1. Introduction
Research has extensively explored the economic challenges faced by US farmers and the innovative models they adopt to enhance profitability and sustainability [1]. However, a notable gap exists in studying the entrepreneurial strategies employed by Western Corn Belt farmers, particularly in eastern South Dakota (SD). This study aims to fill this gap by focusing on the entrepreneurial aspirations of commodity crop producers in this region. Using the diffusion of innovations framework, this research investigates the spread of innovation and entrepreneurialism among SD farmers. It examines the critical roles of frequent training, social networks, and the interplay between age, education level, farm size, and entrepreneurial aspirations.
Global changes have significant impacts on the farm sector. Technological advancements, globalization, rising agricultural input costs, shifts in consumer preferences, climate variability, and significant market fluctuations make farming more competitive and demanding. As a result, farming’s importance is diminishing in many rural communities. These challenges have led to the consolidation of many farms and the adoption of non-farm employment for securing medical insurance or additional income [2,3]. Therefore, producers are encouraged to adopt an entrepreneurial culture to promote agricultural sustainability and remain in business. Agricultural entrepreneurship involves pursuing new farming methods that encourage innovation and strategic planning [4], leading to agricultural sustainability, which aims to achieve soil and water conservation and promotes human health [5,6].
Sustainable agriculture encourages land preservation and seeks to reduce agriculture’s environmental, social, and economic impacts, such as increased input costs, land degradation, and soil and water pollution [7]. However, sustainable agricultural development requires embracing an entrepreneurial culture, focusing on multifunctionality (economic, social, and environmental functions), and encouraging farmers to be innovative [8]. Adopting agricultural entrepreneurship enables farmers to be competitive, seek and recognize new opportunities, and balance environmental conservation with profit maximization. This approach secures adequate financial resources that help farmers adopt best management practices (BMPs) and technology, thus achieving agricultural sustainability [7].
As farmers adjust to meet changing market demands and increased climate and weather variability, understanding the drivers of entrepreneurship adoption among agricultural producers is crucial. The literature shows a growing need to adopt entrepreneurship in the broader rural context, with many farmers incorporating off-farm practices into their businesses to increase profitability and income [9,10]. Although these issues challenge all farmers, this study focuses on farmers in eastern SD as a model system to examine their entrepreneurial aspirations. These aspirations refer to the desires, ambitions, intentions, and motivations that encourage business owners to think entrepreneurially, seek opportunities, and continuously learn and promote self and business development [11,12]. The study site was selected because it lies in the transition zone between the cold semi-arid and hot summer humid continental Köppen climate regions. Farmers in this model system lack access to many natural resources that could help mitigate the effects of a changing climate on their economic risk and profitability [13].
This paper seeks to answer the following questions: (1) What are the entrepreneurial aspirations of Western US Corn Belt farmers? (2) What factors influence entrepreneurial decisions in this model system? (3) To what extent do the socio-demographic characteristics of farmers, such as age, education, and farm size, influence their decisions to adopt entrepreneurship, use social networks, and engage in training?
2. Literature Review
2.1. Understanding Agricultural Entrepreneurship
Agricultural entrepreneurship can be viewed through the broader lens of general entrepreneurship. It encompasses business expansion, leadership development, and acquiring managerial and entrepreneurial skills. However, farmers operate in a unique setting that requires different methods and conceptualizations compared to urban businesses [14]. The relationship between farmers and their operations is complex; a farmer can simultaneously be an owner, tenant, manager, and subcontractor. This complexity necessitates diverse managerial and entrepreneurial skills for success [9].
Many Western US Corn Belt farmers must enhance their managerial skills and entrepreneurial understanding. The Corn Belt is a critical agricultural region with a significant impact on the global economy, making it essential to study the changes in its agricultural sector [15,16]. A crucial aspect is the need for SD agricultural producers to restructure their activities and adopt entrepreneurship to sustain their businesses and promote sustainable agriculture. The literature shows that farmers must adopt entrepreneurial skills and strategies to succeed economically [17]. Policies that do not encourage agricultural entrepreneurship hinder farmers from developing advanced skills and engaging in long-term strategic planning. Therefore, entrepreneurship must be incorporated into farm business practices, and policies must be revised accordingly [14,18,19].
2.2. Transitioning Agricultural Practices
Restructuring agriculture involves transitioning from conventional to multifunctional and entrepreneurial businesses and shifting from production-focused to market-oriented approaches [20]. For instance, organic markets require a land certification and the nonuse of chemical fertilizers and pesticides. While conventional and non-conventional farmers produce products for different markets, they require different skills. Conventional farming often relies on chemical use and tillage, which traditionally require less professional and organizational skills passed down through generations [20,21]. In contrast, entrepreneurial agriculture demands advanced professional and organizational skills from formal education and training. This approach also requires managerial and entrepreneurial skills, opportunity exploitation, and strategic planning [19].
2.3. Influences on Entrepreneurship Adoption
Factors such as education, farm size, and age influence farmers’ adoption of entrepreneurship and engagement in training. Younger and more educated farmers are more likely to embrace entrepreneurship and best management practices (BMPs) [22,23]. Entrepreneurial agriculture involves strategic, tactical, and operational planning. Strategic planning covers long-term and short-term decision making. Tactical planning focuses on short-term production progress, and operational planning includes tasks like planting and harvesting [24]. Some studies in the literature indicate that many farmers, even in developed countries, lack strategic plans with clear visions and missions [18]. Those who have plans often focus on short-term strategies instead of long-term planning. Farmers with advanced management skills better manage financial resources and reduce input costs [18,25].
2.4. Leveraging Social and Human Capital
To acquire these entrepreneurial and managerial skills or enhance their existing ones, farmers may utilize their social connections, education, and training. These resources, referred to as social capital and human capital, are critical for fostering agricultural entrepreneurship.
2.5. The Role of Human and Social Capital in Farm Entrepreneurship
Human and social capital significantly influence farmers’ values, goals, decision making, and strategies. Human capital includes skills and knowledge acquired through education and training, helping farmers manage their operations effectively [6,26]. Social capital encompasses social contacts and networks, providing access to information, resources, and opportunities from external organizations like consulting agencies and professional associations [26,27].
Studies show that education, age, farming experience, training participation, and social network utilization affect farmers’ motivations and goals to adopt conservation practices [28]. Social capital also helps farmers access funding and business services. Human and social capital shape farmers’ strategies to enhance economic success, meet business demands, and achieve sustainability [29]. For instance, business training can teach farmers strategic planning and record-keeping skills, improving their business management. Similarly, local conferences, training workshops, and neighborhood connections allow farmers to share information about innovative ideas and BMPs [9,30].
Farmers acquire human capital in various ways. Some enter the agriculture sector with entrepreneurial skills, while others develop these skills later. Factors like prior entrepreneurial experience, age, and education influence farmers’ success in expanding operations, increasing profitability, and establishing personal wealth [25,31]. Entrepreneurial success involves recognizing, evaluating, and using opportunities, leading to innovations and social networks that increase funding sources, access to training and business advice, and the adoption of practices like crop diversification [32,33,34].
2.6. Diffusion of Innovations Theory
Social networks are crucial for entrepreneurial success, especially for farmers, as they facilitate the diffusion of innovations. Diffusion is the process through which innovations are communicated and shared [35]. The diffusion of innovations theory examines how social networks adopt and disseminate new information or ideas. It highlights the speed and timeliness of adopting new ideas from their introduction to widespread use [35,36]. In this context, the diffusion of innovation theory focuses on the likelihood of farmers to adopt and spread new innovations and knowledge about entrepreneurship and BMPs. The theory emphasizes the speed and timeliness with which individuals adopt new ideas from when they become available until they are widely spread [35].
In this study, we use the diffusion of innovations theory to examine how farmers’ social connections with family, peers, and other farmers enable them to adopt and spread new practices and technologies [37,38]. This framework helps us understand how farmers disseminate new entrepreneurial ideas and the impact of demographic characteristics on their adoption of these ideas.
In summary, agricultural producers must be entrepreneurially oriented to remain competitive. They should continually assess their skills and strategies, leverage social networks to learn and adopt new ideas, and engage in continuous professional development. Enhanced competencies can improve market competitiveness and increase the adoption of environmentally friendly practices [39]. Farmers should prioritize strategic planning, develop formal business plans, and set long-term goals. Regularly evaluating and adjusting these plans helps achieve better outcomes [40]. Business plans should be reviewed annually to accommodate necessary adjustments, allowing for better future planning and resource management flexibility [41].
The diffusion of innovations theory provides valuable insights into the role of social networks in entrepreneurial success among farmers. By understanding how farmers adopt and spread new ideas, we can better support their efforts to maximize profits and implement best management practices.
More research is needed on agricultural entrepreneurship and its role in farm business development. Previous studies have paid little attention to the entrepreneurial aspirations of farmers in the Western US Corn Belt and strategies to overcome economic challenges due to recent agricultural changes [42]. This study examines how adopting entrepreneurship can help Western US Corn Belt farmers succeed economically and promote sustainable agriculture. It analyzes the impact of education, farm size, and age on their decisions to embrace entrepreneurship and seek training opportunities.
2.7. Hypotheses
Based on the existing literature that we reviewed, we developed the following hypotheses:
Operators of larger farms are more likely to have entrepreneurial aspirations.
Operators of larger farms are more likely to perceive building social networks and seeking training opportunities as essential to their farm operations.
Younger farmers are more likely to have entrepreneurial aspirations.
Education is positively associated with farmers’ entrepreneurial aspirations.
Farmers who seek training opportunities and regularly update their knowledge are more likely to be entrepreneurial.
Farmers with more robust social networks are more likely to have entrepreneurial aspirations and access training opportunities.
3. Materials and Methods
3.1. Data Collection
3.1.1. Study Design
This large-scale quantitative study examined the entrepreneurial strategies used by farmers in the Western Corn (Zea mays) Belt to overcome economic challenges and achieve agricultural sustainability. The focus was on eastern SD as the model system, and we used mail and online survey data collected from January to March 2018.
We surveyed commodity crop producers in SD about their plans and aspirations to adopt and implement innovative and entrepreneurial ideas and practices. Additionally, we investigated how these farmers disseminate and receive information regarding new innovations and best management practices to enhance agricultural sustainability.
The sample included farm operations in 34 SD counties east of the Missouri River, where most of the state’s corn and soybean farming activities occur. This region spans a climate transition zone from tallgrass prairie in eastern SD to mixed-grass prairie in central SD. The Köppen climate classification for the area is humid continental, with annual precipitation of 51.1 cm. Climate predictions indicate increased extreme events (e.g., flooding, drought, temperature extremes, and intense storms) and warming trends (e.g., earlier springs and more growing degree days per season) over the next 20 to 50 years [43,44]. Key agricultural products in this region include corn (Zea mays), soybean (Glycine max), wheat (Triticum aestivum), livestock, and ethanol. In eastern SD, corn is the primary source of ethanol and its coproducts.
3.1.2. Sample Selection
We developed a sampling plan and selected the sample from the eastern SD farming population. Through a Freedom of Information Act request, we obtained a list of 10,000 farming operations participating in 2016 Farm Service Agency (FSA) programs. We then selected 3000 operations using proportionate stratified random sampling according to the number of farming operations in the study counties. We assigned a unique code to each subject as an identification number. The questionnaire was directed toward the individual in the operation responsible for most land management decisions. Initially, we sent an advance letter to those in the sample. Half of these letters included USD 2 to test whether it would increase response rates and a link to complete the questionnaire online. We followed up with mail surveys for those who did not respond, including addressed and stamped return envelopes. We sent a reminder postcard and a second paper copy of the survey two weeks later, following the Tailored Design Method [45]. Due to some respondents quitting farming or being inactive, 650 surveys were returned due to wrong addresses or indicating that the operators no longer farmed any land. We received 708 responses, resulting in a 30% response rate.
3.1.3. Research Instruments
As the sampling section indicates, we collected data through mail and online surveys. The survey included 11 entrepreneurial items focused on three themes: (a) how farmers think and plan entrepreneurially, including plans to adopt agricultural sustainability in their farming operations (N = 5), (b) farmers’ use of social networks to learn new and innovative ideas and increase their awareness of funding opportunities (N = 3), and (c) farmers’ use of training and knowledge to be entrepreneurial (N = 3). A variety of questions, such as operation and operator characteristics, usage of conservation practices, and operator attitudes, were included in the survey. These questions were asked on a four-point Likert scale (where 1 = strongly agree, 2 = agree, 3 = disagree, and 4 = strongly disagree).
3.2. Data Analysis
Data were entered into QuestionPro software (Survey software for individuals and teams,
We used various SPSS statistical data analysis tools, including bivariate and multivariate analyses. For the bivariate analysis, we applied Spearman’s rank-order correlation to examine the relationships between socio-demographic factors, the three entrepreneurialism scales, and the relationship between training/networks and entrepreneurialism. Spearman’s rank-order correlation, a nonparametric alternative to Pearson’s correlation coefficient, helps reduce or preserve Type I error rates below the nominal alpha level [46].
To examine the relationships between age, education level, and farm size and farmers’ attitudes toward adopting entrepreneurship, utilizing social networks, and participating in training programs, we used t-tests, an analysis of variance (ANOVA), and a multivariate regression analysis. We grouped items under three scales: entrepreneurship, networking, and training. These statistical methods allowed us to compare means across different groups and analyze their influence on these scales. We used two-tailed t-tests to compare means between two groups and an ANOVA to compare means among three or more groups [47]. After the ANOVA revealed no significant differences between the means of the three scales, we applied a multiple regression analysis to test the relationships between a single dependent variable (such as the three scales) and several independent variables, including education and farm size. Additionally, we used one scale (e.g., the training scale) as an independent variable to predict the value of a single dependent variable. The regression analysis helped us identify the key factors affecting our dependent variables more precisely [48].
One of our primary research questions focuses on how farmers’ socio-demographic characteristics, such as age, education, and farm size, influence their decisions to adopt entrepreneurship, use social networks, and engage in training. Therefore, t-tests and an ANOVA to test group means and a regression analysis to predict the value of dependent variables are the most appropriate statistical tools for this study.
In this section, we present the results from the bivariate analyses using Spearman’s rank-order correlation to examine the relationships between socio-demographic factors and the three entrepreneurialism scales. We also looked at the relationships between training/networks and entrepreneurialism. Additionally, we used bivariate statistics (t-tests and ANOVA) and a multivariate analysis (multiple regression) to test the impacts of age, education level, and farm size on farmers’ attitudes toward adopting entrepreneurship, using their social networks, and participating in training programs offered by various public and private institutions. Only valid percentages are reported, with missing cases excluded using pairwise deletion.
4. Results
4.1. Key Farmer and Operation Characteristics
Most survey respondents were males (97.2%), with 73.2% being 50 years old or above. The average age of the respondents was 57.7 years, aligning with the SD Department of Agriculture’s 2017 data showing that the average age of a rancher or farmer was 56 years. Regarding education, the respondents reported the following levels: 2.5% had less than a high school education, 25.1% were high school graduates or had a GED, 34.7% had some college or technical school experience, 32.0% were college graduates, and 5.7% had post-graduate degrees.
Regarding farm size, 38.6% of participants operated farms ranging from 1 to 499 acres in 2017, which the Economic Research Service (ERS) classifies as small. Additionally, 23.1% operated farms ranging from 500 to 999 acres (large farms), and 38.3% operated very large farms (1000+ acres). The average number of acres operated by respondents was 1150, slightly below the SD average of 1397 acres (SD Department of Agriculture, 2017).
4.2. Thinking and Acting Entrepreneurially
Regarding whether commodity crop farmers in eastern South Dakota think and act entrepreneurially (see Figure 1), the highest percentage strongly agree (31.2%) with the following statement: “I am always taking steps to protect the land I farm from increased weather variability (e.g., diversifying crops, building soil quality, adding drainage or irrigation systems)”. A significant percentage (27.1%) also strongly agree with the following statement: “I am constantly trying to find ways to increase my income or profit (e.g., creating new ideas, finding new innovations, trying various technologies and management practices)”.
Most producers agreed (strongly agreed and agreed) that they pay close attention to funding sources and often look for ways to diversify their operations. However, the only statement most respondents disagreed with was having a written business plan. Nearly sixty percent (59.5%) disagreed or strongly disagreed with always having one.
About half of the respondents agreed (agree and strongly agree combined) with all three statements about the importance of social networks in their farming businesses (see Figure 2). They were the most likely to agree that they actively sought opportunities to build their social networks for business purposes. The next highest agreement was with the idea that social networks help increase awareness about funding opportunities. Lastly, the respondents agreed that they use social networks to gather innovative business ideas.
Most respondents agreed that they are willing to seek training opportunities to improve their farm businesses and update their knowledge to be more environmentally sustainable. However, only about one-third regularly attend SDSU’s Extension training workshops (see Figure 3). Our descriptive statistics reveal that around 40% of respondents receive information from private consultants and companies (e.g., agronomists), and about 39% receive it from friends, family members, or neighbors. This indicates that limited information about the SDSU Extension Program’s training and workshops reaches farmers.
4.3. Bivariate Relationships
To test the correlation between participants’ socio-demographic characteristics and three entrepreneurial scales (Entrepreneurial Scale, Networking Scale, and Training Scale), we used the following variables: age (interval level), education level (ordinal level), and farm size (interval level). Given the ordinal level of measurement for education, we computed Spearman’s rank-order correlation to examine these relationships. Gender was excluded from the analysis due to the small percentage of female respondents (2.7%).
Table 2 shows statistically significant correlations between farmers’ socio-demographic characteristics (age and farm size) and several scales. Age had a weak negative correlation with the entrepreneurial scale, suggesting that entrepreneurial aspirations decrease with age. In contrast, farm size had weak positive correlations with all three scales. This implies that farmers with larger operations are more likely to have entrepreneurial aspirations, engage in networking, and seek training opportunities.
Overall, no strong relationships were found between the socio-demographic characteristics and the three scales. However, moderate positive correlations were observed between the entrepreneurial scale and the training scale, the networking scale and the training scale, and the entrepreneurial scale and the networking scale. These correlations were statistically significant, indicating that agreement with one scale tends to coincide with agreement with the others.
4.4. t-Test Results
Two-tailed t-tests were conducted to determine whether there are statistical differences in means related to age and education regarding farmers’ entrepreneurial thinking and aspirations. Specifically, the analysis compared younger farmers (ages 20–59) with older farmers (60+) and farmers with a college degree against those without one. The t-tests examined differences in three dependent variables: the Entrepreneurial Scale, the Networking Scale, and the Training Scale. Age and education served as the independent variables.
The results indicate that younger farmers score significantly higher on the Entrepreneurial Scale than older farmers. However, the scores on the Networking Scale or the Training Scale were not significantly different between the two age groups (see Table 3).
Table 4 shows no significant differences in the Entrepreneurial Scale, Networking Scale, or Training Scale based on college degrees. Similarly, the two-sample t-test results reveal no significant differences in entrepreneurialism scales when comparing different education levels.
4.5. ANOVA Results
We used a one-way analysis of variance (ANOVA) to assess whether there were significant differences in entrepreneurialism across different farm sizes. The analysis included three dependent variables—entrepreneurialism, networking, and training—measured at the interval level. These variables are suitable for calculating mean variances. The farm sizes—small, large, and very large—served as independent variables and were treated as ordinal.
Our results show statistically significant differences in entrepreneurialism, networking, and training across the three farm size groups. For entrepreneurialism, the mean scores were 13.6 for small farms, 14.1 for large farms, and 14.8 for very large farms. For networking, the means were 7.2 for small farms, 7.4 for large farms, and 7.9 for very large farms. For training, the means were 5.5 for small farms, 5.8 for large farms, and 6.2 for very large farms (see Table 5). This indicates that farm size impacts farmers’ entrepreneurial practices, their engagement in social networks, and their pursuit of training opportunities.
However, when examining the results of multiple comparisons, some differences between specific pairs were not statistically significant. For example, the differences in entrepreneurialism between small and large farms, and between large and very large farms, were not significant. Similarly, differences in networking between small and large farms, and differences in training between small and large farms as well as between large and very large farms, were not significant.
4.6. Multivariate Analysis Results
We conducted a multiple regression analysis to assess the simultaneous impact of education, farm size, age, and two additional variables on each outcome variable (see Table 6). We analyzed three models: Model 1 used the Entrepreneurialism Scale as the dependent variable, Model 2 focused on the Network Scale, and Model 3 examined the Training Scale. This approach enabled us to identify which predictor variable most effectively explains changes in the outcome variable while controlling for the effects of the other predictor variables. We ensured that all assumptions were met for the three models we tested. The adjusted R2 shows that our predictor variables explain 52% of the total change in the dependent variable. In Model 3, we ran the same predictor variables and the Training Scale as a dependent variable, and the adjusted R2 shows that the predictor variables explain 60% of the variability in the dependent variable.
Model 1 shows that education, age, networking, and training significantly predict entrepreneurialism, as expected, when the other variables are held constant. However, an unexpected finding is that as education increases, entrepreneurialism decreases.
Model 2 reveals that only entrepreneurialism and training are significant predictors of networking and move in the expected direction when the other variables are controlled.
Model 3 shows that education, farm size, age, entrepreneurialism, and networking are all significant predictors of training, again in the expected direction, after accounting for other variables.
Our multiple regression results support and challenge some of our bivariate analysis findings. For example, while the bivariate analysis shows no strong correlation between age and the three scales, multiple regression reveals that age significantly predicts entrepreneurialism and training. Similarly, although our two-sample t-test shows that education (college degree vs. no college degree) does not significantly impact the three scales, multiple regression indicates that education is a significant predictor of entrepreneurialism and training. Lastly, both bivariate and multivariate analyses demonstrate that all three scales positively influence each other.
5. Discussion
This study explored whether farmers in SD have entrepreneurial aspirations and motivations that could support agricultural sustainability. The sample consisted of commodity crop producers from the east of the Missouri River, where most of SD’s commodity crops, such as corn and soybeans, are grown. Since the sample was randomly selected from a group mainly representing eastern SD’s farm operations, the findings can be generalized to the broader farming population in this region.
Our analysis, which included correlation, ANOVA, and multiple regression, showed varied results. However, most findings support the hypothesis that SD commodity crop farmers exhibit entrepreneurial thinking and behavior. This behavior varies based on age, education, and farm size. Many SD crop producers actively seek innovative methods to improve their businesses and are dedicated to protecting their land from increasing weather variability. Most also monitor funding opportunities to help expand their businesses and stay competitive in the global market. Additionally, the respondents reported exploring ways to diversify their operations.
Despite these entrepreneurial intentions, most farmers lack written business plans. This finding aligns with previous research [18] indicating that many farmers lack strategic business plans. Experts suggest that written business plans are crucial for entrepreneurial success. Farmers should engage in training programs to enhance their professional, managerial, and entrepreneurial skills [18,49].
While most participants in our study are eager to seek training and update their knowledge to maintain environmental sustainability, only about one-third regularly attend South Dakota Extension training workshops. This limited participation may be due to a lack of information about available training programs focused on developing entrepreneurial skills. Additionally, some farmers may live far from training locations or be unaware of South Dakota Extension’s involvement in certain workshops.
To address these issues, further research is needed to evaluate the types and quality of training programs available to farmers beyond South Dakota Extension workshops and understand how these programs influence farmers’ decision making. The respondents also mentioned using their social networks to stay informed about funding opportunities and innovative ideas.
This study revealed that the socio-demographic characteristics of farmers, including age, education level, and farm size, influence their adoption of entrepreneurship, networking, and training activities. Younger farmers, for example, tend to be more innovative and entrepreneurial, which aligns with findings from [10,50] highlighting their higher profitability and innovative capabilities. Additionally, younger farmers are more likely to invest in environmentally friendly practices and adopt new technologies and entrepreneurial strategies, as supported by [30,51].
Applying the diffusion of innovations theory helps explain how age and education impact the adoption of new ideas. The ANOVA and multiple regression analyses show that farmers with larger farms are more inclined to pursue entrepreneurship, engage in networking, and seek training to enhance their skills. This supports the notion that larger farm operators are generally more interested in developing their entrepreneurial abilities.
However, our study presents a nuanced view. Although younger farmers are more open to innovative ideas and technologies, they often manage smaller farms compared to their older counterparts, who typically run larger operations [52]. This discrepancy may limit younger farmers’ ability to engage in entrepreneurial practices as effectively as older farmers.
This research contributes to agricultural entrepreneurship and innovation in the literature, particularly within the US Corn Belt region. To our knowledge, this is the first study to specifically investigate how farmers’ training and networking influence their entrepreneurial motivations in this region.
The implications for farmers and policymakers are as follows: (1) Farmers in the US Corn Belt should embrace entrepreneurship by leveraging their social networks to enhance training and learning opportunities. Maximizing interactions with institutions offering new innovations and technologies is crucial. (2) Policymakers, when developing agriculture-related policies, should incorporate strategies that support farm entrepreneurship to promote sustainable agriculture. Additionally, they should help farmers increase and diversify their funding opportunities to stay competitive globally. Experiences from the US, Malaysia, and several European countries (including the Netherlands, Poland, Finland, Sweden, and the UK) show that training, entrepreneurial skills, and improved use of social networks significantly contribute to adopting innovations and best management practices [9,14,53].
This study has three main limitations: (1) The Underrepresentation of Female Farmers. Only about 2% of surveyed farmers are female, which reflects a lack of available data on the percentage of female farm operators in SD. (2) Limited Information on Training Programs. There is insufficient data on training programs focusing on developing SD farmers’ entrepreneurial skills. While many participants seek training and update their skills for sustainability, only about one-third regularly attend Extension workshops. More detailed information on these programs could offer better insights into how producers use available resources to enhance their entrepreneurial skills. (3) Inactive Respondents. Some respondents were no longer active in farming, leading to 650 surveys being returned due to incorrect addresses or inactive producers. This affected the response rate.
Further research is needed to explore why commodity crop farmers often lack interest in adopting strategic planning. This includes understanding why they may not create written business plans with sustainable long-term goals, short-term production decisions, or record their operations. Additionally, future studies should investigate the reasons behind the low participation rates in Extension training workshops designed to enhance farmers’ entrepreneurial skills and competencies. It would also be valuable to assess whether farmers benefit from Extension websites and to examine the effectiveness of different training opportunities.
Another key area for future research is the impact of farmers’ age on their use of social networks to discover innovative ideas and seek training. It is also important to study how farmers plan to engage in crop diversification, crop rotation, or other strategies to boost their profits, particularly given the perception that farming is less profitable today. Finally, conducting a comparative study on the entrepreneurial tendencies of SD farmers versus those in other parts of the country and the world would provide valuable insights.
6. Conclusions
This study investigated the entrepreneurial aspirations and motivations of SD farmers and their potential contributions to agricultural sustainability. By analyzing a randomly selected sample of commodity crop producers from eastern SD, the results can be applied to the broader farming population in this region.
The findings reveal that most SD commodity crop farmers exhibit entrepreneurial thinking and behavior. They actively seek innovative methods to improve their businesses, diversify their operations, and protect their farmland from increasing weather variability. Farmers also proactively monitor funding opportunities to expand their businesses and remain competitive globally. However, many lack formal written business plans, which are crucial for strategic planning and entrepreneurial success.
Additionally, while most farmers are eager to update their knowledge and skills through training, only about one-third regularly attend Extension training workshops. This low attendance may stem from inadequate information dissemination, logistical challenges like distance from training venues, or limited use of social networks to access and share information. The diffusion of innovations theory can explain how age and education affect the spread and adoption of new ideas and practices. Social networks are vital for enabling farmers to share knowledge and learn from each other.
This study also highlights that age, education level, and farm size significantly influence farmers’ adoption of entrepreneurial practices, networking, and training involvement. Younger farmers tend to have more entrepreneurial aspirations due to their higher innovativeness and openness to new ideas and technologies. Larger farm operators are more likely to embrace entrepreneurship, engage in networking, and seek training opportunities, indicating a greater capacity or interest in developing entrepreneurial skills. However, younger farmers, while more innovative and inclined to pursue training, often operate smaller farms, which may limit their ability to fully engage in and adopt entrepreneurial practices. This underscores the need for targeted support to help younger farmers overcome these challenges.
Conceptualization, A.A. and J.D.U.-S.; Methodology, A.A. and D.K.; Software, A.A. and J.D.U.-S.; Validation, A.A., J.D.U.-S. and D.K.; Formal analysis, A.A.; Investigation, A.A., J.D.U.-S., D.K. and T.W.; Resources, A.A.; Data curation, A.A. and J.D.U.-S.; Writing—original draft, A.A.; Writing—review & editing, A.A., J.D.U.-S., D.K., T.W. and D.C.; Supervision, J.D.U.-S.; Project administration, J.D.U.-S., D.K. and T.W.; Funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.
The project with reference IRB-1812009-EXP was approved by the Institutional Review Board (IRB) to protect human subjects through an expedited review on 13 December 2018. The proposed activity was deemed to have no greater than minimal risk and congruent with expedited category numbers (6) and (7), as outlined in 45 CFR 46, section 110. The IRB must approve any changes to the protocol or related documents before implementation. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of South Dakota State University (protocol codes (6) and (7) outlined in 45 CFR 46, section 110).
Written informed consent was obtained from all participants in this study. They signed consent forms and were informed about the confidentiality of their information and their right to withdraw at any time or not answer any questions should they decline.
The data used in this study are from the 2018 South Dakota Community Crop Producer survey that the authors conducted with funding from the South Dakota Corn Utilization Council. The authors confirm that a set of raw data supporting the findings of this study is available from the corresponding author upon request. Due to ethical considerations, the authors removed any information that might disclose the identities of the respondents. A descriptive summary of the data used in this study is publicly available on the South Dakota State University website under “2018 South Dakota Commodity Crop Producer Survey Results”, which can be accessed at
This study is part of a collaborative project between social and natural science researchers from South Dakota State University and the South Dakota Corn Utilization Council to examine South Dakota farmers’ land management practices and attitudes to increase their economic and environmental sustainability. We thank South Dakota agricultural producers for their participation and Emireth Cancino (a South Dakota State University undergraduate student) for assisting in conducting the survey.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Percentage of SD producers who use social networks for entrepreneurialism.
Entrepreneurial item scales.
| Scale | Variable Wording (Number of Responses) | Variable Mean (Standard Deviation) | Scale Mean (SD.) | Scale Alpha | Item Alpha, If Removed |
|---|---|---|---|---|---|
| Entrepreneurship Scale | I am constantly trying to find ways to increase my income or profit (e.g., creating new ideas, finding new innovations, and trying various technologies and management practices). (N = 690) | 3.2 (0.6) | 14.0 (3.1) | 0.71 | 0.64 |
| I always have a written business plan for my farm operation. (N = 688) | 2.4 (0.7) | 0.66 | |||
| I am always taking steps to protect my farmland from increased weather variability (e.g., diversifying crops, building soil quality, and adding drainage or irrigation systems). (N = 620) | 3.2 (0.6) | 0.68 | |||
| I pay close attention to various funding sources (e.g., government subsidies or private loans) that may impact my operation. (N = 693) | 2.7 (0.8) | 0.68 | |||
| I am often looking for ways to diversify my farm operation. (N = 691) | 2.9 (0.7) | 0.63 | |||
| Network Scale | I always seek opportunities to create or build social networks to help my business. (N = 690) | 2.6 (0.7) | 7.4 (2.2) | 0.84 | 0.80 |
| My social networks help increase my awareness of funding opportunities such as government subsidies or programs. | 2.4 (0.7) | 0.80 | |||
| I use my social networks to learn new innovative ideas that help develop my business. (N = 685) | 2.6 (0.7) | 0.75 | |||
| Training Scale | I am always willing to seek training opportunities to grow, improve, and expand my farm business. (N = 691) | 2.9 (0.7) | 5.7 (1.4) | 0.78 | 0.53 |
| I constantly update my knowledge and skills to ensure my business remains environmentally sustainable. (N = 691) | 3.0 (0.6) | 0.55 | |||
| I regularly attend South Dakota Extension training workshops. (N = 690) | 2.1 (0.7) | N/A | N/A | 0.78 |
Correlations between socio-demographic characteristics and entrepreneurial scales.
| Variable | Age | Level of Education | Farm Size | Entrepreneurial Scale | Networking Scale | Training Scale |
|---|---|---|---|---|---|---|
| Age | -- | |||||
| Level of education | −0.184 *** | -- | ||||
| Farm size | −0.041 | 0.007 | -- | |||
| Entrepreneurial scale | −0.164 ** | −0.008 | 0.224 *** | -- | ||
| Networking scale | −0.007 | 0.005 | 0.146 ** | 0.462 *** | -- | |
| Training scale | −0.079 | 0.061 | 0.256 *** | 0.496 *** | 0.458 *** | -- |
Note: *** indicates p < 0.001 and ** indicates p < 0.01.
Two-sample t-test results comparing entrepreneurialism scales by age group.
| Younger Farmers (20–59) | Older Farmers (60+) | ||||
|---|---|---|---|---|---|
| Scale | Mean | SD | Mean | SD | t-Test |
| Entrepreneurial | 14.4 | 2.4 | 13.9 | 3.0 | 2.49 * |
| Networking | 7.5 | 1.9 | 7.5 | 2.0 | NS |
| Training | 5.9 | 1.2 | 5.8 | 1.3 | NS |
Note: SD = standard deviation; NS = not significant; * indicates p < 0.05.
Two-sample t-test results comparing entrepreneurialism scales by education.
| No College Degree | College Degree | ||||
|---|---|---|---|---|---|
| Scale | Mean | SD | Mean | SD | t-Test |
| Entrepreneurial | 14.2 | 2.7 | 14.0 | 2.8 | NS |
| Networking | 7.4 | 2.1 | 7.6 | 1.9 | NS |
| Training | 5.7 | 1.3 | 5.9 | 1.3 | NS |
Note: SD = standard deviation; NS = not significant.
ANOVA on entrepreneurial scales by farm size.
| Sum of Squares | df | Mean Square | F | Sig. | ||
|---|---|---|---|---|---|---|
| Entrepreneurial Scale | Between Groups | 159.9 | 2 | 80.0 | 12.2 | 0.000 ** |
| Within Groups | 3997.0 | 608 | 6.6 | |||
| Total | 4156.9 | 610 | ||||
| Network Scale | Between Groups | 55.8 | 2 | 27.9 | 7.5 | 0.000 *** |
| Within Groups | 2250.8 | 608 | 3.7 | |||
| Total | 2306.7 | 610 | ||||
| Training Scale | Between Groups | 51.0 | 2 | 25.5 | 16.8 | 0.000 *** |
| Within Groups | 922.0 | 608 | 1.5 | |||
| Total | 973.0 | 610 | ||||
Note: *** indicates p < 0.001 and ** indicates p < 0.01.
Multiple regression models.
| Model 1 (Entrepreneurial Scale) | Model 2 | Model 3 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | B | Std. Error | Beta | t (Sig.) | B | Std. Error | Beta | t (Sig.) | B | Std. Error | Beta | t (Sig.) |
| Education | −0.298 | 0.089 | −0.091 | −3.325 ** | 0.53 | 0.067 | 0.23 | 0.792 | 0.175 | 0.56 | 0.083 | 3.120 ** |
| Farm Size | 0.080 | 0.97 | 0.23 | 0.826 | −0.072 | 0.72 | −0.029 | −0.994 | 0.257 | 0.60 | 0.114 | 4.313 *** |
| Age | −0.032 | 0.006 | −0.146 | −0.5310 *** | 0.007 | 0.005 | 0.043 | 1.455 | 0.010 | 0.004 | 0.69 | 2.577 * |
| Entrepreneurial Scale | NA | 0.250 | 0.29 | 0.352 | 8.647 *** | 0.292 | 0.023 | 0.450 | 12.850 *** | |||
| Network Scale | 0.447 | 0.52 | 0.317 | 8.647 *** | NA | 0.337 | 0.031 | 0.368 | 10.716 *** | |||
| Training Scale | 0.746 | 0.058 | 0.483 | 12.850 *** | 0.481 | 0.045 | 0.440 | 10.716 *** | NA | |||
| Constant | 7.453 | 0.594 | - | 12.552 *** | −0.292 | 0.499 | - | −0.585 | −0.369 | 0.417 | - | −0.884 |
| R2 (N) | 0.571 (594) | 0.523 (594) | 0.601 (594) | |||||||||
Note: *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
References
1. Key, N. Larger Farms and Younger Farmers Are More Vulnerable to Financial Stress; Economic Research Service, U.S. Department of Agriculture: Washington, DC, USA, 2019.
2. Barbieri, C.; Edward, M.; Butler, L. Understanding the Nature and Extent of Farm and Ranch Diversification in North America. Rural Sociol.; 2008; 73, pp. 205-229. [DOI: https://dx.doi.org/10.1526/003601108784514543]
3. Dimitri, C.; Effland, A.; Conklin, N. The 20th Century Transformation of US Agriculture and Farm Polic; The United States Department of Agriculture: Washington, DC, USA, 2005.
4. Condor, R. Entrepreneurship in Agriculture: A Literature Review. Int. J. Entrep. Small Bus.; 2018; 40, pp. 517-562. [DOI: https://dx.doi.org/10.1504/IJESB.2020.109013]
5. Petrzelka, P.; Korsching, P.F. Farmers’ Attitudes and Behavior toward Sustainable. Agric. Environ. Educ.; 1996; 28, pp. 38-58. [DOI: https://dx.doi.org/10.1080/00958964.1996.9942814]
6. Gadanakis, Y.; Campos-González, J.; Jones, P. Linking Entrepreneurship to Productivity: Using a Composite Indicator for Farm-Level Innovation in UK Agriculture with Secondary Data. Agriculture; 2024; 14, 409. [DOI: https://dx.doi.org/10.3390/agriculture14030409]
7. Dotsiuk, S. Entrepreneurship as a Basis for Sustainable Development of the Agricultural Sector of the Econom. Green Blue Digit. Econ. J.; 2023; 4, pp. 10-21. [DOI: https://dx.doi.org/10.30525/2661-5169/2023-2-2]
8. Díaz-Pichardo, R.; Cantú-González, C.; López-Hernández, P.; McElwee, G. From Farmers to Entrepreneurs: The Importance of Associative Behavior. J. Entrep.; 2012; 21, pp. 91-116.
9. Morgan, L.S.; Marsden, T.; Miele, M.; Morley, A. Agricultural Multifunctionality and Farmers’ Entrepreneurial Skills: A Study of Tuscan and Welsh Farmers. Rural Stud.; 2010; 26, pp. 116-129. [DOI: https://dx.doi.org/10.1016/j.jrurstud.2009.09.002]
10. Morris, W.; Henley, A.; Dowell, D. Farm Diversification, Entrepreneurship, and Technology Adoption: Analysis of Upland Farmers in Wales. Rural Stud.; 2017; 53, pp. 132-143. [DOI: https://dx.doi.org/10.1016/j.jrurstud.2017.05.014]
11. Henley, A. Entrepreneurial Aspiration and Transition into Self-employment: Evidence from British Longitudinal Data. Entrep. Reg. Dev.; 2007; 19, pp. 253-280. [DOI: https://dx.doi.org/10.1080/08985620701223080]
12. Kwong, C.; Thompson, P. The When and Why: Student Entrepreneurial Aspirations. J. Small Bus. Manag.; 2016; 54, pp. 299-318. [DOI: https://dx.doi.org/10.1111/jsbm.12146]
13. Joshi, D.; Ulrich-Schad, J.; Wang, T.; Dunn, B.H.; Bruggeman, S.A.; Clay, D.E. Grassland retention in the North America Midwest following periods of high commodity prices and climate variability. Soil Sci. Soc. Am. J.; 2019; 83, pp. 1290-1298. [DOI: https://dx.doi.org/10.2136/sssaj2019.03.0090]
14. McElwee, G. A Taxonomy of Entrepreneurial Farmers. Int. J. Entrep. Small Bus.; 2008; 6, pp. 465-478. [DOI: https://dx.doi.org/10.1504/IJESB.2008.019139]
15. Wilson, P.; Harper, N.; Darling, R. Explaining Variation in Farm and Farm Business Performance in Respect to Farmer Behavioral Segmentation Analysis: Implications for Land Use Policies. Land Use Policy; 2013; 30, pp. 147-156. [DOI: https://dx.doi.org/10.1016/j.landusepol.2012.03.006]
16. United States Department of Agriculture Agriculture in the Midwest. Available online: https://www.climatehubs.usda.gov/hubs/midwest/topic/agriculture-midwest (accessed on 14 July 2024).
17. Dias, C.S.L.; Rodrigues, R.G.; Ferreira, J.J. What is New in the Research on Agricultural Entrepreneurship?. Rural Stud.; 2019; 65, pp. 99-115. [DOI: https://dx.doi.org/10.1016/j.jrurstud.2018.11.003]
18. Stanford-Billington, C.; Cannon, A. Do Farmers Adopt A Strategic Planning Approach to the Management of Their Businesses?. J. Farm Manag.; 2010; 14, pp. 3-40.
19. Rambo, A.T. The Agrarian Transformation in Northeastern Thailand: A Review of Recent Research. Southeast Asian Stud.; 2017; 6, pp. 211-245.
20. Lovell, R.; Shennan, C.; Thuy, N.N. Sustainable and conventional intensification: How gendered livelihoods influence farming practice adoption in the Vietnamese Mekong River Delta. Environ. Dev. Sustain.; 2021; 23, pp. 7089-7116. [DOI: https://dx.doi.org/10.1007/s10668-020-00905-9]
21. Amata, I.A. The Use of Non-Conventional Feed Resources (NCFR) for Livestock Feeding in the Tropics: A Review. J. Glob. Biosci.; 2014; 3, pp. 604-613.
22. Prokopy, L.S.; Floress, K.; Arbuckle, J.G.; Church, S.P.; Eanes, F.R.; Gao, Y.; Gramig, B.M.; Ranjan, P.; Singh, A.S. Adoption of agricultural conservation practices in the United States: Evidence from 35 years of quantitative literature. Soil Water Conserv.; 2019; 74, pp. 520-534. [DOI: https://dx.doi.org/10.2489/jswc.74.5.520]
23. Shackleton, S.; Masterson, V.; Hebinck, P.; Speranza, C.I.; Spear, D.; Tengö, M. Livelihood and Landscape Change in Africa: Future Trajectories for Improved Well- Being under a Changing Climate. Land; 2019; 8, pp. 114-121. [DOI: https://dx.doi.org/10.3390/land8080114]
24. McElwee, G. Developing Entrepreneurial Skills of Farmers: A Literature Review of Entrepreneurship in Agriculture; University of London: London, UK, The Commission of the European Community: Brussels, Belgium, 2005.
25. Urban, B.; Xaba, G. Enterprise Skills and Performance: An Empirical Study of Smallholder Farmers in Kwa-Zulu Natal. Contemp. Manag.; 2016; 13, pp. 222-245.
26. Emery, M.; Flora, C. Spiraling-Up: Mapping Community Transformation with Community Capitals Framework. Community Dev.; 2009; 37, pp. 19-35. [DOI: https://dx.doi.org/10.1080/15575330609490152]
27. Ramirez, A. The Influence of Social Networks on Agricultural Technology Adoption. Procedia-Soc. Behav. Sci.; 2013; 79, pp. 101-116. [DOI: https://dx.doi.org/10.1016/j.sbspro.2013.05.059]
28. Baumgart-Getz, A.; Prokopy, L.S.; Floress, K. Why Farmers Adopt Best Management Practice in the United States: A Meta-Analysis of the Adoption Literature. J. Environ. Manag.; 2012; 96, pp. 17-25. [DOI: https://dx.doi.org/10.1016/j.jenvman.2011.10.006]
29. Laurwere, C. The Role of Agricultural Entrepreneurship in Dutch Agriculture of Today. Agric. Econ.; 2005; 33, pp. 229-238. [DOI: https://dx.doi.org/10.1111/j.1574-0862.2005.00373.x]
30. Rizzo, G.; Migliore, G.; Schifani, G.; Vecchio, R. Key factors influencing farmers’ adoption of sustainable innovations: A systematic literature review and research agenda. Org. Agric.; 2024; 14, pp. 57-84. [DOI: https://dx.doi.org/10.1007/s13165-023-00440-7]
31. Pannell, D.; Marshall, G.R.; Barr, N.; Curtis, A.; Vanclay, F.; Wilkinson, R. Understanding and Promoting Adoption of Conservation Practices by Rural Landholders. Aust. J. Exp. Agric.; 2006; 46, pp. 1407-1424. [DOI: https://dx.doi.org/10.1071/EA05037]
32. Makhbul, Z.M.; Hasun, F.M. Entrepreneurial Success: An Exploratory Study among Entrepreneurs. Int. J. Bus. Manag.; 2011; 6, pp. 116-125. [DOI: https://dx.doi.org/10.5539/ijbm.v6n1p116]
33. Hansson, H.; Ferguson, R.; Olofsson, C.; Rantamäki-Lahtinen, L. Farmers’ Motives for Diversifying their Farm Business—The Influence of Family. Rural Stud.; 2013; 32, pp. 240-250. [DOI: https://dx.doi.org/10.1016/j.jrurstud.2013.07.002]
34. Taragola, N.; Marchand, F.; Meul, M.; Passel, S.V.; Dessein, J. Development of an Entrepreneur Scan as a Driving Force for Sustainable Farming. Proceedings of the World Conference on Entrepreneurship and Sustainability; Dublin, Ireland, 11–14 June 2014.
35. Rogers, E.M. Diffusion of Innovations; 5th ed. A Division of Macmillan Publishing Co., Inc.: New York, NY, USA, 2003; 576.
36. Dearing, J.W. Applying Diffusion of Innovation Theory to Intervention Development. Res. Soc. Work Pract.; 2009; 19, pp. 503-518. [DOI: https://dx.doi.org/10.1177/1049731509335569]
37. Quinn, C.E.; Halfacre, A.C. Place Matters: An Investigation of Farmers’ Attachment to Their Land. Hum. Ecol. Rev.; 2014; 20, pp. 117-132. [DOI: https://dx.doi.org/10.22459/HER.20.02.2014.06]
38. Nielsen, M.S. Passing on the Good Vibes: Entrepreneurs’ Social Support. J. Entrep. Innov.; 2019; 21, pp. 1465-7503. [DOI: https://dx.doi.org/10.1177/1465750319842530]
39. Lauwere, C.K.; Enting, I.; Vermeulen, P.; Verhaar, K. Modern Agricultural Entrepreneurship. Proceedings of the 13th International Farm Management Congress; Wageningen, The Netherlands, 7–12 July 2002.
40. Noordhuizen, J.P.M.; van Egmond, M.J.; Jorritsma, R. Veterinary Advice for Entrepreneurial Dutch Dairy Farmers—From Curative Practice to Coach-Consultant: What Needs to be Changed?. Tijdschr. Voor Diergeneeskd.; 2008; 133, pp. 1-8.
41. Dickes, L.A.; Robinson, K.L. An Institutional Perspective of Rural Entrepreneurship. Am. J. Entrep.; 2014; 7, pp. 44-57.
42. Alsos, G.A.; Carter, S.; Ljunggren, E.; Welter, W. Researching Entrepreneurship in Agriculture and Rural Development. The Handbook of Research on Entrepreneurship in Agriculture and Rural Development; Edward Elgar Publishing: Cheltenham, UK, 2011.
43. Steiner, J.L.; Briske, D.D.; Brown, D.P. Vulnerability of Southern Plains agriculture to climate change. Clim. Chang.; 2017; 146, pp. 201-218. [DOI: https://dx.doi.org/10.1007/s10584-017-1965-5]
44. National Centers for Environmental Information (NCEI). 2021 U.S. Billion-Dollar Weather and Climate Disasters in Historical Context-Hazard and Socioeconomic Risk Mapping; National Oceanic and Atmospheric Adminstration (NOAA): Silver Spring, MD, USA, 2021.
45. Dillman, D.A.; Smyth, J.D.; Christian, L.M. Internet, Phone, Mail, and Mixed Mode Surveys: The Tailored Design Method; 4th ed. John Wiley & Sons Inc.: Hoboken, NJ, USA, 2014; 528.
46. Bishara, A.J.; Hittner, J.B. Testing the Significance of a Correlation with Nonnormal Data: Comparison of Pearson, Spearman, Transformation, and Resampling Approaches. Psychol. Metthods; 2012; 17, pp. 399-417. [DOI: https://dx.doi.org/10.1037/a0028087] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22563845]
47. Mishra, P.; Singh, U.; Pandey, C.M.; Mish, P. Application of Student’s t-test, Analysis of Variance, and Covariance. Ann. Card. Anaesth.; 2019; 22, pp. 407-411. [DOI: https://dx.doi.org/10.4103/aca.ACA_94_19]
48. Moore, A.W.; Anderson, B.; Das, K.; Wong, W.-K. Chapter 15—Combining Multiple Signals for Biosurveillance. Handbook of Biosurveillance; Academic Press: Cambridge, MA, USA, Elsevier: Amsterdam, The Netherlands, 2006; 620.
49. Tassell, V.W.; Keller, L.H. Farmers’ Decision-Making: Perceptions of the Importance, Uncertainty, and Controllability of Selected Factors. Agribusiness; 1991; 7, pp. 523-535. [DOI: https://dx.doi.org/10.1002/1520-6297(199111)7:6<523::AID-AGR2720070603>3.0.CO;2-N]
50. Thephavanh, M.; Neil, J.; Philp, M.; Nuberg, I.; Denton, M. Narrative Insights Reveal the Motivations of Young Agricultural Entrepreneurs in Laos. Sustainability; 2022; 14, 13113. [DOI: https://dx.doi.org/10.3390/su142013113]
51. Mozzato, D.; Gatto, P.; Defrancesco, E.; Bortolini, L.; Pirotti, F.; Pisani, E.; Sartori, L. The Role of Factors Affecting the Adoption of Environmentally Friendly Farming Practices: Can Geographical Context and Time Explain the Differences. Sustainability; 2018; 10, 3101. [DOI: https://dx.doi.org/10.3390/su10093101]
52. Żmija, K.; Fortes, A.; Tia, M.N.; Šūmane, S.; Ayambila, S.N.; Żmija, D.; Satoła, Ł.; Sutherland, L.-A. Small farming and generational renewal in the context of food security challenges. Glob. Food Secur.; 2020; 26, 100412. [DOI: https://dx.doi.org/10.1016/j.gfs.2020.100412]
53. Rezai, G.; Mohamed, Z.; Shamsudin, M.N. Informal Education and Developing Entrepreneurial Skills among Farmers in Malaysia. Int. J. Humanit. Soc. Sci.; 2011; 5, pp. 906-913.
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Details
; Ulrich-Schad, Jessica D 2
; Kolady, Deepthi 3
; Wang, Tong 3
; Clay, David 4
1 Department of Social Sciences, Qatar University, Doha P.O. Box 2713, Qatar
2 Department of Sociology & Anthropology, Utah State University, Logan, UT 84322, USA;
3 Department of Agricultural Economics, Oklahoma State University, Stillwater, OK 74078, USA;
4 Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57007, USA;