2018 SWCS International Annual Conference July 29-August 1, 2018 Albuquerque Convention Center

1. Meta-Review of Barriers and
Motivations for Farmers to
Adopt Conservation Practices
J. Arbuckle, Sarah Church, Francis Eanes, Kristin Floress, Yuling Gao,
Ben Gramig, Linda Prokopy, Pranay Ranjan, Ajay Singh
SWCS 2018

2. Genesis of This Project
• Prokopy et al. 2008, Baumgart-Getz et al. 2012:
• Reviewed 55 studies published from 1982-2007 in the U.S.

3. Genesis of This Project
• Prokopy et al. 2008, Baumgart-Getz et al. 2012:
• Reviewed 55 studies from 1982-2007 in the U.S.
• Findings:
• No consistent determinants of conservation adoption
• Most often positively associated with conservation adoption: education,
capital, income, farm size, access to information, positive environmental
attitudes, environmental awareness, and social networks
• Since publication:
• Increasing numbers of qualitative papers
• Overall explosion of research in this area
• Walton Family Foundation interest

4. Genesis of This Project
• Prokopy et al. 2008, Baumgart-Getz et al. 2012:
• Reviewed 55 studies from 1982-2007 in the U.S.
• Findings:
• No consistent determinants of conservation adoption
• Most often positively associated with conservation adoption: education,
capital, income, farm size, access to information, positive environmental
attitudes, environmental awareness, and social networks
• Since publication:
• Increasing numbers of qualitative papers
• Overall explosion of research in this area
• Walton Family Foundation interest

5. Overview of Panel
• Generating data
• Coding studies
• Study level summary
• Quantitative analysis results
• Qualitative analysis results
• What this means for practice
• What this means for research
• Your questions and comments

6. Generating Rows of Data:
Finding Papers
Dr. Sarah Church, Purdue University

7. Overview
*Adapted from Card, 2012. Applied meta-analysis for social science
research. The Guilford Press, NY.
• Inclusion/exclusion criteria
• Methods
• Searching for literature
• Coding study characteristics,
determining inclusion/exclusion
• Coding study results

13. Coding Papers, aka where did
the last 2 years of our lives go??
Dr. Ben Gramig, University of Illinois at Urbana-
Champaign

14. Coding Methods
• Primary and secondary coder for each
paper
• Coding – excel template with dropdowns
• Study-level and individual level study
sheets
• Each study on its own sheet for easy
tracking
• Primary coded, then sent code sheet to
secondary
• Secondary reviewed coding, agreed
or disagreed with primary
• All disagreements discussed
between primary and secondary
until resolved
• Remaining issues brought to full
group
Image credit: by Stephen Kirkpatrick, organic crops in high tunnel. Courtesy of
USDA NRCS.

15. Study Level Characteristics Coding
• Author affiliation
• Funding source
• Theoretical grounding/theories used
• General methodological approach
• Data collection and sampling methods
• Year data collected
• Population and sample sizes,
descriptions
• Geographic scope
• Crops and livestock
• DV, Barriers
• Inclusion decision
Image credit: by Jeff Vanuga. Grazing rotation, courtesy of USDA NRCS.

16. Dependent
variables
Target
population
Exclusion criteria
• Adoption/behaviors
• Willingness to adopt
• Willingness to accept
payments
• Interest in
participating in
practices, programs
• Agricultural
producers:
• Conventional
• Specialty
• Organic
• Agroforestry
• Livestock
• Row crops
• Urban
• Reviews,
discussions
• Sample selectivity
models
• >1 paper reporting
on same model
Image credit: by Lance Cheung, taken at Sunny Ridge Farm, MD.
Courtesy of USDA NRCS.

17. Individual Study Results Coding
• Some papers had multiple analyses
• DV, DV measure type and IV/IV measure type
• For all DVs and IVs
• Binary, categorical, ordinal, continuous as described
by authors
• Measure notes – scales used, meaning of coding
(e.g. 0/1 =not adopt/adopt; 1-5 agreement from
strongly disagree to strongly agree)
• Analysis # and method
• Vote count (pos, neg, insig)
• p-value range, threshold used in paper, p-value
• Model results (coefficients, t statistics, R2, etc.)
Image credit: by Jeff Vanuga, lagoon waste management system for 900 head hog
farm. Courtesy of USDA NRCS.

18. Coding Methods
• DV, IV category and
subcategories NOT assigned
during coding
• Full team meeting – 2 days in-
person
• Weekly calls with majority
Attitudes
(10 subcategories)
Awareness
(3 subcategories)
Farm Characteristics
(21 subcategories)
Economic Factors
(18 subcategories)
Farmer Characteristics
(12 subcategories)
Behaviors
(10 subcategories)

19. Study Level Characteristics
Dr. Pranay Ranjan, Purdue University

20. Study Level Characteristics
• Key characteristics of each study
(39 data fields)
• Theoretical grounding, data
collection method, sampling
method, response rate, population
description, geographic scope,
types of crops/livestock…
• 107 quantitative studies looking at
adoption (excluding willingness)
Theoretical grounding
Sampling method Response rate
Data collection
method

21. Study Level Characteristics
44%
26%
24%
2%
3%
Theoretical grounding (n=107)
Complete theoretical framework
No theory employed
Theory used in lit review
Theory incorporated into discussion
Other
29%
27%
21%
20%
3%
Specific theory (n=76)
Multiple
Microeconomic theory
Other
Diffusion/innovation
Theory of Planned Behavior

22. Study Level Characteristics
56%
18%
10%
5%
3%
2%
2%
3%1%
Data collection method (n=107)
Mail survey
Secondary quantitative data
Structured interview (quantitative)
Phone survey
Other qual/quant data
Drop-off pick-up
Semi-structured interview (qualitative)
Other
Web survey

23. Study Level Characteristics
29%
17%
16%
15%
12%
7%
4%
Sampling method (n=107)
Simple random
Census
Stratified random
Not described
Other
Non-random
Systematic random

24. Study Level Characteristics
Sample size statistics
N Missing Mean Median Std. Dev. Min Max
Population size 32 75 83800.53 4027.5 353643.817 110 2000000
Initial sample size 67 40 3743.67 1200 8496.656 38 52000
Final sample size 99 8 1915.69 553 5313.954 25 42229
Response rate 61 46 0.49 0.47 0.218 0.15 0.95

25. Making Sense of >5,000
Rows of Data
Dr. Linda Prokopy
Purdue University

26. Overall Vote Count: Statistical Significance of IVs, All
Models, All Studies
9.08
73.49
17.43
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
Overall Vote Count
Negative Insig Positive

27. Statistical Methods
1. Hypothesized direction of variables
2. For positive hypotheses, where coefficient available:
• Positive coefficients recoded to 1
• Negative or zero coefficients recoded to 0
3. Binomial confidence intervals for proportions were calculated
4. Proportion of positive coefficient examined (number
positive/total number for that variable)
Bushman and Wang, 2009

49. Key Findings: Positive Predictors
• Positive attitudes towards programs and practices
• Existing behaviors
• Vulnerable systems
• Large farms
• Seeking and using information
• Positive evaluation of information
• Education
• Stewardship identity
• Age the only negative predictor

50. Motivations and Barriers to
Conservation Adoption
Evidence from Qualitative Research
Dr. Sarah Church and Dr. Pranay Ranjan
Purdue University

51. Qualitative data: What and why?
• More and more qualitative studies since 2008
• Additional perspective for the 2018 study
• Farmer voice
• Provides understanding and nuance
• To inform conservation programs, plans, and policies

52. Qualitative data analysis: How?
Arbuckle and Roesch-McNally 2015

53. Coding Methods
• Coding framework
− Quantitative foundation
− Refined inductively
• 48 articles
− 2 researchers
− 24 per researcher
• 2nd review on 24 articles
• Codebook refinement - definitions
• Reports to full group
Attitudes
(10 sub-codes)
Motivations
(18 sub-codes)
Barriers
(16 sub-codes)

54. Coding Challenges
Nuance in farmer responses“Filter strips and habitats were most
often viewed as inconvenient, because
they required breaking up fields or
changing management practices. One
farmer did not intend to adopt many
filter strips because he knew his landlord
would not permit it, and he preferred to
use cover crops in this situation because
they would not take land out of
production.” (Kalcic et al. 2015)
– Decision making is complex!

55. Results
1 1 1
3
1
4 4
5
8
7
5
8
0
1
2
3
4
5
6
7
8
9
1996 1997 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Numberofarticles
Year published

56. Results: Motivations and barriers
13
13
15
16
25
27
15
18
18
10
29
19
0 5 10 15 20 25 30
Farm characteristics
Social norms, community aspects
Government programs
Practice
Economics
Farm management
Number of articles
Sub-codes
Motivation Barrier

57. Results: Barrier sub-codes
10
11
13
13
15
16
25
27
0 5 10 15 20 25 30
Tenure, land
Risk
Farm characteristics
Social norms, community aspects
Government programs
Practice
Economics
Farm management
Number of articles
Sub-code

58. Results: Barrier – farm management
8
11
13
15
0 5 10 15
Timing
Status quo bias
Incompatibility physical,
business
Management effort increased
Number of articles
Sub-sub-code

59. Results: Barrier – farm management, effort
“Some producers noted
the increased operational
requirements associated
with grassed waterways,
including time and
resources necessary to
maintain the waterway
and the increased time
and effort required to
manage crops around the
waterway.” (Reimer, et al.,
2012)
8
11
13
15
0 5 10 15
Timing
Status quo bias
Incompatibility physical,
business
Management effort increased
Number of articles
Sub-sub-code

60. Results: Barrier – economics
4
5
5
9
14
0 5 10 15
Farm profitability
Commodity market favorable
Cost high, on-going
Yield reduced
Implementation cost
Number of articles
Sub-sub-code

61. Results: Barrier – economics, implementation cost
“Cost. I'm doing the
numbers for cover crops
right now and it’s a lot of
money for establishment.”
(Krajewski, 2017)
4
5
5
9
14
0 5 10 15
Farm profitability
Commodity market favorable
Cost high, on-going
Yield reduced
Implementation cost
Number of articles
Sub-sub-code

62. Results: Barrier – practice
2
3
3
3
4
0 1 2 3 4
Resource competition
Aesthetics, bad
Current use, negative
Increased weeds
Time, lack
Number of articles
Sub-sub-code

63. Results: Barrier – practice, lack of time
“I don’t have time to go
build a thousand electric
fences all over the place.
And I do rotate my cattle
on my native grass but
not any intensive system.”
(King et al., 2017)
2
3
3
3
4
0 1 2 3 4
Resource competition
Aesthetics, bad
Current use, negative
Increased weeds
Time, lack
Number of articles
Sub-sub-code

64. Results: Motivation sub-codes
15
16
17
18
18
19
21
29
31
0 5 10 15 20 25 30 35
Farm characteristics
Trusted information, advice, networks
Environmental awareness
Government programs
Social norms, community aspects
Farm management
Operator characteristics
Economics
Benefit
Number of articles
Sub-codes

65. Results: Motivation – benefit
6
10
11
12
19
0 5 10 15 20
Nutrient loss reduction
Soil health
Off-farm benefit
On-farm benefit
Erosion reduction
Number of articles
Sub-sub-codes

66. Results: Motivation – benefit, erosion reduction
6
10
11
12
19
0 5 10 15 20
Nutrient loss reduction
Soil health
Off-farm benefit
On-farm benefit
Erosion reduction
Number of articles
Sub-sub-codes
“Farmers also linked the
RG [rotational grazing]
practice with
improvements in soil
conservation on their
property…‘…on a rainy
day. You drive past the
neighbors and you will see
brown water running out
of the fields, it runs clear
as can be all [on our
farm]...’” (Brummel & Nelson
2014)

67. Results: Motivation – economics
6
7
9
12
13
0 5 10 15
Labor reduced
Profit income increased
Yield improved
Economic benefits, general
Reduced input cost
Number of articles
Sub-sub-code

68. Results: Motivation – economics, reduced input cost
6
7
9
12
13
0 5 10 15
Labor reduced
Profit income increased
Yield improved
Economic benefits, general
Reduced input cost
Number of articles
Sub-sub-code
“‘The ground pounders
are just spending a
tremendous amount of
money on iron,
horsepower, fuel, and
labor, where I’m not.’”
(Reimer, et al., 2012)

69. Results: Motivation – operator characteristics
4
20
0 5 10 15 20
Farmer type
Farmer identity
Number of articles
Sub-sub-code

70. Results: Motivation – stewardship identity
4
20
0 5 10 15 20
Farmer type
Farmer identity
Number of articles
Sub-sub-code
“I wanna see the land
preserved as much as
possible. So we don’t farm
it to death or farm it in a
way that it washes away
or whatever.”
(Druschke, 2013)

71. Results: Motivation – innovator identity
4
20
0 5 10 15 20
Farmer type
Farmer identity
Number of articles
Sub-sub-code
“…‘New ideas come up,
whether from other
ranches, the university, or
even ourselves (what we
might think and just to try
new things) and that is
how we learn and develop
and become better land
and grass managers.’”
(Kennedy et al., 2016)

72. Lessons Learned
Dr. Ben Gramig, University of Illinois at Urbana-
Champaign

73. Lessons Learned—Implications for Diffusion
• Information seeking/networking:
• more field days, support for ag networks and social learning
• more info about trust, change agents needed
• better funding for Extension, more use of non-traditional actors?

74. Lessons Learned—Implications for Diffusion
• Information seeking/networking:
• more field days, support for ag networks and social learning
• more info about trust, change agents needed
• better funding for Extension, more use of non-traditional actors?
• Positive attitudes toward programs and practices:
• inadequacy of “information deficit” approach re: water quality problems and
conservation adoption
• economics of practice

75. Lessons Learned—Implications for Diffusion
• Diverse/vulnerable systems:
• need for more targeted assistance, less “random acts of conservation”

76. Lessons Learned—Implications for Diffusion
• Diverse/vulnerable systems:
• need for more targeted assistance, less “random acts of conservation”
• Stewardship identity:
• more strategic/targeted message-framing

77. Lessons Learned—Implications for Diffusion
• Diverse/vulnerable systems:
• need for more targeted assistance, less “random acts of conservation”
• Stewardship identity:
• more strategic/targeted message-framing
• Trialability from adopting other cons. practices:
• more flexible programs: an argument for incrementalism?

78. Lessons Learned—Implications for Diffusion
• Diverse/vulnerable systems:
• need for more targeted assistance, less “random acts of conservation”
• Stewardship identity:
• more strategic/targeted message-framing
• Trialability from adopting other cons. practices:
• more flexible programs: an argument for incrementalism?
• Wherefore land tenure?
• larger operators adopting a practice on all acres, no matter what
• oversimplified measurement

79. Lessons Learned—For Technology Transfer
• No consistently strong predictors
• Plurality generally “not significant”
• Substantial number of results opposite of hypothesized direction
• Most predictor variable “positive” predictors. What about barriers?

80. Lessons Learned—Future Adoption Research
• Survey space and time are limited, can’t ask about or measure all
potential factors
• Many adoption studies have major weaknesses
• lack of theoretical foundations, theory building
• need for clarity in research design, data collection, and analysis
• non-scientific sampling
• lack of measurement consistency across studies, not validated
• measurement error
• Bottom line: Quantitative adoption studies are not as helpful as we
would like
• Need for collaborative effort to address weaknesses, build consistency to
compare results

81. Future Analyses
• Does the dependent variable (the actual practice or program) matter?
• Effect size analysis
• Willingness vs. adoption
• Drilling down into the independent variables
• Have explanatory variables changed over time?

82. Acknowledgments
• Study Team: J. Arbuckle, Kristin
Floress, Ben Gramig, Sarah Church,
Francis Eanes, Yuling Gao, Linda
Prokopy, Pranay Ranjan, Ajay Singh
• Funding: Walton Family
Foundation, USDA-NIFA multi-state
project NC1190