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Estimation of wheat nitrogen status under drip irrigation with canopy spectral indices

Published online by Cambridge University Press:  02 October 2014

X. L. JIN
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
W. Y. DIAO
Affiliation:
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
C. H. XIAO
Affiliation:
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
F. Y. WANG
Affiliation:
Institute of Cotton, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, China
B. CHEN
Affiliation:
Institute of Cotton, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, China
K. R. WANG
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
S.-K. LI
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
Corresponding

Summary

Crop nitrogen (N) status is an important indicator of crop health and predictor of subsequent crop yield. The present study was conducted to analyse the relationships between nitrogen nutrition index (NNI), nitrogen biomass difference (ΔNB) and spectral indices in wheat, and then attempt to improve field N management. Spectral indices and concurrent sample N and biomass parameters were obtained from the Shihezi University experimental site in Xinjiang, China during 2009 and 2010. The results showed that all spectral indices were significantly correlated with NNI. Regression functions with the highest determination coefficient (R 2) and the lowest root mean square error (RMSE) were used to improve prediction of NNI, and then the selected spectral index was used to estimate NNI and ΔNB. The strongest relationships were observed for the products of modified normalized difference 705 × biomass dry weight (BND705) and the enhanced vegetation index 2 (EVI2) for estimating NNI. There were also strong relationships between the NNI and the normalized NNI (ΔNNI) as well as between ΔNNI and ΔNB, with a linear relationship between ΔNB and the spectral index BND705 and a linear relationship between ΔNB and the spectral index EVI2. These results indicated that BND705 and EVI2 can be used to improve the accuracy of NNI estimation, and the correlations of ΔNB and NNI with BND705 and EVI2 can be used to further improve field N management in wheat.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2014 

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