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

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
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

4. Lake Erie History In ‘60s, huge nutrient problems
Cuyahoga river burns in 1969
Clean Water Act passes in 1972
P levels stable from
Improving from
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-Anabaena bloom of 2009 was the largest in recent years in our sampling region
…until 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)
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

21. 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

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)
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

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
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

27. Results: Marginal Effect on Probability that Program is “Best”
Variable
Class 1 (70%)
Class 2 (30%)
Payment
0.0025***
0.0019**
FS Width
***
Paperwork
*** **
Years
0.0016
Status Quo
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

28. 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

29. 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

30. 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

31. 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

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