In the Dutch poultry meat production chain, first week mortality (FWM) of the chicks is an important measure to quality and is therefore highly related to the price of the chicks that the broiler farm has to pay to the hatchery. Therefore, next to the total number of broiler eggs produced per hen and hatchability, this figure is often used as a measure of efficiency in the breeder-hatchery-broiler production chain. In this study, factors that are related to chick mortality in the first week at broiler farms were investigated. Field data obtained from 2 commercial Dutch hatcheries, for which 482 broiler farms voluntarily recorded FWM of 16,365 flocks of broiler chicks over the years 2004, 2005, and 2006, were analyzed. These represented 79% of the total number of day-old chicks delivered to separate broiler farms. First week mortality was significantly related to breeder age, egg storage length at the hatchery, season, strain, feed company of the breeder farm, year, and hatchery. Furthermore, FWM differed significantly between chicks originating from eggs of different breeder flocks and which were kept for grow-out at different broiler farms.

The aim of this research was to explore factors that are related with hatchability in the field. Data from 3 Dutch hatcheries for the years 2004, 2005, and 2006 were analyzed using a random regression model with the method of restricted maximum likelihood. In total, 24,234 batches of 724,750,444 eggs, originating from 511 breeder flocks, were included. Annually, 241,583,481 eggs were set on average, which is 37% of the total annual eggs set in the Netherlands. A significant difference in hatchability among eggs from different breeder flocks was found. Hatchability was significantly related with flock age, egg storage length, strain, feed company, season, year, as well as hatchery (P < 0.001). There was also significant interaction between flock age and age at first delivery, egg storage length at hatchery, strain, feed company, and season. Other 3-way interaction terms were also significant. The variation in hatchability was larger among the breeder farms than within breeder farms. The average estimated difference in hatchability among the hatcheries was 8%. The average estimated hatchability at 25 wk of age was 66%; it increased to 86% between 31 and 36 wk and decreased to 50% at 65 wk of age. On average, an extra day of storage until d 7 reduced hatchability by 0.2% and from d 7 to 14 by 0.5%. Eggs from older flocks were less sensitive to prolonged storage, whereas they were more sensitive to season. Hatchability was greater during late summer than during spring. The average estimated differences in hatchability among strains and feed companies of the breeder farms were 8 and 2%, respectively. Based on the relations found, optimization of hatchery results depends not only on good management at the hatchery but also on the hatching egg quality and therefore on the breeder farm management. It can be concluded that production data that are collected by the hatcheries can be used to adjust the management decisions at hatcheries as well as breeder farms.

The objective of this study was to develop a management information system to evaluate the tactical management of a breeder flock using individual farm analysis with a deterministic simulation model (IFAS). Individual farm analysis is a method that evaluates the performance of individual farms by comparing them with standards. In the first step of IFAS, a farm accounting system is used to compare performance indicators of a flock with the same performance indicators of the average of a group of flocks that produced in the same time period. In the next step, a deterministic simulation model is used to determine the factors causing the traced deviations in performances. Then, relevant deviations are determined based on the economic and statistical importance of each traced deviation. Finally, the deviations are identified by relevance to give farmers an indication of their strong and weak management practices.

The aim of this study was to propose a procedure for optimising the cost-effectiveness of vector borne disease surveillance using a scenario tree model and cost-effectiveness analysis. The surveillance systems for Bluetongue Virus serotype 8 (BTV-8) implemented in Switzerland and Belgium were used as examples. In twenty four different, simulated population structures, passive surveillance and five designs of active surveillance were investigated. The influence of surveillance system design and parameters such as farmer disease awareness, veterinary disease awareness, herd and within-herd design prevalence on the overall surveillance system sensitivity were assessed. Furthermore, the cost-effectiveness of mandatory and voluntary vaccination regimes in relation to disease surveillance was investigated. Under the assumption that BTV-8 manifests clinically, freedom from disease in a population can be established with almost certainty over the period of one year using clinical surveillance alone. Additional investment in active surveillance would therefore economically only be justified, if no clinical manifestation is suspected or other surveillance objectives are to be provided such as early detection. The best cost-effectiveness is obtained by sampling more herds rather than more animals within a herd. Mandatory vaccination reduces the cost of surveillance by 0.26 € per vaccine and voluntary vaccination only marginally reduces the cost of risk-based surveillance, by reducing the population at risk. Finally, in populations with predominantly dairy cattle, bulk-tank milk testing is the method of choice to actively demonstrate freedom from disease.

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.