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 (R_C²) and root mean square error for calibration (RMSEC) of NC models with fresh leaf were 0.940 and 0.226, respectively, whereas the R_C² 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 (R_CV²) and root mean square error of cross-validation (RMSECV), and the determination coefficient (R_V²) and root mean square error of external validation (RMSEP). With fresh leaf, the R_CV² and RMSECV of NC models were 0.866 and 0.243, respectively, while the R_V² was >0.800 and RMSEP was <0.500. With leaf powder, the R_CV² and RMSECV of NC models were 0.900 and 0.202, respectively, whereas the R_V² 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.

Key words: fresh leaf, leaf powder, near infrared reflectance spectroscopy, nitrogen content, rice