Background and Aim: Pig industries are currently facing a crisis in terms of protein and energy costs. Proteases were used to increase protein digestibility and metabolizable energy (ME) in diets. This study evaluated the effects of protease supplementation on in vitro protein digestibility and productive performance in starter-to-finisher pigs. Materials and Methods: A total of 691 starter pigs were randomly allocated into three dietary treatments using a randomized complete block design. Diets were provided in three phases according to body weight (BW): Starter, grower, and finisher phases. Each phase was fed for 30, 60, and 24 days of treatment diets as T1: basal diet and T2 and T3: the basal diet supplemented with 240 ppm protease reduced by 50 kcal/kg ME plus 1% crude protein (CP) and by 100 kcal/kg ME plus 2% CP, respectively. Protease and in vitro protein digestibility were measured. BW and feed intake were recorded to calculate the average daily gain (ADG), average daily feed intake (ADFI), feed-to-gain (F:G), and gain-to-feed (G:F) ratios. Results: There were no significant differences (p > 0.05) in the percentage of in vitro protein digestibility between the groups with and without protease supplementation. In the finisher phase, T2 had lower (p < 0.05) ADFI and F:G than T1 and T3. Overall, T3 had lower (p < 0.05) ADG, ADFI, and F:G than T1 and T2. Conclusion: Protease supplementation significantly affects protein digestibility. Supplementing basal diets with 240 ppm protease reduced ME by 50 kcal/kg and CP by 1% without affecting ADG, ADFI, F:G, and G:F ratios for starter-to-finisher pigs. Keywords: digestibility, performance, pig, production, protease.

Preweaning mortality remains a major constraint to productivity in hyperprolific sow herds. We investigated whether birth weight (BW), subcutaneous fat thickness, and management factors could improve early identification of high-risk piglets. A total of 5,967 live-born piglets from 473 Landrace × Yorkshire sows were followed from birth to weaning under commercial tropical production conditions. Daily mortality identified the first three days after birth as the critical period, accounting for approximately 45% of all preweaning deaths. Decision tree analysis identified biologically meaningful thresholds for BW (0.73 kg) and belly fat thickness (BFT; 0.50 mm), defining three risk groups: normal birth weight (NBW), low birth weight with high BFT (LBW–HBT), and low birth weight with low BFT (LBW–LBT). Compared with NBW piglets, mixed-effects Cox proportional hazards models showed markedly greater mortality hazards during the critical period for LBW–HBT (hazard ratio [HR] = 10.42; P < 0.001) and LBW–LBT piglets (HR = 23.26; P < 0.001). Among piglets surviving beyond this period, mortality remained approximately 3.5-fold higher in both low-birth-weight groups. Cross-fostering reduced early mortality, whereas non-cross-fostered piglets had a 38% greater hazard of death (P = 0.007). Among seven machine learning algorithms, Random Forest achieved the highest predictive performance (F1-score = 86.7%; area under the receiver operating characteristic curve = 0.777), with BW emerging as the most influential predictor. Integrating BW and BFT enables robust risk stratification at birth and provides a practical framework for precision livestock management through targeted interventions during the narrow window when most preweaning deaths occur.

Stillbirth remains a major cause of piglet loss in hyperprolific sow herds. This observational study investigated the effects of farrowing kinetics, including birth weight (BW), birth order (BO), and expulsion interval (EI), on stillbirth risk. Data from 117 Yorkshire × Landrace sows and 1,778 piglets were analyzed using linear mixed models (LMM) and generalized linear mixed models (GLMM), with sow included as a random effect to account for litter clustering. BW, BO, time of farrowing, and EI were categorized into three (low, medium, high), four (Q1–Q4), four (H1–H4), and seven (≤5, 6–10, 11–15, 16–20, 21–30, 31–45, and >45 min) groups, respectively. Birth weight groups were not associated with BO (p = 0.22) or EI (p = 0.49). Univariate GLMM analysis showed that stillbirth risk increased across BO quartiles, with Q4 piglets exhibiting a higher stillbirth rate than Q1 piglets (9.1% vs. 1.6%; p < 0.01), whereas time of farrowing and EI groups were not associated with stillbirth risk (p > 0.05). In the multivariable GLMM, lower BW (OR = 0.49 per 0.25 kg increase; p < 0.001), longer cumulative expulsion interval (CEI) (OR = 1.71 per 60 min increase; p < 0.001), and greater within-litter birth weight variation (BWCV; OR = 1.51 per 10% increase; p = 0.077) were associated with an increased risk of stillbirth. In contrast, BO quartile, EI group, litter size, and time of farrowing were not retained in the final model. Kaplan–Meier survival analysis demonstrated a progressive decline in survival probability throughout the farrowing process (p < 0.01), with a median survival time of 450 min. These findings indicate that prolonged cumulative farrowing duration, rather than individual birth intervals or birth sequence alone, is the primary farrowing-related determinant of stillbirth risk. Therefore, reducing total farrowing duration and providing targeted assistance to late-born and low-birth-weight piglets may represent practical strategies for reducing stillbirth in hyperprolific sow herds.

Stillbirth remains a major cause of piglet loss in hyperprolific sow herds. This observational study investigated the effects of farrowing kinetics, including birth weight (BW), birth order (BO), and expulsion interval (EI), on stillbirth risk. Data from 117 crossbred sows between Landrace sires and Yorkshire dams and 1726 piglets were analyzed using linear mixed models (LMM) and generalized linear mixed models (GLMM), with sow included as a random effect to account for litter clustering. BW, BO, time of farrowing (TF), and EI were categorized into three (BWG, low, medium, high), four (BOQ, Q1–Q4), four (H1–H4), and seven (≤5, 6–10, 11–15, 16–20, 21–30, 31–45, and >45 min) groups, respectively. BWG were not associated with BO (p = 0.22) nor with EI (p = 0.49). Univariate GLMM analysis showed that stillbirth risk increased across BOQ, with Q4 piglets exhibiting a higher stillbirth rate than Q1 piglets (9.1% vs. 1.6%; p < 0.01), whereas TF and EI groups were not associated with stillbirth risk (p > 0.05). In the multivariable GLMM, lower BW (OR = 0.49 per 0.25 kg increase; p < 0.001), longer cumulative expulsion interval (CEI) (OR = 1.71 per 60 min increase; p < 0.001), and greater within-litter birth weight variation (BWCV; OR = 1.51 per 10% increase; p = 0.077) were associated with an increased risk of stillbirth. In contrast, BOQ, EI group, litter size (LS), and TF were not retained in the final model. Reverse Kaplan–Meier survival analysis demonstrated a progressive ascend in the probability of being stillborn throughout the farrowing process (p < 0.01), with a median stillborn time of 450 min. These findings indicate that prolonged cumulative farrowing duration, rather than individual birth intervals or birth sequence alone, is the primary farrowing-related determinant of stillbirth risk. These findings suggest that reducing CEI, providing timely assistance to late-born piglets and lowering BWCV may help decrease stillbirth losses in hyperprolific sow herds.