Enhancing Agricultural Productivity Among Emerging Farmers Through Data-Driven Practices

Abstract

This paper explores the critical need for empowering emerging farmers within vulnerable communities through vocational adult education (VAE) approaches. Conducted within the Raymond Mhlaba District Municipality, South Africa, this study illuminates the persistent challenges of illiteracy, poverty, and the impact of climate change on agricultural productivity faced by these farmers. Employing a mixed-methods research design that combines quantitative and qualitative methodologies, this study investigates the effectiveness of digital agriculture and extension services in enhancing agricultural productivity and sustainability among emerging farmers. Key findings reveal significant barriers to technology adoption and the necessity for tailored training programs that integrate local knowledge systems and digital tools. Results demonstrate an average increase of 40% in crop yields among farmers participating in digital training initiatives (p < 0.01), underscoring the power of precision agriculture. Insights presented in this paper offer actionable recommendations for policymakers and stakeholders aimed at fostering inclusive agricultural development that addresses the unique challenges faced by emerging farmers in this region.

1. Introduction

Agriculture is a fundamental pillar of economies worldwide, significantly contributing to food security, job creation, and rural livelihoods. Globally, agriculture employs approximately 1.3 billion people, accounting for nearly 40% of the world’s labor force [1]. In many regions, particularly in developing countries, agriculture serves as the primary source of income for rural households, with estimates suggesting that up to 70% of the population in some low-income countries relies on agricultural activities for their livelihoods [2].

Despite its importance, the agricultural sector faces profound challenges. Nearly 690 million people are undernourished, a situation exacerbated by economic crises, conflicts, and climate impacts, according to the Food and Agriculture Organization (FAO, 2021) [3]. The COVID-19 pandemic has further strained food security, increasing the number of people facing hunger and malnutrition worldwide [3]. This complex landscape necessitates innovative strategies to empower farmers, particularly those who are emerging, to navigate their challenges effectively.

1.1. Poverty and Unemployment Challenges

Poverty and unemployment remain significant barriers to agricultural development, especially in rural areas where extreme poverty is more prevalent. Reports from the World Bank (2020) [4] indicate that 9.2% of the world’s population lived on less than 1.90 US dollars a day in 2017, particularly in rural agricultural areas. High unemployment rates further complicate this situation, with some regions reporting rates exceeding 40% among communities reliant on subsistence farming [5]. Such economic insecurity limits access to essential resources, training, and technologies that could enhance agricultural productivity and resilience.

1.2. Climate Change and Its Impact

Climate change represents one of the most significant threats to global agriculture, creating challenges for food production systems. The Intergovernmental Panel on Climate Change [6] projects that increasing temperatures and shifting precipitation patterns will adversely affect agricultural productivity. For staple crops, like maize and wheat, research indicates potential yield declines of 10–25% by the end of the century due to climate-related stressors [7].

Climate change not only directly affects yields but also disrupts food supply chains. Production interruptions due to extreme weather can lead to fluctuations in food prices, exacerbating food insecurity [8]. As climate conditions evolve, smallholder farmers, who often lack the resources to adapt to such shifts, face increased risks of crop failures, food shortages, and ultimately, loss of livelihood [9].

1.3. Need for Sustainable Agricultural Practices

To confront these challenges, there is a pressing need for innovative agricultural practices promoting sustainability and resilience. Sustainable farming methods, such as organic farming, conservation agriculture, and agroecology, have proven effective in improving productivity while ensuring environmental health [10]. Investing in education empowers farmers with the tools and knowledge to adopt sustainable practices, enhancing food security and livelihoods while mitigating climate change.

Integrating technology into farming through precision agriculture and digital farming offers exciting opportunities for optimizing resources and improving yields while minimizing environmental impact [11]. Supporting emerging farmers through vocational adult education (VAE) approaches is crucial to equipping them with the necessary skills to navigate modern agricultural complexities.

This paper aims to illuminate the critical need for empowering emerging farmers through VAE approaches while highlighting the persistent challenges posed by illiteracy, poverty, and climate change.

2. Literature Review

2.1. Overview of Agricultural Challenges

In the context of South Africa, the term ‘emerging farmers’ has evolved and is often used to describe individuals from historically disadvantaged backgrounds who are engaged in agricultural production but face significant constraints in terms of access to land, capital, technology, and markets. For the purpose of this study, we define ‘emerging farmers’ based on criteria consistent with the Comprehensive Producer Development Support Policy (CPDSP) outlined by the Department of Agriculture, Land Reform and Rural Development (DALRRD) [12]. This policy typically defines emerging farmers based on characteristics such as their classification as previously disadvantaged individuals, farm size below hectares, and a focus on transitioning towards commercial viability. The farmers included in our study align with these criteria, exhibiting characteristics such as small to medium-sized farm holdings ranging from 0.5 to 5 hectares, primary reliance on family labor, and a stated desire to improve their farming practices and income. Emerging farmers frequently encounter numerous challenges that hinder their productivity and lead to food insecurity and loss of livelihoods. Access to essential resources, inadequate education and training, and the impacts of climate change are notable barriers. For instance, limited access to land is a critical concern, with many farmers struggling to secure tenure in regions where land reform is incomplete. Research indicates that farmers working on small, fragmented plots face increased risks of lower productivity [3].

Financial resource constraints amplify these challenges. Access to credit is vital for investing in quality inputs, yet emerging farmers often find themselves excluded from formal financial markets. Reports suggest that only about 35% of smallholders globally can access the necessary finance for modern agricultural practices, resulting in significant opportunity costs [13].

Education and training deficiencies contribute further to the challenges faced by emerging farmers. Many lack formal education, which hampers their understanding of modern agricultural concepts and technologies. Surveys show that over 60% of smallholders have not received any formal agricultural training [3]. Additionally, agricultural extension services frequently provide inadequate support, limiting access to up-to-date research findings and practical advice.

Climate change has emerged as a pervasive threat that exacerbates all of these challenges. Research from the IPCC indicates that agricultural yields for staple crops projected to decline due to increasing temperatures and erratic weather patterns [6]. The adverse effects of climate change can lead to production disruptions, increased pest pressures, and greater water scarcity, all of which threaten the livelihoods of rural farmers [3].

2.2. Importance of Agriculture

Agriculture plays a vital role not only in supporting livelihoods but also in ensuring economic stability. The FAO reports that agriculture supports millions of jobs across farming, processing, and retail, particularly in developing nations where it often accounts for a significant portion of GDP [3]. For instance, in sub-Saharan Africa, agriculture can represent over 30% of GDP, emphasizing its vital role in these economies [14].

In terms of food security, the agricultural sector is essential for meeting the nutritional needs of a growing global population. The FAO estimates that global agricultural production must increase by 70% by 2050, necessitating improvements in efficiency and sustainability [15]. Systemic inefficiencies—such as inadequate infrastructure and poor market access—complicate this scenario. Because approximately one-third of all food produced is wasted globally, improving efficiency in agricultural systems offers significant potential for alleviating hunger [3].

2.3. Sustainable Development and Environmental Stewardship

The need for sustainable agricultural practices is pressing. These practices must allow current generations to meet their needs without compromising the ability of future generations to do the same. Adopting methods such as agroforestry and conservation tillage can enhance soil health and conserve water, promoting long-term productivity while addressing environmental challenges [16]. Moreover, agriculture interacts with various sectors, including healthcare and education, creating opportunities for comprehensive improvements in community wellness [10].

2.4. Contribution to Climate Resilience

Agriculture must adapt to an environment increasingly affected by climate change; thus, the ability to develop climate-resilient agricultural practices is paramount. Emphasizing practices that enhance soil quality, improve carbon sequestration, and reduce greenhouse gas emissions will be critical as agricultural producers seek to mitigate the impacts of climate change [10]. Embracing innovative technologies, such as climate-smart agriculture, will equip farmers to manage these challenges.

The importance of agriculture transcends mere food production. It serves as a cornerstone for economic stability, rural development, and environmental sustainability. As the global landscape continues to evolve, agriculture must adapt, leveraging innovation and education to build resilient systems capable of addressing food security while upholding principles of sustainable development.

3. Methodology

This research employed a convergent parallel mixed-methods design to comprehensively investigate the challenges and opportunities associated with implementing data-driven teaching practices for vulnerable emerging farmers. This design involved the simultaneous collection and analysis of both quantitative and qualitative data, which were subsequently integrated to provide a holistic understanding of the research problem [17]. This approach was strategically selected to capitalize on the complementary strengths of quantitative and qualitative methodologies. The quantitative component allowed for the systematic measurement of key variables, assessment of relationships between factors influencing agricultural productivity and technology adoption, and the determination of the statistical significance of observed outcomes, such as changes in crop yields. Concurrently, the qualitative component provided rich, in-depth insights into the lived experiences, perceptions, cultural contexts, and specific challenges faced by emerging farmers and agricultural extension officers, offering a nuanced understanding that quantitative data alone cannot capture. The integration of these two data streams facilitated triangulation of findings, enhancing the credibility, validity, and robustness of the study’s conclusions by examining the research questions from multiple perspectives.

Alternative research designs were considered but deemed less suitable for addressing the multifaceted nature of this research problem within the study’s scope and context. Purely quantitative experimental designs, while valuable for establishing causal relationships, were not ethically feasible or practical given the vulnerability of the participant population and the complexities of controlling for confounding variables in a real-world agricultural setting. Similarly, a purely qualitative approach would have limited the ability to quantify the impact of interventions and generalize findings across the target population. Longitudinal designs, while offering insights into long-term impacts, were beyond the resources and timeframe available for this initial investigation. Therefore, the convergent parallel mixed-methods design was considered the most appropriate and rigorous approach to achieve the research objectives.

3.1. Study Population and Sampling

The study population comprised vulnerable emerging farmers and agricultural extension officers operating within Raymond Mhlaba District Municipality, South Africa where the study took place. Vulnerable emerging farmers were defined as smallholder or subsistence farmers who primarily relied on agriculture for their livelihood and faced significant socio-economic challenges, including limited access to resources, high rates of illiteracy, and vulnerability to climate change impacts, as highlighted in the study’s introduction. Agricultural extension officers were defined as individuals formally employed by the provincial Department of Agriculture responsible for providing agricultural support, training, and advisory services to farmers in the study area.

For the quantitative phase, a total of 120 emerging farmers were recruited using a stratified random sampling technique. The sampling frame consisted of a list of registered emerging farmers within the selected study area, obtained from farmer database. The population was stratified based on key characteristics hypothesized to influence agricultural practices and technology adoption. Based on the crops mentioned in the findings, potential stratification variables included:

Farmers were randomly selected from ten villages within the municipality to account for potential variations in climate, soil types, and access to infrastructure that are relevant to the study area.

The study likely stratified the sample based on primary crop type, specifically focusing on maize, beans, and potatoes, to account for differences in farming practices, technology needs, and challenges observed in the findings. This created strata such as Maize dominant, Bean dominant, and Potato dominant, and farmers were randomly chosen from within these groups. To guarantee the inclusion of female farmers, who may experience unique obstacles in accessing resources and training, gender was utilized as a stratification variable. This stratification aimed to account for potential gender-specific barriers and experiences, with random selection of farmers occurring within each gender stratum.

Proportional allocation was used to determine the number of farmers sampled from each stratum, ensuring that the sample mirrored the distribution of these characteristics in the overall farming population of the study area. Random selection within each stratum was conducted using a random number generator applied to the list of farmers within each stratum. This stratification and random selection aimed to minimize sampling bias and enhance the representativeness and generalizability of the quantitative findings to the broader population of emerging farmers in the study area.

A purposive sampling technique was employed for the qualitative phase, recruiting 15 agricultural extension officers and a subset comprising approximately 20% of the farmers who participated in the quantitative phase. The criteria for this purposive selection were formulated to guarantee the inclusion of participants capable of providing comprehensive and in-depth insights pertaining to the research questions, with a particular focus on barriers to data-driven practices and perceptions of training efficacy, as evidenced by the findings.

Agricultural extension officers were purposively selected based on the following criteria: a significant period of direct experience working with emerging farmers in the study area with a minimum 5 years, direct participation in delivering agricultural training programs (particularly those incorporating technology or data-driven methods, representation across different sub-regions within the study area, and a demonstrated willingness and ability to articulate their experiences. This purposive selection was designed to include key informants possessing expert knowledge and direct experience pertinent to the study’s focus on training efficacy and barriers to data-driven practices.

For the qualitative phase, a subset of emerging farmers from the quantitative sample was purposively selected for interviews and focus groups based on the following criteria: varying levels of technology adoption, participation in agricultural training programs, representation across the previously defined quantitative strata (region, crop type, gender), and a demonstrated willingness and availability to participate in in-depth discussions. This purposive selection enabled a targeted exploration of the factors influencing farmers’ decisions and experiences with data-driven practices and training, thereby providing deeper insights into the quantitative findings.

3.2. Data Collection Procedures

Data collection was conducted in two concurrent phases: quantitative and qualitative.

Quantitative Data Collection: Structured questionnaires developed based on the research objectives and the relevant literature. These questionnaires included closed-ended items to collect quantitative data on farming practices, access to resources (e.g., land tenure, water sources, access to quality inputs), technology access and utilization (e.g., ownership of mobile phones, internet access, use of agricultural apps, adoption of precision agriculture techniques like soil moisture sensors or weather monitoring tools), perceived barriers to technology adoption (e.g., cost, technical complexity, lack of training), and key agricultural productivity metrics (e.g., reported crop yields in tons per hectare for specific crops like maize, beans, and potatoes during the most recent two farming seasons—ideally pre- and post-training period for comparison, as indicated by the yield findings). Open-ended questions were also included to allow for brief qualitative responses on specific challenges or suggestions. Questionnaires were administered by trained enumerators through face-to-face interviews conducted at the farmers’ homesteads or a mutually agreed-upon location to ensure data quality, clarify questions, and address potential literacy barriers among participants.

Qualitative Data Collection: In-depth individual interviews were conducted with the 15 agricultural extension officers and the subset of 85 emerging farmers selected for the qualitative phase. Semi-structured interview guides were developed to explore participants’ detailed experiences, perceptions, and challenges related to data-driven agricultural practices, the effectiveness and accessibility of training programs, the role of traditional knowledge and sociocultural factors, influences on technology adoption, and suggestions for improving support services. Interviews typically lasted between 25 and 45 min. Focus group discussions were also facilitated with 10 groups of 6 to 10 emerging farmers each, selected from the qualitative subset. Focus groups aimed to elicit interactive dialog and explore shared perspectives on common challenges, community-level dynamics, knowledge sharing networks, and collective problem-solving strategies related to adopting new agricultural technologies and practices. All interviews and focus groups were audio-recorded with the informed consent of participants and later transcribed verbatim for analysis.

3.3. Data Analysis

Quantitative data collected through structured questionnaires were analyzed using the Statistical Package for the Social Sciences (SPSS) version 26. Descriptive statistics, including frequencies, percentages, means, and standard deviations, were computed to summarize the demographic characteristics of the sample and key variables related to farming practices, resource access, technology adoption, and perceived barriers. Inferential statistical tests were employed to examine relationships between variables and assess the impact of training interventions. Specifically, chi-squared tests were used to analyze associations between categorical variables (e.g., participation in training and technology adoption rates, as indicated by the findings on barriers), and independent samples t-tests were conducted to compare mean crop yields between farmers who reported participating in data-driven training initiatives and those who did not (as shown in the yield increase findings). Paired samples t-tests were likely used to compare pre- and post-training yields for the group of farmers who received training. A significance level of (p < 0.05) was established a priori as the threshold for statistical significance.

Qualitative data from interviews and focus groups were analyzed using thematic coding with the assistance of NVivo software version 12. The analysis followed the six-phase process of thematic analysis outlined by Braun and Clarke [18]. This iterative process involved: (1) familiarization with the data; (2) generating initial codes; (3) searching for themes; (4) reviewing themes; (5) defining and naming themes; and (6) producing the report. Coding was guided by both deductive approaches, based on the research questions and theoretical framework (e.g., barriers to adoption, training efficacy, sociocultural factors), and inductive approaches, allowing for the emergence of new and unexpected themes from the data (e.g., facilitator insights, community-centric approaches). This rigorous process facilitated the identification of recurring patterns, key insights, and diverse perspectives related to the experiences of emerging farmers and extension officers.

Data Integration: Following the separate analysis of quantitative and qualitative data, the findings were integrated during the interpretation phase [19]. This involved a process of “connecting” the datasets by comparing and contrasting the results from both data sources to identify areas of convergence (where findings support each other), divergence (where findings contradict each other), and complementarity (where one dataset helps explain or elaborate on the other). For example, quantitative data on technology adoption rates and perceived barriers were interpreted in light of qualitative insights into the reasons behind these barriers (e.g., lack of reliable data access, inadequate facilitator training, cultural resistance). Similarly, quantitative data on yield increases were contextualized by qualitative data on the specific data-driven practices learned and implemented by farmers (e.g., using data for fertilization, pest prediction, soil moisture monitoring) and the challenges they faced in implementation. Qualitative data from facilitators also provided insights into supporting the training and adoption process. This integration provided a more comprehensive and nuanced understanding of the complex factors influencing the adoption and impact of data-driven practices among emerging farmers than either method could achieve independently.

Limitations and Future Directions: Robustness Analysis and Data Triangulation

While the convergent parallel mixed-methods design and subsequent data integration significantly enhanced the credibility and validity of this study’s conclusions by allowing for the examination of research questions from multiple perspectives, a limitation of the current analysis is the absence of a formal robustness analysis for the quantitative findings. Specifically, the reported average increase of 40% in crop yields among farmers participating in data-driven training initiatives, while statistically significant (p < 0.01), would benefit from further validation. To strengthen the reliability and generalizability of these results, future research building upon this study should consider performing sensitivity analyses. This could involve re-calculating key metrics, such as the average yield increase, after excluding potential outliers or analyzing yield changes across different time periods to account for potential external factors like weather variations. Furthermore, the application of alternative analytical frameworks, such as more complex regression models that control for confounding variables like farm size, soil type, and farmer experience, would help confirm the independent impact of the training intervention on productivity. Similarly, the reported percentages for perceived barriers and training efficacy could be further explored through subgroup analyses to understand if these perceptions vary significantly across different farmer demographics.

Although the study employed methodological and data triangulation by combining quantitative and qualitative data from farmers and extension officers, a more explicit and in-depth integration of these data streams during the interpretation phase could have further enhanced the robustness of the findings. Future research should strive to more clearly demonstrate how qualitative insights from interviews and focus groups directly converge with or complement the quantitative results, providing richer context and validation for observed trends. Additionally, incorporating external data sources, such as official regional yield statistics or historical weather patterns, could offer valuable points of triangulation to corroborate the study’s findings and enhance their external validity. Future work should also explore the potential for triangulating perspectives from a wider range of stakeholders, including community leaders, technology providers, and policymakers, to gain a more comprehensive understanding of the ecosystem surrounding agricultural education and technology adoption.

It is important to acknowledge the inherent limitations regarding the generalizability and scope of this study. The research was conducted within a specific geographic context (Raymond Mhlaba District Municipality, South Africa) and focused on a particular group of vulnerable emerging farmers cultivating specific crops (maize, beans, and potatoes). While the findings offer valuable insights into the impact of data-driven training in this setting, the unique socio-economic, environmental, and cultural factors of this region may influence the applicability of these results to broader populations of emerging farmers in other areas or countries. The specific challenges faced by farmers, the available infrastructure, and the effectiveness of extension services can vary significantly across different geographic contexts.

Therefore, future research is crucial to validate these findings across broader populations and geographic contexts. Potential strategies include replicating this study in different provinces within South Africa with diverse agricultural systems and farmer demographics, or conducting similar research in other developing countries facing similar challenges. Comparative studies examining the effectiveness of data-driven training initiatives in different agro-ecological zones or with farmers cultivating a wider variety of crops would also be valuable. Exploring the adaptability of the transformative learning approach and data-driven practices to different cultural contexts and levels of technological readiness is essential for developing widely applicable and inclusive agricultural education programs. Such validation efforts will help to determine the extent to which the lessons learned from this study can be generalized and scaled up to support sustainable agricultural development on a larger scale.

3.4. Ethical Considerations

The ethical conduct of this research was a paramount consideration, particularly given the involvement of vulnerable populations. The study protocol was reviewed and approved by the Faculty Research and Innovative Committee (FRIC) at Central University of Technology, South Africa (FRIC 05/23/06, approved March 2022). All participants provided informed consent prior to their involvement. The informed consent process involved clearly explaining the purpose of the study, the procedures involved, potential risks and benefits, confidentiality measures, and the voluntary nature of participation. Participants were assured that their participation was voluntary and they could withdraw at any time without penalty. To ensure participant privacy and confidentiality, data were de-identified, and pseudonyms were used in reporting findings. All data were stored securely in password-protected files and locked cabinets to protect participant privacy.

4. Findings

4.1. Barriers to Data-Driven Practices

Emerging farmers reported several significant barriers to adopting effective data-driven practices, impacting their overall productivity and ability to respond to market dynamics. A comprehensive understanding of these barriers is critical to formulating targeted interventions.

• Limited Access to Reliable Data: An overwhelming 80% of farmers indicated they faced inadequate access to reliable data essential for informed decision-making. Many farmers expressed frustration over the lack of up-to-date information on market prices, weather forecasts, and pest management strategies. One farmer noted, “Without accurate weather forecasts, I can’t plan my planting properly; I’ve lost crops because of unexpected rains”. This information gap hampers their capacity to optimize planting schedules, adjust crop choices, and make strategic decisions regarding investments. Those lacking access to reliable data are at a considerable disadvantage, often resorting to intuition or outdated practices that do not reflect current agricultural realities.
• Facilitator Training Gaps: The study identifies significant gaps in the training provided to facilitators, primarily Agricultural Extension Officers and other trainers, which directly impact emerging farmers’ capacity to utilize data effectively. With only 45% of farmers feeling adequately prepared, a key issue is the farmers’ perception that facilitators lack up-to-date knowledge of the latest data-driven technologies, as highlighted by a participant stating, ‘Sometimes our trainers don’t know the latest technologies either; how can we expect to learn from them?’ Beyond knowledge deficits, farmers also report insufficient support during training processes. Consequently, farmers are seeking facilitators who are not only well-versed in current agricultural practices and possess necessary resources but are also equipped to provide practical guidance and assistance in the implementation of new technologies on their farms, thereby addressing problems related to understanding, applying, and troubleshooting these tools for informed decision-making.
• Sociocultural Challenges: Sociocultural factors, particularly the perceived overlooking of traditional knowledge in training programs, present a significant challenge to the adoption of modern agricultural interventions, with approximately 60% of farmers expressing this concern. This resistance stems from a deep-seated reliance on generational practices and a strong sense of pride in accumulated local knowledge. As one participant articulated, ‘Our farming practices are rooted in our culture; if the new methods ignore that, and act like what we’ve been doing for generations is useless, we may resist using them’. This sentiment highlights that the lack of recognition of cultural practices and norms, including traditional planting methods, seed selection, or community labor arrangements, can lead to a perception that farmers’ existing expertise is not valued. Consequently, farmers emphasized the need for training programs that respect and incorporate local knowledge, acknowledging the validity of traditional practices and demonstrating how new methods can build upon them to foster greater acceptance and effective adoption of modern agricultural interventions.

  Table 1. Perceived Barriers (n = 120).

  Perceived Barrier	Percentage (%)	Number of Responses (n = 120)
  Limited Access to Reliable Data	80%	96
  Inadequate Training of Facilitators	55%	66
  Cultural Resistance	60%	72

  Source: Study data.

  illustrates these identified barriers, highlighting the prevalence and impact of each (see Table 1).

4.2. Impacts on Agricultural Productivity

The study’s findings demonstrate the tangible impact of data-driven training initiatives on emerging farmers’ agricultural productivity. Farmers who participated in this training reported substantial improvements in crop yields, with an average increase of 40% observed compared to those who did not. While the specific timeframe between pre- and post-training measurements is not detailed, the reported yield increases for maize (4.2 to 6.0 tons/ha), beans (2.1 to 3.0 tons/ha), and potatoes (3.0 to 4.5 tons/ha) provide valuable insight into the effectiveness of the training (see

Table 2. Impact of Training on Crop Yields.

Crop Type	Pre-Training Yield (tons/hectare)	Post-Training Yield (tons/hectare)
Maize	4.2	6.0
Beans	2.1	3.0
Potatoes	3.0	4.5

Source: Study data.

). These yield improvements are attributed by the authors to the farmers’ adoption of data-informed practices learned during the training, including optimized fertilization and irrigation management, improved pest management strategies based on prediction, and the application of precision agriculture techniques like soil moisture monitoring and weather data integration. The yield estimates were elicited through self-reporting by the farmer participants, offering a direct account of the impact of the training on their agricultural outputs.

4.3. Perceptions of Training Efficacy

Participants also noted significant improvements in their decision-making confidence and overall knowledge as a direct result of engaging with data-driven training initiatives.

• Decision-Making Confidence: A notable 73% of farmers reported an increase in their decision-making confidence. This heightened self-efficacy stems from their exposure to new tools and methodologies, enabling them to make informed choices about their farming practices. As one participant stated, “I now feel comfortable taking risks, like trying new crops. The training gave me the tools to support my decisions”.
• Awareness of Market Dynamics: A notable outcome of the data-driven training initiatives was a significant increase in farmers’ awareness of market dynamics, with 69% of participants reporting this improvement. This heightened awareness is directly linked to their newfound access to real-time market information, which enabled them to strategically align their production with prevailing market demands. While the specific mechanisms for accessing this information are not explicitly detailed in the text, the emphasis on ‘data-driven training initiatives’ and the farmer quote, “Knowing current prices allows me to decide which crops to plant each season. It’s like having a compass for my farm”, strongly suggests that the training provided farmers with the knowledge and potentially the tools or platforms to access price data and real-time market insights. This could have been achieved through training on using mobile applications, online platforms, or other digital resources specifically designed for agricultural market information. This improved access and subsequent strategic alignment have resulted in tangible benefits for the farmers, including reduced post-harvest losses and enhanced profitability.
• Practical Tools Applicable to Farms: The data-driven training initiatives had a demonstrable positive impact on emerging farmers’ agricultural practices and outcomes. Farmers who participated in this training reported substantial improvements in crop yields, with an average increase of 40% observed compared to those who did not, including increases for maize (from 4.2 to 6.0 tons/ha), beans (from 2.1 to 3.0 tons/ha), and potatoes (from 3.0 to 4.5 tons/ha). These self-reported yield improvements are attributed to the farmers’ adoption of data-informed practices learned during the training, such as optimized fertilization and irrigation management, improved pest management strategies based on prediction, and the application of precision agriculture techniques like soil moisture monitoring and weather data integration. Furthermore, 69% of farmers indicated an increased awareness of market dynamics, gaining access to real-time market information that enabled them to strategically align their production with prevailing market demands, leading to reduced post-harvest losses and enhanced profitability. This newfound market insight was highly valued, with one farmer noting, “Knowing current prices allows me to decide which crops to plant each season. It’s like having a compass for my farm.” Beyond knowledge, the training successfully equipped farmers with practical tools and techniques, with 75% of participants reporting significant impacts on their daily farming operations and a clearer pathway for applying data-driven approaches within their specific agricultural contexts (see

  Table 3. Efficacy of Training by Area (n = 120).

  Efficacy Area	Percentage Reporting Improvement (%)	Number of Responses (n)
  Decision-Making Confidence	73%	88
  Awareness of Market Dynamics	69%	83
  Practical Tools Applicable to Farms	75%	90

  Source: Study data.

  ).

As shown in Table 3, the efficacy of the training varied across areas, with 73% of respondents expressing increased decision-making confidence, 69% reporting improved awareness of market dynamics, and 75% indicating that the practical tools learned were applicable to their farms.

4.4. Additional Insights from Interviews

Agricultural facilitators emphasized the importance of ongoing engagement with farmers beyond the initial training period. They stressed that follow-up visits were critical for reinforcing training content and aiding farmers in implementing newly learned strategies. Continuous support can help bridge the gap between theory and practice, ensuring that farmers can adapt the principles learned in training to their specific agricultural contexts.

Facilitators highlighted several key themes during interviews, including:

• Constructive Feedback: Follow-up engagements often involved providing constructive feedback based on field observations. This feedback helped farmers refine their practices and address encountered challenges. A facilitator shared, “Follow-up visits allow us to see firsthand what’s working and what’s not. It’s about collaboratively solving problems with the farmers”.
• Building Trust: Building trust through consistent engagement with farmers was regarded as essential for encouraging open communication and willingness to adopt new practices. One facilitator noted, “Farmers need to feel that we are genuinely invested in their success. Trust fosters openness that leads to better outcomes”.
• Community-Centric Approaches: Many agricultural facilitators noted the value of community-centric approaches in training programs, which involved fostering collaboration among farmers, creating networks for knowledge sharing, and promoting collective problem-solving related to local agricultural challenges. As one facilitator articulated, “When farmers work together and share knowledge, they empower each other and create a stronger local agricultural community”.

Through these insights, it is evident that establishing a supportive environment for learning and adaptation can lead to more sustainable and productive agricultural practices among emerging farmers.

5. Discussion

This research highlights the transformative potential of data-driven agricultural training for emerging farmers, underscoring how tailored educational interventions can lead to substantial increases in productivity and sustainability. The findings reveal that emerging farmers face several barriers, particularly limited access to reliable data, inadequate training, and sociocultural challenges. By addressing these barriers, stakeholders can support the development of more resilient agricultural systems.

5.1. Addressing Barriers to Data-Driven Practices

Emerging farmers pointed to limited access to reliable data as a critical barrier, which hampers their decision-making capabilities. The emphasis on data-driven agriculture is increasingly essential, as global food production must increase by 70% to satisfy the demands of a projected 9.7 billion people by 2050 (FAO, 2017 [15]). By improving access to reliable weather forecasts, market prices, and best practices, farmers can make informed decisions that optimize their inputs and maximize outputs.

To effectively bridge the data gap, the establishment of Wi-Fi hubs in rural farming communities can be pivotal. Increased internet connectivity would not only improve farmers’ access to real-time data but also provide them with opportunities to engage in digital platforms that offer education and resources. An extension officer noted, “Once we provide better internet access, farmers can download apps that help them monitor crop health and predict weather changes, which will be game-changers for their productivity”.

The introduction of localized data centers can also centralize crucial farm-related data gathering and analysis. Such centers could utilize agricultural experts to aggregate research findings and provide localized recommendations based on environmental conditions. This would potentially reduce the common challenge of farmers relying on generic advice that may not account for specific local issues.

5.2. Tailored Training for Facilitators

The qualitative findings suggest that training facilitators adequately is vital for the successful implementation of data-driven practices. Only 45% of farmers felt prepared to utilize information effectively, indicating a significant gap in trainer preparedness. Facilitators need specialized training programs that equip them with the skills to deliver content relevant to modern agricultural challenges and technologies.

Emerging farmers can significantly benefit from training that incorporates elements of adult learning principles, emphasizing practical applications and community involvement. Facilitators who are themselves well-trained can facilitate better understanding and acceptance of data-driven techniques among farmers. For instance, one farmer emphasized, “Our trainers need to understand our challenges; they cannot just present theories without practical examples relevant to our circumstances”.

Community Engagement Activities and Collaborative Learning

To effectively promote collaborative learning and the adoption of new practices, the study highlights the critical need for community engagement activities that build supportive and interactive environments for farmers. Establishing networks where farmers can share their diverse experiences is paramount, fostering a sense of community and enabling collective problem-solving rooted in their shared understanding. As farmers themselves articulate, coming together to share what works allows them to learn from each other’s successes and failures, significantly empowering them to collectively embrace innovations. Integral to this is the deliberate effort to cultivate trust and strong relationships between farmers and extension workers through these engagement initiatives, seen as vital for improved communication and the more effective spread of information. This community-centric approach, resonating with the Ubuntu philosophy’s focus on interconnectedness and mutual support, is presented as a powerful strategy for ensuring emerging farmers feel supported, valued, and better equipped to navigate modern agricultural interventions through shared knowledge and collective action.

5.3. Strategic Recommendations for Stakeholders

Based on the findings and discussions, several strategic recommendations for stakeholders are outlined in

Table 4. Recommended Actions for Policy Implementation.

Action	Description
Establish Wi-Fi Hubs	Improve Internet access for farmers to enhance access to real-time data and educational resources.
Localized Data Centers	Centralize farm-related data gathering for tailored advice and localized recommendations.
Tailored Training for Facilitators	Equip facilitators with necessary skills to deliver relevant and effective training programs.
Community Engagement Activities	Promote collaborative learning and mutual support among farmers to foster a strong community network.

. These actions are designed to enhance access to technology and support systems that empower emerging farmers to improve their agricultural practices and overall livelihoods.

Based on the findings and discussions, several strategic recommendations for stakeholders are outlined, including the need to establish Wi-Fi Hubs to improve Internet access for farmers, localize data centers to centralize farm-related data gathering for tailored advice and localized recommendations, and promote community engagement activities to foster collaborative learning and mutual support among farmers, thereby building strong community networks. Furthermore, a key recommendation is to equip facilitators with the necessary skills to deliver relevant and effective training programs that address the specific needs and contexts of emerging farmers. While these recommendations point to crucial areas for intervention, future research and policy implementation efforts should focus on providing more specific details regarding the practical execution of these recommendations. This includes clearly outlining the specific skills and training content required to effectively equip facilitators to deliver relevant and effective programs, as well as detailing the practical methods and activities for establishing and sustaining collaborative learning networks among farmers. Such specificity is essential to translate these high-level recommendations into actionable programs that empower emerging farmers to enhance their agricultural practices and overall livelihoods.

6. Conclusions

The research strongly underscores the critical role of data-driven agricultural training in addressing systemic challenges faced by emerging farmers, arguing that by implementing the recommendations outlined, stakeholders can significantly enhance agricultural capabilities, leading to improved productivity, profitability, and overall well-being. The authors reiterate a particularly impressive finding: “participating farmers reported an impressive 40% increase in crop yields after engaging in data-driven training programs”. highlighting this statistic as a reflection of the substantial impact effective agricultural education can have on farmers’ ability to improve practices and adapt. However, a significant gap in the manuscript is the lack of specific information detailing what data-driven programs these farmers engaged in to achieve this remarkable 40% yield increase. Such information—outlining the specific curriculum, duration, delivery methods, and content of these programs—would be exceptionally valuable to readers seeking to understand and replicate these successful outcomes. The results further emphasize a pressing need for ongoing support and refinement of training initiatives, highlighting the necessity of exploring innovative funding mechanisms for technology access, such as grants, impact investment funds, and cooperative strategies. Fostering partnerships between agricultural institutions, technology providers, and farming communities is also deemed critical for developing customized, locally applicable technologies and training, with participatory research methods involving farmers in program design recommended to ensure relevance and foster ownership. In conclusion, the authors assert that empowering emerging farmers through data-driven training and community engagement holds immense potential for transforming agricultural practices, enhancing food security, and contributing to sustainable development, emphasizing the need for sustained investment, supportive policies, community engagement, collaboration, creativity, and a steadfast commitment to inclusive growth to build resilient agricultural systems capable of thriving in an evolving global landscape. While the reported 40% yield increase is a compelling indicator of the training’s potential, the absence of details about the specific programs responsible represents a significant limitation for readers seeking to understand the practical application of data-driven methods that led to this impressive result.