Chin J Plan Ecolo ›› 2010, Vol. 34 ›› Issue (6): 704-712.DOI: 10.3773/j.issn.1005-264x.2010.06.010

• Research Articles • Previous Articles     Next Articles

Estimating leaf nitrogen content with near infrared reflectance spectroscopy in rice

ZHANG Yu-Sen; YAO Xia; TIAN Yong-Chao; CAO Wei-Xing; and ZHU Yan*   

  1. Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2009-07-07 Online:2010-06-01 Published:2010-06-01
  • Contact: ZHU Yan

Abstract: Aim Our primary objective was to establish an effective method of near infrared reflectance spectroscopy (NIRS) for estimating leaf nitrogen content in rice, which would help with nitrogen diagnosis and dressing fertilization in rice production.
Methods Using the techniques of partial least square (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR), we established four NIRS-based models for estimating nitrogen content (NC) in fresh leaf and leaf powder of rice cultivars under varied nitrogen application rates.
Important findings The coefficient of determination (RC2) and root mean square error for calibration (RMSEC) of NC models with fresh leaf were 0.940 and 0.226, respectively, whereas the RC2 and RMSEC of NC models with leaf powder were 0.977 and 0.136, respectively. We tested the accuracy of models with independent experiment datasets by the determination coefficient (RCV2) and root mean square error of cross-validation (RMSECV), and the determination coefficient (RV2) and root mean square error of external validation (RMSEP). With fresh leaf, the RCV2 and RMSECV of NC models were 0.866 and 0.243, respectively, while the RV2 was >0.800 and RMSEP was <0.500. With leaf powder, the RCV2 and RMSECV of NC models were 0.900 and 0.202, respectively, whereas the RV2 and RMSEP were 0.944 and 0.142, respectively. Overall, the performance of the models with leaf powder is better than that with fresh leaf in rice.