, 870-888. doi: 10.4236/as.2015.68084.
References
[1]   Brumbley, S.M., Snyman, S.J., Gnanasambandam, A., Joyce, P., Herman, S.R., da Silva, J.A.G., McQualter, R.B., Wang, M., Egan, B.T. and Patterson, A.H. (2008) Sugarcane. In: Kole, C. and Hall, T.C., Eds., Compendium of Transgenic Crop Plants: Transgenic Sugar, Tuber and Fiber Crops, Blackwell Publishing Ltd., Oxford.

[2]   Alonso-Pippo, W., Luengo, C.A., Koehlinger, J., Garzone, P. and Cornacchia, G. (2008) Sugarcane Energy Use: The Cuban Case. Energy Policy, 36, 2163-2181.
http://dx.doi.org/10.1016/j.enpol.2008.02.025

[3]   Barnes, A.C. (1974) The Sugar Cane. Halsted Press.

[4]   Goldemberg, J., Coelho, S.T. and Guardabassi, P. (2008) The Sustainability of Ethanol Production from Sugarcane. Energy Policy, 36, 2086-2097.
http://dx.doi.org/10.1016/j.enpol.2008.02.028

[5]   Mackintosh, D. (2000) Sugar Milling. In: Hogarth, D.M. and Allsopp, P.G., Eds., Manual of Canegrowing, Bureau of Sugar Experiment Stations, Brisbane, 369-377.

[6]   Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J. and Ritchie, J.T. (2003) The DSSAT Cropping System Model. European Journal of Agronomy, 18, 235-265.
http://dx.doi.org/10.1016/S1161-0301(02)00107-7

[7]   Keating, B.A., Robertson, M.J., Muchow, R.C. and Huth, N.I. (1999) Modelling Sugarcane Production Systems I. Development and Performance of the Sugarcane Module. Field Crops Research, 61, 253-271.
http://dx.doi.org/10.1016/S0378-4290(98)00167-1

[8]   Knox, J.W., Rodríguez Díaz, J.A., Nixon, D.J. and Mkhwanazi, M. (2010) A Preliminary Assessment of Climate Change Impacts on Sugarcane in Swaziland. Agricultural Systems, 103, 63-72.
http://dx.doi.org/10.1016/j.agsy.2009.09.002

[9]   Marin, F.R., Jones, J.W., Singels, A., Royce, F., Assad, E.D., Pellegrino, G.Q. and Justino, F. (2013) Climate Change Impacts on Sugarcane Attainable Yield in Southern Brazil. Climate Change, 117, 227-239.
http://dx.doi.org/10.1007/s10584-012-0561-y

[10]   Singels, A., Jones M., Marin F., Ruane, A.C. and Thorburn, P. (2014) Predicting Climate Change Impacts on Sugarcane Production at Sites in Australia, Brazil and South African Using the Canegro Model. Sugar Tech, 16, 347-355.
http://dx.doi.org/10.1007/s12355-013-0274-1

[11]   Biggs, J.S., Thorburn, P.J., Crimp, S., Masters, B. and Attard, S.J. (2013) Interactions between Climate Change and Sugarcane Management Systems for Improving Water Quality Leaving Farms in the Mackay Whitsunday Region, Australia. Agriculture, Ecosystems & Environments, 180, 79-89.
http://dx.doi.org/10.1016/j.agee.2011.11.005

[12]   Park, S., Creighton, C. and Howden, M. (2007) Climate Change and the Australian Sugar Industry, Impacts, Adaptation and R&D Opportunities. SRDC Technical Report. Sugar Research and Development Corporation, Brisbane.

[13]   Webster, A.J., Thorburn, P.J., Roebeling, P.C., Horan, H.L. and Biggs, J.S. (2009) The Expected Impact of Climate Change on Nitrogen Losses from Wet Tropical Sugarcane Production in the Great Barrier Reef Region. Marine & Freshwater Research, 60, 1159-1164.
http://dx.doi.org/10.1071/MF08348

[14]   Cai, W., Crimp, S., Jones, R., McInnes, K., Durack, P., Cechet, B., Bathols, J. and Wilkinson, S. (2005) Climate Change in Queensland Under Enhanced Greenhouse Conditions. Report 2004-2005. CSIRO Marine and Atmospheric Research, Melbourne.

[15]   Santos, D. and Sentelhas, P. (2014) Climate Change Scenarios and Their Impact on Water Balance and Sugarcane Yield in Southern Brazil. Sugar Tech, 16, 356-365.
http://dx.doi.org/10.1007/s12355-013-0293-y

[16]   Doorenbos, J. and Kassam, A.H. (1979) Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Food and Agriculture Organization of the United Nations, Rome.

[17]   Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York.
http://dx.doi.org/10.1007/978-0-387-21606-5

[18]   Jones, D.A., Wang, W. and Fawcett, R. (2009) High-Quality Spatial Climate Data-Sets for Australia. Australian Meteorological and Oceanographic Journal, 58, 233-248.

[19]   R Core Team (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.

[20]   Liu, D.L. and Scott, B.J. (2001) Estimation of Solar Radiation in Australia from Rainfall and Temperature Observations. Agriculture and Forest Meteorology, 106, 41-59.
http://dx.doi.org/10.1016/S0168-1923(00)00173-8

[21]   Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J. and Taylor, K.E. (2007) The WCRP CMIP3 Multi-Model Dataset: A New Era in Climate Change Research. Bulletin of the American Meteorological Society, 88, 1383-1394.
http://dx.doi.org/10.1175/BAMS-88-9-1383

[22]   PCMDI (2002) 20th Century Climate in Coupled Models: A CMIP Pilot Project.

[23]   Nakicenovic, N. and Swart, R. (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the International Panel on Climate Change. Cambridge University Press, Cambridge.

[24]   Timbal, B., Wang, Y. and Evans, A. (2011) Downscaling Climate Change Information: An Essential Ingredient to Incorporate Uncertainties into Adaptation Policies. Proceedings of the 19th International Congress on Modelling and Simulation (MODSIM2011), Perth, 12 to 16 December 2011, 1652-1658.

[25]   Timbal, B., Fernandez, E. and Li, Z. (2009) Generalization of a Statistical Downscaling Model to Provide Local Climate Change Projections for Australia. Environmental Modelling & Software, 24, 341-358.
http://dx.doi.org/10.1016/j.envsoft.2008.07.007

[26]   Bari, M.A., Amirthanathan, G.E. and Timbal, B. (2010) Climate Change and Long Term Water Availability in Western Australia—An Experimental Projection. Proceedings of the 2010 International Congress on Environmental Modelling and Software, Ottawa, 5-8 July 2010, 180-188.

[27]   Liu, D.L., Timbal, B., Mo, J. and Fairweather, H. (2011) A GIS-Based Climate Change Adaptation Strategy Tool. International Journal of Climate Change Strategies and Management, 3, 140-155.
http://dx.doi.org/10.1108/17568691111128986

[28]   Teng, J., Chiew, F.H.S., Timbal, B., Wang, Y., Vaze, J. and Wang, B. (2012) Assessment of an Analogue Downscaling Method for Modelling Climate Change Impacts on Runoff. Journal of Hydrology, 472-473, 111-125.
http://dx.doi.org/10.1016/j.jhydrol.2012.09.024

[29]   IPCC (2001) Climate Change 2001: The Scientific Basis. In: Houghton, J.T., et al., Eds., Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 881 p.

[30]   Tebaldi, C. and Lobell, D. (2008) Towards Probabilistic Projections of Climate Change Impacts on Global Crop Yields. Geophysical Research Letters, 35, L08705.
http://dx.doi.org/10.1029/2008gl033423

[31]   Semenov, M.A. and Stratonovitch, P. (2010) Use of Multi-Model Ensembles from Global Climate Models for Assessment of Climate Change Impacts. Climate Research, 41, 1-14.
http://dx.doi.org/10.3354/cr00836

[32]   Armour, J., Nelson, P., Daniells, J., Rasiah, V. and Inman-Bamber, N. (2012) Nitrogen Leaching From the Root Zone of Sugarcane and Bananas in the Humid Tropics of Australia. Agriculture, Ecosystems and Environment, 180, 68-78.
http://dx.doi.org/10.1016/j.agee.2012.05.007

[33]   Inman-Bamber, N.G. and McGlinchey, M.G. (2003) Crop Coefficients and Water-Use Estimates for Sugarcane Based on Long-Term Bowen Ratio Energy Balance Measurements. Field Crops Research, 83, 125-138.
http://dx.doi.org/10.1016/S0378-4290(03)00069-8

[34]   Inman-Bamber, N.G., Attard, S.J., Verrall, S.A., Webb, W.A. and Baillie, C. (2007) A Web-Based System for Scheduling Irrigation in Sugarcane. Proceedings of the 2007 International Society of Sugar Cane Technologists Congress, Durban, 29 July-2 August 2007, 459-464.

[35]   Inman-Bamber, N.G., Culverwell, T.L. and McGlinchey, M.G. (1993) Predicting Yield Responses to Irrigation of Sugar Cane from a Growth Model and Field Records. Proceedings of the South African Sugar Technologists’ Association, 67, 66-72.

[36]   Inman-Bamber, N.G. (1991) A Growth Model for Sugar-Cane Based on a Simple Carbon Balance and the CERES- Maize Water Balance. South African Journal of Plant Soil, 8, 93-99.

[37]   Singels, A., van den Berg, M., Smit, M.A., Jones, M.R. and van Antwerpen, R. (2010) Modelling Water Uptake, Growth and Sucrose Accumulation of Sugarcane Subjected to Water Stress. Field Crops Research, 117, 59-69.
http://dx.doi.org/10.1016/j.fcr.2010.02.003

[38]   Inman-Bamber, N.G., Attard, S.J. and Spillman, M.F. (2004) Can Lodging Be Controlled through Irrigation? Proceedings of the 2004 Conference of the Australian Society of Sugar Cane Technologists, Queensland, 4-7 May 2004, 1-11.

[39]   Singels, A. and Bezuidenhout, C.N. (2002) A New Method of Simulating Dry Matter Partitioning in the Canegro Sugarcane Model. Field Crops Research, 78, 151-164.
http://dx.doi.org/10.1016/S0378-4290(02)00118-1

[40]   Thorburn, P.T., Biggs, J.S., MacDonald, B.C.T., Allen, D.E. and Denmead, O.T. (2013) What Causes Nitrous Oxide Emissions from Some Sugarcane Crops to Be So High? Proceedings of the Australian Society of Sugar Cane Technologists, Townsville, 16-18 April 2013.

[41]   Inman-Bamber, N.G., Zund, P.R. and Muchow, R.C. (2000) Water Use Efficiency and Soil Water Availability for Sugarcane. Proceedings of the Australian Society of Sugar Cane Technologists, 22, 264-269.

[42]   Robertson, M.J., Wood, A.W. and Muchow, R.C. (1996) Growth of Sugarcane under High Input Conditions in Tropical Australia. I. Radiation Use, Biomass Accumulation and Partitioning. Field Crops Research, 48, 11-25.
http://dx.doi.org/10.1016/0378-4290(96)00041-X

[43]   Inman-Bamber, N.G., Everingham, Y. and Muchow, R. (2001) Modelling Water Stress Response in Sugarcane: Validation and Application of the Apsim-Sugarcane Model. Proceedings of the 10th Australian Agronomy Conference, Hobartn, 28 January to 1 February 2001.

[44]   Attard, S. and Inman-Bamber, N. (2011) Irrigation Scheduling in the Central Region: Making Every Drop Count. Proceedings of the Australian Society of Sugar Cane Technologists, Mackay, 4-6 May 2011.

[45]   Basnayake, J., Jackson, P., Inman-Bamber, N.G. and Lakshmanan, P. (2012) Sugarcane for Water-Limited Environments: Genetic Variation in Cane Yield and Sugar Content in Response to Water Stress. Journal of Experimental Botany, 63, 6023-6033.
http://dx.doi.org/10.1093/jxb/ers251

[46]   Sexton, J., Basnayake, J., Everingham, Y., Inman-Bamber, G., Lakshmanan, P. and Jackson, P. (2014) Detailed Trait Characterisation Is Needed for Simulation of Cultivar Responses to Water Stress. Proceedings of the 36th Conference of the Australian Society of Sugar Cane Technologists, Gold Coast, 29 April-1 May 2014, 82-92.

[47]   Challinor, A.J., Smith, M.S. and Thornton, P. (2013) Use of Agro-Climate Ensembles for Quantifying Uncertainty and Informing Adaptation. Agricultural & Forest Meteorology, 170, 2-7.
http://dx.doi.org/10.1016/j.agrformet.2012.09.007

[48]   Everingham, Y.L., Smyth, C.W. and Inman-Bamber, N.G. (2009) Ensemble Data Mining Approaches to Forecast Regional Sugarcane Crop Production. Agricultural & Forest Meteorology, 149, 689-696.
http://dx.doi.org/10.1016/j.agrformet.2008.10.018

[49]   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. Agricultural & Forest Meteorology, 149, 333-348.
http://dx.doi.org/10.1016/j.agrformet.2008.08.015

[50]   Murphy, J.M., Sexton, D.M.H., Barnett, D.N., Jones, G.S., Webb, M.J., Collins, M. and Stainforth, D.A. (2004) Quantification of Modelling Uncertainties in a Large Ensemble of Climate Change Simulations. Nature, 430, 768-772.
http://dx.doi.org/10.1038/nature02771

[51]   Tao, F. and Zhang, Z. (2013) Climate Change, Wheat Productivity and Water Use in the North China Plain: A New Super-Ensemble-Based Probabilistic Projection. Agricultural & Forest Meteorology, 170, 146-165.
http://dx.doi.org/10.1016/j.agrformet.2011.10.003

[52]   Tao, F., Zhang, Z., Liu, J. and Yokozawa, M. (2009) Modelling the Impacts of Weather and Climate Variability on Crop Productivity over a Large Area: A New Super-Ensemble-Based Probabilistic Projection. Agricultural & Forest Meteorology, 149, 1266-1278.
http://dx.doi.org/10.1016/j.agrformet.2009.02.015

[53]   Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
http://dx.doi.org/10.1023/A:1010933404324

[54]   Krogh, A. and Vedelsby, J. (1995) Neural Network Ensembles, Cross Validation, and Active Learning. In: Tesauro, G., Tourestzky, D.S., Leen, T.K., Eds., Advances in Neural Information Processing Systems, MIT Press, Cambridge, 231-238.

[55]   Friedman, J.H. and Popescu, B.E. (2003) Importance Sampled Learning Ensembles. Department of Statistics, Stanford University, Stanford.

[56]   Masson, D. and Knutti, R. (2011) Climate Model Genealogy. Geophysical Research Letters, 38, L08703.
http://dx.doi.org/10.1029/2011gl046864

[57]   Power, S.B., Delage, F., Colman, R. and Moise, A. (2011) Consensus on Twenty-First-Century Rainfall Projections in Climate Models More Widespread than Previously Thought. Journal of Climatology, 25, 3792-3809.
http://dx.doi.org/10.1175/JCLI-D-11-00354.1

[58]   Sexton, J., Everingham, Y. and Timbal, B. (2015) Harvest Projections for the Australian Sugar Industry. International Journal of Climate Change Strategies and Management, 7, 41-57.
http://dx.doi.org/10.1108/IJCCSM-03-2013-0018

[59]   Everingham, Y.L., Stoeckl, N.E., Cusack, J. and Osborne, J.A. (2011) Quantifying the Benefits of a Long-Lead ENSO Prediction Model to Enhance Harvest Management—A Case Study for the Herbert Sugarcane Growing Region, Australia. International Journal of Climatology, 32, 1069-1076.
http://dx.doi.org/10.1002/joc.2333

[60]   Good, P.I. (1997) Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer-Verlag, New York.

[61]   Everingham, Y., Sexton, J. and Timbal, B. (2013) Downscaled Rainfall Projections for the Burdekin, Mackay and NSW. Proceedings of the 35th Conference of the Australian Society of Sugar Cane Technologists, 16-18 April 2013, Townsville, 1-10.

[62]   Inman-Bamber, N.G. (2007) Economic Impact of Water Stress on Sugar Production in Australia. Proceedings of the 2007 Conference of the Australian Society of Sugar Cane Technologists, Cairns, 8-11 May 2007, 167-175.

[63]   Park, S., Crimp, S., Inman-Bamber, N.G. and Everingham, Y. (2010) Sugarcane. In: Stokes, C., Howden, M., Eds., Adapting Agriculture to Climate Change, CSIRO Publishing, Collingwood, 85-99.

[64]   Stokes, C.J. and Howden, M. (2010) Adapting Agriculture to Climate Change: Preparing Australian Agriculture, Forestry and Fisheries for the Future. CSIRO Publishing, Collingwood.

[65]   Jakku, E., Thorburn, P., Everingham, Y. and Inman-Bamber, G. (2007) Improving the Participatory Development of Decision Support Systems for the Australia Sugar Industry. Proceedings of the 2007 Conference of the Australian Society of Sugar Cane Technologists, Cairns, 8-11 May 2007, 41-49.

[66]   Skocaj, D., Everingham, Y. and Schroeder, B. (2013) Nitrogen Management Guidelines for Sugarcane Production in Australia—Can These Be Modified for Wet Tropical Conditions Using Seasonal Climate Forecasting? Springer Science Reviews, 1, 51-71.
http://dx.doi.org/10.1007/s40362-013-0004-9

[67]   Rötter, R.P., Carter, T.R., Olesen, J.E. and Porter, J.R. (2011) Crop-Climate Models Need an Overhaul. Nature Climate Change, 1, 175-177.
http://dx.doi.org/10.1038/nclimate1152

[68]   Marin, F.R., Ribeiro, R.V. and Marchiori, E.R. (2014) How Can Crop Modelling and Plant Physiology Help to Understand the Plant Response to Climate Change? A Case Study with Sugarcane. Theoretical and Experimental Plant Physiology, 26, 49-63.
http://dx.doi.org/10.1007/s40626-014-0006-2

 
 
Top