Departamento de Ciencia Animal, Instituto de Ciencia y Tecnología Animal, Universitat Politècnica de Valencia, Valencia, España.
Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, Urbana, USA.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad048.
Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein, and fat is transferred to milk (protein, lactose, and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein, and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%), and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals vs. predicted were positive for milk fat energy, zero for lactose, and negative for milk energy in protein and CH4. This model suggested for use with mixed diets and by-products to obtain balanced macronutrient supply, methane emissions, and milk performance during mid lactation could be an interesting tool to help farmers simulate scenarios that increase milk fat and protein, evaluate CH4 emissions, without the costs of running animal trials.
饲料成本是奶农最大的支出。因此，提高牛奶生产效率（牛奶中的脂肪和蛋白质是奶农的主要收入来源）对奶业具有重要的经济意义。本研究的主要目的是开发一个动态能量分配模型，以描述和量化碳水化合物、蛋白质和脂肪的日粮能量如何转化为奶山羊的牛奶（蛋白质、乳糖和脂肪）。此外，由于全球范围内对牲畜对全球变暖的贡献越来越关注，还对甲烷（CH4）排放进行了量化。为了建模目的，使用了 158 个个体山羊的观测值，并将其随机分为 2/3 用于模型开发和 1/3 用于内部评估。对于外部评估，使用了文献中的 20 个不同的能量平衡研究（77 个观测值）。CH4 中能量损失的 RMSPE 为 13.2%，脂肪中能量的 RMSPE 为 16.8%，蛋白质中能量的 RMSPE 为 19.4%，乳糖中能量的 RMSPE 为 22.3%。所有变量的平均偏差均接近零，乳糖中乳能的斜率偏差接近零，接近 1%（1.01%），蛋白质和 CH4 的斜率偏差分别约为 3%和 10%。对于 CH4 和乳能成分的能量，随机偏差大于 85%，这表明存在非系统性误差，模型中的方程适当拟合了数据。残差的分析似乎随机分布在零附近。残差与预测值的回归线斜率为正值，用于乳脂能，为零，用于乳糖，为负值，用于蛋白质和 CH4 中的乳能。该模型建议在使用混合日粮和副产品时使用，以获得平衡的宏量营养素供应、甲烷排放和泌乳中期的牛奶产奶量，这可能是一个有趣的工具，可以帮助农民模拟增加牛奶脂肪和蛋白质的情况，评估 CH4 排放，而无需进行动物试验的成本。
