A time-series approach to mapping livestock density using household survey data

Abstract

More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo, and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.

The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense: A causal inference-based approach

“…We attempted to account for measurement error arising from estimation of wealth and livestock density in South Sudan, however these measurement error models do not account for errors in the raw data used for livestock or wealth mapping, for instance a systematic under-reporting of livestock ownership due to concerns about increased taxation. Furthermore, we did not implement measurement error models in the remaining study countries, however we did restrict analyses to years bounded by those with available input data used to generate livestock density estimates [24]. We expect such measurement error to be non-differential with respect to HAT risk, biasing our effect estimates towards the null in expectation.…”

“…Exposure. Exposure is defined as livestock density, parameterized using a time-series of maps we have created and detailed in a separate publication [24]. We use the term "livestock" to refer collectively to cattle and pigs, however all analyses are conducted separately for each species.…”

Assessing and addressing the global state of food production data scarcity

“…Countrylevel data can mask substantial subnational heterogeneity and rapid local changes between infrequent national surveys. This situation is increasingly relevant for rapidly expanding research and policy applications, for example, in development research, animal health, economics, environmental adaptation and mitigation science 50 . For more spatially explicit research, the Gridded Livestock of the World (GLW) 51,52 dataset is the global standard, mapping populations of cattle, buffalo, horses, sheep, goats, pigs, chickens and ducks in 2010 and 2015.…”

Gridded livestock density database and spatial trends for Kazakhstan

“…Furthermore, livestock grazing systems have a broader, bi-directional interaction with climate change (CC). CC is widely characterized by drought and heat stress, causing loss of livestock production, feed availability (quality and quantity), and uneven distribution of water resources and disease vectors 5 . Meanwhile, the intensification of livestock densities and disturbances such as overgrazing cause environmental changes such as greenhouse gas emissions, elevated albedo, alterations in nitrogen and phosphorus cycles, and loss of biodiversity and ecosystem services 6 – 8 .…”

“…To complement the GLW project, Meisner et al . 5 developed a longitudinal (2000–2020) gridded cattle and pig density dataset for Malawi and Uganda with a resolution of 0.017° (~1.88 km), and Li et al . 21 developed cattle and sheep density datasets at five-year intervals.…”