Introduction
During the last decade, agricultural technology has provided better methods for growing agricultural products that includes the expanded use of greenhouses, hydroponic cultivation, improved seeds, modern irrigation systems, highly sophisticated machinery and improved integrated pest management systems. These improved technology-based methods increase the efficiency and sustainability of farm production. However, access to these technologies has been uneven by farm size and across ethno-racial groups. Farmers with high levels of human capital, large operations, substantial credit reserves, membership in networks and associations with resources enjoy the greatest access to modern farming technology.

Farmers with small operations, especially farmers of color and women experience discrimination in obtaining agricultural credit (NSAC, 2019), have limited credit reserves, and few belong to producer networks and associations that could provide information and resources to access modern farming technology. In addition, sometimes small farmers purchase machinery for their attachment value rather than for their contributions to the farming operation. In other cases, they simply fail to conduct the proper investment analysis for the purchase. For example, Michigan Food and Farming Systems (MIFFS) field operators found that in some cases, small farmers purchase a particular piece of equipment (new or used) that is not appropriate for the size of their agricultural operations and is used only a few weeks per year.

Research supports the proposition that decision makers are motivated by the acquisition of relational goods, the need for internal and/or external validation, belonging, altruism, and the desire for increased commodities. These factors are relevant for studies of small and ethno-racial minority farmers because their decisions may involve trade-offs between increased commodity gains and relational goods; that is, assets infused with socio-emotional features. In other words, important economic decisions such as the adoption of new technology are not motivated exclusively by potential commodity gains but by increases in relational goods. Robison et al. (2020) found support for the idea that decision makers vary in their preference for commodities relative to relational goods based on exchange partner and the type of goods exchanged.
The Julian Samora Research Institute (JSRI), in cooperation with MIFFS, conducted an exploratory study on how relational goods produced in social capital rich relationships influence farming operations, including farmers’ willingness to adopt new technology. Preliminary results presented here from the study are based on a telephone survey completed with thirty-eight Latino farmers located in Michigan. The data were analyzed using appropriate statistical techniques and summarized in this paper.

Literature Review
As far as we can determine, this study is the first to examine the influence of social capital and relational goods on technology adoption among U.S. farmers generally and specifically among Latino farmers. We found several related studies that examine the adoption of new technology by farmers where social capital constitutes an important asset especially for small farmers, but none conducted in the U.S.

A study conducted by dos Santos et al. (2021) in two South Brazilian states, Rio Grande do Sul and Paraná, notes that innovative farmers are “those who are motivated to establish social connections and seek external sources of information to enhance performance” (p. 92), and that “farmers who networked frequently were more likely to invest more in innovative technologies” (p. 92). Dos Santos et al. (2021) hold that networking is related to knowledge acquisition and assimilation and becomes a precursor to the process of influencing Potential Absorptive Capacity (PAC), or the ability to assimilate new knowledge, which is a precursor to Realized Absorptive Capacity (RAC), which involves transforming and utilizing new knowledge which directly affects Financial Performance (FP).

Hunecke and colleagues (2017) examined the impact of social capital on the adoption of irrigation technology and irrigation scheduling among wine producers in Central Chile. They found that trust in institutions and support for formal and informal networks have a positive impact on the adoption of both irrigation technology and irrigation scheduling technologies. They also found that human capital is positively related to network participation.  

Ntume et al. (2015) examined the role of social capital in technology adoption and livestock development in Uganda. They collected data from 107 livestock farmers with varying sizes of livestock operations. They found that social capital played a key role in livestock development, technology adoption, dissemination, and participation in Extension activities. In addition, they found that 75.7% of the respondents’ livestock rearing and management practices were influenced by social networks of friends, relatives, and neighbors, while only 24.3% followed traditional methods.  

Finally, Nyongesa Nato et al. collected data from 120 beneficiaries of training programs in Kenya presented by the African Institute for Capacity Development (AICAD). They found that group involvement and social support, two important components of the social capital framework, were positively associated with and significantly influenced adoption of appropriate agricultural production technologies.

Other studies of social capital, such as that by Robison and Ritchie (2010), have found that social capital, sympathy, empathy, trust, and regard for another person or group, the object of social capital, resides in networks—making clear the connection between network participation and social capital.    

Survey Design
This project surveyed socially disadvantaged Latino farmers with small operations in Southwest Michigan in 2021. The main purpose of the study is to explore how farmers perceive the importance of each of five social capital motives (Robison et.al., 2011) when explaining their current production techniques and equipment and the adoption of new agricultural methods and equipment.  

The survey included several questions intended to measure the extent and reliability of farmers’ networks that support their farming activities and that provide farming related advice. The configuration of these networks depends on three types of social capital: bonding (based on inherited commonalities), linking (based mostly on earned commonalities, and bridging (based on asymmetric levels of social capital). We also included social media as a new source of information. The survey instrument also contained questions about the type of tools and equipment currently used and the need for external help to operate them.

Another section of the survey instrument included questions about respondents’ willingness to adopt new and appropriate technology. Since farmers consider a greenhouse a technological advance for their operations, we asked them if they currently owned or operated a greenhouse and, if they do not currently have one, their willingness to acquire and operate one. We asked them about their willingness to consider the adoption of new tools, equipment, and appropriate technology for their farming operations. We included a question regarding the success of their current farming operations to determine the levels of how efficient and successful they consider their operations using the categories of very successful, successful, average, not successful, disorganized, and poor.  

Finally, we included a demographic profile section where they reported their sex, race/ethnicity, educational attainment, type of producer (full-time, part-time, or weekends), type of organization with which they are affiliated, family structure, years working in agriculture, and years living in current community. Data collection based on the survey instrument was done via phone interviews due to current restrictions related to the COVID-19 pandemic. A total of 38 Latino farmers were interviewed in Spanish. Participants in this study were randomly selected from a long list of farmers with similar characteristics developed by MIFFS and JSRI.

Results
Profile of survey respondents
According to the 2017 U.S. Census of Agriculture, the average age of farmers in the United States is 57.5 years old. Table 1 shows that the average age of Latino farmers who participated in the study is 47.6 years old, almost 10 years younger than the national average. Most of these farmers fall under the socially disadvantaged, beginning farmers category with small farm operations. In addition, while U.S. farmers have been on their current farms an average of 21.3 years, farmers in our study have been working in agriculture an average of 14.6 years, nearly 7 years lower than the U.S. average. There is only a year of difference between the years participating farmers have been living in their current community (15.6 years) and the years that they have been working in agriculture, which suggests that Latino farmers moved to their current communities with the main intention of operating their own farms.

The survey results show that there is a huge gap between the percentage of male and female farmers participating in the study, with male farmers accounting for 84.2% of the participants, while female farmers represented only 15.8%. These figures differ from the national figures included in the 2017 Census of Agriculture, which reports up to 64% of male farmers and 36% of female farmers, with a 20% average increase of female farmers in the last 10 years.

When participating farmers were asked about their race, 31.6% identified as White, 2.6% as Black, 2.6% as Multiracial, and 47.4% as “Other”, which could indicate an Indigenous identity. Regarding ethnicity, a high percentage of respondents identified themselves as Mexican (84.2%), indicating that the majority of Latino farmers in this region of Michigan are Mexican.

The educational attainment of Latino farmers who participated in this exploratory study is low; 63.2% of them did not graduate from high school and 23.7% had attained only a high school diploma or GED. Some of these farmers are former migrant farmworkers who started working on agriculture-related jobs at early ages with few possibilities to attend a formal school system.

Another interesting feature presented in Table 1 has to do with the type of farmer they indicated being. Approximately 71.1% of them indicated they are part-time producers, either part-time per se (47.4%) or weekend only producers (23.7%). At the national level, 1 in 3 farmers (33.3%) are part-time operators. Only 28.9% of the project’s participants are full-time farmers. Due to their current situation, farmers in our study have to generate income outside their farms to support their families and their farming operations.

Table 1.  Demographic Characteristics of Latino Respondents

Demographics	Mean	Standard Deviation
Age	47.6 years	15.6
Sex:    Male    Female	84.2% 15.8%
Race:    White    Black    Multiracial    Other    No Answer	31.6% 2.6% 2.6% 47.4% 15.8%
Ethnic Background:    Mexican    Cuban    Puerto Rican    Central American\Caribbean    Spaniard	81.6% 5.3% 5.3% 2.6% 5.3%
Education Attainment:    Did not graduate from high school    High school graduate or GED    Some college/currently attending    Associate degree    College graduate	63.2% 23.7% 2.6% 5.3% 5.3%
Producer Status:    Full-time producer    Part-time producer    Weekends producer	28.9% 47.4% 23.7%
Family Structure:    Two biological parents    One biological and one non biological parent    Divorced parents    Did not reply	87.2% 5.1% 2.6% 5.1%
Type of Farmer:    Successful farmer    Average farmer    Not successful farmer	32.4% 54.1% 13.5%
Years Working in Agriculture	14.6 years	11.6
Years Living in Community	15.6 years	9.0

 

When asked about the type of farmer they consider themselves relative to success, 54.1% reported being an average farmer, while 32.4% described themselves as successful farmers who intend to keep improving, and 13.5% reported being poor, disorganized, and not successful farmers. The relative high percentage of successful farmers, 1 in 3, is encouraging since most of these farming operators face a series of constraints due to their low levels of human, financial, and social capital.

Up to 87.2% of the participants come from traditional families with two biological parents, indicating strong family ties that constitute the base of bonding social capital. This type of social capital in many situations serves as a safety net characterized by a high exchange of information among family members, along with physical, financial, and socio-emotional goods.
Table 2 shows the number of times that participants in the study reported asking for advice from different sources during the past year. The sources included in Table 2 are related to the three different types of social capital: bonding (close family members), linking (friends and other farmers), and bridging (Extension agents and service providers).

Table 2.  Source of Advice for Operations by Relative Importance

Sources	Number	Minimum	Maximum	Mean	Std. Deviation
Close family members	26	1	12	3.08	2.50
Friends and other farmers	32	1	8	2.94	1.70
Extension agents and service providers	19	1	14	2.84	3.08
Social media	20	1	15	4.05	3.47
Others	1	4	4	4.00

 

According to the figures presented in the table, social media constitutes their most common source of information, with a maximum of 15 contacts and a mean of 4.05 times during the past year. We were surprised by these results considering the low educational attainment of these farmers and the general portrayal of them as having limited access to online sources. A second source of information was close family members, with a maximum of 12 contacts and a mean of 3.08 times, followed by friends and other farmers with a maximum of 8 contacts and a mean of 2.94 times. The last two findings are related to two different types of social capital, bonding and linking. Bonding social capital is based on strong ties among family members, and linking is established among peers who work in related activities, in this case other farmers.

Further, the source of information with the lowest mean (2.84) and a maximum of 14 contact times are Extension agents and service providers. Previous focus groups conducted by JSRI (2016) also found that there was limited contact between Latino farmers and Extension agents in Michigan. One explanation has to do with the cultural gaps between Latino farmers and service providers. There are cultural factors such as language and the limited knowledge that Latino farmers have of Extension and other service agencies, as well as the limited prioritization of serving Latino farmers among these agencies and their relative lack of multicultural capabilities, such as the limited number of Extension agents who can communicate in Spanish. In addition, Extension agents tend to focus on supporting large farming operations and devote far less time and resources to small operations.

Table 3 shows the relative importance that Latino farmers assign to the advice received for their farming operations from different social capital related sources included in the survey instrument. Usually, the advice received is associated with the type of crops suitable to the area, market opportunities, necessary tools and equipment, irrigation, and other factors. To facilitate the importance of the advice received we included in the survey instrument a ten-point Likert scale ranging from 0 (Not important) to 10 (Very important).

Table 3.  Importance of Source of Advice on Farming Operation Decisions

Sources	Number	Minimum	Maximum	Mean	Std. Deviation
Close family members	28	5	10	8.43	1.75
Friends and other farmers	33	2	10	7.64	1.85
Extension agents & Service Providers	21	2	10	7.76	2.21
Social media	21	1	10	6.33	2.73
Others	2	7	10	8.50	2.12

 

Table 3 shows that participant farmers assigned high marks to the advice they obtained for their farming operations from close family members (mean = 8.43). It is well-known that Latinos in general have strong family ties and base their decisions on the advice and/or agreement among family members. “Others” had a high mean but represented only two respondents. Extension agents and service providers had the next highest mean as a source of information (mean = 7.76), followed closely by friends and other farmers (mean = 7.64). Social media obtained the lowest marks (mean of 6.33) for its importance as a source of information for farming operations.

In a previous paragraph, we reported that participant farmers contacted social media more frequently than other sources when searching for information for their farming operations, but they assigned the lowest importance marks to these sources of information, which might mean they are surfing the Internet for information but do not have much confidence in what they find.

Table 4 shows the type of tools and equipment that participant Latino farmers currently are using and the required external help for their operations. The types of equipment used range from manual tools to sophisticated equipment operated by computers.

Table 4.  Type of Tools and Equipment Used that Required External Help for Farm Operations

Description	Used by Participants		Required External Help for its Operation
	Frequency	Percent	Frequency	Percent
Manual tools	34	87.2%	10	25.6%
Tractors and other small equipment	35	89.7%	32	82.1%
Equipment shared with others	1	2.6%	2	5.1%
Equipment operated by computer	1	2.6%	3	7.7%

 

According to Table 4, socially disadvantaged Latino farmers with small operations who participated in this study reported currently using either manual tools (87.2%) and/or small tractors and other equipment (89.7%), with only a few of them reporting that they share equipment with other farmers or use technologically advanced equipment operated by computers. Ten farmers (25.6%) reported requiring external help or assistance for operating manual tools, while up to 32 of them (82.1%) required external assistance to operate tractors and other small equipment.

Table 5 shows the sources available to farmers searching for external information on how to operate, maintain, and buy or sell farming equipment. Respondents mainly reached out to friends and other farmers (92.3%), obtained information from social media (53.8%), or from Extension agents and service providers (35.9%). Only six of them (15.4%) rely on close family members and one (2.6%) reported obtaining information from other sources. The low number of farmers who turned to their close family members searching for this information could be explained by the likelihood that most of their relatives do not work in agriculture and have little knowledge on how to operate tools and equipment. As a result, they tend to rely on friends and other farmers who are currently working in agriculture.

Table 5.  Sources Farmers Contacted for External Information to Operate, Maintain, and Buy or Sell Farming Equipment.

Sources of Information	To operate and maintain equipment		To buy or sell equipment
	Frequency	Percent	Frequency	Percent
Close family members	6	15.4%	15	38.5%
Friends and other farmers	36	92.3%	24	61.5%
Extension agents and service providers	14	35.9%	8	20.5%
Social media	21	53.8%	23	59.0%
Other	1	2.6%	2	5.1%

 

When participant farmers look for advice to buy or sell farming equipment, they are most likely to rely on friends and other farmers (61.5%) and social media (59.0%). Close family members (38.5%) became the third source of information to buy or sell farming equipment. It is important to note that to make any new financial investment decision most Latinos consult with relatives and ask for their support to make the investment, in this case to buy new farm equipment. Finally, only 20.5% of the participants ask for advice from Extension agents and service providers.

An examination of the extent of farmers’ external networks (not shown) found that few of them are members of four types of community organization. Eight of them (20.5%) belong to a producers’ association, seven (17.9%) are members of a religious organization, five (12.8%) participate in a community service organization, and only three are in a social organization. These results are related to those obtained in the social capital motives section below that show that most Latino farmers do not feel part of or do not have a sense of belonging to the community in which they live, including the farming community and its organizations.

Adoption of New Technology
The main purpose of this pilot study is to evaluate the willingness of Latino farmers to adopt new and appropriate technology for their agricultural operations, how social capital could facilitate obtaining the new technology, and the role social capital motives play in the process. Since in the last few years greenhouses became the symbol of new and advanced agricultural technology, we asked participants if they currently own and operate a greenhouse or if they are willing to buy one of these facilities. In addition, we asked them if they are willing to adopt any new and appropriate technology for their operations. Table 6 summarizes the results to these questions.

Table 6.  Greenhouse Ownership and the Adoption of New Technology

Description	YES		NO		Total*
	Number	Percent	Number	Percent
Currently own or operate a greenhouse	9	24.3%	28	75.7%	37
Interested in buying a and operating a greenhouse	27	73.0%	10	27.%	37
Considering the adoption of new tools and appropriate technology	36	100.0%	0	0	36

*In rows 1 and 2 there was 1 missing respondent, and in row 3 there were 2 missing cases.

Table 6 shows that 9 (24.3%) of 37 respondents currently own and operate a greenhouse. As stated before, most of these farmers are operating small farms and are part-time farmers with limited resources. We assume farmers currently operating a greenhouse have a very simple one, usually built by them and without major technological devices (i.e., automatic irrigation, control of temperature, etc.). On the other hand, most of them, 28 farmers representing 75.7% of the respondents, do not currently have or operate one of these agricultural production facilities. We can also observe that up to 73.0% of the participants (27) reported being interested in buying and operating a greenhouse.

The last row shows the participants’ willingness to adopt new tools and appropriate technology for their farming operations; 36 of them (100.0%) report an eagerness to adopt this type of technology. This is the central focus of this study, to examine if and how Latino, part-time, low-income farmers are searching for means to make their farming operations more efficient and sustainable through the adoption of new and appropriate technology.

Analysis of Social Capital Motives
Robison et al. (2011) developed a social capital maximizing model on commodities and relational goods. In the process they identified five distinct motives. The demand for relational goods refutes the assumptions that choices are mostly about selfish acquisition of commodities. Other motives include the demand for internal and external validation, belonging, and altruism.

Consider a description of the five motives and the needs they satisfy. According to Robison et al. (2020), “Our need for physical goods and services motivates us to find ways to maintain and increase our own consumption of commodities now and in the future” (p. 1292). They call this motive the “own consumption” motive, which is related to the “selfishness of preference” motive that underlies much of neoclassical economic theory. The other four social capital motives are related to our desire to satisfy socio-emotional needs. The need for internal validation leads decision makers to invest in social capital in relation to the ideal self which is the source of internal validation. Robison et al. (2020) call this motive the “self-respect” motive.

The need for external validation leads decision makers to invest in the social capital others have for them. This motive leads to efforts designed to win the good-will and approval of important others. Robison et al. (2020) refer to this motive as the “good-will” motive. They state, “The good-will of others can be viewed as the social capital others provide us from which we receive external validation” (p. 1292).

They further state that “Our need for connectedness motivates us to change our feelings of empathy toward other people, causes, and organizations, especially when we lack the ability or resources to change the empathetic feelings and attitudes others have towards us. This motive calls for us to increase the social capital we have for others” (p. 1292). They refer to this motive as the “belonging” motive.

Finally, they argue that “Our social capital (empathetic) connections to others internalize their well-being, motivating us to act in their interest often by sharing our resources with them” (p. 1292). They refer to this motive as the “sharing” motive. In addition, vicariously sensing the well-being of others often provides information about the social environment that could not be gained by other means.

An analysis of the data obtained by the project assessed the relative importance of the motives in two different scenarios: tools and equipment already purchased and possible purchases of tools and equipment in the future. We compared the relative importance of motives in the two scenarios to determine if past purchases influenced motives for future purchases.   

Table 7 summarizes the relative importance of the five motives in percentages that sum to 100% in the two scenarios. Our results suggest that the motives for future purchases of tools and equipment varies from those purchased in the past.

The neoclassical model that maximizes profit assumes that decision makers are completely selfish. Our results do not support this assumption even in the case where profit maximization might be assumed. Farmers’ allocations for this motive in the first scenario, why they own or operate agricultural equipment in their farming operations, range from a minimum of 30% to a maximum of 100% resulting in a mean of 87.3%. The allocations in the second scenario, why they would adopt new tools, equipment, and appropriate technology for their farming operations in the future, range from a minimum of 25% to a maximum of 100%, with a mean of 78.4%, which scores 10% lower than in the first scenario.

Table 7. Farmers’ Motivations When Making Important Investments.

Motivations	Number		Min.		Max.		Mean		Standard Deviation
	S1	S2	S1	S2	S1	S2	S1	S2	S1	S2
To save money and time - Own consumption	26	35	30	25	100	100	87.3	78.4	19.1	19.4
To show to my friends - Self-respect	2	11	5	5	10	80	7.5	18.2	3.5	21.2
I always should use the best - Good-will	11	26	10	10	50	100	25.5	23.9	13.1	18.2
To join a community of efficient agricultural producers – Belonging	1	1	10	10	10	10	10	10	0	0
To support the business that sells it – Sharing	1	9	25	5	25	25	25	13.9	0	7.4

Source: Estimated by the authors.; S1 (Scenario 1); S2 (Scenario 2)

 

A different pattern was found in the participant farmers’ allocations for the second motive, self-respect. The importance of the internal validation as their motive to operate tools and agricultural equipment is relatively low in both scenarios. In the first scenario farmers’ allocations range from a minimum of 5% to a maximum of 10% with a mean of 7.5%. The mean allocation for this motive increased almost 10% in the second scenario to 18.2% with a range of 75% between a minimum of 5% to a maximum of 80%.

The difference of means for farmers’ allocations on the two scenarios of the third motive, good-will, is very low (1.6%), although the result for this motive ranks second in importance among the five social capital motives. In the first scenario, allocations range from a minimum of 10% to a maximum of 50% with a mean of 25.5%. The range for the second scenario goes from a minimum of 10% to a maximum of 100% with a mean of 23.9%. The relative high marks of this motive show the importance that participant farmers give to external validation when operating or buying appropriate agricultural tools and equipment for their farming operations.

The results related to the fourth motive, belonging, show that participant farmers allocated, in both scenarios, very low marks to this motive. In addition, very few of them, 1 in the first and 1 in the second scenario, made allocations equal to10 for this motive. These results show that these farmers do not sense they belong to a community of efficient and productive farmers and tend to operate within a small, close knit social group of other Latino farmers.

Finally, results for the fifth social capital motive, sharing, show a similar pattern to that of the fourth motive. Only one farmer made an allocation in the first scenario and 9 in the second.  Farmers’ allocations in the second scenario range from a minimum of 5% to a maximum of 25% with a mean equal to 13.9%, which is the lowest among the means of the fourth top social capital motives.

In summary, participant farmers consider the own consumption motive as their priority, with lower scores than expected in profit maximizing neoclassical economic theory. We observe a decline of almost 10% in this motive when farmers are trying to buy new equipment for their operations. The second most important social capital motive for operating and/or owning appropriate agricultural tools and equipment is related to their external validation, which increases when they plan to buy new equipment. Internal validation becomes the third important motive for operating or buying agricultural equipment with practically similar scores in both scenarios. Latino farmers do not consider themselves members of a community of efficient and productive farmers; usually they are isolated within a network of farmers working in similar conditions with limited access to information about production techniques, market opportunities, product prices, and access to credit. All these constitute constraints for their operations. Since they do not consider themselves members of the large community of farmers, their information channels are very constrained, with only a few of them willing to share their agricultural knowledge and opportunities with other non-Latino farmers and to support local businesses and their employees that sell tools and equipment for their operations.

Statistical analysis compares the results of each of the social capital motives in both scenarios using a T-test. We find that the difference of means for the first motive, own consumption, is highly significant at .000, for the second motive, self-respect or internal validation, the difference of means is significant at .018, and for the third motive, goodwill, or external validation, is also significant at .000. We did not estimate T-test for the last two motives due to the low number of farmers who made allocations to these motives.

Additional Statistical Analyses
Further statistical analysis yielded several statistically significant relationships between some of the motives and other variables. For example, there is a negative correlation (-.422 significant at .032) between employing equipment to save time and money with years working in agriculture, with farmers with many years working in agriculture not concerned about using equipment to save money and time. On the other hand, we found a positive correlation (+.644 significant at .033) between buying equipment and years working in agriculture; the longer participant farmers have their farms, the more willing they are to buy equipment to save money and make their agricultural production more productive and sustainable.

Another significant positive correlation (significant at .02) exists between the second motive, internal validation, and the farmers’ ages; the older the farmers, the more their willingness to buy equipment to show friends and family members that they updated their farming practices. On the other hand, we found a statistically significant negative correlation (0.01) between the farmers’ internal validation motive related to the tools and equipment they are currently employing and years living in the community; the longer they have lived in the community, the less they are interested in showing these tools and equipment to their relatives and friends. When these farmers are planning to buy new equipment there is a negative correlation (significant at .021) between their own consumption motive and the years living in the current community. The longer they live in their communities the less interested they are in buying new equipment to make their farming operations more efficient.

Further statistical analysis found a negative correlation (significant at .06) between the view that farmers have about the type of farmers they are and the number of years they have been working in agriculture. The longer they have been farming, the more efficient they consider their agricultural operations (1 = efficient and successful; 2 = average; 3 = not successful, disorganized, and poor). Additional results show that farmers with high educational attainment are working more as part-time or weekend farmers (significant at .001). Most of these farmers usually have a job outside their farms and consider farming a hobby or as an extra source of income. Finally, we found a strong negative correlation (significant at .000) between the years farmers have been working in agriculture and the type of farmers they are (1 = full time; 2 = part-time; 3 = weekends), with the longer the farmer is working in agriculture, the higher the likelihood that he/she is a full-time farmer.

Conclusion
This exploratory study provides an examination of the relative importance of social capital motives for Latino farmers relative to using and/or buying tools and equipment for their farming operations. Because of the limited resources associated with the small size of their farms and the limited financial capital for their operations compared with farmers who have been farming for many years on farms with large acreages, Latino farmers have very limited networks from which they can obtain advice and information necessary for improving their farming operations. It is interesting to note, according to the results presented above, that social media (i.e., Facebook, YouTube, and others) are a main source of information these farmers have for their farming operations, followed by close family members, which they consider as the most reliable source of information.

Latino farmers with small operations in our study are mainly using tools and small equipment on their farms and obtain external information on how to operate this equipment from other farmers and friends and appeal to social media sources. Most of them are considering the adoption of appropriate technology, which includes buying or building a greenhouse.

The analysis of the social capital motives shows that since tools and farming equipment are physical goods, farmers allocate relatively high percentages to the own consumption motive (selfishness, but in this case practical), higher when they consider the current use of tools and equipment (87.3%) compared to the case when they are planning to buy this equipment (78.4%), although in both cases lower than the 95% stated by the neoclassical economic model.

An inverse trend was found in the examination of farmers’ percent allocations for the second motive, self-respect (internal validation), 7.5% when using the equipment versus 18.2% when willing to buy the equipment. The third motive, good-will (external validation), resulted with the second highest marks with 25.5% for the first scenario and 23.9% for the second scenario.  The other two motives, belonging and sharing, received percent allocations from very few farmers.

T-tests were run to compare the means for each of the two scenarios listed above, the results show that the mean percent allocations are equal in both scenarios. Additional statistical analysis found important correlations between social capital motives and other variables such as years working in agriculture, age, years living in the community, and between educational attainment and the perception farmers have about their farming operations and conditions.

Given the importance of the results obtained in this exploratory study, we expect to expand the study to other groups of socially disadvantaged, ethno-racial, female operated, low resourced, and veteran farmers. A multi-state study could also help make comparisons among all these groups and clarify their perceptions on the role of social capital and motives in the adoption of new and appropriate technology by all these farmers.

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