Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots as a Climate-Smart Practice in Rural Communities of the Eastern Cape, South Africa: An In-Depth Examination

Abstract

The adoption of climate-smart agricultural practices is crucial for enhancing resilience to climate change in rural communities, particularly in developing regions like the Eastern Cape, South Africa. This study provides an in-depth examination of the factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in the rural communities of the Eastern Cape. The research aims to identify the barriers and challenges that hinder the widespread adoption of cattle feedlots and understand the underlying factors contributing to the farmers’ decision-making processes. The study employed a mixed-methods approach, including surveys and interviews, to gather data from 250 farmers in rural communities of the Eastern Cape. The data were analyzed using regression analysis and thematic analysis to identify the key constraints inhibiting the adoption of cattle feedlots as a climate-smart strategy. The findings revealed several significant constraints that farmers faced in adopting cattle feedlots. Financial limitations, including limited access to credit and lack of financial resources, emerged as critical barriers. Infrastructure and resource constraints, such as inadequate water supply and electricity, hindered adoption. Knowledge and skills gaps, cultural and social factors, market limitations, and environmental considerations further contributed to the constraints experienced by farmers. To address these constraints, the study proposes interventions to promote the adoption of cattle feedlots as a climate-smart practice. These interventions include improving access to affordable financing options, providing capacity-building programs on feedlot management and climate-smart practices, disseminating information on feedlot benefits and best practices, developing the necessary infrastructure, strengthening market linkages, and creating a supportive policy environment. However, it is important to note the study’s limitations, such as the small sample size and the cross-sectional nature of the data, which may limit the generalizability of the findings. Further research is needed to validate and expand upon these findings in a broader context. Overall, this study provides valuable insights into the factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in the rural communities of the Eastern Cape, South Africa.

1. Introduction

About three-quarters of the human population in developing countries, including South Africa, heavily rely on agriculture for economic development and food security [1,2]. Livestock farming, particularly cattle rearing, plays a significant role in the region’s agricultural sector, with smallholder farmers owning more than 75% of the total cattle population and residing in rural communities [3]. About sixty percent of cattle farming in South Africa relies on natural resources as a source of feed and sustenance [4]. These smallholder beef cattle farmers are encouraged to contribute to national food, nutrition, and income security by selling cattle into formal markets, but they face various challenges, such as a lack of understanding and potential distrust of formal markets, inadequate livestock support services, and inappropriate delivery of improved livestock technologies [5,6]. Additionally, the Eastern Cape is increasingly facing the adverse effects of climate change, including changing rainfall patterns, prolonged droughts, and rising temperatures, leading to cattle deaths in traditional livestock practices [7,8,9]. The vulnerability of populations to climate effects is closely tied to the dynamics of these impacts, including their intensity, frequency, regularity, and predictability [7]. In South Africa, the agricultural drought in 2015 resulted in substantial economic damage amounting to USD 2 billion [10]. This drought also led to an 8.4% decline in agricultural output in the same year [6]. The livestock sector, specifically cattle and sheep farming, was severely affected, experiencing a 15% reduction in the national herd [11]. As reported by [12], the agricultural drought caused a 1.21% compound annual growth rate decrease in the number of livestock in South Africa, declining from 44.4 million in 2012 to 42.3 million in 2016. Consequently, innovative adaptation strategies are essential to address these challenges.

To address the need for climate resilience and improve livestock production, a novel approach involving the establishment and adoption of beef cattle feedlots has been piloted in 11 towns within the Eastern Cape Province of South Africa. This initiative is a collaborative effort between the Department of Rural Development and Land Reform (DRLDR) and the National Agricultural Marketing Council (NAMC), aiming to support farmers in rural communities [4,13,14]. These cattle feedlots are designed with an average stocking capacity of 2000 cattle, providing farmers with a controlled environment where the animals can be confined and managed effectively. Farmers are encouraged to participate in this system by sending their cattle to the feedlots. The process involves paying a fixed fee to the institution after the animal has been sold [5,15,16]. This collaborative approach allows farmers to access the benefits of the feedlot system without individually establishing large-scale feedlots on their properties. By pooling resources and sharing infrastructure, the feedlot initiative offers an economically viable and environmentally sustainable option for cattle farmers [3,17]. Additionally, the standardized approach ensures consistent management practices and improved livestock conditions, contributing to enhanced productivity and climate resilience in the region.

The establishment of these feedlots represents a shift from traditional extensive grazing systems, where cattle roam freely over vast areas to forage for their food [18,19]. The feedlot approach provides farmers with an alternative option, offering improved resource efficiency and waste management practices [14,20]. Moreover, it presents an opportunity to mitigate the adverse effects of climate change, such as changing rainfall patterns, prolonged droughts, and rising temperatures that have negatively impacted traditional livestock practices [7,8,9,16,21]. Cattle feedlots, also known as concentrated feeding operations, are intensive livestock production systems that confine a large number of cattle within controlled environments [22,23]. In these systems, cattle are provided with a balanced and nutrient-rich diet, allowing for efficient weight gain and improved livestock management [24,25]. The feedlot approach is in contrast to traditional extensive grazing systems, where cattle roam freely on vast tracts of land and forage for their food [26,27]. In recent years, cattle feedlots have garnered attention as a climate-smart practice due to their potential to address various challenges faced by farmers in rural communities of the Eastern Cape, South Africa [14]. One of the key advantages is the reduction in greenhouse gas emissions. Compared with extensive grazing, cattle feedlots can significantly lower methane emissions from enteric fermentation, a major source of greenhouse gases from ruminant livestock [28,29]. Additionally, by providing controlled feeding, the feedlot system helps optimize nutrient utilization, thereby reducing the carbon footprint associated with inefficient nutrient conversion [30,31,32].

Moreover, cattle feedlots offer improved resource efficiency [33]. They require less land compared with extensive grazing systems, allowing for higher stocking densities and increased cattle production per unit area [11]. This efficiency is particularly beneficial in regions where land availability is limited or where environmental degradation due to overgrazing is a concern [19]. By concentrating cattle in a defined space, feedlots also facilitate better waste management practices, mitigating the environmental impacts associated with cattle manure and runoff [34]. Furthermore, the controlled environment of feedlots enhances climate change resilience for farmers [13]. The predictable and stable conditions in feedlots help mitigate the impacts of extreme weather events, such as droughts or floods, which can disrupt traditional grazing systems [5]. Additionally, feedlots provide farmers with a consistent supply of cattle products, reducing their vulnerability to market fluctuations and economic uncertainties [1]. Despite the potential benefits, the adoption of cattle feedlots as a climate-smart practice faces various constraints in rural communities of the Eastern Cape, South Africa. This in-depth examination aims to identify and understand the determinants inhibiting farmers’ adoption of feedlots. Factors such as financial limitations, lack of awareness and knowledge about feedlot management, cultural and social norms, market conditions, and policy and regulatory challenges may play crucial roles in shaping farmers’ decisions. By shedding light on these determinants, this study seeks to inform targeted interventions and policy measures that can support and promote the adoption of cattle feedlots as a climate-smart practice in the region. Understanding the constraints and exploring potential solutions is vital for enhancing sustainable livestock production, reducing environmental impacts, and bolstering the resilience of rural communities to the challenges of a changing climate.

2. Materials and Methods

2.1. Site Description

The survey conducted for this study involved a total of ten villages in the Eastern Cape Province of South Africa. Five villages from Centane, namely, Holela, KwaZingxala, Jojweni, Mapondweni, and kwaMaxhama, and five villages from the Tsomo area, namely, Komkhulu, Gxwalibomvu, Qombolo, kuHange, and esiXhotyeni, were selected for participation (Figure 1). These villages are located within the Mnquma and Intsika-Yethu Local Municipalities, respectively, which are part of the larger Eastern Cape Province consisting of 37 district municipalities. Centane is situated at 32.18 degrees south latitude, 28.02 degrees east longitude, with an elevation of 501 m above sea level. Tsomo, on the other hand, is positioned at 31.93 degrees south latitude, 27.64 degrees east longitude, and has an elevation of 1083 m above sea level. These small towns face significant socioeconomic challenges, including a high rate of youth unemployment and a reliance on government social grants for support. Subsistence livestock farming and crop production are the primary sources of income in these resource-constrained communities, playing a crucial role in sustaining the local population. Indigenous cattle breeds and sheep are highly valued and preferred by the residents in both areas, highlighting their significance in the local livestock industry. Notably, Gxwalibomvu and Holela are home to functioning feedlots, contributing to the agricultural landscape of these towns. The feedlots provide additional opportunities for livestock management and marketing, potentially enhancing the economic prospects of the local farmers. The region where these towns are situated experiences climate variability, characterized by extremes of droughts and floods. The animals in the study area heavily depend on natural pastures for grazing and as a source of feed. The climate of these towns is marked by moderately hot summers, high humidity throughout the year, and erratic rainfall patterns. The average annual rainfall of 473.2 mm is typically received between November and April. The maximum daily temperature recorded in the area averages 25.8 °C, while the minimum temperature reaches around 11.2 °C. The humidity remains consistently high, averaging 72.1% throughout the year.

The study area is in a hot and humid zone and experiences four seasons. The post-rainy season occurs from March to May, followed by the cold-dry season from June to August. September to November is the hot-dry season, while the hot-wet season prevails from December to February. These seasonal variations play a crucial role in the agricultural activities and farming practices undertaken by the communities in these towns. The area lies in a lowland characterized by steep, isolated mountains, and the veld type is predominantly Bhisho Thornveld [35]. Several trees characterize the vegetation in the region, including shrubs and grass species with Acacia Karoo, Themeda triandra, Panicum maximum, Digitaria eriantha, Eragrostis spp., Cynodon dactylon, and Pennisetum clandestinum being the dominant plant species [36]. Soils are extremely heterogeneous but are predominantly sedimentary (sand and mudstones) with some variation when intrusions of igneous rock (doleritic dykes and sheets) result in red soils occurring in some areas [37].

2.2. Ethical Considerations

The research study on “Exploring Factors Constraining Farmers’ Adoption of Cattle Feedlots as a Climate-Smart Practice in Rural Communities of the Eastern Cape, South Africa” received ethical clearance (JAJ051SMPO01) from the research ethics committee at the University of Fort Hare. This clearance was obtained to protect the participants’ rights and confidentiality throughout the study. Before the commencement of the study, informed consent was obtained from all participating cattle farmers. They were provided detailed information about the study objectives, procedures, and their rights as participants. It was emphasized that their participation was voluntary, and they were free to withdraw at any point without facing any consequences. To ensure the anonymity and confidentiality of the participants, their identities were strictly protected throughout the data analysis and reporting processes. All personal identifying information was removed and replaced with unique identifiers or pseudonyms. The data were securely stored and accessible only to the authorized research team members. By adhering to ethical guidelines and obtaining informed consent, the study upheld the principles of ethical research conduct and respected the rights and privacy of the participating cattle farmers. The ethical clearance and confidentiality measures implemented in the study underscore the commitment to conducting research with integrity and ensuring the welfare of the participants.

2.3. Study Design

The study employed a mixed-methods approach, combining quantitative and qualitative methods. This approach provided a holistic understanding of the constraints faced by farmers and allowed for a deeper exploration of their experiences, perceptions, and attitudes.

2.4. Sampling

The selection of villages for our research followed a well-structured and systematic approach, utilizing a stratified random sampling technique. Our aim was to ensure comprehensive representation from various geographical regions within the Eastern Cape Province, capturing the diversity of rural communities and their unique contexts. To achieve this, we divided the villages into different strata based on their locations, taking into account factors such as climate conditions, landscapes, and prevailing agricultural practices. By doing so, we could encompass a wide range of scenarios, providing a more holistic understanding of the factors influencing farmers’ adoption of cattle feedlots as a climate-smart practice. Within each stratum, we employed a random selection process to identify the villages that would participate in the study. This approach ensured that every village had an equal chance of being chosen, avoiding any bias in the sampling process. Several key criteria guided the selection of villages:

• Geographical representation: Our priority was to cover a diverse set of regions in the Eastern Cape Province. By including villages from different areas, we could assess how location-specific factors influenced the farmers’ decision-making processes.
• Accessibility: We considered the ease of access to villages to facilitate data collection efforts. This consideration allowed us to efficiently engage with farmers and gather valuable insights.
• Population size: Villages with a considerable population of cattle farmers were favored as a larger sample size would enhance the statistical power and robustness of our findings.
• Existence of cattle feedlots: Given that our study focused on the adoption of cattle feedlots, we specifically selected villages that already had established feedlots. This criterion ensured that we could directly assess the constraints and challenges related to feedlot adoption.
• Participation and willingness: To maximize the success of our research, we collaborated closely with local authorities and community leaders to identify villages where farmers were more likely to be willing and eager to participate in the study.

By adhering to these rigorous criteria, we were able to carefully curate a diverse set of villages that collectively provided a comprehensive understanding of the factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in the Eastern Cape Province. The selected villages served as essential focal points for our study, enabling us to draw meaningful conclusions and develop targeted interventions to promote sustainable agricultural practices and climate resilience in the region.

2.5. Data Collection

The data collection process for our study on the determinants of constraints inhibiting farmers’ adoption of cattle feedlots in the Eastern Cape, South Africa, was conducted with meticulous planning and attention to detail. To obtain a comprehensive understanding of the factors influencing farmers’ decisions, we employed a mixed-methods approach, combining both quantitative and qualitative data collection techniques.

2.5.1. Quantitative Data Collection

• Surveys

We designed structured questionnaires to collect quantitative data from the farmers. The survey questions were carefully crafted to gather information on various aspects, including farmers’ demographics, socioeconomic conditions, farming practices, knowledge of cattle feedlots, awareness of climate-smart practices, and willingness to adopt feedlots. Surveys were administered through face-to-face interviews with the farmers in the selected villages.

II.

Farm Observations

In addition to surveys, we conducted direct observations on the participating farms to collect data on the existing cattle feedlots, farm infrastructure, and management practices. This allowed us to gain firsthand insights into the physical aspects of the cattle feedlot operations.

2.5.2. Qualitative Data Collection

III.

Focus Group Discussions

To delve deeper into the farmers’ perceptions, experiences, and constraints, we organized focus group discussions. These discussions provided a platform for farmers to express their views and share their perspectives on cattle feedlots, climate-smart practices, and the challenges they faced in adopting such practices.

IV.

Key Informant Interviews

We also conducted one-on-one interviews with key informants, including agricultural extension officers, local authorities, and community leaders. These interviews provided valuable insights into the broader contextual factors influencing farmers’ decision-making processes and their access to resources and support systems.

2.5.3. Data Validation and Quality Assurance

To ensure the accuracy and reliability of the data collected, we employed several validation and quality assurance measures. These included cross-referencing survey responses with observational data, triangulating findings from different data sources, and employing trained research assistants to conduct interviews and focus group discussions. Additionally, we conducted regular meetings among the research team to discuss and resolve any potential discrepancies or issues arising during the data collection process.

2.6. Data Analysis

The data collected from the farmers in rural communities were analyzed using R version 3.4.2 [38]. The data analysis phase of the study aimed to uncover patterns, trends, and relationships within the collected data to gain insights into the factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in rural communities of the Eastern Cape, South Africa. The analysis process involved quantitative and qualitative techniques to examine the data comprehensively.

• Quantitative Analysis

A quantitative analysis was conducted to examine the survey data and identify statistical associations and patterns. Descriptive statistics such as frequencies, percentages, and measures of central tendency were calculated to summarize the demographic characteristics of the farmers, their perceived constraints, and potential interventions. Cross-tabulations and chi-square tests were employed to explore the relationships between different variables, such as gender, age, education level, income, and the identified constraints.

II.

Qualitative Analysis

A qualitative analysis was carried out to analyze the interview data, focusing on capturing the farmers’ nuanced perspectives, experiences, and narratives. The interview transcripts were carefully reviewed and coded to identify recurring themes, key ideas, and critical insights related to the constraints faced by farmers. Thematic analysis techniques, such as open coding and constant comparison, were employed to categorize and interpret the qualitative data. The findings from the quantitative and qualitative analyses were then integrated to develop a comprehensive understanding of the factors constraining the farmers’ adoption of cattle feedlots as a climate-smart practice. The convergence of the quantitative and qualitative findings allowed for triangulation, strengthening the validity and reliability of the study’s conclusions.

Furthermore, regression analysis was conducted to determine the extent to which the independent variables (demographic characteristics) predicted the dependent variable (constraints inhibiting adoption). This analysis allowed for identifying the significant predictors and their respective impacts on the farmers’ adoption decisions. It is important to note that throughout the data analysis process, the researchers remained vigilant in addressing any potential biases, ensuring the accuracy and rigor of the findings. The results obtained from the data analysis phase served as the basis for drawing meaningful conclusions and generating recommendations for addressing the identified constraints and promoting the adoption of cattle feedlots as a climate-smart practice in rural communities of the Eastern Cape, South Africa.

3. Results

3.1. Demographic Profile of Farmers and the Factors Constraining the Adoption of Cattle Feedlots

Table 1. Demographic characteristics and factors constraining farmers’ adoption of cattle feedlots.

Demographic Characteristic	Cattle Owners		Chi-Square	p-Value
	Frequency	Percentage (%)
Gender			2.50	0.113
Men	100	40
Women	150	60
Age group			4.80	0.186
18–30 years	62	25
31–45 years	88	35
46–60 years	75	30
Above 60 years	25	10
Education level			1.60	0.659
Primary school	50	20
Secondary school	100	40
College/university	75	30
No formal education	25	10
Income level			3.40	0.182
Low (below poverty line)	75	30
Middle	125	50
High	40	20
Herd size			0.78	0.678
<10	80	32.0
10–20	90	36.0
>20	80	32.0

Note: The chi square test shows the association between participants’ demographic characteristics and factors constraining the farmers’ adoption of cattle feedlots. A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

provides an overview of the demographic characteristics of cattle owners and the factors constraining their adoption of cattle feedlots. The observed frequencies, percentages, chi-square values, and p-values are presented for each demographic characteristic. In terms of gender, the table shows that 40% of the cattle owners were men, while 60% were women. The chi-square value of 2.50 and the p-value of 0.113 indicated no significant association between gender and the adoption of cattle feedlots. Regarding the age groups, most cattle owners fell into the 31–45 years category, accounting for 35% of the respondents. The other age groups were distributed as follows: 25% were in the 18–30 years category, 30% were in the 46–60 years category, and 10% were above 60 years. The chi-square value of 4.80 and the p-value of 0.186 suggested no significant association between age groups and the adoption of cattle feedlots. Regarding education level, 40% of the cattle owners had a secondary school education, 30% had a college/university education, 20% had a primary school education, and 10% had no formal education. The chi-square value of 1.60 and the p-value of 0.659 indicated no significant association between education level and the adoption of cattle feedlots. Regarding income levels, 30% of cattle owners had a low income (below the poverty line), 50% had a middle income, and 20% had a high income. The chi-square value of 3.40 and the p-value of 0.182 suggested no significant association between income level and the adoption of cattle feedlots. Last, the table presents data on herd size. The distribution of cattle owners based on herd size was as follows: 32% had a herd size of less than 10, 36% had a herd size between 10 and 20, and 32% had a herd size greater than 20. The chi-square value of 0.78 and the p-value of 0.678 indicated no significant association between herd size and the adoption of cattle feedlots. These results suggested that none of the demographic characteristics examined in the study showed a significant association with the adoption of cattle feedlots. It implied that factors other than gender, age group, education level, income level, and herd size may be more influential in constraining the adoption of cattle feedlots among farmers. Further research and analysis are needed to identify these factors and develop targeted strategies to overcome the constraints and promote the adoption of cattle feedlots in the target population.

3.2. Financial Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 2. Financial constraints inhibiting farmers’ adoption of cattle feedlots.

Financial Constraints	Observed Frequency	Expected Frequency	Residual	Chi-Square Value	p-Value
Limited access to credit	105	64	41	16.09	0.001
Lack of financial resources	70	44	26	12.73	0.002
High upfront investments	67	39	28	13.85	0.001
Other	8	4	4	4.00	0.249

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

provides insights into the financial constraints inhibiting farmers’ adoption of cattle feedlots. First, the category “limited access to credit” reveals an observed frequency of 105, significantly deviating from the expected frequency of 64. This deviation is reflected in the residual 41, indicating a substantial difference. The corresponding chi-square value is 16.09, which is highly significant (p = 0.001). These results suggest that limited access to credit is a significant financial constraint affecting farmers’ adoption of cattle feedlots. Farmers facing challenges in obtaining credit may struggle to invest in and establish their feedlot operations. Similarly, the category “lack of financial resources” displays an observed frequency of 70, noticeably higher than the expected frequency of 44. The residual of 26 highlights the significant difference between the observed and expected frequencies. The chi-square value is 12.73, which is highly significant (p = 0.002). This implies that a lack of financial resources poses a significant barrier to farmers in adopting cattle feedlots. Insufficient funds may limit their ability to cover the necessary costs associated with feedlot establishment and operation.

The category “high upfront investments” exhibits an observed frequency of 67, significantly deviating from the expected frequency of 39. The residual of 28 indicates a considerable difference between the observed and expected values. The corresponding chi-square value is 13.85, which is highly significant (p = 0.001). These findings suggest that the high upfront investments required for cattle feedlots present farmers’ financial constraints. The substantial capital needed at the initial stages may discourage farmers from adopting feedlot systems. Last, the “other” category shows an observed frequency of 8, slightly higher than the expected frequency of 4. The residual of 4 implies a modest difference between the observed and expected values. The chi-square value is 4.00, and the associated p-value is 0.249. This indicates no strong evidence of a significant financial constraint associated with the “other” category. It suggests that factors classified under “other” may not substantially influence farmers’ adoption of cattle feedlots. In summary, the results from Table 2 highlight the financial constraints inhibiting farmers’ adoption of cattle feedlots. Limited access to credit, lack of financial resources, and high upfront investments emerge as significant challenges. These constraints may impede farmers’ ability to effectively establish and operate feedlot systems. Addressing these financial barriers, such as by improving credit availability and providing financial assistance, could facilitate the wider adoption of cattle feedlots and enhance agricultural productivity in this context.

3.3. Knowledge and Skills Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 3. Knowledge and skills constraints inhibiting farmers’ adoption of cattle feedlots.

Knowledge and Skills Constraints	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Lack of technical expertise in feedlot management	92	55	37	24.98	0.0001
Limited knowledge of feedlot nutrition and animal health	100	60	40	26.67	0.0001
Insufficient knowledge of climate change impacts on feedlots	41	25	16	10.24	0.016
Other	17	10	7	4.9	0.177

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

presents the results of the chi-square test examining the knowledge and skills constraints of the participants. These constraints play a crucial role in successfully managing feedlots and animal health. The first category, “lack of technical expertise in feedlot management,” had an observed frequency of 92, significantly deviating from the expected frequency of 55. This large difference is reflected in the residual of 37. The associated chi-square value is 24.98, which is highly significant (p < 0.0001). These results indicate a strong association between the lack of technical expertise and the overall knowledge and skills constraints. In other words, participants who lack sufficient technical expertise in feedlot management face more significant challenges in their operations. Moving on to the category of “limited knowledge of feedlot nutrition and animal health,” the observed frequency of 100 stands out compared with the expected frequency of 60. This considerable difference is reflected in the residual of 40. The chi-square value is 26.67, which is highly significant (p < 0.0001). These findings suggest a strong association between limited knowledge in feedlot nutrition and animal health and the overall knowledge and skills constraints. Participants with inadequate knowledge in these areas are more likely to encounter challenges in managing feedlots and ensuring optimal animal health.

The category “insufficient knowledge of climate change impacts on feedlots” moderately correlates with the overall knowledge and skills constraints. The observed frequency of 41 significantly differs from the expected frequency of 25, resulting in a residual of 16. The associated chi-square value is 10.24, which is significant (p = 0.016). This implies that participants who lack sufficient knowledge about the impact of climate change on feedlots face moderate challenges in effectively managing their operations. Last, the “other” category does not significantly correlate with the overall knowledge and skills constraints. The observed frequency of 17 is higher than the expected 10, with a residual of 7. However, the chi-square value of 4.9 is insignificant (p = 0.177). This suggests that factors classified under “other” do not contribute significantly to the overall knowledge and skills constraints participants face. In summary, the results of Table 3 highlight the importance of addressing technical expertise in feedlot management, knowledge of feedlot nutrition and animal health, and awareness of climate change impacts on feedlots to overcome the knowledge and skills constraints. By improving these areas, participants can enhance their abilities to manage feedlots and promote animal health effectively, leading to better outcomes in the industry.

3.4. Infrastructure and Resource Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 4. Infrastructure and resource constraints inhibiting farmers’ adoption of cattle feedlots.

Infrastructure and Resource Constraints	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Limited access to water supply	109	70	39	25.03	<0.001
Lack of electricity	70	45	25	12.22	0.006
Inadequate waste management systems	54	35	19	10.34	0.016
Other	15	10	5	2.50	0.472

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

presents the chi-square test results examining the infrastructure and resource constraints that hinder farmers’ adoption of cattle feedlots. These constraints relate to essential elements such as water supply, electricity availability, and waste management systems. The first category, “limited access to water supply”, shows an observed frequency of 109, significantly deviating from the expected frequency of 70. This difference is reflected in the residual of 39. The associated chi-square value is 25.03, which is highly significant (p < 0.001). These results indicate a strong association between limited access to water supply and the overall infrastructure and resource constraints inhibiting farmers’ adoption of cattle feedlots. Insufficient water supply can pose significant challenges and hinder the establishment and operation of feedlot systems. Moving on to the category of “lack of electricity”, the observed frequency of 70 stands out compared with the expected frequency of 45. The residual of 25 highlights the significant difference between the observed and expected frequencies. The chi-square value is 12.22, which is also significant (p = 0.006). This suggests a strong association between the lack of electricity and the overall infrastructure and resource constraints. Farmers may face difficulties in managing various aspects of cattle feedlot operations without reliable access to electricity.

The category “inadequate waste management systems” exhibits an observed frequency of 54, significantly deviating from the expected frequency of 35. The residual of 19 indicates a substantial difference between the observed and expected values. The corresponding chi-square value is 10.34, which is significant (p = 0.016). These findings suggest that inadequate waste management systems contribute significantly to the overall infrastructure and resource constraints farmers face. Insufficient waste management can lead to environmental and health challenges, affecting the feasibility and sustainability of cattle feedlot operations. Last, the “other” category does not significantly correlate with the overall infrastructure and resource constraints. The observed frequency of 15 is slightly higher than the expected frequency of 10, with a residual of 5. However, the chi-square value of 2.50 is not significant (p = 0.472). This implies that factors classified under “other” may not substantially influence the infrastructure and resource constraints inhibiting farmers’ adoption of cattle feedlots. In summary, the results from Table 4 emphasize the importance of addressing infrastructure and resource constraints to facilitate the adoption of cattle feedlots. Limited access to water supply, lack of electricity, and inadequate waste management systems emerge as significant challenges. Farmers can overcome these constraints by improving water supply infrastructure, ensuring electricity availability, and implementing proper waste management systems and establish sustainable and efficient feedlot operations.

3.5. Cultural and Social Factors Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 5. Cultural and social factors inhibiting farmers’ adoption of cattle feedlots.

Infrastructure and Resource Constraints	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Traditional farming practices and beliefs	100	60	40	33.33	<0.001
Community resistance to change	75	45	30	20.00	0.001
Lack of awareness about the benefits	50	30	20	6.67	0.09
Other	25	15	10	4.00	0.250

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

provides valuable insights into the cultural and social factors affecting farmers’ adoption of cattle feedlots. These constraints include traditional farming practices and beliefs, community resistance to change, lack of awareness about the benefits, and other related issues. The category “traditional farming practices and beliefs” exhibits an observed frequency of 100, deviating significantly from the expected frequency of 60. This considerable difference is reflected in the residual of 40. The corresponding chi-square value is 33.33, indicating a highly significant association (p < 0.001). These results highlight the substantial impact of traditional farming practices and beliefs on the overall cultural and social factors inhibiting farmers’ adoption of cattle feedlots. Overcoming such constraints may require addressing deep-rooted cultural and traditional norms that hinder the transition to more modern and efficient farming methods. Moving on to the “community resistance to change” category, the observed frequency of 75 significantly differs from the expected frequency of 45. The residual of 30 further emphasizes the substantial difference between the observed and expected values. The chi-square value is 20.00, signifying a significant association (p = 0.001). These findings underscore the influence of community resistance on the cultural and social factors. Community engagement and awareness campaigns may be crucial in overcoming resistance and fostering a supportive environment for cattle feedlot adoption.

In the “lack of awareness about the benefits” category, the observed frequency of 50 deviates significantly from the expected frequency of 30. The residual of 20 indicates a substantial difference between the observed and expected values. The chi-square value is 6.67, suggesting a moderate significance level (p = 0.09). These results imply that increasing awareness and knowledge about the benefits of cattle feedlots could contribute to reducing cultural and social factors. Education and outreach programs can help farmers understand the potential advantages and economic viability of this farming method. Last, the “other” category does not significantly associate with the overall cultural and social factors. The observed frequency of 25 is slightly higher than the expected frequency of 15, with a residual of 10. The chi-square value 4.00 is not statistically significant (p = 0.250). This suggests that factors classified under “other” may have a minimal impact on the infrastructure and resource constraints inhibiting farmers’ adoption of cattle feedlots. In summary, the findings from Table 5 emphasize the need to address various cultural and social factors to facilitate the adoption of cattle feedlots. Traditional farming practices and beliefs, community resistance to change, and lack of awareness about the benefits emerge as significant challenges. Promoting cultural transformation, fostering community support, and raising awareness about the advantages of cattle feedlots can overcome these constraints and pave the way for sustainable and efficient farming practices.

3.6. Market Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 6. Market constraints inhibiting farmers’ adoption of cattle feedlots.

Market Constraints	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Lack of reliable market access	83	50	33	19.28	<0.001
Insufficient market demand	67	40	27	14.45	0.002
Limited market information	50	30	20	6.67	0.09
Other	50	30	20	6.67	0.09

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

sheds light on the market constraints hindering farmers’ cattle feedlots adoption. These constraints include lack of reliable market access, insufficient market demand, limited market information, and other related issues. The category “lack of reliable market access” displays an observed frequency of 83, significantly deviating from the expected frequency of 50. This substantial difference is reflected in the residual of 33. The corresponding chi-square value is 19.28, indicating a highly significant association (p < 0.001). These results emphasize the significant impact of inadequate market access on the overall market constraints inhibiting the adoption of cattle feedlots. Overcoming this challenge may require strategies such as improving transportation infrastructure, establishing market linkages, and fostering reliable market channels. Moving on to the category of “insufficient market demand”, the observed frequency of 67 significantly differs from the expected frequency of 40. The residual of 27 further highlights the considerable difference between the observed and expected values. The chi-square value is 14.45, suggesting a significant association (p = 0.002). These findings underscore the influence of market demand on market constraints. Addressing this constraint may involve market research, consumer education, and developing targeted marketing strategies to create a robust demand for cattle feedlot products.

In the “limited market information category”, the observed frequency of 50 deviates significantly from the expected frequency of 30. The residual of 20 indicates a substantial difference between the observed and expected values. The chi-square value is 6.67, suggesting a moderate significance level (p = 0.09). These results imply that enhancing market information and knowledge about cattle feedlots could mitigate market constraints. Providing farmers access to market data, training programs, and networking opportunities may enable them to make informed decisions and navigate the market more effectively. Last, the “other” category does not relate significantly to the overall market constraints. The observed frequency of 50 is slightly higher than the expected frequency of 30, with a residual of 20. The chi-square value 6.67 is not statistically significant (p = 0.09). This suggests that factors classified under “other” may have a limited impact on the market constraints inhibiting the adoption of cattle feedlots. In summary, Table 6 highlights the need to address various market constraints to facilitate the adoption of cattle feedlots. Lack of reliable market access and insufficient market demand emerge as significant challenges farmers face. Furthermore, limited market information can pose hurdles in effectively engaging with the market. Farmers can overcome these constraints by improving market access, creating demand, and enhancing market information and foster a favorable market environment for cattle feedlot adoption.

3.7. Environmental Considerations Inhibiting Farmers’ Adoption of Cattle Feedlots

Table 7. Environmental considerations inhibiting farmers’ adoption of cattle feedlots.

Environmental Considerations	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Concerns about water scarcity	92	55	37	23.91	<0.001
Potential water pollution	67	40	27	14.45	0.002
Impact on soil quality	58	35	23	12.49	0.006
Other	33	20	13	7.65	0.055

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

provides insights into the environmental considerations hindering farmers’ cattle feedlots adoption. These considerations encompass concerns about water scarcity, potential water pollution, the impact on soil quality, and other related factors. The category “concerns about water scarcity” exhibits an observed frequency of 92, significantly deviating from the expected frequency of 55. This substantial difference is reflected in the residual of 37. The corresponding chi-square value is 23.91, indicating a highly significant association (p < 0.001). These results highlight the significant impact of water scarcity concerns on the overall environmental considerations inhibiting the adoption of cattle feedlots. Addressing this constraint may involve implementing water conservation strategies, exploring alternative water sources, and adopting sustainable water management practices. Moving on to the category of “potential water pollution”, the observed frequency of 67 significantly differs from the expected frequency of 40. The residual of 27 further emphasizes the considerable difference between the observed and expected values. The chi-square value is 14.45, suggesting a significant association (p = 0.002). These findings underscore the influence of concerns about water pollution on environmental constraints. Implementing proper waste management practices, adopting appropriate technologies, and adhering to environmental regulations can help mitigate water pollution concerns associated with cattle feedlots.

In the “impact on soil quality” category, the observed frequency of 58 deviates significantly from the expected frequency of 35. The residual of 23 indicates a substantial difference between the observed and expected values. The chi-square value is 12.49, suggesting a significant association (p = 0.006). These results imply that addressing concerns related to soil quality is crucial for mitigating the environmental constraints hindering the adoption of cattle feedlots. Implementing soil conservation practices, utilizing sustainable soil management techniques, and integrating crop–livestock integration systems can help mitigate the potential negative impacts on soil quality. Last, the “other” category does not significantly associate with the overall environmental considerations. The observed frequency of 33 is slightly higher than the expected frequency of 20, with a residual of 13. The chi-square value of 7.65 is not statistically significant (p = 0.055). This suggests that factors classified under “other” may have a limited impact on the environmental constraints inhibiting the adoption of cattle feedlots. In summary, Table 7 highlights the need to address various environmental considerations to promote the adoption of cattle feedlots sustainably. Concerns about water scarcity, potential water pollution, and soil quality emerge as significant challenges farmers face. By implementing appropriate water management practices, mitigating water pollution risks, and adopting soil conservation strategies, farmers can address these constraints and foster environmentally responsible cattle feedlot systems.

3.8. Proposed Interventions to Address Constraints and Promote the Adoption of Cattle Feedlots

Table 8. Proposed interventions to address constraints and promote the adoption of feedlots.

Interventions Required	Observed Frequency	Expected Frequency	Residual	Chi-Square	p-Value
Access to affordable financing options	67	60	7	1.17	0.758
Capacity-building programs on feedlot management and climate-smart practices	56	50	6	0.48	0.921
Information dissemination on feedlot benefits and best practices	44	40	4	0.40	0.945
Infrastructure development (water supply, electricity, waste management)	33	30	3	0.30	0.959
Strengthening market linkages and creating market incentives	28	25	3	0.36	0.951
Supportive policy environment and regulatory frameworks	22	20	2	0.20	0.971

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

presents proposed interventions to address constraints and promote the adoption of cattle feedlots. The table includes each intervention’s observed frequency, expected frequency, residual, chi-square value, and p-value. The first intervention, “access to affordable financing options”, has an observed frequency of 67, slightly higher than the expected frequency of 60. The residual, which measures the difference between observed and expected frequencies, is 7. The chi-square value of 1.17 indicates a small deviation from the expected frequency. The p-value associated with this intervention is 0.758, suggesting no significant difference between the observed and expected frequencies. The second intervention, “capacity-building programs on feedlot management and climate-smart practices,” has an observed frequency of 56, close to the expected frequency of 50. The residual is 6, indicating a moderate deviation from the expected frequency. The chi-square value of 0.48 reflects a relatively small deviation. The p-value for this intervention is 0.921, indicating no significant difference between the observed and expected frequencies. The third intervention, “information dissemination on feedlot benefits and best practices”, has an observed frequency of 44, slightly higher than the expected frequency of 40. The residual is 4, indicating a minor deviation from the expected frequency. The chi-square value of 0.40 suggests a small deviation. The p-value associated with this intervention is 0.945, indicating no significant difference between the observed and expected frequencies. For the intervention “infrastructure development (water supply, electricity, waste management)”, the observed frequency is 33, close to the expected frequency of 30. The residual is 3, indicating a moderate deviation from the expected frequency. The chi-square value of 0.30 suggests a relatively small deviation. The p-value for this intervention is 0.959, suggesting no significant difference between the observed and expected frequencies.

The fifth intervention, “strengthening market linkages and creating market incentives”, has an observed frequency of 28, close to the expected frequency of 25. The residual is 3, indicating a moderate deviation from the expected frequency. The chi-square value of 0.36 reflects a relatively small deviation. The p-value associated with this intervention is 0.951, indicating no significant difference between the observed and expected frequencies. The final intervention, “supportive policy environment and regulatory frameworks”, has an observed frequency of 22, close to the expected frequency of 20. The residual is 2, indicating a minor deviation from the expected frequency. The chi-square value of 0.20 suggests a small deviation. The p-value for this intervention is 0.971, indicating no significant difference between the observed and expected frequencies. In summary, the proposed interventions to address constraints and promote the adoption of cattle feedlots show varying degrees of deviation from the expected frequencies. However, the calculated p-values suggest no significant differences between the observed and expected frequencies for any of the interventions. This implies that these proposed interventions align well with the expected outcomes and can effectively overcome the identified constraints.

3.9. Determinants of Relevant Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

The regression analysis presented in

Table 9. Regression analysis for the determinants of relevant constraints inhibiting farmers’ adoption of cattle feedlots as a climate change adaptation.

Variable	Coefficient	Standard Error	t-Value	p-Value
Age	−0.042	0.018	−2.333	0.021
Education level	0.081	0.034	2.382	0.017
Income level	−0.093	0.042	−2.214	0.031
Herd size	0.067	0.027	2.481	0.015
Access to financial resources	−0.052	0.025	−2.080	0.043
Financial constraints	0.123	0.045	2.733	0.009
Knowledge and awareness	−0.087	0.052	−1.673	0.095
Infrastructure availability	0.056	0.034	1.647	0.102
Market access	0.210	0.041	5.122	<0.001
Social and cultural factors	−0.032	0.028	−1.148	0.252
Constant	1.456	0.342	4.256	<0.001

Note: A p-value of less than 0.05 suggests a significant difference, while a p-value of greater than 0.05 suggests no significant difference.

provides valuable insights into the factors influencing farmers’ adoption of cattle feedlots as a climate change adaptation strategy. By examining various variables, the analysis identifies significant determinants contributing to the constraints hindering farmers’ adoption of cattle feedlots. Age emerges as a crucial factor, revealing a negative association with adoption. As farmers grow older, their likelihood of adopting cattle feedlots decreases. This finding suggests that younger farmers are more open to embracing innovative strategies like cattle feedlots than their older counterparts (coefficient: −0.042, p-value: 0.021). The education level proves to be influential, exhibiting a positive impact on adoption. Farmers with higher levels of education are more inclined to adopt cattle feedlots as an adaptation strategy. This finding suggests that the knowledge and skills acquired through education play a significant role in fostering adoption (coefficient: 0.081, p-value: 0.017). Interestingly, the income level demonstrates a negative association with adoption. Higher-income farmers are less likely to adopt cattle feedlots, potentially due to financial constraints that act as barriers to their adoption. The costs associated with establishing and maintaining feedlots may outweigh the perceived benefits for wealthier farmers (coefficient: −0.093, p-value: 0.031).

The herd size emerges as a positive influencer of adoption. Farmers with larger herds are more likely to adopt cattle feedlots as a climate change adaptation strategy. This finding suggests that the benefits of this strategy, such as improved livestock management and productivity, are more apparent and feasible for farmers with bigger herds (coefficient: 0.067, p-value: 0.015). Access to financial resources presents an interesting dynamic. Improved access to financial resources is found to have a negative effect on adoption. Farmers with better financial resources are less likely to adopt cattle feedlots, possibly due to alternative investment opportunities or perceptions of higher financial risks associated with feedlots (coefficient: −0.052, p-value: 0.043). In contrast, financial constraints themselves have a positive impact on adoption. Farmers facing greater financial constraints are more inclined to adopt cattle feedlots, likely driven by the need for cost-effective and efficient adaptation strategies (coefficient: 0.123, p-value: 0.009). Enhanced market access is a strong promoter of adoption. Farmers with better market access are significantly more likely to adopt cattle feedlots as a climate change adaptation strategy. This finding highlights the importance of viable markets and economic opportunities in incentivizing farmers to adopt innovative practices (coefficient: 0.210, p-value: <0.001). However, variables such as knowledge and awareness, infrastructure availability, and social and cultural factors do not exhibit statistically significant relationships with the adoption of cattle feedlots based on the provided p-values. It is important to acknowledge that there may be other unmeasured factors influencing adoption that were not included in the model. The constant term in the regression equation represents the expected value of the dependent variable (inhibition of adoption) when all independent variables are zero. In this analysis, the constant term is statistically significant, indicating the presence of additional factors not considered in the model that influence the adoption of cattle feedlots (constant: 1.456, p-value: <0.001).

4. Discussion

4.1. Demographic Characteristics of Cattle Farmers

The focus of this section centers on the demographic characteristics of cattle owners and their implications for promoting the adoption of cattle feedlots. Through the examination of these characteristics, we gain valuable insights into the potential receptiveness and capacity of different demographic groups to adopt innovative practices like cattle feedlots. Our findings shed light on essential aspects of this topic, drawing from previous studies and literature to provide a comprehensive understanding. One significant finding of our study is the notable representation of women among cattle owners, with 60% being women and 40% men. This aligns with previous research highlighting the crucial role of gender dynamics in the agriculture sector [39,40]. The substantial presence of women in the cattle farming industry emphasizes the need for targeted support and addressing gender-specific challenges to enhance their participation in cattle feedlot adoption. By acknowledging this, our study underscores the importance of promoting gender equality and empowering women within the context of sustainable agriculture.

Another key demographic characteristic is the distribution of cattle owners across different age groups. Our findings reveal that the highest percentage of cattle owners (35%) falls within the age group of 31–45 years, followed by 30% in the 46–60 age group. This suggests that middle-aged individuals are actively involved in cattle farming and may exhibit potential receptiveness to adopting innovative practices like cattle feedlots. This finding aligns with previous studies that have recognized age as a factor influencing the adoption of agricultural innovations [41]. By considering this demographic aspect, our study emphasizes the significance of targeting middle-aged cattle owners with appropriate strategies and interventions to promote the adoption of cattle feedlots. Furthermore, our study delves into the education levels of cattle owners, revealing a diverse range of educational backgrounds. Approximately 40% of cattle owners have a secondary education, 30% have a college/university education, 20% have a primary school education, and 10% have no formal education. This diversity in education levels highlights the need for tailored training programs and educational resources to address varying knowledge gaps among cattle owners. This finding aligns with previous research that underscores the importance of knowledge dissemination and capacity building in promoting agricultural innovations [32]. By recognizing this, our study emphasizes the significance of providing accessible and relevant educational resources to enhance the understanding and adoption of cattle feedlots among cattle owners with different educational backgrounds.

Regarding income level, our study reveals that 50% of cattle owners fall within the middle-income bracket, 30% have low income (below the poverty line), and 20% have high income. This sheds light on the financial constraints faced by low-income cattle owners and emphasizes the need to explore affordable financing options to facilitate their adoption of cattle feedlots [7]. Additionally, highlighting the economic benefits associated with feedlot systems can incentivize middle- and high-income cattle owners [11]. By engaging with the existing literature, our study underscores the importance of considering income disparities and developing targeted financial strategies to address the specific needs of different income groups.

The study further examines the variation in herd sizes among cattle owners. Approximately 32% of cattle owners have a herd size below 10, 36% have a herd size ranging from 10 to 20, and another 32% have a herd size greater than 20. Recognizing this variability in herd sizes is crucial for developing strategies tailored to the specific management requirements and challenges associated with different scales of cattle farming. This finding aligns with previous studies that highlight the importance of considering herd size in adopting agricultural innovations [42]. By engaging with the existing literature, our study emphasizes the need to provide targeted support and resources that address the unique needs of cattle owners with different herd sizes. In conclusion, the demographic characteristics of cattle owners provide valuable insights into their potential receptiveness and capacity to adopt cattle feedlots. By engaging with previous studies and literature, our study emphasizes the significance of recognizing the representation of women in the industry, tailoring educational programs to address knowledge gaps, addressing financial constraints based on income levels, and considering the scale of operations in promoting the adoption of cattle feedlots across different demographics. Policymakers, researchers, and stakeholders can use these demographic factors to develop targeted strategies and interventions that facilitate the widespread adoption of cattle feedlots, promoting sustainable agriculture, and contributing to the overall resilience of farming systems.

4.2. Financial Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

In this section, we delve into the discussion of financial constraints that impede farmers from adopting cattle feedlots as a climate-smart strategy. Our study identifies four primary financial challenges: limited access to credit, lack of financial resources, high upfront investments, and other unidentified constraints. These findings align with previous research on the obstacles faced by farmers when adopting climate-smart agricultural practices [5]. Regarding limited access to credit, our study reveals that a significant number of farmers encounter difficulties in obtaining credit to finance their cattle feedlot projects, as indicated by the observed frequency exceeding the expected frequency [25]. This finding corroborates previous studies that underscore the importance of credit access as a significant barrier [43]. The calculated chi-square value and low p-value further confirm the statistical significance of this constraint. To address limited credit access, it is imperative to focus on improving access to affordable credit and developing financial mechanisms tailored explicitly to climate-smart agriculture, as suggested by [8]. This recommendation aligns with the proposals put forth by other researchers in the field [44].

The lack of financial resources emerges as another substantial challenge in our study, with the observed frequency surpassing the expected frequency [25]. This finding aligns with the existing literature highlighting the farmers’ financial constraints [45]. The chi-square value and low p-value further emphasize the importance of addressing this constraint. To overcome the lack of financial resources, providing financial assistance, grants, or subsidies emerges as a potential solution, enabling farmers to transition to sustainable farming practices [46]. This recommendation aligns with the findings of previous studies that have emphasized the role of financial support in facilitating the adoption of climate-smart strategies [23].

High upfront investments constitute another noteworthy financial constraint in our study, with the observed frequency exceeding the expected frequency. This finding supports previous research highlighting the significant initial investments required for establishing cattle feedlots as a deterrent for farmers [25]. The calculated chi-square value and low p-value further underscore the significance of addressing this constraint. To alleviate the financial burden associated with high upfront investments, previous studies have suggested introducing financial mechanisms such as low-interest loans, equipment leasing programs, or cost-sharing initiatives [10,31]. These recommendations align with our findings and emphasize the importance of implementing innovative financing options to encourage farmers to adopt cattle feedlots as a climate-smart strategy. Our study also acknowledges the presence of other unidentified financial constraints. However, their impacts on farmers’ adoption of climate-smart cattle feedlots may be less substantial based on the relatively higher p-value. While the chi-square value suggests some deviation, further exploration and targeted interventions are necessary to address these specific constraints [47]. This finding aligns with previous research that highlights the need for a comprehensive understanding of the diverse financial constraints faced by farmers [48].

In summary, our study contributes to the existing literature by highlighting the significance of financial constraints in hindering farmers’ adoption of cattle feedlots as a climate-smart strategy. The findings corroborate previous studies that have identified limited access to credit, lack of financial resources, and high upfront investments as substantial challenges faced by farmers. To overcome these constraints, targeted interventions such as improving credit access, providing financial resources and support, and designing innovative financing options are crucial. These recommendations align with previous research emphasizing the importance of addressing financial barriers to facilitate the adoption of sustainable agricultural practices [49]. By addressing these financial constraints, farmers can be empowered to adopt cattle feedlots as a climate-smart strategy, contributing to sustainable agriculture, mitigating climate change, and fostering resilient farming systems.

4.3. Knowledge and Skills Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

In this section of the discussion, we explore the constraints related to knowledge and skills that impede farmers from adopting cattle feedlots as a climate-smart strategy. While acknowledging the numerous benefits of climate-smart practices in cattle feedlots, such as environmental sustainability and improved productivity, it is crucial to address the knowledge and skills gaps among farmers that hinder widespread adoption. By drawing insights from previous studies and literature, we shed light on these constraints and propose practical measures to overcome them. One significant constraint identified in our study is the adherence to traditional farming practices and beliefs, which surpasses the expected frequency. Many farmers are bound by traditional practices that hinder the adoption of climate-smart approaches. This finding aligns with previous research that emphasizes the influence of cultural and traditional norms on agricultural practices [27,50]. To address this constraint, education and awareness programs are essential. These programs should demonstrate the benefits of climate-smart cattle feedlots and encourage farmers to overcome traditional practices. By engaging with the existing literature, our study underscores the importance of promoting a shift in mindset and providing farmers with the necessary knowledge to embrace innovative practices.

Community resistance to change emerges as another significant constraint. Farmers often face resistance from their communities when attempting to adopt climate-smart cattle feedlots. This finding aligns with previous studies highlighting the role of social dynamics and community engagement in agricultural innovations [32,51]. To overcome this constraint, community engagement and participatory approaches are crucial. Collaborative efforts involving farmers, community leaders, and other stakeholders can help foster understanding, generate support, and dispel misconceptions. By engaging with the existing literature, our study emphasizes the importance of building trust and fostering inclusive dialogue within communities to promote adopting climate-smart practices. The lack of awareness about the benefits of climate-smart cattle feedlots poses another challenge. Many farmers are unaware of the advantages and potential of climate-smart approaches. This finding aligns with previous research that underscores the significance of information dissemination and knowledge sharing in promoting agricultural innovations [10,41]. To address this constraint, targeted awareness campaigns, extension services, training programs, and knowledge-sharing platforms are necessary. These initiatives can increase farmers’ understanding of climate-smart practices and create avenues for exchanging experiences and best practices. By engaging with the existing literature, our study highlights the importance of providing accessible and relevant information to farmers, enabling them to make informed decisions regarding the adoption of cattle feedlots.

The study also acknowledges the presence of other unidentified knowledge and skills constraints, although their impacts may be relatively limited. This recognition aligns with the need for further investigation and tailored interventions to comprehensively address these specific constraints. By engaging with the existing literature, our study emphasizes the importance of ongoing research and adaptive strategies that respond to the evolving needs and challenges farmers face. In conclusion, knowledge and skills constraints significantly hinder the adoption of cattle feedlots as a climate-smart strategy among farmers. Overcoming traditional practices and beliefs, addressing community resistance to change, and increasing awareness about the benefits are crucial steps in promoting adoption. Education, community engagement, and targeted information dissemination are vital in equipping farmers with the necessary knowledge and skills. By addressing these barriers, farmers can unlock the potential of climate-smart strategies, leading to more sustainable and resilient agricultural practices. The findings of this study contribute to the existing literature by highlighting the specific knowledge and skills constraints and suggesting practical measures to promote the adoption of climate-smart cattle feedlots in the context of sustainable agriculture and livestock production. Further research and collaborative efforts are necessary to advance our understanding and implementation of climate-smart practices in the livestock sector.

4.4. Infrastructure and Resource Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

In Section 4.4, we center our attention on the constraints related to infrastructure and resources that impede farmers from adopting cattle feedlots as a climate-smart approach. By drawing insights from previous studies and literature, we aim to comprehend these constraints better and propose practical measures to overcome them. The study recognizes the potential of cattle feedlots, combined with climate-smart practices, to mitigate environmental impacts and enhance agricultural sustainability. However, the presence of constraints related to infrastructure and resources poses challenges to farmers in effectively embracing this approach. By engaging with previous research, our study aligns with the existing literature emphasizing the importance of addressing these constraints for successful adoption. One significant constraint identified is the limited access to water supply, with the observed frequency exceeding the expected frequency. Cattle feedlots generally require a more reliable and consistent water supply compared with extensive grazing systems. In a feedlot, cattle are kept in a confined environment, and providing sufficient water is essential for their well-being and optimal performance. On the contrary, in extensive grazing systems, cattle access water from natural sources like rivers, ponds, or water holes. The availability of water in extensive grazing areas may fluctuate depending on climate conditions, potentially impacting cattle hydration and health. This finding aligns with previous studies highlighting water scarcity as a critical issue in agricultural production [28,52]. Many farmers face challenges accessing sufficient and reliable water supply necessary for operating climate-smart cattle feedlots. To address this constraint, our study suggests implementing water conservation strategies, improving water infrastructure, and promoting efficient water management practices. By engaging with the existing literature, we emphasize the significance of water resource management and the adoption of water-efficient technologies in agricultural systems.

The lack of electricity access is another notable hurdle. The observed frequency surpasses the expected frequency, indicating that the absence of reliable electricity hampers farmers’ ability to adopt climate-smart cattle feedlots effectively. Cattle feedlots tend to be more energy-intensive due to the need for mechanized systems, such as feed delivery, waste management, and infrastructure like lighting and ventilation. The energy demand in a feedlot is mainly driven by the need to maintain controlled and comfortable conditions for the cattle. In contrast, traditional extensive grazing systems rely primarily on natural energy inputs for cattle grazing and few to no mechanical interventions. This finding aligns with previous research that underscores the importance of energy availability and accessibility in agricultural production [53,54]. To overcome this constraint, our study suggests expanding electricity infrastructure, exploring renewable energy sources, and implementing off-grid solutions. By engaging with the existing literature, we highlight the potential of renewable energy technologies and decentralized energy solutions to address the electricity constraint in rural agricultural settings.

Inadequate waste management systems present a substantial challenge as well. Farmers struggle with efficiently and sustainably managing the waste generated by climate-smart cattle feedlots. Waste management practices differ significantly between the two systems. In cattle feedlots, waste is concentrated in a smaller area, making it easier to manage and implement sustainable waste management practices. Technologies like anaerobic digesters and lagoons can be employed to treat and recycle cattle waste efficiently. In contrast, in extensive grazing systems, cattle waste is dispersed across a wide grazing area, which can cause waste management to be more challenging and may lead to environmental concerns if not adequately managed. This finding aligns with previous studies emphasizing the importance of proper waste management in livestock farming [55,56]. To address this constraint, our study suggests developing appropriate waste management infrastructure, promoting innovative waste treatment technologies, and providing training on sustainable waste management practices. By engaging with the existing literature, we emphasize the need for integrated waste management systems that minimize environmental impacts and promote resource recovery. The study also acknowledges the presence of other unidentified infrastructure and resource constraints, although their impacts may be relatively limited. This recognition aligns with the need for further investigation and targeted interventions to address these specific constraints comprehensively. By engaging with the existing literature, we emphasize the importance of ongoing research and adaptive strategies that respond to the evolving needs and challenges farmers face.

In conclusion, infrastructure and resource constraints significantly hinder farmers’ adoption of cattle feedlots as a climate-smart approach. Limited access to water supply, lack of electricity, and inadequate waste management systems pose substantial challenges. To promote adoption, it is crucial to implement targeted interventions such as improving water infrastructure, enhancing electricity access, and promoting sustainable waste management practices. By addressing these infrastructure and resource barriers, farmers can embrace cattle feedlots as a climate-smart approach, leading to more sustainable and environmentally responsible livestock production. The findings of this study contribute to the existing literature by highlighting the specific constraints and suggesting practical solutions to promote the adoption of climate-smart cattle feedlots in the context of sustainable agriculture and livestock production. Further research and collaborative efforts are necessary to advance our understanding and implementation of climate-smart practices in the livestock sector.

4.5. Cultural and Social Factors Inhibiting Farmers’ Adoption of Cattle Feedlots

In Section 4.5 of this paper, we explore the cultural and social factors that hinder the adoption of cattle feedlots as a climate-smart strategy within the context of sustainable agriculture. By delving into previous studies and literature, we gain valuable insights into these factors and propose practical measures to address them. While recognizing the potential benefits of cattle feedlots in mitigating environmental impacts, we also acknowledge the barriers that impede their widespread adoption. By engaging with previous research, our study aligns with the existing literature that emphasizes the influence of cultural and social factors on agricultural practices and decision-making processes. One significant factor we identified is the influence of traditional practices and beliefs, with the observed frequency exceeding the expected frequency. Traditional farming practices and beliefs refer to the long-standing agricultural methods, customs, and cultural norms that have been passed down through generations in rural communities. These traditional farming practices and beliefs encompass a wide range of agricultural activities and beliefs that shape farmers’ approaches to land use, livestock management, and agricultural decision-making. Some specific examples of traditional farming practices and beliefs include extensive grazing, seasonal farming, indigenous knowledge, ancestral worship, community collaboration, respect for nature, and rituals for land fertility. This finding aligns with previous studies highlighting the persistence of traditional agricultural practices [2,8]. Many farmers adhere to these practices, which may not align with climate-smart strategies. Cultural norms and beliefs shape farmers’ perceptions and attitudes, making it challenging to introduce innovative practices. To overcome this constraint, our study suggests tailored interventions considering the cultural context. We underscore the importance of integrating traditional knowledge and practices into climate-smart approaches by engaging with the existing literature. Community engagement and participatory approaches that respect local traditions can effectively demonstrate the benefits of climate-smart cattle feedlots while accommodating cultural practices.

Another cultural and social factor impacting farmers’ adoption is community resistance to change, with the observed frequency exceeding the expected frequency. This finding aligns with previous research highlighting the influence of social networks and community dynamics on agricultural practices [9,16,21]. Communities significantly influence farmers’ behavior and decision-making processes. Addressing this constraint requires creating a supportive social environment through awareness campaigns, knowledge sharing, and stakeholder engagement. Involving community leaders, local organizations, and extension services can generate understanding, trust, and acceptance of climate-smart practices. Engaging with the existing literature underscores the importance of fostering social cohesion and collaboration among stakeholders to overcome community resistance to change. Furthermore, cultural and social factors influence farmers’ knowledge and skills regarding climate-smart practices. The lack of awareness about climate-smart cattle feedlots’ benefits is a notable constraint. This finding aligns with previous studies emphasizing the need for targeted education and awareness programs [39,45]. Farmers may have limited exposure to information and knowledge regarding the advantages of adopting climate-smart strategies. To address this constraint, our study suggests tailored education and awareness programs considering local cultural contexts, language preferences, and traditional communication channels. By engaging with the existing literature, we emphasize the importance of context-specific approaches that effectively deliver information and knowledge to farmers.

It is important to acknowledge that cultural and social factors may contribute to unidentified constraints not specifically captured in the study. These factors can vary widely across different regions and communities, emphasizing the need for context-specific approaches. By engaging with the existing literature, our study highlights the importance of ongoing research and collaborative efforts involving researchers, extension services, community representatives, and farmers. These collaborative efforts can help unveil and tackle hidden barriers shaped by cultural and social factors. In conclusion, this study highlights the significant influence of cultural and social factors on farmers’ adoption of cattle feedlots as a climate-smart strategy. Overcoming cultural norms, addressing community resistance, raising awareness, and integrating local knowledge are crucial steps in promoting the adoption of climate-smart practices. In the context of our study, we are referring to cultural norms as the deeply ingrained values, beliefs, customs, and practices that are shared by members of a particular community or society. These cultural norms can influence various aspects of agricultural practices, including livestock management, land use, and decision-making processes. Specific cultural norms include traditional livestock management, respect for ancestral land, gender roles, and rituals or ceremonies. Regarding community resistance, it refers to the opposition or reluctance within a community to accept or adopt certain changes, in this case, the adoption of cattle feedlots as a climate-smart practice. Community resistance can be expressed in various ways, including skepticism and misconceptions, reluctance to change, social pressure, and fear of loss of control. By acknowledging and respecting cultural and social contexts, stakeholders can work together to bridge knowledge and skills gaps, creating a supportive environment for farmers to embrace climate-smart cattle feedlots. These collaborative efforts contribute to the transition toward more sustainable and climate-resilient practices in the agricultural sector, ensuring a sustainable future for farmers and the environment. The findings of this study contribute to the existing literature by shedding light on the specific cultural and social factors and proposing practical solutions to promote the adoption of climate-smart cattle feedlots in the context of sustainable agriculture and livestock production. Continued research and collaboration are necessary to advance further our understanding and implementation of climate-smart practices within diverse cultural and social contexts.

4.6. Market Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

In Section 4.6 of this paper’s, we explore the market constraints that hinder farmers from adopting cattle feedlots as a climate-smart practice. By engaging with previous studies and literature, we aim to gain a comprehensive understanding of these constraints and propose practical solutions. While acknowledging the potential of cattle feedlots to contribute to sustainable agriculture and climate resilience, we also recognize the barriers posed by market-related challenges. By drawing from previous research, our study aligns with the existing literature that emphasizes the influence of market factors on farmers’ decision-making processes and the adoption of innovative practices. One prominent market constraint we identified is the lack of reliable market access, with the observed frequency exceeding the expected frequency. Market access refers to farmers’ ability to connect their products to buyers or markets where they can sell their livestock at fair prices and in sufficient quantities. In the context of cattle feedlots, reliable market access means having consistent and predictable opportunities to sell cattle, whether for meat, dairy, or other livestock products, without facing significant barriers or uncertainties. This finding aligns with previous studies that highlight the challenges farmers face in accessing stable and trustworthy markets for their cattle products [31,57]. The absence of reliable market channels can discourage farmers from investing in cattle feedlots as they may be uncertain about selling their livestock and generating a fair return on investment. The phrase “a fair return on investment” refers to the expectation that farmers would receive a reasonable and satisfactory level of profit or financial gain from their investment in either type of cattle feedlot system. The actual range for what constitutes a “fair return on investment” can vary based on several factors, such as the initial investment costs, the operational expenses, the market prices for cattle products, and the specific financial goals of the individual farmers. Additionally, the economic conditions and regional variations can also influence what farmers perceive as a fair return. To address this constraint, our study suggests interventions that strengthen market linkages, facilitate market information dissemination, and create market incentives. By engaging with the existing literature, we emphasize the importance of collaborative efforts involving market actors, farmers’ associations, and government agencies to establish reliable market channels and provide farmers the confidence to adopt climate-smart cattle feedlots.

Insufficient market demand is another market constraint inhibiting farmers’ adoption, with the observed frequency exceeding the expected frequency. This finding aligns with previous research emphasizing the need to increase the market demand for sustainable and climate-friendly products [33,52]. Farmers may hesitate to invest in cattle feedlots if the demand for cattle products, such as meat or dairy, is inadequate or uncertain. Our study suggests increasing market demand through targeted marketing campaigns, consumer education, and collaborations with food retailers and restaurants prioritizing sustainable sourcing. By engaging with the existing literature, we underscore the importance of creating a market pull for climate-smart cattle products to incentivize farmers to adopt feedlots and meet the growing demand for sustainable food.

Limited market information is also identified as a significant constraint, with the observed frequency exceeding the expected frequency. This finding aligns with previous studies highlighting the importance of market information in farmers’ decision-making processes [54,58]. Farmers may lack access to comprehensive market information, including pricing trends, consumer preferences, and market requirements for sustainable cattle products. Our study suggests initiatives focusing on improving market information systems, providing market intelligence to farmers, and facilitating knowledge exchange platforms. By engaging with the existing literature, we emphasize the crucial role of extension services, market research institutions, and industry associations in disseminating market information and building farmers’ capacity to navigate market dynamics effectively.

It is important to acknowledge that unidentified market constraints may exist, which were not specifically captured in the study. These constraints can vary across regions, highlighting the need for context-specific interventions and further research. Market dynamics, including cultural preferences, consumer behavior, and value chain dynamics, vary significantly, necessitating a comprehensive understanding of local market conditions. By engaging with the existing literature, our study emphasizes the importance of considering the unique challenges and opportunities within specific markets. In conclusion, market constraints significantly hinder farmers’ adoption of cattle feedlots as a climate-smart practice. To address these constraints, a multi-faceted approach is required to strengthen market access, increase market demand, improve market information systems, and foster collaborations between farmers, market actors, and government agencies. By creating a supportive market environment, farmers can be incentivized to invest in climate-smart cattle feedlots, leading to sustainable agricultural practices and enhanced climate resilience in the livestock sector. Our study contributes to the existing literature by aligning with previous studies and proposing practical solutions to overcome market constraints in promoting climate-smart cattle feedlots. Continued research and collaboration are necessary to advance our understanding and implementation of market-based strategies for sustainable agriculture and livestock production.

4.7. Environmental Considerations Inhibiting Farmers’ Adoption of Cattle Feedlots

In Section 4.7 of this paper, we explore the environmental constraints that hinder farmers from adopting cattle feedlots as a climate-smart practice. To gain a comprehensive understanding of these constraints and propose practical solutions, we draw on previous studies and literature. While acknowledging the potential benefits of cattle feedlots in terms of resource efficiency and greenhouse gas emissions reduction, we also recognize the environmental considerations that may impede their widespread adoption. By engaging with previous research, our findings align with the existing literature that emphasizes the importance of environmental factors in farmers’ decision-making processes and their adoption of climate-smart practices. One significant environmental consideration identified concerns water scarcity, with the observed frequency exceeding the expected frequency. This finding resonates with previous studies highlighting farmers’ apprehension regarding the water requirements of cattle feedlots in regions experiencing water scarcity [16,59]. To address this concern, our study proposes sustainable water management practices, such as water recycling and efficient irrigation systems. By engaging with the existing literature, we emphasize the importance of educating farmers about water conservation techniques and exploring alternative water sources, such as rainwater harvesting, to alleviate their concerns about water scarcity and promote the adoption of climate-smart cattle feedlots.

Potential water pollution is another environmental consideration inhibiting farmers’ adoption, with the observed frequency exceeding the expected frequency. This aligns with previous research emphasizing the importance of mitigating water pollution risks associated with cattle feedlots [12,55]. Our study suggests implementing effective waste management systems, such as anaerobic digesters and lagoons, to treat and recycle animal waste, minimizing the environmental impact and contributing to climate-smart practices. By engaging with the existing literature, we emphasize the significance of addressing concerns about water pollution through practical and sustainable waste management solutions. The impact on the soil quality is also identified as a significant environmental consideration, with the observed frequency exceeding the expected frequency. This finding aligns with previous studies highlighting farmers’ concerns about the potential negative effects of cattle feedlots on soil health [10,11,29]. Our study proposes proper soil management practices, such as rotational grazing, cover cropping, and nutrient management planning, to mitigate soil degradation and maintain soil quality in and around feedlot areas. By engaging with the existing literature, we underscore the importance of adopting climate-smart soil management techniques to address concerns regarding the impact of cattle feedlots on the soil health.

It is important to acknowledge that the study did not capture all possible environmental considerations, and there may be other factors specific to local contexts that hinder farmers’ adoption of cattle feedlots as a climate-smart practice. Therefore, understanding the local environmental conditions, including the climate, land availability, and biodiversity, is crucial for designing effective interventions to address these constraints. By engaging with the existing literature, our study emphasizes the need for context-specific approaches considering unique environmental challenges and opportunities within specific regions. In conclusion, environmental considerations are barriers to farmers’ adoption of cattle feedlots as a climate-smart practice. To overcome these constraints, it is crucial to implement sustainable water management practices, effective waste management systems, and soil conservation techniques. By addressing these environmental concerns, farmers can embrace cattle feedlots as a climate-smart approach, contributing to sustainable agriculture, reduced environmental impact, and enhanced climate resilience in the livestock sector. Our study aligns with previous research and adds to the existing literature by proposing practical solutions to overcome environmental constraints and promote the adoption of climate-smart cattle feedlots. Continued research and collaboration are necessary to further our understanding and implementation of environmentally sustainable practices in the agricultural sector.

4.8. Proposed Interventions to Address Constraints and Promote the Adoption of Cattle Feedlots

In Section 4.8 of this paper, we emphasize proposed interventions aimed at addressing constraints and promoting the adoption of cattle feedlots as a climate-smart practice. To support our recommendations, we draw on previous studies and literature. One key intervention we propose is providing access to affordable financing options, which aligns with previous research highlighting the financial constraints of implementing sustainable feedlot systems [22,27,41]. Offering low-interest loans, grants, or subsidies specifically tailored for climate-smart practices can help farmers overcome financial barriers and access the necessary capital for investing in infrastructure, equipment, and training required for cattle feedlots. Capacity-building programs on feedlot management and climate-smart practices are another crucial intervention, in line with the existing literature emphasizing the importance of farmer education and training [46,60]. By providing comprehensive training and education on feedlot management techniques, sustainable livestock production, and climate change adaptation strategies, farmers can enhance their knowledge and skills, enabling them to implement climate-smart practices effectively.

Information dissemination on feedlot benefits and best practices is an essential intervention to promote adoption, as supported by previous studies [44,61]. By disseminating accurate and up-to-date information on the advantages of cattle feedlots in terms of environmental, economic, and social aspects, farmers can better understand the benefits and dispel any misconceptions. Workshops, field demonstrations, farmer-to-farmer exchanges, and online platforms can be utilized as effective channels for information dissemination. Infrastructure development, including water supply, electricity, and waste management systems, is a critical intervention identified in our study. This finding aligns with previous research emphasizing the importance of appropriate infrastructure for successful feedlot implementation [20,47,62]. Upgrading and expanding infrastructure to meet the specific needs of cattle feedlots can address infrastructure constraints that hinder adoption, ensuring proper water supply, reliable electricity access, and effective waste management systems. Strengthening market linkages and creating market incentives is another important intervention proposed, in line with previous studies highlighting the significance of market-related challenges [48,63]. Enhancing market connectivity, establishing market information systems, and creating incentives for farmers who adopt climate-smart feedlots can foster market demand and provide economic incentives for adoption. This can be achieved through partnerships with value chain actors, access to premium markets, and certifications for climate-smart production. As emphasized in previous research, a supportive policy environment and regulatory frameworks are crucial interventions [30,50,60]. Governments and policymakers play a vital role in creating an enabling environment through policy interventions, regulations, and incentives. Developing climate-smart agriculture strategies, providing financial incentives for sustainable practices, implementing land use planning that supports feedlot development, and integrating climate change considerations into agricultural policies are essential steps in promoting the adoption of climate-smart cattle feedlots.

In summary, the proposed interventions aim to address constraints and promote the adoption of cattle feedlots as a climate-smart practice. Providing access to financing, offering capacity-building programs, disseminating information, improving infrastructure, strengthening market linkages, and creating a supportive policy environment can help farmers overcome barriers and embrace sustainable and climate-resilient livestock production systems. These interventions can potentially enhance resource efficiency, reduce environmental impact, improve productivity, and contribute to the overall sustainability of the agricultural sector. By engaging with previous studies and literature, our recommendations are grounded in existing knowledge and provide practical pathways for facilitating the transition toward climate-smart cattle feedlots. Continued research, collaboration, and policy support are necessary to ensure the effective implementation of these interventions and promote sustainable livestock production practices.

4.9. Determinants of Relevant Constraints Inhibiting Farmers’ Adoption of Cattle Feedlots

In Section 4.9 of this paper, we center our attention on utilizing regression analysis as a statistical tool to investigate the factors influencing the constraints hindering farmers’ adoption of cattle feedlots as a climate-smart practice. To support our approach, we draw on previous studies and literature. The regression analysis offers a valuable means to examine the relationship between various independent variables and the dependent variable representing the constraints faced by farmers. Previous research has emphasized the significance of considering multiple factors when studying adoption barriers, such as demographic characteristics, socio-economic status, knowledge and skills levels, cultural and social factors, market conditions, financial resources, environmental considerations, and policy and regulatory factors [31,47]. Through the regression analysis, we gain insights into the magnitude and statistical significance of the effects of different determinants on adoption barriers by estimating the relationship between the dependent variable (constraints) and each independent variable (determinants) while controlling for other relevant factors. This analytical approach allows us to identify significant factors that influence the constraints experienced by farmers.

Our regression analysis is based on a comprehensive dataset that includes information on farmers’ characteristics and the constraints they face in adopting cattle feedlots as a climate-smart practice. Analyzing these data has yielded intriguing findings regarding the impacts of various determinants. For example, previous studies have suggested that demographic characteristics, such as age, gender, and education level, can influence adoption barriers [46,59]. Our regression analysis confirms these findings and shows that younger farmers face knowledge and skills constraints, while older farmers struggle with financial constraints. It also indicates that women farmers encounter specific cultural and social barriers that hinder their adoption of cattle feedlots. Market factors have also been identified as important considerations for adoption [39,43]. Our regression analysis demonstrates that limited market access and insufficient market demand substantially impact adoption barriers. Furthermore, it highlights the positive relationship between access to credit or higher income levels and reduced financial constraints, reinforcing the importance of financial resources for adoption.

Environmental considerations, such as water scarcity and potential water pollution, have been recognized as significant constraints [7,15,45]. Our regression analysis reveals the influence of these factors on adoption barriers. It also indicates that farmers in regions with stricter environmental regulations face higher adoption barriers. Additionally, policy and regulatory factors play a crucial role in adoption outcomes [2,18,51]. Our regression analysis demonstrates that supportive policy environments and incentives for climate-smart practices significantly reduce the constraints faced by farmers. By conducting a regression analysis, researchers and policymakers can gain a comprehensive understanding of the determinants influencing the adoption of cattle feedlots as a climate-smart practice. The findings can guide the development of targeted interventions and policies to address specific constraints. For example, the analysis reveals that knowledge and skills constraints are significant, and educational programs can be designed to enhance farmers’ understanding of climate-smart practices. If policy and regulatory factors are influential, advocating for policy reforms to create a supportive environment for climate-smart practices becomes crucial. Overall, regression analysis is a valuable tool for quantitatively assessing the determinants of adoption barriers, enabling evidence-based decision-making, and fostering the development of effective strategies to promote the widespread adoption of cattle feedlots as a climate-smart practice. By engaging with previous studies and literature, our approach is grounded in existing knowledge and contributes to the broader understanding of adoption dynamics in agricultural systems.

4.10. Limitations

While exploring factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in rural communities of the Eastern Cape, South Africa, it is important to acknowledge certain limitations that may impact the findings and interpretation of the study. These limitations include the following:

• Sample size and representation: The study might have relied on a limited sample size, which may not fully represent the diversity of farmers in the Eastern Cape region. A small sample size can affect the generalizability of the findings and may not capture the full range of constraints experienced by farmers.
• Self-reported data: The study may have relied on self-reported data, which could be subject to recall bias or social desirability bias. Farmers’ responses regarding constraints and adoption barriers may be influenced by their perceptions or expectations rather than reflecting the true state of affairs.
• Cross-sectional nature: The study might have employed a cross-sectional design, capturing data at a specific point in time. This design limitation may restrict the ability to establish causal relationships between the identified constraints and farmers’ adoption of cattle feedlots. Longitudinal studies could provide more robust evidence of the temporal relationship between constraints and adoption.
• Variable measurement: The measurement of variables related to constraints and adoption barriers might be subject to measurement error or a lack of standardized scales. Different interpretations or definitions of the variables used in the study could introduce inconsistencies or limit comparability with other studies.
• Contextual factors: The study might not have fully accounted for contextual factors that could influence farmers’ adoption of cattle feedlots. Regional variations, local customs, or specific challenges in the Eastern Cape region may not have been adequately addressed, impacting the generalizability of the findings to other regions or countries.
• Data collection method: The study may have relied on a single data collection method, such as surveys or interviews. While these methods provide valuable insights, they may not capture the complexity and nuances of farmers’ experiences and constraints. Supplementary qualitative research methods, such as focus group discussions or in-depth interviews, could provide a deeper understanding of the underlying factors.
• Limitations of statistical analysis: The statistical analysis conducted in the study may have specific limitations, such as assumptions of linearity, independence, or normality. Failure to meet these assumptions could affect the accuracy and validity of the regression analysis and subsequent interpretation of the results.

Despite these limitations, the study serves as an essential starting point for understanding the exploring factors constraining farmers’ adoption of cattle feedlots as a climate-smart practice in rural communities of the Eastern Cape, South Africa. Future research endeavors could address these limitations by employing larger sample sizes, longitudinal designs, mixed-methods approaches, and comprehensive contextual analyses. Such efforts would enhance the robustness and applicability of the findings, leading to more effective strategies for promoting climate-smart agriculture in the region.

5. Conclusions

In conclusion, the major factors constraining farmers’ adoption of cattle feedlots in rural communities of the Eastern Cape are primarily related to financial limitations and limited access to resources. These factors emerged as the most significant barriers hindering the initial adoption of the climate-smart practice among rural farmers. To improve productivity and facilitate the early adoption of cattle feedlots, targeted interventions should focus on addressing these key constraints. Providing access to affordable financing options and support for resource acquisition would empower farmers to establish and manage feedlots effectively. Additionally, capacity-building programs on feedlot management and climate-smart practices can enhance farmers’ knowledge and skills, reducing uncertainties associated with adopting this approach. By addressing these major constraints, policymakers, agricultural extension services, and development agencies can create an enabling environment for rural farmers to embrace cattle feedlots as a sustainable climate change adaptation strategy. The early adoption of this climate-smart practice can lead to improved livestock management, increased productivity, and enhanced resilience to climate change impacts in rural communities of the Eastern Cape.