Estimating pasture quality of fresh vegetation based on spectral slope of mixed data of dry and fresh vegetation-method development

Rachel Lugassi, Alexandra Chudnovsky, Eli Zaady, Levana Dvash, Naftaly Goldshleger

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes across these ranges were calculated and a partial least squares (PLS) analytical model was constructed for the slopes vs. crude protein (CP) and neutral detergent fiber (NDF) contents. Different datasets with different numbers of fresh/dry samples were constructed to predict CP and NDF contents. When using a mixed-sample dataset with dry-to-fresh ratios of 85%:15% and 75%:25%, the correlations of CP (R2 = 0.95, in both) and NDF (R2 = 0.84 and 0.82, respectively) were almost as high as when using only dry samples (0.97 and 0.85, respectively). Furthermore, satisfactory correlations were obtained with a dry-to-fresh ratio of 50%:50% for CP (R2 = 0.92). The results of our study are especially encouraging because CP and NDF contents could be predicted even though some of the selected spectral regions were directly affected by atmospheric water vapor or water in the plants.

Original languageEnglish
Pages (from-to)8045-8066
Number of pages22
JournalRemote Sensing
Volume7
Issue number6
DOIs
StatePublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Crude protein (CP)
  • Fresh vegetation
  • Neutral detergent fiber (NDF)
  • Pasture quality
  • Reflectance spectroscopy
  • Spectral slope

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