The stochastic modeling to estimate the economic consequences of reproductive performance in dairy cattle
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The stochastic modeling to estimate the economic consequences of reproductive performance in dairy cattle
- 1. The stochastic modeling to estimate the economic consequences of reproductive performance in dairy cattle C. Inchaisri, H. Hogeveen, R. Jorritsma, P.L.A.M., Vos, G.C. van der Weijden 1
- 2. Introduction Reproductive performance of individual cows is the result of physiological interactions Many cow factors are involved Parity Milk production level Health Lactation stage The interactions are complex What are the economic consequences of these interactions? 2
- 3. Objectives To create a simulation model To calculate the economic consequences for reproductive performance of single cows Average Good Poor To calculate the economic benefits of improving individual reproduction parameters. 3
- 4. Methodology Dynamic stochastic simulation model Monte Carlo One week of time step The relations of cow factors were adjusted Literature Authors’ expertise 4
- 5. Dynamics of the model 5
- 7. 1st OvulationOvulation no yes Disorder Estrous Detection no yes yes AI no yes Conception no yes summarize
- 8. Milk production Wood’s Function Depending on Parity, milk declining rate, milk production loss due to gestation Base values Norm (8310±10%) kg/305 days 6 wks at peak 7
- 9. The first ovulation 1st Ovulation time (wks) Depending on parity Primiparous cows = Lognorm(5.36, 5.04, Truncate(1, 10)) Multiparous cows = Lognorm(4.53, 3.08, Truncate(1, 10)) 8
- 11. 1st OvulationOvulation no yes Estrous Detection no yes AI no yes Conception no yes
- 13. 1st OvulationOvulation no yes Disorder Estrous Detection no yes yes AI no yes Conception no yes 40 wks pp. summarize Culling, if not pregnant
- 14. Reproductive cycle 1 Ovulation At least 3 weeks interval Estrous detection Milk production Days in milk 11
- 15. Reproductive cycle 2 Insemination Voluntary waiting period (VWP) Conception Parity Milk production Days in milk 12
- 16. Internal validation Input, processing and output The rationalism method Face validity
- 17. Economic calculationExcellent & Realistic scenarios Milk(kg)/cow/yr Annual Economic losses Revenue and Cost Calves/cow/yr A partial budget approach Culled cow Net Economic Losses No. Services 14 Calving management
- 18. Input of excellent scenario
- 21. 18
- 22. Mean net losses per cow per year 19
- 24. 21 Sensitivity analysis Ovulation 0.90 1.00 0.30 0.70 Estrous detection 0.30 0.70 0.11 0.30 Conception 15 wks 9 wks Incidence rate Voluntary waiting period
- 25. Main causes Decreased milk production Increased culling rate (poor fertility) The conception rate, estrous detection rate and VWP have the large effect on economic These losses could vary largely between cows
- 26. Next steps Which combination of cow factors is responsible for the high and low economic outcomes ???
- 27. Thank you for your attention 24
Hinweis der Redaktion
- Good morning, Lady and gentleman. This afternoon, I would like to talk with you about the overview of my research in topic of………... Firstly, I will give you briefly introduction and the objective of our research and then show you the methodology and the important results and end up with concluding remarks.
- For examples; the cow in first parity show the first ovulation later and given a little bit higher in conception rate than multiparous cows. The cow with high milk production level must be difficult to detect in estrous or making the cow get pregnant than the cow with lower milk production. Sometimes, cow might have the postpartum disorder such as cystic ovary that it can delay insemination time. The lactation stage as well it can effect the successes in estrous detection and conception of dairy cows. The cow in later stage of lactation stage is easier to detect estrous or making the cow get pregnant than in eariler stage of lactation
- Because many factors involve and their changing over the lactation stage. We decide to use dynamic stochastic simulation model by montecarlo in our study. The model was run one week of time step. The relations of cow factors were adjusted based on literature and authors’ expertise
- To make it clear on dynamics of the model, please look at the next slide
- The first step, we identify the cow with its milk production and the first ovulation time.
- we estimate milk production by use Wood’s function. The lactating curve of Wood’s function depends on the level of milk production, MDR/week. We also take the effect of milk production loss from pregnancyaccount in the model. To estimate lactation curve by wood’s function, we use themilk production based on the Dutch dairy cows at reference 8310 kg in 305 days and time of peak milk at 6 wks pp.
- The first ovulation time was predicted by lognormal distribution given the first parity cows show the first ovulation later than multiparous cows
- After the first ovulation occurs, and the second step will be continued dealing with making the cow pregnant. If the cow ovulated in the next cycle and estrous was detected and farmer decide to inseminate the cows and then the conception was evaluated. In other ways, if the cow did not ovulate in that week, the cow will go back to the starting point of cycle and the cycle will run again in the next week. In similar pattern, if cow ovulate but estrous was not detected and the cycel will back to the starting point and run again in the next week. The cycle will run the same for AI and conception step .
- However, sometimes, if disorders occurred, it must delay the reproductive cycle. The cycle was stopped for 4 weeks until the cows recovered. The reproductive will run until 40 wks pp., if cow get pregnant, we summarized the outcome after the cow complete lactation. If cow did not get pregnant within 40 wks pp, we culled that cow.
- In the detail of reproductive cycle. We set the ovulation occurred at least 3 week interval. And estrous detection depend on milk production and days in milk given a little bit lower in detection of estrous in cow with high milk production and given higher detection of estrous in later stage of lactation than in earlier stage of lactation.
- The insemination depend on farmer decision when to start insemination as we call voluntary waiting period. The conception depend on parity, milk production and days in milk.
- To calculate the economic outcome, we used a partial budget approach by calculation additional revenue and revenue forgone of milk production per cow per year, number of calves per year and number of culled cows and sum it up with the extra and reduced cost of number of services and calving management. The economic outcome is presented as annual economic losses per cow by comparison the outcome of excellent scenarios with the realistic reproductive scenarios.
- The average of economic losses in poor scenario is around 282 euros, in average scenario is 88 euros and good scenario is 28 euros per cow per year, if we look on the gap between poor bar and average bar, it is a big gap meaning that improvement the reproductive performance from poor to average can get better benefit, it cost around 200 euro. This must be better than improvement of subclinical mastitis that was reported by my colleagues in Utrecht university that is cost around 89 euro per cow.
- The impact of single reproductive parameter or sensitivity analysis of both in good condition of reproductive performance in red bars and poor condition on the blue bars are showing on the slide. We found that the conception rate has the greatest impact on economic losses in both conditions. The second is estrous detection and the third is VWP but ovulation and incidence rate of disorder have a little impact on economic losses
- It’s interesting to work in the next step to find out which combination of cow factors is responsible for the high and low economic outcomes. We will continue to find out that specific circumstances in the further study. Well, I’m grateful for your time. Thank for listening. I will give my hand over to a chairperson for your comments and questions. Thanks you.