































   Advancements in Agricultural Development 
  Volume 6, Issue 3, 2025 
  agdevresearch.org 

 

1. Karissa Palmer, Doctoral Candidate, Texas A&M, 600 John Kimbrough Blvd., College Station, 77843, 

karissapalmer@tamu.edu,  https://orcid.org/0000-0003-3595-5213  
2. Morgan Orem, Legislative Assistant, U.S. Senate, 317 Russell Senate Office Building, Washington D.C. 20510, 

oremmmorgan@gmail.com  
3. Jean Parrella, Assistant Professor, Virginia Polytechnic Institute and State University, 175 West Campus Dr., Blacksburg, VA 

24061, jparrella@vt.edu,  https://orcid.org/0009-0005-9210-8717  
4. Peng Lu, Assistant Professor, University of Georgia, 405 College Station Rd., Athens, GA 30602, penglu@uga.edu,  

 https://orcid.org/0000-0002-3392-3528  
5. Holli Leggette, Associate Professor, Texas A&M University, 600 John Kimbrough Blvd., College Station, 77843, 

hollileggette@tamu.edu,  https://orcid.org/0000-0003-1339-1947  
6. Jamie Foster, Professor, Texas A&M University, 370 Olsen Blvd. TAMU 2474, College Station, TX 77843, 

jlfoster@ag.tamu.edu,  https://orcid.org/0000-0001-5419-1736  
 

90 

 

You’ve Got Mail: Exploring Predictors of Wheat Farmers’ 
Behaviors Toward Soil Health Conservation Practices 

 
K. Palmer1, M. Orem2, J. Parrella3, P. Lu4, H. Leggette5, J. Foster6 

 
 

Article History 
Received: May 27, 2025 
Accepted: August 12, 2025 
Published: October 7, 2025 
 
 
Keywords 
SDG 13: Climate Action; wheat 
producers; theory of normative 
behavior; identity  
  

Abstract 
The agricultural sector is facing significant challenges due to the projected 
U.S. population in 2050, indicating farmers need to adopt conservation 
practices to sustain the land and feed the growing population. Many 
farmers recognize this need and the importance of adopting sustainable 
practices, and they are likely already doing so. The purpose of our study, 
then, was to explore significant predictors of farmers’ behaviors toward 
soil health practices. Data were collected from a sample of U.S. wheat 
farmers from the top wheat-producing states using a cross-sectional 
survey design. Guided by the theory of normative social behavior, we 
conducted descriptive statistics, an ANOVA, and a multiple linear 
regression to answer two research questions: (a) How do demographic 
characteristics of U.S. wheat farmers influence soil health behavior?; and 
(b) To what extent do descriptive norms, injunctive norms, outcome 
expectations, and identity predict wheat farmers’ behaviors regarding 
soil health practices? Producers were predominantly older, white males 
with a college education, large farms, and conservative political views. 
Demographic characteristics did not significantly influence farmers’ soil 
health behavior, and identity was the only significant predictor of their 
behavior toward soil health practices. Therefore, we recommend tailored 
educational programming related to identity and further research to 
explore predictors of soil health behavior. 

 

https://agnettamu0-my.sharepoint.com/personal/karissa_palmer_agnet_tamu_edu/Documents/Desktop/karissapalmer@tamu.edu
https://orcid.org/0000-0003-3595-5213
mailto:oremmmorgan@gmail.com
mailto:jparrella@vt.edu
https://orcid.org/0009-0005-9210-8717
mailto:penglu@uga.edu
https://orcid.org/0000-0002-3392-3528
mailto:hollileggette@tamu.edu
https://orcid.org/0000-0003-1339-1947
mailto:jlfoster@ag.tamu.edu
https://orcid.org/0000-0001-5419-1736


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Introduction and Problem Statement 
 
According to the U.S. Census Bureau, the population is expected to reach around nine billion by 
2050 (n.d.). Due to this, the agricultural industry is facing intense pressure to produce safe and 
affordable food while also preserving the land, often by adopting conservation practices 
(Claassen et al., 2018). Agricultural producers often recognize the need to adopt conservation 
practices to address challenges posed by climate change and to maintain agricultural 
productivity in a changing climate (Karki et al., 2020), yet adoption is still not widespread. The 
Sustainable Development Goals (SDGs), established by the United Nations, provide an agenda 
aimed at addressing pressing issues such as decreasing poverty, protecting the planet, and 
ensuring all people are healthy and enjoy justice and prosperity in both developed and 
developing countries (United Nations, n.d.). SDG 13 focuses on climate action, which aligns with 
our study, because of its focus on soil health practices that support sustainability and reduce 
climate-related impacts (Campbell et al., 2018).  
 
More specifically, wheat farmers have experienced the consequences of climate change, such 
as increased temperatures and unpredictable precipitation (Karki et al., 2020; Wilson et al., 
2009), prompting the need to adopt sustainable production practices. Miner et al. (2020) 
documented that implementing soil health practices can reduce nutrient loss, enhance weather 
resistance, increase crop yields, and improve yield stability. Soil health practices may include 
implementing conservation tillage or using residue and cover crops (Claassen et al., 2018). 
Previous literature presents mixed results on crop yield and soil health benefits resulting from 
the implementation of no-till and cover crops (Guo et al., 2021; Pittelkow et al., 2015; Yang et 
al., 2020). Edwards et al. (2015) noted no-till farming can decrease field operations, fuel use, 
and machinery requirements, and Wallander et al. (2021) noted cover crops are increasingly 
being adopted due to their environmental benefits and government incentives.  
 

Theoretical and Conceptual Framework 
 
Our research is grounded in the theory of normative social behavior (Rimal & Real, 2003) and 
seeks to explore how wheat farmers engage in soil health practices (i.e., no-till and cover 
crops). The theory of normative social behavior includes four components: descriptive norms, 
injunctive norms, outcome expectations, and identity. Therefore, our study examines the 
influence of descriptive norms, injunctive norms, outcome expectations, and farmer identity on 
behaviors concerning soil health practices.  
 
The theory of planned behavior suggests individuals act logically based on their attitudes, 
subjective norms, and perceived behavioral control (Ajzen, 1985), and it serves as a foundation 
for the development of the theory of normative social behavior (Ajzen & Fishbein, 2000). The 
theory of planned behavior is most effective for assessing individual factors; however, our focus 
was more on social or peer influence because adopting climate-smart practices is frequently 
connected to social norms within rural communities (Gauld & Reeves, 2023). The theory of 
normative social behavior served as our guiding framework because it explains how individuals’ 

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perceptions of others’ behaviors (descriptive norms) shape their own actions through three key 
elements: norms, expectations, and identity (Rimal & Real, 2003). Descriptive norms are beliefs 
about how common or socially acceptable an action (or behavior) is within their group (Rimal & 
Lapinski, 2015) and how it positively influences behavior directly. For example, soil health 
practices widely used or considered socially acceptable among wheat farmers may lead to 
higher adoption rates. Injunctive norms refer to social expectations and the beliefs perceived to 
be morally right or approved by a social group (Rimal & Lapinski, 2015). In the adoption of soil 
health practices, injunctive norms act as a moderating factor, such as the approval or 
disapproval of peers or industry experts. Outcome expectations refer to an individual’s 
perception of the benefits or consequences that may result from behaving in a certain way, and 
they moderate the relationship between descriptive norms and behavior (Rimal & Real, 2003). 
Wheat farmers who adopt soil health practices may see long-term benefits for the environment 
and the consequences of not conserving the land. Last, group identity refers to the extent to 
which individuals see themselves as aligning with a group (Rimal & Real, 2003). For example, 
wheat farmers who see themselves as stewards of the land or environmentally responsible 
might adopt conservation practices matching the shared group identity.  
 
Many studies have examined farmers’ adoption of conservation practices and the factors 
influencing their decision-making. However, our study offers new insights into how the theory 
of normative social behavior affects behavioral intention concerning the adoption of soil health 
practices. Studies like the one conducted by Rezaei-Moghaddam et al. (2020) have found that 
social norms are crucial in farmer decision-making and that influence from other farmers has a 
significant impact on the adoption of environmentally friendly practices. Previous literature 
indicates farmers are currently implementing conservation practices and often view themselves 
as environmental stewards (Burke & Running, 2019). However, many of their beliefs, attitudes, 
and behaviors are focused on productive efficiency, which can impact relationships among 
farmer social groups (Burke & Running, 2019). Vaske et al. (2020) found farmers are willing to 
do the right things for the environment even without government compensation, possibly 
influenced by the opinions of those around them. Therefore, in our study, we applied the 
theory of normative social behavior to determine how descriptive norms, injunctive norms, 
outcome expectations, and identity contribute to the prediction of behavioral intention (see 
Figure 1).  
 
 
 
 
 
 
 
 
 
 
 
 

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Figure 1 
 
Normative Social Behavior Model Adapted from Gauld and Reeves (2023) 

 
Purpose 

 
The purpose of our study was to identify significant psychological and demographic predictors 
of U.S. wheat farmers' behaviors regarding soil health practices, including no-tillage and cover 
cropping. By understanding what influences the adoption of soil health practices, agricultural 
development practitioners can better tailor educational and communication materials to reach 
and serve farmers effectively, ultimately supporting broader sustainability goals. Our research 
questions were:  
1. How do demographic characteristics of U.S. wheat farmers influence soil health behavior? 
2. To what extent do descriptive norms, injunctive norms, outcome expectations, and identity 

predict wheat farmers’ behaviors regarding soil health practices? 
 

Methods 
 
Our study is part of a larger project; therefore, similar methods may appear elsewhere. The 
study employed a cross-sectional survey design, which Fraenkel et al. (2019) described as a 
survey that collects data at a single point in time from a predetermined population. In our 
study, this population was U.S. wheat farmers. Cross-sectional surveys are adaptable and can 
facilitate the collection of larger volumes of data from a broad population (Levin, 2006). The 
survey sought to collect data from U.S. wheat farmers about their adoption behaviors, 
perceived source credibility, scientific goodwill, and socio-demographic factors (Orem et al., 
2024). The sample was U.S. wheat farmers from the top five wheat-producing states at the time 
of the study (i.e., Kansas, North Dakota, Montana, Texas, and Oklahoma; Agricultural Marketing 
Resource Center, 2022). 
 
The socio-demographic characteristics of the sample are detailed in Table 1. The majority of 
participants identified as white (f = 104; 88.89%) males (f = 102; 87.18%) who were not first-
generation farmers (f = 106; 90.60%). The majority of participants were between 55 and 74 

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years old (f = 74; 63.25%). Slightly less than half of the participants had a bachelor’s degree or 
equivalent (f = 41, 35.04%), followed by farmers who had some college or no degree (f = 21, 
17.95%). More than half of participants did not attend a land-grant university (f = 65; 57.02%). 
Additionally, the largest proportion of participants identified their political views as somewhat 
or very conservative (f = 81; 71.05%).  
 
Table 1 
 
Socio-Demographic Characteristics of the Farmers who Participated in the Study (N = 119) 
Characteristic n f % 
Gender 117   
   Male  102 87.18 
   Female  15 12.82 
Age 117   
   65 to 74 years  43 36.75 
   55 to 64 years  31 26.50 
   75 years or older  23 19.66 
   35 to 44 years  7 5.98 
   45 to 54 years  7 5.98 
   25 to 34 years  6 5.13 
Ethnicity  117   
   White  104 88.89 
   Other  13 11.11 
Education  117   
   Bachelor’s or 4-year degree  41 35.04 
   Some college, no degree  21 17.95 
   Graduate or professional degree  19 16.24 
   Trade, technical, or vocational training  13 11.11 
   Associates or 2-year degree  11 9.40 
   High school diploma or GED  11 9.40 
   Some high school or less  1 0.85 
Attend Land Grant 114   
   No  65 57.02 
   Yes  49 42.98 
Political Ideology 114   
   Conservative  81 71.05 
   Moderate/neither liberal or 

conservative 
 23 20.18 

   Liberal  10 8.77 
First-Generation Producer 117   
   No  106 90.60 
   Yes  11 9.40 

 

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Participants in our study also shared details about their agricultural production operations (see 
Table 2). Most participants grew hard red winter wheat (f = 64, 55.17%) followed by hard red 
spring wheat (f = 23; 19.83%). The largest number of participants indicated they farmed 2,000 
to 4,999 acres of wheat (f = 27; 22.88%), 1,000 to 1,999 acres (f = 26; 22.03%), or 500 to 999 
acres (f = 21; 17.80%). Participants were evenly dispersed among five states: Montana (f = 32; 
27.35%), Kansas (f = 29; 24.79%), Oklahoma (f = 20; 17.09%), Texas (f = 19; 16.24%), and North 
Dakota (f = 17; 14.53%).  
 
Table 2 
 
Characteristics of Participants’ Farms (N = 119) 
Characteristic n f % 
Wheat Class 116   
   Hard red winter  64 55.17 
   Hard red spring  23 19.83 
   Hard winter and spring  13 11.21 
   Hard red winter, hard red spring, and Durum   4 3.45 
   Hard red spring and Durum  3 2.59 
   Hard red winter and soft red winter  2 1.72 
   Soft red winter  2 1.72 
   Durum  2 1.72 
   Hard red winter and hard white winter  1 0.86 
   Hard white winter  1 0.86 
   Hard white spring  1 0.86 
Acres Farmed 118   
   2,000 – 4,999 acres  27 22.88 
   1,000 – 1,999 acres  26 22.03 
   500 – 999 acres  21 17.80 
   260 – 499 acres  11 9.32 
   5,000 acres or more  9 7.63 
   140 – 179 acres  7 5.83 
   100 – 139 acres  6 5.08 
   220 – 259 acres  3 2.54 
   10 – 49 acres  3 2.54 
   180 – 219 acres  2 1.69 
   70 – 99 acres  2 1.69 
   50 – 69 acres  1 0.85 
Location 117   
   Montana   32 27.35 
   Kansas  29 24.79 
   Oklahoma  20 17.09 
   Texas  19 16.24 
   North Dakota  17 14.53 

  

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Participants completed a mail survey aimed at exploring soil health practices. We selected a 
mail survey because Coon et al. (2019) and Quinn (2010) suggested that, when targeting rural 
and agricultural audiences, mail surveys yield a higher response rates when compared to online 
surveys. We identified participants using the USDA Farm Service Agency payment list of wheat 
farmers and collected data using a modified version of Dillman et al.’s (2014) method. Data 
were collected in the summer of 2023, and the survey data were manually entered into 
Qualtrics. We received a total of 124 mail responses and, ultimately, obtained 119 usable 
responses after accounting for missing data.  
 
Coberley’s (2020) survey items were used to measure farmers’ soil health practice behaviors, 
specifically cover cropping and no-till practices. Twelve items were assessed on a five-point 
Likert scale (i.e., 1 = strongly disagree; 2 = disagree; 3 = neither agree nor disagree; 4 = agree; 5 
= strongly agree). The 12 items allowed us to measure soil health practice behavior (2 items); 
descriptive norms (2 items); injunctive norms (2 items); outcome expectations (3 items); and 
identity (3 items). See Table 3 for a summary of the items used to measure soil health practice 
behavior. We assessed instrument content validity through a panel of experts and scale 
reliability using Cronbach’s alpha.  
 
  

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Table 3 
 
Summary of Items to Measure Soil Health Practice Behavior Adapted from Coberley (2020) 
Variable  Item 
Soil Health Behavior  1. The use of soil health practices on my farm is important to me. 
(Dependent Variable) 2. I have increased the use of soil health practices on my farm over 

the past five years. 
Descriptive Norms 3. Most farmers use soil health practices on their farm. 
 4. Most farmers have increased their use of soil health practices 

over the past few years. 
Injunctive Norms 5. Most farmers would encourage me to adopt or increase my level 

of soil health practices on my farm. 
 6. Most farmers would encourage other farmers to adopt or 

increase their level of soil health practices on their farms. 
Outcome 
Expectations 

7. The benefits of using soil health practices on my farm outweigh 
the costs such as time or financial burdens. 

 8. Most farmers agree the benefits of using soil health practices on 
their farm outweighs the costs such as time or financial burdens. 

 9. The use of soil health practices increases productivity and income 
on my farm. 

Identity 10. The need to protect and improve the soil on my farm has a strong 
influence on my decision to use soil health practices. 

 11. The need to protect my source of income and improve my 
financial situation has a strong influence on my decision to use 
soil health practices. 

 12. The need to produce higher yields and improve productivity of 
my farm has a strong influence on my decision to use soil health 
practices. 

  
We used Stata and SPSS to conduct the statistical analysis. To answer research question one, 
we conducted a univariate analysis of variance (ANOVA) to examine the effects between the 
dependent variable (soil health behavior) and the independent variables (i.e., gender, age, 
ethnicity, and education). To address research question two, we conducted a correlation 
analysis followed by a simultaneous multiple linear regression, using soil health practice 
behavior as the outcome variable and descriptive norms, injunctive norms, outcome 
expectation, and identity as predictor variables. We conducted an assumption check to 
determine normality of residuals, homoscedasticity (constant variance of residuals), 
multicollinearity, and independence of observations. Our study is not without limitations, 
including a small sample size and a geographic bias toward five states, which limits its 
generalizability to those who completed the survey and those who are representative of the 
top five wheat-producing states. Therefore, our data are not representative of U.S. wheat 
farmers but are representative of only the sample of 119 farmers whom we surveyed. Another 
limitation of conducting a cross-sectional survey is self-reported data, which may not reflect 
participants’ actual behaviors. 

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Findings 
 
Research Question 1: How do Demographic Characteristics of U.S. Wheat Farmers Influence 
Soil Health Behavior? 
To examine research question one, we conducted a univariate ANOVA to determine the effects 
of gender, age, ethnicity, and education on soil health behavior. The overall model was not 
significant F(13,103) = 0.83, p = 0.63, R2 = 0.95. The results revealed that none of the individual 
factors were significant influencers on soil health behavior, indicating that soil health behavior 
did not significantly differ across gender F(1,103) = 0.84, p = .36; age F(5,103) = 1.12, p = .36; 
ethnicity F(1,103) = 1.01, p = .32; or education F(6,103) = 0.23, p = .97.  
 
Table 4 
 
Univariate ANOVA for Soil Health Behavior of U.S. Wheat Producers 
Source Type III SOS df Mean Square F p 
Intercept 1177.92 1 1177.82 649.05 <0.01 
Gender 1.52 1 1.52 0.84 0.36 
Age 10.12 5 2.02 1.12 0.36 
Ethnicity 1.83 1 1.83 1.01 0.32 
Education 2.48 6 0.41 0.23 0.97 
Error 186.93 103 1.82   
Total 8265.00 117    
Corrected Total 206.53 116    

Note. F(13,103) = 0.83, p = 0.63, R2 = 0.95  
 
Research Question 2: How do Descriptive Norms, Injunctive Norms, Outcome Expectations, 
and Identity Predict Farmers’ Soil Health Practice Behaviors? 
The dependent variable soil health behavior was positively and moderately associated with the 
predictor descriptive norms (r = 0.24, p < 0.01) and outcome expectations (r = 0.26, p < 0.01), 
indicating soil health behavior increases as descriptive norms and outcome expectations 
increase. There was also a strong positive association between soil health behavior and identity 
(r = 0.56, p < 0.001) (see Table 5). All predictors were moderately to strongly positively 
correlated, suggesting an increase in one was associated with an increase in the other.  
 
  

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Table 5 
 
Descriptive Data and Interrelationships Among Variables (N = 119) 
 M SD 1 2 3 4 5 
Behavior (1) 4.15 0.66 1.00     
Descriptive norms (2) 3.45 0.82 0.24** 1.00    
Injunctive norms (3) 3.25 0.72 0.16 0.60*** 1.00   
Outcome expectations (4) 3.35 0.64 0.26** 0.25** 0.28** 1.00  
Identity (5) 4.03 0.58 0.56*** 0.28** 0.27** 0.56*** 1.00 

Note. *** indicates p < .001; ** indicates p < .01; * indicates p < .05. 
 
The model including descriptive norms, injunctive norms, outcome expectations, and identity 
explained 33.26% of the variance in soil health behavior. Accounting for the number of 
predictors, the adjusted percentage of variance explained is 30.92%. The F-test shows the 
model explained a statistically significant amount of variation in the outcome, F(4, 114) = 14.20, 
p < 0.001. The predicted soil health behavior is 1.53 when descriptive norms, injunctive norms, 
outcome expectations, and identity are all zero, which is statistically significantly different from 
zero (t(119) = 3.94, p < 0.001). Holding descriptive norms, injunctive norms, and outcome 
expectation constant, each additional unit increase in identity is associated with an increase of 
.67 in soil health behavior, which is statistically significantly different from zero (t(119) = 6.28, p 
< 0.001). One standard deviation increase in identity is associated with 0.59 standard deviation 
increase in soil health behavior on average, indicating a strong positive effect (see Table 6).  
 
Table 6 
 
Regression Model Results with Behavior as the Dependent Variable (N = 119) 
Predictors Soil Health Practice Behavior (α = .76) 
 B (S.E.) t p β 
Intercept  1.53(0.39) 3.94 <0.001  
Descriptive norms (α = .83) 0.11(0.08) 1.41 0.16 0.14 
Injunctive norms (α = .80) -0.05(0.09) -0.57 0.57 -0.06 
Outcome expectations (α = .64) -0.09(0.10) -0.91 0.36 -0.09 
Identity (α = .67) 0.67(0.11) 6.28 <0.001 0.59 

 
Conclusions, Discussion, and Recommendations 

 
For research question one, we sought to describe the personal characteristics that influence 
wheat farmers’ soil health behavior. Our results indicated no demographic differences in 
education, age, ethnicity, and gender in soil health behavior. According to our demographic 
analysis, farmers who participated in this study were predominantly older, white males with 
college educations who owned large farms and held conservative political views. Our 
descriptive analysis indicates trends associated with generational farming, land-grant influence, 
crop specialization, and age distribution. These factors may subsequently affect the norms, 

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expectations, and identities of farmers as discussed by Rimal and Real (2003). The 
overwhelming majority of participants were not first-generation farmers, indicating a tradition 
of multi-generational farming and a legacy of livelihood. Some participants may have an 
advantage due to their knowledge from land-grant universities, which provide exposure to 
research and extension programs, possibly influencing their attitudes toward soil health 
practices. Hard red winter wheat was the primary crop of farmers from Montana, Kansas, North 
Dakota, Oklahoma, and Texas who participated in this study, which could further shape 
farmers’ soil health practices due to the specific planting and soil management needs.  
 
Among wheat farmers in our sample, demographic characteristics, including gender, age, 
ethnicity, and education, were not significantly associated with soil health behavior. However, 
previous studies have investigated demographic factors associated with the adoption of 
conservation practices and found that education, household size, income, age, gender, and 
experience have varying impacts on farmers’ behavior (Burton, 2014; Cheruiyot, 2020). Farmers 
who are younger and/or female as well as farmers who have more experience and knowledge 
about conservation practices are more likely to adopt conservation practices than their 
counterparts (Burton, 2014). Therefore, tailored outreach along with more community-based 
activities where resistant farmers can gain experience and learn about successful conservation 
practices, like no-till and cover crops, are essential in reaching targeted populations. 
 
In response to research question two, we aimed to investigate how descriptive norms, outcome 
expectations, injunctive norms, and identity impact behavior toward soil health practices. We 
discovered identity is the sole significant predictor of soil health practice behavior, which is 
consistent with previous literature (Chekima et al., 2016; Comito et al., 2013; Floress et al., 
2017). Oyserman et al. (2012) discussed that, when a choice becomes connected to a person’s 
identity, it becomes natural or automatic. For example, individuals who identify as a soil health-
conscious wheat farmer will make decisions to maintain the soil, and it will not be a difficult 
decision because they believe in conservation efforts. Typically, farmers have a primary identity 
focused on productivity, like maximizing yields and profitability (Coberley, 2020). As the need 
for more conservation efforts increases, farmers’ identities need to shift from productivity to 
conservation-focused.  
 
Although the correlation table shows norms and expectations are correlated with behavior, 
identity is crucial in motivating wheat farmers to adopt soil health practices. As a result, 
farmers’ intentions to adopt these practices are strongly influenced by their self-identification 
as soil health practitioners, which is consistent with Rimal and Real’s theory of normative social 
behavior (2003). Given identity emerged as the sole significant predictor, initiatives aimed at 
promoting soil health practices should incorporate elements of personal and community 
identity to enhance adoption, as Carlisle (2016) recommended. Additionally, as Rezaei-
Moghaddam et al. (2020) noted, social norms play a vital role in promoting the adoption of 
environmentally friendly practices. Thus, outreach initiatives should go beyond individual 
messaging to foster a sense of collective identity among farmers and should be rooted in 
shared values centered around environmental stewardship, by encouraging community 
engagement and relationship-building (Burke & Running, 2019). Furthermore, although farmers 

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are adopting soil health practices (Burke & Running, 2019), they face social pressures regarding 
implementation. This signifies the need for developing educational resources and programs to 
alleviate pressures and improve farmers’ awareness of the benefits associated with 
implementing soil health practices. Practitioners should use farmers to disseminate information 
about soil health practices, given peer influence and impact (Rezaei-Moghaddam et al., 2020).  
 
We encourage professionals in agricultural leadership, education, and communications to 
investigate how norms, outcome expectations, and identity influence different groups of 
farmers and ranchers, such as those cultivating cotton, raising cattle, or producing corn, to 
identify any distinctions. Additionally, future research should examine the potential of 
removing identity as a predictor and evaluating the variance explained by norms and 
expectations.  
 

Acknowledgments 
 
Funding Information: The study is based upon work supported by the Natural Resources 
Conservation Service, U.S. Department of Agriculture, number NR183A750008G013. 
 
Conflict of interest: There are none. 
 
Previous Dissemination: This article stems from data presented at the American Association for 
Agricultural Education Southern Region Conference in Irving, TX, in 2025:  
 
Orem, M., Leggette, H., Parrella, J., Lu, P., Palmer, K., Foster, J., Neely, H, & Noland, R., (2025, 
February 2-4). Digging into soil health: Farmers’ perspectives on climate-smart practices [Poster 
presentation]. American Association for Agricultural Education Southern Region Conference, 
Irving, TX, United States. 
 
Artificial Intelligence: Artificial intelligence was not used in this work.  
 
Author Contribution Statement: K. Palmer – writing – original draft, data curation, formal 
analysis, validation, methodology; M. Orem – conceptualization, methodology, validation, 
investigation, writing – reviewing & editing; J. Parrella - data curation, formal analysis, 
validation, methodology, writing – reviewing & editing; P. Lu - data curation, formal analysis, 
validation, methodology, writing – reviewing & editing; H. Leggette – supervision, funding 
acquisition, writing – review & editing, validation, methodology, conceptualization; J. Foster - 
supervision, funding acquisition, writing – review & editing 
 

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