Main crop scenarios in IEHIAS

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The text on this page is taken from an equivalent page of the IEHIAS-project.

Scenarios in the Greece case study

To provide a basis for the assessment of health impacts of agricultural land use change in Greece, scenarios had to be developed describing the likely distribution of cropping and livestock systems across the country under different possible 'futures'. Two scenarios were defined for this purpose: a 'business-as-usual' scenario and a 'climate mitigation' scenario. Each of these required some further development of the baseline information provided by the ATEAM model.

(i) Baseline 16x16km dataset from National Statistical Service of Greece (ESYE)

Arable land data at LAU-2 level (General Secretariat of the National Statistical Service of Greece – ESYE) were aggregated to 16x16km and compared to the arable land estimates from the ATEAM model. Differences between the two were normalised with respect to the ESYE data. In addition, crop data (including cereals, cotton, maize and sugar beet) from ESYE LAU-2 level were aggregated to 16x16km, in order to generate a crop distribution for each grid cell. A subset of livestock data (including cows, pigs, sheep and goats) was regressed against grassland area, available from the ATEAM. The remaining animals (including pigs and poultry) are estimated from the GAINS model and are included in the baseline data.

(ii) Business-as-usual scenario

The business-as-usual scenario was derived by projecting current land use estimates forward, under the IPCC A1 Scenario for 2020 and 2050. Changes in arable land in years 2020 and 2050 (from ATEAM) are utilised to project the baseline crop distributions into the future. In addition, three energy crops are included in the future data-set: sunflowers (33.3%), sorghum (33.3%) and cardoon (33.3%). Animal numbers are projected to 2020 and 2050 proportional to the estimated changes in grassland area, as indicated by the ATEAM model.

(iii) Mitigation scenario

The mitigation scenario was derived by taking account of climate change mitigation policies and future CAP developments, within the context of the IPCC B1 scenario. The baseline (2004) crop distribution is modified by reducing the proportion of (water consuming) cotton by 40% in 2020 and by 75% in 2050. The land released as a result is allocated to cereals (25% in 2020, 45% in 2050) and maize (15% in 2020, 30% in 2050). Energy crop and animal data are included in the analysis in a similar manner as in the IPCC A1 scenario.

See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty