Quantitative analysis of nutrients in corn straw and wheat straw using near-infrared spectroscopy

This study aimed to establish a near-infrared prediction model for corn straw and wheat straw by using near-infrared spectroscopy (NIRS). In total, 155 samples of corn straw and 135 samples of wheat straw were collected in the three provinces Gansu, Xinjiang, and Henan. A total of 124 corn stalks was used as calibration set, and 31 samples were used as verification set; 108 wheat straw samples were selected as calibration set, and 27 were used as verification set. Near-infrared prediction models of DM, CP, NDF, ADF, and ADL of corn straw and wheat straw were established by near-infrared spectroscopy combined with modified partial least squares and other stoichiometry methods. The results showed that 1) the average content of DM, CP, NDF, ADF, and ADL was 94.60%, 5.16%, 63.88%, 36.33%, and 3.32%, respectively, in corn straw and 95.35%, 3.42%, 77.31%, 46.59%, and 6.84%, respectively, in wheat straw; 2) the prediction model for CP content in corn straw and wheat straw showed an interactive verification coefficient (1−VR) of > 0.90, and the external verification determination coefficient (RSQ) was > 0.84, the constructed model can be used for accurate prediction; and 3) the 1−VR value of the calibration model for corn straw DM, NDF, and ADF and for wheat straw DM was > 0.80, which roughly predicts the nutrient content. The prediction results of other indicators were suboptimal, thus the respective model requires optimization. In summary, this study provides a theoretical basis for rapid prediction of nutrient content of corn straw and wheat straw in production practice, and we established a near-infrared prediction model through NIRS.