Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016-2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.
Lameness in swine breeding herds is a common cause of compromised animal well-being and economic loss to pig producers. Current lameness assessment methods are subjective and require intensive training. It has been shown that embedded microcomputer-based force plate systems can detect lameness by measuring weight distributions in livestock. The objective of this study was to determine the minimum time required to record data from each individual load cell in the force plate system to obtain accurate sow weight distributions on each leg. Sound and induced lameness states were evaluated to ensure that time requirements were similar for both situations. Lameness was induced in 12 mixed parity sows on Day 0 using a chemical synovitis model. An embedded microcomputerbased force plate system measured weight bearing applied on each foot twice per second for 15 min on days-1, +1, +6 and +10 relative to lameness induction. Data were analyzed using mixed model equations with day relative to lameness induction, time period, foot and the injected foot included as fixed effects and sow within replicate included as a random effect. Results indicate sow weight distributions at 1 and 5 cumulative minutes were not different (p≥0.05) when compared to those cumulative results recorded for 10 min. Comparing weights for each minute across time identified potential data collection problems after 12 min; therefore, 10 min was considered the maximum time required for weight recordings. Results from the present study indicate that recorded data for 1 min could be used as the minimum time required to accurately assess lameness for each individual animal. Results from this study can be used to improve the embedded microcomputer-based force plate use efficiency when evaluating sow lameness.
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