Common Knowledge or Common Sense? Identifying Systematic Misconceptions of Animal Agriculture and Food Familiarity in Higher Education Individuals

Abstract

Knowledge gaps in the context of agriculture contribute to mistrust and negative worldviews of the animal agriculture sector. The purpose of this quasi-experimental survey study was to quantify the perceived connection of participants to food production, assess their understanding, knowledge, and perceptions of animal agriculture (AA) and food production (FP), and determine predictors that may have contributed to their knowledge and perceptions of animal food production. The convenience sample for this study was a southeastern land grant institution, n = 265. An Animal Agricultural Knowledge and Perceptions Questionnaire and a Food Familiarity Index Questionnaire were included in the electronic survey. The study reported that nearly 50% of the participants showed negative perceptions of animal agriculture (p < 0.05) regardless of the food familiarity scores. Natural and self-identified demographic characteristics impacted the knowledge and perceptions of AA including gender, ethnicity, dietary preference, perceived connection to FP, and affiliation with the College of Agriculture (p < 0.05). By identifying topics and ideas that are of great concern and little understanding, future perceptions and purchase intentions can be improved. Additional research should replicate the findings with broader question pools and other demographic groups to identify areas that need improvement in agriculture communication efforts designed to dispel misinformation.

1. Introduction

Within a complex media-driven infosphere, communicators tied to agriculture attempt to reach and inform consumers, but their knowledge can be influenced by diverse worldviews [1]. A worldview is a set of beliefs, ideals, narratives, and expectations about the world that we live in [2]. These beliefs guide all our decisions and actions. In general, a consumer’s attitude tends to improve as they learn more about a topic or issue [3]. In an agricultural context, knowledge deficits also lead to distrust in the agricultural industry. Worldviews or perceptions and trust in agriculture are often formed based on prior household beliefs, exposure to media content, and generational beliefs [3,4]. The public perception of agriculture is vital to its success as an industry [5]. In this study, the term negative worldview does not imply a pessimistic outlook in general, but rather refers to orientations toward animal agriculture that are predominantly critical or skeptical. Such views often arise from ethical or environmental concerns, limited or fragmented exposure to agricultural practices, or cultural and historical associations with farming and food systems. A negative worldview, in this context, reflects less favorable perceptions of livestock production shaped by informational, social, and identity-based factors rather than by direct experience alone. In 2020, 54.1% of consumers focused on the environmental contributions of animal agriculture (AA), 82% focused on health considerations, 58.4% on sustainability potential, and 54.3% focused on animal welfare and the ethics of food production (FP) [6]. Some media use less credible sources of information about agriculture and are vectors to communicate risk, or the uncertainty of facts, and often mislead audiences [7,8]. Ultimately, the perceptions and concerns of consumers affect the supply and demand of products and affect farming practices or policy actions [9,10,11,12]. The disconnect between knowledge of agricultural production among consumers and access to misinformation through the media further distance consumers from their food [3].

Today, people are less able to comprehend the intricate agricultural systems and industries that exist today compared with generations ago [13]. This, together with additional elements to be examined in our study, has widened the knowledge gap between the two sides of FP. Direct on-farm producers, who make up 1.3% of U.S. employment, along with those working in the agriculture and food industries, account for 10.5% of all U.S. employment [14]. These individuals compose the agricultural community, where most consumers are not directly involved in agriculture [15].

Although disconnected, many demographic factors influence how varied populations perceive FP and AA. These characteristics are factors of ethical consumerism or the need and interest to know how food is produced [16]. Ethical consumerism considers animal welfare, low environmental impact, labor conditions, and other practices of producing food [16]. Specifically, women, young consumers, those who avoid consuming meat, highly educated individuals, and people of urban backgrounds tend to have more negative views about modern farming and its effects on health and the environment [17,18,19]. Factors that shape these perceptions amongst diverse populations are also derived from where people obtain their information about food or agriculture. In general, consumers use food labels to evaluate and select healthy foods [20]. Beyond food label literacy, consumers only seek out information about how food is produced if they are motivated by personal or news events [21]. In other words, it can be challenging to pinpoint the sources to which consumers turn to learn more about their food. However, trust has been extensively evaluated in different agricultural resources [22]. A global infodemic has decreased trust in all news sources [23]. Government agencies, extension programs, animal rights organizations, and some agriculture and allied industry organizations can be identified by the public [22]. However, trust in each of these varies, since non-profit organizations are perceived as highly trustworthy, apart from the People for the Ethical Treatment of Animals (PETA) campaigns [22].

By understanding the perceptions and relative knowledge of consumers as well as sources of agricultural information, animal and related industries can pinpoint weaknesses in communication efforts. Identifying topics and concepts of high concern and low understanding can improve future perceptions and buying intentions.

1.1. Conceptual Framework

Scientific knowledge and understanding among citizens are strongly tied to educational exposure of climate moderation, vaccines, bioengineered foods, and more [24,25]. A study from [26] described science as an epistemology, or theories of knowledge, which arises from personal beliefs. Personal beliefs, in other words, reflect what an individual perceives as the truth [27]. Based on the literature and the design of this research study, the conceptual framework was adapted from the foci of connectivism, knowledge gap theory, and systems thinking. In contrast to cognitivism and constructivism, connectivism has no restrictions on where learning occurs, as it embraces the concept that information is always accessible on the Internet and the media [28,29,30]. Negative exposure to the media along with other factors contributes to the gap in understanding how food is produced among agricultural and non-agricultural tribes, which is reflective of knowledge gap theory. Knowledge gap theory or hypothesis explains the lack of knowledge, understanding, or curiosity found within the public or society [31]. Typically, knowledge gap theory is dependent upon socioeconomic class divisions of the public or educational classes [32], however, the model can be applied to connections to agriculture like the current study. Systems thinking is a dynamic epistemological approach to observe how the world works [33,34]. To adapt to the turnover of academia and produce competent, communicative graduates, those found in higher education are cultured in systems thinking methodologies [35]. A Swedish organization called Gapminder aims to educate about misconceptions of the world by leading the audience to approach new information from a fact-based worldview [36,37]. This fact-based worldview includes mitigating instincts such as ‘The Size Instinct’ or ‘The Destiny Instinct’, otherwise known as our intrinsic biases, by implementing systems thinking [36,38].

In this study, we draw on connectivism, knowledge gap theory, and systems thinking as complementary frameworks that together provide a holistic lens for understanding misconceptions about animal agriculture. In this sense, the three frameworks can be thought of as strands of a web that together form the structure of understanding. Connectivism represents the threads themselves, the multiple links through which individuals encounter and exchange information in both digital and social spaces. Knowledge gap theory helps explain why some areas of the web are densely woven while others are sparse, reflecting unequal access to resources and background knowledge. Systems thinking shifts the focus to the integrity of the web as a whole, recognizing that its strength and shape depend on the interconnections between threads rather than any single strand. Misconceptions about agriculture, therefore, are not loose ends that can be tied up in isolation, but points of tension that emerge from the way the web is patterned. By viewing these theories as complementary strands, the framework highlights how information flows, why disparities persist, and how understanding can only be strengthened by addressing the web in its entirety. In combination, these theories guide this study by explaining not only how people encounter agricultural information, but also why gaps in understanding emerge and how a systems-oriented approach can help bridge these gaps.

1.2. Purpose and Research Question

This descriptive and quasi-experimental study describes individuals at a southeastern university and their perceived involvement with food production, where they obtain their information about food and agriculture, animal agriculture knowledge, and perceptions, and whether there are demographic characteristics that influence their agricultural knowledge. The following research questions guided this study:

• How do participants describe their connection to food production and where do they obtain their information about food production?
• Do participants have greater knowledge and/or better perceptions of animal welfare, health and nutrition, or environmental sustainability compared with food familiarity?
• Which demographic predictors influence the level of knowledge and perceptions of food production knowledge and perceptions?

2. Materials and Methods

The research design was descriptive quasi-experimental survey research using convenience sampling to fulfill the stated research questions and purpose. The target population was undergraduate students, graduate students, and faculty at a southeastern land grant institution. Study elements including survey questionnaires and all Institutional Review Board (IRB) documentation were submitted and approved in October 2022 under protocol #22-481 EX 2210. The approval verifies that all participants were at low risk and that anonymity was maintained. Data were collected over a period of three months beginning November 2022 and concluding January 2023 using a researcher-developed, expert-validated online survey through the Qualtrics Survey platform (Version 2022, Provo, UT, USA). The survey was distributed to participants using the appropriate methodologies described by [39], since recruitment emails were delivered through university-issued emails containing the survey link. The survey consisted of the following: an information letter, a multiple-choice questionnaire on animal agriculture knowledge and perceptions (AAKPQ), a 10-point Likert-type scale Food Familiarity Index (FFI), and demographic questions. The convenience sample resulted in 324 responses, of which 265 remained after removing the unfinished questionnaires.

2.1. Participants

The demographic characteristics of the participants were separated by natural and self-identified characteristics. Natural characteristics included descriptors that are natural to the participant, which involves gender, age, ethnicity, and upbringing. Self-identified characteristics included descriptors that reflect the choices made by the individual in his life such as academic role, dietary preference, affiliation with the College of Agriculture, and perceived connection to FP.

Table 1. Natural and self-identified demographic characteristics of participants ^z.

Natural Characteristics	n	%	Self-Identified Characteristics	n	%
Gender			Academic Role
Female	120	45.3	Undergraduate student	81	30.6
Male	139	52.5	Graduate student	115	43.4
Other	6	2.2	Faculty	69	26.0
Age y			Study/Work in College of
Generation Z (18–26)	90	34.0	Agriculture
Millennial (27–42)	115	43.4	Yes	27	10.2
Generation X (43–59)	42	15.8	No	238	89.8
Baby Boomers (60–77)	18	6.8
Ethnicity			Dietary Preference
Caucasian/White	201	75.8	Omnivorous	237	89.4
Hispanic/Latino	9	3.4	Flexitarian	9	3.4
African American/Black	11	4.2	Pescatarian	9	3.4
Asian/Pacific Islander	31	11.7	Vegetarian	6	2.3
Mixed/Other	13	4.9	Other	4	1.5
Upbringing			FFI Group x
Urban	40	15.1	Low	30	11.3
Suburban	163	61.5	Medium	182	68.7
Rural	62	23.4	High	53	20.0

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y Generations defined by Research Guides at [40]. ^x Low contained scores 0–40, medium contained scores 41–79, and high contained scores 80–120.

presents a summary of the demographic results. There were more males than females (52.5%) and millennials (43.4%) for the participant body. The participants were mostly White (75.8%) and were raised in the suburbs (61.5%). Most participants were graduate students (43.4%) that did not work or study at the College of Agriculture (89.8%) and consumed meat (89.4%).

2.2. Animal Agriculture Knowledge and Perceptions Questionnaire (AAKPQ)

The multiple choice AAKPQ contained twelve questions sourced from evidence-based, public resources regarding knowledge of animal agriculture, specifically topics of animal welfare, nutrition, and the environment. Each question was presented with three possible answers weighted in a positive, negative, or extremely negative perception weight, as described in

Table 2. Questions and answer choices presented to the participants in the Animal Agriculture Knowledge and Perception Questionnaire ^z.

Welfare & Wellness y,x,w

1. How old are veal calves when they are harvested? 2 months old 4 months old 6 months old *

2. How much square footage is provided per beef animal in a feed yard? 50–100 sq ft (comparable to a walk-in closet, roughly 8ft × 8ft) 100–150 sq ft (comparable to a small bedroom, roughly 12ft × 12ft) 150–250 sq ft (comparable to a large bedroom, roughly 15ft × 15ft) *

3. Most animal-sourced proteins are sourced from. Factory farms Family-owned farms * Corporate-owned operations

4. Why are dairy calves removed from their mothers earlier than beef calves? Calves get sick soon after birth To keep the udder undamaged * The stress makes them produce more milk

Diet & Health y,v

1. About how many vitamins and minerals are provided in 3.5 ounces of beef? 5–7 8–10 More than 10 *

2. When compared in ounce equivalents, plant-sourced proteins are in protein content compared with animal-sourced. Greater Equal Lesser *

3. One 8-ounce serving of milk has the same amount of calcium compared with how many cups of kale? 1 cup About 3 cups More than 5 cups *

4. According to the CDC, there has been a reduction in E. coli related reports derived from ground beef of. 25% 60% 90% *

Environment and Sustainability v

1. If 10% (39 million people) of the U.S. population were to go vegan, how much of an environmental impact will this have on the carbon footprint? Reduced 0.26% * Reduced 2.6% Reduced 5.5%

2. What percent of water in a beef animal’s diet is not provided by rainwater? 6% * 15% 38%

3. What percent of U.S. greenhouse gas emissions are attributed to livestock? 3.9% * 14.5% 20.7%

4. What percent of animal-sourced foods end up in landfills? 10–20% * 30–40% Higher than 40%

* Denotes correct answer choice. ^z Modeled after [41] ^y [42]. ^x [43]. ^w [44] ^v [45]

. If a person has to choose at random, a score of four questions answered correctly (33%) is likely, whereas scores below 4 reflect a negative perception, and scores 5 and above reflect a positive perception. Questions and answers were randomized to combat survey bias and fatigue.

2.3. Food Familiarity Index (FFI)

The FFI is a 12-item, 10-point Likert-type scale questionnaire developed by researchers to measure and then summarize a person’s perceived relationship to FP while considering the participants’ interests in the origins of their food, perceived familiarity with FP, and other factors [46]. Current FFI questions were modified from another questionnaire to better fit a general involvement with FP [47,48]. Participants were asked to rate their level of agreement with 12 statements where 0 = Strongly Disagree, 5 = Neutral, and 10 = Strongly Agree. An additive score described that the participants were connected to their food at a low, medium, or high level with scores of 0–40, 41–79, and 80–120, respectively. Questions are presented in

Table 3. Food Familiarity Index (FFI) questions presented to the students and faculty.

FFI Item
I go out of my way to accommodate purchase of preferred foods.
I am emotionally connected to procedures and conditions in which food is produced/grown.
I would say I know something about how a majority of the food I eat is raised.
I would devote time and energy to learning about different food systems and current agricultural practices used in food production.
When food is a topic of conversation, I am willing to share my knowledge about how food is grown/produced with others.
I devote time to growing my own food and/or food for others (people or animals) to consume.
I would be concerned if I was not able to study and learn about food and agriculture.
I support agriculture and food production systems.
I make buying decisions based on how and/or where a specific food item was produced.
I seek out others who also know or care about where their food comes from.
I buy goods based on the nutritional composition and health implication.
I am familiar with the safety, quality, and marketing factors of food.

. Questions and answers within this section of the survey were randomized to combat survey bias and fatigue.

2.4. Data Collection and Statistical Analysis

Quantitative data were analyzed using SPSS statistical software (Version 28). Descriptive statistics were calculated to summarize the demographic information and FFI scores. Frequencies and chi-square tests of independence were computed to analyze resources identified in information seeking behavior and AAKPQ scores. One-way ANOVAs were performed to compare the effect of natural and self-identified demographic predictors on AAKPQ scores. Multiple alpha and significance levels were defined for this study. The first, α1 = 0.05, was defined so that differences were declared when p < 0.05. The second, α2 = 0.10, was defined so that trending differences among responses were declared when 0.05 ≤ p < 0.10. The exact p-values are presented to allow the reader to develop independent interpretations. The inventory of knowledge, perception, and food familiarity was found to be reliable (27 items; α = 0.788).

3. Results

3.1. Connection to Food Production

With completion of the FFI, cumulative scoring placed an individual involved with FP in three classifications: low, medium, and high. The largest body of participants was those who identified themselves as involved in agriculture or FP on a medium level. In

Table 4. Frequencies and percentages of Food Familiarity Index (FFI) score placement ^z.

FFI Score y	n	%	M x	SD w
Low	30	11.3	30.38	6.87
Medium	182	68.7	61.71	11.16
High	53	20.0	93.85	8.87

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y Low scores = 0–40, medium scores = 41–79, high scores = 80–120. ^x M = mean ^w SD = standard deviation.

, descriptive comparisons are shown among each level. Of the 12 questions, 120 possible points could be awarded according to the involvement in FP, and the mean for the medium group (M = 61.71) was almost exactly average for the FFI.

3.2. Resourcing Information Regarding Food Production and Agriculture

Frequencies for the resources that the participants used to find information about FP and agriculture are presented in

Table 5. Sources of information about food production and agriculture and food production selected by the participants ^z.

Resources for Information Seeking	n	%
I ask farmers or other people who work in the industry.	52	7.1
I actively seek out information about agriculture through “Googling.”	53	7.2
I ask my parents/guardians/family.	50	6.8
I read/learn about agriculture across social media.	131	17.8
I read/learn about agriculture in the news.	137	18.6
I actively seek out articles regarding agriculture.	38	5.2
I read food labels.	163	22.1
I hear about it in classroom settings.	48	6.5
I read signs/billboards/other public landmarks.	48	6.5
Other—I don’t seek out agriculture.	7	0.9
Other—Specific resources (shows, extension, etc.).	4	0.5
Other—Other people (friends, professors, significant other).	6	0.8

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production.

. The most common sources of information on agriculture identified by the participants were food labels (22.1%), news resources (18.6%), and social networks (17.8%). More people were expected to indicate that they did not seek agricultural resources, but our sample revealed that very few (0.9%) did not actively seek this information. Very few participants identified specific titles of resources (0.5%) including Alabama extension articles, RFD-TV, Clarkson’s Farm, and others.

3.3. Knowledge and Perceptions of Food Production

To determine whether the participants had a negative perception of the AA industry, the frequencies of the total AAKPQ scores were computed and are presented in

Table 6. Percent correct scores for Animal Agriculture Knowledge and Perceptions Questionnaire based on Food Familiarity Index (FFI) group placements ^z.

FFI Group
Score	Low y	Medium x	High w	Full Sample v	Cumulative %
	%	%	%	%
0	3.3	1.6	0.0	1.5	1.5
1	16.7	8.8	5.7	9.1	10.6
2	3.3	22.5	15.1	18.9	29.4
3	33.3	16.5	22.6	19.6	49.1
4	26.7	12.6	15.1	14.7	63.8
5	3.3	9.3	5.7	7.9	71.7
6	6.7	8.8	7.5	8.3	80.0
7	0.0	6.0	5.7	5.3	85.3
8	0.0	6.6	13.2	7.2	92.5
9	6.7	2.2	1.9	2.6	95.1
10	0.0	2.2	0.0	1.5	96.6
11	0.0	2.7	3.8	2.6	99.2
12	0.0	0.0	3.8	0.8	100.0

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y n = 30; Low scores = 0–40, ^x n = 182; Medium scores = 41–79, ^w n = 53; High scores = 80–120, ^v n = 265.

. The participants sampled in this study demonstrated a largely negative worldview of AA overall, as 49.1% of the participants produced an AAKPQ score of three or less. These participants selected answer choices that reflected a negative perception of AA. Worldview estimations were also compared on a food familiarity basis. A score of 0 or the most negative worldview was demonstrated by the low FFI group, where 3.3% of individuals connected to FP on a low level scored 0 correct on the AAKPQ. No individuals in the high FFI group scored a 0 on the AAKPQ. Of the 49.1% of participants that demonstrated a negative worldview of AA, the low FFI group showed the greatest percentage of negative worldviews through a score of 3 or less (56.6%) compared with the medium (49.1%) and high (43.4%) FFI groups. A score of 5 or more indicates a more positive worldview, where 36.2% of participants demonstrated a positive worldview toward AA. The high FFI group had the highest percentage of positive worldviews (41.6%) in comparison to the low (33.4%) and medium (37.8%) FFI groups.

Participants were found to have varying levels of knowledge and perceptions about animal welfare, diet, and health of animal foods, and the environmental or sustainability impacts of AA. Specifically, the welfare category was identified as the category with the most correct answers, and the environment category as having the least correct answers from the participants. The diet category was shown to have the most completed section, where participants correctly answered all four questions in the diet category (n = 17), in contrast to the welfare and environment categories (n = 14). As shown in

Table 7. Frequencies and percentages of correct scores in welfare, diet, and environment categories based on Food Familiarity Index (FFI) group placement ^z.

Question Category and FFI Group	Number Correct
	0		1		2		3		4		Total
	n	%	n	%	n	%	n	%	n	%	n	%
Welfare y
Low	7	19.4	11	11.3	11	12.9	1	3.0	0	0.0	30	11.3
Medium	25	69.4	70	72.2	56	65.9	23	69.7	8	57.1	182	68.7
High	4	11.1	16	16.5	18	21.2	9	27.3	6	42.9	53	20.0
Diet x
Low	10	15.4	11	12.4	4	6.5	4	12.5	1	5.9	30	11.3
Medium	42	64.6	62	69.7	44	71.0	22	68.8	12	70.6	182	68.7
High	13	20.0	16	18.0	14	22.6	6	18.8	4	23.5	53	20.0
Environment w
Low	10	11.2	12	14.8	5	9.6	1	3.4	2	14.3	30	11.3
Medium	63	70.8	56	69.1	35	67.3	20	69.0	8	57.1	182	68.7
High	16	18.0	13	16.0	12	23.1	8	27.6	4	28.6	53	20.0

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y χ²(15) = 279.09, p < 0.001, V = 0.591 ^x χ²(15) = 269.57, p < 0.001, V = 0.581 ^w χ²(15) = 271.48, p < 0.001, V = 0.583.

, the chi-square test of independence revealed a relationship between FFI group placement and animal welfare or wellness knowledge and perceptions with a large effect [χ²(15) = 279.09, p < 0.001, V = 0.59]. Similarly, an association was found for FFI score and diet and health concepts of animal-derived foods with a large effect [χ²(15) = 269.57, p < 0.001, V = 0.58]. A clear association and a large effect were discovered between the FFI score and the notions of the environmental impacts of AA practices [χ²(15) = 271.48, p < 0.001, V = 0.58].

3.4. Predictors of Food Production Knowledge and Perceptions

To better understand what factors are important in forming perceptions about AA, demographic predictors were analyzed for AA category scores and general AAKPQ scores based on natural and self-identified levels.

3.4.1. Natural Characteristics: Gender, Ethnicity, Upbringing, and Age

The ANOVA results for the effects of natural demographic characteristics are presented in

Table 8. ANOVA summary table of Animal Agriculture Production Knowledge and Perceptions by gender and ethnicity ^z.

	SS y	df	MS	F	η2	p-Value
Gender
Between groups	86.937	2	43.468	6.453 *	0.047	0.002
Within groups	1757.124	262	6.707
Total	1844.060	264
Ethnicity
Between groups	84.157	4	21.039	4.663 *	0.046	0.016
Within groups	1759.903	260	6.769
Total	1844.060	264

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y SS = sum of squares; df = degrees of freedom; MS = mean square; F = F-statistic; η² = eta-square. * Denotes Welch’s F-statistic reported.

. There was an effect of gender on knowledge and perceptions at the p < 0.05 level for the three gender groups with a small to medium effect [F(2, 262) = 6.45, p = 0.002, η² = 0.05]. Tukey’s HSD test for multiple comparisons revealed that women had improved AA knowledge and perceptions (M = 2.92, SD = 0.27) compared with men (M = 2.31, SD = 0.09), but not between other genders. The ANOVA findings showed that there was a significant influence of ethnicity on knowledge and perceptions [F(4, 260) = 4.66, p = 0.016, η² = 0.05]. Post hoc comparisons using Tukey HSD test indicated that the mean score for White participants (M = 2.79, SD = 0.20) was significantly higher than the mean scores for Asian participants (M = 1.87, SD = 0.33). There were no differences between other ethnicities. There were no differences for upbringing [F(2, 262) = 1.76, p = 0.17], and no differences were observed between different age groups or generations of participants [F(3, 261) = 0.09, p = 0.97].

3.4.2. Self-Identified Characteristics: Dietary Preference, Academic Role, FFI Group, and College

The one-way between subjects ANOVA results for the effects of self-identified demographic characteristics are presented in

Table 9. ANOVA summary table of food production knowledge and perceptions by upbringing and meat consumption habits ^z.

	SS y	df	MS	F	η2	p-Value
Dietary Preference
Between groups	72.419	4	18.105	2.657	0.039	0.033
Within groups	1771.642	260	6.814
Total	1844.060	264
FFI Group
Between groups	39.040	2	19.520	2.833	0.021	0.061
Within groups	1805.020	262	6.889
Total	1844.060	264
College
Between groups	26.133	1	26.133	3.781	0.014	0.053
Within groups	1817.927	263	6.912
Total	1844.060	264

^z Survey responses utilizing Qualtrics (n = 265) for student and faculty knowledge and perceptions of animal agriculture and connection to food production. ^y SS = sum of squares; df = degrees of freedom; MS = mean square; F = F-statistic; η² = eta-square.

. The ANOVA results report that the knowledge and perceptions of AA were affected by the selection of the diet of the participants with a small effect [F(4, 260) = 2.66, p = 0.03, η² = 0.04]. Post hoc comparisons using the Tukey HSD test indicated that the mean score for omnivores (M = 4.41, SD = 2.64) was different from pescatarians (M = 2.11, SD = 4.86). No other differences were observed between other eating patterns. The ANOVA results for academic role, undergraduate student, graduate student, or faculty revealed that there was no significant effect on AA knowledge or perceptions [F(2, 262) = 0.83, p = 0.44].

Different FFI groups tended to respond differently to knowledge and perceptions of food production, as presented in Table 9 (p = 0.06). Participants in the high-scoring group (M = 4.87, SD = 2.91) tended to score higher on the AAKPQ (p = 0.05) and have better perceptions than the low-scoring group (M = 3.47, SD = 2.08). A participant’s affiliation with the College of Agriculture tended to have different worldviews of AA than those who were not affiliated with the College of Agriculture (p = 0.05). Participants who worked or studied at the College of Agriculture tended to score better on the AAKPQ (M = 5.19, SD = 2.69) than participants outside of the College of Agriculture (M = 4.15, SD = 2.62).

4. Discussion

As students encounter information about animal issues and other topics, they are likely to acquire opinions and incorporate that information either positively or negatively into their own knowledge structures [49]. This study sought to describe knowledge and perceptions of animal agriculture, how people are involved in food production, where information about agriculture is sourced, and whether higher education roles or perceived connection to food production influenced the perceptions of animal agriculture production.

Understanding where people obtain their information about food and agriculture is crucial to understanding how members of industry can communicate with consumers. Social media was the third most identified source of agricultural and food production information (Table 5). This study revealed that even among individuals who turned to social media for agricultural information, personal connection to food origins remained weak. This tension illustrates how digital exposure can increase awareness but does not necessarily deepen understanding or foster meaningful connection.

Knowledge gaps in the context of agriculture also contribute to mistrust of the agricultural sector. Participants were generally less knowledgeable about animal agriculture, and this lack of knowledge often coincided with negative perceptions (Table 6 and Table 7). Almost half of the total responses reflected poor perceptions of the industry (Table 6). These poor perceptions could stem from repeated exposure to misinformation, preconceived biases, or the influence of the “knowledge gap” range [28,50,51]. Importantly, the gap was not uniform: participants demonstrated a higher knowledge on welfare and diet compared with environmental issues, even though environmental concerns dominate public discourse. This disconnect suggests that large-scale environmental data may be less personally relatable, easily misrepresented, or too abstract for individuals to contextualize [8,52]. The “meat paradox” may also explain why participants had greater welfare knowledge; individuals reconcile their consumption behaviors with ethical concerns by seeking more information on welfare while still consuming meat [53,54]. No participants scored a 0 on the FFI, meaning that none demonstrated the most extreme negative orientation toward animal agriculture (i.e., answering all items incorrectly) for respondents in the highly agriculturally affiliated group. Notably, these individuals demonstrated at least some accurate knowledge or more balanced perceptions. These findings suggest that greater exposure to or experience with agricultural systems may buffer against extreme misconceptions, even as the levels of skepticism or critical orientation vary across individuals.

Demographic characteristics also influenced knowledge and perceptions, though often in complex and sometimes unexpected ways. Gender, for example, showed results that differed from prior literature (Table 8). While women are often reported as more attentive to welfare and health-related concerns [19,20,55,56,57], in this study, they demonstrated more positive perceptions of animal food production than anticipated. This finding complicates assumptions that greater awareness necessarily leads to more critical views, suggesting instead that awareness may also foster an appreciation of agricultural practices when information is contextualized. Interestingly, while previous research frequently notes that women are more cautious toward livestock production and more likely to raise concerns about animal welfare and environmental impacts [19,20,55,56,57], our findings indicated that women in this study expressed somewhat more favorable perceptions than men or other gender groups. Several factors may help explain this divergence. Because our sample was drawn entirely from higher education populations, it is possible that women respondents—particularly those with academic or professional exposure to agriculture and life sciences—had greater familiarity with production systems, thereby shaping more balanced perspectives. Cultural and regional influences may also have contributed, as perceptions of animal agriculture often vary across social and demographic contexts. In addition, self-selection may have played a role, with women who hold more positive views of livestock production potentially being more likely to participate in the survey. Finally, evolving public conversations around sustainability and welfare standards may be influencing how women perceive the livestock industry compared with earlier studies. While these explanations provide potential insights, we caution against broad generalizations and recommend that future research explore gender-related differences across more diverse populations to assess whether this outcome reflects a localized phenomenon or a wider shift.

Patterns across racial groups were less pronounced but point toward broader sociological dimensions. Some minority groups may have more complex relationships with agriculture due to historical and cultural associations with labor-intensive, economically marginal, or exploitative practices [58]. These findings highlight that perceptions of agriculture are shaped not only by knowledge, but also by cultural identities and social histories [59].

Similarly, upbringing and age—often assumed to predict perceptions—showed little effect in this study. This suggests that contemporary knowledge and attitudes are increasingly influenced by globalized media and social networks, which may diminish the influence of traditional background factors [20,60]. Self-identified demographics, however, such as dietary preference, were strongly associated with outcomes. Omnivores and meat-eaters demonstrated greater knowledge and more favorable perceptions, while individuals who avoided meat (vegetarians and vegans) consistently reported more critical views. This finding is consistent with prior literature indicating that meat avoidance is often grounded in ethical and environmental considerations [18,20,61].

Academic role did not correspond to differences in knowledge, likely because the study population was limited to higher education participants. However, familiarity with food production and affiliation with agriculture-related disciplines was consistently associated with stronger knowledge and more positive perceptions. This supports the utility of the Food Familiarity Index [46] as a research tool and underscores the influence of disciplinary exposure. At the same time, demographic shifts within agricultural colleges—where fewer students enter with prior farm backgrounds—pose new challenges for agricultural literacy [62,63,64,65]. These students may begin with less experiential grounding, which could shape both their perceptions and their confidence in communicating agricultural issues with broader audiences.

Finally, the findings underscore the role of social contagion in shaping public discourse regarding animal agriculture. Misinformation can spread rapidly through networks, with tipping points amplified by social ties and demographic resonance [66,67,68,69]. This suggests that improving agricultural literacy requires more than correcting inaccuracies; it requires leveraging similar mechanisms of social contagion to amplify accurate, contextualized, and relatable information. Taken together, these results suggest that knowledge, perceptions, and demographic factors interact in complex and sometimes counterintuitive ways. Addressing misconceptions about animal agriculture will require nuanced communication strategies that account not only for what people know, but also for how knowledge intersects with identity, media exposure, and social context.

There were a few limitations to our study including the nonresponse error. To minimize the nonresponse error, early and late responses were compared [70,71]. No significant differences were found between early and late responses considering all demographic variables. A second limitation was that the respondents would answer aimlessly, potentially skewing the results and decreasing the validity of the study. Finally, if participants answered dishonestly, especially regarding their perceived connection to their food, this could also skew the results. A set of more advanced (deeper knowledge) questions within the AAKPQ could have revealed greater separation between the low, medium, and high FFI.

5. Conclusions

Drawing on connectivism, knowledge gap theory, and systems thinking, this study highlights the complexity of how individuals in higher education encounter, process, and interpret information about animal agriculture. Research question 1 employed connectivism, suggesting that learners build networks of understanding from diverse sources, which helps explain why social media can function as both a conduit of misinformation and as a potential channel for accurate, constructive messaging. Knowledge gap theory framed research question 2 and underscores that disparities in access to or comprehension of agricultural information may contribute to uneven perceptions, and our findings suggest that some demographic and experiential characteristics may shape these gaps—though often in modest ways that should be interpreted cautiously. Research question 3 explored how systems thinking reminds us that perceptions of agriculture are embedded in broader, interdependent contexts where education, cultural values, communication networks, and prior experiences interact to influence understanding and trust.

Animal and related industries may be able to improve impressions and future purchase intentions by recognizing issues where concern is high but understanding is limited. However, as this study was confined to university individuals, broader sampling across household consumers, occupational groups, and younger students would be needed to fully validate these patterns. The Food Familiarity Index shows promise as a tool to describe involvement in food production, but additional validation across diverse contexts is necessary before its utility as a segmentation instrument can be firmly established.

While our findings suggest that higher education affiliation itself may mitigate role-based differences, broader sampling outside of the university setting is needed to empirically test whether this homogeneity masks more meaningful role-based distinctions.

Ultimately, this study suggests, not conclusively but provisionally, that effective, evidence-based communication strategies, particularly through social media and other connective networks, may help bridge knowledge gaps and foster greater trust between agricultural and non-agricultural communities. Expanding the scope of measurement tools and diversifying the populations studied will be important steps for advancing this line of research.

Rather than focusing on a restricted number of questions, future investigation should also consider whether there is consistency in the increased understanding and perspectives of welfare, health, and sustainability of animal agriculture.