Welcome to Chinese Journal of Tropical Crops,

Chinese Journal of Tropical Crops ›› 2021, Vol. 42 ›› Issue (2): 393-404.DOI: 10.3969/j.issn.1000-2561.2021.02.014

• Plant Cultivation, Physiology & Biochemistry • Previous Articles     Next Articles

Establishment of Foliar Nutrient Diagnosis Norms for Longan in South China

ZHU Yongcong, WANG Wei, ZHOU Cangmin, BAI Cuihua, YAO Lixian*()   

  1. College of Natural Resources and Environment, South China Agricultural University, Guangzhou, Guangdong 510642, China
  • Received:2020-03-23 Revised:2020-05-01 Online:2021-02-25 Published:2021-02-25
  • Contact: YAO Lixian


Foliar nutrient diagnosis has been developed and utilized in crops for decades. Four methods including the critical value approach (CVA), sufficiency range approach (SRA), modified diagnosis and recommendation integrated system (M-DRIS), and compositional nutrient diagnosis (CND) are commonly adopted to evaluate the leaf nutrient status in crops. The availability and reliability of the four foliar nutrient diagnosis approaches were compared in longan in southern China, with the aim to (1) select the suitable diagnosis method for longan; (2) establish the foliar diagnosis norm; and (3) provide scientific base for nutrient management in longan in southern China. Eight typical longan orchards, located in the main production regions of South China, were chosen in this study during 2017 to 2019. 82 leaf samples at both terminal shoot maturing stage (TSMS) of 2017 and fruit swelling stage (FSS) of 2018, and 49 leaf samples at FSS of 2018 and at TSMS of 2019 were collected, respectively. Foliar nutrient (N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn and B) concentration in all samples was detected. The fruit yield was recorded for each sampled tree at harvest in 2018 and 2019. The fruit yield in each orchard was calculated by multiplying the yield per tree and the plantation density in the orchard. The high yield population for nutrient diagnosis in both years was determined by cluster analysis in fruit yield. Then, the relationship between fruit yield and leaf nutrient concentration was calculated. Further, the above four approaches were used to assess the foliar nutrient status of longan at four stages within two years, respectively. Irregular fruit bearing was commonly observed in most sampled orchards. The maximum concentrations of foliar nutrients were 2-fold to more than 10-fold higher than the minimum levels in the sampled trees at the same growth stage. Significant difference was observed in foliar nutrient contents at FSS and TSMS between 2018 and 2019, respectively. However, the high yield population in both years was characterized by relatively constant foliar nutrient concentrations. Based on the relationship between fruit yield and foliar nutrient concentration, diagnosis indices of a few foliar nutrients were obtained by CVA. The necessity of foliar nutrients was qualitatively evaluated by M-DRIS and CND. Moreover, the diagnosis accuracy for nutrients was related to the diagnosis stage and the severity of nutrient deficiency or abundance, with significant yearly variation. In contrast, diagnosis indices for all nutrients could be calculated by SRA, and maintained relatively stable at FSS or TSMS of both years, regardless of the annual variation of fruit yields. SRA is accepted to compute the foliar nutrient diagnosis norms for longan due to its universal diagnosis indexes with small annual variation and practicability in practice. According to SRA, the optimum foliar nutrient contents for longan in southern China are recommended as N 22.6-24.3 g/kg, P 1.56-1.86 g/kg, K 10.2-11.3 g/kg, Ca 5.7-7.1 g/kg, Mg 1.07-1.25 g/kg, S 1.39-1.52 g/kg, Fe 34.2-41.3 mg/kg, Mn 30.8-51.3 mg/kg, Cu 6.3-7.6 mg/kg, Zn 18.2-21.9 mg/kg, B 17.1-24.0 mg/kg at TSMS, and N 20.8-21.7 g/kg, P 1.29-1.44 g/kg, K 8.0-9.5 g/kg, Ca 15.6-19.1 g/kg, Mg 1.29-17.20 g/kg, S 1.56-1.73 g/kg, Fe 52.8-67.5 mg/kg, Mn 43.9-73.9 mg/kg, Cu 5.1-5.9 mg/kg, Zn 32.2-38.3 mg/kg, B 24.3-28.1 mg/kg at FSS.

Key words: longan, foliar nutrient diagnosis, diagnosis index, sufficiency range approach

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