Dynamic production monitoring in pig herds II:Modeling and monitoring farrowing rate at herd level

Abstract:Good management in animal production systems is becoming of paramount importance. The aim of this paper was to develop a dynamic moni-toring system for farrowing rate. A farrowing rate model was implemented us-ing a Dynamic Generalized Linear Model (DGLM). Variance components were pre-estimated using an Expectation-Maximization (EM) algorithm applied on a dataset containing data from 15 herds, each of them including insemination and farrowing observations over a period ranging from 150 to 800 weeks. The model included a set of parameters describing the parity-specific farrowing rate and the re-insemination effect. It also provided reliable forecasting on weekly basis. Sta-tistical control tools were used to give warnings in case of impaired farrowing rate. For each herd, farrowing rate profile, analysis of model components over time and detection of alarms were computed. Together with a previous model for litter size data and a planned similar model for mortality rate, this model will be an important basis for developing a new, dynamic, management tool

Farm Level Economic Implications of Genetic Selection for Improving Milk Fat Composition

The objective of the study was to assess the farm level economic implications of value-adding genetic selection strategies to improve milk fat composition. Selection based on a quantitative trait (ratio of total saturated to total unsaturated fatty acids in milk) or a known genotype (for the DGAT1 gene) was considered. Technical and economic performance of hypothetical herds were computed by a herd optimization and simulation model. It was assumed that the herds are already bred for the specific milk composition, and the transition period was not considered. Correlated effects of the selection scenarios on milk production, female fertility, and functional longevity traits were accounted for. Results showed that increasing the total unsaturated fatty acids in milk by traditional selection leads to lower net revenue, whereas selection based on DGAT1 genotype results in slightly higher net revenue. Our results, therefore, suggest that genetic selection based on DGAT1 genotype is a more profitable strategy for dairy farmers than selection based on phenotypes for SFA/UFA ratio.dairy cattle, genetic selection, milk composition, farm economics, Livestock Production/Industries,

Multi-level hierarchic markov processes as a framework for herd management support

This third manual is an updated version of manuals previously published as Dina Notat No. 84. It describes versio