A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving the representation of cattle grazing patterns in the European Union
Improving the sustainability of the European cattle sector requires improved knowledge not only of the density of cattle, but also of the grazing patterns. Only in this way can the potential negative impacts of cattle related to local ecosystem degradation, as well as positive ones such as preserving cultural landscapes through grazing, be analyzed. While data on livestock distribution often used in scientific analyses can provide estimates on density, the separation between the livestock that has access to outdoor grazing and those that remain indoors is not available. This is problematic because it prevents the identification of the intensity and type of grassland management, as well as the consequential environmental impacts of grazing livestock. Here we present an approach where we combined agricultural and veterinary statistics, in-situ data, expert surveys and machine learning to develop a map of grazing cattle distribution for the wider European Union region. Our approach and the resulting data allow for the differentiation between cattle that are actually grazing versus those that do not. We also compare our method to traditional approaches that do not have a clear separation between grazing and non-grazing cattle, illustrating the implications that this can have for agricultural, land use and environmental assessments.
Improving the representation of cattle grazing patterns in the European Union
Improving the sustainability of the European cattle sector requires improved knowledge not only of the density of cattle, but also of the grazing patterns. Only in this way can the potential negative impacts of cattle related to local ecosystem degradation, as well as positive ones such as preserving cultural landscapes through grazing, be analyzed. While data on livestock distribution often used in scientific analyses can provide estimates on density, the separation between the livestock that has access to outdoor grazing and those that remain indoors is not available. This is problematic because it prevents the identification of the intensity and type of grassland management, as well as the consequential environmental impacts of grazing livestock. Here we present an approach where we combined agricultural and veterinary statistics, in-situ data, expert surveys and machine learning to develop a map of grazing cattle distribution for the wider European Union region. Our approach and the resulting data allow for the differentiation between cattle that are actually grazing versus those that do not. We also compare our method to traditional approaches that do not have a clear separation between grazing and non-grazing cattle, illustrating the implications that this can have for agricultural, land use and environmental assessments.
Improving the representation of cattle grazing patterns in the European Union
Žiga Malek (author) / Zoriana Romanchuk (author) / Orysia Yaschun (author) / Gwyn Jones (author) / Jan-Erik Petersen (author) / Steffen Fritz (author) / Linda See (author)
2024
Article (Journal)
Electronic Resource
Unknown
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