Spatial allocation of farming systems and farming indicators in Europe

Markus Kempen; Berien S. Elbersen; Igor Staritsky; Erling Andersen; Thomas Heckelei

doi:10.1016/j.agee.2010.08.001

Spatial allocation of farming systems and farming indicators in Europe

Markus Kempen, Berien S. Elbersen, Igor Staritsky, Erling Andersen, Thomas Heckelei

22 Citationer (Scopus)

Abstract

In this article an approach to spatially allocate farm information to a specific environmental context is presented. At this moment the European wide farm information is only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. This spatial flexibility allows providing input data to economic or bio-physical models at their desired resolution. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. The allocation procedure recovers the spatial farm type distributions satisfactorilly thereby providing information of significant value for further analysis in a multidisciplinary context.

Originalsprog	Engelsk
Tidsskrift	Agriculture, Ecosystems & Environment
Vol/bind	142
Udgave nummer	1-2
Sider (fra-til)	51-62
Antal sider	12
ISSN	0167-8809
DOI	https://doi.org/10.1016/j.agee.2010.08.001
Status	Udgivet - jul. 2011

Adgang til dokumentet

10.1016/j.agee.2010.08.001

Citationsformater

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abstract = "In this article an approach to spatially allocate farm information to a specific environmental context is presented. At this moment the European wide farm information is only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. This spatial flexibility allows providing input data to economic or bio-physical models at their desired resolution. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. The allocation procedure recovers the spatial farm type distributions satisfactorilly thereby providing information of significant value for further analysis in a multidisciplinary context. ",

author = "Markus Kempen and Elbersen, {Berien S.} and Igor Staritsky and Erling Andersen and Thomas Heckelei",

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T1 - Spatial allocation of farming systems and farming indicators in Europe

AU - Kempen, Markus

AU - Elbersen, Berien S.

AU - Staritsky, Igor

AU - Andersen, Erling

AU - Heckelei, Thomas

PY - 2011/7

Y1 - 2011/7

N2 - In this article an approach to spatially allocate farm information to a specific environmental context is presented. At this moment the European wide farm information is only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. This spatial flexibility allows providing input data to economic or bio-physical models at their desired resolution. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. The allocation procedure recovers the spatial farm type distributions satisfactorilly thereby providing information of significant value for further analysis in a multidisciplinary context.

AB - In this article an approach to spatially allocate farm information to a specific environmental context is presented. At this moment the European wide farm information is only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. This spatial flexibility allows providing input data to economic or bio-physical models at their desired resolution. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. The allocation procedure recovers the spatial farm type distributions satisfactorilly thereby providing information of significant value for further analysis in a multidisciplinary context.

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DO - 10.1016/j.agee.2010.08.001

M3 - Journal article

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JO - Agriculture, Ecosystems & Environment

JF - Agriculture, Ecosystems & Environment

IS - 1-2

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