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GIS-based methodology for tracking the grazing cattle site use.

Monica C M Parlato1, Francesca Valenti2, Simona M C Porto3

  • 1Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020, Legnaro, PD, Italy.

Heliyon
|July 22, 2024
PubMed
Summary

This study uses wearable sensors and Internet of Things (IoT) technology to monitor livestock in extensive farming systems. A GIS-based methodology with custom algorithms identified preferred grazing areas and animal core areas, aiding in environmental management.

Keywords:
Animal behaviorGPSGrazing cowsIoTKernel density estimationSpatial analysisTracking

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Area of Science:

  • Livestock Management and Animal Behavior
  • Geographic Information Systems (GIS) and Spatial Analysis
  • Internet of Things (IoT) and Sensor Technology

Background:

  • Increasing need for effective livestock monitoring in extensive farming systems where farmer-animal interaction is limited.
  • Challenges in long-distance herd management necessitate remote and real-time monitoring solutions.
  • Internet of Things (IoT) offers potential for overcoming distance barriers in livestock tracking.

Purpose of the Study:

  • To develop and apply a GIS-based methodology for analyzing animal distribution in grazing areas.
  • To identify frequently occupied territorial areas and individual core areas of livestock using spatial analysis.
  • To explore the application of collected data for insights into feeding behavior and soil erosion prevention.

Main Methods:

  • Deployment of custom-designed wearable LP-GPS collars with SigFox communication in two distinct grazing areas.
  • Data collection at 20-min and 10-min intervals for Case Study I and II, respectively.
  • Analysis using Geographical Information Systems (GIS) software, including Heatmap and Kernel Density Estimation (KDE) algorithms via a web application.

Main Results:

  • Heatmap visualizations revealed the temporal-spatial distribution patterns of animals within their grazing environments.
  • Kernel Density Estimation (KDE) successfully classified preferred territorial areas and identified individual animal core areas.
  • The GIS-based methodology effectively pinpointed animal locations and their spatial usage patterns.

Conclusions:

  • The developed GIS-based methodology provides a robust framework for analyzing livestock spatial distribution in extensive systems.
  • Identified animal positions and preferred areas offer valuable insights into potential feeding behaviors.
  • The approach can contribute to environmental management by aiding in the prevention of issues like soil erosion.