biostudies-literatureScriban AAgropolis InternationalFondation TotalEnergiesFrench agricultural research centre for international development (Cirad)Agropolis Fondation6964https://www.ebi.ac.uk/biostudies/studies/S-EPMC11548461biostudies-literatureUnknown24(21)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 changeSensors (Basel, Switzerland)GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel.PMC11548461AF 1802-001, FT C002181ANR-10-LABX-0001-01)Scriban ANabeneza SCesaro JDCornelis DDelay EVayssieres JSalgado PfalseGPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel.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 change2024-01-01T00:00:00Z2024 Oct2026-04-08T15:25:52.642Z2025-04-06T09:31:20.476ZS-EPMC115484613951786010.3390/s24216964