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Impact of climate change and carbon dioxide fertilization effect on irrigation water demand and yield of soybean in Serbia

Published online by Cambridge University Press:  08 April 2015

M. JANCIC*
Affiliation:
Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq 8, 21000 Novi Sad, Serbia
B. LALIC
Affiliation:
Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq 8, 21000 Novi Sad, Serbia
D. T. MIHAILOVIC
Affiliation:
Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq 8, 21000 Novi Sad, Serbia
G. JACIMOVIC
Affiliation:
Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq 8, 21000 Novi Sad, Serbia
*
*To whom all correspondence should be addressed. Email: [email protected]

Summary

The Decision Support System for Agrotechnology Transfer (DSSAT) v. 4·2 crop model was used to estimate climate change impacts on soybean yield in Serbia in simulations for 2030 and 2050 integration periods using three global climate change models (GCMs): the European Centre Hamburg Model (ECHAM), The Hadley Centre Coupled Model (HadCM) and the National Center for Atmospheric Research Parallel Climate Model (NCAR-PCM) under two scenarios from the IPCC Special Report on Emissions Scenarios (IPCC 2001): A1B SRES and A2 SRES. Input data included weather data from a 1971–2000 baseline period from ten weather stations assimilated from the Republic Hydrometeorological Service of Serbia. Output results from the three GCMs under the two scenarios for 2030 and 2050 were statistically downscaled with the ‘Met & Roll’ weather generator for predicted climate conditions. Mechanical and chemical soil properties were collected in the vicinity of weather stations and analysed by the Agency for Environmental Safety in Belgrade. Genetic coefficients, for the soybean maturity group II variety, were slightly modified using the DSSAT-SOYGRO model ones. The results showed a considerable benefit of carbon dioxide fertilization on soybean yield and yield increases at all locations. The greatest estimated yield increases obtained using outputs the HadCM model for 2030 both scenarios; in 2050, however, the A2 scenario resulted in smaller increase in yield at some locations. The highest increase in yield was in the central and eastern parts of Serbia. Analyses of the climate change impacts on irrigation demand showed a great increase in the irrigation demand amount per growing season. The average irrigation demand reached the highest values in the southern and eastern parts of Serbia. Water productivity reached highest values in eastern and central locations, while the minimum is expected in the most southern and northern location. According to all results it can be concluded that soybean will benefit greatly under climate change conditions and that soybean cropping, currently most concentrated in the Vojvodina region in northern Serbia, expanding in the central part and one location in eastern Serbia.

Type
Climate Change and Agriculture Research Papers
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Allen, L. H. Jr., Boote, K. J., Jones, J. W., Jones, P. H., Valle, R. R., Acock, B., Rogers, H. H. & Dahlman, R. C. (1987). Response of vegetation to rising carbon dioxide: photosynthesis, biomass, and seed yield of soybean. Global Biogeochemical Cycles 1, 114.Google Scholar
Alexandrov, V., Eitzinger, J., Cajic, V. & Oberforster, M. (2002). Potential impact of climate change on selected agricultural crops in north-eastern Austria. Global Change Biology 8, 372389.CrossRefGoogle Scholar
Downing, T. E., Harrison, P. A., Butterfield, R. E. & Lonsdale, K. G. (2000). Climate Change. Climatic Variability and Agriculture in Europe. An Integrated Assessment. Research Report No. 21. Oxford, UK: Environmental Change Institute, University of Oxford.Google Scholar
Eitzinger, J., Orlandini, S., Stefanski, R. & Naylor, R. E. (2010). Climate change and agriculture: introductory editorial. Journal of Agricultural Science, Cambridge 148, 499500.CrossRefGoogle Scholar
Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B. & Wood, R. A. (2000). The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16, 147168.Google Scholar
Harrison, P., Butterfield, R. & Downing, T. (1995). Climate Change and Agriculture in Europe - Assessment of Impacts and Adaptation. Report No. 9. Oxford, UK: Environmental Change Unit, University of Oxford.Google Scholar
Hoogenboom, G., Jones, J. W. & Boote, K. J. (1990). Nitrogen fixation, uptake and remobilization in legumes: A modeling approach. In Proceedings of IBSNAT Symposium: Decision Support System for Agrotechnology Transfer – Part II, Posters. pp. 138–186. Honolulu, Hawaii, USA: Dept. of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii.Google Scholar
Hoogenboom, G., Jones, J. W. & Boote, K. J. (1991). A decision support system for prediction of crop yield, evapotranspiration and irrigation management. In Irrigation and Drainage: Proceedings of the 1991 National Conference (Ed. Ritter, W. F.), pp. 198204. New York: ASCE.Google Scholar
Hoogenboom, G., Tsuji, G. Y., Pickering, N. B., Curry, R. B., Jones, J. W., Singh, U. & Godwin, D. C. (1995). Decision support system to study climate change impacts on crop production. In Climate Change and Agriculture: Analysis of Potential International Impacts. (Eds Rosenzweig, C., Allen, L. H. Jr., Harper, A., Hollinger, S. E. & Jones, J. W.), pp. 5175. ASA Special Publication No. 59 Madison, Wisconsin, USA: ASA.Google Scholar
Hoogenboom, G., Jones, J. W., Porter, C. H., Wilkens, P. W., Boote, K. J., Batchelor, W. D., Hunt, L. A. & Tsuji, G. Y. (2003). Decision Support System for Agrotechnology Transfer Version 4.0. Volume 1: Overview. Honolulu, HI, USA: University of Hawaii.Google Scholar
Hoogenboom, G., Paz, J. O., Salazar, M. & Garcia, A. G. (2012). Agricultural Irrigation Water Demand Forecast: Procedures for Estimating Monthly Irrigation Demands. Tifton, GA, USA: NESPAL. Available from: http://www.nespal.org/sirp/waterinfo/state/awd/AgWaterDemand_IrrAmt_Detail.htm (accessed January 2015).Google Scholar
Houghton, J. T., Meira Filho, L. G., Callander, B. A., Harris, N., Kattenberg, A. & Maskell, K. (1996). Climate Change 1995. The Science of Climate Change. Cambridge, UK: Cambridge University Press.Google Scholar
International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) (1984). Experimental Design and Data Collection Procedures for IBSNAT. The Minimum Data Set for Systems Analysis and Crop Simulation. Technical Report 1. Honolulu, Hawaii, USA: Department of Agronomy and Soil science, University of Hawaii.Google Scholar
International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) (1986). Experimental Design and Data Collection Procedures for IBSNAT: the Minimum Data Set for Systems Analysis and Crop Simulation. Technical Report 1 (2nd edition). Honolulu, Hawaii, USA: Department of Agronomy and Soil science, University of Hawaii.Google Scholar
International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) (1988). Experimental Design and Data Collection Procedures for IBSNAT: the Minimum Data Set for Systems Analysis and Crop Simulation. Technical Report 1 (3rd edn). Honolulu, Hawaii, USA: Department of Agronomy and Soil science, University of Hawaii.Google Scholar
International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) (1989). Decision Support System for Agrotechnology Transfer v 2·1 (DSSAT v 2·1). Honolulu, Hawaii, USA: Department of Agronomy and Soil science, University of Hawaii.Google Scholar
IPCC (2001). IPCC Third Assessment Report. Working Group I: The Scientific Basis. Cambridge, UK: Cambridge University Press.Google Scholar
IUSS Working Group WRB (2007). World Reference Base for Soil Resources 2006; First Update 2007. World Soil Resources Reports 103. Rome: FAO.Google Scholar
Jones, J., Boote, K., Jagtap, S., Hoogenboom, G. & Wilkerson, G. (1988). SOYGRO v5·41 Soybean Crop Growth Simulation Model, User's Guide. Florida Agricultural Experiment Station Journal 8304, IFAS, Gainesville, FL: University of Florida.Google Scholar
Kucharik, C. J. & Serbin, S. P. (2008). Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environmental Research Letters 3, 034003. doi:10.1088/1748-9326/3/3/034003.Google Scholar
Kumar, A., Pandey, V., Shekh, A. M., Dixit, S. K. & Kumar, M. (2008). Evaluation of cropgro–soybean (Glycine max. [L] (Merrill) model under varying environment condition. American–Eurasian Journal of Agronomy 1(2), 3440.Google Scholar
Lal, M., Singh, K. K., Srinivasan, G., Rathore, L. S., Naidu, D. & Tripathi, C. N. (1999). Growth and yield responses of soybean in Madhya Pradesh, India, to climate variability and change. Agricultural and Forest Meteorology 93, 5370.Google Scholar
Lalic, B., Eitzinger, J., Mihailovic, D. T., Thaler, S. & Jancic, M. (2012). Climate change impacts on winter wheat yield change-which climatic parameters are crucial in Pannonian lowland? The Journal of Agriculture Science, Cambridge 151, 757774.Google Scholar
Laprise, R. (2008). Regional climate modelling. Journal of Computational Physics 227, 36413666.Google Scholar
Lauer, J. (2002). Methods for calculating corn yield. Agronomy Advice Field Crops 28, 47–33.Google Scholar
Mall, R. K., Lal, M., Bhatia, V. S., Rathore, L. S. & Singh, R. (2004). Mitigating climate change impact on Soybean productivity in India: a simulation study. Agricultural and Forest Meteorology 121, 113125.Google Scholar
Moayeri, M., Pazira, E., Siadat, H., Abbasi, F. & Kaveh, F. (2011). Influence of planting and irrigation management methods on maize water productivity improvement in a semiarid region. World Applied Sciences Journal 13, 12181228.Google Scholar
Paknejad, F., Pad, P. F., Ilkaee, M. N. & Fazeli, F. (2012). Simulation of soybean growth under sowing date management by CROPGRO model. American Journal of Agriculture and Biological Sciences 7, 143149.Google Scholar
Prasad, P. V. V. & Staggenborg, S. A. (2008). Impacts of drought and/or heat stress on physiological, developmental, growth, and yield processes of crop plants. In Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes (Eds Ajuha, L. R., Reddy, V. R., Saseendran, S. A. & Yu, Q.), pp. 301356. Madison, WI, USA: American Society of Agronomy/ Crop Science Society of America / Soil Science Society of America.Google Scholar
Priestley, C. H. B. & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large scale parameters. Monthly Weather Review 100, 8192.Google Scholar
RHSS (Republic Hidrometeorology Service of Serbia) (2012). Osnovne Klimatske Karakteristike na Teritoriji Srbije (Standardni Normalni Period 1961–1990) (in Serbian). Belgrade, Republic of Serbia: RHSS. Available from: http://www.hidmet.gov.rs/podaci/meteorologija/latin/Klima_Srbije.pdf (accessed January 2015).Google Scholar
Ritchie, J. & Otter, S. (1985). Description of and performance of CERES-Wheat: A user-oriented wheat yield model. In ARS Wheat Yield Project (Eds Willis, W. O.), pp. 159175. Washington, DC: Department of Agriculture, Agricultural Research Service ARS-38.Google Scholar
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U. & Tompkins, A. (2003). The Atmospheric General Circulation Model ECHAM-5: Model Description. Report No. 349. Hamburg, Germany: Max-Planck-Institut fur Meteorologie.Google Scholar
Rosenzweig, C. & Iglesias, A. (1994). Implication of Climate Change for International Agriculture: Crop Modeling Study. Washington, DC: EPA.Google Scholar
Sathaye, J. A., Dixon, R. K. & Rosenzweig, C. (1997). Climate change country studies. Applied Energy 36, 225235.Google Scholar
Siqueira, O. J. F., Farias, J. R. B. & Sans, L. M. A. (1994). Potential effects of global climate change for Brazilian agriculture: applied simulation studies for wheat, maize, and soybeans. In Implications of Climate Change for International Agriculture: Crop Modeling Study (Eds Rosenzweig, C., Iglesias, A.), pp. BRAZIL-135. EPA report 230-B-94-003. Washington, DC, USA: U.S. Environmental Protection Agency.Google Scholar
Sirotenko, O. D., Abashina, H. V. & Pavlova, V. N. (1997). Sensitivity of the Russian agriculture to changes in climate. CO2 and tropospheric ozone concentrations and soil fertility. Climate Change 36, 217232.Google Scholar
Sivakumar, M. V. K. & Motha, R. (2007). Managing Weather and Climate Risks in Agriculture. Berlin, Germany: Springer.Google Scholar
Southworth, J., Pfeifer, R. A., Habeck, M., Randolph, J. C., Doering, O. C., Johnston, J. J. & Rao, D. G. (2002). Changes in soybean yields in the Midwestern United States bas result of future change in climate variability, and CO2 . Climatic Change 53, 447475.Google Scholar
Statistical Office of the Republic of Serbia (2012). Statistical Yearbook of the Republic of Serbia 2012. Belgrade, Serbia: Statistical Office of the Republic of Serbia. Available from: http://pod2.stat.gov.rs/ObjavljenePublikacije/G2012/pdf/G20122007.pdf (accessed February 2015).Google Scholar
Stigter, K. (2010). Applied Agrometeorology. Berlin, Germany: Springer.CrossRefGoogle Scholar
Thornton, P. K., Bowen, W. T., Ravelo, A. C., Wilkens, P. W., Farmer, G., Brock, J. & Brink, J. E. (1997). Estimating millet production for famine early warning: an application of crop simulation modeling using satellite and ground based data in Burkina Faso. Agriculture and Forest Meteorology 83, 95112.Google Scholar
Travasso, M. I., Magrin, G. O., Rodriguez, G. R. & López, G. M. (2009). Potential impacts of climate change on soybean yields in the Argentinean pampas and adaptation measures for future sustainable production. IOP Conference Series: Earth Environmental Science 6, 372045. doi: 10.1088/1755-1307/6/37/372045.Google Scholar
Tsuji, G., Hoogenboom, G. & Thornton, P. K. (1998). Understanding Options for Agricultural Production. Dordrecht, Netherlands: Kluwer Academic Publishers.Google Scholar
Washington, W. M., Weatherly, J. W., Meehl, G. A., Semtner, A. J. Jr., Bettge, T. W., Craig, A. P., Strand, W. G. Jr., Arblaster, J. M., Wayland, V. B., James, R. & Zhang, Y. (2000). Parallel climate model (PCM) control and transient simulations. Climate Dynamics 16, 755774.Google Scholar
Watson, R. T., Zinyowera, M. C. & Moss, R. H. (1996). Climate Change 1995 – Impacts, Adaptation and Mitigation of Climate Change. Contribution of WG II to the Second Assessment Report of the IPCC. Cambridge, UK: Cambridge University Press.Google Scholar
Wittwer, S. H. (1995). Food, Climate, and Carbon Dioxide – The Global Environment and World Food Production. New York: Lewis Publishers.Google Scholar
Wolf, J. & Van Diepen, C. A. (1995). Effects of climate change on grain maize yield potential in the European Community. Climatic Change 29, 299331. http://www.fao.org/ag/agp/agpc/doc/counprof/serbiamontenegro/serbiamont.htm Google Scholar