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.
Highlights
The quality of the plants consumed by livestock in pastures is an important factor for their productivity
In a previous study [28], we found that when using slopes across selected spectral ranges of dried and ground vegetation, it was possible to evaluate several pasture quality indicators with high accuracy, such as crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration
To identify the required threshold ratio to predict CP and NDF content in fresh vegetation, as mentioned in section 2.5, we reduced the original proportion of the dried samples from 85% to 75%, 50% and
Summary
The quality of the plants consumed by livestock in pastures is an important factor for their productivity. The food’s potential quality is assessed by indicators such as crude protein (CP). Reflectance spectroscopy of solid particles in the visible-near infrared-shortwave infrared (VIS-NIR-SWIR) spectral range is a well-known technique for the rapid and quantitative assessment of chemical composition in many materials [3,4,5]. This is a rapid, cost-effective, nonchemical and nondestructive technique and for the most part, no sample preparation is needed. Vegetation spectra absorb in the VIS range (350–780 nm) due to photosynthetic pigments
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