多环境燕麦干草主要品质成分近红外光谱模型构建及应用OA

To develop a rapid quality detection model for oat(Avena sativa)hay applicable across environ-ments,849 samples were collected at three sites(Baoding and Zhangjiakou in Hebei,Guyuan in Ningxia)for two years.Crude protein(CP),neutral detergent fiber(NDF),acid detergent fiber(ADF),soluble sugars(SS),and total potassium(K)were measured.Prediction models were established using near-infrared spec-troscopy(NIRS)combined with partial least squares regression(PLSR).All five traits showed broad,near-normal distributions,indicating good sample representativeness.The CP,SS,and K models performed well,with cross-validation coefficients(R²)≥0.89,ratio of performance to deviation(RPD)≥3.0,and valida-tion set correlations(r)of 0.84,0.86,and 0.83,respectively,demonstrating strong external predictive abil-ity.The NDF and ADF models achieved R²≥0.85 and RPD≥2.5,with r values of 0.78 and 0.79,meet-ing requirements for routine screening.Based on model predictions and forage quality standards,56 high-qual-ity forage oat accessions were identified.This study provides an efficient technical platform for high-throughput phenotyping and quality-based breeding in oat germplasm.