Multi-scale land-use disaggregation modelling: Concept and application to EU countries. (August 2016)

• Record Type: Journal Article
• Authors: Lamboni, Matieyendou; Koeble, Renate; Leip, Adrian
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 183
• Page End: 217
• Publication Date: 2016-08
• Subjects: Bayesian modelling -- Homogenous spatial unit -- Prediction of land-use areas -- Spatial disaggregation -- Uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.028
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 10 5 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75 % of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning. Highlights: Development of a scale independent model to disaggregate land use data. Generate high resolution maps for crops cultivated in EU-28. Validation of model results by comparison with detailed survey data.