A Framework for Modeling Animal Welfare Assessment in Sustainable Livestock Systems

Thesis Abstract

Effective assessment of animal welfare within sustainable livestock systems remains a critical challenge, owing to the complexity of balancing production efficiency, ethical considerations, and environmental impacts. Current evaluation frameworks often lack consistency, scalability, or integration into broader sustainability metrics, thereby limiting their utility for stakeholders seeking to improve livestock management practices. This study aims to develop a comprehensive, model-based framework capable of quantifying and predicting animal welfare outcomes in diverse livestock systems, facilitating informed decision-making aligned with sustainability principles. The specific objectives include (1) identifying key animal welfare indicators relevant to sustainable livestock production; (2) developing an integrative conceptual model that links animal health, behavior, environmental conditions, and management practices; (3) formulating a mathematical or computational model to synthesize these indicators into a composite welfare score; and (4) validating the framework through empirical data collected from diverse livestock farms. The research adopts a mixed-methods approach, combining qualitative and quantitative techniques within a cross-sectional design. The population comprises 150 livestock farms across the region, representing cattle, sheep, and poultry systems. A stratified random sampling technique ensures representative diversity in farm size, management system, and geographic location. Data collection employs structured questionnaires for farm management data and animal health records, coupled with on-farm welfare assessments using standardized protocols such as Welfare Quality® indicators. Additionally, semi-structured interviews with farm managers and animal health experts inform contextual understanding. The instrument validity is ensured through expert review, and reliability is tested via Cronbach’s alpha coefficients exceeding 0.85. Analytical methods include multivariate regression analysis to identify significant predictors of animal welfare outcomes, as well as factor analysis to reduce dimensionality of indicators. Structural equation modeling (SEM) is used to test the hypothesized relationships within the proposed conceptual framework, allowing for the assessment of direct and indirect effects of management practices and environmental factors on animal welfare. The model is further refined through sensitivity analysis to ensure robustness and applicability across different livestock systems. Data analysis is performed using SPSS, AMOS, and R statistical software packages, with results interpreted within the context of established theoretical frameworks such as the Five Domains Model and the Theory of Planned Behavior, which underpin behavioral and welfare assessments. Expected findings include a validated, user-friendly model that objectively integrates multiple welfare indicators into a single composite score, correlated with farm management practices, environmental variables, and production parameters. The results are anticipated to demonstrate significant relationships between variables such as pasture quality, stocking density, and welfare scores, confirming the utility of the framework in real-world settings. The study contributes to knowledge by extending existing welfare assessment tools into a holistic, systems-level model tailored for sustainable livestock production, filling a notable gap in the literature where current models tend to focus on isolated indicators rather than integrated assessments. The research concludes with evidence-based recommendations for policymakers, livestock producers, and animal welfare practitioners to adopt the framework for routine welfare evaluations and sustainable farm management. It advocates for integrating the model into regional certification schemes and developing decision-support tools grounded in the framework’s principles. Further research suggestions include longitudinal testing of the model’s predictive capacity over time, adaptation for specific species or regional contexts, and integration with environmental impact assessments to strengthen sustainability evaluations comprehensively. This study advances theoretical understanding of animal welfare within sustainability paradigms and offers a practical, scalable tool for improving livestock management globally.