Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat

Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat Canopy spectral reflectance (CSR) is a cost-effective, rapid, and non-destructive remote sensing and selection tool that can be employed in high throughput plant phenotypic studies. The objectives of the current study were to evaluate the predictive potential of vegetative indices as a high-throughput phenotyping tool for nitrogen use efficiency in soft red winter wheat (SRWW) (Triticum aestivum L.) and determine the optimum growth stage for employing CSR. A panel of 281 regionally developed SRWW genotypes was screened under low and normal N regimes in two crop seasons for grain yield, N uptake, nitrogen use efficiency for yield (NUEY) and nitrogen use efficiency for protein (NUEP). Vegetative indices were calculated from CSR and the data were analyzed by year and over the 2 years. Multiple regression and Pearson’s correlation were used to obtain the best predictive models and vegetative indices. The chosen models explained 84 and 83 % of total variation in grain yield and N uptake respectively, over two crop seasons. Models further accounted for 85 and 77 % of total variation in NUEY, and 85, and 81 % of total variation in NUEP under low and normal N conditions, respectively. In general, yield, NUEY and NUEP had greater than 0.6 R2 values in 2011–2012 but not in 2012–2013. Differences between years are likely a result of saturation of CSR indices due to high biomass and crop canopy coverage in 2012–2013. Heading was found to be the most appropriate crop growth stage to sense SRWW CSR data for predicting grain yield, N uptake, NUEY, and NUEP. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat

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Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9385-2
Publisher site
See Article on Publisher Site

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