Pith Number

pith:LDDGNOJN

pith:2025:LDDGNOJNQDLWTN7FB2SDHRNMB2

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AI-based framework to predict animal and pen feed intake in feedlot beef cattle

Alex S. C. Maia, Hugo F. M. Milan, Izabelle A. M. A. Teixeira, John B. Hall

An AI framework with two new environmental indices and XGBoost predicts daily feed intake for individual feedlot cattle and pens with low error rates.

arxiv:2511.17663 v1 · 2025-11-20 · cs.LG · cs.AI · cs.SY · eess.SY

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

the best-performing machine learning model (XGBoost) accuracy was RMSE of 1.38 kg/day for animal-level and only 0.14 kg/(day-animal) at pen-level.

C2weakest assumption

That the two new environmental indices and the XGBoost model trained on data from a single Idaho feedlot will generalize to other locations, breeds, and management systems without substantial retraining or performance loss.

C3one line summary

An AI framework introduces InComfort-Index and EASI-Index, then uses XGBoost to predict cattle feed intake at animal level (RMSE 1.38 kg/day) and pen level (RMSE 0.14 kg/day-animal) from 16.5 million samples across 19 experiments.

References

13 extracted · 13 resolved · 0 Pith anchors

[1] https://doi.org/10.3390/ani10091486 ArunKumar KE, Blake NE, Walker M, Yost TJ, Mata Padrino D, Holásková I, Yates JW, Hatton J, Wilson ME. Predicting dry matter intake in cattle at scale using gradien 2025 · doi:10.3390/ani10091486

[2] Negative relationship between dry matter intake and the temperature-humidity index with increasing heat stress in cattle: A global meta-analysis 2017

[3] XGBoost: A scalable tree boosting system · doi:10.1007/s00484-021-02138-6

[4] Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages = · doi:10.1145/2939672.2939785

[5] Measuring the feeding behavior of lactating dairy cows in early to peak lactation · doi:10.3390/ani11051261

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-07-21T01:20:39.903389Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

58c666b92d80d769b7e50ea433c5ac0ebfc21ecd77de6e2040f7fa9423598aae

Aliases

arxiv: 2511.17663 · arxiv_version: 2511.17663v1 · doi: 10.48550/arxiv.2511.17663 · pith_short_12: LDDGNOJNQDLW · pith_short_16: LDDGNOJNQDLWTN7F · pith_short_8: LDDGNOJN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LDDGNOJNQDLWTN7FB2SDHRNMB2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 58c666b92d80d769b7e50ea433c5ac0ebfc21ecd77de6e2040f7fa9423598aae

Canonical record JSON

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      "eess.SY"
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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-11-20T22:43:53Z",
    "title_canon_sha256": "cb93b8ea38dc21b4380540bd213f38bfe338a51bfb097379d48355a6399622a0"
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