Abstract: To develop a rapid quality detection model for oat （Avena sativa） hay applicable across environments， 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 spectroscopy （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 validation set correlations （r） of 0.84， 0.86， and 0.83， respectively， demonstrating strong external predictive ability. The NDF and ADF models achieved R² ≥ 0.85 and RPD ≥ 2.5， with r values of 0.78 and 0.79， meeting requirements for routine screening. Based on model predictions and forage quality standards， 56 high-quality forage oat accessions were identified. This study provides an efficient technical platform for high-throughput phenotyping and quality-based breeding in oat germplasm.

Key words: Oat hay, Near-infrared spectroscopy, Quality components, Prediction model, Partial least squares, Germplasm resources

摘要： 为构建适用于多环境的燕麦（Avena sativa L.）干草品质快速检测模型，本研究收集了河北保定、张家口和宁夏固原两年三点的849份样品，测定粗蛋白（Crude protein，CP）、中性洗涤纤维（Neutral detergent fiber，NDF）、酸性洗涤纤维（Acid detergent fiber，ADF）、可溶性糖（Soluble sugar，SS）和全钾（Total potassium，K）含量，并结合近红外光谱与偏最小二乘法建立预测模型。结果表明：五项指标均呈宽范围正态分布，代表性广泛。CP，SS和K模型预测精度较高，交叉验证决定系数（R²）均≥0.89，相对分析误差（Ratio of performance to deviation，RPD）≥3.0，验证集相关系数（r）分别为0.84，0.86和0.83，外部验证结果可靠。NDF和ADF模型的R² ≥0.85，RPD≥2.5，验证集r分别为0.78和0.79，能满足常规筛查需求。基于模型预测结果与饲草品质标准，筛选出56份优质饲用燕麦种质。本研究为燕麦种质资源的高通量表型鉴定及品质育种提供高效技术支撑。

关键词: 燕麦干草, 近红外光谱, 品质成分, 预测模型, 偏最小二乘法, 种质资源