Skip to main content

Advertisement

SpringerLink
Log in

Bayesian estimation of crop yield function: drought based wheat prediction model for tigem farms

  • Published:
Environmental and Ecological Statistics Aims and scope Submit manuscript

Abstract

Drought is one of the biggest threats that affects agriculture. Based on recent climatic observations and future projections, drought continues to increase its harmful impact on agricultural productivity especially in the arid areas of Turkey. Wheat farming in these arid and semi-arid areas such as Central Anatolia depends heavily on precipitation, thus monitoring for drought is needed. The timing of precipitation is also as important as its quantity. This study makes use of a crop and location specific model developed by FAO to simulate water related variables such as evapotranspiration, water deficiency and water satisfaction index to estimate the crop yield function for rain-fed wheat production in the arid regions of Turkey. A spatio-temporal yield model is estimated by Bayesian method known as Markov Chain Monte Carlo. By standardizing the simulated variables over normalized difference vegetation index (NDVI), impact of drought related variables on wheat yield is studied and two variables are found. Use of NDVI, as a numeraire, comes in handy in many ways. For actual evapotranspiration estimate, it strengthens separation between evaporation and transpiration and, for water deficiency, it better represents the drought properties of farms for the terrain chosen.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Diggle PJ, Heagerty PJ, Liang KY, Zeger SL (2002) Analysis of longitudinal data. Oxford University Press, Oxford

    Google Scholar 

  • Gelfand AE (1995) Model determination using sampling-based methods. In: Gilks WR, Richardson S, Spiegelhalter DJ (eds) Markov chain monte carlo in practice. Chapman Hall, London, pp 145–161

  • Gilks WR, Richardson S, Spiegelhalter DJ (2000) Markov chain monte carlo in practice. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  • Gommes R (2007) Non-Parametric crop yield forecasting. In: Proceedings of the 2nd international workshop on crop and rangeland monitoring in Eastern Africa

  • Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitation. J Clim 23:618–632

    Article  Google Scholar 

  • Kucharik CJ, Serbin SP (2008) Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environ Res Lett 3:1–10

    Article  Google Scholar 

  • Mukhala E, Hoefsloot P (2004) AgroMetShell manual. FAO, Rome

    Google Scholar 

  • Ntzoufras I (2009) Bayesian modeling using WinBUGS. Wiley series in computational statisitcs. Wiley, New Jersey

  • Rojas O (2007) Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya. Int J Remote Sens 28(17):3775–3793

    Article  Google Scholar 

  • Simsek O, Mermer A, Yildiz H, Ozaydin KA, Cakmak B (2007) Agrometshell modeli kullanilarak Turkiye’de bugdayin verim tahmini. Tarim Bilimleri Dergisi Ankara Universitesi Ziraat Fakultesi 13(3):299–307

    Google Scholar 

  • Spiegelhalter D, Best N, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B Met 64(4):583–616

    Article  Google Scholar 

  • Van Keulen H, Van Laar HH (1986) The relation between water use and crop production. In: van Keulen H, Wolf J (eds) Modelling of agricultural production: weather, soils and crops. Pudoc, Wageningen, pp 117–129

  • Yerdelen A, Mermer A, Dedeoglu F, Yildiz H, Kaya Y, Suzer S, Ocal MB (2008) Edirne ilinde urun deseninin cografi bilgi sistemleri ve uzaktan algilama yontemleri kullanilarak belirlenmesi ve aycicegi verim tahmini. Tarla Bitkileri Merkez Arastirma Enstitusu Dergisi 17:1–2

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Actuarial Sciences, Hacettepe University, 06800, Ankara, Turkey

    Kasirga Yildirak

  2. Department of Statistics, Middle East Technical University, 06530, Ankara, Turkey

    Zeynep Kalaylıoglu

  3. GIS Division, General Directorate of Agricultural Research, Turkish Ministry of Agriculture and Rural Affairs, Ankara, Turkey

    Ali Mermer

Authors
  1. Kasirga Yildirak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeynep Kalaylıoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Mermer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kasirga Yildirak.

Additional information

This study was supported by Food and Agriculture Organization of the United Nations under the joint program of MDGF-1680.

Handling Editor: Ashis SenGupta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yildirak, K., Kalaylıoglu, Z. & Mermer, A. Bayesian estimation of crop yield function: drought based wheat prediction model for tigem farms. Environ Ecol Stat 22, 693–704 (2015). https://doi.org/10.1007/s10651-015-0327-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10651-015-0327-6

Keywords

Search

Navigation