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Insights on Farmer Preferences for Filter Strip Programs in Lake Erie Region

This analysis explores farmer preferences for Best Management Practices (BMPs) to combat nutrient pollution in Lake Erie, examining factors influencing adoption and effectiveness of filter strip programs. The study addresses the economic implications and government responses to mitigate nutrient pollution issues.

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Insights on Farmer Preferences for Filter Strip Programs in Lake Erie Region

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  1. 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

  2. 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

  3. 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 Howard: An Analysis of Farmer Preferences Regarding BMPs

  4. 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

  5. Microcystis in Lake Erie • The Microcystis-Anabaenabloom 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

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

  15. 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

  16. 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

  17. Survey Howard: An Analysis of Farmer Preferences Regarding BMPs

  18. Survey Howard: An Analysis of Farmer Preferences Regarding BMPs

  19. 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

  20. Variables (Alternative-specific) Howard: An Analysis of Farmer Preferences Regarding BMPs

  21. Variables (Farmer-specific) Models including age, income, environmental stewardship, and whether farmer grows organic yield same results. Howard: An Analysis of Farmer Preferences Regarding BMPs

  22. 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

  23. Variables (Field-specific) Howard: An Analysis of Farmer Preferences Regarding BMPs

  24. 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

  25. 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

  26. Results: 2nd Stage Coefficients Howard: An Analysis of Farmer Preferences Regarding BMPs

  27. Results: Marginal Effect on Probability that Program is “Best” *, **, and *** denote statistical significance at the 90%, 95%, and 99% levels, respectively Howard: An Analysis of Farmer Preferences Regarding BMPs

  28. Results: Relative Importance of Independent Variables Howard: An Analysis of Farmer Preferences Regarding BMPs

  29. Results: Relative Importance of Independent Variables Howard: An Analysis of Farmer Preferences Regarding BMPs

  30. Results: 2nd Stage Profiles *, **, and *** denote statistical significance at the 90%, 95%, and 99% levels, respectively Howard: An Analysis of Farmer Preferences Regarding BMPs

  31. Implications • How do we improve adoption rates? • 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

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

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