Advertisement

Temporal Analysis of Adverse Weather Conditions Affecting Wheat Production in Finland

  • Vladimir KuzmanovskiEmail author
  • Mika Sulkava
  • Taru Palosuo
  • Jaakko Hollmén
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

Growing conditions of agricultural crops are increasingly affected by global climate change. Not only the overall agro-climatic conditions are changing, but also climatic variability and the occurrence of extreme weather events are becoming more frequent. This will affect crop yields and impact food supply both locally and globally. Located in the north, with short growing seasons and long days, Finland is not an exception. Drought- and temperature-related adverse events have been identified as most harmful abiotic factors on the production. Farmers try to mitigate with a range of management options. However, they need to adapt them over time as the climate is changing.

This study aims to identify the most adverse weather events that affect the spring wheat production in Finland and to ascertain if there have been changes on the most harmful abiotic weather-related factors during the last decades. Adverse weather conditions studied include frequency and length of periods with exceptional snow, drought, intensive rainfall and extreme heat. This was studied by modeling the wheat production using the adverse weather events as predictors with different lengths of training period (consecutive number of years) using LASSO regression.

The results reveal clear shift from early season drought and periodical intensive rainfall to the adverse effects of frequent and long periods of extremely high temperatures during later development stages.

Keywords

Wheat production Adverse weather event Data analysis Time series 

Notes

Acknowledgment

Authors acknowledge the Slovenian Ministry of Education, Science and Sport for funding the work through funding agreement C3330-17-529020.

References

  1. 1.
    Allen, M., et al.: Technical summary: Global warming of 1.5 \(^{\circ }\)C (2019). http://pure.iiasa.ac.at/id/eprint/15716/
  2. 2.
    FMI, F.M.I.: Daily weather data (2019). https://en.ilmatieteenlaitos.fi/download-observations
  3. 3.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)CrossRefGoogle Scholar
  4. 4.
    Mäkinen, H., et al.: Sensitivity of European wheat to extreme weather. Field Crops Res. 222, 209–217 (2018)CrossRefGoogle Scholar
  5. 5.
    Natural Resource Institute Finland - LUKE: Agricultural statistics (2019). http://statdb.luke.fi/PXWeb/pxweb/en/LUKE
  6. 6.
    Olesen, J., et al.: Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34(2), 96–112 (2011)CrossRefGoogle Scholar
  7. 7.
    Peltonen-Sainio, P., Rajala, A., Seppälä, R.: Viljojen kehityksen ja kasvun ABC. Maa- ja elintarviketalous, Maa- ja elintarviketalouden tutkimuskeskus (2005). (in Finnish)Google Scholar
  8. 8.
    Peltonen-Sainio, P., Jauhiainen, L., Niemi, J., Hakala, K., Sipiläinen, T.: Do farmers rapidly adapt to past growing conditions by sowing different proportions of early and late maturing cereals and cultivars? Agric. Food Sci. 22, 331–341 (2013)CrossRefGoogle Scholar
  9. 9.
    Peltonen-Sainio, P., et al.: Harmfulness of weather events and the adaptive capacity of farmers at high latitudes of Europe. Clim. Res. 67, 221–240 (2016)CrossRefGoogle Scholar
  10. 10.
    Pietola, K., Myyrä, S., Jauhaianen, L.: Predicting the yield of spring wheat by weather indices in Finland: implications for designing weather index insurances. Agric. Food Sci. 20(4), 269–286 (2011)CrossRefGoogle Scholar
  11. 11.
    Porter, J.R., Gawith, M.: Temperatures and the growth and development of wheat: a review. Eur. J. Agron. 10(1), 23–36 (1999)CrossRefGoogle Scholar
  12. 12.
    Rötter, R.P., Höhn, J., Trnka, M., Fronzek, S., Carter, T.R., Kahiluoto, H.: Modelling shifts in agroclimate and crop cultivar response under climate change. Ecol. Evol. 3(12), 4197–4214 (2013)CrossRefGoogle Scholar
  13. 13.
    Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Tibshirani, R., Wainwright, M., Hastie, T.: Statistical Learning with Sparsity: The LASSO and Generalizations. Chapman and Hall/CRC, Boca Raton (2015)zbMATHGoogle Scholar
  15. 15.
    Trnka, M., et al.: Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change. 4, 637 (2014)CrossRefGoogle Scholar
  16. 16.
    Zadoks, J.C., Chang, T.T., Konzak, C.F.: A decimal code for the growth stages of cereals. Weed Res. 14(6), 415–421 (1974)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vladimir Kuzmanovski
    • 1
    • 3
    Email author
  • Mika Sulkava
    • 2
  • Taru Palosuo
    • 2
  • Jaakko Hollmén
    • 3
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Natural Resources Institute Finland (Luke)HelsinkiFinland
  3. 3.Department of Computer ScienceAalto UniversityAaltoFinland

Personalised recommendations