Abstract: Abstract: Real-time monitoring of nutrient content of green forage in silage is essential to understanding how the nutrition change with time. In this paper we present a method to estimate nutrient content of the green forage using near-infrared (NIR) spectroscopy. Based on calibrated NIR models, a optimization model was modified and applied to estimate the nutrient. All green forage samples were collected from a grassland and their spectroscopy scanning was conducted in laboratory under controlled temperature and humidity. The results of 230 samples were used to train the chemometric algorithmic model, and the local optimization model was constructed using the modified partial least squares (M-PLS) algorithm combined with local random sample technique, local optimization and discontinuous adjustment of model parameters, and cross validation. For both silage and green forages, we measured nitrogen content, neutral detergent fiber (NDF) and acid detergent fiber (ADF) in 120 samples each. As a comparison, a global calibration model was also constructed based on the full-length waveband and applied to validate against the silage forage samples. The results showed that the square error of the prediction was 1.02 for nitrogen, 16.56 for nutrient NDF and 13.47 for nutrient ADF. The standard prediction errors were small and the correlation coefficients were higher than 0.9, with the relative derivation greater than 3. The model calibrated against the silage forage samples was able to predict nutrient content in both silage samples and green forage samples with SEP being 0.90 for nitrogen, 14.11 for NDF and 9.98 for ADF. The associated correlation coefficients were higher than 0.9, with the RPD greater than 3. All these results meet the standard for fast detection. The model calibrated locally can deal with non-linear molecular structure and non-uniform response of NIR spectroscopy. Experimental examination revealed that the locally calibrated model was more effective than the global model, and we can thus conclude that the NIR calibration model against the silage samples is able to predict nutrient content of green forage samples, especially the locally calibrated model.