Balancing accuracy, interpretability, and stability in machine-learning models - Live-weight prediction of Andean sheep from morphometric traits - Research article

Aug 5, 2026

This research predicts the live weight of Corriedale lambs using morphometric measurements and machine-learning algorithms, leveraging a homogeneous dataset of 291 five-month-old lambs. Morphological traits included body length, withers height, thoracic girth, rump width, abdominal girth, cannon bone length, chest depth, and live weight. Models compared were multiple linear regression, ridge regression, decision trees, random forest, and XGBoost. Results identified ModG and ridge as the most accurate and stable, with low MSE (0.083), RMSE (~0.287–0.288), strong fit (R2=0.89, RAdj2=0.88), and low CV%, supporting faster data-driven decisions. XGBoost remained a robust alternative. The approach enables precise feeding allocation, market-weight classification, and early detection of growth deviations to improve flock profitability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

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Abstract

Introduction

SCIENTIA AGROPECUARIA Scientia Agropecuaria Web page: [http://revistas.unitru.edu.pe/index.php/scientiaagrop](http://revistas.unitru.edu.pe/index.php/scientiaagrop) RESEARCH ARTICLE Balancing accuracy, interpretability, and stability in machine-learning models: Live-weight prediction of Andean sheep from morphometric traits Jordan Ninahuanca1 * ; Edgar Garcia-Olarte1 ; Ide Unchupaico Payano1 ; Vicky Sarapura2 Kevin Zenteno Vera1 ; Carlos Quispe Eulogio3 ; Edith Ancco Gomez3 ; Mohamed Mohamed M. Hadi4 Carolina Miranda-Torpoco4 ; Wilhelm Guerra Condor3 1 Facultad de Zootecnia, Universidad Nacional del Centro del Perú, Huancayo, 12006, Av. Mariscal Castilla N° 3909, Junín, Perú . 2 Facultad de Ciencias Forestales y del Ambiente, Universidad Nacional del Centro del Perú, Huancayo, 12006, Av. Mariscal Castilla N° 3909, Junín, Perú . 3 Facultad de Ciencias de la Salud, Universidad Peruana Los Andes, Huancayo, 12002, Perú . 4 Facultad de Ingeniería, Universidad Peruana Los Andes, Huancayo, 12002, Perú . * Corresponding [author:](author: jninahuanca@uncp.edu.pe)[ jninahuanca@uncp.edu.pe](author: jninahuanca@uncp.edu.pe) (J. Ninahuanca) . Received: 28 January 2025. Accepted: 22 July 2025. Published: 8 August 2025. Abstract The objective of this research was to predict the live weight of Corriedale lambs using morphological measurements and machine learning algorithms. A total of 291 five-month-old lambs from the Corpacancha Production Unit of SAIS PACHACÚTEC SAC were used. These animals represented a homogeneous group in terms of age, sex, and genetics, as they belonged to the Corriedale breed and were offspring of "Category A" ewes. Morphological measurements recorded included Body Length (BL), Withers Height (WH), Thoracic Girth (TG), Rump Width (RW), Abdominal Girth (AG), Cannon Bone Length (CBL), Chest Depth (CD), and Live Weight (LW). The models evaluated were Multiple Linear Regression, Ridge Regression, Decision Trees, Random Forest, and XGBoost. The comparative analysis of the machine learning models identified ModG and Ridge asthe most accurate and stable options, standing out for their low Mean Squared Error (MSE = 0.083) and Root Mean Squared Error (RMSE ≈ 0.287 – 0.288). Additionally, they exhibited the highest coefficients of determination (R2 = 0 .89, RAdj2 = 0.88), indicating excellent predictive capability and data fit. Their low coefficient of variation (CV%) confirms their stability, establishing them as the best choices for applications where precision is paramount, such as predicting critical values in production processes and high-demand scientific studies. While XGBoost proved to be a robust alternative with an MSE of 0.119, an RMSE of 0.345, and a relative error of 2.22% . These findings confirm that prioritizing models that balance accuracy, interpretability, and stability enable faster, data-driven decision-making in Corriedale sheep production. Such an approach optimizes feed allocation, classifies lambs by market weight, and promptly detects growth deviations, thereby improving overall flock profitability. Keywords: biometrics; predictive models; mathematical models; young sheep; zoometrical. DOI: [https://doi.org/10.17268/sci.agropecu.2025.037](https://doi.org/10.17268/sci.agropecu.2025.037) [Cite this article:](Cite this article:) Ninahuanca, J., Garcia-Olarte, E., Unchupaico Payano, I., Sarapura, V., Zenteno Vera, K., Quispe Eulogio, C., Ancco Gomez, E., Mohamed, M., Miranda-Torpoco, C., & Guerra Condor, W. (2025) . Balancing accuracy, interpretability, and stability in machine-learning models: Liveweight prediction of Andean sheep from morphometric traits. Scientia Agropecuaria, 16(4), 487-498. 1. Introduction Across the Andean highlands of South America, sheep husbandry remains a cornerstone of rural livelihoods, and ovine meat is increasingly preferred over other animal proteins by local consumers (Ninahuanca Carhuas et al., 2025). The regional sheep sector never

FAQs

What is the main goal of the study?

To predict the live weight of Corriedale lambs using morphological measurements and machine-learning models.

Which morphometric traits were used as inputs?

Body Length (BL), Withers Height (WH), Thoracic Girth (TG), Rump Width (RW), Abdominal Girth (AG), Cannon Bone Length (CBL), Chest Depth (CD), and Live Weight (LW).

Which models performed best for accuracy and stability?

ModG and ridge showed the highest accuracy and stability, with low MSE (0.083), RMSE (~0.287–0.288), and strong determination coefficients (R2=0.89, RAdj2=0.88).

Aug 5, 2026

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