Herding resilience: Surveys and Bayesian spatial models for Africa's livestock

Tamás Krisztin; Michiel van Dijk; Philipp Piribauer

doi:10.1016/j.envdev.2025.101141

Herding resilience: Surveys and Bayesian spatial models for Africa's livestock

Tamás Krisztin^*, Michiel van Dijk, Philipp Piribauer

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

This paper proposes a novel method for mapping livestock distribution in Africa using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). Using a Bayesian spatial statistical model, we produce maps of livestock distribution at a resolution of 1 km². Our case study in Malawi, covering 2010 and 2019, demonstrates the effectiveness of the method in mapping five livestock species. We compare our results with the Gridded Livestock of the World (GLW) database and use the maps to assess livestock vulnerability to climate-related flood risks under different climate scenarios. This approach provides a rapid, data-rich tool for policy makers to assess climate risks to livestock, which is critical for sustainable agricultural development and environmental management in data-poor regions.

Original language	English
Article number	101141
Number of pages	12
Journal	Environmental Development
Volume	54
DOIs	https://doi.org/10.1016/j.envdev.2025.101141
Publication status	Published - Jun 2025

Keywords

African agriculture
Climate resilience
Household surveys
INLA models
Livestock distribution

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.envdev.2025.101141Licence: CC BY

https://edepot.wur.nl/687237Licence: CC BY

Cite this

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title = "Herding resilience: Surveys and Bayesian spatial models for Africa's livestock",

abstract = "This paper proposes a novel method for mapping livestock distribution in Africa using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). Using a Bayesian spatial statistical model, we produce maps of livestock distribution at a resolution of 1 km2. Our case study in Malawi, covering 2010 and 2019, demonstrates the effectiveness of the method in mapping five livestock species. We compare our results with the Gridded Livestock of the World (GLW) database and use the maps to assess livestock vulnerability to climate-related flood risks under different climate scenarios. This approach provides a rapid, data-rich tool for policy makers to assess climate risks to livestock, which is critical for sustainable agricultural development and environmental management in data-poor regions.",

keywords = "African agriculture, Climate resilience, Household surveys, INLA models, Livestock distribution",

author = "Tam{\'a}s Krisztin and \{van Dijk\}, Michiel and Philipp Piribauer",

year = "2025",

month = jun,

doi = "10.1016/j.envdev.2025.101141",

language = "English",

volume = "54",

journal = "Environmental Development",

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TY - JOUR

T1 - Herding resilience

T2 - Surveys and Bayesian spatial models for Africa's livestock

AU - Krisztin, Tamás

AU - van Dijk, Michiel

AU - Piribauer, Philipp

PY - 2025/6

Y1 - 2025/6

N2 - This paper proposes a novel method for mapping livestock distribution in Africa using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). Using a Bayesian spatial statistical model, we produce maps of livestock distribution at a resolution of 1 km2. Our case study in Malawi, covering 2010 and 2019, demonstrates the effectiveness of the method in mapping five livestock species. We compare our results with the Gridded Livestock of the World (GLW) database and use the maps to assess livestock vulnerability to climate-related flood risks under different climate scenarios. This approach provides a rapid, data-rich tool for policy makers to assess climate risks to livestock, which is critical for sustainable agricultural development and environmental management in data-poor regions.

AB - This paper proposes a novel method for mapping livestock distribution in Africa using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). Using a Bayesian spatial statistical model, we produce maps of livestock distribution at a resolution of 1 km2. Our case study in Malawi, covering 2010 and 2019, demonstrates the effectiveness of the method in mapping five livestock species. We compare our results with the Gridded Livestock of the World (GLW) database and use the maps to assess livestock vulnerability to climate-related flood risks under different climate scenarios. This approach provides a rapid, data-rich tool for policy makers to assess climate risks to livestock, which is critical for sustainable agricultural development and environmental management in data-poor regions.

KW - African agriculture

KW - Climate resilience

KW - Household surveys

KW - INLA models

KW - Livestock distribution

U2 - 10.1016/j.envdev.2025.101141

DO - 10.1016/j.envdev.2025.101141

M3 - Article

AN - SCOPUS:85216210849

SN - 2211-4645

VL - 54

JO - Environmental Development

JF - Environmental Development

M1 - 101141

ER -

Herding resilience: Surveys and Bayesian spatial models for Africa's livestock

Abstract

Keywords

UN SDGs

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