Department of Agricultural Science, University of Sassari, 07100 Sassari, Italy.
Department of Animal Science, Food and Nutrition (DIANA), Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, 29100 Piacenza, Italy.
J Dairy Sci. 2021 Dec;104(12):12679-12692. doi: 10.3168/jds.2020-19764. Epub 2021 Sep 30.
Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.
许多用于评估农场绩效的指标只是农场运营的部分指标，这些指标被认为是整个农场效率的最佳预测指标。这项工作的主要目的是使用常见的部分指标来识别综合的多个盈利能力指标，这些指标通常可从单个农场获得，以便更好地支持奶牛养殖过程的短期决策。数据是从意大利 90 个奶牛场的农民面对面访谈中收集的，用于计算 16 个部分指标，这些指标涵盖了目前用于奶牛场饲料效率和经济效益的几乎所有指标。这些部分指标描述了饲料效率、能量利用、饲料成本、奶-饲料价格比、饲料成本以上的收入、饲料成本相等的收入、货币校正奶和饲料成本的讨价还价能力。饲料成本的计算基于泌乳牛或整个牛群，牛奶销售收入的确定考虑或不考虑牛奶质量付款。然后应用多元因子分析对 16 个部分指标进行分析，以确定简化和潜在结构。结果表明，5 个因子解释了 70%的变异性。原始部分指标中的每一个都与所有因子以不同的比例相关联，这是通过多元因子分析的负载得分来表示的。根据负载得分，我们将这 5 个因子标记为“经济效益”、“能量利用”、“盈亏平衡点”、“奶-饲料价格”和“农场的讨价还价能力”，按解释共同度的降序排列。前 3 个因子共占总共同度的 83%。饲料效率与因子 1（53%的负载）和因子 2（66%的负载）相似相关。只有因子 4 受到农场位置的显著影响。牛奶产量和牛群规模对因子 1 和因子 2有显著影响。我们的多元方法消除了部分指标的多重共线性问题，为农场饲料经济提供了简单有效的描述。所提出的方法允许在单个农场和地区层面上评估、基准测试和排名奶牛群的绩效，并具有在其他地区使用或复制的高机会。
