Research · Agriculture 4.0
A research question, not a farm
We have no farm, no flock and no data. What we have is a question that matters to Canadian livestock and food-loss research, and a published method to answer it.
How much of each Qurbani animal is actually used?
Every Eid al-Adha, Muslims across Canada perform Qurbani: an animal is sacrificed and its meat shared with family, neighbours and people in need. How much of each animal is used, and how much is lost as hide, offal, bone, fat and wool, is not routinely measured. We have not found a published measurement of it, in Canada or elsewhere.
A method to measure it already exists. The Qurbani Utilisation Index (QUI) is a methodology note published by Awqaf New Zealand, a registered New Zealand charity, under an open licence (CC BY-NC 4.0). Version 3 was issued on 22 September 2026. It adapts the FAO food-loss methodology behind UN Sustainable Development Goal indicator 12.3.1(a).
Three series, not one headline figure
What the index would report
No one has run the method yet, so every reading below is empty. We show it that way on purpose.
Food loss rate
Carcass meat, edible offal and blood lost between pre-slaughter handling and the point before retail. The only series aligned directly with the FAO method.
Sub-product recovery rate
Hide, bone, horn and wool recovered, against the quantity generated.
Utilization rate
Value realized for social development, against the total realizable value of the animal.
Five stages of physical loss, and one set apart
- S1Pre-Eid holding
- S2Slaughter
- S3Carcass recovery
- S4The fifth quarterHide, offal, bone, blood, fat and wool
- S5Distribution
- S6Foregone productive lifeMilk, wool and lambs never produced. Modelled, not measured, and never added to S1–S5.
Benyounis, H. (2026). Qurbani Utilisation Index (QUI-Awqaf NZ): measuring zero Qurbani waste — a methodology note for the Qurbani supply chain (v3, 22 September 2026). Awqaf New Zealand. Zenodo. https://doi.org/10.5281/zenodo.22700036
The note contains no empirical findings: no one has run it yet. Citing it does not imply a partnership or an endorsement.
What Agriculture 4.0 means here
Ordinary, checkable tools first. AI only where it earns its place.
Agriculture 4.0 is the use of digital technology, data, artificial intelligence and automation to make farming more efficient and more sustainable. Each layer below comes with the test it must pass before it is used.
| Layer | What it does | Test before using it |
|---|---|---|
| Identification | Builds on the approved ear tags that Canadian cattle and sheep already carry under national livestock identification rules (CFIA), plus a lot number for each batch. | Can every kilogram be traced to an animal or lot? |
| Measurement | Calibrated scales at each stage, time-stamped records, cold-chain temperature logs. | Would an independent auditor get the same numbers? |
| Shared records | A simple dashboard that operators and researchers read from the same data. | Does it improve data quality at a justified cost? |
| AI, narrowly | Flags impossible weights, duplicate IDs and missing records. Drafts English, French and Arabic training material. People review every result. | Does it beat a simple non-AI baseline? |
| Privacy | Personal and commercially sensitive records stay under local control and are never sent to unapproved AI systems. | Does it meet PIPEDA and Alberta’s PIPA? |
What we have and do not have
What we have
- A registered Canadian charity in Alberta, with a community behind it.
- A published, openly licensed method, ready to test.
- A question that matters to Canadian livestock and food-loss research.
What we do not have
- No farm and no flock.
- No data. The first measurement has not been made.
- No agreement with any university, yet.
Universities
How we would like to work with universities
Start small
An honours or Master’s project on the QUI needs no memorandum. A willing supervisor and a student are enough to begin.
Declare our interest
We would prefer the answer to come out a certain way. That is exactly why the measurement should be made by people for whom it would not.
The first paper is yours
A measurement an interested party produces alone will not be believed, and should not be. The first publication belongs to the researchers.
Supervise a student project, or tell us who should.
Work with us