Data-driven prediction of cattle weight gain for evaluating key growth factors with machine learning approaches Article Swipe
Kenneth Tuyishime
,
Michael Adelani Adewusi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10791-025-09798-6
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10791-025-09798-6
Accurate prediction of cattle weight gain is critical for optimizing herd management, improving productivity, and promoting sustainable livestock practices. Traditional monitoring methods, such as manual data collection and periodic surveys, often lack the precision and timeliness required for reliable forecasting. This study evaluated multiple machine learning models Linear Regression, Decision Tree Regression, XGBoost, and Support Vector Regression (SVM) to predict cattle weight at 36 months and identify the key factors influencing growth. A comprehensive historical dataset was preprocessed to handle missing values, correct inconsistencies, and engineer relevant features. Models were assessed using R-squared (R 2 ), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) to evaluate predictive accuracy and robustness. Results indicated that weights at 30, 24, and 18 months were the strongest predictors of final weight, demonstrating the cumulative effect of early growth. Additional influential factors included breed (notably Zebu), grazing type, and cattle movement frequency, while birth weight showed a negative association, reflecting compensatory growth in lighter-born calves. Environmental variables such as temperature and seasonal conditions had moderate but consistent effects. The Decision Tree model, with the highest predictive performance (R 2 = 0.927; MAE = 2.21 kg; RMSE = 2.95 kg), provided the most interpretable and actionable insights. Linear Regression, XGBoost, and SVM offered complementary predictions but with slightly lower accuracy or interpretability. These findings provide quantitative insights into cattle growth dynamics and demonstrate the utility of machine learning for data-driven decision-making in livestock management. The study supports the adoption of predictive models, improved data collection protocols, and targeted management interventions during critical growth periods to optimize cattle production sustainably.
Related Topics
Concepts
Decision tree
Machine learning
Mean squared error
Support vector machine
Artificial intelligence
Statistics
Mathematics
Beef cattle
Computer science
Random forest
Regression
Tree (set theory)
Predictive modelling
Linear regression
Livestock
Feature selection
Key (lock)
Linear model
Regression analysis
Forage
Information gain ratio
Decision tree learning
Mean squared prediction error
Herd
Dairy cattle
Data mining
Breed
Data collection
Stepwise regression
Selection (genetic algorithm)
Pearson product-moment correlation coefficient
Econometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10791-025-09798-6
- https://link.springer.com/content/pdf/10.1007/s10791-025-09798-6.pdf
- OA Status
- diamond
- References
- 23
- OpenAlex ID
- https://openalex.org/W7106282236
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106282236Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10791-025-09798-6Digital Object Identifier
- Title
-
Data-driven prediction of cattle weight gain for evaluating key growth factors with machine learning approachesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-21Full publication date if available
- Authors
-
Kenneth Tuyishime, Michael Adelani AdewusiList of authors in order
- Landing page
-
https://doi.org/10.1007/s10791-025-09798-6Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10791-025-09798-6.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10791-025-09798-6.pdfDirect OA link when available
- Concepts
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Decision tree, Machine learning, Mean squared error, Support vector machine, Artificial intelligence, Statistics, Mathematics, Beef cattle, Computer science, Random forest, Regression, Tree (set theory), Predictive modelling, Linear regression, Livestock, Feature selection, Key (lock), Linear model, Regression analysis, Forage, Information gain ratio, Decision tree learning, Mean squared prediction error, Herd, Dairy cattle, Data mining, Breed, Data collection, Stepwise regression, Selection (genetic algorithm), Pearson product-moment correlation coefficient, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
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| referenced_works | https://openalex.org/W4405872760, https://openalex.org/W2113281985, https://openalex.org/W4389237989, https://openalex.org/W3165319944, https://openalex.org/W2077052683, https://openalex.org/W4385318219, https://openalex.org/W2556347197, https://openalex.org/W3094525182, https://openalex.org/W3088885014, https://openalex.org/W429766147, https://openalex.org/W1889308939, https://openalex.org/W3033615074, https://openalex.org/W4396900774, https://openalex.org/W2587466508, https://openalex.org/W2999840167, https://openalex.org/W2905099077, https://openalex.org/W2885770726, https://openalex.org/W3179864758, https://openalex.org/W2462583090, https://openalex.org/W3119306837, https://openalex.org/W1973388069, https://openalex.org/W3011832530, https://openalex.org/W3132295338 |
| referenced_works_count | 23 |
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