Challenges of Integrating Biophysical Information into Agricultural Sector Models

Daniel G. De La Torre Ugarte, Lixia Lambert, Burton English, Brad Wilson

Linking Biophysical and Economic Models of Biofuel Production and Environmental Impacts November 13-14, 2008 Gleacher Center, Chicago IL.

POLYSYS Modules and Interaction

Crop Supply (7 /305/ 3110 Regions) Expected Returns & Available Acreage Acreage Allocation Based on Expected Returns Acreage, Production, Expenditures Production Price Available for Domestic Consumption Livestock (U.S.) Export Use Domestic use Food Use Feed Use Bioenery Use Total Use Price (U.S.) Crop Demand Value of Exports & Production Gov’t Payments Cash Receipts Gross & Net Realized Income Production Expenses (U.S.) Ag Income

Our Initial Motivation

• • • • • Analysis of economic and environmental tradeoffs Sustainability context: erosion, N,P,K, chemical Economic tradeoffs: net returns, net farm income, government cost, price changes National and regional policy instruments Several sector models have integrated biophysical models since the mid 1980’s

Connection between economic and environmental analysis level

Net Farm Income Net Returns Government Costs Prices Variability LINKS Erosion N, P, K Leaching Chemical Risk Water use Carbon Embodied Energy

Interaction with Environmental Module

Environmental (305 Regions) Soil Erosion Yield Impacts Nitrogen Runoff, Leaching Phosphorus Runoff, Leaching Chemical Risk Index* Other Environmental Variables

* Chemical Risk Index from Kovach, J., C. Petzoldt, J. Degni, and J. Tette (1992).

Crop Supply (305 Regions) Crop Demand (U.S.) Livestock (U.S.) Ag Income (U.S.)

Soils • ST ATSGO

Integration of EPIC

EPIC Environmental Indicators POLYSYS • Land Allocation by Soil Type • Rotations Rotations • AP AC Budgeting System

POLYSYS Regions (305) ASDs

Nation

Levels of Aggregation

State Farm USDA Region Agricultural Statistic District

Changes in Chemical Risk

Environmental Impacts from Maximizing Alternative Practices (ACE)

Main Challenges and decisions

• • • • • Geographic aggregation analytical level What to include: Crops, rotations, practices, land, soils, etc.

Diverse resolution for economic and environmental data Average environmental impacts vs. dynamic environmental impacts Shrinking agricultural economic databases

Analytical Resolution

• • • Economic: – Lower resolution better economic data more reliable output – High resolution lower reliability of economic output Environmental: – Lower resolution, too much aggregation, less significance of environmental impacts – Higher resolution better significance of environmental output Compromise: objectives, data, computer power, $$$

Changes in Soil Carbon*: No LANDSAT - LANDSAT

*POLYSYS estimates Carbon changes based on West, Marland, King, Post, Jain, and K Andrasko (2003)

• • • •

Comprehensiveness

Land: cropland, pasture, idle, forest – Begins with research objectives, driven by complexity of the forthcoming issues and availability of biophysical data Crops, rotations, livestock activities, forest – Economically/environmentally meaningful for resources, region, nation, market. Biophysical parameters ?

Agricultural practices – Current practices, and alternative practices from more likely to less likely. Biophysical parameters ?

Soils and landscapes – Extensive representation, study objectives. Biophysical parameters ?

• • • •

Data Sources Resolution

Economic – – Cost of production: ERS Resource Regions Crop Price: NASS state Environmental – SSURGO: MUID – Land use history: county, NRI point (1992) Link – Yield: NASS county – Practices: Tillage (CTIC county), ARMS (ERS Regions) Shrinking agricultural economic databases availability and/or resolution: cost of production, NRI,

APAC Budgeting System

• Provides Consistent, Crop- System Budgets For Research – Critical in Assessing Policy & Environmental Changes • Much of The Data Required Comes From Databases Built Into The System

Machinery Specifications Prices ***

USDA ERS

ABS Databases

Fertilizer Composition Prices ***

USDA NASS

Wage Rates By Region

USDA NASS

Irrigation Costs Yield Impacts

USDA Farm & Ranch Irrigation Survey

Chemical Prices Compatability ***

DRPA Inc.

Meister Publishing

Other Seed Costs ***

USDA NASS, Others

ABS Flexibility

• ABS Supplies The Needs of Several Different Models: – POLYSYS, FLIPSIM, EPIC – ABS Data Are Readily Incorporated Into These Models • Has Supported a Range of Research Projects – Sustainable Agriculture, Biomass, Various Biotechnologies, Boll Weevil Eradication

ABS Output

Corn-Moldboard Plow ITEM COMMODITY: Corn PREVIOUS CROP: Corn REGION TYPE: POLYSYS REGION I.D.: CT-1 PRODUCTION REGION: Northeast CODE CATEGORY CODE TILLAGE: Moldboard Plow FARM SIZE: Large IRRIGATION: Dry PRACTICES: N.A.

N.A.

N.A.

SOIL TYPE: 0 E.I. INDEX: 0 ALT. NUTRIENTS: None 400001D0.C01

NAME UNITS PRICE NATIONAL INDEX QUANTITYAMOUNT

REVENUE

2053

VARIABLE EXPENSES

1 SEED 2655 8 1 Corn, Grain Corn Seed, Hybrid BU 2.7000

1,000 kernels 0.9713

1 1 92.34

25.33

SUBTOTAL

249.33

24.60

24.60

2 109 200 AP A 300 C FERTILIZER & LIME 2 Urea (44%-46% N) 2 2 P2O5 K20 * * LB LB LB 0.1569

0.3016

0.1275

1 1 1 281.00

41.00

50.00

44.08

12.37

6.38

Static vs Dynamic Impacts

• • • • Most implementations imply fix static environmental parameters into economic models While most physical processes occur in the mid or long term, annual/seasonal impacts maybe critical: yield, water However when looking 10, 25 or more years into the future this could be critical Full integration should not be a problem with current computer power

POLYSYS Regions (3110 Counties)

POLYSYS County Regions (3111)

ALMANAC

• • • Developed by ARS-USDA in 1992 to simulate the impact of agronomic decisions on crop biomass production Compiles soil erosion, economic, hydrological, weather, nutrient, plant growth dynamics, and crop management information Simulates plant competition up to 10 crops growing at the same time (unique from EPIC)

ALMANAC

• • • Does not require local calibration of plant parameters or hydrological components it is ideal for regional-level analyses Has been widely used to estimate yield response to climate and differences in land and water management at a specific location The most recent version of ALMANAC has incorporated additional parameters including evapotranspiration rates and water table information (Kiniry et al., 2005).

Geodatabase PRISM weather data Soil layer, landform, and acreage data (SSURGO) Tillage, fertilizer and other management data (ABS)

ALMANAC

ALMANAC Input File

ALMANAC ALMANAC Output: • Yield • Water: -Precipitation -Transpiration & ET -Potential plant water -Surface runoff • Fertilizer -Loss -Uptake -Mineralize -Fixed • • Soil erosion Temperature

Geodatabase Daily and monthly weather data (Weather Data) Soil layer, landform, and acreage data (SSURGO) Tillage, fertilizer and other management data (ABS) Crop parameters (USDA-ARS)

ALMANAC Input File

ALMANAC

ALMANAC Output File

Environmental indicators Land Allocation Decisions Environmental Effects (7/305/3110 Regions) Crop Supply (7 /305/ 3110 Regions) Expected Returns & Available Acreage Acreage Allocation Based on Expected Returns Acreage, Production, Expenditures Export Use Food Use Feed Use Bioenery Domestic use Price (U.S.) Crop Demand Production Price Available for Domestic Consumption ( Livestock U.S.) Value of Exports & Production Gov’t Payments Cash Receipts Gross & Net Realized Income Production Expenses Ag Income

Final Remarks

• • • • Data availability and compatibility is one of the major challenges Remote sensing and GIS systems developing new sources of data Use alternative biophysical data based on need, strength, simplicity Processing power, usually not a limiting factor

Thanks

!

Bio-based Energy Analysis Group http://beag.ag.utk.edu/ Agricultural Policy Analysis Center http://agpolicy.org/ Department of Agricultural Economics, Institute of Agriculture University of Tennessee http://www.agriculture.utk.edu/