In this study, we analyzed data from 2992 cows to comprehensively evaluate the adult weight (WEI), a key growth and body-size indicator, in West China cattle, aiming to estimate the related phenotypic and genetic parameters. The analysis focused on four weight traits while considering non-genetic factors such as parity, season, year, and birth weight. Data were processed and corrected using a MIXED procedure and a multi-trait animal model. Results showed that these non-genetic factors significantly affected the weight traits (p < 0.05), which had high heritability (0.25–0.39) (p < 0.01). WEI is crucial for improving the genetic traits of cattle in western China and provides innovative approaches for optimizing herd management, enhancing the efficiency of genetic selection, and boosting beef cattle productivity.

The daily milk yield (DMY) is defined as the sum of milk produced during morning, midday, and evening milkings. This metric is the key parameter for quantifying a cow’s absolute production level and serves as the foundation for calculating economic traits such as 305-day total milk yield, fat yield, and protein yield, making it essential for evaluating lactation performance. Based on the data cleaning pipeline, 2992 valid records were retained from 3332 initial records through rigorous quality-control screening, this study systematically evaluated three critical lactation traits—daily milk yield and its milk components (milk fat percentage and milk protein percentage)—using complete lactation records from Huaxi cows and estimated their phenotypic and genetic parameters. Non-genetic factors (parity, season, and mature body weight) were corrected via the MIXED procedure coupled with a multi-trait animal model. The results showed that all considered non-genetic factors significantly influenced the lactation traits (p < 0.05), while daily milk yield (DMY), milk fat percentage (FP), and milk protein percentage (PP) all exhibited moderate to high heritability (0.29–0.38) (p < 0.01). Genetic evaluation of DMY and its milk components provides quantitative evidence for precision selection and optimized mating decisions in the Huaxi dairy population, thereby accelerating genetic progress in milk production, improving herd profitability, and promoting the development of the regional dairy industry.

Background: Huaxi cattle are a local breed in Xinjiang, China. This breed has gained increasing attention from farmers and breeders for its dual-purpose capabilities in both meat and dairy production. Beyond this, these animals are highly adapted to extreme environmental conditions, making them unique and valuable for livestock-enhancing programs and the food industry. To maximize economic potential, effective selection strategies should be designed to enhance production (milk and meat) and identify individuals with superior genetics, as assessed by Estimated Breeding Values (EBVs). Therefore, the current study aimed to construct selection indices for 12-month body weight (WEI-12) as a primary objective and daily milk yield (DMY) using the Smith-Hazel index method in Huaxi cattle reared in Xinjiang, China–an area that has not previously been studied. Methods: Based on estimated genetic parameters, including heritability, genetic and phenotypic variances, and covariances for the WEI-12 and DMY traits in Huaxi cattle, a selection index was calculated using the classical Smith-Hazel method. The mathematical model was fitted in RStudio (version 4.5.2, 2026) using the dplyr, tidyr, and readxl packages. Results: Nine indices (C1 to C9) were constructed by applying varying conditions, including adjustments to economic weights, selection intensity, genetic correlation, and heritability, to identify the most effective index and assess its sensitivity to selection. Among the nine indices, C1, C2, C7, C8, and C9 exhibited the highest expected genetic gains of 2.723, 2.703, 2.7152, 2.6543, and 2.9049, respectively–along with accuracy rates of 0.19, 0.18, 0.18, 0.18, and 0.19, respectively. These indices demonstrated positive genetic gain potential for WEI-12 and DMY traits under ideal conditions (e.g., high selection intensity of 10% and the original estimated genetic parameters). Furthermore, minor alterations to these ideal conditions, specifically in genetic parameters, resulted in a negligible impact on the index ’s performance. Notably, small changes were observed when genetic correlation and heritability were modified by + 10% and –20%, respectively, suggesting the index’s robustness. Conclusion: The present research established a theoretical framework utilizing pedigree based estimated genetic parameters for the construction of selection indices to increase genetic gain in 12-month body weight (primary goal) and daily milk production. This approach could be utilized by breeders in developing sustainable and productive breeding plans for Huaxi cattle. The results highlight the importance of incorporating economically relevant traits and genomic information (e.g., gBLUP) to enhance the accuracy and sustainability of breeding programs.

This study used pepper straw as the fermentation substrate to investigate the effects of different molasses addition levels on the nutritional quality, fermentation quality, and microbial community of pepper straw in micro-storage. A control group (CON) and groups with different molasses addition levels (PA (2.0%), PB (4.0%), PC (6.0%)) were established. The results showed that the addition of molasses effectively reduced the dry matter (DM) loss rate in micro-storage of pepper straw. Compared with the control group, the 4% and 6% treatment groups significantly reduced the dry matter (DM) loss rate. In the 6% treatment group, crude protein (CP) content increased by 1.51 percentage points; at the same time, it significantly increased the relative abundance of Lactobacillus in the fermentation system, lowered the pH, and increased the concentrations of lactic acid (LA) and acetic acid (AA), with the 6% treatment group exhibiting the lowest pH value of 4.18. In summary, the optimal addition range is 4.0–6.0%. Considering production costs, we recommend an addition level of 4.0%.