Lugassi, R., Civil Engineering Faculty, Ariel University, Ariel, Israel
Chudnovsky, A., Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Zaady, E., Department of Natural Resources, Agricultural Research Organization, Gilat Research Center, Mobile Post, Negev, Israel
Dvash, L., Department of Natural Resources and Agronomy, Institute of Field and Garden Crops, Agricultural Research Organization, The Volcani Center, Bet Dagan, Israel
Goldshleger, N., Civil Engineering Faculty, Ariel University, Ariel, Israel, Soil Erosion Research Station, Ministry of Agriculture, Bet Dagan, Israel

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. © 2015 by the authors.