• Anderson, W. B., R. Seager, W. Baethgen, and M. Cane, 2017: Crop production variability in North and South America forced by life-cycles of the El Niño Southern Oscillation. Agric. For. Meteor., 239, 151165, https://doi.org/10.1016/j.agrformet.2017.03.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, W. B., R. Seager, W. Baethgen, M. Cane, and L. You, 2019: Synchronous crop failures and climate-forced production variability. Sci. Adv., 5, eaaw1976, https://doi.org/10.1126/sciadv.aaw1976.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, W. B., E. Han, W. Baethgen, L. Goddard, Á. G. Muñoz, and A. W. Robertson, 2020: The Madden-Julian Oscillation affects maize yields throughout the tropics and subtropics. Geophys. Res. Lett., 47, e2020GL087004, https://doi.org/10.1029/2020GL087004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 10831126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. K. Tippett, M. L. L’Heureux, S. Li, and D. G. DeWitt, 2012: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Amer. Meteor. Soc., 93, 631651, https://doi.org/10.1175/BAMS-D-11-00111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. K. Tippett, M. Ranganathan, and M. L. L’Heureux, 2019: Deterministic skill of ENSO predictions from the North American multimodel ensemble. Climate Dyn., 53, 72157234, https://doi.org/10.1007/s00382-017-3603-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brouwer, C., and M. Heibloem, 1986: Irrigation water management: Irrigation Water Needs. FAO, accessed 1 February 2021, https://www.fao.org/3/s2022e/s2022e02.htm#2.3.

  • Cane, M. A., G. Eshel, and R. W. Buckland, 1994: Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature. Nature, 370, 204205, https://doi.org/10.1038/370204a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ceglar, A., M. Turco, A. Toreti, and F. J. Doblas-Reyes, 2017: Linking crop yield anomalies to large-scale atmospheric circulation in Europe. Agric. For. Meteor., 240–241, 3545, https://doi.org/10.1016/j.agrformet.2017.03.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ceglar, A., A. Toreti, C. Prodhomme, M. Zampieri, M. Turco, and F. J. Doblas-Reyes, 2018: Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast. Sci. Rep., 8, 1322, https://doi.org/10.1038/s41598-018-19586-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes, 2005: Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather–yield relationships. J. Appl. Meteor., 44, 516531, https://doi.org/10.1175/JAM2212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deryng, D., W. J. Sacks, C. C. Barford, and N. Ramankutty, 2011: Simulating the effects of climate and agricultural management practices on global crop yield. Global Biogeochem. Cycles, 25, GB2006, https://doi.org/10.1029/2009GB003765.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • FAO, 2016: 2015–2016 El Niño—Early action and response for agriculture, food security and nutrition. Accessed 25 April 2020, http://www.fao.org/emergencies/resources/documents/resources-detail/en/c/340660.

  • Friedman, J. H., T. Hastie, and R. Tibshirani, 2010: Regularization paths for generalized linear models via coordinate descent. J. Stat. Software, 33, 122, https://doi.org/10.18637/jss.v033.i01.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, R. J., and et al. , 2011: Long-range forecasting and the global framework for climate services. Climate Res., 47, 4755, https://doi.org/10.3354/cr00963.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grassini, P., K. Eskridge, and K. Cassman, 2013: Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun., 4, 2918, https://doi.org/10.1038/ncomms3918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harada, Y., and et al. , 2016: The JRA-55 reanalysis: Representation of atmospheric circulation and climate variability. J. Meteor. Soc. Japan, 94, 269302, https://doi.org/10.2151/jmsj.2016-015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayashi, K., L. Llorca, S. Rustini, P. Setyanto, and Z. Zaini, 2018: Reducing vulnerability of rainfed agriculture through seasonal climate predictions: A case study on the rainfed rice production in Southeast Asia. Agric. Syst., 162, 6676, https://doi.org/10.1016/j.agsy.2018.01.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heino, M., M. J. Puma, P. J. Ward, D. Gerten, V. Heck, S. Siebert, and M. Kummu, 2018: Two-thirds of global cropland area impacted by climate oscillations. Nat. Commun., 9, 1257, https://doi.org/10.1038/s41467-017-02071-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heino, M., J. H. A. Guillaume, C. Müller, T. Iizumi, and M. Kummu, 2020: A multi-model analysis of teleconnected crop yield variability in a range of cropping systems. Earth Syst. Dyn., 11, 113128, https://doi.org/10.5194/esd-11-113-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerl, A. E., R. W. Kennard, and K. F. Baldwin, 1975: Ridge regression: Some simulations. Commun. Stat., 4, 105123, https://doi.org/10.1080/03610927508827232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., 2020: Crop forecasting service for world’s food agencies. NARO Tech. Rep. 4, 6-9, accessed 1 February 2021, https://www.naro.affrc.go.jp/publicity_report/publication/laboratory/naro/naro_technical_report/134176.html.

  • Iizumi, T., and W. Kim, 2019: Recent improvements to global seasonal crop forecasting and related research. Adaptation to Climate Change in Agriculture, T. Iizumi, R. Hirata, and R. Matsuda, Eds., Springer, 97110, https://doi.org/10.1007/978-981-13-9235-1_7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., and T. Sakai, 2020: The global dataset of historical yields for major crops 1981–2016. Sci. Data, 7, 97, https://doi.org/10.1038/s41597-020-0433-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., H. Sakuma, M. Yokozawa, J.-J. Luo, A. J. Challinor, M. E. Brown, G. Sakurai, and T. Yamagata, 2013: Prediction of seasonal climate-induced variations in global food production. Nat. Climate Change, 3, 904908, https://doi.org/10.1038/nclimate1945.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., J.-J. Luo, A. J. Challinor, G. Sakurai, M. Yokozawa, H. Sakuma, M. E. Brown, and T. Yamagata, 2014a: Impacts of El Niño–Southern Oscillation on the global yields of major crops. Nat. Commun., 5, 3712, https://doi.org/10.1038/ncomms4712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., G. Sakurai, and M. Yokozawa, 2014b: Contributions of historical changes in sowing date and climate to U.S. maize yield trend: An evaluation using large-area crop modeling and data assimilation. Nogyo Kisho, 70, 7390, https://doi.org/10.2480/agrmet.D-13-00023.

    • Search Google Scholar
    • Export Citation
  • Iizumi, T., and et al. , 2014c: Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. Global Ecol. Biogeogr., 23, 346357, https://doi.org/10.1111/geb.12120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., H. Sakuma, M. Yokozawa, J.-J. Luo, A. J. Challinor, G. Sakurai, and T. Yamagata, 2016: Characterizing the reliability of global crop prediction based on seasonal climate forecasts. The Indo-Pacific Climate Variability and Predictability, T. Yamagata and S. Behera, Eds., World Scientific Publisher, 281304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., M. Kotoku, W. Kim, P. C. West, J. S. Gerber, and M. E. Brown, 2018a: Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-based yield datasets at multiple resolutions. PLOS ONE, 13, e0203809, https://doi.org/10.1371/journal.pone.0203809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iizumi, T., Y. Shin, W. Kim, M. Kim, and J. Choi, 2018b: Global crop yield forecasting using seasonal climate information from a multi-model ensemble. Climate Serv., 11, 1323, https://doi.org/10.1016/j.cliser.2018.06.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jha, S., V. Kumar Sehgal, R. Raghava, and M. Sinha, 2016: Teleconnections of ENSO and IOD to summer monsoon and rice production potential of India. Dyn. Atmos. Oceans, 76, 93104, https://doi.org/10.1016/j.dynatmoce.2016.10.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, E. K., and et al. , 2008: Current status of ENSO prediction skill in coupled ocean–atmosphere models. Climate Dyn., 31, 647664, https://doi.org/10.1007/s00382-008-0397-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, G., and et al. , 2016: Global and regional skill of the seasonal predictions by WMO lead centre for long-range forecast multi-model ensemble. Int. J. Climatol., 36, 16571675, https://doi.org/10.1002/joc.4449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, G., J.-B. Ahn, V. N. Kryjov, W.-S. Lee, D.-J. Kim, and A. Kumar, 2021: Assessment of MME methods for seasonal prediction using WMO LC-LRFMME hindcast dataset. Int. J. Climatol., 41, E2462–E2481, https://doi.org/10.1002/joc.6858.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and et al. , 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koide, N., A. W. Robertson, A. V. M. Ines, J.-H. Qian, D. G. DeWitt, and A. Lucero, 2013: Prediction of rice production in the Philippines using seasonal climate forecasts. J. Appl. Meteor. Climatol., 52, 552569, https://doi.org/10.1175/JAMC-D-11-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawless, J. F., and P. Wang, 1976: A simulation study of ridge and other regression estimators. Commun. Stat., 5, 307323, https://doi.org/10.1080/03610927608827353.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D., A. Sibley, and J. Ivan Ortiz-Monasterio, 2012: Extreme heat effects on wheat senescence in India. Nat. Climate Change, 2, 186189, https://doi.org/10.1038/nclimate1356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, J., G. J. Carbone, and P. Gao, 2017: Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014. Agric. For. Meteor., 237–238, 196208, https://doi.org/10.1016/j.agrformet.2017.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Met Office, 2013: Relative Operating Characteristic (ROC). Met Office, accessed 19 April 2020, https://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/gpc-outlooks/user-guide/interpret-roc.

  • Min, Y.-M., V. N. Kryjov, and S. M. Oh, 2014: Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real-time forecasts (2008–2013). J. Geophys. Res. Atmos., 119, 12 13212 150, https://doi.org/10.1002/2014JD022230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monfreda, C., N. Ramankutty, and J. A. Foley, 2008: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles, 22, GB1022, https://doi.org/10.1029/2007GB002947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, C., and et al. , 2017: Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications. Geosci. Model Dev., 10, 14031422, https://doi.org/10.5194/gmd-10-1403-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Najafi, E., I. Pal, and R. Khanbilvardi, 2020: Larger-scale ocean-atmospheric patterns drive synergistic variability and world-wide volatility of wheat yields. Sci. Rep., 10, 5193, https://doi.org/10.1038/s41598-020-60848-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakaegawa, T., M. Sugi, and K. Matsumaru, 2003: A long-term numerical study of the potential predictability of seasonal mean fields of water resource variables using MRI/JMA-AGCM. J. Meteor. Soc. Japan, 81, 10411056, https://doi.org/10.2151/jmsj.81.1041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naylor, R. L., W. P. Falcon, D. Rochberg, and N. Wada, 2001: Using El Niño/Southern Oscillation climate data to predict rice production in Indonesia. Climatic Change, 50, 255265, https://doi.org/10.1023/A:1010662115348.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nobre, G. G., J. E. Hunink, B. Baruth, J. C. J. H. Aerts, and P. J. Ward, 2019: Translating large-scale climate variability into crop production forecast in Europe. Sci. Rep., 9, 1277, https://doi.org/10.1038/s41598-018-38091-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parkes, B., T. P. Higginbottom, K. Hufkens, F. Ceballos, B. Kramer, and T. Foster, 2019: Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change. Environ. Res. Lett., 14, 124089, https://doi.org/10.1088/1748-9326/ab5ebb.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porter, J. R., and M. A. Semenov, 2005: Crop responses to climatic variation. Philos. Trans. Roy. Soc. London, 360B, 20212035, https://doi.org/10.1098/rstb.2005.1752.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team, 2018: R: A language and environment for statistical computing. Accessed 25 April 2020, https://www.R-project.org/.

  • Roberts, M. J., N. O. Braun, T. R. Sinclair, D. B. Lobell, and W. Schlenker, 2017: Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett., 12, 095010, https://doi.org/10.1088/1748-9326/aa7f33.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenzweig, C., and D. Hillel, 2008: Climate Variability and the Global Harvest. Oxford University Press, 280 pp.

  • Sacks, W. J., D. Deryng, J. A. Foley, and N. Ramankutty, 2010: Crop planting dates: An analysis of global patterns. Global Ecol. Biogeogr., 19, 607620, https://doi.org/10.1111/j.1466-8238.2010.00551.x.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., and T. Yamagata, 2003: Possible impacts of Indian Ocean Dipole mode events on global climate. Climate Res., 25, 151169, https://doi.org/10.3354/cr025151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and et al. , 2019: Tropical rainfall predictions from multiple seasonal forecast systems. Int. J. Climatol., 39, 974988, https://doi.org/10.1002/joc.5855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schauberger, B., C. Gornott, and F. Wechsung, 2017: Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting. Global Change Biol., 23, 47504764, https://doi.org/10.1111/gcb.13738.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semenov, M. A., and F. J. Doblas-Reyes, 2007: Utility of dynamical seasonal forecasts in predicting crop yield. Climate Res., 34, 7181, https://doi.org/10.3354/cr034071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, L., H. H. Hendon, O. Alves, J. Luo, M. Balmaseda, and D. Anderson, 2012: How predictable is the Indian Ocean dipole? Mon. Wea. Rev., 140, 38673884, https://doi.org/10.1175/MWR-D-12-00001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sivakumar, M. V. K., and J. Hansen, 2007: Climate Prediction and Agriculture. Springer, 306 pp.

  • Smith, D. M., A. A. Scaife, and B. P. Kirtman, 2012: What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ. Res. Lett., 7, 015602, https://doi.org/10.1088/1748-9326/7/1/015602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stan, C., D. M. Straus, J. S. Frederiksen, H. Lin, E. D. Maloney, and C. Schumacher, 2017: Review of tropical-extratropical teleconnections on intraseasonal time scales. Rev. Geophys., 55, 902937, https://doi.org/10.1002/2016RG000538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stone, R., G. Hammer, and T. Marcussen, 1996: Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature, 384, 252255, https://doi.org/10.1038/384252a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strazzo, S., D. C. Collins, A. Schepen, Q. J. Wang, E. Becker, and L. Jia, 2019: Application of a hybrid statistical–dynamical system to seasonal prediction of North American temperature and precipitation. Mon. Wea. Rev., 147, 607625, https://doi.org/10.1175/MWR-D-18-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tack, J., A. Barkley, and L. L. Nalley, 2015: Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. USA, 112, 69316936, https://doi.org/10.1073/pnas.1415181112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takaya, Y., and et al. , 2018: Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): Atmosphere–land–ocean–sea ice coupled prediction system for operational seasonal forecasting. Climate Dyn., 50, 751765, https://doi.org/10.1007/s00382-017-3638-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tibshirani, R., 1996: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc., 58B, 267288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.

    • Search Google Scholar
    • Export Citation
  • Toreti, A., and et al. , 2019: Using reanalysis in crop monitoring and forecasting systems. Agric. Syst., 168, 144153, https://doi.org/10.1016/j.agsy.2018.07.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USDA, 1994: Major world crop areas and climatic profiles. World Agricultural Outlook Board, Agricultural Handbook 664, 293 pp., https://naldc.nal.usda.gov/download/CAT88895275/PDF.

  • van Oort, P. A. J., T. Zhang, M. E. de Vries, A. B. Heinemann, and H. Meinke, 2011: Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agric. For. Meteor., 151, 15451555, https://doi.org/10.1016/j.agrformet.2011.06.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and et al. , 2009: Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Climate Dyn., 33, 93117, https://doi.org/10.1007/s00382-008-0460-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 100, Academic Press, 648 pp.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., and C. M. Godfrey, 2002: Diagnostic verification of the IRI net assessment forecasts, 1997–2000. J. Climate, 15, 13691377, https://doi.org/10.1175/1520-0442(2002)015<1369:DVOTIN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WMO, 2002: Standardised Verification System (SVS) for Long-Range Forecasts (LRF). New Attachment II-9 to the Manual on the GDPS, Vol. 1, WMO-485, 24 pp., https://clima1.cptec.inpe.br/gpc/pdf/svs.pdf.

  • WMO, 2019: Expert Team on Sector-Specific Climate Indices (ET-SCI). WMO, accessed 1 February 2021, https://www.wmo.int/pages/prog/wcp/ccl/ccl17/focusarea/fa3/CCl-17FA3ET-SCIWMO.php.

  • Yuan, C., and T. Yamagata, 2015: Impacts of IOD, ENSO and ENSO Modoki on the Australian winter wheat yields in recent decades. Sci. Rep., 5, 17252, https://doi.org/10.1038/srep17252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zambrano, F., A. Vrieling, A. Nelson, M. Meroni, and T. Tadesse, 2018: Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices. Remote Sens. Environ., 219, 1530, https://doi.org/10.1016/j.rse.2018.10.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, H., and T. Hastie, 2005: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc., 67B, 301320, https://doi.org/10.1111/j.1467-9868.2005.00503.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation

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  • 1 Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan
  • | 2 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
  • | 3 Global Environment and Marine Department, Japan Meteorological Agency, Tokyo, Japan
  • | 4 Aerological Observatory, Japan Meteorological Agency, Tsukuba, Japan
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Abstract

Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0097.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Toshichika Iizumi, iizumit@affrc.go.jp

Abstract

Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0097.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Toshichika Iizumi, iizumit@affrc.go.jp

Supplementary Materials

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