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This study aimed to estimate green herbage biomass (GBM) and crude protein (CP) concentrations of a mixed-sown pasture in Hokkaido, Japan using ground based hyperspectral measurements and geostatistical analysis. The mixed-sown pasture consisted of a relatively flat section renovated by over-seeding a grass (Subunit 1, 2.6 ha) and a hilly aged section (Subunit 2, 5.0 ha). Hyperspectral reflectance and plant data were collected for 22 days in August 2009 from 88 plots within the two subunits. For mapping, separate spectral readings, without plant sampling, were obtained from a total of 347 plots along permanent transects in the pasture. Genetic algorithm-based wavebands selection with partial least squares (GA-PLS) regression analyses was performed to predict GBM and CP concentrations using both reflectance and first derivative reflectance (FDR) datasets. Then, geostatistical analysis with semivariograms was conducted to determine sampling interval of GBM and CP concentration in Subunits 1 and 2. In the GA-PLS analysis, the most accurate results were obtained by calibration of GBM using FDR (cross-validated coefficient of determination, inline image; cross-validated root mean square error, RMSECV = 42) and of the CP concentration using raw reflectance (inline image, RMSECV = 2.02). Geostatistical analysis with semivariograms showed that, at the landscape scale, the GBM patch sizes in Subunits 1 and 2 were 31 and 67 m, respectively, and those of the CP concentration were 37 and 54 m. These values indicate that the spatial distribution patterns of these pasture parameters were more heterogeneous in Subunit 1 than in Subunit 2. The analysis result also indicates that the sampling interval for GBM and CP concentration should be <15 m. This work shows the ability to estimate the spatial distribution of GBM and CP concentration for the implementation of site-specific grazing management.