Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Audsley, E.; Trnka, Miroslav; Sabaté, Santiago; Maspons, Joan; Sánchez, Anabel; Sandars, Daniel L.; Bálek, Jan; Pearn, Kerry R.

doi:10.1007/s10584-014-1164-6

Cited by 36 publications

(40 citation statements)

References 14 publications

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“…Impacts resulting from future socio-economic change have been shown, in some cases, to be greater than impacts based on future climate change alone 2,3,4,5,6 . It is often through the socio-economic drivers that cross-sectoral impacts become evident, as policy effects in one sector can have indirect effects in others, and these effects are lost in single sector studies.…”

Section: States: "Little Information Is Available On Integrated and Cmentioning

confidence: 99%

“…The IAP is a spatiallyexplicit modelling platform that operates on a 10 x 10 minute grid for the countries of the European Union plus Norway and Switzerland. It has been thoroughly validated (Supplementary Table 1) and widely applied in climate change impact 2,4,6,14,15 , adaptation 16 and vulnerability 17 assessment, in robust policy analysis 18 , and has been tested extensively through model sensitivity 19 and uncertainty analysis 20,21 . It was applied with and without coupling of the individual sectoral models for a number of scenario experiments for the 2050s that included different SRES emissions scenarios 22 , climate change models 23 and the socio-economic storylines underlying the SRES scenarios 22 .…”

Section: States: "Little Information Is Available On Integrated and Cmentioning

confidence: 99%

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Climate change impact modelling needs to include cross-sectoral interactions

et al. 2016

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Climate change impact assessments often apply models of individual sectors such as agriculture, forestry and water use without considering interactions between these sectors. This is likely to lead to misrepresentation of impacts, and consequently to poor decisions about climate adaptation. However, no published research assesses the differences between impacts simulated by single sector and integrated models. Here we compare 14 indicators derived from a set of impact models run within single sector and integrated frameworks across a range of climate and socio-economic scenarios in Europe. We show that single sector studies misrepresent the spatial pattern, direction and magnitude of most impacts because they omit the complex interdependencies within human and environmental systems. The discrepancies are particularly pronounced for indicators such as food production and water exploitation which are highly influenced by other sectors through changes in demand, land suitability and resource competition. Furthermore, the discrepancies are greater under different socio-economic scenarios than different climate scenarios, and at the sub-regional rather than Europe-wide scale.

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Section: States: "Little Information Is Available On Integrated and Cmentioning

confidence: 99%

Section: States: "Little Information Is Available On Integrated and Cmentioning

confidence: 99%

Climate change impact modelling needs to include cross-sectoral interactions

et al. 2016

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“…Audsley et al (2014) defined available water capacity, saturation to permanent wilting point, soil stoniness and soil texture variables, based on the EUSOILS dataset on soil type-grid combinations, up to 47 different soil types. Moriondo et al (2009) defined the water balance and soil properties (thickness and texture) as variables at grid point scale based on the EUSOILS database, the soil type having the highest frequency within each 50×50 km grid point grid (in every soil mapping unit, SMU) being considered as representative for the whole unit.…”

Section: Datamentioning

confidence: 99%

Impacts of climate on technical efficiency in the Hungarian arable sector

Vígh¹,

Fertő²,

Fogarasi³

2018

Stud. Agr. Econ.

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“…A literature-based meta-analysis to provide a semi-quantitative overview of current and possible future climate-related risks to agriculture is presented in Schaap et al [60]. Audsley et al [61] apply the neural network approach to derive meta-models from outputs of previously developed very complex and integrated models. The benefit of applying the meta-model instead of the complex integrated model is the reduced run-time, which increases the feasibility of the model to be applied for explorative analyses.…”

Section: Meta-modelsmentioning

confidence: 99%

Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models?

Holzkämper

2017

Agriculture

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Climate change poses a challenge to agricultural production and its impacts vary depending on regional focus and on the type of production system. To avoid production losses and make use of emerging potentials, adaptations in agricultural management will inevitably be required. Adaptation responses can broadly be distinguished into (1) short-term incremental responses that farmers often choose autonomously in response to observed changes and based on local knowledge and experiences, and (2) long-term transformative responses that require strategic planning, and which are usually implemented at a larger spatial scale. Models can be used to support decision making at both response levels; thereby, different features of models prove more or less valuable depending on the type of adaptation response. This paper presents a systematic literature review on the state-of-the-art in modelling for adaptation planning in agricultural production systems, investigating the question of which model types can be distinguished and how these types differ in the way they support decision making in agricultural adaptation planning. Five types of models are distinguished: (1) empirical crop models; (2) regional suitability models; (3) biophysical models; (4) meta-models; and (5) decision models. The potential and limitations of these model types for providing decision-support to short-and long-term adaptation planning are discussed. The risk of maladaptation-adaptation that implies negative consequences either in the long term or in a wider context-is identified as a key challenge of adaptation planning that needs more attention. Maladaptation is not only a risk of decision making in the face of incomplete knowledge of future climate impacts on the agricultural production system; but it can also be a threat if the connectedness of the agroecosystem is not sufficiently acknowledged when management adaptations are implemented. Future research supporting climate change adaptation efforts should thus be based on integrated assessments of risk and vulnerabilities (considering climate variability and uncertainty). To secure adaptation success in the long term, frameworks for monitoring management adaptations and their consequences should be institutionalised.

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Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Cited by 36 publications

References 14 publications

Climate change impact modelling needs to include cross-sectoral interactions

Climate change impact modelling needs to include cross-sectoral interactions

Impacts of climate on technical efficiency in the Hungarian arable sector

Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models?

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