GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel.

ABSTRACT: In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state change

SUBMITTER: Scriban A 

PROVIDER: S-EPMC11548461 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature