Economic analysis of the De Laval activity meter system for heat detection : a case study on farm level
Bibliographic record
Abstract
During the last centuries dairy cow breeding has strived to increase milk yield resulting in a dairy cow that produces large quantities of milk. However, high milk yield has been shown to be correlated with decreased reproductive performance (Nebel & McGilliard; 1993). The problem with low fertility among dairy cows has become one of the more costly problems for the dairy industry as of today. \n \nLow fertility causes a number of problems that affect herd profitability. Replacement and calving interval are two measures used to describe dairy herd performance. The maximum milk yield and net farm income is associated with a calving interval of 12 – 13 months (Hollman. 1984). The replacement rate also affects the economic result of the dairy herd. Jagannatha et al (1999) calculate the optimal replacement rate to 20%. However, low fertility tends to increase the calving interval and the number of cows culled increases due to infertility. \n \nThe activity meter system provides a management tool that facilitates the detection of cows in heat and provides information that helps to determine the most suitable time of insemination. The objective of this thesis is to evaluate the economic impact of an automated estrus detection system on the dairy farm. The study analyses the impact of improved pregnancy rate on replacement, milk yield, feed costs, breeding costs and the number of calves born. These parameters in combination affect net farm income associated with the introduction of activity metering. \n \nIn order to evaluate the consequences of activity metering a simulation model has been developed. The model is a modified waiting line simulation model where pregnancy rate determines the time each cow has to wait for a subsequent insemination ultimately leading to pregnancy. The pregnancy rate is a stochastic parameter with a uniform distribution. If a cow does not become pregnant after a number of inseminations she is culled due to infertility. Based on the pregnancy rate the model calculates the calving interval for each cow and creates an average herd calving interval. Based on the individual calving interval an average daily milk yield is calculated for the herd. Daily average herd milk yield is matched with the corresponding cost of the feed mix. \n \nBased on farm visits and international dairy statistics two case farms have been identified. The first farm is situated in Northern Italy. It is managed as a typical Western European dairy farm. The production is highly specialized and cows do not graze on pasture. Herd size is 400 cows. The other farm is situated in Southwest Great Britain. This farm is characterized as a less intensive operation with a smaller herd, 150 cows. These cows produce a lower annual milk yield compared to the Italian herd. The herd is fed on pasture only to a small extent. \n \nThe simulation is carried out by comparing the net revenues of the farm, based on the parameters mentioned, before and after the introduction of the activity metering system. The difference in net revenue between the two cases is the annual net revenue of the activity meter investment. The simulation reveals a positive net revenue in all cases when activity metering is used. The net revenue per cow and year of the investment ranges from 70 to 251 € for the Italian farm and 8 to 178 € for the U.K. farm. The economic impact of the system is more pronounced for the Italian farm. The result change as the initial reproductive performance of the diary herd is improved. If management is characterized by good skills, resulting in a relatively high pregnancy rate prior to introducing the system, the economic impact of the system is not as high, compared to a situation with less skilled management. However, in all cases the simulations display a positive gain in net revenue from using automated estrus detection. \n \nMilk yield, feed cost and replacement rate have the most pronounced impacts on the result. Changes in these parameters account for the larger share of the net revenue of the system. Consequently, initial reproductive performance and the ability of the system to facilitate improvements in reproductive management are of critical importance to the result.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".