an analysis of farmer preferences regarding filter strip programs greg howard work in collaboration...

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

[email protected]

http://aede.osu.edu/

Howard: An Analysis of Farmer Preferences Regarding BMPs

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Lake Erie: A Big Freaking DealDrinking water for 11 million people

Over 20 power plants300 marinas in Ohio alone40% of all Great Lakes charter boatsOne of top 10 sport fishing locations in

the worldThe 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


3

Nutrient PollutionHigh nutrient loads in lakes can

cause harmful algal blooms (HABs)

Why are large algal blooms harmful?◦Released toxins◦Lower water quality◦Hypoxic (dead) zones


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Lake Erie HistoryIn ‘60s, huge nutrient problems

◦Cuyahoga river burns in 1969◦Clean Water Act passes in 1972

P levels stable from 1970-75Improving 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


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Microcystis in Lake Erie• The Microcystis-Anabaena bloom of

2009 was the largest in recent years in our sampling region

2011

…until 2011 Source: Tom Bridgeman, UT and

Jeffrey M. Reutter, Ohio Sea Grant


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Government ResponseRegulationMarket-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)


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Best Management Practices

Soil testing and variable-rate application

Avoiding fertilizer application before storm events or in winter

Winter cover cropsFilter stripsRetention areasConservation tillage/No tillField retirement


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Where is the Economic Problem?

Question facing government: How to make these programs better?1. More effective practices2. Greater adoption rates (more acres

enrolled)3. Lower cost


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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?


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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


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Preference HeterogeneityLatent 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


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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


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FindingsEveryone likes more money and

less paperworkMajority 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


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Rest of the TalkSurvey and data

Model

Results

Implications and conclusion


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SurveySent 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 farmersResponse rate ≈ 40%


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SurveyQuestions 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


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Survey


18

Survey


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Model: Conditional Logit

T

tI

initx

nits

X

Xsi

1

1

n

)exp(

)exp()|(Pr

S

sns

nsn

Z

Zs

1

)exp(

)exp()(Pr

Probability that farmer nwill choose a series of tpolicy 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


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Variables (Alternative-specific)

Variable 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 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


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Variables (Farmer-specific)Variable Description Ran

geAvg. Std. Dev.

Risk Tolerant

= 1 if risk tolerant in farm

practices

{0,1} 0.38 0.38

High School

=1 if high school education or less

{0,1} 0.42 0.42

First Gen =1 if first generation

farmer

{0,1} 0.14 0.35

Norm Till =1 if engages in conventional

tillage

{0,1} 0.25 0.43

Lake Erie Algae

Level of awareness for Lake Erie algae

issues

{0,1,2}

1.11 0.69Models including age, income, environmental stewardship, and whether farmergrows organic yield same results.


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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 wellIndependent variables are

exogenous and correlated with expected FS Efficacy

Predicted values for FS Efficacy are used in the 2nd stage estimation


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Variables (Field-specific)Variable 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, 1} 0.34 0.47

Width 75 = 1 if 75 foot filter strip

{0, 1} 0.33 0.47

Slope < 2 =1 if slope of field is < 2 degrees

{0, 1} 0.51 0.50

Slope > 5 =1 if slope of field is > 5 degrees

{0, 1} 0.10 0.30

D 25-75 Indicator variables for distance to the

nearest surface water in feet

{0, 1} 0.19 0.39

D > 75 {0, 1} 0.25 0.43


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Results: 1st Stage3 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


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Results: 1st Stage ClassesClass 1: Most profit-driven

◦(marginally significant)

Class 2: Better educated, already enrolled in PES programs

Class 3: Older, more risk averse


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Results: 2nd Stage CoefficientsIndependent Variable

Traditional

Analysis

Latent Class Analysis

Class 1 (70%)

Class 2 (30%)

Payment Positive Positive Positive

FS Width Negative Negative -------

Paperwork Negative Negative Negative

Years ------ ------- -------

Status Quo Positive ------- Positive (Large)

FS Efficacy Positive Positive -------


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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


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Results: Relative Importance of Independent Variables

Independent Variable

Traditional 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% 7% 30%

FS Efficacy 14% 26% 16%


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Results: Relative Importance of Independent Variables

Independent Variable

Traditional 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% 7% 30%

FS Efficacy 14% 26% 16%


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Results: 2nd Stage ProfilesClass 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


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ImplicationsHow 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.)


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Thank You!Support provided by NSF Coupled

Human and Natural Systems Program (GRT00022685)

an analysis of farmer preferences regarding filter strip programs greg howard work in collaboration...

Documents

analysis of farmer preferences

lake erie historyin

year2000 grand lake

water limit20 ppb

ohio alone40

lake erie maumee bay

bmps1 ppb

ohio sea grant