Environmental Impact Modeling

of Food and Non-Food Crop Management for EU25

Erwin Schmid

University 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

Ind

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Resou

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Fully Integrated Model

Pro

du

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on

facto

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TopoData

EPIC is part of a model family

Field Scale:

EPIC

Environmental Policy

Integrated Climate

Watershed Scale

APEX 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

Pre

cip

ita

tio

n in

mm

EPIC Input data - Soil

up 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 DE13

1 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