Advertisement

Theoretical and Applied Climatology

, Volume 136, Issue 1–2, pp 377–390 | Cite as

A framework for standardized calculation of weather indices in Germany

  • Markus MöllerEmail author
  • Juliane Doms
  • Henning Gerstmann
  • Til Feike
Original Paper

Abstract

Climate change has been recognized as a main driver in the increasing occurrence of extreme weather. Weather indices (WIs) are used to assess extreme weather conditions regarding its impact on crop yields. Designing WIs is challenging, since complex and dynamic crop-climate relationships have to be considered. As a consequence, geodata for WI calculations have to represent both the spatio-temporal dynamic of crop development and corresponding weather conditions. In this study, we introduce a WI design framework for Germany, which is based on public and open raster data of long-term spatio-temporal availability. The operational process chain enables the dynamic and automatic definition of relevant phenological phases for the main cultivated crops in Germany. Within the temporal bounds, WIs can be calculated for any year and test site in Germany in a reproducible and transparent manner. The workflow is demonstrated on the example of a simple cumulative rainfall index for the phenological phase shooting of winter wheat using 16 test sites and the period between 1994 and 2014. Compared to station-based approaches, the major advantage of our approach is the possibility to design spatial WIs based on raster data characterized by accuracy metrics. Raster data and WIs, which fulfill data quality standards, can contribute to an increased acceptance and farmers’ trust in WI products for crop yield modeling or weather index-based insurances (WIIs).

Notes

Acknowledgements

We are very grateful to two anonymous reviewers who provided valuable advice on how to improve the manuscript. Parts of this study were funded by the German Federal Ministry of Food and Agriculture and managed by the Federal Office for Agriculture and Food (BLE), contract no. 2815707915.

References

  1. Acevedo E, Silva P, Silva H (2002) Wheat growth and physiology. FAO Plant Production and Protection Series. FAO, Rome, ItalyGoogle Scholar
  2. Adeyinka A, Krishnamurti C, Maraseni T, Chantarat S (2016) The viability of weather-index insurance in managing drought risk in rural Australia. Int J Rural Manag 12:125–142CrossRefGoogle Scholar
  3. Barnett BJ, Mahul O (2007) Weather index insurance for agriculture and rural areas in lower-income countries. Am J Agric Econ 89:1241–1247CrossRefGoogle Scholar
  4. Castañeda-Vera A, Barrios L, Garrido A, Mínguez I (2014) Assessment of insurance coverage and claims in rainfall related risks in processing tomato in Western Spain. Eur J Agron 59:39–48CrossRefGoogle Scholar
  5. Chen W, Hohl R, Tiong L (2017) Rainfall index insurance for corn farmers in shandong based on high-resolution weather and yield data. Agric Finance Rev 77:337–354CrossRefGoogle Scholar
  6. Chmielewski FM, Müller A, Bruns E (2004) Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961 - 2000. Agric For Meteorol 121:69–78CrossRefGoogle Scholar
  7. Chuine I, Kramer K, Hänninen H (2003) Plant development models Schwartz M (ed), vol 39, An integrative environmental science, Kluwer Academic Publishers, Dordrecht, The Netherlands, Tasks for vegetation science, PhenologyGoogle Scholar
  8. Conradt S, Finger R, Bokuševa R (2015) Tailored to the extremes: Quantile regression for index-based insurance contract design. Agric Econ 46(4):537–547CrossRefGoogle Scholar
  9. Conradt S, Finger R, Spörri M (2015) Flexible weather index-based insurance design. Clim Risk Manag 10:106–117CrossRefGoogle Scholar
  10. Cruz S, Monteiro A, Santos R (2012) Automated geospatial web services composition based on geodata quality requirements. Comput Geosci 47:60–74CrossRefGoogle Scholar
  11. Dalhaus T, Finger R (2016) Can gridded precipitation data and phenological observations reduce basis risk of weather index-based insurance? Weather Clim Soc 8:409–419CrossRefGoogle Scholar
  12. Dalhaus T, Musshoff O, Finger R (2018) Phenology Information Contributes to Reduce Temporal Basis Risk in Agricultural Weather Index Insurance. Scientific Reports 8(1):46+.  https://doi.org/10.1038/s41598-017-18656-5 CrossRefGoogle Scholar
  13. Davis J (2002) Statistics and data analysis in geology. John Wiley & SonsGoogle Scholar
  14. Doms J, Gerstmann H, Möller M (2017) Modeling of dynamic weather indexes by coupling spatial phenological and precipitation data – A practical application in the context of weather index-based insurances. In: Contribution presented at the XV EAAE Congress ”Towards Sustainable Agri-food Systems: Balancing Between Markets and Society”, European Association of Agricultural Economists (EAAE), Parma, ItalyGoogle Scholar
  15. Doms J, Hirschauer N, Marz M, Boettcher F (2018) Is the hedging efficiency of weather index insurance overrated? A farm-level analysis in regions with moderate natural conditions in Germany. Agric Finance Rev  https://doi.org/10.1108/AFR-07-2017-0059
  16. FAOSTAT (2015) FAOSTAT: FAO Statistical database. Tech. rep., Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
  17. Field C, Barros V, Stocker T (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University PressGoogle Scholar
  18. Gerstmann H, Doktor D, Gläßer C, Möller M (2016) Phase: A geostatistical model for the kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Comput Electron Agric 127:726–738CrossRefGoogle Scholar
  19. Gömann H, Bender A, Bolte A, Dirksmeyer W, Englert H, Feil JH, Frühauf C, Hauschild M, Krengel S, Lilienthal H, Löpmeier FJ, Müller J, Mußhoff O, Natkhin M, Offermann F, Seidel P, Schmidt M, Seintsch B, Steidl J, Strohm K, Zimmer Y (2015) Agrarrelevante Extremwetterlagen und Möglichkeiten von Risikomanagementsystemen: Studie im Auftrag des Bundesministeriums für Ernährung und Landwirtschaft (BMEL), Thünen Rep, vol 30. Johann Heinrich von Thünen-Institut, Braunschweig, GermanyGoogle Scholar
  20. Goodwin B, Mahul O (2004) Risk modeling concepts relating to the design and rating of agricultural insurance contracts. World Bank Policy Research Working Paper 3392, World Bank, Washington, D.CCrossRefGoogle Scholar
  21. Grassini P, van Bussel L, Wart JV, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, van Ittersum M, Cassman K (2015) How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res 177:49–63CrossRefGoogle Scholar
  22. Hengl T, Heuvelink G, Rossiter D (2007) About regression-kriging: from equations to case studies. Comp Geosci 33(10):1301–1315CrossRefGoogle Scholar
  23. Hiemstra P, Pebesma E, Twenhöfel C, Heuvelink G (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network. Comp Geosci 35:1711–1721CrossRefGoogle Scholar
  24. Hijmans RJ (2016) raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster, R package version 2.5-8
  25. Kaspar F, Zimmermann K, Polte-Rudolf C (2014) An overview of the phenological observation network and the phenological database of Germany’s national meteorological service (Deutscher Wetterdienst). Adv Sci Res 11:93–99CrossRefGoogle Scholar
  26. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26CrossRefGoogle Scholar
  27. Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, New York, Heidelberg, Dordrecht, LondonCrossRefGoogle Scholar
  28. Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z (2014) caret: Classification and Regression Training. http://CRAN.R-project.org/package=caret, R package version 6.0-24
  29. Leblois A, Quirion P (2013) Agricultural insurances based on meteorological indices: realizations, methods and research challenges. Meteorol Appl 20:1–9CrossRefGoogle Scholar
  30. Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2:165–168CrossRefGoogle Scholar
  31. Lokers R, Knapen R, Janssen S, van Randen Y, Jansen J (2016) Analysis of Big Data technologies for use in agro-environmental science. Environ Modell Software 84:494–504CrossRefGoogle Scholar
  32. Lüttger AB, Feike T (2018) Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany. Theor Appl Climatol 132:15–29CrossRefGoogle Scholar
  33. McMaster G, Wilhelm W (2003) Phenological responses of wheat and barley to water and temperature: improving simulation models. J Agric Sci 141:129–147CrossRefGoogle Scholar
  34. Möller M, Birger J, Gidudu A, Gläßer C (2013) A framework for the geometric accuracy assessment of classified objects. Int J Remote Sens 34:8685–8698CrossRefGoogle Scholar
  35. Möller M, Gerstmann H, Gao F, Dahms TC, Förster M (2017) Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk. CATENA 150:192–205CrossRefGoogle Scholar
  36. Mourtzinis S, Edreira J, Conley S, Grassini P (2017) From grid to field: Assessing quality of gridded weather data for agricultural applications. Eur J Agron 82:163–172CrossRefGoogle Scholar
  37. Nellis M, Price K, Rundquist D (2009) Remote sensing of cropland agriculture. In: Warner T, Nellis M, Foody G (eds) The SAGE Handbook of Remote Sensing, vol 1. SAGE Publications, London, UK, pp 368–380Google Scholar
  38. Okpara J, Afiesimama E, Anuforom A, Owino A, Ogunjobi K (2017) The applicability of standardized precipitation index: drought characterization for early warning system and weather index insurance in West Africa. Nat Hazards 89:555–583CrossRefGoogle Scholar
  39. Overpeck J, Meehl G, Bony S, Easterling D (2011) Climate data challenges in the 21st century. Sci 331(6018):700–702CrossRefGoogle Scholar
  40. Pelka N, Musshoff O (2013) Hedging effectiveness of weather derivatives in arable farming - is there a need for mixed indices? Agric Finance Rev 73:358–372CrossRefGoogle Scholar
  41. Pietola K, Myyrä S, Jauhiainen L, Peltonen-Sainio P (2011) Predicting the yield of spring wheat by weather indices in Finland: Implications for designing weather index insurances. Agric Food Sci 20:269–286CrossRefGoogle Scholar
  42. Poudel M, Chen S, Huang W (2016) Pricing of rainfall index insurance for rice and wheat in Nepal. J Agric Sci Technol 18:291–302Google Scholar
  43. R Core Team (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/
  44. Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission – A new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57:241–262CrossRefGoogle Scholar
  45. Rauthe M, Steiner H, Riediger U, Mazurkiewicz A, Gratzki A (2013) A central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorol Z 22(3):235–256CrossRefGoogle Scholar
  46. Rezaei E, Siebert S, Hüging H, Ewert F (2018) Climate change effect on wheat phenology depends on cultivar change. Sci Rep 8(4891)Google Scholar
  47. Schwartz M (ed.) (2006) Phenology: an integrative environmental science, Tasks for Vegetation Science, vol 39, Kluwer Academic Publishers, Dordrecht, The NetherlandsGoogle Scholar
  48. Skees J, Gober S, Varangis P, Lester R, Kalavakonda V (2001) Developing rainfall-based index insurance in Morocco. World Bank Policy Research Working Paper 2577, World Bank, Washington, D.C.Google Scholar
  49. Ssymank A (1994) Neue Anforderungen im europäischen Naturschutz: Das Schutzgebietssystem Natura 2000 und die FFH-Richtlinie der EU. Natur Land 69:395–406Google Scholar
  50. Stoppa A, Hess U (2003) Design and use of weather derivatives in agricultural policies: the case of rainfall index insurance in Morocco. In: International Conference Agricultural Policy Reform and the WTO, Where are we heading, Capri (Italy)Google Scholar
  51. Szoecs E (2016) esmisc: Misc Functions. https://github.com/EDiLD/esmisc/blob/master/R/read_regnie.R, R package version 0.0.2
  52. Turvey CG (2001) Weather derivatives for specific event risks in agriculture. Rev Agric Econ 23:333–351CrossRefGoogle Scholar
  53. Vijaya Kumar P, Rao V, Bhavani O, Dubey A, Singh C, Venkateswarlu B (2016) Sensitive growth stages and temperature thresholds in wheat (Triticum aestivum L.) for index-based crop insurance in the Indo-Gangetic Plains of India. J Agric Sci 154:321–333CrossRefGoogle Scholar
  54. World Bank (2011) Weather index insurance for agriculture: guidance for development practitioners. No. 50 in Agriculture and Rural Development Discussion Paper World Bank, Washington, D.CCrossRefGoogle Scholar
  55. Zhang J, Zhang Z, Tao F (2017) Performance of temperature-related weather index for agricultural insurance of three main crops in China. Int J Disaster Risk Sci 8:78–90CrossRefGoogle Scholar
  56. Zhao M, Peng C, Xiang W, Deng X, Tian D, Zhou X, Yu G, He H, Zhao Z (2013) Plant phenological modeling and its application in global climate change research: overview and future challenges. Environ Rev 21:1–14CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated PlantsInstitute for Strategies and Technology AssessmentKleinmachnowGermany
  2. 2.Martin Luther University Halle-WittenbergInstitute of Agricultural and Nutritional Sciences, Agribusiness Management GroupHalle (Saale)Germany
  3. 3.Martin Luther University Halle-WittenbergInstitute of Geosciences and GeographyHalle (Saale)Germany

Personalised recommendations