Construction of a near-infrared prediction model for nutrient content in different parts (tissues) of corn silage

The purpose of this study was to establish a near-infrared (NIR) prediction model for nutrient content in different parts (tissues) of silage corn using near-infrared spectroscopy (NIRS). Two hundred and twenty-seven silage corn samples were collected from Minqin County, Gansu Province. Near-infrared prediction models for dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE), neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) contents in the different parts (tissues) of silage corn were established using the improved partial least squares (MPLS) method. There were three derivative treatments and ten spectral scattering treatments included in the model. The results showed that the coefficient of determination for validation (RSQv) and the ratio of performance to deviation for validation (RPD) of the ADF content prediction model were higher than those for other components, while RSQv and RPD for the NDF, CP, and OM contents were lower than those for ADF. The RSQv values for ADF, NDF, CP, and OM were all greater than 0.9, while the RPD was greater than 2.5, which means that they can be used for accurate analysis during actual production. The RSQv and RPD for EE content were 0.701 and 1.838, respectively, and the RSQv and RPD for DM and ADL were 0.525 and 1.549, and 0.631 and 1.602, respectively. These two models cannot be used in actual production and the models need to be further optimized. In conclusion, near infrared prediction models for ADF, NDF, CP, and OM contents in different parts (tissues) of silage corn were established, and these models can be applied to production practice.