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Open AccessArticle

Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants

1
Hamilton Center, Agriculture Victoria Research, Hamilton, Victoria 3300, Australia
2
School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3086, Australia
3
Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
4
Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Victoria 3010, Australia
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(6), 293; https://doi.org/10.3390/agronomy9060293
Received: 16 April 2019 / Revised: 27 May 2019 / Accepted: 3 June 2019 / Published: 6 June 2019
The nutritive value (NV) of perennial ryegrass is an important driver of productivity for grazing stock; therefore, improving NV parameters would be beneficial to meat and dairy producers. NV is not actively targeted by most breeding programs due to NV measurement being prohibitively slow and expensive. Nondestructive spectroscopy has the potential to reduce the time and cost required to screen for NV parameters to make targeted breeding of NV practical. The application of a field spectrometer was trialed to gather canopy spectra of individual ryegrass plants to develop predictive models for eight NV parameters for breeding programs. The targeted NV parameters included acid detergent fibre, ash, crude protein, dry matter, in vivo dry matter digestibility, in vivo organic matter digestibility, neutral detergent fibre, and water-soluble carbohydrates. The models were developed with partial least square regression. Model predicted ranking of plants had R2 between (0.87 and 0.39) and lab rankings of highest preforming plants. The highest ranked plants, which are generally the selection target for breeding programs, were accurately identified with the canopy-based model at a speed, cost and accuracy that is promising for NV breeding programs. View Full-Text
Keywords: hyperspectral sensors; perennial ryegrass; nutritive value; multiple linear regression; nondestructive; recurrent selection; near infrared spectroscopy hyperspectral sensors; perennial ryegrass; nutritive value; multiple linear regression; nondestructive; recurrent selection; near infrared spectroscopy
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MDPI and ACS Style

Smith, C.; Cogan, N.; Badenhorst, P.; Spangenberg, G.; Smith, K. Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants. Agronomy 2019, 9, 293.

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