An Analysis of Farmer Preferences Regarding Filter Strip Programs Greg Howard Work in collaboration with Dr. Brian Roe Department of AED Economics Ohio State University November19, 2012 howard.761@osu.edu

Lake Erie: A Big Freaking Deal Drinking water for 11 million people Over 20 power plants 300 marinas in Ohio alone 40% of all Great Lakes charter boats One of top 10 sport fishing locations in the world The most valuable freshwater commercial fishery in the world (Walleye capital of the world) Coastal county tourism value is over $10 billion (7 coastal counties = over 25% of Ohio 88-county total) Issues with nutrient pollution Phosphorous and Nitrogen Howard: An Analysis of Farmer Preferences Regarding BMPs

Nutrient Pollution High nutrient loads in lakes can cause harmful algal blooms (HABs) Why are large algal blooms harmful? Released toxins Lower water quality Hypoxic (dead) zones 1 ppb WHO drinking water limit 20 ppb WHO swimming limit 60 ppb highest level for Lake Erie till this year 84 ppb highest level for Grand Lake St. Marys till last year 2000+ Grand Lake St. Marys 2010 1200 Lake Erie Maumee Bay area 2011 Howard: An Analysis of Farmer Preferences Regarding BMPs

Lake Erie History In ‘60s, huge nutrient problems Cuyahoga river burns in 1969 Clean Water Act passes in 1972 P levels stable from 1970-75 Improving from 1975-95 How did we do it? Point source reductions Majority of loading in 1970 was point source Now agriculture accounts for 2/3 of loading 1995-present: Getting worse Howard: An Analysis of Farmer Preferences Regarding BMPs

Microcystis in Lake Erie The Microcystis-Anabaena bloom of 2009 was the largest in recent years in our sampling region …until 2011 Source: Tom Bridgeman, UT and Jeffrey M. Reutter, Ohio Sea Grant 2011 Howard: An Analysis of Farmer Preferences Regarding BMPs

Government Response Regulation Market-based Solutions Nutrient taxes Nutrient trading programs (Ohio River Basin) Payment for Ecosystem Services (PES) programs Pay farmers for implementation of Best Management Practices (BMPs) Howard: An Analysis of Farmer Preferences Regarding BMPs

Best Management Practices Soil testing and variable-rate application Avoiding fertilizer application before storm events or in winter Winter cover crops Filter strips Retention areas Conservation tillage/No till Field retirement Howard: An Analysis of Farmer Preferences Regarding BMPs

Where is the Economic Problem? Question facing government: How to make these programs better? 1. More effective practices 2. Greater adoption rates (more acres enrolled) 3. Lower cost Howard: An Analysis of Farmer Preferences Regarding BMPs

More Specifically… How do farmer perceptions of filter strip effectiveness influence filter strip program choice? Do farmers exhibit substantial preference heterogeneity for filter strip programs? Howard: An Analysis of Farmer Preferences Regarding BMPs

Perceptions of Filter Strip Effectiveness Ma, Swinton, Lupi, and Jolejole-Foreman (2012) Consider a series of cropping systems, and control for farmer perceptions of ecosystem services from a cropping system Qualitative, and possibly endogenous This study uses a quantitative measure and instruments for perceived efficacy using a two-stage estimation Howard: An Analysis of Farmer Preferences Regarding BMPs

Preference Heterogeneity Latent Class Analysis (LCA) allows for preference heterogeneity Farmers belong to one of several latent (unobserved) groups For each group, variables of interest (predictors) can have different marginal effects Can use other variables (covariates) to inform class membership Howard: An Analysis of Farmer Preferences Regarding BMPs

Latent Class Analysis (LCA) Example: Effect of LeBron James endorsement Some people are more likely to buy a product if James endorses it Other people (Ohioans and New Yorkers) may be less likely to buy if James endorses Assuming preference homogeneity Little or no effect of endorsement LCA can capture differences Howard: An Analysis of Farmer Preferences Regarding BMPs

Findings Everyone likes more money and less paperwork Majority are more likely to choose program if perceived efficacy is higher No status quo bias Minority for whom perceived efficacy little or no impact Large status quo bias Howard: An Analysis of Farmer Preferences Regarding BMPs

Rest of the Talk Survey and data Model Results Implications and conclusion Howard: An Analysis of Farmer Preferences Regarding BMPs

Survey Sent to 2000 Ohio corn and soybean farmers in Maumee watershed December-February 2012 Tailored Design Method (Dillman 2007) Completed surveys entered to win a pair of OSU football tickets Pilot tested with farmers Response rate ≈ 40% Howard: An Analysis of Farmer Preferences Regarding BMPs

Survey Questions regarding Demographic information Field characteristics “Consider one of your fields where runoff is a potential problem and where no filter strip exists…” PES program enrollment Preferences regarding hypothetical filter strip programs Howard: An Analysis of Farmer Preferences Regarding BMPs

Survey Howard: An Analysis of Farmer Preferences Regarding BMPs

Survey Howard: An Analysis of Farmer Preferences Regarding BMPs

Model: Conditional Logit Probability that farmer n will choose a series of t policy alternatives i, conditional on the farmer belonging to class s: X is a policy alternative-specific variable Probability that farmer n belongs to class s: Z is a farmer-specific variable Howard: An Analysis of Farmer Preferences Regarding BMPs

Variables (Alternative-specific) Description Range Avg. Std. Dev. Payment Dollars per acre {0, 125, 175, 200, 250} 126.4 96.1 FS Width Filter strip width in feet {0, 25, 75} 33.4 31.1 Paperwork Hours per year {0, 2, 5, 10} 3.8 Years Length of program {0, 5, 10} 5.1 4.1 FS Efficacy Decrease in probability of runoff [-90, 100] 11.9 19.0 Status Quo =1 if current program {0, 1} 0.3 0.5 Howard: An Analysis of Farmer Preferences Regarding BMPs

Variables (Farmer-specific) Description Range Avg. Std. Dev. Risk Tolerant = 1 if risk tolerant in farm practices {0,1} 0.38 High School =1 if high school education or less 0.42 First Gen =1 if first generation farmer 0.14 0.35 Norm Till =1 if engages in conventional tillage 0.25 0.43 Lake Erie Algae Level of awareness for Lake Erie algae issues {0,1,2} 1.11 0.69 Models including age, income, environmental stewardship, and whether farmer grows organic yield same results. Howard: An Analysis of Farmer Preferences Regarding BMPs

Model: First Stage (Endogenous Efficacy) OLS with FS Efficacy as dependent variable and field-specific variables as independent variables Latent Class Analysis used in 1st stage as well Independent variables are exogenous and correlated with expected FS Efficacy Predicted values for FS Efficacy are used in the 2nd stage estimation Howard: An Analysis of Farmer Preferences Regarding BMPs

Variables (Field-specific) Description Range Avg. Std. Dev. Drainage Field has working drainage tile {0, 1} 0.88 0.33 Width 25 =1 if 25 foot filter strip 0.34 0.47 Width 75 = 1 if 75 foot filter strip Slope < 2 =1 if slope of field is < 2 degrees 0.51 0.50 Slope > 5 =1 if slope of field is > 5 degrees 0.10 0.30 D 25-75 Indicator variables for distance to the nearest surface water in feet 0.19 0.39 D > 75 0.25 0.43 Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: 1st Stage 3 Classes (40%, 40%, 20%) Class 1 and Class 2: Wider filter strips and absence of drainage tile increase efficacy Class 2 believe filter strips are much more effective than Class 1 (21 vs. 6) Distance to water and slope not significant Class 3: Filter strips do nothing, regardless of field attributes Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: 1st Stage Classes Class 1: Most profit-driven (marginally significant) Class 2: Better educated, already enrolled in PES programs Class 3: Older, more risk averse Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: 2nd Stage Coefficients Independent Variable Traditional Analysis Latent Class Analysis Class 1 (70%) Class 2 (30%) Payment Positive FS Width Negative ------- Paperwork Years ------ Status Quo Positive (Large) FS Efficacy Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: Marginal Effect on Probability that Program is “Best” Variable Class 1 (70%) Class 2 (30%) Payment 0.0025*** 0.0019** FS Width -0.0039*** -0.0027 Paperwork -0.0257*** -0.0235** Years -0.0124 0.0016 Status Quo -0.1288 0.5047** FS Efficacy 0.0105** 0.0060* Observations 526 R2 0.7920 *, **, and *** denote statistical significance at the 90%, 95%, and 99% levels, respectively Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: Relative Importance of Independent Variables Traditional Analysis Latent Class Analysis Class 1 (70%) Class 2 (30%) Payment 38% 32% 27% FS Width 19% 15% 12% Paperwork 16% 13% 14% Years 2% 7% 1% Status Quo 11% 30% FS Efficacy 26% Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: Relative Importance of Independent Variables Traditional Analysis Latent Class Analysis Class 1 (70%) Class 2 (30%) Payment 38% 32% 27% FS Width 19% 15% 12% Paperwork 16% 13% 14% Years 2% 7% 1% Status Quo 11% 30% FS Efficacy 26% Howard: An Analysis of Farmer Preferences Regarding BMPs

Results: 2nd Stage Profiles Class 1 Class 2 Risk Tolerant* 26% 15% High School 37% 45% First Gen 10% 17% Norm Till*** 16% 35% Lake Erie Algae: Somewhat aware 51% 55% Lake Erie Algae: Very aware* 31% 20% *, **, and *** denote statistical significance at the 90%, 95%, and 99% levels, respectively Howard: An Analysis of Farmer Preferences Regarding BMPs

Implications How do we improve adoption rates? How do we lower costs? Increase payments, decrease paperwork Target those most likely to belong to Class 1 Educate farmers on value of FSs How do we lower costs? Decrease paperwork Focus on education Education on the benefits of filter strips Education on the impacts of nutrient pollution (Lake Erie, Grand Lake St. Mary’s, etc.) Howard: An Analysis of Farmer Preferences Regarding BMPs

Thank You! Support provided by NSF Coupled Human and Natural Systems Program (GRT00022685) Howard: An Analysis of Farmer Preferences Regarding BMPs