Theoretical and Applied Climatology

, Volume 134, Issue 3–4, pp 1005–1014 | Cite as

Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment

  • Nasrin Salehnia
  • Hossein ZareEmail author
  • Sohrab Kolsoumi
  • Mohammad Bannayan
Original Paper


Meteorological drought indices associated with soil moisture status have potential for varying applications including predictive power for crop yields estimation. The Keetch-Byram Drought Index (KBDI) was initially developed to estimate forest flammability, based on quantification of the moisture deficiency in upper soil layer as a function of daily precipitation and maximum air temperature. In this study, we characterized the utility of KBDI to accurately trace and monitor vegetation change and crop yield fluctuation in a semi-arid environment. It is tried to find any temporal association for both the 16-day MODIS-derived NDVI and KBDI from 2002 to 2012 and the correlation between KBDI and wheat and barley yield from 1984 to 2010. Correlation between KBDI and NDVI showed a general seasonal pattern with strongest correlation in mid-growing season, but this varied across study locations. Warmer locations with very sparse vegetation showed weaker association between KBDI and NDVI. Although a robust correlation between KBDI and winter cereal crop yield was not achieved based on winter (wet and cold season) data, spring cereal crop yield was correlated with KBDI.



We would like to thank K. Grace Crummer (Institute for Sustainable Food Systems, University of Florida) for editing the manuscript to improve the language.


  1. Arpaci A, Eastaugh CS, Vacik H (2013) Selecting the best performing fire weather indices for Austrian ecoregions. Theor Appl Climatol 114(3-4):393–406. CrossRefGoogle Scholar
  2. Bannayan M, Sanjani S (2011) Weather conditions associated with irrigated crops in an arid and semiarid environment. Agric For Meteorol 151(12):1589–1598. CrossRefGoogle Scholar
  3. Bannayan M, Sanjani S, Alizadeh A, Sadeghi Lotfabadi S, Mohamadian A (2010) Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crop Res 118:105–114. CrossRefGoogle Scholar
  4. Bannayan M, Lakzian A, Gorbanzadeh N, Roshani A (2011) Variability of growing season indices in northeast of Iran. Theor Appl Climatol 105(3-4):485–494. CrossRefGoogle Scholar
  5. Bannayan M, Mansoori H, Eyshi Rezaei E (2014) Estimating climate change, CO2 and technology development effects on wheat yield in northeast Iran. Int J Biometeorol 58:395. CrossRefGoogle Scholar
  6. Beranová R, Kyselý J (2015) Links between circulation indices and precipitation in the Mediterranean in an ensemble of regional climate models. Theor Appl Climatol 123(3-4):693–701. CrossRefGoogle Scholar
  7. Brolley JM, O’Brien JJ, Schoof J, Zierden D (2007) Experimental drought threat forecast for Florida. Agric For Meteorol 145(1-2):84–96. CrossRefGoogle Scholar
  8. Brown JF, Wardlow BD, Tsegaye T, Hayes MJ, Reed BC (2008) The vegetation drought response index: a new integrated approach for monitoring drought stress in vegetation. GISci Remote Sens 45(1):1548–1603. CrossRefGoogle Scholar
  9. Chen CF, Nguyen TS, Li YC (2011) Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Appl Geogr 31(2):463–475. CrossRefGoogle Scholar
  10. Ciais P, Reichstein M, Viovy N, Granier A, Ogee J, Allard V, Aubinet M, Buchmann N, Bernhofer C, Carrara A, Chevallier F, De Noblet N, Friend AD, Friedlingstein P, Grunwald T, Heinesch B, Keronen P, Knoh A, Krinner G, Loustau D, Manca G, Matteucci G, Miglietta F, Ourciva JM, Papale D, Pilegaard K, Rambal S, Seufert G, Soussana JF, Sanz MJ, Schulze ED, Vesala T, Valentini R (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437(7058):529–533. CrossRefGoogle Scholar
  11. Darmawan S, Takeuchi W, Shofiyati R, Sari D K, Wikantika K (2014) Seasonal analysis of precipitation, drought and vegetation index in Indonesian paddy field based on remote sensing data. IOP Conf Ser Environ Earth Sci 20. CrossRefGoogle Scholar
  12. Dimitrakopoulos AP, Bemmerzouk AM (2003) Predicting live herbaceous moisture content from a seasonal drought index. Int J Biometeorol 47:73–79. CrossRefGoogle Scholar
  13. Dolling K, Chu P-S, Fujioka F (2005) A climatological study of the Keetch/Byram drought index and fire activity in the Hawaiian Islands. Agric For Meteorol 133(1-4):17–27. CrossRefGoogle Scholar
  14. Eyshi Rezaei E, Mohammadian A, Koohi M, Bannayan M (2013) Comparative analysis of drought indices for drought zone scheme of northern Khorasan Province of Iran. Not Sci Biol 3(3):62–66CrossRefGoogle Scholar
  15. Farhangfar S, Bannayan M, Khazaei HM, Mousavi Baygi M (2015) Vulnerability assessment of wheat and maize production affected by drought and climate change. Int J Disaster Risk Sci 13:37–51. CrossRefGoogle Scholar
  16. Garcia-Prats A, Del Campo A, Fernandes TJG, Molina AJ (2015) Development of a Keetch and Byram—based drought index sensitive to forest management in Mediterranean conditions. Agric For Meteorol 205:40–50. CrossRefGoogle Scholar
  17. Gausman HW (1974) Leaf reflectance of near-infrared. Photogramm Eng 10:183–191Google Scholar
  18. Houghton J T, Ding Y, Giggs D, van Noguet M, del Linden P, Dai X, Maskell A, Johnson C A (2001) Climate change: the scientific basis. Cambridge University Press, Cambridge doi:, 128, 581, 1038, 1039CrossRefGoogle Scholar
  19. Hu MQ, Mao F, Sun H, Hou YY (2011) Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region. Int J Appl Earth Obs 13(11):24–33. CrossRefGoogle Scholar
  20. Janis M, Johnson MB, Forthun G (2002) Near-real time mapping of Keetch-Byram drought index in the south-eastern United States. Int J Wildland Fire 11(4):281–289. CrossRefGoogle Scholar
  21. Katiraie-Boroujerdy P, Nasrollahi N, Hsu K, Sorooshian S (2016) Quantifying the reliability of four global datasets for drought monitoring over a semiarid region. Theor Appl Climatol 123(1-2):387–398. CrossRefGoogle Scholar
  22. Keetch J J, Byram O M (1968) A drought index for forest fire control. USDA For Serv Southeastern For and Range Exp Stn Res Pap SE-38Google Scholar
  23. Klink K, Wiersma JJ, Crawford CJ, Stuthman DD (2014) Impacts of temperature and precipitation variability in the Northern Plains of the United States and Canada on the productivity of spring barley and oat. Int J Climatol 34(8):2805–2818. CrossRefGoogle Scholar
  24. Kogan FN (1990) Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int J Remote Sens 11(8):1405–1419. CrossRefGoogle Scholar
  25. Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. Bull Am Meteorol Soc 76(5):655–668.<0655:DOTLIT>2.0.CO;2 CrossRefGoogle Scholar
  26. Liu Y, Stanturf J, Goodrick S (2010) Wildfire potential evaluation during a drought event with a regional climate model and NDVI. Eco Inform 5(5):418–428. CrossRefGoogle Scholar
  27. Miller TD (1999) Growth stages of wheat: identification and understanding improve crop management. SCS-1999-16. Texas Agricultural Extension Service, the Texas A&M University System, College StationGoogle Scholar
  28. Narasimhan B, Srinivasan R (2005) Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric For Meteorol 133(1-4):69–88. CrossRefGoogle Scholar
  29. Nazemosadat MJ (2000) Winter drought in Iran: association with ENSO. Drought Netw News 13:1Google Scholar
  30. Peltonen-Sainio P, Jauhiainen L, Hakala K (2011) Crop responses to temperature and precipitation according to long-term multilocation trials at high-latitude conditions. J Agric Sci 148(01):49–62. CrossRefGoogle Scholar
  31. Petros G, Antonis M, Marianthi T (2011) Development of an adapted empirical drought index to the Mediterranean conditions for use in forestry. Agric For Meteorol 151(2):241–250. CrossRefGoogle Scholar
  32. Piao S, Mohammat A, Fang J, Cai Q, Feng J (2006) NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Glob Environ Chang 16(4):340–348. CrossRefGoogle Scholar
  33. Quiring SM, Ganesh S (2010) Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150(3):330–339. CrossRefGoogle Scholar
  34. Rouse JW, Hass RH, Schell JA (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: The third earth resources technology Satellite-1 symposium, Greenbelt, MD, 309–317Google Scholar
  35. Sellers PJ, Berry J, Collatz GJ, Field CB, Hall FG (1992) Canopy reflectance, photosynthesis and transpiration. III A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens Environ 42(3):187–216. CrossRefGoogle Scholar
  36. Taufik M, Setiawan BI, Van Lanen HAJ (2015) Modification of a fire drought index for tropical wetland ecosystems by including water table depth. Agric For Meteorol 203:1–10. CrossRefGoogle Scholar
  37. Trnka M, Dubrovsky M, Zalud Z (2004) Climate change impacts and adaptation strategies in spring barley production in the Czech Republic. Clim Chang 64(1/2):227–255. CrossRefGoogle Scholar
  38. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150. CrossRefGoogle Scholar
  39. Tucker CJ, Sellers PJ (1986) Satellite remote sensing of primary production. Int J Remote Sens 7(11):1395–1416. CrossRefGoogle Scholar
  40. Wang J, Rich PM, Price KP (2003) Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int J Remote Sens 24(11):2345–2364. CrossRefGoogle Scholar
  41. Xanthopoulos G, Maheras G, Gouma V, Gouvas M (2006) Is the Keetch–Byram drought index (KBDI) directly related to plant water stress? Forest Ecol Manag 234(supplement 1):S27. CrossRefGoogle Scholar
  42. Zhang F, Zhang L, Wang X, Hung J (2013) Detecting agro-droughts in southwest of China using MODIS satellite data. J Integr Agric 12(1):159–168. CrossRefGoogle Scholar

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© Springer-Verlag GmbH Austria, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Faculty of AgricultureFerdowsi University of MashhadMashhadIran

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