Reducing phosphorus loading to Lake Erie
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2017 SWCS Annual Conference July 30-August 2, 2017 Monona Terrace Convention Center
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Reducing phosphorus loading to Lake Erie
- 1. SCHOOL OF ENVIRONMENT & NATURAL RESOURCES THE OHIO STATE UNIVERSITY Reducing phosphorus loading to Lake Erie: Closing the efficacy gap among future adopters Robyn S. Wilson, Ph.D. The Environmental and Social Sustainability Lab Twitter: @RiskWilson SWCS Annual Conference ~ Madison, WI ~ August 1, 2017
- 2. School of Environment & Natural Resources | Environmental and Social Sustainability Lab
- 3. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Recommended 40% reduction in total P - filter strips - subsurface application - winter cover
- 4. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Linking behavioral & physical models
- 5. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Survey Methods ā¢ Mail survey of corn and soybean farmers (over 50 acres) living in the western Lake Erie basin ā Stratified by farm size ā¢ 50-249 acres (15%) ā¢ 250-499 (13%) ā¢ 500-999 (22%) ā¢ 1000-1999 (31%) ā¢ 2000+ (19%) ā¢ Response rate: ~30%
- 6. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Three key questionsā¦ ā¢ What is the likelihood of voluntary adoption of recommended practices? ā¢ What factors are driving adoption of recommended practices? ā¢ To what extent can these factors be leveraged to promote necessary changes in practices?
- 7. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Three key questionsā¦ ā¢ What is the likelihood of voluntary adoption of recommended practices? ā¢ What factors are driving adoption of recommended practices? ā¢ To what extent can these factors be leveraged to promote necessary changes in practices?
- 8. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Voluntary adoption? Improving water quality Neutral attitudes Status quo behaviors? Negative attitudes āBadā behaviors? Positive attitudes āGoodā behaviors?
- 9. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Current farmer adoption 2016 Survey 2017+? 2015 adopters 2016 intended adopters b Motivated future adopters? c Possible Future The Need d Cover crops 27% 20% 38% 58% 58% Subsurface placementa 25% 36% 29% 65% 50% a Banding or in-furrow with seed b Those reporting intentions to ādefinitelyā using it in 2016 c Those reporting intentions to ālikelyā use it in 2016 d Based on multi-model study for the 40% reduction in total P, assuming 78% adoption of filter strips (Scavia et al. 2017)
- 10. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Three key questionsā¦ ā¢ What is the likelihood of voluntary adoption of recommended practices? ā¢ What factors are driving adoption of recommended practices? ā¢ To what extent can these factors be leveraged to promote necessary changes in practices?
- 11. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Factors driving adoption Planting Cover Crops (n=426) Subsurface Placement (n=420) Innovators Future Adopters Innovators Future Adopters Age Farm Income > HS Education 2.2x Total Farmed Acresa 1.1x Field Rented Field in No-till 2.6x High Issue Attentiveness - .54x High Nutrient Loss Concern High Perceived Efficacy 14.9x 3.4x 10.7x 3.9x High Perceived Barriers - .17x - .34x a = in 100 acre increments Multinomial logistic regression, p<..05, comparison group: laggards R2 = 24-27%R2 = 34-39%
- 12. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Change over timeā¦ 2012 Survey 2014 Survey 2016 Survey Nutrient loss concern (on farm)a 50% 71% 71% Nutrient loss concern (Lake Erie) a 59% 68% Likelihood nutrient loss impact profitb 83% 91% Likelihood nutrient loss impact water qualityb 79% 89% WLEB algae awareness 79% 84% 97% 4R familiarity (at least slightly) 70% 93% Exposure to 4R info (at least some) 60% 96% Control over farm impact on water qualitya 64% 63% Control over nutrient loss on farma 57% 54% a Chose 4-6 on scale of 0-6 b Somewhat to extremely likely
- 13. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Three key questionsā¦ ā¢ What factors are driving adoption of recommended practices? ā¢ What is the likelihood of voluntary adoption of recommended practices? ā¢ To what extent can these factors be leveraged to promote necessary changes in practices?
- 14. School of Environment & Natural Resources | Environmental and Social Sustainability Lab 0 10 20 30 40 50 60 70 80 Predictedprobabilityofadoption Increases in Efficacy (%) Laggards Future Adopters Innovators Goal: 50% Adoption Necessary Efficacy Increase: 45% Subsurface Placement Adoption
- 15. School of Environment & Natural Resources | Environmental and Social Sustainability Lab 0 10 20 30 40 50 60 70 80 90 Predictedprobabilityofadoption Increases in Efficacy (%) Laggards Future Adopters Innovators Goal: 58% Adoption Necessary Efficacy Increase: 90% Cover Crop Adoption
- 16. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Recap ā¢ What is the likelihood of voluntary adoptionā¦? ā High ā 30 to 40% of farmers are motivated to change (roughly Ā½ of non-adopters) ā¢ What factors are driving adoptionā¦? ā Self-efficacy: Confidence in oneās ability to take action ā Response-efficacy: A belief that practices are effective ā¢ To what extent can these factors be leveragedā¦? ā It dependsā¦efficacy can be leveraged for practices low in risk and uncertainty ā Peer to peer, group deliberation, goal settingā¦
- 17. School of Environment & Natural Resources | Environmental and Social Sustainability Lab Questions?? Wilson.1376@osu.edu @RiskWilson ā¢ Website: ā http://ohioseagrant.osu.edu/maumeebay ā http://4rcertified.org/research/ ā¢ Thanks to WLEB Farmers!!!! ā¢ Funding: ā NSF CNH Program ā The 4R Fund ā¢ Collaborators: ā Jay Martin, Elena Irwin, Brian Roe, Seyoum Gebremariam, Noel Aloysius, Wendong Zhang, Greg Howard, Greg LaBarge, Kevin King, Tom Bruulsema, Carrie Vollmer-Sanders, and more! ā Tara Ritter, Lizzie Burnett, Ajay Singh, Avishek Konar, Alex Heeren
Editor's Notes
- The results here indicate that for cover crops, an individual is 80% more likely to be an innovator (vs. a laggard) for every one unit increase in efficacy (on a scale of 1 to 10), an individual is 72% less likely to be an innovator (vs. a laggard) for every unit increase in perceived barriers (on a scale of -2 to 2), an individual is 29% less likely to be an innovator when they only have a high school education, and 31% less likely to be an innovator when they use conventional tillage. Similarly for cover crops, an individual is 40% more likely to be a future adopter than a laggard with every one unit increase in efficacy, and 43% less likely to be a future adopter versus a laggard for every one unit increase in perceived barriers to cover crop adoption. For subsurface placement, an individual is 59% more likely to be an innovator versus a laggard for every one unit increase in efficacy, and 55% less likely to be an innovator if they only have a high school education. Individuals are 70% more likely to be a future adopter versus a laggard when they fall into the 100-250K farm income category, 28% more likely to be a future adopter versus laggard for every one unit increase in efficacy, and 24% less likely to be a future adopter versus laggard for every one unit increase in issue attentiveness (on a scale from 0 not at all attentive to 6 extremely attentive).
- The results here indicate that for cover crops, an individual is 80% more likely to be an innovator (vs. a laggard) for every one unit increase in efficacy (on a scale of 1 to 10), an individual is 72% less likely to be an innovator (vs. a laggard) for every unit increase in perceived barriers (on a scale of -2 to 2), an individual is 29% less likely to be an innovator when they only have a high school education, and 31% less likely to be an innovator when they use conventional tillage. Similarly for cover crops, an individual is 40% more likely to be a future adopter than a laggard with every one unit increase in efficacy, and 43% less likely to be a future adopter versus a laggard for every one unit increase in perceived barriers to cover crop adoption. For subsurface placement, an individual is 59% more likely to be an innovator versus a laggard for every one unit increase in efficacy, and 55% less likely to be an innovator if they only have a high school education. Individuals are 70% more likely to be a future adopter versus a laggard when they fall into the 100-250K farm income category, 28% more likely to be a future adopter versus laggard for every one unit increase in efficacy, and 24% less likely to be a future adopter versus laggard for every one unit increase in issue attentiveness (on a scale from 0 not at all attentive to 6 extremely attentive).
- The results here indicate that for cover crops, an individual is 80% more likely to be an innovator (vs. a laggard) for every one unit increase in efficacy (on a scale of 1 to 10), an individual is 72% less likely to be an innovator (vs. a laggard) for every unit increase in perceived barriers (on a scale of -2 to 2), an individual is 29% less likely to be an innovator when they only have a high school education, and 31% less likely to be an innovator when they use conventional tillage. Similarly for cover crops, an individual is 40% more likely to be a future adopter than a laggard with every one unit increase in efficacy, and 43% less likely to be a future adopter versus a laggard for every one unit increase in perceived barriers to cover crop adoption. For subsurface placement, an individual is 59% more likely to be an innovator versus a laggard for every one unit increase in efficacy, and 55% less likely to be an innovator if they only have a high school education. Individuals are 70% more likely to be a future adopter versus a laggard when they fall into the 100-250K farm income category, 28% more likely to be a future adopter versus laggard for every one unit increase in efficacy, and 24% less likely to be a future adopter versus laggard for every one unit increase in issue attentiveness (on a scale from 0 not at all attentive to 6 extremely attentive).