A biomechanics and machine learning framework for sustainable livestock behavior analysis and regional economic optimization

Abstract

This study proposes an integrated framework combining biomechanics, spatial econometrics, and machine learning to enhance sustainable livestock management. Using panel data from 11 provinces over a two-decade period, we analyse the nonlinear relationship between livestock concentration and regional economic output using Cobb-Douglas functions and dynamic panel regression with GMM estimation. Location entropy indices reveal spatial disparities in livestock agglomeration, while robustness is ensured via VIF, root tests, and LSDVC correction. A case study on meerkat behavior demonstrates the effectiveness of a hybrid SVM model in classifying key behaviors—vigilance, resting, foraging, and running—from over 82,000 labeled video bouts. The model achieves high classification accuracy and interpretability. Results highlight the synergy of biomechanical analysis and intelligent classification in improving animal welfare monitoring, while spatial models inform balanced regional livestock development. The proposed framework offers actionable insights for eco-agriculture policy, smart farming systems, and sustainable rural economic planning.