17 Upcoming Challenges in Land Use Science—An International Perspective
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view and train researchers that fail to understand the philosophies and theoreticalmethodological backgrounds of other disciplines (Bromham et al. 2016). A huge
hampering factors for land use sciences consists in the historically separately developed research in land use sectors, such as agriculture and forestry, that often have
similar disciplinary approaches, but fail in the cross-sectoral integration and fail
in cooperation from a systemic, landscape perspective that is essential for deriving
suitable policy recommendations (e.g. Klein et al. 2005; Mickwitz et al. 2009). The
lack in a systemic understanding is another challenge in further developing land
use sciences. Many of the data acquisition, monitoring and modelling approaches
are still oriented towards the micro-scale, miss spatial representativeness and thus
fail to contribute to integrative assessments at superior scales and decision levels
(e.g. Anderson 2018; De Palma et al. 2018). There is often no real valid relation
between spot-oriented sampling or monitoring regarding up-scaling approaches to
regional or global scales, but vice versa, also no real attempts to down-scale and
validate outcomes from global assessments and modelling approaches with regard
to their local reliability (e.g. Kolosz et al. 2018; Le Clec’h et al. 2018; Malenovský
et al. 2019). Most of the modelling approaches in land use sciences are purely data
driven and miss making use of such theoretical frameworks as provided by the socialecological system concept (Colding and Barthel 2019). Sustainability and ecosystem
services assessments focus often too narrowly on singular ecosystems or ecosystem
types and thus do not contribute to holistic and integrative landscape-oriented planning and policy recommendations (von Haaren et al. 2019). Consequently, one of
the most important challenges consists in an improved implementation of a systemic
perspective and in the focussing of systemic architectures including all subsystems,
subcomponents, their interrelations and the quality of these interrelations (e.g. Langhammer et al. 2019). Graph-node theory based approaches such as Bayesian Belief
Networks (BBN) or Artificial Neural Networks (ANN) would provide adequate solutions that can either make use of local, indigenous and expert knowledge in drafting
the system architecture (BBN), or make use of artificial intelligence algorithms to
harvest data sets (ANN) (e.g. Marcot and Penman 2018; Schmidt et al. 2018). While
ANN are reliant on the amount and quality of the available data, BBN and similar
approaches hold the huge potential to serve also as a transdisciplinary method to
approach the understanding of land use systems, integrate multiple knowledge types
and data sets and combine qualitative and quantitative data sets. This is of even
higher relevance, since challenges for land use science called by Future Earth are the
co-design of research and subsequently the co-development of new knowledge (Liu
et al. 2018). Using system architectures as a starting point in the discourse between
science and practice reveals knowledge gaps and research needs in understanding
specific land use systems and—in the sense of social-ecological frameworks—in
generalizing their structure and functioning (Gu et al. 2018). The identified “nodes”
(i.e. sub-systems) can be critically reflected considering the availability of data or
methods to parameterize them. Finally, by the step-wise integration of knowledge
to parameterize the subsystems and describe their interactions helps to co-develop
knowledge on the system, but also knowledge on potential intervention scales or
decision levels to accomplish sustainable development (see e.g. Kampelmann et al.
327
view and train researchers that fail to understand the philosophies and theoreticalmethodological backgrounds of other disciplines (Bromham et al. 2016). A huge
hampering factors for land use sciences consists in the historically separately developed research in land use sectors, such as agriculture and forestry, that often have
similar disciplinary approaches, but fail in the cross-sectoral integration and fail
in cooperation from a systemic, landscape perspective that is essential for deriving
suitable policy recommendations (e.g. Klein et al. 2005; Mickwitz et al. 2009). The
lack in a systemic understanding is another challenge in further developing land
use sciences. Many of the data acquisition, monitoring and modelling approaches
are still oriented towards the micro-scale, miss spatial representativeness and thus
fail to contribute to integrative assessments at superior scales and decision levels
(e.g. Anderson 2018; De Palma et al. 2018). There is often no real valid relation
between spot-oriented sampling or monitoring regarding up-scaling approaches to
regional or global scales, but vice versa, also no real attempts to down-scale and
validate outcomes from global assessments and modelling approaches with regard
to their local reliability (e.g. Kolosz et al. 2018; Le Clec’h et al. 2018; Malenovský
et al. 2019). Most of the modelling approaches in land use sciences are purely data
driven and miss making use of such theoretical frameworks as provided by the socialecological system concept (Colding and Barthel 2019). Sustainability and ecosystem
services assessments focus often too narrowly on singular ecosystems or ecosystem
types and thus do not contribute to holistic and integrative landscape-oriented planning and policy recommendations (von Haaren et al. 2019). Consequently, one of
the most important challenges consists in an improved implementation of a systemic
perspective and in the focussing of systemic architectures including all subsystems,
subcomponents, their interrelations and the quality of these interrelations (e.g. Langhammer et al. 2019). Graph-node theory based approaches such as Bayesian Belief
Networks (BBN) or Artificial Neural Networks (ANN) would provide adequate solutions that can either make use of local, indigenous and expert knowledge in drafting
the system architecture (BBN), or make use of artificial intelligence algorithms to
harvest data sets (ANN) (e.g. Marcot and Penman 2018; Schmidt et al. 2018). While
ANN are reliant on the amount and quality of the available data, BBN and similar
approaches hold the huge potential to serve also as a transdisciplinary method to
approach the understanding of land use systems, integrate multiple knowledge types
and data sets and combine qualitative and quantitative data sets. This is of even
higher relevance, since challenges for land use science called by Future Earth are the
co-design of research and subsequently the co-development of new knowledge (Liu
et al. 2018). Using system architectures as a starting point in the discourse between
science and practice reveals knowledge gaps and research needs in understanding
specific land use systems and—in the sense of social-ecological frameworks—in
generalizing their structure and functioning (Gu et al. 2018). The identified “nodes”
(i.e. sub-systems) can be critically reflected considering the availability of data or
methods to parameterize them. Finally, by the step-wise integration of knowledge
to parameterize the subsystems and describe their interactions helps to co-develop
knowledge on the system, but also knowledge on potential intervention scales or
decision levels to accomplish sustainable development (see e.g. Kampelmann et al.