Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

Prediction of yield and the contribution of legumes in legume-grass mixtures using field... Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R 2 values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R 2 values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

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Publisher
Springer US
Copyright
Copyright © 2008 by Springer Science+Business Media, LLC
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/s11119-008-9078-9
Publisher site
See Article on Publisher Site

Abstract

Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R 2 values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R 2 values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied.

Journal

Precision AgricultureSpringer Journals

Published: Sep 10, 2008

References

  • Assessment of pasture production in the Italian Alps using spectrometric and remote sensing
    Boschetti, M; Bocchi, S; Brivio, PA
  • Estimation of green/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression
    Cho, MA; Skidmore, A; Corsi, F; Wieren, SE; Sobhan, I
  • Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments
    Fitzgerald, GJ; Rodriguez, D; Christensen, LK; Belford, R; Sadras, VO; Clarke, TR

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