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

REVIEW 2 major objections 2 minor 13 references

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

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

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.

T0 review reviewed 2026-05-17 challenge →

load-bearing objection The paper introduces two new environmental indices and reports strong XGBoost RMSE numbers on a very large single-site cattle dataset, but all results stay within one Idaho feedlot with no external checks shown. the 2 major comments →

arxiv 2511.17663 v1 pith:LDDGNOJN submitted 2025-11-20 cs.LG cs.AIcs.SYeess.SY

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

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

classification cs.LG cs.AIcs.SYeess.SY

keywords feed intake predictionXGBoostenvironmental indicesfeedlot beef cattlemachine learningprecision livestock farmingthermal comfortbig data agriculture

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper develops an AI-based system to forecast how much feed beef cattle consume each day in feedlots by combining machine learning with fresh measures of environmental conditions and animal behavior. Researchers created the InComfort-Index from weather data alone and the EASI-Index that mixes weather with observed feeding patterns, then trained models on more than 16 million records from one Idaho facility. The best model, XGBoost, achieved a root mean square error of 1.38 kg per day at the animal level and 0.14 kg per day per animal at the pen level, suggesting the approach could support more precise feeding decisions.

Core claim

Using longitudinal data from over 16.5 million samples across 19 experiments at a single Idaho feedlot together with weather records, the work shows that the EASI-Index, which integrates meteorological variables with feed intake behavior, combined with the XGBoost algorithm produces accurate predictions of individual animal feed intake (RMSE 1.38 kg/day) and pen-level aggregation (RMSE 0.14 kg/day-animal).

What carries the argument

The EASI-Index, a hybrid environmental measure that combines meteorological variables with observed feed intake behavior to improve intake forecasts beyond purely weather-based indices.

Load-bearing premise

That the environmental indices and XGBoost model trained on data from one Idaho feedlot will deliver similar accuracy when used with cattle in other locations, breeds, or management systems.

What would settle it

Testing the trained model on feed intake and weather data from a feedlot in a different climate or region and measuring whether the RMSE stays near 1.38 kg/day for animals and 0.14 kg/day-animal for pens.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

The framework enables daily forecasts that can guide precision feeding for each animal in a feedlot.
Accurate intake predictions support efforts to reduce feed waste during cattle finishing.
Pen-level results allow resource optimization without requiring individual monitoring for every animal.
The method provides a basis for climate-adaptive management by incorporating weather effects into intake estimates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The indices could be retrained on data from other regions to check whether the same low error rates hold outside Idaho conditions.
Pen-level accuracy raises the possibility of using the model for group-level feeding adjustments in systems with limited individual sensors.
Real-time linkage to weather stations might allow the predictions to trigger automatic changes in feed delivery schedules.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

The paper introduces two new environmental indices and reports strong XGBoost RMSE numbers on a very large single-site cattle dataset, but all results stay within one Idaho feedlot with no external checks shown.

read the letter

This paper creates two new indices, InComfort-Index from weather data alone and EASI-Index that folds in feed intake behavior, then plugs them into standard ML models to predict individual and pen-level feed intake. The headline numbers are RMSE of 1.38 kg/day at animal level and 0.14 kg per day per animal at pen level, drawn from more than 16.5 million samples across 19 experiments at one facility in Carmen, Idaho. That scale of real longitudinal data is the clearest strength here. It lets the authors move past toy examples and show concrete error on actual feedlot records paired with local weather station data. The indices themselves are a straightforward domain extension that tries to capture thermal effects on feeding without overcomplicating the inputs. The work is honest about what each index does well and where it falls short for the two target tasks. The main soft spot is the complete lack of external validation. Every training and test point comes from the same Idaho site and management system. The EASI-Index pulls in behavioral signals that are likely tuned to local patterns, so the low pen-level error could shrink or disappear when the weather distribution or animal genetics shift. The abstract gives no details on how the 19 experiments were split, what baselines were used, or whether any form of cross-site hold-out was attempted. Those gaps make it hard to judge whether the framework is ready for the precision-management uses the authors mention. This is the kind of paper that matters to people working on smart livestock systems or precision feeding tools. A reader who already deals with animal intake data or environmental indices in agriculture will get the most out of it. It deserves a serious referee because the dataset size and the new indices give the work enough substance to review, even though the generalization claim needs more evidence. I would send it for peer review and ask the authors to add a clear validation plan or at least report performance on a temporal or experiment-level hold-out that mimics a new location.

Referee Report

2 major / 2 minor

Summary. The paper develops an AI-based framework for predicting daily feed intake in feedlot beef cattle at both individual-animal and pen-aggregated levels. It introduces two new environmental indices (InComfort-Index from meteorological variables alone and EASI-Index that additionally incorporates feed-intake behavior), combines them with machine-learning models trained on >16.5 M samples from 19 experiments conducted at a single Idaho feedlot (2013–2024), and reports that XGBoost achieves RMSE = 1.38 kg/day at the animal level and 0.14 kg/(day-animal) at the pen level. The work positions the framework as a tool for precision livestock management, feed-waste reduction, and climate-adaptive operations.

Significance. If the reported accuracy proves robust under distribution shift, the combination of large-scale longitudinal intake records with compact environmental indices could supply a practical, deployable component for precision feeding systems. The empirical scale (>16.5 M samples) and the explicit separation of thermal-comfort versus intake-prediction performance of the two indices are concrete strengths that would be useful to the livestock-modeling community.

major comments (2)

[Abstract / Results] Abstract and results sections: all training, validation, and test data originate from 19 experiments at a single facility (Nancy M. Cummings Research Extension & Education Center, Carmen, ID). No external test set, cross-site evaluation, or transfer experiment on a different breed, climate, or management regime is reported. Because the headline claim is that the framework supports “precision management of feedlot cattle” across locations, the absence of any out-of-distribution test directly undermines the transportability assertion.
[Methods / Results] Methods / Results: the abstract and available text give no description of the cross-validation strategy, the train/test split across the 19 experiments, or any baseline models (linear regression, random forest, etc.). Without these details it is impossible to judge whether the reported RMSE values reflect genuine predictive power or site-specific correlations captured by the EASI-Index.

minor comments (2)

[Abstract] Abstract: the phrase “only 0.14 kg/(day-animal)” at pen level should be accompanied by the corresponding animal-level figure for direct comparison; the current wording is ambiguous.
[Abstract] Notation: the units “kg/(day-animal)” are clear but should be defined explicitly the first time they appear to avoid reader confusion with per-animal versus total-pen quantities.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment below and describe the revisions we will make to improve the manuscript.

read point-by-point responses

Referee: [Abstract / Results] Abstract and results sections: all training, validation, and test data originate from 19 experiments at a single facility (Nancy M. Cummings Research Extension & Education Center, Carmen, ID). No external test set, cross-site evaluation, or transfer experiment on a different breed, climate, or management regime is reported. Because the headline claim is that the framework supports “precision management of feedlot cattle” across locations, the absence of any out-of-distribution test directly undermines the transportability assertion.

Authors: We agree that the data come exclusively from one facility and that this constrains claims of broad transportability. The 19 experiments span 11 years and capture substantial within-site variation in weather and management, but they do not substitute for multi-site testing. In the revision we will add an explicit limitations paragraph, moderate language in the abstract and conclusions to refer to “similar feedlot operations” rather than “across locations,” and list cross-site validation as a priority for future work. revision: partial
Referee: [Methods / Results] Methods / Results: the abstract and available text give no description of the cross-validation strategy, the train/test split across the 19 experiments, or any baseline models (linear regression, random forest, etc.). Without these details it is impossible to judge whether the reported RMSE values reflect genuine predictive power or site-specific correlations captured by the EASI-Index.

Authors: We acknowledge that these methodological details were insufficiently described. The revised manuscript will expand the Methods section to specify the cross-validation procedure, the exact train/validation/test partitioning across the 19 experiments (including whether splits were performed at the experiment level), and performance of baseline models (linear regression and random forest) alongside XGBoost. These additions will allow readers to assess whether the reported accuracy exceeds what site-specific correlations alone would produce. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ML predictions on held-out data from single-site longitudinal records

full rationale

The paper reports standard supervised machine-learning results (XGBoost RMSE on animal- and pen-level feed intake) obtained after constructing two environmental indices as input features. These performance numbers arise from training and testing on held-out samples within the >16.5 M record dataset; they do not reduce algebraically to fitted constants or to the target variable by construction. No self-definitional equations, uniqueness theorems, or load-bearing self-citations appear in the derivation chain. The work is therefore self-contained as an applied predictive-modeling study whose validity hinges on external generalization rather than internal circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The framework rests on standard supervised-learning assumptions plus two newly defined indices whose construction details are not fully specified in the abstract.

free parameters (1)

XGBoost hyperparameters
Learning rate, tree depth, and regularization parameters chosen during model training; not reported in abstract.

axioms (1)

domain assumption The 19 experiments provide representative samples of feedlot conditions
Invoked when claiming general applicability of the trained models.

invented entities (2)

InComfort-Index no independent evidence
purpose: Summarize meteorological variables into a single thermal-comfort score
New composite index introduced in the paper; no independent validation outside this dataset.
EASI-Index no independent evidence
purpose: Hybrid index combining environment and observed feed-intake behavior
New composite index introduced in the paper; no independent validation outside this dataset.

reviewed 2026-05-17 · how reviews work

0 comments

Cite this review

Pith. "Pith review of AI-based framework to predict animal and pen feed intake in feedlot beef cattle." pith.science (2026). https://pith.science/paper/LDDGNOJN

@misc{pith2026251117663,
  author       = {Pith},
  title        = {Pith review of: AI-based framework to predict animal and pen feed intake in feedlot beef cattle},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LDDGNOJN}},
  note         = {Machine review of arXiv:2511.17663}
}

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read the original abstract

Advances in technology are transforming sustainable cattle farming practices, with electronic feeding systems generating big longitudinal datasets on individual animal feed intake, offering the possibility for autonomous precision livestock systems. However, the literature still lacks a methodology that fully leverages these longitudinal big data to accurately predict feed intake accounting for environmental conditions. To fill this gap, we developed an AI-based framework to accurately predict feed intake of individual animals and pen-level aggregation. Data from 19 experiments (>16.5M samples; 2013-2024) conducted at Nancy M. Cummings Research Extension & Education Center (Carmen, ID) feedlot facility and environmental data from AgriMet Network weather stations were used to develop two novel environmental indices: InComfort-Index, based solely on meteorological variables, showed good predictive capability for thermal comfort but had limited ability to predict feed intake; EASI-Index, a hybrid index integrating environmental variables with feed intake behavior, performed well in predicting feed intake but was less effective for thermal comfort. Together with the environmental indices, machine learning models were trained and 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. This approach provides a robust AI-based framework for predicting feed intake in individual animals and pens, with potential applications in precision management of feedlot cattle, through feed waste reduction, resource optimization, and climate-adaptive livestock management.

Figures

Figures reproduced from arXiv: 2511.17663 by Alex S. C. Maia, Hugo F. M. Milan, Izabelle A. M. A. Teixeira, John B. Hall.

**Figure 3.** Figure 3: Feeding behavior traits and feed intake for one experimental animal. (A) Total eating [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 4.** Figure 4: Diurnal variation of meteorological variables recorded by Corvallis (continuous blue lines; 2013–2019) and Salmon (dashed orange lines; 2020–2024) weather stations. Means were smoothed using a centered 3-hour moving average [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: Yearly variation of meteorological variables recorded by Corvallis (continuous blue [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Environmental indices throughout the year. (A) InComfort-Index primarily reflects [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗

**Figure 7.** Figure 7: Performance of machine learning models to predict individual animal feed intake. (A) [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗

**Figure 8.** Figure 8: Relative importance of predictors in the best-performing model (XGBoost) calculated [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗

**Figure 9.** Figure 9: SHAP summary plot for the best performing model (XGBoost) ranked by predictor [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗

**Figure 10.** Figure 10: Simulation of predicted feed intake as a function of environmental indices using the [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗

**Figure 11.** Figure 11: Performance of the best prediction model (XGBoost; [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

XGBoost RMSE of 1.38 kg/day animal-level and 0.14 kg/(day-animal) pen-level using InComfort-Index and EASI-Index on 16.5M samples from single Carmen, ID feedlot
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Two novel environmental indices developed via PCA and gradient-boosted trees; no mention of recognition cost or golden-ratio identities

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages

[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 gradient boosting regression techniques and Gaussian process boosting regression with Shapley additive explanation explainable artificial intelligence, MLflow, and ...

work page doi:10.3390/ani10091486 2025
[2]

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

doi:10.3168/ jds.2017-13676 Chang-Fung-Martel J, Harrison MT, Brown JN, Rawnsley R, Smith AP, Meinke H. Negative relationship between dry matter intake and the temperature-humidity index with increasing heat stress in cattle: A global meta-analysis. International Journal of Biometeorology 65:2099–2109,

work page 2017
[3]

XGBoost: A scalable tree boosting system

https://doi.org/10.1007/s00484-021-02138-6 30 / 33 Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 785– 794,

work page doi:10.1007/s00484-021-02138-6
[4]

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

https://doi.org/10.1145/2939672.2939785 DelCurto-Wyffels HM, Dafoe JM, Parsons CT, Boss DL, DelCurto T, Wyffels SA, Van Emon, M.L, Bowman JGP. The influence of environmental conditions on intake behavior and activity by feedlot steers fed corn or barley -based diets. Animals 11(5):1261,

work page doi:10.1145/2939672.2939785
[5]

Measuring the feeding behavior of lactating dairy cows in early to peak lactation

https://doi.org/10.3390/ani11051261 DeVries TJ, von Keyserlingk MAG, Weary DM, Beauchemin KA. Measuring the feeding behavior of lactating dairy cows in early to peak lactation. Journal of Dairy Science 86(10):3354–3361,

work page doi:10.3390/ani11051261
[6]

Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets

https://doi.org/10.1016/j.atech.2017.12.001 Gorczica MT, Milan HFM, Maia ASC, Gebremedhin KG. Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets. Computers and Electronics in Agriculture 151:286-294,

work page doi:10.1016/j.atech.2017.12.001 2017
[7]

31/33 Mader TL, Johnson LJ, Gaughan JB

https://doi.org/10.3168/jds.2016-11047 pg. 31/33 Mader TL, Johnson LJ, Gaughan JB. A comprehensive index for assessing environmental stress in animals. Journal of Animal Science 88(6)2:153–2165,

work page doi:10.3168/jds.2016-11047 2016
[8]

Economically sustainable shade design for feedlot cattle

https://doi.org/10.2527/jas.2009- 2586 Maia ASC, Moura GAB, Fonsêca VFC, Gebremedhin KG, Milan HFM, Chiquitelli Neto M, Simão BR, Campanelli VPC, Pacheco RDL. Economically sustainable shade design for feedlot cattle. Frontiers in Veterinary Science, 10:1110671,

work page doi:10.2527/jas.2009- 2009
[9]

Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids

https://doi.10.3389/fvets.2023.1110671 Montanholi YR, Swanson KC, Palme R, Schenkel FS, McBride BW, Lu D, Miller SP. Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids. Animal 4(5)6:92–701,

work page doi:10.3389/fvets.2023.1110671 2023
[10]

Prediction of optimum supplemental heat for piglets

https://doi.org/10.1017/S1751731110000032 Milan HFM, Maia ASC, Gebremedhin KG. Prediction of optimum supplemental heat for piglets. Transaction of the ASABE 62:321-342,

work page doi:10.1017/s1751731110000032
[11]

LightGBM: A highly efficient gradient boosting decision tree

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY . LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30:2017 32 / 33 Løvendahl P, Munksgaard L. An investigation into genetic and phenotypic variation in time budgets and yield of dairy cows. Journal of Dairy Science 99:408–417, 2016 Lun...

work page 2017
[12]

33/33 Wolfert S, Ge L, Verdouw C, Bogaardt MJ

https://doi.org/10.1016/j.jtherbio.2018.08.005 pg. 33/33 Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big Data in Smart Farming – A review. Agricultural Systems, 153:69–80,

work page doi:10.1016/j.jtherbio.2018.08.005 2018
[13]

https://doi.org/10.1016/j.agsy.2017.01.023

work page doi:10.1016/j.agsy.2017.01.023 2017

This paper was first reviewed by grok-4.3 on May 17, 2026.

[1] [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 gradient boosting regression techniques and Gaussian process boosting regression with Shapley additive explanation explainable artificial intelligence, MLflow, and ...

work page doi:10.3390/ani10091486 2025

[2] [2]

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

doi:10.3168/ jds.2017-13676 Chang-Fung-Martel J, Harrison MT, Brown JN, Rawnsley R, Smith AP, Meinke H. Negative relationship between dry matter intake and the temperature-humidity index with increasing heat stress in cattle: A global meta-analysis. International Journal of Biometeorology 65:2099–2109,

work page 2017

[3] [3]

XGBoost: A scalable tree boosting system

https://doi.org/10.1007/s00484-021-02138-6 30 / 33 Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 785– 794,

work page doi:10.1007/s00484-021-02138-6

[4] [4]

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

https://doi.org/10.1145/2939672.2939785 DelCurto-Wyffels HM, Dafoe JM, Parsons CT, Boss DL, DelCurto T, Wyffels SA, Van Emon, M.L, Bowman JGP. The influence of environmental conditions on intake behavior and activity by feedlot steers fed corn or barley -based diets. Animals 11(5):1261,

work page doi:10.1145/2939672.2939785

[5] [5]

Measuring the feeding behavior of lactating dairy cows in early to peak lactation

https://doi.org/10.3390/ani11051261 DeVries TJ, von Keyserlingk MAG, Weary DM, Beauchemin KA. Measuring the feeding behavior of lactating dairy cows in early to peak lactation. Journal of Dairy Science 86(10):3354–3361,

work page doi:10.3390/ani11051261

[6] [6]

Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets

https://doi.org/10.1016/j.atech.2017.12.001 Gorczica MT, Milan HFM, Maia ASC, Gebremedhin KG. Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets. Computers and Electronics in Agriculture 151:286-294,

work page doi:10.1016/j.atech.2017.12.001 2017

[7] [7]

31/33 Mader TL, Johnson LJ, Gaughan JB

https://doi.org/10.3168/jds.2016-11047 pg. 31/33 Mader TL, Johnson LJ, Gaughan JB. A comprehensive index for assessing environmental stress in animals. Journal of Animal Science 88(6)2:153–2165,

work page doi:10.3168/jds.2016-11047 2016

[8] [8]

Economically sustainable shade design for feedlot cattle

https://doi.org/10.2527/jas.2009- 2586 Maia ASC, Moura GAB, Fonsêca VFC, Gebremedhin KG, Milan HFM, Chiquitelli Neto M, Simão BR, Campanelli VPC, Pacheco RDL. Economically sustainable shade design for feedlot cattle. Frontiers in Veterinary Science, 10:1110671,

work page doi:10.2527/jas.2009- 2009

[9] [9]

Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids

https://doi.10.3389/fvets.2023.1110671 Montanholi YR, Swanson KC, Palme R, Schenkel FS, McBride BW, Lu D, Miller SP. Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids. Animal 4(5)6:92–701,

work page doi:10.3389/fvets.2023.1110671 2023

[10] [10]

Prediction of optimum supplemental heat for piglets

https://doi.org/10.1017/S1751731110000032 Milan HFM, Maia ASC, Gebremedhin KG. Prediction of optimum supplemental heat for piglets. Transaction of the ASABE 62:321-342,

work page doi:10.1017/s1751731110000032

[11] [11]

LightGBM: A highly efficient gradient boosting decision tree

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY . LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30:2017 32 / 33 Løvendahl P, Munksgaard L. An investigation into genetic and phenotypic variation in time budgets and yield of dairy cows. Journal of Dairy Science 99:408–417, 2016 Lun...

work page 2017

[12] [12]

33/33 Wolfert S, Ge L, Verdouw C, Bogaardt MJ

https://doi.org/10.1016/j.jtherbio.2018.08.005 pg. 33/33 Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big Data in Smart Farming – A review. Agricultural Systems, 153:69–80,

work page doi:10.1016/j.jtherbio.2018.08.005 2018

[13] [13]

https://doi.org/10.1016/j.agsy.2017.01.023

work page doi:10.1016/j.agsy.2017.01.023 2017

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

The reading

Core claim §

What carries the argument

Load-bearing premise §

What would settle it §

If this is right

Where Pith is reading between the lines

discussion (0)

Lean theorems connected to this paper

Core claim

Load-bearing premise

What would settle it