Accurate predictive modelling facilitates efficient and effective trait selection in animal breeding and can decrease costs while maximizing profits when raising economically important animals. The objective of this study was to extend a previously developed bioeconomic model and computer program to calculate the marginal economic values (MEVs) and economic weights (EWs) for direct and maternal pig traits affected by new reproductive traits, namely the number of sow functional teats (NFTs) and boar sperm quality traits (SQTs) that included sperm volume, sperm concentration, motility percentage and percentage of abnormal spermatozoa. The MEV of NFTs represented the cost differences between naturally and artificially reared piglets until weaning and the cost differences between naturally and artificially reared finished animals. The MEVs of SQTs expressed the saved costs for artificial insemination, assuming a decreased price per insemination dose when improving the SQTs. The absolute and relative EWs for the newly defined complex of traits in the breeding objectives for pig breeds involved in the Czech national three-way crossing system (Czech Large White [CLW], Czech Landrace [CL] and Pietrain [PN]) were calculated using gene flow methods. The NFT trait was included only for dam breeds, and the relative EW averaged 3.6% of the total economic importance based on the genetic standard deviations of all 19 simultaneously evaluated traits in CLW and CL breeds. The relative EWs of the four SQTs comprised 2.0% of the total economic importance of the 19 traits in the CLW and CL dam breeds and 8% of the total economic importance of the 18 traits in the PN sire breed. Therefore, inclusion of the NFTs for dam breeds and SQTs for sire breeds in the breeding goal is recommended to aid in obtaining ideal outcomes with optimal economic values.