TY - GEN
T1 - In-season estimation of spring maize nitrogen status with GreenSeeker active canopy sensor
AU - Xia, Tingting
AU - Miao, Yuxin
AU - Mi, Guohua
AU - Khosla, R.
AU - Wu, Dali
AU - Shao, Hui
AU - Xu, Xinxing
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - Precision nitrogen (N) management (PNM) is a promising strategy to improve N use efficiency and protect the environment while maintaining or increasing crop yield. In-season non-destructive diagnosis of crop N status is crucial for the success of this strategy. The objectives of this study were to (i) evaluate how well the GreenSeeker active canopy sensor can non-destructively estimate N status indicators of spring maize (Zea mays L.) in Northeast China and (ii) evaluate different N status diagnostic approaches based on N nutrition index (NNI) estimated via GreenSeeker sensor measurements. Two N rate field experiments involving 6 N rates (0, 60, 120,180, 240, and 300 kg N ha-1) were conducted in 2014 in Lishu County, Jilin Province in Northeast China. The results indicated that across sites and growth stages, GreenSeeker-based vegetation indices explained 89%-90% and 80%-86% of maize aboveground biomass and plant N uptake variability, respectively. The performance of GreenSeeker for estimating N status indicators from crop growth stage V7 to V10 was better than early growth stages (V5 and V6). The normalized difference vegetation index (NDVI) became saturated when aboveground biomass reached about 3.1 t ha-1 or plant N uptake reached about 75 kg ha-1; whereas no obvious saturation effect was found with ratio vegetation index (RVI). Across growth stages, about 50% of variability in maize N concentration was explained, but the standard error of estimate (SEa) was not acceptable. The NNI values were significantly correlated with GreenSeeker-based vegetation indices, with R2 being 0.64-0.80 at a specific growth stage. It is concluded that the GreenSeeker sensor has good potential for in-season non-destructive diagnosis of spring maize N status at V7-V8, but more studies are needed to further evaluate and improve its performance for practical applications.
AB - Precision nitrogen (N) management (PNM) is a promising strategy to improve N use efficiency and protect the environment while maintaining or increasing crop yield. In-season non-destructive diagnosis of crop N status is crucial for the success of this strategy. The objectives of this study were to (i) evaluate how well the GreenSeeker active canopy sensor can non-destructively estimate N status indicators of spring maize (Zea mays L.) in Northeast China and (ii) evaluate different N status diagnostic approaches based on N nutrition index (NNI) estimated via GreenSeeker sensor measurements. Two N rate field experiments involving 6 N rates (0, 60, 120,180, 240, and 300 kg N ha-1) were conducted in 2014 in Lishu County, Jilin Province in Northeast China. The results indicated that across sites and growth stages, GreenSeeker-based vegetation indices explained 89%-90% and 80%-86% of maize aboveground biomass and plant N uptake variability, respectively. The performance of GreenSeeker for estimating N status indicators from crop growth stage V7 to V10 was better than early growth stages (V5 and V6). The normalized difference vegetation index (NDVI) became saturated when aboveground biomass reached about 3.1 t ha-1 or plant N uptake reached about 75 kg ha-1; whereas no obvious saturation effect was found with ratio vegetation index (RVI). Across growth stages, about 50% of variability in maize N concentration was explained, but the standard error of estimate (SEa) was not acceptable. The NNI values were significantly correlated with GreenSeeker-based vegetation indices, with R2 being 0.64-0.80 at a specific growth stage. It is concluded that the GreenSeeker sensor has good potential for in-season non-destructive diagnosis of spring maize N status at V7-V8, but more studies are needed to further evaluate and improve its performance for practical applications.
KW - Active canopy sensor
KW - Biomass
KW - Nitrogen nutrition index
KW - Plant nitrogen concentration
KW - Plant nitrogen uptake
KW - Precision nitrogen management
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U2 - 10.1109/Agro-Geoinformatics.2015.7248155
DO - 10.1109/Agro-Geoinformatics.2015.7248155
M3 - Conference contribution
AN - SCOPUS:84960457048
T3 - 2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
SP - 390
EP - 395
BT - 2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
Y2 - 20 July 2015 through 24 July 2015
ER -