The objective of this study was to estimate the genetic parameters of litter size and piglet weight from farrowing to weaning in KHAPS Black sows. The genetic parameters investigated were the direct (h²_d), maternal (h²_m), realized (h²_r), and total (h²_T) heritability, as well as correlations (r_d, r_m, and r_dm) within and between traits. The analyses were performed using single- and three-trait animal models with and without maternal genetic effects. In the three-trait model with maternal genetic effect, all estimates of h²_d and h²_m were significantly different from zero except the h²_d of mean birth weight. Positive values of r_d and r_m between traits were observed as expected in the range of 0.322–1.000. Negative values of r_dm were found within and between traits and were less associated with mean piglet weight traits than litter size traits. Estimates of h²_T were consistently larger than those of h²_r in both the single- and three-trait model analyses. In addition, the three-trait model can take into account the association between the traits, so the estimates are more accurate with smaller SEs. In conclusion, maternal genetic effects were not negligible in this study, and thus, a multiple-trait animal model with maternal genetic effects and full pedigree is recommended to assist future pig breeding decisions in this new breed.

Laparoscopic artificial insemination (LAI) has emerged as a cornerstone technology for genetic improvement in small ruminants. By directly depositing semen into the uterine horns through minimally invasive surgery, LAI effectively bypasses the cervical anatomical barrier that hinders transcervical insemination. This approach has elevated pregnancy rates with frozen–thawed semen from 20 to 40% using conventional methods to 60–70%, establishing LAI as the “gold standard” in small ruminant reproduction. This review develops an integrated framework centered on LAI, systematically highlighting how emerging technologies directly enhance its precision and automation. Artificial intelligence–driven multivariate prediction models now enable pregnancy rate forecasting (AUC = 0.86), while computer vision technologies provide highly accurate estrus detection (98.56% accuracy), optimizing insemination timing. Robotic-assisted systems further refine surgical precision, and the integration of Internet of Things (IoT) and digital twin platforms enables end-to-end intelligent reproductive management. Economic evaluations indicate that LAI delivers significant returns on investment when the genetic value of disseminated germplasm is sufficiently high. Although technical complexity and equipment costs remain challenges, the integration of LAI with emerging technologies is driving a paradigm shift toward precision and intelligent management in the small ruminant industry, offering critical support for global food security and sustainable development.