豆粕有效能值预测模型构建

doi:10.16431/j.cnki.1671-7236.2025.11.017

摘要/Abstract

摘要： 【目的】通过湿化学法、体外法和体内法相结合的方式对10个豆粕(SBM)样品营养价值进行系统评价，分析其有效能值与营养成分含量、瘤胃发酵参数以及产气量的相关关系，构建相关有效能值预测模型。【方法】采用湿化学法检测10个豆粕原料的常规营养组成，基于体外法测定样品体外发酵指标；选择36头16月龄的西门塔尔杂交牛，分为1个对照组和5个试验组，每组6头，采用栓系饲喂，试验期共42 d，每21 d为1期，其中16 d预饲期和5 d采样期，测定饲料原料有效能值。在上述测定结果基础上构建豆粕有效能值预测模型。【结果】10个豆粕样品的中性洗涤纤维(NDF)含量在17.28％～33.35％，粗蛋白质(CP)含量在40.18％～50.81％，干物质(DM)含量在88.01％～95.40％。豆粕体外发酵产生的乙酸(Ace)含量为79.23～88.03 mmol/L；丙酸(Pro)含量为40.45～48.69 mmol/L；丁酸(But)含量为23.32～38.06 mmol/L。豆粕的消化能(DE)为12.13～16.67 MJ/kg，代谢能(ME)为10.01～13.97 MJ/kg。有效能值与各项指标的相关性分析结果显示，丁酸、总挥发性脂肪酸(TVFA)、产气量(GP_{3 h}和GP_{9 h})与有效能值相关性较高(r分别为0.669、0.687、0.687和0.724)，并构建豆粕有效能值预测模型：DE＝－14.904+0.0857 TVFA+0.351 Pro+0.0912 Ace－0.845 Ibut－0.0381 GP_{9 h}(R²＝0.755)；ME＝－4.039－0.125 GP_{9 h}+0.0343 TVFA+0.125 GP_{3 h}+0.0328 But－0.270 Pro(R²＝0.648)。【结论】豆粕有效能值与其体外发酵和产气指标相关性较高；以丁酸、TVFA和产气量为要素初步构建了豆粕有效能值预测模型，可为豆粕的高效利用提供参考。

关键词: 豆粕; 营养价值评定; 有效能值; 体外发酵

Abstract: 【Objective】 The nutritional value of 10 soybean meal (SBM) samples was systematically evaluated by a combination of wet chemical,in vitro and in vivo methods to analyze the correlation between their effective energy values and nutrient content,rumen fermentation parameters and gas production,and construct a prediction model for the related effective energy values. 【Method】 The conventional nutrient composition of 10 soybean meal samples was examined by wet chemical method,The in vitro fermentation indicators of the samples were determined based on the in vitro method and the effective energy values of the feedstuffs were determined by in vivo method. The predictive model for effective energy values of soybean meal was established on the above measurement results.Thirty-six 16-month-old Simmental hybrid cattle were selected and divided into one control group and five treatment groups,with six cattle in each group.They were fed by tethered feeding.The experiment lasted for 42 days,with each 21-day period as one stage,including a 16-day pre-feeding period and a 5-day sampling period. 【Result】 The results obtained revealed that the neutral detergent fibre (NDF) content of 10 soybean meal samples ranged from 17.28% to 33.35%,the crude protein (CP) content ranged from 40.18% to 50.81%,and the dry matter (DM) content ranged from 88.01% to 95.40%.The acetic acid (Ace) content produced by in vitro fermentation of soybean meal ranged from 79.23 to 88.03 mmol/L,the propionic acid (Pro) content ranged from 40.45 to 48.69 mmol/L,and the butyric acid (But) content ranged from 23.32 to 38.06 mmol/L.The digestible energy (DE) of soybean meal ranged from 12.13 to 16.67 MJ/kg,and the metabolisable energy (ME) ranged from 10.01 to 13.97 MJ/kg.The analysis of correlation between the effective energy value and various indicators showed that butyric acid,total volatile fatty acids (TVFA) and gas production (GP_{3 h} and GP_{9 h}) were the four indicators most highly correlated with the effective energy value (r were 0.669,0.687,0.687 and 0.724,respectively).The final predictive model for the effective energy value of soybean meal was established as follows:DE＝－14.904＋0.0857 TVFA＋0.351 Pro＋0.0912 Ace－0.845 Ibut－0.0381 GP_{9 h}(R²＝0.755)；ME＝－4.039－0.125 GP_{9 h}＋0.0343 TVFA＋0.125 GP_{3 h}＋0.0328 But－0.270 Pro(R²＝0.648). 【Conclusion】 The results of this study indicated that the effective energy value of soybean meal was highly correlated with its in vitro fermentation and gas production indicators.A preliminary prediction model for the effective energy value of soybean meal was constructed based on factors such as butyric acid,total volatile fatty acids,and gas production,which could provide reference for the efficient utilization of soybean meal.

Key words: soybean meal; nutritional value assessment; effective energy value; in vitro fermentation

中图分类号:

S816.1

邢承前, 吴兆海, 赵连生, 孙梦旭, 卜登攀. 豆粕有效能值预测模型构建[J]. 中国畜牧兽医, 2025, 52(11): 5207-5216.

XING Chengqian, WU Zhaohai, ZHAO Liansheng, SUN Mengxun, BU Dengpan. Construction of Effective Energy Value Prediction Model for Soybean Meal[J]. China Animal Husbandry & Veterinary Medicine, 2025, 52(11): 5207-5216.

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