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Cereal Research Communications

, Volume 37, Issue 4, pp 603–610 | Cite as

Irrigation timing in maize by using the crop water stress index (CWSI)

Agronomy

Abstract

The aim of the investigation was to determine if the CWSI could be used to determine when the irrigation water should be applied to maize under Hungarian changeable weather. Three water treatments were applied: “Ad libitum” watering in lysimeter’s growing chambers, irrigated treatment (use of CWSI), and rainfed control. The theoretical approximation of crop water stress index was applied in our long-term investigation. Among the 17 consecutive seasons between 1989 and 2005 included in the study there were seven arid and four humid seasons. The remaining summers had semi-humid character. To represent the influence of drought on water stress index of maize we chose the year of 2000, the driest and warmest season from the beginning of weather observations of Keszthely. As a counterpoint stress index results of 1997 served as an example of humid seasons. The degree of the deviation in CWSI as a result of different weather may vary, but the tendency in arid or humid seasons remained the same. The basic parameter of CWSI, the canopy temperatures were measured using a hand-held infrared thermometer. Relevant meteorological and plant data were collected locally. Our final conclusion was that there is no problem in use of CWSI during arid summers, where radiation is undisturbed and the cloudiness does not disturb the canopy temperature sampling. In arid seasons increases in assimilatory surface size and yield of maize in irrigated treatments were between 20–35% and 10–30%, respectively. The extra production seems to be enough to cover the irrigation costs. There is also no problem in humid seasons, where the sky is often covered by clouds, but there is no need to irrigate maize in Hungary. In semi-humid weather not only the environmental circumstances may disturb the plant temperature measurements and calculation of CWSI (doubtful results caused by changeable radiation and wind), but the probable yield surplus — below 5% — is also negligible. The irrigation costs are higher than the production gain during semi-humid seasons. In Hungary about one third of the seasons have characteristics of an arid weather where sampling of canopy temperatures provides reliable basis to calculate CWSI and plan irrigation timing.

Keywords

maize irrigation crop water stress index (CWSI) arid and humid seasons 

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References

  1. Abraham, N., Hema, P.S., Saritha, E.K., Subramannian, S. 2000. Irrigation automation based on soil electrical conductivity and leaf temperature. Agric. Water Management 45:145–157.CrossRefGoogle Scholar
  2. Anda, A. 1993. Surface temperature as an important parameter of plant stand. Idõjárás 97:259–269. (In Hungarian)Google Scholar
  3. Anda, A. 2001a. Micro-meteorological observations and modification of canopy microclimate. DSc Thesis. Budapest, Hungary, 149 pp. (In Hungarian)Google Scholar
  4. Anda, A. 2001b. Influence of crop water stress index on the development of different maize hybrids. Georgikon for Agric. 12:40–54.Google Scholar
  5. Anda, A. 2002. Slices of plant-water relation in reflection to investigations carried out at Agrometeorological Research Station of Keszthely. Idõjárás 106:137–160. (In Hungarian)Google Scholar
  6. Anda, A. 2003. Remotely-sensed canopy temperatures used for irrigation timing linked to crop water stress index (CWSI). Supplement of International Conference on Water-saving Agriculture and Sustainable Use of Water and Land Resources (ICWSAWLR), Yangling, Shaanxi, P. R. China, 26–29 October 2003. p. 14. (Abstract in J. of Experimental Bot. Suppl. 1:54.)Google Scholar
  7. Anda, A., Ligetvári, F. 1991. Infrared Thermometry in Scheduling Irrigation. ICID Bulletin, Spec. Tech. Session, Beijing, China 1-B:210–220.Google Scholar
  8. Bajwa, S., Vories, E. 2007. Spatial analysis of cotton canopy responses to irrigation in a moderately humid area. Irrig. Sci. 25:429–441.CrossRefGoogle Scholar
  9. Clawson, K. L., Blad, B. L. 1982. Infrared thermometry for scheduling irrigation of corn. Agron. J. 74:311–316.CrossRefGoogle Scholar
  10. Gardner, B. R., Blad, B.L., Mauer, R.E., Watts, D.G. 1981. Relationship between crop temperature and physiological and phenological development of differentially irrigated corn. Agron. J. 73:743–747.CrossRefGoogle Scholar
  11. Idso, S. B., Jackson, R.D., Pinter, Jr., P.J., Reginato, R.J., Hatfield, J.L. 1981. Normalizing the stress degree parameter for environmental variability. Agric. Meteorol. 24:45–55.CrossRefGoogle Scholar
  12. Irmak, S., Dorota Z., Haman and Ruhi Bastug, 2000. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agron. J. 92:1221–1227.CrossRefGoogle Scholar
  13. Jackson, R.D. 1982. Canopy temperature and crop water stress. Advances in Irrigation 1:43–85.CrossRefGoogle Scholar
  14. Jensen, H.E., Svendsen, H., Jensen, S.E., Mogensen, V.O. 1990. Canopy-air temperature of crops grown under different irrigation regimes in a temperature humid climate. Irrig. Sci. 11:181–188.CrossRefGoogle Scholar
  15. Keener, M.E., Kirchner, P.L. 1983. The use of canopy temperature as an indicator of drought stress in humid regions. Agric. Meteorol. 28:339–349.CrossRefGoogle Scholar
  16. Kocsis, T., Anda, A. 2006. A csapadék alakulása a keszthelyi hosszú idõsoros meteorológiai megfigyelések alapján (Long-term investigations on precipitation at Keszthely). J. Central Eur. Agric. 7:699–708.Google Scholar
  17. Kumar, V.P., Ramakrishna Y.S., Bhaskara Rao, D.V., Sridhar G., Srinivasa Rao, G., Rao, G.G.S.N. 2005. Use of remote sensing for drought stress monitoring, yield prediction and varietal evaluation in castor beans (Ricinus communis L.). Int. J. Remote Sensing 26:5525–5534.CrossRefGoogle Scholar
  18. Kumar, V.P., Ramakrishna, Y.S., Ramana Rao, B.V., Khandgonda, I.R., Victor, U.S., Srivastava, N.N., Rao, G.G.S.N. 1999. Assessment of plant-extractable soil water in castor beans (Ricinus communis L.) using infrared thermometry. Agric. Water Management 39:69–83.CrossRefGoogle Scholar
  19. Payero, J.O., Merlin, S.R., Irmak, S., Tarkalson, D. 2006. Yield response of corn to deficit irrigation in a semi-arid climate. Agric. Water Management 84:101–112.CrossRefGoogle Scholar
  20. Penman, H. L. 1948. Natural evaporation from open water, bare soil and grass. Proc. of the Royal Soc. A. 193:120–145.CrossRefGoogle Scholar
  21. Pennington, D.A., Heatherly, L. 1989. Effects of changing solar radiation on canopy-air temperature of cotton and soybean. Agric. Forest Met. 46:1–14.CrossRefGoogle Scholar
  22. STATA 5.0 (1996) Stata Corporation LP Texas, USA, www.stata.com
  23. Svendsen, H., Jensen, H.E., Jensen, S.E., Mogensen, V.O. 1991. Crop canopy temperature and meteorological conditions. in: Thomson, A., Jensen, A., Jensen, H.E. (eds), Proc. from the Workshop on Remote Sensing. Sastrup Castle, Grena, Denmark.Google Scholar
  24. Wanjura, D.F., Upchurch, D.R. 1997. Accounting for humidity in canopy-temperature-controlled irrigation scheduling. Agric. Water Management 34:217–231.CrossRefGoogle Scholar
  25. Wanjura, D.F., Upchurch, D.R., Mahan, J.R. 2006. Behaviour of temperature-based water stress indicators in BIOTIC-controlled irrigation. Irrig. Sci. 24:223–232.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2009

Authors and Affiliations

  1. 1.Georgikon FacultyUniversity of PannoniaKeszthelyHungary

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