{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Scriban A"],"funding":["Agropolis International","Fondation TotalEnergies","French agricultural research centre for international development (Cirad)","Agropolis Fondation"],"pagination":["6964"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11548461"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(21)"],"pubmed_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"],"journal":["Sensors (Basel, Switzerland)"],"pubmed_title":["GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel."],"pmcid":["PMC11548461"],"funding_grant_id":["AF 1802-001, FT C002181","ANR-10-LABX-0001-01)"],"pubmed_authors":["Scriban A","Nabeneza S","Cesaro JD","Cornelis D","Delay E","Vayssieres J","Salgado P"],"additional_accession":[]},"is_claimable":false,"name":"GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel.","description":"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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-04-08T15:25:52.642Z","creation":"2025-04-06T09:31:20.476Z"},"accession":"S-EPMC11548461","cross_references":{"pubmed":["39517860"],"doi":["10.3390/s24216964"]}}