Integrated modeling of agricultural land
management decisions and Lake Erie
ecosystem services
Elena Irwin
Department of Agricultural, Environmental and Development Economics
Iowa State FEW Workshop, October 12-13, 2015
Support provided by grants from the National Science Foundation Coupled Human and
Natural Systems Program (GRT00022685) and the Ohio Sea Grant program
Lake Erie human behaviorecosystem services research
• Focus: Develop a set of models to project how policies
influence agricultural land management in the watershed,
Lake Erie harmful algal blooms (HABs) and ecosystem services
Jay Martin, FABE
Elena Irwin, AEDE
Stuart Ludsin, EEOB
Erik Nisbet, COMM
Noel Aloysius, FABE
Brian Roe, AEDE
Eric Toman, SENR
Robyn Wilson, SENR
Carlo DeMarchi, CWRU
Mike Fraker, EEOB
Wendong Zhang,
Zhang, Iowa
Iowa State
State
Funding from NSF Coupled Human and
Natural Systems Program (GRT00022685)
and the NOAA/Ohio Sea Grant Program
Project Website: http://ohioseagrant.osu.edu/maumeebay
Study Region: W. Lake Erie basin & Maumee River watershed
Lake Erie
East
Central
West
Maumee
River
Agricultural land use in the Maumee watershed
and harmful algal blooms in Lake Erie
2007 Land Use
Yellow = Corn
Green = Soybeans
Dark Red = Wheat
Orange = Hay/Pasture
Dark Green = Forest
2011 toxic algae bloom in Lake Erie. At its peak, the bloom cover
990 square miles of the lake's surface area. Source: Ohio DNR
2011
Sources: Kevin Czajkowski, University of Toledo (above)
Heidelberg University (below)
Source: Thomas Bridgeman, University of Toledo
Main research goals: Policy scenarios and
assessment of key benefits and costs
• Nutrient management policy scenarios
• Fertilizer tax
• Incentives (payments, cost share) for BMP adoption, including
•
•
•
•
•
4 R’s (Right source at the Right rate and Right time in the Right place)
Cover crops
Filter strips
Controlled drainage
Grid sampling
• Spatial targeting (of tax or monetary incentives)
• Information campaigns
• Benefits and costs of alternative policies
• Change in agricultural profits
• Change in ecosystem services due to changes in HABs, including
• Recreational services (fishing, beach going)
• Residential amenities (housing values)
• Health costs (drinking water treatment)
Lake Erie-land coupled human-natural systems modeling
(Steve’s sandwich!)
Policy support
(surveys of Ohio
residents, Maumee
farmers, residents)
Policies
Farmer land
management
decisions
P runoff from
field into
watershed
Lake hydrodynamic-lower
food web model and
statistical models
Costs of
policy ($)
Benefits of
policy ($)
P loadings
to Lake
Erie
Changes in
ecosystem
services
Econometric models of
crop choice, fertilizer
demand & BMP adoption
(survey of 7,500 farmers)
Spatial land-watershed
simulation model w
SWAT (data on 187k rural
land parcels, 256 HRUs)
Improved
ecosystem
services
Non-market
valuation (survey
of anglers, Ohio
residents)
Survey data collected includes…
• Farm characteristics
• Type of operation (size; crop mix; with livestock)
• Amount of land rented, crop insurance
• Farm sales
• Operator characteristics
• Characteristics: Experience. education, farm income
• Behavioral attitudes, beliefs: Risk attitudes, perceived efficacy,
environmental stewardship, familiar with 4R’s
• Field-specific responses
•
•
•
•
Crop choice in 2012, 2013, inputs, prices
Land characteristics (soil type, slope, size)
Fertilizer application rates (N and P)
Other management practices (soil testing, broadcast, manure, crop
rotation)
• Two round of mail surveys from Jan – Apr 2014
• Farmers in the watershed from Ohio, Indiana, Michigan
• Response rate: ~ 38%
Land use data: field-level crop rotation (2006-2009)
Total number of rural parcels: 187,622
Total percent in cropland: 83%
Source: Common Land Unit boundaries overlaid with the Cropland Data Layer (USDA) , 2006-2012
Land use data: field-level crop rotation (2010-2012)
Total number of rural parcels: 187,622
Total percent in cropland: 83%
Source: Common Land Unit boundaries overlaid with the Cropland Data Layer (USDA) , 2006-2012
Some key findings so far
• Policy preferences of Ohio residents (Econometric model)
• Controlling for cost to household, residents have a preference
for reducing nutrient pollution via implementing regulations
and PES programs over fertilizer taxes. No significant
differences between support for PES and regulations
• Farmer decision making (Econometric models)
• Fertilizer demand: Demand elasticity is greater w higher quality
land, multi-year applications, farmers’ knowledge of 4R’s;
elasticity varies w crop choice
• BMP adoptions:
• All farmers are profit-maximizers; some are also
conservationalists
• Perceived efficacy and conservation identity are positive and
significant across all or most; other factors vary with BMP (still
working on costs)
• Farmers tend to fall into one of several classes: innovators (2245%), future adopters (45-55%), laggards (10%)
• Farmers require the least compensation for grid sampling and
require the most compensation for implementing strict nutrient
limits (15 ppm P). Using winter cover crops falls somewhere in
between.
Some key findings so far
• BMPs and P loadings (SWAT model)
• Injection is very effective at reducing TP and DRP
• Seasonal timing of broadcast matters: spring application
generates more runoff and increase in HABs; fall runoff settles
out in lake and increases hypoxia
• Integrated analysis (Farmer model w estimated
parameters and SWAT)
• A one-year 50% P-fertilizer tax generates 15-20% reduction in P
application, but only 2-3% predicted reduction in edge-of-field P
loadings  legacy effects
Challenges
• Spatial mismatch between the land unit (parcel) and
hydrologic response unit (HRU)
• About 155,000 cropland parcels; at best we can disaggregate HRUs
into about 2,300 units (252 subwatersheds)
• Challenges in translating predicted land management and crop
changes at parcel scale to impacts on P loadings at HRU scale
• Linking farmer variables to spatial land simulation model
• Lack individual data for whole population of farmers
• Ideal world: Individual data for all farmers AND land ownership and
rental agreements for all fields
• The best we can do is draw randomly from distributions of
characteristics from ag census or surveyed farmers (county level) or
use means of surveyed farmers (township level)  translate into
distributions of crop and BMP choices at subwatershed level
• Extreme policy scenarios needed to achieve desired
reductions
• Need to better understand whether this is a true causal relationship
(e.g., due to legacy effects) or model misspecification
Bigger challenges and questions
• Current model: Can generate changes over time, but
farmer decision making is static
• Myopic behavior; parameters estimated from single snapshot
• Forward-looking behavior and modeling
• Expectations: Farmer cares about future prices, soil P; does not
care about future P loadings; however, social planner cares
about all these things
• Behavioral parameters: Out of sample prediction is a challenge
due to lack of data AND potential behavioral feedbacks, e.g.,
• Perceived efficacy may change over time due to learning
• Interactions between farmers and policy could be a repeated game
• Type of model should be determined by research goals
• Optimal policy design  social planner model
• Realistic description of behavior and system  agent-based
model with lots of data to specify rules
Stepping back…
• What is the purpose of IAMs?
• Describe current: develop a model of the coupled
human-natural system to better understand the
observed behavior, interactions, outcomes
• Project future: use model to generate future scenarios
• Prescribe policy: choose management actions to max
social welfare or to ensure non-declining social welfare
or to avoid bad outcomes
• Different welfare goals
• Efficiency: max intertemporal social welfare
• Sustainability: non-declining social welfare over time
• Dasgupta (2009), Arrow, et al. (2003), etc: In imperfect
economies, we should assess sustainability based on a forecast
of the actual economy, not an optimized economy