ABSTRACT
Poultry companies record and store vast amounts of data on their flocks, primarily for production monitoring and reporting. Data covering flock location/identity, numbers of chicks placed, weekly average bodyweights, crude mortality and culls, feed conversion efficiency and pickup for slaughter information would be commonly kept. Histories of anticoccidial programmes and routine medications should also be documented. Some operations may also record breeder source identities, hatchery information, vaccination details, day-old chick weights and sex (if grown separately). Other more specific records are useful but seldom recorded routinely (e.g. disease-specific mortality or morbidity, lighting programme, farm visitor records, serviceman records of litter quality, ventilation assessments and problem notes). Laboratory records from breeder flocks are also available and taken to reveal the success of vaccination procedures and/or to evaluate the possible exposure to wild pathogens. Routine serological monitoring of broiler flocks, commonly for infectious bursal disease, infectious bronchitis and ND is also sometimes carried out. These resources are of immense value but are often underutilized. They are of use for the monitoring of performance and health. Often the computerized storage format is not easily amenable to formal analysis techniques and considerable editing is necessary to produce a database capable of straightforward statistical manipulation. The time needed to translate data into an efficiently analysable format is, however, completely worthwhile. A baseline for each parameter should be determined15 for each operation. The knowledge of baseline data, seasonally adjusted, can be a useful technique for early detection of problems or for gaining an understanding of why performance may have improved. There is always a natural level of variability in field data, and assessment of performance must be considered within the framework of this variation to avoid invalid conclusions. A very instructive method of evaluating data on any of the parameters is to display them in a statistical control run chart16. Run charts plot data consecutively over time and confine the individual points between control lines calculated at three times the standard deviation (above and below the mean). As these are built up over time the value increases. These provide visual evaluation of trends occurring within an operation. Points plotted between the control lines represent natural variation. A single point outside the control lines would represent a significant outlier and an exception to the general process. Trends can become apparent from a successive run of points on one side of the mean line and can signal that performance is changing from a common source causation (either getting worse or better, depending on the direction). Identified changes can then be more thoroughly analysed against other known factors by standard epidemiological procedures. Any of the above parameters can be examined in this way.
