Integrated modeling of agricultural land management

decisions and Lake Erie ecosystem services

Support provided by grants from the National Science Foundation Coupled Human and Natural Systems Program (GRT00022685) and the Ohio Sea Grant program

Elena IrwinDepartment of Agricultural, Environmental and Development Economics

Iowa State FEW Workshop, October 12-13, 2015

Lake Erie human behavior-ecosystem services research

Jay Martin, FABE Brian Roe, AEDEElena Irwin, AEDE Eric Toman, SENRStuart Ludsin, EEOB Robyn Wilson, SENRErik Nisbet, COMM Carlo DeMarchi, CWRUNoel Aloysius, FABE Mike Fraker, EEOB

• 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

Project Website: http://ohioseagrant.osu.edu/maumeebay

Funding from NSF Coupled Human and Natural Systems Program (GRT00022685) and the NOAA/Ohio Sea Grant Program

Wendong Zhang, Iowa StateWendong Zhang, Iowa State

Study Region: W. Lake Erie basin & Maumee

River watershed

West

Central

East

MaumeeRiver

Lake Erie

Agricultural land use in the Maumee watershed and harmful algal blooms in Lake Erie

Sources: Kevin Czajkowski, University of Toledo (above)Heidelberg University (below)

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

Source: Thomas Bridgeman, University of Toledo

2007 Land UseYellow = CornGreen = SoybeansDark Red = WheatOrange = Hay/PastureDark Green = Forest

2011

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!)

Improved ecosystem

services

Non-market valuation (survey of anglers, Ohio

residents)

Costs of policy ($)

Benefits of policy ($)

Farmer land management

decisionsPolicies

Econometric models of crop choice, fertilizer

demand & BMP adoption (survey of 7,500 farmers)

Policy support (surveys of Ohio

residents, Maumee farmers, residents)

P runoff from field into

watershed

P loadings to Lake

Erie

Spatial land-watershed simulation model w

SWAT (data on 187k rural land parcels, 256 HRUs)

Changes in ecosystem

services

Lake hydrodynamic-lower food web model and

statistical models

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)

Source: Common Land Unit boundaries overlaid with the Cropland Data Layer (USDA) , 2006-2012

Total number of rural parcels: 187,622Total percent in cropland: 83%

Total number of rural parcels: 187,622Total 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)

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 (22-45%), 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