Rapid determination of biomass feedstock properties is of value for the production of biomass densification briquetting fuel with high quality. In the present study, visible and near-infrared (Vis-NIR) spectroscopy was employed to build prediction models of componential contents, i.e. moisture, ash, volatile matter and fixed-carbon, and calorific value of three selected species of agricultural biomass feedstock, i.e. pine wood, cedar wood, and cotton stalk. The partial least squares (PLS) cross validation results showed that compared with original reflection spectra, PLS regression models developed for first derivative spectra produced higher prediction accuracy with coefficients of determination (R2) of 0.97, 0.94 and 0.90, and residual prediction deviation (RPD) of 6.57, 4.00 and 3.01 for ash, volatile matter and moisture, respectively. Good prediction accuracy was achieved with R2 of 0.85 and RPD of 2.55 for fixed carbon, and R2 of 0.87 and RPD of 2.73 for calorific value. It is concluded that the Vis-NIR spectroscopy is promising as an alternative of traditional proximate analysis for rapid determination of componential contents and calorific value of agricultural biomass feedstock.