Post on 25-Feb-2016
description
Environmental Impact Modeling
of Food and Non-Food Crop Management for EU25
Erwin SchmidUniversity of Natural Resources and Applied Life Sciences Vienna (BOKU)
European Non-Food Agriculture (ENFA) EU-Projectkick-off meeting
Hamburg, 10 May 2005
Objective EPIC Model Hydrological Response Units - HRU Spatial and temporal representation of
EU25 Link to EUFASOM
Outline
Objective: ENFA Components
Forest Inventory and
ManagementAlternatives
Traditional Agricultural
Technologies
Soil Data
Climate Data
Management
Data
Simulation of Environmental Field Impacts with EPIC
Non-Food Technologies / Engineering
Models
Microeconomic Models and Analyses
Existing and Potential
Agricultural or Other Policies
Indu
stry
Dem
and
s
Reso
urce
En
dow
men
ts
Fully Integrated Model
Prod
ucti
on
fact
ors
TopoData
EPIC is part of a model family
Field Scale:EPIC
Environmental Policy
Integrated Climate
Watershed ScaleAPEX
Agricultural Policy Environmental
eXtender
SWAT Soil Water
Assessment Tool
EPIC simulates many Processes:
on a daily time step
Weather: generated or actualHydrology: evapotranspiration, runoff,
percolation, 5 PET equations,...Erosion: wind and water, 7 erosion equationsCarbon sequestration: plant residue, manure,
leaching, sediment,...Crop growth: NPK uptake, stresses, yields,
N-fixation,...Fertilization: application, runoff, leaching,
mineralization, denitrification, volatilization, nitrification,...
Tillage: mixing, harvest efficiencies,...Irrigation and furrow diking,...Drainage: depth,... Pesticide: application, movement, degradation,...Grazing: trampling, efficiency,...Manure application and transport,...Crop rotations: inter-cropping, weed competition,
annual and perennial crops, trees,...
EPIC Input data
1. Weather
2. Soil
3. Topography
4. Crop Rotation / Management
4 major components:
EPIC Input data - Weather• actual daily weather or/and generated• Tmin, Tmax, Precipitation,
Solar Radiation,Wind Speed, Relative Humidity(for Penman/Monteith)
• monthly statistics (long run daily weather) meanstandard deviationskew coefficient for daily precipitationprobability of a wet day after a wet dayprobability of a wet day after a dry dayaverage rain dayswind speed?!?
0
400
800
1200
1600
1 8 15 22 29 36 43 50
Years
Prec
ipita
tion
in m
m
EPIC Input data - Soilup to 10 soil layers
essential:• Soil Albedo• Hydrological Soil Group (A, B, C, D)
for each layer:• Sand Content (%)• Silt Content (%)• Soil pH• Organic Carbon Content (%)• Calcium Carbonate Content (%)• Bulk density of layer, moist (t/m3)• Coarse Fragment Content (vol%)
EPIC Input data - Topography
• average field size (ha)
• slope length (m)• slope steepness (m/m)
• elevation• latitude/longitude
EPIC Input data - Management
Crop rotation (crops, grass/legumes, trees)• date of planting • date, type, & amount of fertilization (kg/ha)• date & amount of irrigation (mm/ha)• date & amount of pesticides (kg/ha of active
ingredients)• date of tillage operation (plough, harrow spike,
field cultivator, thinning,...) • date of harvesting (expected yield), grazing,...
INSEA:Data for HRU delineation in EU25
GROUP DATA SET DESCRIPTION MARS Monitoring of agriculture with remote sensing (50 km) EAST ANGLIA
Tyndall Centre for Climate Change Research (0.5°) climate
EMEP Monitoring and evaluation of the long-range transmission of air-pollutants in EUROPE (50 km)
soil ESDB v.2 The European soil database v. 2. (10km/1km) topography GTOPO30 Global digital elevation model (30 arc seconds) land cover CORINE/PELCOM Combined CORINE & PELCOM (1 km)
NEW CRONOS New Cronos Regional Statistics (NUTS1, NUTS2) agricultural statistics LUCAS Land use and land cover area frame statistical survey
project data admin. regions
AGISCO Geographic information system of European commission data
reference grids
SWU JRC Soil and waste unit reference grid (10km).
INSEA-Concept: HRU delineation
Climate Zones
Elevation Classes
Topographic Classes
Soil Classes
... <300m 300-600 600-1100
>1100
slope (0-2%, 2-5%,...)
soil texture (sand, silt, clay),
soil depths, stoniness
slow changing physical parameters
Political Boundary
Land Categories
Crop Rotation
Management Alternatives
NUTSII arable land grass land pastures
permanent crops forest
5 year base-run alternative1 alternative2
management related parameters
HRUintersect
Slope classes:
1k-based HRU delineation
Texture classes: 1 – coarse2 – medium3 – medium fine4 – fine5 – very fine6 – no texture7 – rock8 – peat
Depth to rock classes:1 – shallow (< 40 cm)2 – moderate (40-80 cm)3 – deep (80-120 cm)4 – very deep (>120 cm)
Elevation classes: 1 – 0-300 m lowland2 – 300-600 m upland3 – 600-1100 m high mts.4 – > 1100 m very high mts.
Climate:Annual rainfall
Volume of stones:1 – without2 – moderate3 – stony
Altitude:1. < 300 m2. 300-600 m3. > 600 m
Texture:1. Coarse2. Medium3. Medium-fine4. Fine 5. Very fine
Soil Depth:1. shallow2. medium3. deep
Stoniness:1. Low content2. Medium content3. High content
Delineated coverage
intersect
Delineated coverage DE131 km ESRI GRID Database
Zone Processingspecific per Land Categories
Dataset of input parameters specific for NUTS2 and Land categories
Dataset of input parameters specific for NUTS2 and Land categories
PTF Rules
Processing in MS ACCESS
EPIC INPUT DATABASE for soil and topography parameters
INSEA-Concept: Spatial and Temporal HRU-Representation for EU25
Field Level (HRU)
site specific effects on yield and environmental indicators (carbon seq.,
N, sediment transport) in kg/ha
NUTS II level
Country levelFASOMweighing of effects of landuse shares in NUTS II
AROPAjweighing of HRU effects by crop, soil, and landuse shares
EPICtemporal: weather (30 yr)spatial: soil topography management
Farm1 Farm2 Farm3 Farm4 Farm5
Country Country
Discussion:Non-Food Crop Management
In EPIC, crops are specified with 56 parametersAbout 130 crops/trees are specified in the EPIC crop file.
• Miscanthus, Switchgrass, Red Canary Grass• Willow, Poplar, Eucalyptus, Kenaf
Jim Kiniry (ALMANAC model) USDA-ARS, Grassland, Soil and Water Research Lab, Temple, TX