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Potential adaptation strategies for rainfed soybean production in the south-eastern USA under climate change based on the CSM-CROPGRO-Soybean model

Published online by Cambridge University Press:  11 March 2015

Y. BAO ,
G. HOOGENBOOM ,
R. W. McCLENDON  and
J. O. PAZ
Y. BAO
Affiliation:
College of Engineering, The University of Georgia, Athens, Georgia 30602, USA
G. HOOGENBOOM*
Affiliation:
College of Engineering, The University of Georgia, Athens, Georgia 30602, USA AgWeatherNet Program, Washington State University, Prosser, Washington 99350-8694, USA
R. W. McCLENDON
Affiliation:
College of Engineering, The University of Georgia, Athens, Georgia 30602, USA
J. O. PAZ
Affiliation:
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi 39762, USA
*
*To whom all correspondence should be addressed. Email: gerrit.hoogenboom@wsu.edu
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Summary

Due to the potential impact of climate change and climate variability on rainfed production systems, both farmers and policy makers will have to rely more on short- and long-term yield projections. The goal of this study was to develop a procedure for calibrating the Cropping System Model (CSM)-CROPGRO-Soybean model for six cultivars, to determine the potential impact of climate change on rainfed soybean for five locations in Georgia, USA, and to provide recommendations for potential adaptation strategies for soybean production in Georgia and other south-eastern states. The Genotype Coefficient Calculator (GENCALC) software package was applied for calibration of the soybean cultivar coefficients using variety trial data. The root mean square error (RMSE) between observed and simulated grain yield ranged from 201 to 413 kg/ha for the six cultivars. Generally, the future climate scenarios showed an increase in temperature which caused a decrease in the number of days to maturity for all varieties and for all locations. This will benefit late-planted soybean production slightly, while the increase in precipitation and carbon dioxide (CO2) concentration will result in a yield increase. This was the highest for Calhoun and Williamson and ranged from 31 to 49% for the climate change projections for 2050. However, a large reduction in precipitation caused a decrease in yield for Midville, especially based on the climate scenarios of the Global Climate Models (GCMs) Commonwealth Scientific and Industrial Research Organisation's model CSIRO-Mk3.0 and Geophysical Fluid Dynamics Laboratory's model GFDL-CM2.1. Overall, Calhoun, Williamson, Plains and Tifton will probably be more suitable for rainfed soybean production over the next 40 years than Midville. Farmers might shift to a later planting date, around 5 June, for the locations that were evaluated in the present study to avoid potential heat and drought stress during the summer months. The cultivars AG6702, AGS758RR and S80-P2 could be selected for rainfed soybean production since they had the highest rainfed yields among the six cultivars. In general, the present study showed that there are crop management options for soybean production in Georgia and the south-eastern USA that are adapted for the potential projected climate change conditions.

Type
Climate Change and Agriculture Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 5 , July 2015 , pp. 798 - 824
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Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Alexandrov, V. A. & Hoogenboom, G. (2000). Vulnerability and adaptation assessments of agricultural crops under climate change in the Southeastern USA. Theoretical and Applied Climatology 67, 45–63.CrossRefGoogle Scholar
Anothai, J., Patanothai, A., Jogloy, S., Pannangpetch, K., Boote, K. J. & Hoogenboom, G. (2008). A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials. Field Crops Research 108, 169–178.CrossRefGoogle Scholar
Ash, M. (2013). Oil Crops Outlook: Oilseed Production Gains for 2013/14 Could Outpace Global Consumption. Washington, DC: Economic Research Service, U.S. Department of Agriculture. Available from: http://www.ers.usda.gov/media/1105552/ocs13e.pdf (verified August 2014).Google Scholar
Ash, M. & Dohlman, E. (2001). Oil Crops Outlook: Stronger World Demand Lifts Soybean and Product Exports. Washington, DC: U.S. Department of Agriculture. Available from: http://usda.mannlib.cornell.edu/usda/ers/OCS//2000s/2001/OCS-12-12-2001.pdf (verified August 2014).Google Scholar
Ash, M., Dohlman, E. & Wittenberger, K. (2008). Oil Crops Outlook: Slowing Domestic Demand Weighs on Soybean Prices. Washington, DC: Economic Research Service, U.S. Department of Agriculture. Available from: http://usda.mannlib.cornell.edu/usda/ers/OCS//2000s/2008/OCS-12-12-2008.pdf (verified August 2014).Google Scholar
Boerma, R., Day, D., Harris, G. H. Jr, Harrison, K. A., Hussey, R., Kemerait, B., McPherson, R. M., Phillips, D., Prostko, E. P., Roberts, P., Raymer, P., Sconyers, L., Smith, N. & Sumner, P. E. (2007). 2007 Georgia Soybean Production Guide. Athens, GA: College of Agricultural and Environmental Sciences Cooperative Extension Service, The University of Georgia. Available from: http://www.caes.uga.edu/commodities/fieldcrops/soybeans/documents/2007ProductionGuide2.pdf. (verified 12 May 2014).Google Scholar
Boote, K. J., Jones, J. W., Hoogenboom, G. & Pickering, N. B. (1998). The CROPGRO model for grain legumes. In Understanding Options for Agricultural Production (Eds Tsuji, G. Y., Hoogenboom, G. & Thornton, P. K.), pp. 99–128. Systems Approaches for Sustainable Agricultural Development no. 7. Dordrecht, The Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Carbone, G. J., Kiechle, W., Locke, C., Mearns, L. O., McDaniel, L. & Downtown, M. W. (2003). Response of soybean and sorghum to varying spatial scales of climate change scenarios in the Southeastern United States. Climatic Change 60, 73–98.CrossRefGoogle Scholar
Ceccarelli, S., Grando, S., Maatougui, M., Michael, M., Slash, M., Haghparast, R., Rahmanian, M., Taheri, A., Al-Yassin, A., Benbelkacem, A., Labdi, M., Mimoun, H. & Nachit, M. (2010). Plant breeding and climate changes. Journal of Agricultural Science, Cambridge 148, 627–637.CrossRefGoogle Scholar
Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A. & Whetton, P. (2007). Regional climate projections. In Climate Change 2007: The Physical Science Basis (Eds Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. & Miller, H. L.), pp. 847–940. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK & New York: Cambridge University Press.Google Scholar
CLIMsystems (2010). User's Guide for SimCLIM. Hamilton, New Zealand: CLIMsystems Ltd.Google Scholar
Curry, R. B., Jones, J. W., Boote, K. J., Peart, R. M., Allen, L. H. Jr & Pickering, N. B. (1995). Response of soybean to predicted climate change in the USA. In Climate Change and Agriculture: Analysis of Potential International Impacts (Eds Rosenzweig, C., Allen, L. H. Jr, Jones, J. W., Tsuji, G. Y. & Hildebrand, P.), pp. 163–181. ASA Special Publication No. 59. Madison, WI: American Society of Agronomy.Google Scholar
Day, J. L., Coy, A. E. & Rose, P. A. (2001). 2001 Soybean, Sorghum Grain and Silage, Grain Millet, and Summer Annual Forages Performance Tests, Research Report No. 676. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E., Rose, P. A. & Lee, R. D. (2002). 2002 Soybean, Sorghum Grain and Silage, Grain Millet, and Summer Annual Forages Performance Tests, Research Report No. 685. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Rose, P. A. (2003). 2003 Soybean, Sorghum Grain and Silage, Grain Millet, and Summer Annual Forages Performance Tests, Research Report No. 691. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Rose, P. A. (2004). 2004 Soybean, Sorghum Grain and Silage, and Summer Annual Forages Performance Tests, Research Report No. 697. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Rose, P. A. (2005). 2005 Soybean, Sorghum Grain and Silage, and Summer Annual Forages Performance Tests, Research Report No. 702. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Rose, P. A. (2006). 2006 Soybean, Sorghum Grain and Silage, and Summer Annual Forages Performance Tests, Research Report No. 708. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Gassett, J. D. (2007). 2007 Soybean, Sorghum Grain and Silage, Summer Annual Forages, and Sunflower Performance Tests, Research Report No. 713. Athens, GA: University of Georgia.Google Scholar
Day, J. L., Coy, A. E. & Gassett, J. D. (2008). 2008 Soybean, Sorghum Grain and Silage, Summer Annual Forages, and Sunflower Performance Tests, Research Report No. 718. Athens, GA: University of Georgia.Google Scholar
Doherty, R. M., Mearns, L. O., Reddy, K. R., Downton, M. W. & McDaniel, L. (2003). Spatial scale effects of climate scenarios on simulated cotton production in the Southeastern USA. Climatic Change 60, 99–129.CrossRefGoogle Scholar
Eitzinger, J., Orlandini, S., Stefanski, R. & Naylor, R. E. L. (2010). Climate change and agriculture: introductory editorial. Journal of Agricultural Science, Cambridge 148, 499–500.CrossRefGoogle Scholar
Garcia y Garcia, A. & Hoogenboom, G. (2005). Evaluation of an improved daily solar radiation generator for the southeastern USA. Climate Research 29, 91–102.CrossRefGoogle Scholar
Goffe, W. L., Ferrier, G. D. & Rogers, J. (1994). Global optimization of statistical functions with simulated annealing. Journal of Econometrics 60, 65–99.CrossRefGoogle Scholar
Grimm, S. S., Jones, J W., Boote, K. J. & Hesketh, J. D. (1993). Parameter estimation for predicting flowering date of soybean cultivars. Crop Science 33, 137–144.CrossRefGoogle Scholar
Guerra, L. C., Hoogenboom, G., Garcia y Garcia, A., Banterng, P. & Beasley, J. P. Jr (2008). Determination of cultivar coefficients for the CSM-CROPGRO-Peanut model using variety trial data. Transaction of the ASABE 54, 1471–1481.CrossRefGoogle Scholar
Harris, G. H. Jr, Harrison, K. A., Kemerait, B., Prostko, E. P., Roberts, P., Smith, N., Sumner, P. E. & Woodruff, J. (2008). 2008 Georgia Soybean Production Guide. Athens, GA: College of Agricultural and Environmental Sciences Cooperative Extension Service, The University of Georgia.Google Scholar
Hoogenboom, G. (1996). The georgia automated environmental monitoring network. In Proceedings of the 22nd Agricultural and Forest Meteorology Conference, Atlanta, Georgia, pp. 343–346. Boston, MA: American Meteorological Society.Google Scholar
Hoogenboom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Boote, K. J., Hunt, L. A., Singh, U., Lizaso, J. L., White, J. W., Uryasev, O., Royce, F. S., Ogoshi, R., Gijsman, A. J., Tsuji, G. Y. & Koo, J. (2012). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 [CD-ROM]. Honolulu, Hawaii: University of Hawaii.Google Scholar
Hulme, M., Wigley, T. M. L., Barrow, E. M., Raper, S. C. B., Centella, A., Smith, S. & Chipanshi, A. C. (2000). Using a Climate Scenario Generator for Vulnerability and Adaptation Assessments: MAGICC and SCENGEN Version 2.4 Workbook. Norwich, UK: Climatic Research Unit.Google Scholar
Hunt, L. A. & Boote, K. J. (1998). Data for model operation, calibration, and evaluation. In Understanding Options for Agricultural Production (Eds Tsuji, G. Y., Hoogenboom, G. & Thornton, P. K.), pp. 9–39. Systems Approaches for Sustainable Agricultural Development no. 7. Dordrecht, The Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Hunt, L. A., Pararajasingham, S., Jones, J. W., Hoogenboom, G., Imamura, D. T. & Ogoshi, R. M. (1993). GENCALC: Software to facilitate the use of crop models for analyzing field experiments. Agronomy Journal 85, 1090–1094.CrossRefGoogle Scholar
Jones, J. W., Boote, K. J., Hoogenboom, G., Jagtap, S. S. & Wilkerson, G. G. (1989). SOYGRO V5.42, Soybean Crop Growth Simulation Model. User's Guide. Florida Agricultural Experiment Station Journal No. 8304. Gainesville, FL: International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) and University of Florida.Google Scholar
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. & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal Agronomy 18, 235–265.CrossRefGoogle Scholar
Kenny, G. J., Warrick, R. A., Mitchell, N. D., Mullan, A. B. & Salinger, M. J. (1995). CLIMPACTS: An integrated model for assessment of the effects of climate change on the New Zealand environment. Journal of Biogeography 22, 883–895.CrossRefGoogle Scholar
Kenny, G. J., Warrick, R. A., Campbell, B. D., Sims, G. C., Camilleri, M., Jamieson, P. D., Mitchell, N. D., McPherson, H. G. & Salinger, M. J. (2000). Investigating climate change impacts and thresholds: an application of the CLIMPACTS integrated assessment model for New Zealand agriculture. Climatic Change 46, 91–113.CrossRefGoogle Scholar
Kenny, G. J., Ye, W., Flux, T. & Warrick, R. A. (2001). Climate variations and New Zealand agriculture – The CLIMPACTS system and issues of spatial and temporal scale. Environment International 27, 189–194.CrossRefGoogle ScholarPubMed
Mavromatis, T., Boote, K. J., Jones, J. W., Irmak, A., Shinde, D. & Hoogenboom, G. (2001). Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Science 41, 40–51.CrossRefGoogle Scholar
Mearns, L. O., Giorgi, F., McDaniel, L. & Shields, C. (2003 a). Climate scenarios for the southeastern U.S. based on GCM and regional model simulations. Climatic Change 60, 7–35.CrossRefGoogle Scholar
Mearns, L. O., Giorgi, F., Whetton, P., Pabon, D., Hulme, M. & Lal, M. (2003 b). Guidelines for Use of Climate Scenarios developed from Regional Climate Model Experiments. Bonn, Germany: IPCC-TGICA. Available from: http://www.ipcc-data.org/guidelines/dgm_no1_v1_10-2003.pdf (verified August 2014).Google Scholar
Mirza, M. M. Q., Warrick, R. A. & Ericksen, N. J. (2003). The implications of climate change on floods of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh. Climatic Change 57, 287–318.CrossRefGoogle Scholar
Naeve, S. L. & Orf, J. H. (2007). Quality of the 2007 Soybean Crop from the United States. St. Paul, MN: University of Minnesota Extension. Available from: http://www.extension.umn.edu/agriculture/soybean/seed/docs/2007-US-Soybean-Quality-Report.pdf (verified August 2014).Google Scholar
Nakicenovic, N. & Swart, R. (eds) (2000). Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press.Google Scholar
Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J. & Hanson, C. E. (eds) (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.Google Scholar
Perkins, H. F., Moss, R. B. & Hutchins, A. (1978). Soils of the Southwest Georgia Branch Experiment Station. Research Bulletin 217. Athens, GA: Georgia Agricultural Experiment Station.Google Scholar
Perkins, H. F., McCreery, R. A., Lockaby, G. & Perry, C. E. (1979). Soils of the Southeast Georgia Branch Experiment Station. Research Bulletin 245. Athens, GA: University of Georgia College of Agriculture Experiment Station.Google Scholar
Perkins, H. F., Owen, V., Hammel, J. E. & Price, E. A. (1982). Soil Characteristics of the Plant Science Farm of the University of Georgia College Experiment Station. Research Bulletin 287. Athens, GA: University of Georgia College of Agriculture Experiment Station.Google Scholar
Perkins, H. F., Owen, V. R. & Worley, E. E. (1983). Soils of the Northwest Georgia Research Experiment Station. Research Bulletin 302. Athens, GA: College of Agriculture Experiment Station, University of Georgia.Google Scholar
Perkins, H. F., Schuman, L. M., Boswell, F. C. & Owen, V. (1985). Soil Characteristics of the Bledsoe and Beckham Research Farms of the Georgia Station. Research Bulletin 332. Athens, GA: University of Georgia Agricultural Experiment Station.Google Scholar
Perkins, H. F., Hook, J. E. & Barbour, N. W. (1986). Soil Characteristics of Selected Areas of the Coastal Plain Experiment Station and ABAC Research Farms. Research Bulletin 346. Athens, GA: Georgia Agricultural Experiment Station, College of Agriculture, the University of Georgia.Google Scholar
Persson, T., Garcia y Garcia, A., Paz, J. O., Fraisse, C. W. & Hoogenboom, G. (2010). Reduction in greenhouse gas emissions due to the use of bio-ethanol from wheat grain and straw produced in the south-eastern USA. Journal of Agricultural Science, Cambridge 148, 511–527.CrossRefGoogle Scholar
Semenov, M. A. & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35, 397–414.CrossRefGoogle Scholar
Soler, C. M. T., Sentelhas, P. C. & Hoogenboom, G. (2007). Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy 27, 165–177.CrossRefGoogle Scholar
Soler, C. M. T., Maman, N., Zhang, X., Mason, S. C. & Hoogenboom, G. (2008). Determining optimum planting dates for pearl millet for two contrasting environments using a modelling approach. Journal of Agricultural Science, Cambridge 146, 445–459.CrossRefGoogle Scholar
Tsvetsinskaya, E. A., Mearns, L. O., Mavromatis, T., Gao, W., McDaniels, L. & Downton, M. W. (2003). The effect of spatial scale of climatic change scenarios on simulated maize, winter wheat, and rice production in the Southeastern United States. Climatic Change 60, 37–71.CrossRefGoogle Scholar
White, J. W., Hoogenboom, G., Kimball, B. A. & Wall, G. W. (2011). Methodologies for simulating impacts of climate change on agricultural production. Field Crops Research 124, 357–368.CrossRefGoogle Scholar
Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P. & Mearns, L. O. (2004). Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Cambridge, UK: IPCC. Available from: http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf (verified August 2014).Google Scholar
Woodruff, J., Whitaker, J., Prostko, E., Roberts, P., Kemerait, R., Smith, N., Smith, A., Sumner, P., Harrison, K. & Harris, G. (2010). 2010 Georgia Soybean Production Guide. Athens, GA: College of Agricultural and Environmental Sciences Cooperative Extension Service, The University of Georgia. The 2014 Georgia Soybean Production Guide can be accessed from: http://www.caes.uga.edu/commodities/fieldcrops/soybeans/documents/2014GeorgiaSoybeanProductionGuide.pdf (25 September 2014).Google Scholar