SmartHerd Decision Support Framework for Adaptive Livestock Management Under Changing Environmental Conditions

Abstract

Climate change presents a growing threat to the wellbeing and productivity of livestock as well as profit-making through its impact on the livestock production system. Existing livestock herding practices are based mostly on fixed thresholds and reactive measures, which do not suffice for the new challenges presented by climate change. The purpose of this research is to outline an innovative framework called SmartHerd, which is designed to facilitate adaptive herding of livestock under changing conditions of climate. The SmartHerd decision support framework involves the combination of real-time monitoring of environmental factors and animals' physiologic state, multi-tiered classification of risks, and adaptive recommendation system based on rule logic into a modular structure appropriate for any farm regardless of its size and resourcefulness. In contrast with existing approaches in developing decision support frameworks, which are focused primarily on a single stressor or a production-related indicator, the proposed framework brings together a number of sources of information – temperature-humidity index values, animal-specific physiologic data, and herd-specific production performance indicators – into one decision layer. The design rationale for each layer, present a comparative analysis of existing precision livestock farming and decision support literature, and outline evaluation criteria for framework adoption across low-resource and high-resource farm settings. The paper further discusses implementation pathways, data governance considerations, and barriers to adoption, including cost, connectivity, and farmer trust. By consolidating dispersed monitoring and decision-making functions into a coherent and adaptable structure, SmartHerd aims to support more resilient, welfare-conscious, and economically sustainable livestock management as environmental variability intensifies globally.