Simulation of Crop Yields Using ERA-40: Limits to Skill and Nonstationarity in Weather–Yield Relationships

A. J. Challinor Centre for Global Atmospheric Modelling, Department of Meteorology, and Department of Agriculture, The University of Reading, Reading, United Kingdom

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T. R. Wheeler Department of Agriculture, The University of Reading, Reading, United Kingdom

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J. M. Slingo Centre for Global Atmospheric Modelling, Department of Meteorology, The University of Reading, Reading, United Kingdom

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P. Q. Craufurd Department of Agriculture, The University of Reading, Reading, United Kingdom

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D. I. F. Grimes Department of Meteorology, The University of Reading, Reading, United Kingdom

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Abstract

Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Corresponding author address: Dr. Andrew Challinor, GCAM, Department of Meteorology, The University of Reading, P.O. Box 243, Earley Gate, Reading RG6 6BB, United Kingdom. ajc@met.rdg.ac.uk

Abstract

Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Corresponding author address: Dr. Andrew Challinor, GCAM, Department of Meteorology, The University of Reading, P.O. Box 243, Earley Gate, Reading RG6 6BB, United Kingdom. ajc@met.rdg.ac.uk

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  • Andersson, E. Coauthors 2005. Assimilation and modeling of the atmospheric hydrological cycle in the ECMWF forecasting system. Bull. Amer. Meteor. Soc. 86:387402.

    • Search Google Scholar
    • Export Citation
  • Annamalai, H., J. M. Slingo, K. R. Sperber, and K. Hodges. 1999. The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP–NCAR reanalyses. Mon. Wea. Rev. 127:11571186.

    • Search Google Scholar
    • Export Citation
  • Atwood, J., S. Shaik, and M. Watts. 2003. Are crop yields normally distributed? A reexamination. Amer. J. Agric. Econ. 85:888901.

  • Basso, B., J. T. Ritchie, F. J. Pierce, R. P. Braga, and J. W. Jones. 2001. Spatial validation of crop models for precision agriculture. Agric. Syst. 68:97112.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L. and J. Shukla. 1988. Integration of space and in situ observations to study global climate change. Bull. Amer. Meteor. Soc. 69:11301143.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., J. H. Ball, M. Bosilovich, P. Viterbo, Y. Zhang, and W. B. Rossow. 2003. Intercomparison of water and energy budgets for five Mississippi subbasins between ECMWF reanalysis (ERA-40) and NASA Data Assimilation Office fvGCM for 1990–1999. J. Geophys. Res. 108.8618, doi:10.1029/2002JD003127.

    • Search Google Scholar
    • Export Citation
  • Boote, K. J. and J. W. Jones. 1998. Simulation of crop growth: Cropgro model. Agricultural Systems Modeling and Simulation, R. M. Peart and R. B. Curry, Eds., Marcel Dekker, 651–692.

    • Search Google Scholar
    • Export Citation
  • Camberlin, P. and M. Diop. 1999. Inter-relationships between groundnut yield in Senegal, interannual rainfall variability and sea-surface temperatures. Theor. Appl. Climatol. 63:163181.

    • Search Google Scholar
    • Export Citation
  • Challinor, A. J., J. M. Slingo, T. R. Wheeler, P. Q. Craufurd, and D. I. F. Grimes. 2003. Toward a combined seasonal weather and crop productivity forecasting system: Determination of the spatial correlation scale. J. Appl. Meteor. 42:175192.

    • Search Google Scholar
    • Export Citation
  • Challinor, A. J., J. M. Slingo, T. R. Wheeler, and F. J. Doblas-Reyes. 2004a. Probabilistic hindcasts of crop yield over western India. Tellus in press.

    • Search Google Scholar
    • Export Citation
  • Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes. 2004b. Design and optimisation of a large-area process-based model for annual crops. Agric. For. Meteor. 124:99120.

    • Search Google Scholar
    • Export Citation
  • Corte-Real, J., H. Xu, and B. D. Qian. 1999. A weather generator for obtaining daily precipitation scenarios based on circulation patterns. Climate Res. 13:6175.

    • Search Google Scholar
    • Export Citation
  • Davis, C. A., K. W. Manning, R. E. Carbone, S. B. Trier, and J. D. Tuttle. 2003. Coherence of warm-season continental rainfall in numerical weather prediction models. Mon. Wea. Rev. 131:26672679.

    • Search Google Scholar
    • Export Citation
  • FAO/UNESCO 1974. Southeast Asia. Vol. 9, FAO/UNESCO Soil Map of the World, 1:5 000 000, UNESCO, 149 pp.

  • Gadgil, S., P. R. S. Rao, K. N. Rao, and K. Savithiri. 1999. Farming strategies for a variable climate. Indian Institute of Science CAOS Tech. Rep. 99AS7, 46 pp.

  • Gadgil, S., P. R. S. Rao, and K. N. Rao. 2002. Use of climate information for farm-level decision making: Rainfed groundnut in southern India. Agric. Syst. 74:431457.

    • Search Google Scholar
    • Export Citation
  • Gershunov, A., N. Schneider, and T. Barnett. 2001. Low-frequency modulation of the ENSO–Indian monsoon rainfall relationship: Signal or noise? J. Climate 14:24862492.

    • Search Google Scholar
    • Export Citation
  • Gibson, J. K., P. Kållberg, and S. Uppala. 1996. The ECMWF Re-Analysis (ERA) project. ECMWF Newsletter, No. 73, ECMWF, 7–17.

  • Gibson, J. K., P. Kållberg, S. Uppala, A. Hernandez, A. Nomura, and E. Serrano. 1997. Era description. Re-Analysis (ERA) Project Report Series 1, ECMWF, 72 pp.

  • Guerif, M. and C. L. Duke. 2000. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agric. Ecosyst. Environ. 81:5769.

    • Search Google Scholar
    • Export Citation
  • Hansen, J. W. and J. W. Jones. 2000. Scaling-up crop models for climatic variability applications. Agric. Syst. 65:4372.

  • Hoshen, M. B., E. Worrall, A. P. Morse, and M. C. Thomson. 2003. A weather-driven model of malaria transmission. Malaria J. 3.doi:10.1186/1475-2875-3-32.

    • Search Google Scholar
    • Export Citation
  • Jones, D. and E. M. Barnes. 2000. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Agric. Syst. 65:137158.

    • Search Google Scholar
    • Export Citation
  • Just, R. E. and Q. Weninger. 1999. Are crop yields normally distributed? Amer. J. Agric. Econ. 81:287304.

  • Kakani, V. G. 2001. Quantifying the effects of high temperature and water stress in groundnut. Ph.D. thesis, University of Reading, 309 pp.

  • Kalnay, E. Coauthors 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc. 77:437471.

  • Kulkarni, B. S. and S. N. N. Pandit. 1988. A discrete step in the technology trend for sorghum yields in Parbhani, India. Agric. For. Meteor. 42:156165.

    • Search Google Scholar
    • Export Citation
  • Kumar, K. K., B. Rajagopalan, and M. A. Cane. 1999. On the weakening relationship between the Indian Monsoon and ENSO. Science 284:21562159.

    • Search Google Scholar
    • Export Citation
  • Lansigan, F. P., W. L. De Los Santos, and J. O. Coladilla. 2000. Agronomic impacts of climate variability on rice production in the Philippines. Agric. Ecosyst. Environ. 82:129137.

    • Search Google Scholar
    • Export Citation
  • Legler, D. M., K. J. Bryant, and J. J. O’Brien. 1999. Impact of ENSO-related climate anomalies on crop yields in the U.S. Climatic Change 42:351375.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., T. Mavromatis, and E. Tsvetsinskaya. 1999. Comparative response of EPIC and CERES crop models to high and low spatial resolution climate change scenarios. J. Geophys. Res. 104:D6,. 66236646.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., W. Easterling, C. Hays, and D. Marx. 2001. Comparison of agricultural impacts of climate change calculated from the high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Climatic Change 51:131172.

    • Search Google Scholar
    • Export Citation
  • Moss, C. B. and J. S. Shonkwiler. 1993. Estimating yield distributions with a stochastic trend and nonnormal errors. Amer. J. Agric. Econ. 75:10561062.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N. Coauthors 2004. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull. Amer. Meteor. Soc. 85:853872.

    • Search Google Scholar
    • Export Citation
  • Parthasarathy, B., K. R. Kumar, and A. A. Munot. 1992. Forecast of rainy-season food-grain production based on monsoon rainfall. Indian J. Agric. Sci. 62:18.

    • Search Google Scholar
    • Export Citation
  • Parthasarathy, B., A. A. Munot, and D. R. Kothawale. 1995. All India monthly and seasonal rainfall series: 1871–1993. Theor. Appl. Climatol. 49:217224.

    • Search Google Scholar
    • Export Citation
  • Pathak, H. Coauthors 2003. Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains. Field Crops Res. 80:223234.

    • Search Google Scholar
    • Export Citation
  • Priestly, C. H. B. and R. J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev. 100:8192.

    • Search Google Scholar
    • Export Citation
  • Ramirez, O. A., S. Misra, and J. Field. 2003. Crop-yield distributions revisited. Amer. J. Agric. Econ. 85:108120.

  • Reddy, P. S. 1988. Groundnut. Indian Council of Agricultural Research, 583 pp.

  • Rodó, X., M. Pascual, G. Fuchs, and A. S. G. Faruque. 2002. Enso and cholera: A non-stationary link related to climate change? Proc. Natl. Acad. Sci. 99:1290112906.

    • Search Google Scholar
    • Export Citation
  • Selvaraju, R. 2003. Impact of El Niño–Southern Oscillation on Indian foodgrain production. Int. J. Climatol. 23:187206.

  • Semenov, M. A. and E. M. Barrow. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35:397414.

    • Search Google Scholar
    • Export Citation
  • Semenov, M. A. and R. J. Brooks. 1999. Spatial interpolation of the LARSWG stochastic weather generator in Great Britain. Climate Res. 11:137148.

    • Search Google Scholar
    • Export Citation
  • Sinclair, T. R. and N. Seligman. 2000. Criteria for publishing papers on crop modelling. Field Crops Res. 68:165172.

  • Sivakumar, M. V. K. and P. S. Sarma. 1986. Studies on water relations of groundnut. Agrometeorology of Groundnut (Proceedings of the International Symposium held at ICRISAT Sahelian Center, Niamey, Niger), M. V. K. Sivakumar and S. M. Virimani, Eds., ICRISAT, 83–98.

  • Southworth, J., J. C. Randolph, M. Habeck, O. C. Doering, R. A. Pfeifer, D. G. Rai, and J. J. Johnston. 2000. Consequences of future climate change and changing climate variability on maize yields in the midwestern United States. Agric. Ecosyst. Environ. 82:139158.

    • Search Google Scholar
    • Export Citation
  • Torrence, C. and P. J. Webster. 1999. Interdecadal changes in the ENSO–monsoon system. J. Climate 12:26792690.

  • Troccoli, A. and P. Kållberg. 2004. Precipitation correction in the ERA-40 reanalysis. ERA-40 Project Report Series 13, ECMWF, 10 pp.

  • Tsvetsinskaya, E. A., L. O. Mearns, and W. E. Easterling. 2001a. Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part I: Simulation of surface fluxes over the growing season. J. Climate 14:692709.

    • Search Google Scholar
    • Export Citation
  • Tsvetsinskaya, E. A., L. O. Mearns, and W. E. Easterling. 2001b. Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part II: Atmospheric response to crop growth and development. J. Climate 14:711729.

    • Search Google Scholar
    • Export Citation
  • Virmani, S. M. and N. J. Shurpali. 1999. Climate prediction for sustainable production of rainfed groundnuts in SAT: Crop establishment risks in the Anantapur region. International Crop Research Institute for the Semi–Arid Tropics Tech. Manual 4, 50 pp.

  • Wheeler, T. R., P. Q. Craufurd, R. H. Ellis, J. R. Porter, and P. V. V. Prasad. 2000. Temperature variability and the annual yield of crops. Agric. Ecosyst. Environ. 82:159167.

    • Search Google Scholar
    • Export Citation
  • Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics 1:8083.

  • Wilks, D. S. 2002. Realizations of daily weather in forecast seasonal climate. J. Hydrometeor. 3:195207.

  • Wilks, D. S. and R. L. Wilby. 1999. The weather generation game: A review of stochastic weather models. Prog. Phys. Geogr. 23:329357.

    • Search Google Scholar
    • Export Citation
  • Wu, R. and B. Wang. 2002. A contrast of the east Asian summer monsoon–ENSO relationship between 1962–77 and 1978–93. J. Climate 15:32663279.

    • Search Google Scholar
    • Export Citation
  • Yu, Q., H. Hengsdijk, and J. D. Liu. 2001. Application of a progressive-difference method to identify climatic factors causing variation in the rice yield in the Yangtze Delta, China. Int. J. Biometeor. 45:5358.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., T. Li, B. Wang, and G. Wu. 2002. Onset of the summer monsoon over the Indochina Peninsula: Climatology and interannual variations. J. Climate 15:32063221.

    • Search Google Scholar
    • Export Citation
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