Environmental and Ecological Statistics

, Volume 22, Issue 4, pp 681–691 | Cite as

Adjusting SPI for crop specific agricultural drought



Standardized Precipitation Index (SPI) is one of the most used drought monitoring tools. It is easy to compute as it only needs cumulative precipitation amount for an input. However, it is more of a meteorological drought index rather than an agricultural one. In this study, we reconstruct SPI for monitoring a specific crop by calibrating the cut-off values that separates drought classes. For this purpose, two objectives to optimize are obtained: area under receiver operating characteristics (ROC) curve and misclassification rate of a multivariate decision model. By maximizing the area under ROC curve, we are able to calibrate thresholds for the realized states of the drought. By multivariate decision problems, crop and location specific information is used to regulate the size of the classes so that they can reveal agricultural wise meaningful information. Rain-fed wheat monitoring at Polatli station of Turkey is studied for an implementation.


Agricultural drought monitoring CART Receiver operating characteristics curve Standardized Precipitation Index 


  1. Alley WM (1984) The Palmer Drought Severity Index: limitations and assumptions. J Climate Appl Meteorol 23:1100–1109CrossRefGoogle Scholar
  2. Aslan S (2010) Comparison of missing value imputation methods for meteorological time series data, Msc Thesis, Middle East Technical University, Department of StatisticsGoogle Scholar
  3. Breiman Leo, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CAGoogle Scholar
  4. Gommes R (2007) Non-parametric crop yield forecasting. In: Proceedings of the 2nd International workshop on crop and rangeland monitoring in Eastern AfricaGoogle Scholar
  5. Green DM, Swets JA (1966) Signal detection theory and psychophysics. Wiley, New YorkGoogle Scholar
  6. 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 Climate 23:618–632CrossRefGoogle Scholar
  7. Kurum E, Yildirak K, Weber GW (2011) A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve. Cent Eur J Oper Res. doi: 10.1007/s10100-011-0224-5
  8. McKee TB, Edwards DC (1997) Characteristics of 20th century drought in the United States at Multiple Time Scales. Atmospheric Science Paper No. 634 Climatology Report No. 97-2, Department of Atmospheric Science Colorado State University Fort Collins, CO 80523–81371, May 1997Google Scholar
  9. Monteith JL (1965) Evaporation and Environment. In: Fogg GE (ed) Symposium of the society for experimental biology, the state and movement of water in living organisms, vol 19. Academic Press Inc, New York, pp 205–234Google Scholar
  10. Mukhala E, Hoefsloot P (2004) AgroMetShell manual. FAO, RomeGoogle Scholar
  11. Palmer WC (1965) Meteorological drought. Research Paper No. 45. US Weather Bureau, Washington, D.CGoogle Scholar
  12. Palmer WC (1968) Keeping track of crop moisture conditions, nationwide: the new Crop Moisture Index. Weatherwise 21:156–161CrossRefGoogle Scholar
  13. Penman HL (1948) Natural evaporation from open water, bare soil, and grass. Proc R Soc London A193:120–146CrossRefGoogle Scholar
  14. 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):3775–3793CrossRefGoogle Scholar
  15. 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):299–307Google Scholar
  16. 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. Simulation monographs. PUDOC, WageningenGoogle Scholar
  17. Wilhite DA, Hayes MJ, Knutson C, Smith KH (2000) Planning for drought: moving from crisis to risk management. J Am Water Resour Assoc 36:697–710CrossRefGoogle Scholar
  18. Yildirak K, Kalaylioglu Z, Mermer A (2011) Bayesian estimation of crop yield function: drought based wheat prediction model for TIGEM Farms, FAO Report MDG F 1680Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Actuarial SciencesHacettepe UniversityAnkaraTurkey
  2. 2.Institute of Applied MathematicsMiddle East Technical UniversityAnkaraTurkey

Personalised recommendations