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Precision Agriculture

, Volume 12, Issue 5, pp 682–698 | Cite as

Spectral characterization and prediction of nutrient content in winter leaves of litchi during flower bud differentiation in southern China

  • Shuisen Chen
  • Dan Li
  • Yingfang Wang
  • Zhiping Peng
  • Weiqi Chen
Article

Abstract

Nutrient availability can affect the cracking rate of the litchi fruit tree, and the nutrient content of the canopy can be used to monitor availability and fertilizer needs of the litchi orchard. In this study, we analyzed the correlation between calcium (Ca), magnesium (Mg) and potassium (K) in canopy leaves and four indices of in situ spectral data of litchi canopy. These were reflectance (R), reciprocal-logarithm-transformed reflectance (log(1/R)), the first-order derivative of reflectance (dR) and the first-order derivative of reciprocal-logarithm-transformed reflectance (dlog(1/R)). The results showed that the spectra of the litchi canopy have common spectral characteristics. The correlation between the selected nutrients of the litchi canopy and R or log(1/R) was weak, that between the nutrients and dR was significant and with dlog(1/R) it was the most significant. The 1262 nm wavelength for dR and 1018 nm one for dlog(1/R) had the most significant correlation with Ca. The 1293 nm wavelength for dR and 1601 nm one for dlog(1/R) had the most significant correlation with Mg. The 1686 nm wavelength for dR and 1337 nm one for dlog(1/R) had the most significant correlation with K. Therefore, those wavelengths were chosen to create the regression equations for prediction. The linear regression equations performed the best when predicting canopy nutrient content of litchi for Ca at 1018 nm, Mg at 1601 nm and K at 1337 nm. Further, the formulated application of K2O played an important role in reducing the amount of K2O applied (50.6 g/plant on average) and in increasing the yield of the litchi tree by 4.4 kg/plant on average. These results are relevant for implementing a precision agriculture approach for litchi production and for environmental protection in South China or in other litchi production areas of the world. Other nutrients that affect litchi growth need to be studied in the future.

Keywords

Litchi (Litchi chinensis Soon.Winter canopy Nutrient content Calcium (Ca) Magnesium (Mg) Potassium (K) Spectral model Fertilizer application 

Notes

Acknowledgments

This study was partly supported by two Science & Technology Plan Funds of Guangdong Province of China grants (2009B020305003, 2010B020315029 and 2006B1001014) and a National Science Foundation of China grant (40771160) of China. The authors wish to thank Xiaojun Yang, Qinhuo Liu, Jian-guang Wen, and Min Chen for their help during field experiments, sampling and manuscript improvement. We also acknowledge the helpful comments from two anonymous reviewers.

References

  1. Ayala-Silva, T., & Beyl, C. A. (2005). Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Advances in Space Research, 35, 305–317.PubMedCrossRefGoogle Scholar
  2. Bogrekci, I., & Lee, W. S. (2005). Spectral phosphorus mapping using diffuse reflectance of soils and grass. Biosystems Engineering, 91, 305–312.CrossRefGoogle Scholar
  3. Chen, X. D., Chen, J. S., Zhang, F. B., & Yang, S. H. (1998). The effect of different nitrogen and potassium fertilizing application on litchi production. Guangdong Agricultural Science, 2, 27–30 (in Chinese).Google Scholar
  4. Chen, J. Y., Tian, Q. J., & Shi, R. H. (2005). Study on simulation of rice leaf’s chlorophyll concentration via the spectrum. Remote Sensing Information, 5, 12–16 (in Chinese).Google Scholar
  5. Cheng, Y. S., Hu, C. S., Hao, E. B., & Yu, G. R. (2003). Analysis and extraction of hyperspectral information feature of winter wheat under N-stress condition. Resources Science, 25, 86–93 (in Chinese).Google Scholar
  6. Dai, L. Z., Guo, Y. C., & Xie, X. Y. (1995). Status of mineral nutrition of Litchi chinensis and effect of chemicals on controlling its nutrition condition. Journal of Fujian Academy of Agricultural Science, 10, 48–53 (in Chinese with English abstract).Google Scholar
  7. Ferwerda, J. G., & Skidmore, A. K. (2007). Can nutrient status of four woody plant species be predicted using field spectrometry? ISPRS Journal of Photogrammetry and Remote Sensing, 62, 406–414.CrossRefGoogle Scholar
  8. Fu, G. N., Zhang, X. M., Zeng, Y. N., Xie, Y. H., Xie, S. G., & Li, C. B. (2007). Determination on the sufficient ranges of mineral nutrient contents in the leaves of Nuomici litchi (Litchi chinensis Sonn). Chinese Journal of Soil Science, 38(2), 291–295 (in Chinese).Google Scholar
  9. Guo, Y. C., Dai, L. Z., Tong, W. S., & Li, Y. L. (1992). Litchi tree nutrient, flower condition research and adjusting and controlling technology. Fujian Fruits, 3, 1–5.Google Scholar
  10. Kong, W. S., Wang, X. Z., & Tang, Y. L. (2004). An approach to the action of derivative spectra for agronomic parameter determination in cotton. Journal of Southwest Agricultural University, 26, 5–9.Google Scholar
  11. Li, J. G., Gao, F. F., Huang, H. B., Tan, Y. W., & Luo, J. T. (1999). Preliminary studies on the relationship between calcium and fruit-cracking in litchi fruit. Journal of South China Agricultural University, 20, 45–49.Google Scholar
  12. Li, W., Zhang, S. H., Zhang, Q., Dong, C. W., & Zhang, S. Q. (2007). Rapid prediction of available N, P and K content in soil using near—infrared reflectance spectroscopy. Transactions of the Chinese Society of Agricultural Engineering, 23, 55–59.Google Scholar
  13. Li, Y. X., Zhu, Y., & Cao, W. X. (2006). Characterizing canopy hyperspectral and multispectral reflectance under different N-application conditions in wheat. Journal of Triticae Crops, 26, 103–108.Google Scholar
  14. Lin, X., Zhang, X. Q., Li, C. Y., & Shen, X.-H. (2001). Study on the physiology of nutrition in autumn shoots of litchi. Journal of South China Normal University, 4, 98–100.Google Scholar
  15. Lu, S. M., Chen, X. D., & Yang, S. H. (1993). Research on characteristics of litchi’s nitrogen, phosphorus and potassium nutrient absorption. Guangdong Agricultural Science, 1, 23–26.Google Scholar
  16. Ludwig, B., Khanna, P. K., Bauhus, J., & Hopmans, P. (2002). Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability. Forest Ecology and Management, 171, 121–132.CrossRefGoogle Scholar
  17. Menzel, C. M., Carseldine, M. L., & Simpson, D. R. (1988). The effect of fruiting status on nutrient composition of litchi during the flowering and fruiting season. Journal of Horticultural Science, 63, 547–556.Google Scholar
  18. Niu, Z., Chen, Y.-H., Sui, H.-Z., Zhang, Q. Y., & Zhao, C. J. (2000). Mechanism analysis of leaf biochemical concentration by high spectral remote sensing. Journal of Remote Sensing, 4, 125–129 (in Chinese).Google Scholar
  19. Rossel, R. A. V., Walvoort, D. J. J., McBratney, A. B., Janikc, L. J., & Skjemstad, J. O. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131, 59–75.CrossRefGoogle Scholar
  20. Soil Science Society of China. (1999). Soil agricultural chemistry analytical methods. Beijing, China: Agricultural Science Technology Press.Google Scholar
  21. Sun, J. Y., Li, M. Z., Tang, N., & Zheng, L. H. (2007). Spectral characteristics and their correlation with soil parameters of black soil in northeast China. Spectroscopy and Spectral Analysis, 27, 1502–1505 (in Chinese).Google Scholar
  22. Wang, Y. F., Chen, S. S., Li, D., Zhou, X., & Wang, J. F. (2010). Spectral characterization and nutrition prediction of litchi canopy leaves during flower bud differentiation. Transactions of the Chinese Society of Agricultural Engineering, 26, 216–225.Google Scholar
  23. Xu, Y. M., & Lin, Q. Z. (2005). Experimental study on total nitrogen concentration in soil by VNIR reflectance spectrum. Geography and Geo-Information Science, 21, 19–22 (in Chinese).Google Scholar
  24. Xue, L. H., Cao, W. X., Luo, W. H., Jiang, D., Meng, L. Y., & Zhu, Y. (2003). Diagnosis of nitrogen status in rice leaves with the canopy spectral reflectance. Scientia Agricultura Sinica, 36, 807–812.Google Scholar
  25. Yao, L. X., Xu, C. X., Mo, Q. A., He, G. M., Zhou, X. C., & Chen, W. Z. (2004). Study on combined application of potassium and magnesium of litchi. South China Fruits, 33, 32–33.Google Scholar
  26. Yi, Q. X., Huang, J. F., & Wang, X. Z. (2007). Hyperspectral estimation models for crude fibre concentration of corn. Journal of Infrared and Millimeter Waves, 26, 393–400.Google Scholar
  27. Yu, F.-J., Min, S.-G., Ju, X.-T., & Zhang, F.-S. (2002). Determination the content of nitrogen and organic substance in dry soil by using near infrared diffusion reflectance spectroscopy. Chinese Journal of Analysis Laboratory, 21, 49–51.Google Scholar
  28. Zhao, D. L., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, Sailaja. (2007). Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26, 335–344.CrossRefGoogle Scholar
  29. Zheng, G. D., & Zhang, X. M. (2008). Nutritional characteristics of litchi nutrition research diagnosis at home and abroad. Journal of Anhui Agricultural Sciences, 36, 489–538.Google Scholar
  30. Zhou, X. C., Sam, P., Xie, F., Mo, W. X., & Yao, L. X. (2001). Nutritive peculiarity of famous-brand litchi products and effect of potassium, sulfur and magnesium fertilizer application. Guangdong Agricultural Science, 5, 31–33.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Shuisen Chen
    • 1
    • 2
  • Dan Li
    • 1
  • Yingfang Wang
    • 1
    • 2
  • Zhiping Peng
    • 3
  • Weiqi Chen
    • 4
  1. 1.Guangzhou Institute of GeographyGuangzhouChina
  2. 2.College of InformaticsSouth China Agriculture UniversityGuangzhouChina
  3. 3.Soil & Fertilizer InstituteGuangdong Academy of Agricultural SciencesGuangzhouChina
  4. 4.Department of GeographyFlorida State UniversityTallahasseeUSA

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