Culling before testing in swine: Identification of culling strategy and estimation of culling precision

The aim of this simulation study was to identify culling strategy and to es timate culling precision based on various characteristics available in fiel d data in order to evaluate the ability to detect situations in which adjus tment for missing data should be applied in genetic evaluation. Data were s imulated for age at 100 kg of Live weight (AGE) measured on the farm. Culli ng was done within (C-W/IN) or over (C-OVER) litters by deleting records fr om the simulated datasets with culling intensities of .33 and .67. The cull ing variate (CVAR) used indicated the culling precision and had genetic and phenotypic correlations of 1.00, .75, .50, .25, or .00 with AGE (r(CVAR,AG E)). We were able to distinguish between culling strategies C-OVER and C-W/ IN by means of decision rules based on proportion of tested animals per lit ter. Estimates of r(CVAR,AGE) were obtained from calibration curves for lin ear regression coefficients of litter average or within-litter variance for AGE on proportion of tested animals, and within-and between-litter varianc e (V-W and V-B) for AGE. Moderate to high r(CVAR,AGE) could be identified w ith little error by using V-W or V-B in C-W/IN and VW in C-OVER. Within-lit ter variance and the weighted average of the estimates from all four charac teristics were well able to detect r(CVAR,AGE) values of .50 and higher in both C-W/IN and C-OVER. In conclusion, characteristics of swine field data with missing observations contain information that-makes it possible to det ermine culling strategy, intensity, and precision. This information can be used to decide whether missing data should be replaced by their expected va lues in genetic evaluation.