Dynamic production monitoring in pig herds I: Modeling and monitoring litter size at herd and sow level

Abstract Monitoring animal production results in real time is a challenge. Existing management information systems (MIS) in pig production are typically based on static statements of selected key figures. The objective of this paper is to develop a dynamic monitoring system for litter size at herd and sow level, with weekly updates. For this purpose, a modified litter size model, based on an existing model found in the literature, is implemented using dynamic linear models (DLMs). The variance components are pre-estimated from the individual herd database using a maximum-likelihood technique in combination with an Expectation–Maximization (EM) algorithm applied on a larger dataset with observations from 15 herds. The model includes a set of parameters describing the parity-specific mean litter sizes (herd level), a time trend describing the genetic progress (herd level), and the individual sow effects (sow level). It provides reliable forecasting with known precision, on a weekly basis, for future production. Individual sow values, useful for the culling strategy, are also computed. In a second step, statistical control tools are applied. Shewhart Control Charts and V-masks are used to give warnings in case of impaired litter size results. The model is applied on data from 15 herds, each of them including a period ranging from 150 to 800 weeks. For each herd, the litter size profile, the litter size over time, the sow individual effect and sow economic value, are computed. Perspectives for further development of the model can take into account indices including conception rate, service rate, mortality rate etc. Such a model can be used as a basis for developing a new, dynamic, management tool.