This study focused on two large-scale dairy farms in the Lanzhou region, targeting key indicators of dairy cow production performance. By systematically analyzing Dairy Herd Improvement (DHI) data, this study aimed to investigate the variation patterns of milk composition, milk urea nitrogen (MUN), somatic cell count (SCC), peak milk yield, and days to peak yield, clarify the main influencing factors, and provide data-driven support and practical guidance for precision feeding management on dairy farms.
A total of 30 consecutive DHI test records from January 2022 to December 2024 were collected from the two farms. Descriptive statistics and trend analyses were performed using SPSS 26.0 software. Combined with farm management logs (including records on diet formulation, immunization and health care, environmental regulation, etc.), the driving factors behind changes in each indicator were comprehensively analyzed to ensure scientific reliability of the results.
Milk composition was jointly influenced by dietary adjustments and genetic improvements. Farm A exhibited significant fluctuations in milk fat percentage due to modifications in feed formula, while Farm B achieved progressive annual increases in milk protein percentage through optimized protein supplementation and breeding enhancements. Both farms significantly reduced SCC values by improving barn environments and standardizing milking procedures. MUN levels remained generally stable, though energy-nitrogen balance during peak lactation requires attention. Peak milk yield showed a steady increasing trend, days to peak yield gradually decreased, and overall feeding management levels continued to improve.
Through systematic analysis of three-year DHI data, this study identified the variation characteristics and key influencing factors of production performance indicators on large-scale dairy farms in the Lanzhou region. The targeted management recommendations provided can serve as an important reference for controlling feed costs, improving milk quality, and optimizing feeding management models on dairy farms.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |