Developing a Holistic Behavioral Assessment Framework for Sheep Welfare Optimization

Thesis Abstract

In the context of increasing global emphasis on animal welfare and sustainable livestock production, this study addresses the critical need for a comprehensive and holistic assessment framework for evaluating sheep behavior to optimize welfare outcomes. The traditional approaches to sheep welfare assessment largely focus on physiological indicators and production metrics, often overlooking the behavioral components that are vital to understanding the animals’ overall well-being. This research aims to develop and validate a multidimensional behavioral assessment framework that incorporates behavioral, environmental, and physiological parameters, thereby providing a holistic tool for welfare evaluation. The specific objectives include identifying key behavioral indicators of sheep welfare, integrating these indicators into a conceptual framework, and empirically testing the framework’s effectiveness in diverse sheep management systems. Employing a mixed-methods research design, the study combines qualitative observations with quantitative data analysis. The population comprises 450 sheep across three commercial farms with differing management practices, selected via stratified random sampling to ensure representativeness. Data collection involved ethological observations using a structured ethogram developed for this study, behavioral questionnaires for farm personnel, and physiological measures such as cortisol levels and heart rate variability monitored through wearable sensors. These instruments underwent validity and reliability testing, including inter-observer reliability assessments and calibration of physiological equipment. Quantitative data were analyzed through multivariate techniques such as principal component analysis (PCA) to identify core behavioral indicators, followed by multiple regression analysis to examine relationships between behavioral, physiological, and environmental variables. Qualitative data from open-ended survey responses were subjected to thematic analysis, providing contextual insights into farm management practices affecting welfare. Preliminary findings are expected to reveal specific behavioral patterns indicative of positive and negative welfare states, such as grooming, grazing, and social interaction frequencies, which significantly correlate with physiological stress markers. The framework aims to distinguish welfare stressors stemming from environmental factors like pen design, stocking density, and handling procedures. It is anticipated that the study will demonstrate the utility of integrating behavioral with physiological data to produce a robust, scalable assessment tool adaptable across various sheep production systems. The findings are expected to contribute novel insights into behavioral biomarkers of welfare, reinforcing the theoretical foundations laid by the Five Domains Model and the Animal Welfare Assessment Framework. This research’s contribution to knowledge lies in bridging the existing gap between behavioral science and practical welfare assessment, offering a validated holistic framework that enhances accuracy and efficiency in sheep welfare monitoring. By providing a comprehensive tool that accounts for behavioral, physiological, and environmental dimensions, the study advances normative and applied knowledge in animal welfare science and livestock management. Moreover, the framework can serve as a foundational model for future studies across diverse livestock species and production contexts. The main conclusions will emphasize the importance of multidimensional assessment approaches in welfare evaluation, advocating for the adoption of the developed framework within farm management practices and policy guidelines. It will recommend that stakeholders adopt integrated behavioral and physiological monitoring protocols, supported by user-friendly analytical systems. Additionally, the study will suggest avenues for further research, including longitudinal validation of the framework, adaptation to specific breeds, and integration with emerging technologies such as machine learning for predictive welfare management. Overall, this research aims to significantly enhance the scientific basis for sheep welfare assessment and contribute towards sustainable, ethical, and welfare-centered sheep production systems.