Gross Megan A, Holder Amanda L, Moehlenpah Alexi N, Freetly Harvey C, Goad Carla L, Beck Paul A, DeVuyst Eric A, Lalman David L
Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74078, USA.
Division of Agriculture and Natural Sciences, College of the Ozarks, Branson, MO 65726, USA.
Transl Anim Sci. 2024 Jan 4;8:txae001. doi: 10.1093/tas/txae001. eCollection 2024.
Six existing equations (three for nonlactating and three for lactating; NRC, 1987, Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950; NRC, 1996, Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791; Hibberd and Thrift, 1992. Supplementation of forage-based diets. J. Anim. Sci. 70:181. [Abstr]) were evaluated for predicting feed intake in beef cows. Each of the previously published equations are sensitive to cow-shrunk BW and feed energy concentration. Adjustments in feed intake prediction are provided for level of milk yield in NRC (1987. Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950) and NRC (1996 Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equations. The equation published in 1996 used data generated between 1979 and 1993. Our objectives were to validate the accuracy of the published equations using more recent data and to propose alternative prediction models. Criteria for inclusion in the evaluation dataset included projects conducted or published since 2002, direct measurement of feed intake, adequate protein supply, and pen feeding (no metabolism crate data). After removing outliers, the dataset included 53 treatment means for nonlactating cows and 32 treatment means for lactating cows. Means for the nonlactating dataset were dry matter intake (DMI) = 13.2 ± 2.9 kg/d, shrunk body weight (SBW) = 578 ± 83.9 kg, body condition score = 5.7 ± 0.73, and Mcal net energy for maintenance (NEm)/kg of feed = 1.27 ± 0.15 Mcal/kg. Means for the lactating dataset were DMI = 14.6 ± 2.24 kg/d, SBW = 503 ± 73.4 kg, body condition score = 4.7 ± 0.58, and Mcal NE/kg feed = 1.22 ± 0.16. Simple linear regression was used to determine slope, intercept, and bias when observed DMI () was regressed against predicted DMI (). The NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) nonlactating equation underestimated feed intake in diets moderate to high in energy density with intercept differing from 0 and slope differing from one ( ≤ 0.01). Average deviation from observed values was 2.4 kg/d. Similarly, when the NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equation was used to predict DMI in lactating cows, the slope differed from one (< 0.01) with average deviation from observed values of 3.0 kg/d. New models were developed by pooling the two datasets and including a categorical variable for stage of production (0 = nonlactating and 1 = lactating). Continuous variables included study-average SBW and diet NE, Mcal/kg. The best-fit empirical model accounted for 68% of the variation in daily feed intake with standard error of the estimate Sy root mean squared error = 1.31. The proposed equation needs to be validated with independent data.
对六个现有方程（三个用于非泌乳母牛，三个用于泌乳母牛；NRC，1987年，《预测产食动物的采食量》。华盛顿特区：美国国家科学院出版社，美国国家科学院；doi:10.17226/950；NRC，1996年，《肉牛营养需求》，第7版修订版：1996年更新。华盛顿特区：美国国家科学院出版社；doi:10.17226/9791；Hibberd和Thrift，1992年。以粗饲料为基础的日粮的补充。《动物科学杂志》70:181。[摘要]）进行了评估，以预测肉牛的采食量。之前发表的每个方程对母牛空腹体重和饲料能量浓度都很敏感。NRC（1987年，《预测产食动物的采食量》。华盛顿特区：美国国家科学院出版社，美国国家科学院；doi:10.17226/950）和NRC（1996年，《肉牛营养需求》，第7版修订版：1996年更新。华盛顿特区：美国国家科学院出版社；doi:10.17226/9791）的方程针对产奶量水平对采食量预测进行了调整。1996年发表的方程使用了1979年至1993年期间生成的数据。我们的目标是使用更新的数据验证已发表方程的准确性，并提出替代预测模型。纳入评估数据集的标准包括2002年以来开展或发表的项目、采食量的直接测量、充足的蛋白质供应以及围栏饲养（无代谢笼数据）。去除异常值后，数据集包括53个非泌乳母牛的处理均值和32个泌乳母牛的处理均值。非泌乳数据集的均值为干物质采食量（DMI）=13.2±2.9千克/天，空腹体重（SBW）=578±83.9千克，体况评分=5.7±0.73，维持净能（NEm）/千克饲料=1.27±0.15兆卡/千克。泌乳数据集的均值为DMI=14.6±2.24千克/天，SBW=503±73.4千克，体况评分=4.7±0.58，NE/千克饲料=1.22±0.16。当将观察到的DMI（）与预测的DMI（）进行回归时，使用简单线性回归来确定斜率、截距和偏差。NRC（1996年，《肉牛营养需求》，第7版修订版：1996年更新。华盛顿特区：美国国家科学院出版社；doi:10.17226/9791）的非泌乳方程在能量密度中等至高的日粮中低估了采食量，截距与0不同，斜率与1不同（≤0.01）。与观察值的平均偏差为2.4千克/天。同样，当使用NRC（1996年，《肉牛营养需求》，第7版修订版：1996年更新。华盛顿特区：美国国家科学院出版社；doi:10.17226/9791）的方程预测泌乳母牛的DMI时，斜率与1不同（<0.01），与观察值的平均偏差为3.0千克/天。通过合并两个数据集并纳入一个生产阶段的分类变量（0=非泌乳，1=泌乳）开发了新模型。连续变量包括研究平均SBW和日粮NE，兆卡/千克。最佳拟合经验模型解释了每日采食量变化的68%，估计标准误差Sy均方根误差=1.31。所提出的方程需要用独立数据进行验证。
