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Use of Seasonal Climate Forecasts in Agricultural Decision-Making for Crop Disease Management

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Adaptation to Climate Change in Agriculture

Abstract

Recently, seasonal climate forecasts (SCFs) and their advances have gained increasing attention in agricultural communities, specifically because of their potential to improve climate risk management by increasing preparedness and thus enhance agricultural and economic outcomes. Seasonal predictions of crop diseases and insect pests that provide timely and accurate forecasts are especially valuable not only to farmers and extension workers (i.e., to inform their crop management decisions) but also to governments (i.e., to increase national-level disaster preparedness). In this study, we used a case study in Bicol, Philippines, to introduce an array of implementation strategies to facilitate the use of SCFs in the agricultural sector. To demonstrate the full potential of SCFs in the Bicol region, we developed and applied seasonal disease predictions for rice, with sequential activities that included a baseline study on disease epidemics in the target area, the examination of available SCFs, the development of a decision support system for seasonal disease predictions, and an evaluation of this system using SCF hindcasts. Finally, we disseminated the resulting seasonal disease predictions in the target area using an agro-met bulletin. The present study demonstrated a successful example of a developmental framework for the application of SCFs to agricultural decision-making with the support of relevant SCF-linked agricultural models. These implementation strategies, in combination with the lessons learned, can help guide prospective efforts of establishing similar climate services that utilize SCFs in developing countries to improve the outcomes and thus lead to enhanced and sustainable food security.

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References

  • Ahn JB, Kim HJ (2013) Improvement of one-month lead predictability of the wintertime AO using a realistically varying solar constant for a CGCM. Meteorol Appl 21:415–418

    Article  Google Scholar 

  • Boer GJ (2005) An evolving seasonal forecasting system using Bayes’ theorem. Atmos Ocean 43:129–143

    Article  Google Scholar 

  • Bunsri T (2017) Simulation of severity of rice blast disease in Prachin Buri using plant disease epidemiological model: simulation of rice blast disease. In: Proceedings of the 22nd annual meeting in mathematics (AMM 2017)

    Google Scholar 

  • Canal N, Deudon O, Le Bris X, Gate P, Pigeon G, Regimbeau M, Calvet J-C (2017) Anticipation of the winter wheat growth based on seasonal weather forecasts over France. Meteorol Appl 24:432–443

    Article  Google Scholar 

  • Cantelaube P, Terres J-M (2005) Seasonal weather forecasts for crop yield modelling in Europe. Tellus A: Dynamic Meteorol Oceanogr 57:476–487

    Article  Google Scholar 

  • Capa-Morocho M, Ines AV, Baethgen WE, Rodríguez-Fonseca B, Han E, Ruiz-Ramos M (2016) Crop yield outlooks in the Iberian Peninsula: connecting seasonal climate forecasts with crop simulation models. Agric Syst 149:75–87

    Article  Google Scholar 

  • Cash DW, Clark WC, Alcock F, Dickson NM, Eckley N, Guston DH et al (2003) Knowledge systems for sustainable development. Proc Natl Acad Sci 100(14):8086–8091

    Article  CAS  PubMed  Google Scholar 

  • Challinor A (2009) Towards the development of adaptation options using climate and crop yield forecasting at seasonal to multi-decadal timescales. Environ Sci Pol 12:453–465

    Article  Google Scholar 

  • Challinor AJ, Slingo JM, Wheeler TR, Doblas-Reyes FJ (2005) Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles. Tellus A: Dynamic Meteorol Oceanogr 57(3):498–512

    Article  Google Scholar 

  • CountryStat Philippines (2013) Regional profile: Bicol. http://countrystat.psa.gov.ph/?cont=16&r=5. Accessed 20 Dec 2016)

  • Duku C, Sparks AH, Zwart SJ (2016) Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate. Clim Chang 135(3–4):569–583

    Article  CAS  Google Scholar 

  • Epstein ES (1969) A scoring system for probability forecasts of ranked categories. J Appl Meteorol 8:985–987

    Article  Google Scholar 

  • FAO (2015) The impact of natural hazards and disasters on agriculture and food and nutrition security: a call for action to build resilient livelihoods. (online) Available at: http://www.fao.org/3/a-i4434e.pdf

  • Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA (2001) Current approaches to seasonal to interannual climate predictions. Int J Climatol 21(9):1111–1152

    Article  Google Scholar 

  • Guston DH (2001) Boundary organizations in environmental policy and science: anintroduction. Sci Technol Hum Values 26:399–408

    Article  Google Scholar 

  • Han E, Ines AV (2017) Downscaling probabilistic seasonal climate forecasts for decision support in agriculture: a comparison of parametric and non-parametric approach. Climate Risk Manage 18:51–65

    Article  Google Scholar 

  • Han E, Ines AV, Baethgen WE (2017) Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture. Environ Modell Softw 95:102–114

    Article  Google Scholar 

  • Han E, Baethgen WE, Ines AV, Mer F, Souza JS, Berterretche M, Atunez G, Barreira C (2018) SIMAGRI: An agro-climate decision support tool. Comput Electron Agric. https://doi.org/10.1016/j.compag.2018.06.034

    Article  Google Scholar 

  • Hansen JW, Indeje M (2004) Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya. Agric For Meteorol 125(1–2):143–157

    Article  Google Scholar 

  • Hansen JW, Mason SJ, Sun L, Tall A (2011) Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Exp Agric 47(2):205–240

    Article  Google Scholar 

  • Hossain M, Gascon F, Revilla I (1995) Constraints to growth in rice in the Philippines. J Agric Econ Dev 33:1–2

    Google Scholar 

  • Iizumi T, Shin Y, Kim W, Kim M, Choi J (2018) Global crop yield forecasting using seasonal climate information from a multi-model ensemble. Clim Serv 11:13–23

    Article  Google Scholar 

  • Ines A, Han E (2014) Predict WTD: a temporal downscaling tool for seasonal climate forecast. IRI/Columbia University, New York

    Google Scholar 

  • IPCC (2014) In: Core Writing Team, Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva

    Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change) (2012) In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York

    Google Scholar 

  • Janowiak JE, Xie P (1999) CAMS_OPI: a global satellite-raingauge merged product for real-time precipitation monitoring applications. J Clim 12:3335–3342

    Article  Google Scholar 

  • Kanamitsu M, Kumar A, Juang HMH, Schemm JK, Wang W, Yang F et al (2002) NCEP dynamical seasonal forecast system 2000. Bull Am Meteorol Soc 83(7):1019–1038

    Article  Google Scholar 

  • Kang HS, Boo KO, Cho CH (2011) Introduction to KMA-Met office joint seasonal forecasting system and evaluation of its hindcast ensemble simulations. NOAA/NWS Science and Technology Infusion Climate Bulletin

    Google Scholar 

  • Kharin VV, Zwiers FW (2003) Improved seasonal probability forecast. J Clim 16:1684–1701

    Article  Google Scholar 

  • Kim KH, Cho J (2016) Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information. Clim Chang 134(1–2):327–339

    Article  Google Scholar 

  • Kim KH, Cho J, Lee YH, Lee WS (2015) Predicting potential epidemics of rice leaf blast and sheath blight in South Korea under the RCP 4.5 and RCP 8.5 climate change scenarios using a rice disease epidemiology model, EPIRICE. Agric For Meteorol 203:191–207

    Article  Google Scholar 

  • Kirchhoff CJ, Lemos MC, Dessai S (2013) Actionable knowledge for environmental decision making: broadening the usability of climate science. Annu Rev Environ Resour 38:393–414

    Article  Google Scholar 

  • Koide N, Robertson AW, Ines AV, Qian JH, DeWitt DG, Lucero A (2013) Prediction of rice production in the Philippines using seasonal climate forecasts. J Appl Meteorol Climatol 52(3):552–569

    Article  Google Scholar 

  • Lim EP, Hendon HH, Langford S, Alves O (2012) Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall. CAWCR technical report no. 051

    Google Scholar 

  • Liou CS, Chen JH, Terng CT, Wang FJ, Fong CT, Rosmond TE, Kuo HC, Shiao CH, Cheng MD (1997) The second generation global forecast system at the central weather bureau in Taiwan. Weather Forecast 12:653–663

    Article  Google Scholar 

  • Lyon B, Cristi H, Verceles ER, Hilario FD, Abastillas R (2006) Seasonal reversal of the ENSO rainfall signal in the Philippines. Geophysical Res Letters 33(24):L24710

    Google Scholar 

  • Martins MA, Tomasella J, Rodriguez DA, Alvalá RC, Giarolla A, Garofolo LL et al (2018) Improving drought management in the Brazilian semiarid through crop forecasting. Agric Syst 160:21–30

    Article  Google Scholar 

  • Mason SJ, Goddard L (2001) Probabilistic precipitation anomalies associated with EN SO. Bull Am Meteorol Soc 82(4):619–638

    Article  Google Scholar 

  • Meinke H, Stone RC (2005) Seasonal and inter-annual climate forecasting: the new tool for increasing preparedness to climate variability and change in agricultural planning and operation. Climate Change 70:221–253

    Article  Google Scholar 

  • Merryfield WJ et al (2013) The Canadian seasonal to interannual prediction system. Part I: models and initialization. Mon Weather Rev 141:2910–2945

    Article  Google Scholar 

  • Molod A et al (2012) The GEOS-5 atmospheric general circulation model: mean climate and development from MERRA to Fortuna. Technical report series on global modeling and data assimilation, p 28

    Google Scholar 

  • Murphy AH (1969) On the ranked probability skill score. J Appl Meteorol 8:988–989

    Article  Google Scholar 

  • Murphy AH (1971) A note on the ranked probability skill score. J Appl Meteorol 10:155–156

    Article  Google Scholar 

  • Philippine Department of Agriculture, Regional Office No. 5 (2014) Bicol annual report 2014. http://bicol.da.gov.ph/index.php/reports-documentation/1207-da-bicol-annual-report-2014

  • Power SB, Plummer N, Alford P (2007) Making climate models more useful. Aust J Agric Res 58:945–951

    Article  Google Scholar 

  • Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn P et al (2013) The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric For Meteorol 170:166–182

    Article  Google Scholar 

  • Savary S, Nelson A, Willocquet L, Pangga I, Aunario J (2012) Modeling and mapping potential epidemics of rice diseases globally. Crop Prot 34:6–17

    Article  Google Scholar 

  • Scinocca JF, Mcfarlane NA, Lazare M, Li J, Plummer D (2008) The CCCma third generation AGCM and its extension into the middle atmosphere. Atmos Chem Phys 8:7055–7074

    Article  CAS  Google Scholar 

  • Sittisak I, Saruda H, Angkool W, Thidarat B (2017) Numerical solution of the differential equation for simulation of the rice blast disease. J Appl Sci Environ Manag 21(7):1272–1275

    Google Scholar 

  • Syktus J, McKeon G, Flood N, Smith I, Goddard L (2003) Evaluation of a dynamical seasonal climate forecast system for Queensland. In: Science for drought, Proceedings of the National Drought Forum, Brisbane, April 2003. Queensland Department of Primary Industries. Ed. R Stone and I Partridge, pp 160–173

    Google Scholar 

  • Takaya Y, Yasuda T, Ose T, Nakaegawa T (2010) Predictability of the mean location of typhoon formation in a seasonal prediction experiment with a coupled general circulation model. J Meteorol Soc Japan 88:799–812

    Article  Google Scholar 

  • Trosnikov IV, Kaznacheeva VD, Kiktev DB, Tolstikh MA (2005) Assessment of potential predictability of meteorological variables in dynamical seasonal modeling of atmospheric circulation on the basis of semi-Lagrangian model SL-AV. Russian Meteorol Hydrol 12:5–17

    Google Scholar 

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Acknowledgment

This work was supported by the APEC Climate Center. We are grateful to the partners of the USAID-funded Bicol Agri-Water Project for their sincere support for this collaborative work, especially Dr. Eunjin Han and Dr. Amor Ines for their kind assistance with the predictWTD downscaling tool within the Climate-Agriculture-Modeling and Decision Tool.

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Correspondence to Kwang-Hyung Kim .

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Kim, KH., Shin, Y., Lee, S., Jeong, D. (2019). Use of Seasonal Climate Forecasts in Agricultural Decision-Making for Crop Disease Management. In: Iizumi, T., Hirata, R., Matsuda, R. (eds) Adaptation to Climate Change in Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-13-9235-1_12

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