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

In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared-shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, partial least squares (PLS) regression analysis was applied to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a success rate of: 73%-96% for CP, 72%-87% for NDF and 60%-85% for MEC. Moreover, only one spectral range, 1748-1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Importantly, five of the six selected spectral regions were not affected by water absorbance. With some modifications, this rationale can be applied to further analyses of pasture quality from airborne sensors. © 2014 by the authors.