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

• Autores: Ninahuanca Carhuas, Jordan, Garcia-Olarte, Edgar, Unchupaico Payano, Ide, Sarapura, Vicky, Zenteno Vera, Kevin, Quispe Eulogio, Carlos, Ancco Gomez, Edith, M. Hadi, Mohamed Mohamed, Miranda-Torpoco, Carolina, Guerra Condor, Wilhelm
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional de Trujillo
• Repositorio: Revistas - Universidad Nacional de Trujillo
• Lenguaje: inglés; español
• OAI Identifier: oai:ojs.revistas.unitru.edu.pe:article/6221
• Enlace del recurso: https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/6221
• Nivel de acceso: acceso abierto
• Materia: biometrics; predictive models; mathematical models; young sheep; zoometrical

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 as the 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.