Factors Influencing Farmer Interest and Participation in Great Lakes Agricultural Incentive Programs
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This study examined factors influencing farmer interest and participation in Great Lakes Restoration Initiative agricultural incentive programs. The researchers surveyed over 600 farmers across four watersheds. They found farmer interest in programs was highest among younger, more educated farmers with larger farms who believed conservation practices had beneficial impacts. However, barriers like lack of program flexibility and information reduced the effect of interest on actual participation. Program structures needed to address small farm disadvantages and promote demonstrated success to increase adoption.
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Factors Influencing Farmer Interest and Participation in Great Lakes Agricultural Incentive Programs
- 1. Researching the effectiveness of Great Lakes Restoration Initiative agricultural incentive programs Callia Tellez, undergraduate researcher, Udall Scholar Robyn S Wilson, PhD and Hugh Walpole, PhD candidate, Environmental and Social Sustainability Lab School of Environment and Natural Resources The Ohio State University
- 2. 2 School of Environment and Natural Resources This project is supported with funding from the Great Lakes Restoration Initiative through the U.S. Environmental Protection Agency. https://www.glc.org/work/reap
- 3. 3 • Great Lakes Basin investments • $100 million GLRI Incentives • $5.6 billion 2018 USDA Farm Bill budget • Incentive program benefits • On-farm and watershed-level benefits • Assessing effectiveness with farmer behavior • Why changes in adoption occur • Influence of program structure Project Context credit: nrcs.usda.gov
- 4. 4 • Who is interested in government incentive programs? • Why isn’t everyone who is interested participating? Research Questions
- 5. 5 • People who are concerned about water quality? Conservation oriented-thinkers? • Perceptions of off-farm benefits related to increased practice adoption and program participation(e.g. Reimer, Thompson, & Prokopy, 2012; Yeboah, Lupi, & Kaplowitz, 2015) • Environmental awareness (consequences) is a weak predictor of adoption (e.g. Baumgart-Getz, Prokopy, & Floress, 2012; Prokopy, Floress, Klotthor-Weinkauf, & Baumgart-Getz, 2008) Who is interested?
- 6. 6 Study Sample • 3500 farmers 4 EPA priority watersheds Stratified by counties intersecting the watershed • Final sample N= 616 (17.6%) 11% Saginaw 24% Lower Fox 25% Genesee 40% Maumee
- 7. 7 Study Variables • Farm characteristics E.g., farm size, tillage, presence of livestock, rent/own • Farmer characteristics E.g., age, education, farming experience • Socio-psychological measures E.g., concern, farmer identity, barriers to adoption • Behaviors/outcomes Cover crop and vegetated buffer use Participation in government programs
- 8. 8 Study Variables • Watershed/farm-level concern • E.g., Nutrient loss from your farm negatively impacting watershed • E.g., Managing soil health on your farm • Broad response efficacy • My actions on my farm have measurable impact on watershed • Productivist/ conservationist identity (Arbuckle, 2013) (McGuire et al., 2015) • E.g., has the highest yields per acre • E.g., minimizes nutrient runoff into waterways • Cover crop/ vegetative buffers response efficacy • Cover crops/vegetative buffers can reduce nutrient loss on my farm • Responsibility • It is the responsibility of farmers to help protect waters
- 9. 9 Study Variables • Watershed/farm-level concern • E.g., Nutrient loss from your farm negatively impacting watershed • E.g., Managing soil health on your farm • Broad response efficacy • My actions on my farm have measurable impact on watershed • Productivist/ conservationist identity (Arbuckle, 2013) (McGuire et al., 2015) • E.g., has the highest yields per acre • E.g., minimizes nutrient runoff into waterways • Cover crop/ vegetative buffers response efficacy • Cover crops/vegetative buffers can reduce nutrient loss on my farm • Responsibility • It is the responsibility of farmers to help protect waters
- 10. 10 Study Variables • Watershed/farm-level concern • E.g., Nutrient loss from your farm negatively impacting watershed • E.g., Managing soil health on your farm • Broad response efficacy • My actions on my farm have measurable impact on watershed • Productivist/ conservationist identity (Arbuckle, 2013) (McGuire et al., 2015) • E.g., has the highest yields per acre • E.g., minimizes nutrient runoff into waterways • Cover crop/ vegetative buffers response efficacy • Cover crops/vegetative buffers can reduce nutrient loss on my farm • Responsibility • It is the responsibility of farmers to help protect waters
- 11. 11 Study Variables • Watershed/farm-level concern • E.g., Nutrient loss from your farm negatively impacting watershed • E.g., Managing soil health on your farm • Broad response efficacy • My actions on my farm have measurable impact on watershed • Productivist/ conservationist identity (Arbuckle, 2013) (McGuire et al., 2015) • E.g., has the highest yields per acre • E.g., minimizes nutrient runoff into waterways • Cover crop/ vegetative buffers response efficacy • Cover crops/vegetative buffers can reduce nutrient loss on my farm • Responsibility • It is the responsibility of farmers to help protect waters
- 12. 12 Study Variables • Watershed/farm-level concern • E.g., Nutrient loss from your farm negatively impacting watershed • E.g., Managing soil health on your farm • Broad response efficacy • My actions on my farm have measurable impact on watershed • Productivist/ conservationist identity (Arbuckle, 2013) (McGuire et al., 2015) • E.g., has the highest yields per acre • E.g., minimizes nutrient runoff into waterways • Cover crop/ vegetative buffers response efficacy • Cover crops/vegetative buffers can reduce nutrient loss on my farm • Responsibility • It is the responsibility of farmers to help protect watershed
- 13. 13 Survey Responses Mean Min Max Age 59 23 93 Years of farming experience 36 2 90 Education Some college Some high school Graduate degree Farm’s annual net income below $50,000 48% <$50,000 >$500,000 Sole manager 65% - - Manage livestock 44% - - Total number of acres owned 1039 18 12,000 Percent acres rented 44% 0% 100%
- 14. 14 Program Participation Yes No Unsure Participation in GLRI funded programs 11.9% 70.9% 18.1 Participation government-funded programs 29.5% 70.5% - Future participation government- funded programs 15.8% 39.7% 44.5%
- 15. 15 • Who is interested in government incentive programs? • Linear regression • DV= interest in program participation Research Questions
- 16. 16 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop/ veg. buffers adoption - - Age .032 Education .018 Watershed/ farm-level concern - - Conservationist/ Productivist Identity - - Responsibility - - Broad response efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - -
- 17. 17 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop/ veg. buffers adoption - - Age .032 Education .018 Watershed/ farm-level concern - - Conservationist/ Productivist Identity - - Responsibility - - Broad response efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - -
- 18. 18 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop/ veg. buffers adoption - - Age .032 Education .018 Watershed/ farm-level concern - - Conservationist/ Productivist Identity - - Responsibility - - Broad response efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - -
- 19. 19 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop/ veg. buffers adoption - - Age .032 Education .018 Watershed/ farm-level concern - - Conservationist/ Productivist Identity - - Responsibility - - Broad response efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - -
- 20. 20 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop/ veg. buffers adoption - - Age .032 Education .018 Watershed/ farm-level concern - - Conservationist/ Productivist Identity - - Responsibility - - Broad response efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - -
- 21. 21 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop adoption - - Vegetative buffer adoption - - Age .032 Education .018 Watershed-level concern - - Farm-level concern - - Conservationist Identity - - Productivist Identity - - Responsibility - - Self-efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - - The probability of interest in participation increases… • Younger farmers • Educated farmers • Larger farms • Response efficacy (belief that practices are impactful and beneficial)
- 22. 22 • Who is interested in government incentive programs? • Why isn’t everyone who is interested participating? Research Questions
- 23. 23 Program interest moderation model Current Participation Interest +
- 24. 24 Program interest moderation model Current Participation Interest Program barriers and farm characteristics + -
- 25. 25 Program interest moderation model Current Participation Interest Program barriers + -
- 26. 26 Moderation: Program Participation Barriers Program barriers Information about government programs is not readily available Programs are not flexible to meet the specific needs of my farm Programs are not long enough to allow the practice to start paying for itself The program payments are too small The program payments are too slow I would prefer if payments were based on actual reductions in nutrient loss I would prefer if payments were higher to start but decreased over time There is too much paperwork required to participate There are too many restrictions on how land in programs is managed -2 strongly disagree, -1 disagree, 0 neither disagree nor agree, 1 agree, 2 strongly agree
- 27. 27 School of Environment and Natural Resources Program interest and participation by barriers -2 -1.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 1 1.5 ParticipationLogodds Program Interest Low barrier Moderate barrier High barrier
- 28. 28 Moderation: Program Participation Barriers Program barriers Information about government programs is not readily available Programs are not flexible to meet the specific needs of my farm Programs are not long enough to allow the practice to start paying for itself The program payments are too small The program payments are too slow I would prefer if payments were based on actual reductions in nutrient loss I would prefer if payments were higher to start but decreased over time There is too much paperwork required to participate There are to many restrictions on how land in programs is managed
- 29. 29 Moderation: Program Participation Barriers Program barriers Information about government programs is not readily available Programs are not flexible to meet the specific needs of my farm Programs are not long enough to allow the practice to start paying for itself The program payments are too small The program payments are too slow I would prefer if payments were based on actual reductions in nutrient loss I would prefer if payments were higher to start but decreased over time There is too much paperwork required to participate There are to many restrictions on how land in programs is managed The effect of interest on program participation weakens… • NOT with barriers related to payment structure • Barriers related to program structure • Information access, flexibility, restrictions
- 30. 30 Program interest moderation model Current Participation Interest Farm size +
- 31. 31 School of Environment and Natural Resources Program interest and participation by farm size -3 -2.5 -2 -1.5 -1 -0.5 0 -1 -0.5 0 0.5 1 1.5 ParticipationLogodds Program Interest 1.00 1.89 2.91Farm Size
- 32. 32 Characteristics Predicting Program Interest Effect Sig. Farm size .054 Cover crop adoption - - Vegetative buffer adoption - - Age .032 Education .018 Watershed-level concern - - Farm-level concern - - Conservationist Identity - - Productivist Identity - - Responsibility - - Self-efficacy .008 Cover crops response efficacy .029 Grass buffers response efficacy .000 Lack of practice knowledge - - The probability of interest in participation increases… • Younger farmers • Educated farmers • Larger farms • Response efficacy (belief that practices are impactful and beneficial)
- 33. 33 School of Environment and Natural Resources Program interest and participation by farm size -3 -2.5 -2 -1.5 -1 -0.5 0 -1 -0.5 0 0.5 1 1.5 ParticipationLogodds Program Interest 1.00 1.89 2.91Farm Size
- 34. 34
- 35. 35 Recommendations • Addressing program barriers • Access to information, program flexibility, program restrictions • Addressing small farm disadvantage • Interest overcomes farm size • Promoting interest among farmers • Giving voice to younger farmers • Measuring and demonstrating success
- 36. 36 Thank you! Callia Tellez Tellez.13@osu.edu This project is supported with funding from the Great Lakes Restoration Initiative through the U.S. Environmental Protection Agency.
- 37. 37 Study Variables Farm-level concern Watershed- level concern Nutrient loss from your farm negatively impacting watershed X - Nutrient loss from agriculture negatively impacting watershed X - The management decisions of other farmers in your community X - Additional government regulation or rules related to ag nutrient loss - - A lawsuit filed against farmers because of nutrient loss to watershed - X Managing soil health on your farm - X Making an annual profit - X Passing your farm on to the next generation - X Cronbach’s alpha: .693 .788
- 38. 38 Study Variables A good farmer is one who… Productivist Identify Conservationist Identity …has the highest yields per acre X - …gets their crops planted first X - …has the highest profits per acre X - …has the most up-to-date equipment X - …uses the latest seed and chemical technology X - …considers health of waterways - X …minimizes soil erosion - X Thinks beyond their own farm to the social and ecological health of their watershed - X …maintains or increases soil organic matter - X …minimizes nutrient runoff into waterways - X …manages for both profitability and minimization of environmental impact - X Cronbach’s alpha: .790 .890 (Arbuckle, 2013) (McGuire et al., 2015)
- 39. 39 Study Variables Belief items It is the responsibility of farmers to help protect waters Responsibility My actions on my farm have measurable impact on watershed Broad response efficacy Cover crops can reduce nutrient loss on my farm Cover-crop response efficacy Grass buffers can reduce nutrient loss on my farm Vegetative buffers response efficacy I am unsure of what steps to take to reduce nutrient loss Practice knowledge Likert Scale: -2: Strongly disagree to 2: Strongly Agree
- 40. 40 Moderation: Program Participation Barriers Program Barriers Levels of M Effect of X on Y Sig. Information not readily available Low 1.05 .0000 Medium .79 .000 High .53 .0005 Not flexible for specific farming needs Low Medium High Too many restrictions on land management Low Medium High
- 41. 41 Moderation: Program Participation Barriers Program Barriers Levels of M Effect of X on Y Sig. Information not readily available Low 1.05 .0000 Medium .79 .000 High .53 .0005 Not flexible for specific farming needs Low 1.11 .000 Medium .79 .000 High .45 .0013 Too many restrictions on land management Low Medium High
- 42. 42 Moderation: Program Participation Barriers Program Barriers Levels of M Effect of X on Y Sig. Information not readily available Low 1.05 .0000 Medium .79 .000 High .53 .0005 Not flexible for specific farming needs Low 1.11 .000 Medium .79 .000 High .45 .0013 Too many restrictions on land management Low .99 .0000 Medium .76 .0000 High .53 .0001
- 43. 43 Moderation: Program Participation Barriers Characteristic Size Effect Sig. Farm size Small 1.01 .0000 Medium .80 .0000 Large .55 .0000 Johnson-Neyman Value= 3.68 % above= 10.88
Editor's Notes
- Today’s presentation comes from a component of the interdisciplinary project Researching the Effectiveness of Agricultural Programs which uses socio-economic analysis to evaluate federal incentives for conservation agriculture.
- More specifically, farmers in the Great Lakes Basin have received over 100$ million in GLRI incentives for on-farm conservation practices. Practices such as planting cover crops, adopting no-till, edge of field vegetative buffers, interventions that reduce the non-point source runoff of nutrients into the watershed. These incentives are also used in Farm Bill conservation programs for example EQUIP and CSP. And in 2018 USDA budget included $5.6 billion in funding for Farm Bill conservation programs. Incentive programs aim to off-set the short-term costs of adopting conservation practices or provide annual payments for retiring land from production. Incentive programs are promoted based on their dual benefits as conservation practices benefit water quality but also could provide on-farm benefit for example improved soil health, reduced erosion, and increased production efficiency Our portion of the project uses farmer behavior as an indicator of effectiveness trying to understand why changes in practice adoption occur and the influence of program structure on adopters and participants. In a preliminary analysis I found that farm size and stated “interest” in incentive programs were the strongest predictors of participation. And that seems very obvious that if you’re in a program you’d be interested but as a piece of our assessment I wanted to explore this further particularly how the two predictors related.
- Literature tells us a lot of about the types of farmers, on-farm and outside factors that influence adoption So we can investigate similar socio-psychological factors and how they relate to interest
- In the early spring of 2019 we sent out a mailed and online survey to 3500 farmers in 4 EPA priority watersheds
- Our survey asked about
- We used this information to create scales or represent some of these socio-psychological factors commonly used in this field such as watershed and farm-level concern
- Response efficacy in general, the actions you do on your farm have a measurable impact on the watershed
- Again, these scales are commonly used in ag conservation psych and are based off of perceptions of what a ”good farmer” does
- Believing that cover crops and vegetative buffers are effective on your farm These were measured in the same way, but practice specific opposed to broad
- In answering who is interested… We’ll use a linear regression to compare how several variables impact the likelihood of being interested in government incentive programs.
- More likely to be interested
- Increases probability The probability of adoption increases Bullet younger farmers etc… (last bullet point) call it out as response efficacy
- To answer this we’ll use a moderated model where we hypothesize that interest has some positive effect on participation. When you have high interest, you are likely to participate
- But we also believe that some factors such as program barriers and farm characteristics are negatively impacting this positive effect
- First we’ll focus on program barriers
- Our survey included several barriers to participation on a likert scale of strongly disagree to strongly agree We hypothesized that each of these barriers would weaken the positive relationship between interest and participation. So rather than grouping the items as a combined barriers variable, we considered each barrier individually, which gives us more detail as to which specific concepts are interrupting that effect of interest on participation Now three barriers turned out as significantly weakening the positive relationship between interest and participation and that was the belief that
- This is another way we can look at the effect of barriers on the relationship between interest and participation. This is an example using the first barriers, but all three show the same pattern When you perceive barriers as low, interest has a highly positive effect on participation But when your perception of barriers is high, then your interest does not have as much of an effect on your odds of participating So these barriers are weakening the relationship between interest and participation But the good news is
- the barriers that serve as significantly decrease the effect of interest on participation are not related to payment structures.
- But actually barriers related to program structure. This is good because it’s harder to address payment structure issues when funding from tax dollars but program structure can be addressed and corrected based on feedback.
- Let’s look at the effect of farm size on the relationship between interest and current participation Qualitative feedback in open ended
- And in fact we do see a difference in the relationship between program interest and program participation based on farm size If you have low interest, you’re potentially more likely to participate if you have a larger farm. This suggests that maybe programs are built for large farms whether its easier for them to enroll or there is a difference in experienced barriers especially considering that larger farms just have higher economies of scale, time and personnel resources If you’re a small farm, you have to be very interested in programs to overcome the possible disadvantage of owning a small operation But this is also telling us that having high interest overcomes differences in farm size! And remember…
- It was younger, more educated farmers who had high response efficacy who had high interest
- So if you’re a young or educated farmer or you really believe these practices are effective and can make a difference on your farm you’re more likely to participate even if you have a small farm. But if you’re older farmer, aren’t sure if these practices will work for your farm which is the majority demographic of our agricultural decision makers, it’s a lot less likely you’re going to enroll in an incentive program
- This difference in farm size matters especially when we consider that 90 percent of all U.S farms are small family farms. We need to ask ourselves what we’re aiming for here because if it’s just to address water quality then we can keep targeting the large and industrial farms But what is it doing for a culture shift towards conservationism when we’re promoting conservation practices through incentive programs that are accessible to less than 10% of our farms These are some of the most challenging times for farmers and they are receiving a lot of messaging that they are expected, as stewards of the land to be adopting expensive and sometimes risky practices. Not the mention the perception of societal blame for water quality impairment. So we need to be actively assessing that our policy measures are available and inclusive to 90% of our farming decision makers
- So first we saw that certain program barriers weaken the relationship between interest and program participation. But they were not barriers related to program structure, they were barriers that can more realistically be addressed Then we saw evidence that unless you have really high interest in a program to start, if you’re a small farm you are lot less likely to enroll in a program than a large farm. But that high interest overcomes the advantages and disadvantages that come with farm size differences If you’re a young or educated farmer or you believe that practices are effective at reducing nutrient loss on your farm you are more likely to have that interest in programs and participate even if you’re a small farmer. And we know that farmers rely on each other as trusted sources of information when it comes to decision making so how are we giving voice to the younger generation of farmers who are engaging in conservation. If response efficacy promotes interest which supports participation then we need to increase our projects and programs that measure the success of practices and really focus on outreach, sharing and demonstrating success of conservation practices to farmers
- Watershed-level concern Identity Responsibility self-efficacy response efficacy knowledge in a table
- Watershed-level concern Identity Responsibility self-efficacy response efficacy knowledge in a table
- Do the visuals for these
- There’s no significant effect of interest on participation for the top 10% of farms, so for those in big farms, interest has no impact on your decision to participate.