2019 SWCS International Annual Conference July 28-31, 2019 Pittsburgh, Pennsylvania

1. Evaluating the consistency
of conservation practice
adoption among farmers in
the Western Lake Erie Basin
Margaret Beetstra, PhD Candidate
Robyn Wilson, PhD
Mary Doidge, PhD
School of Environment and Natural Resources
SWCS
July 30, 2019

2. • Harmful Algal Blooms
(HABs) in the Western
Lake Erie Basin (WLEB)
going back to the 1970s
(De Pinto et al. 1986)
• The five worst HABs on
record occurred since
2011 (NOAA 2017)
• HABs in Lake Erie
largely driven by high
levels of dissolved
reactive phosphorus in
the Maumee River (Ohio
LEPTF 2010)
ISSUE & CONTEXT
Source: Froehlich, n.d.
2

3. CONSERVATION ADOPTION
• Capital, income, access to information,
positive environmental attitudes,
environmental awareness, and utilization
of social networks impact adoption (Prokopy et
al. 2008; Baumgart-Getz et al. 2012)
• Looking specifically at two practices, cover
crops (e.g., Arbuckle & Roesch-McNally 2015; Burnett et al. 2018;
Roesch-McNally et al. 2017) and subsurface
placement (e.g., Wilson et al. 2018)
• Adoption can increase when conservation
practice relative advantage, compatibility,
and observability are clear (Reimer et al. 2012)
3

4. CONSERVATION PRACTICES
Subsurface Placement Cover Crops
Images from blancharddemofarms.org/practices
• Reduce dissolved
reactive phosphorus
(DRP) & total
phosphorus (King et al.
2015; Williams et al. 2016)
• Reduce phosphorus
runoff (Scavia et al. 2017)
• Reduce DRP & total
phosphorus runoff (Kalcic
et al. 2016)
4
• Potentially mixed results
for DRP

5. THEORETICAL FRAMEWORK: EFFICACY
• Previous research identifies
conservation practice barriers related to:
• Response efficacy (Tosakana et al. 2010)
• Self-efficacy (Arbuckle & Roesch-McNally 2015)
• Adoption correlates with the perceived
efficacy of the practice (Burnett et al. 2018;
Wilson et al. 2014; Zhang et al. 2016)
• Farmers were up to 10-15x more likely
to use cover crops and subsurface
placement as perceived efficacy
increased (Wilson et al. 2018)
6

6. • A variety of behavioral theories support the importance
of perceived efficacy for driving change (Floyd et al. 2000;
Armitage and Conner 2001; Ajzen 2002)
– Theory of Planned Behavior: intentions are a precursor to
behavior influenced by an individual’s attitudes, subjective
norms, and perceived behavioral control (Ajzen 2002)
– Protection Motivation Theory (Rogers 1975, 1983) & Extended
Parallel Process Model (Witte 1992): take action to protect
oneself based upon event’s severity, personal vulnerability,
response efficacy, and self-efficacy
• Considering both self-efficacy and response efficacy in
the present work
THEORETICAL FRAMEWORK: CONNECTING
EFFICACY TO BEHAVIOR
7

7. • How have conservation practice adoption,
intention, and efficacy changed over time?
• How well do conservation practice use
intentions translate to actual adoption?
• Do changes in efficacy help to explain
changes in adoption over time?
RESEARCH QUESTIONS
8

8. • Corn and soybean farmers with 50+ acres in the
WLEB
• Stratified by farm size:
• 50-499 acres (32%)
• 500-999 acres (25%)
• 1000-1999 acres (24%)
• 2000+ acres (18%)
• Combination online and mail survey given in early
2016 and early 2018 about the previous planting
season
• 362 panel responses
SURVEY DETAILS
9

9. MEASUREMENT
10
Variable Measurement
Response efficacy, field
Not at all (0), A little (1), Somewhat (2), A
good deal (3), To a great extent (4)
Response efficacy,
watershed
Not at all (0), A little (1), Somewhat (2), A
good deal (3), To a great extent (4)
Self-efficacy
Cannot do at all (0), May be able to do
(50), Absolutely can do (100)
Adoption Yes/No
Intention
Will not do it (0), Am unlikely to do it (1),
Am likely to do it (2), Will definitely do it
(3)

10. RESULTS: SAMPLE DESCRIPTIVES
Variable Mean (SD) Range
Age (years) 58 (11) 26-95
Sex (% female) 2 (1) —
Educational Attainment 3.2 (1.3), ~some college 2-6
Total Farm Income 2.5 (1.3), ~$175,000 1-5
Off-Farm Income (% yes) 75 (43) —
Acres Owned 499 (483) 7-4000
Acres Rented 751 (794) 0-4900
11

11. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017
Current Use
Range
Current Use
Range
Subsurface
placement
Cover crops
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

12. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017
Current Use
Range
Current Use
Range
Subsurface
placement
30-37% 29-39%
Cover crops
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

13. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017
Current Use
Range
Current Use
Range
Subsurface
placement
30-37% 29-39%
Cover crops 24-31% 24-33%
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

14. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017 2016 2018
Current Use
Range
Current Use
Range
Intend to
Use Range
Intend to
Use Range
Subsurface
placement
30-37% 29-39%
Cover crops 24-31% 24-33%
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

15. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017 2016 2018
Current Use
Range
Current Use
Range
Intend to
Use Range
Intend to
Use Range
Subsurface
placement
30-37% 29-39% 62-70% 70-79%
Cover crops 24-31% 24-33%
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

16. CONSERVATION PRACTICE USE IN THE WLEB
2015 2017 2016 2018
Current Use
Range
Current Use
Range
Intend to
Use Range
Intend to
Use Range
Subsurface
placement
30-37% 29-39% 62-70% 70-79%
Cover crops 24-31% 24-33% 56-64% 49-60%
Sources: Beetstra et al. 2018; Wilson et al. 2018
5

17. CATEGORIES OF ADOPTION: WAVE 1/WAVE 2
0
10
20
30
40
50
60
70
Discontinuers Rejecters Early
Adopters
Late
Adopters
%ofRespondents
Subsurface Placement Cover Crops
Y/N N/N Y/Y N/Y
12

18. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
SE 
REF 
REW 
CoverCrops
Lagged
intention
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

19. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE 
REF 
REW 
CoverCrops
Lagged
intention
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

20. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF 
REW 
CoverCrops
Lagged
intention
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

21. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW 
CoverCrops
Lagged
intention
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

22. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW  0.3 0.3 0.0 0.2
CoverCrops
Lagged
intention
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

23. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW  0.3 0.3 0.0 0.2
CoverCrops
Lagged
intention
2.42,4 1.31,3,4 2.62,4 1.81,2,3
SE 
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

24. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW  0.3 0.3 0.0 0.2
CoverCrops
Lagged
intention
2.42,4 1.31,3,4 2.62,4 1.81,2,3
SE  -2.44 -0.44 3.3 10.71,2
REF 
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

25. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW  0.3 0.3 0.0 0.2
CoverCrops
Lagged
intention
2.42,4 1.31,3,4 2.62,4 1.81,2,3
SE  -2.44 -0.44 3.3 10.71,2
REF  -0.0 -0.14 0.0 0.32
REW 
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

26. Variable 1: Y/N Mean 2: N/N Mean 3: Y/Y Mean 4: N/Y Mean
Subsurface
Placement
Lagged
intention
2.32,4 1.51,3,4 2.52,4 1.91,2,3
SE  5.2 13.23 1.32 9.8
REF  0.0 0.2 0.0 0.2
REW  0.3 0.3 0.0 0.2
CoverCrops
Lagged
intention
2.42,4 1.31,3,4 2.62,4 1.81,2,3
SE  -2.44 -0.44 3.3 10.71,2
REF  -0.0 -0.14 0.0 0.32
REW  -0.1 0.1 0.1 0.1
1 Significantly different from the Y/N mean at the 0.05 level
2 Significantly different from the N/N mean at the 0.05 level
3 Significantly different from the Y/Y mean at the 0.05 level
4 Significantly different from the N/Y mean at the 0.05 level
13

27. RESULTS: LOGISTIC REGRESSION
DV: Practice Adoption Wave 2
Variable Subsurface placement Cover crops
Lagged intentionA 0.07 0.19***
Self-efficacy change -0.00 0.00
Response efficacy watershed
change
-0.12*** -0.01
Response efficacy field
change
0.08** -0.01
Practice adoption wave 1 0.24*** 0.16***
Average practice-specific
barriers faced
-0.12*** -0.14***
Pseudo R2 0.14 0.24
N 207 217
A Binary variable; ** Significant at the 0.01 level; *** Significant at 0.001 level; Controlling for age,
acres owned, acres rented, total farm income, off-farm income, and educational attainment
14

28. RESULTS: LOGISTIC REGRESSION
DV: Practice Adoption Wave 2
Variable Subsurface placement Cover crops
Lagged intentionA 0.07 0.19***
Self-efficacy change -0.00 0.00
Response efficacy watershed
change
-0.12*** -0.01
Response efficacy field
change
0.08** -0.01
Practice adoption wave 1 0.24*** 0.16***
Average practice-specific
barriers faced
-0.12*** -0.14***
Pseudo R2 0.14 0.24
N 207 217
A Binary variable; ** Significant at the 0.01 level; *** Significant at 0.001 level; Controlling for age,
acres owned, acres rented, total farm income, off-farm income, and educational attainment
14

29. RESULTS: LOGISTIC REGRESSION
DV: Practice Adoption Wave 2
Variable Subsurface placement Cover crops
Lagged intentionA 0.07 0.19***
Self-efficacy change -0.00 0.00
Response efficacy watershed
change
-0.12*** -0.01
Response efficacy field
change
0.08** -0.01
Practice adoption wave 1 0.24*** 0.16***
Average practice-specific
barriers faced
-0.12*** -0.14***
Pseudo R2 0.14 0.24
N 207 217
A Binary variable; ** Significant at the 0.01 level; *** Significant at 0.001 level; Controlling for age,
acres owned, acres rented, total farm income, off-farm income, and educational attainment
14

30. RESULTS: LOGISTIC REGRESSION
DV: Practice Adoption Wave 2
Variable Subsurface placement Cover crops
Lagged intentionA 0.07 0.19***
Self-efficacy change -0.00 0.00
Response efficacy watershed
change
-0.12*** -0.01
Response efficacy field
change
0.08** -0.01
Practice adoption wave 1 0.24*** 0.16***
Average practice-specific
barriers faced
-0.12*** -0.14***
Pseudo R2 0.14 0.24
N 207 217
A Binary variable; ** Significant at the 0.01 level; *** Significant at 0.001 level; Controlling for age,
acres owned, acres rented, total farm income, off-farm income, and educational attainment
14

31. RESULTS: LOGISTIC REGRESSION
DV: Practice Adoption Wave 2
Variable Subsurface placement Cover crops
Lagged intentionA 0.07 0.19***
Self-efficacy change -0.00 0.00
Response efficacy watershed
change
-0.12*** -0.01
Response efficacy field
change
0.08** -0.01
Practice adoption wave 1 0.24*** 0.16***
Average practice-specific
barriers faced
-0.12*** -0.14***
Pseudo R2 0.14 0.24
N 207 217
A Binary variable; ** Significant at the 0.01 level; *** Significant at 0.001 level; Controlling for age,
acres owned, acres rented, total farm income, off-farm income, and educational attainment
14

32. • Looking at change in efficacy
• Efficacy is changing
– Subsurface placement: 55% saw an increase in overall
perceived efficacy
– Cover crops: 44% saw an increase in overall perceived
efficacy
• Effect of change in efficacy on adoption seems
limited
– Subsurface placement: Changes in response efficacy
influence adoption
– Cover crops: Prior intentions seem to play a more
important role than changing efficacy
CONCLUSIONS
15

33. • Practice-specific solutions critical
• Increase field-level response efficacy
– Decision support tools
– Coordination with researchers to learn about
practice effectiveness
– Communicating success
• Need to further investigate negative
watershed response efficacy results for
subsurface placement
FINAL THOUGHTS
16

34. Questions?
Thank you to our funder:
4R Research Fund
Margaret Beetstra: beetstra.2@osu.edu
Robyn Wilson: wilson.1376@osu.edu
Mary Doidge: doidge.13@osu.edu
17

36. RESULTS: CHANGES OVER TIME
Intentions Adoption
Wave 1
Mean
Wave 2
Mean
p-value
Wave 1
Mean
Wave 2
Mean
p-value
Subsurface
placement
1.90 2.09 0.020 0.36 0.35 0.774
Cover crops 1.74 1.57 0.002 0.30 0.28 0.395
Self-efficacy A
Response efficacy B
(watershed level)
Response efficacy B
(farm level)
Wave 1
Mean
Wave 2
Mean
p-value
Wave 1
Mean
Wave 2
Mean
p-value
Wave 1
Mean
Wave 2
Mean
p-value
Subsurface
placement
62.95 71.47 <0.001 2.69 2.87 0.003 2.73 2.88 0.034
Cover
crops
60.96 62.02 0.496 2.63 2.63 0.876 2.54 2.54 0.960
A Self-efficacy was captured on a scale of 0 (cannot do at all) to 100 (absolutely can do)
B Response efficacy was captured on a scale of 0 (not at all) to 4 (to a great extent)

37. RESULTS: MODERATION – subsurface placement
mean_bar_subsurf -.1086043 .0405171 -2.68 0.007 -.1880164 -.0291922
edu_final -.0476161 .0559793 -0.85 0.395 -.1573336 .0621014
off_farm_income_yn -.1026917 .0624246 -1.65 0.100 -.2250417 .0196582
total_farm_income -.0115078 .037607 -0.31 0.760 -.0852162 .0622006
acres_rented -.0000207 .0000512 -0.40 0.686 -.0001211 .0000796
acres_owned -.0000592 .0000795 -0.74 0.456 -.0002151 .0000966
age -.0013346 .0033171 -0.40 0.687 -.007836 .0051669
subsurf_yn_wave1 .2397683 .0554994 4.32 0.000 .1309916 .3485451
subsurf_int_eff .0074247 .0041476 1.79 0.073 -.0007045 .0155539
eff_subsurf_diff -.0087688 .0033847 -2.59 0.010 -.0154027 -.0021349
lag_int_subsurf_bin .0202579 .0843668 0.24 0.810 -.1450979 .1856138
dy/dx Std. Err. z P>|z| [95% Conf. Interval]
Delta-method
Change in efficacy
Intention 1 Behavior 2

38. RESULTS: MODERATION – cover crops
mean_bar_cvrcrop -.1331773 .0349751 -3.81 0.000 -.2017272 -.0646273
edu_final -.0401408 .052584 -0.76 0.445 -.1432035 .0629218
off_farm_income_yn .0373971 .0586217 0.64 0.524 -.0774993 .1522935
total_farm_income .0055668 .0190821 0.29 0.770 -.0318333 .042967
acres_rented -.00003 .0000332 -0.90 0.366 -.0000951 .0000351
acres_owned .0000424 .000045 0.94 0.347 -.0000459 .0001306
age -.0026195 .0028242 -0.93 0.354 -.0081549 .002916
cvrcrop_yn_wave1 .1626212 .0437659 3.72 0.000 .0768415 .2484008
cvrcrop_int_eff .0024888 .0056479 0.44 0.659 -.0085809 .0135585
eff_cvrcrop_diff .0006503 .0049161 0.13 0.895 -.008985 .0102857
lag_int_cvrcrop_bin .1743547 .0549894 3.17 0.002 .0665774 .2821319
dy/dx Std. Err. z P>|z| [95% Conf. Interval]
Delta-method