# Spatiotemporal Mapping of Grazing Livestock Behaviours Using Machine Learning Algorithms

## TL;DR

This study uses machine learning to map how livestock behaviors affect grasslands, showing that rotational grazing better manages grazing pressure than continuous grazing.

## Contribution

The novel use of GPS tracking and machine learning to analyze spatiotemporal patterns of livestock behaviors under different grazing systems.

## Key findings

- The KNN model with SMOTE-ENN resampling achieved high accuracy in classifying livestock behaviors.
- Continuous grazing failed to reduce grazing pressure despite lower intensity due to persistent spatial clustering.
- Rotational grazing led to more even temporal activity and reduced spatial clustering with lower grazing intensity.

## Abstract

Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering and temporal concentration patterns of livestock behaviours are critical yet underexplored factors that significantly influence grassland ecosystems. This study investigated the spatiotemporal patterns of livestock behaviours under different grazing management systems and grazing-intensity gradients (GIGs) in Wenchang, China, using high-resolution GPS tracking data and machine learning classification. the K-Nearest Neighbours (KNN) model combined with SMOTE-ENN resampling achieved the highest accuracy, with F1-scores of 0.960 and 0.956 for continuous and rotational grazing datasets. The results showed that the continuous grazing system failed to mitigate grazing pressure when grazing intensity was reduced, as the spatial clustering of livestock behaviours did not decrease accordingly, and the frequency of temporal peaks in grazing behaviour even showed an increasing trend. Conversely, the rotational grazing system responded more effectively, as reduced GIGs led to more evenly distributed temporal activity patterns and lower spatial clustering. These findings highlight the importance of incorporating livestock behavioural patterns into grassland monitoring and offer data-driven insights for sustainable grazing management.