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NIRS Calibration Strategies for the Botanical Composition of Grass-Clover Mixtures

  • M. Cougnon
  • C. Van Waes
  • J. Baert
  • D. Reheul
Conference paper

Abstract

In literature, different calibrations to predict the species composition of grass legumes mixtures or mixtures of different grass species are described. Mostly, these calibrations were developed using so called “artificial samples”. These artificial samples are obtained by mixing pure (ground) material of the species for which the calibration is developed in known proportions. The plant material used for these artificial samples may have been grown in mixtures or in pure stands. Calibrations based on artificial samples mostly have very good calibration statistics but fail to predict real validation samples. “Real samples” are obtained by hand separation of species mixtures into the different species followed by recomposition. The advantage of the use of artificial samples relative to real samples is that a lot of calibration samples with a different composition can be obtained with a relative small labour input. We built calibrations to predict the white clover content in grass clover mixtures, based on real and artificial samples with the same composition, and validated them with the same independent samples. Calibrations based on real samples performed far better than calibrations based on artificial samples. The failure of the latter can be explained by the lack of environmental variation in their spectra. We recommend a calibration strategy based on fewer but more diverse hand sorted samples, rather than making a lot of artificial samples that contain relatively little spectral information.

Keywords

Real Sample Grass Species White Clover Tall Fescue Calibration Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Chataigner F, Surault F, Huyghe C, Jullier B (2010) Determination of botanical composition in multispecies forage mixtures by near infrared reflectance spectroscopy. In: Huyghe C (ed) Sustainable use of genetic diversity in forage and turf breeding, Springer, Heidelberg, pp 199–203CrossRefGoogle Scholar
  2. Coleman S, Barton F, Meyer R (1985) The use of near-infrared reflectance spectroscopy to predict species composition of forage mixtures. Crop Sci 25:834–837CrossRefGoogle Scholar
  3. Locher F, Heuwinkel H, Gutser R, Schmidhalter U (2005) Development of near infrared reflectance spectroscopy calibrations to estimate legume content of multispecies legume-grass mixtures. Agron J 97:11–17CrossRefGoogle Scholar
  4. Petersen J, Barton F, Windham W, Hoveland C (1987) Botanical composition definition of tall fescue-white clover mixtures by near infrared reflectance spectroscopy. Crop Sci 27:1077–1080CrossRefGoogle Scholar
  5. Pittman W, Piacitelli C, Aiken G, Barton F (1991) Botanical composition of tropical grass-legume pastures estimated with near infrared reflectance spectroscopy. Agron J 83:103–107CrossRefGoogle Scholar
  6. Shaffer J, Jung G, Shenk J, Abrams S (1990) Estimation of the botanical composition in alfalfa/ryegrass mixtures by near infrared spectroscopy. Agron J 82:669–673CrossRefGoogle Scholar
  7. Shenk J, Westerhaus M (1991) Population structuring of near infrared spectra and modified partial least square regression. Crop Sci 31:1548–1555CrossRefGoogle Scholar
  8. Surault F, Briand M, Veron R. Huyghe C (2006) NIRS calibration equations to determine species contribution in grassland swards. In: Lloveras J (ed) Grassland science in Europe Vol 11, 598–600Google Scholar
  9. Wachendorf M, Ingwersen B, Taube F (1999) Prediction of the clover content of red clover and white clover-grass mixtures by near-infrared reflectance spectroscopy. Grass Forage Sci 54:87–90CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Plant Production, Faculty of Bioscience EngineeringGhent UniversityGhentBelgium
  2. 2.ILVO Plant SciencesMerelbekeBelgium

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