Author(s): ZHANG Yu-Sen | YAO Xia | TIAN Yong-Chao | CAO Wei-Xing | ZHU Yan
Journal: Chinese Journal of Plant Ecology
ISSN 1005-264X
Volume: 34;
Issue: 06;
Start page: 704;
Date: 2010;
Keywords: fresh leaf | leaf powder | near infrared reflectance spectroscopy | nitrogen content | rice
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
Journal: Chinese Journal of Plant Ecology
ISSN 1005-264X
Volume: 34;
Issue: 06;
Start page: 704;
Date: 2010;
Keywords: fresh leaf | leaf powder | near infrared reflectance spectroscopy | nitrogen content | rice
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
