Virtual fence technology for enhancing adaptive management of rangelands

Authors: Derner, Justin; GUAN, KAIYU - University Of Illinois; THOMA, GREG - Colorado State University; PENG, BIN - University Of Illinois; YE, LEXUAN - University Of Illinois; Lents, Clay

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: Managing grazing lands for ecosystem services can be challenging given spatial constraints associated with physical infrastructure (i.e., fences) that constrain management flexibility. Increasing variability in weather and climate poses additional complexity and risks for managers on these lands. Here, we introduce virtual fence + as a transformational tool that integrates numerous technologies to facilitate precision livestock management. Virtual fence + integrates on-animal sensors for animal movement, behavior and health, advanced remote sensing and Internet-of-Things providing near-real time environmental monitoring information, machine and deep learning, artificial intelligence, big data analytics, process-based modeling, and Life Cycle Assessment. Expected outcomes include improved quantification of generated ecosystem services attributable to the producer management decisions, optimization of spatial and temporal livestock placement on landscapes, and enhanced adaptive grazing management across multiple spatial and temporal scales that de-risks vulnerabilities associated with global change.

Technical Abstract: Global grazing lands support rural and pastoral communities and livelihoods through the provision of numerous ecosystem services. Increasing climatic variability, combined with limited experiential knowledge of managers to these environmental conditions, exacerbates local-level management issues for co-sustainability of ecosystem services and environmental resources. Opportunities exist for transformational technology managing grazing animals across multiple spatial and temporal scales in a changing climate without diminishing the food producing capacity of global grazing lands. Virtual fence + is the integration of numerous technologies to facilitate precision livestock management, similar in concept as to the effectiveness of precision farming for croplands. Virtual fence + integrates on-animal sensors for animal movement, behavior and health, advanced remote sensing and Internet-of-Things providing near-real time environmental monitoring information, machine and deep learning, artificial intelligence, big data analytics, process-based modeling, and Life Cycle Assessment. Incorporating model-data fusion into virtual fence + integrates observational data streams with process-based models to improve the quantification of generated ecosystem services attributable to the producer management decisions. This provides opportunities to establish trust in ecosystem service markets to accomplish pathways towards climate-neutral and/or carbon-neutral futures. Virtual fence + facilitates the optimization of spatial and temporal livestock placement on landscapes as a precision-management tool for 1) providing habitat for species of conservation concern, 2) implementing targeted grazing for control of invasive species, 3) improving functional heterogeneity of vegetation structure to enhance biodiversity and wildlife-livestock coexistence, 4) mitigating environmental resource concerns in sensitive areas of the landscape, like riparian areas, 5) enhancing intake of high-quality forage leading to reduced methane emissions and improved livestock weight gains, and 6) altering the spatial distribution of nitrogen deposition by grazing animals. Virtual fence + will facilitate adaptive management across multiple spatial and temporal scales and enhance the resilience of global grazing lands by de-risking vulnerabilities associated with global change.