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Photosynthesis, yield, and chemical composition of Tieguanyin tea plants (Camellia sinensis (L.) O. Kuntze) in response to irrigation treatments

Author

Listed:
  • Chen, X.H.
  • Zhuang, C.G.
  • He, Y.F.
  • Wang, L.
  • Han, G.Q.
  • Chen, C.
  • He, H.Q.

Abstract

Tieguanyin Oolong tea (Camellia sinensis (L.) O. Kuntze) is a name brand important commodity for Anxi county, Fujian province in China. Four-year-old tea plants at a tea plantation in Anxi were subjected to six different irrigation treatments (i.e. 5, 10, 15, 20, and 25d irrigation intervals for T1 to T5 with a rate of 3.5kg water per plant, plus a non-irrigated control). After 50d of irrigation treatments, leaf water potential was -1.70, -2.34, -2.48, -2.89, -3.55, and -4.92MPa for treatment T1, T2, T3, T4, T5, and control, respectively. Leaf biomass yield increased by 32.8%, 21.9%, and 21.3% for T1, T2, and T3, respectively, compared to control. The net photosynthesis (Pn), stomatal conductance (gs) and transpiration (E) decreased with irrigation interval increasing. Tea polyphenol (TP) and free amino acid (AA) decreased when the irrigation intervals were increased, but caffeine (CA) content apparently increased as the irrigation intervals were increased. To balance irrigation water demand and tea yield and quality, it is recommended that the irrigation interval should be set at 10d with a rate of 3.5kg water per plant for the optimal production in Anxi, Fujian province of China.

Suggested Citation

  • Chen, X.H. & Zhuang, C.G. & He, Y.F. & Wang, L. & Han, G.Q. & Chen, C. & He, H.Q., 2010. "Photosynthesis, yield, and chemical composition of Tieguanyin tea plants (Camellia sinensis (L.) O. Kuntze) in response to irrigation treatments," Agricultural Water Management, Elsevier, vol. 97(3), pages 419-425, March.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:3:p:419-425
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    Cited by:

    1. Ruiming, Fang & Shijie, Song, 2020. "Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network," Agricultural Water Management, Elsevier, vol. 236(C).

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