Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583; National Animal Nutrition Program, University of Kentucky, Lexington 40546; Land O'Lakes Inc., Arden Hills, MN 55126.
Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583; National Animal Nutrition Program, University of Kentucky, Lexington 40546.
J Dairy Sci. 2020 Apr;103(4):3786-3803. doi: 10.3168/jds.2019-16702. Epub 2020 Feb 26.
Millions of feed composition records generated annually by testing laboratories are valuable assets that can be used to benefit the animal nutrition community. However, it is challenging to manage, handle, and process feed composition data that originate from multiple sources, lack standardized feed names, and contain outliers. Efficient methods that consolidate and screen such data are needed to develop feed composition databases with accurate means and standard deviations (SD). Considering the interest of the animal science community in data management and the importance of feed composition tables for the animal industry, the objective was to develop a set of procedures to construct accurate feed composition tables from large data sets. A published statistical procedure, designed to screen feed composition data, was employed, modified, and programmed to operate using Python and SAS. The 2.76 million data received from 4 commercial feed testing laboratories were used to develop procedures and to construct tables summarizing feed composition. Briefly, feed names and nutrients across laboratories were standardized, and erroneous and duplicated records were removed. Histogram, univariate, and principal component analyses were used to identify and remove outliers having key nutrients outside of the mean ± 3.5 SD. Clustering procedures identified subgroups of feeds within a large data set. Aside from the clustering step that was programmed in Python to automatically execute in SAS, all steps were programmed and automatically conducted using Python followed by a manual evaluation of the resulting mean Pearson correlation matrices of clusters. The input data set contained 42, 94, 162, and 270 feeds from 4 laboratories and comprised 25 to 30 nutrients. The final database included 174 feeds and 1.48 million records. The developed procedures effectively classified by-products (e.g., distillers grains and solubles as low or high fat), forages (e.g., legume or grass-legume mixture by maturity), and oilseeds versus meal (e.g., soybeans as whole raw seeds vs. soybean meal expellers or solvent extracted) into distinct sub-populations. Results from these analyses suggest that the procedure can provide a robust tool to construct and update large feed data sets. This approach can also be used by commercial laboratories, feed manufacturers, animal producers, and other professionals to process feed composition data sets and update feed libraries.
每年由测试实验室生成的数以百万计的饲料成分记录是宝贵的资产，可以用于造福动物营养界。然而，管理、处理和处理源自多个来源、缺乏标准化饲料名称且包含异常值的饲料成分数据具有挑战性。需要有效的方法来整合和筛选这些数据，以便使用准确的均值和标准差 (SD) 开发饲料成分数据库。考虑到动物科学界对数据管理的兴趣以及饲料成分表对动物产业的重要性，目标是开发一组程序，从大数据集中构建准确的饲料成分表。使用了一种已发表的统计程序来筛选饲料成分数据，对其进行了修改和编程，使其能够在 Python 和 SAS 中运行。从 4 家商业饲料测试实验室收到的 276 万条数据用于开发程序和构建总结饲料成分的表格。简要地说，对实验室之间的饲料名称和营养素进行了标准化，并删除了错误和重复的记录。使用直方图、单变量和主成分分析来识别和删除关键营养素超出均值±3.5 SD 的异常值。聚类程序在大型数据集内识别饲料的亚组。除了在 Python 中自动执行的聚类步骤外，所有步骤都使用 Python 编程并自动执行，然后手动评估聚类的结果皮尔逊相关矩阵。输入数据集包含来自 4 家实验室的 42、94、162 和 270 种饲料，包含 25 到 30 种营养素。最终数据库包含 174 种饲料和 148 万条记录。所开发的程序有效地对副产品（例如，酒糟和可溶性物分为高脂肪或低脂肪）、饲料（例如，按成熟度分为豆科或豆科-禾本科混合物）和油籽与粕（例如，整粒生大豆与豆粕膨化机或溶剂提取）进行了分类。这些分析的结果表明，该程序可以提供一种强大的工具来构建和更新大型饲料数据集。这种方法也可以由商业实验室、饲料制造商、动物生产者和其他专业人员用于处理饲料成分数据集和更新饲料库。
