Lecture – Physiology basics → what data must answer

ANSCI 4040 – Dairy Data Analysis & Analytics

Ass. Prof. Dr. Miel Hostens

Learning goals

  • Refresh core reproductive physiology (enough to interpret data)
  • Translate biology into questions your database must answer
  • Identify common failure modes (estrus, ovulation, luteolysis, anovulation)
  • Connect management levers to measurable outcomes

Why repro is a data problem

  • Reproductive efficiency drives:
    • Milk production dynamics (lactation length, calving pattern)
    • Replacement rate, culling risk, heifer inventory, genetic progress
    • Labor and treatment costs
  • The industry’s challenge: fertility declined then rebounded—requiring integration of physiology, management, nutrition, genetics, economics, and records.

Conceptual model: “open → pregnant”

How fast the program turns eligible non-pregnant cows into pregnant cows.

Key workflow:

  • Calve → VWP (voluntary waiting period)

  • Detect heat or timed AI (TAI) program

  • Inseminate (service)

Conceptual model: “open → pregnant”

  • Conceive (P/AI)

  • Maintain pregnancy (loss matters)

  • Diagnose (preg check)

  • Resync or rebreed non-pregnant cows

Minimal physiology you must know

Estrous cycle in cattle:

  • ~3 weeks, but longer in lactating cows vs heifers (≈23 d vs ≈21 d).

  • Ovarian structures:

    • Follicles (dominant follicle → ovulation)

    • Corpus luteum (CL → progesterone)

    • Luteolysis timing differs (heifers vs lactating cows).

Minimal physiology you must know

Six key hormones and what they mean for data interpretation:

  • Progesterone (P4): from CL; maintains pregnancy; blocks estrus / GnRH surge

  • Estradiol (E2): from follicle; standing heat; triggers GnRH surge

  • GnRH: hypothalamus; triggers LH surge

Minimal physiology you must know

  • LH: surge causes ovulation; pulses drive dominant follicle growth

  • FSH: initiates follicular wave

  • PGF2α: uterus; regresses CL (luteolysis)

What high milk production changes (and why metrics mislead)

High milk production is associated with:

  • Larger ovulatory follicles but lower circulating E2

  • Larger luteal tissue volume but lower circulating P4

  • Shorter estrus duration at higher production

  • Higher risk of double ovulation (twinning risk)

Analytics consequence: “heat detection looks worse” partly because estrus expression is shorter, not only because labor/observation is worse.

Estrus: behavior vs biology

  • Standing heat is driven by E2 and ends when E2 drops below threshold (often before ovulation).
  • AI timing should relate to onset of estrus (biologically tied to GnRH/LH surge), not just “was she seen?”
  • Therefore: “no observed heat” ≠ “no ovulation”

Anovulation: big hidden driver

  • Anovulation commonly defined as failure to ovulate around ~60–80 DIM
  • Multi-study summaries show ~20–25% anovular cows around 60–70 DIM (large between-farm variation).
  • Two phenotypes (useful for interpreting progesterone/ultrasound data):
    • “Small-follicle” anovular (insufficient LH pulses → follicles don’t reach ovulatory size)
    • “Large-follicle/cystic” anovular (high E2 fails to induce GnRH/LH surge) 1

Management lever: timed AI programs

Why TAI exists:

  • Detection-of-estrus-only programs often delay first service and reduce service rate.

  • Ovsynch (GnRH → PGF2α → GnRH → TAI) coordinates ovulation and CL regression, enabling timed insemination without estrus detection.

  • Presynchronization (PGF2α and/or GnRH combinations) aims to start protocols at an optimal cycle stage and address anovulation.

“What questions must the data answer?”

For any herd (and each pen/group):

  1. Eligibility / exposure
    • Who is eligible to be bred this week (DIM, VWP, health holds)?
  2. Submission
    • Who was submitted to insemination (observed heat or protocol)?
  3. Service quality
    • Was the insemination recorded correctly (date/time, technician, semen, bull)?

“What questions must the data answer?”

  1. Fertility outcome
    • Who conceived (preg check), who didn’t, who lost?
  2. Timeliness
    • How long did it take from calving → first service → conception?
  3. Bottleneck diagnosis
    • Is low pregnancy rate due to service rate or P/AI?

Take-home summary

  • Physiology gives you mechanisms; metrics give you signals
  • High production alters hormone dynamics → affects estrus expression and twinning risk
  • Anovulation is common around breeding start and requires protocol-aware interpretation
  • Your monitoring framework must separate:
    • Exposure (service/submission) from success (conception/pregnancy)