Standardized data in the broiler value chain

Abstract

In the Dutch broiler chain, data are collected as a routine practice. However, there is wide variation in the content of data collected and in data collection systems. This variability hampers the use of field data in management information systems to support decisions. The objective of this study was to analyze the quality of data and to standardize the content of data sets in the broiler production chain. To evaluate the quality of data, data sets from 3 Dutch hatcheries, from 23,637 batches of eggs, were assessed. The quality of data was assessed intuitively based on 7 quality attributes. To standardize the content of the data set, a protocol was proposed and validated. The protocol was validated at 30 breeder farms, 3 hatcheries, and 104 broiler farms by using 3 quality attributes: consistency, uniformity, and completeness. Results of the data quality analysis of the 3 Dutch hatcheries showed that the data sets had some fields with inaccurate, incorrect, inconsistent, nonuniform, incomprehensible, missing relevant, or incomplete data. Results of the validation protocol were as follows: feedback was obtained from 23 (77%) breeder farms, 3 (100%) hatcheries, and 7 (7%) broiler farms. Of all the questions, on average 88% were answered on breeder farms; 57, 65, and 82% were answered at each of the 3 hatcheries, respectively; and 79% were answered on the broiler farms. Data collected at 2 hatcheries were more consistent than those collected at the third hatchery. Hatchery data were less consistent than breeder farm data, but the number of data entries at hatcheries far exceeded the number at the farm level. Data from the hatcheries, breeder farms, and broiler farms were not always uniform, possibly because of differences in management strategies. This protocol enables the listing of relevant and standard contents of a data set whereby information exchange along the chain can be simplified. However, it is recommended that the protocol be supplemented with some rules for data collection and management, for example, that variables must be recorded in the provided fields, and that a variable must have one and only one name or code, the same unit of measurement, and the same definition.

Comparison of Chinese Broiler Production Systems in Economic Performance and Animal Welfare

“…One of the main reasons may be due to the management level and attitude of managers. Many researchers have started to consider the impact of management on animal welfare levels [30,31]. Further research is therefore required to analyze management factors in WQA.…”