草业学报2025，Vol.34Issue(11)：150-160,11.DOI:10.11686/cyxb2024481
饲草中性洗涤纤维48h消化率预测模型的构建
Construction of a predictive model for the 48-hour digestibility of neutral detergent fiber in forage
摘要
Abstract
Forage is the primary source of dietary fiber for ruminants.Neutral detergent fiber(NDF)is an important indicator used to measure the fiber content of roughage.Its digestibility is a key parameter for evaluating forage quality and animal dry matter intake.Currently,the in vitro 48-hour NDF digestibility(NDFD48)is commonly used to assess forage NDF digestibility.An accurate measure of the NDFD48 value of roughage is important for balancing animal diets.Presently,roughage NDFD48 can be measured using the rumen nylon bag technique,laboratory semi-in vitro methods,or near-infrared spectroscopy,but these are often constrained by test conditions and unavailability of equipment.Given the biological significance of NDFD48,this study aimed to develop a computational method to predict NDFD48.Fiber indicators(NDF and acid detergent fiber,ADF)and digestibility indicators(NDFD48)from NASEM(2021)were used as a test set to build the NDFD48 model.Fourteen articles from the Journal of Dairy Science along with relevant indicators,and laboratory-measured values,were selected as two validation sets for the model.The results showed that the calculated NDFD48 values were significantly correlated with the measured NDFD48 values in the two validation sets(P<0.001),with R² values of 0.89 and 0.85,respectively.The model was further validated using the concordance correlation coefficient(CCC),achieving CCC values of 0.93 and 0.91.This model requires fewer input indicators,is easy to compute,and demonstrates high accuracy.Based on the evaluation performed here,the model can provide forage NDF estimates suitable for production applications and forage nutrition prediction.关键词
粗饲料/纤维消化率/简化算法Key words
roughage/fiber digestibility/simplified algorithm引用本文复制引用
梁韵仪,陈雅坤,何可可,杨嘉宇,赵连生,卜登攀..饲草中性洗涤纤维48h消化率预测模型的构建[J].草业学报,2025,34(11):150-160,11.基金项目
国家重点研发计划(2022YFD1301002),云南省重大科技专项计划(202402AE090032),中国农业科学院科技创新工程(ASTIP-IAS-17),首农食品集团自立科技项目(SNSPKJ(2022)02)和家畜产业技术体系北京市创新团队(BAIC05-2024)资助. （2022YFD1301002）
