Motivation of milking high productivity cows under the conditions of robotic sys-tems
Bibliographic record
Abstract
Scientific research was carried out at the TDV “Terezine” farm of Bila Tserkva district, Kyiv region, on a newly created dairy farm for 500 cows with robotic milking systems of the De Laval Company. The herd of cows has been housed in a room with new volume-planning and technological solutions of 36 m wide and 10.5 m high. In the center of the room are eight robotic systems where the animals enter for milking at their request. The article highlights the research results into the structure of the milking herd of a dairy farm. It has been established that at the farm with robotic milking systems for 500 cows, there are animals with 1–6 lactations. The most significant part of the herd consists of cows of the first lactation (142 heads) and the smallest one - of the sixth lactation (11 heads). The parts of cows of the third, fourth, and fifth lactation are, respectively, 3rd – heads, 4th – 45 heads, and 5th – 28 heads. Such a structure of the herd indicates that the cows are highly productive. We studied the milking frequency of cows of different ages under robotic milking depending on the lactation period and their productivity. It has been shown that the technology of milking cows using robotic milking systems radically differs from that of milking on traditional machines. It was found that first-born cows had the lowest number of milking times per lactation (2.17 times), and cows of the second lactation had the highest (2.24 times) with a gradual decrease in the third (2.21 times) and the fourth (2.18 times). The decrease in the frequency of milking in the third and fourth lactation is because as the age of lactating cows increases, milk productivity increases, and, accordingly, the size of the udder, which does not require frequent release. Studies have proven that the higher the daily milk yield, the more often the cow comes to be milked. So, the first-born cows with an average daily milk yield of 10–20 kg go to milking 2.12 times, and with a productivity of 20–30 kg, the number of milking increases by 0.1 time, and with daily milk yields of 31–40 kg and 41–50 kg, the need in milking increases by 0.35 and 0.43 times. It has also been established that the need for milking is 0.52 times higher in animals of second lactation than in first-born cows. It has also been established that when the daily productivity of cows increases, the intervals between milking are shortened. With a daily milking of 10–20 kg, the average interval between milking is 10.5 hours, and with a milking of 40 kg and above – 7.3 hours. The most extended interval between milking is observed at night between the evening and morning milking. Based on the obtained research results, the farm's work schedule is suggested to be adjusted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".