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

Published on Aug 1, 2015in Precision Agriculture3.36
· DOI :10.1007/s11119-014-9385-2
K. Pavuluri2
Estimated H-index: 2
(VT: Virginia Tech),
Bee Khim Chim2
Estimated H-index: 2
(VT: Virginia Tech)
+ 3 AuthorsWade Everett Thomason17
Estimated H-index: 17
(VT: Virginia Tech)
Abstract
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.
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References63
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#2Scott C. Rowntree (UW: University of Wisconsin-Madison)H-Index: 5
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#2G. Berger (VT: Virginia Tech)H-Index: 4
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#1Oorbessy Gaju (University of Nottingham)H-Index: 12
#2Vincent Allard (Blaise Pascal University)H-Index: 22
Last.Simon Orford (Norwich Research Park)H-Index: 18
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#1Yingying Yang (Fujian Agriculture and Forestry University)H-Index: 2
#2Shiwu Gao (Fujian Agriculture and Forestry University)H-Index: 6
Last.Youxiong Que (Fujian Agriculture and Forestry University)H-Index: 19
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#1Katherine Frels (NU: University of Nebraska–Lincoln)H-Index: 6
#2Mary J. Guttieri (NU: University of Nebraska–Lincoln)H-Index: 9
Last.P. Stephen Baenziger (NU: University of Nebraska–Lincoln)H-Index: 24
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