Indigenous communities in Guatemala face persistent social, economic, and political inequities that limit their participation in development decision-making, including agricultural and rural development initiatives. Leadership development programs often rely on Western leadership models that many times do not reflect Indigenous epistemologies and communal leadership practices. The purpose of this study was to develop and validate an empirical instrument to measure the perceptions of Maya-Mam and Xinka communities regarding the leadership competencies that characterize effective indigenous leaders. Guided by Indigenous Maya-Mam and Xinka leadership practices, an initial 20-item instrument was developed, pilot-tested, and validated. Data were collected from 138 Indigenous community members using both paper-based and online surveys. Exploratory factor analysis retained 13 items across a four-factor structure: service, mediation, Indigenous values, and experience, accounting for 43.71% of the total variance following rotation. The overall scale demonstrated evidence of convergent and discriminant validity, indicating that the resulting instrument is a psychometrically sound, culturally grounded tool for measuring community perceptions of Indigenous leadership competencies. This instrument provides agricultural development practitioners, extension professionals, and development organizations with a culturally connected tool to support their assessments and strengthen Indigenous leadership capacity.
The implementation of ChLOE at Ohio State University provided science teachers with authentic agricultural experiences for integrating agriculture in their classes through a professional development session and field trip with their students. The purpose of this explanatory mixed-methods study is to evaluate the ChLOE’s impact on teacher participants’ knowledge, beliefs, and application of the program content. The quantitative questionnaire indicated that the program was beneficial for students and teachers through the reported implementation of the pedagogies taught during the professional development and immersive experiences during the field trip. The qualitative follow-up interviews indicated that teachers perceived high student engagement during the field trip and a plethora of intended and reported behavior change through the integration of professional development and field trip within the classroom. The expansion on the quantitative results by the qualitative suggests that the behavior changes are congruent with the additional beliefs and knowledge acquired by the teachers throughout the ChLOE program. OSU should align educator programs, including ChLOE, with evolving teacher needs and evaluate student outcomes to ensure relevance to instructional goals. Overall, teacher professional development programs should cater to a variety of teacher needs and curriculum.
Information and communication technologies (ICTs) are now central to modern extension and advisory services. Smartphones have become popular in the Caribbean, but their use in the farming sector remains largely unknown. A sample of 559 farmers from Barbados, Dominica, Grenada, Jamaica, St. Lucia, St. Vincent & the Grenadines, Suriname, and Trinidad and Tobago was conveniently selected. Demographic data, barriers, needs, perceptions, and intentions were assessed. Results showed that in every country investigated, most respondents were smartphone users, with use ranging from 58% to 89%. There was a significant association between user group and country of residence, and between age, education, and years in farming, with age and education showing moderate associations. Perceptions of barriers differed based on access to training, the time available to use the devices, and the cost of smartphones. Most farmers, regardless of user status, were interested in regular ICT training programs, assistance to purchase devices, and technical support for smartphones. The study provides implications for smartphone use, factors associated with use, and barriers to be overcome and support needed. Policy makers and Extension agencies can use the findings to help increase smartphone adoption for farming across the Caribbean.
This exploratory sequential study explored teacher perspectives about building relatedness in their School-Based Agricultural Education (SBAE) program by utilizing semi-structured interviews, and the data were used to build an instrument to further examine the use of relatedness. In the qualitative phase, we utilized a basic qualitative design and purposively sampled (n = 8) Ohio teachers. We found that teachers shared four relatedness themes in their SBAE program: attainable relationships, student commitment, accessible mentorship, and student collaboration. In the quantitative phase, a stratified probabilistic sampling of our target audience (n = 320), which was the National Association of Agricultural Educators (NAAE) Region IV teachers. The quantitative results demonstrated that teachers perceived that it was somewhat true that each of the relatedness themes were present in their programs. Additionally, it was found that demographic data had no relationship to the presence of the relatedness themes. From these findings, it is recommended that SBAE teachers use pedagogical and programmatic strategies that allow the four themes of attainable relationships, student commitment, accessible mentorship, and student collaboration to flourish within their SBAE program.
This descriptive and correlational study investigated the adoption of generative artificial intelligence (AI) by agricultural educators in Alabama, focusing on their perceptions, attitudes, and experiences (N = 80). Grounded by Rogers’ diffusion of innovation theory and Davis’s technology acceptance model, a mixed-mode survey design was used to assess educators’ awareness, perceived benefits, competencies, and barriers to AI adoption. Findings revealed a significant experiential divide across all measured themes, where early-career educators (with ≤5 years of experience) reported significantly higher awareness, perceived benefits, competence, and optimism about overcoming barriers than experienced educators (with ≥ 15 years of experience). The primary barrier to adoption was a shared and uniform high level of concern regarding the pedagogical and ethical implications of AI. This contrast suggests that the central challenge is a pedagogical adoption gap between educators’ operational skills and their deeper apprehension of reconciling AI with their professional identity. This study confirms prior research on technology adoption while identifying a novel ethical barrier associated with the use of generative AI in agricultural education. The findings support the recommendation of differentiated approaches to enhance the confidence of experienced educators and reinforce the ethical best practices for early-career educators.
In today’s changing landscape of artificial intelligence, agricultural leaders and professionals need to be equipped with strategies to help them navigate the adoption and implementation of these tools. Understanding the technical implications of AI is critical to how one communicates and engages with others; however, it is but a piece of the puzzle when considering the complex nature of working with people. In order to be truly effective, agricultural leaders and professional also need to be prepared to use behaviors of emotional intelligence and critical thinking to help mitigate concerns people may have about the impacts of AI in their work. Through emotionally engaged thinking, agricultural leaders can be ready to address the human-centered concerns about the adoption and implementation of AI tools.
Recent generative AI offers personalized, high-quality advice to smallholder farmers in resource-limited settings. Yet, most large language models (LLMs) lack training data for diverse agroecologies, often yielding generic, inaccurate, or locally misaligned advice. Digital Green adapted Reinforcement Learning from Human Feedback (RLHF) to agricultural advisory to deliver highly localized, relevant, information. This refined tool, called Farmer.Chat, is an AI assistant supporting over 670,000 farmers in India, Kenya, Ethiopia, and Nigeria with text, image, and voice-based content. This paper details Digital Green's RLHF approach: a web-based annotation tool, multi-phase implementation, and quality assurance. Over 25,000 expert-reviewed Q&A pairs yielded significant improvements in response quality, tone, context, and cultural fit, especially for region-specific agricultural queries. The work outlines key lessons, cost/equity, and replication guidance. It calls for researchers, governments, and NGOs to pool validated Q&A data, strengthening global AI systems. Future work explores multimodal RLHF (image, voice, video), aiming to foster a global, inclusive, evidence-based ecosystem for AI agricultural advice.
This article is a written reflection of common themes shared by experts at the second biennial symposium and in their accompanying special issue articles, presented as a forward-looking story integrating themes of purpose, ethics, and human dimensions into decisions about the responsible use of artificial intelligence and digitial technologies as innovations for agricultural development.
Advancements in Agricultural Development hosted its second symposium, centered on the theme of “Applications of Artificial Intelligence and Digital Technologies in Agricultural Development Research and Practice,” co-hosted by the University College Dublin School of Agriculture and Food Science, on October 13 and 14, 2025. Invited experts from Auburn University, Digital Green, Harper Adams University, International Food Policy Research Institute, the University of Florida, University College Dublin, and Utah State University shared papers that advanced our understanding of the role of these emerging technologies in our extension and teaching efforts. This article summarizes each of the papers presented. We are proud of this special issue and hope the agricultural development community finds it useful.
This study explores how generative AI (GAI) tools for agricultural extension can be designed and evaluated more responsibly. While current GAI systems offer scalable, personalized advice, they often ignore the lived realities of smallholder farmers—especially women—by relying on generic datasets and rigid evaluation metrics. We investigate three complementary methods: adversarial testing to expose gendered and contextual blind spots in model outputs; deliberative stakeholder engagement using the C-H-A-T framework, which focused on Collective knowledge, Human insight, Augmentation, and Trust, to surface value tensions and design trade-offs; and field-level insights from extension officers to uncover trust-building, diagnostic reasoning, and social intelligence absent from static GAI interactions. Together, these approaches reveal that responsible GAI requires more than technical accuracy. It demands participatory design processes that foreground user realities, surface stakeholder assumptions, and account for social and institutional context. We recommend developing gender-responsive benchmarks, embedding reflexive, participatory design methods, and modeling advisory reasoning based on real-world extension practice. The findings contribute to a growing agenda for responsible AI development—highlighting the importance of aligning GAI tools not only with technical goals, but with the social, cultural, and political contexts in which they operate.
This study investigates the applicability, practicality, and effectiveness of a low-cost AI foundational model (FM) in agricultural extension through the development, fine-tuning, and evaluation of a custom GPT named Utah PeachBot, built using OpenAI’s GPT platform. The research focused on facilitating real-time, evidence-based advisory service support for Extension agents assisting small-scale peach producers in Utah. Methods involved training the GPT with curated, research-based horticultural resources and assessing model outputs through an expert panel of six Extension agents. Results showed high reliability and accuracy for general inquiries about peach cultivation. However, inconsistencies in regional specificity and the practicality of recommendations emerged as limitations. Feedback indicated a need for iterative fine-tuning of the model through continuous expert feedback and integration of local, context-specific data. Recommendations include a phased approach to implementing customized GPTs in agricultural advisory services to improve information dissemination, decision-making quality, and operational efficiency within extension systems.
Agricultural innovation is pivotal to meeting global food, climate, and livelihood challenges. This study systematically reviews publications from 2021 to 2025 on agricultural technology adoption. We combine Everett Rogers’s diffusion of innovation theory with a supervised machine learning technique: Random Forest (RF), an ensemble tree-based method within the broader AI toolkit, to identify key factors that influence adoption outcomes across 571 cases from 531 publications. Our stepwise approach integrates systematic bibliometric searches, rigorous textual coding and numerical conversion, and RF modeling to synthesize diverse empirical evidence into actionable, data-driven guidance. The RF model, which demonstrates good predictive performance, highlights extension access, climate risk awareness, and perceived relative advantage (along with perceived simplicity and training participation) as the most influential predictors of adoption decisions. Education, or more broadly, innovation literacy, emerges as essential in specific local contexts but less influential across all cases, while peer networks exert moderate, context-dependent effects. These findings suggest that extension messages and programs should emphasize clear, observable benefits, manageable complexity, and climate-related risk information that directly address farmers’ needs and concerns. Overall, this integrated methodological approach provides robust and nuanced insights, offering practical guidance for agricultural development policy, extension strategies, and future research.
Extant research supporting digital mental health interventions for farmers and the successful delivery of psychological interventions by laypeople is predominantly nomothetic (aggregate, group-level). Since conclusions we draw from inter-individual data may not apply at the intra-individual level, it is important to cultivate a diverse evidence base for these topics. Adding alternative methods, such as idiographic (individual-level) single-case experimental designs is imperative. Akin to a pilot randomized-controlled trial, the present study examined the feasibility and suitability of a quasi-randomized multiple-baseline single-case experimental design for testing agricultural advisors’ experiences of training in a digital acceptance and commitment therapy intervention. 18 agricultural advisors enrolled in the study and were asked to (i) complete a three-item measure daily for 55 days, (ii) attend two 2.5-hour training sessions via Zoom, and (iii) complete three longer surveys preintervention (Time 1), immediately after the intervention (Time 2), and three months postintervention (Time 3). Appropriate participant retention, data missingness, and errors were observed, suggesting that the present method is feasible and suitable. In addition, outcomes were generally consistent with expectations at the nomothetic level at Times 2 and 3. Future research should employ single-case experimental designs and target various levels of analysis (psychological, sociocultural, and biophysiological).
This research contributes to the growing discourse on participatory approaches for digital agricultural innovation design. Specifically, it details practical insights and learnings from applying the first three phases of a design thinking approach to the design of a digital animal health innovation. Design thinking proved effective as it facilitated direct engagement with end users (through focus groups and a co-design workshop) and leveraged specific design thinking techniques (user personas and ‘How might we…?’ questions). This facilitated the identification of key end-user needs and the co-creation of tailored solutions, successfully informing the tools design. Several context specific insights and learnings are garnered from this study for future researchers aiming to replicate these approaches in an agricultural context. These include due consideration for the busy farming calendar to ensure effective stakeholder participation and engagement with the process; attention to environmental factors when engaging stakeholders on farm; and the recommended use of boundary objects in participatory research to facilitate mutual understanding and rich discussion. This research suggests that considering these factors is imperative for successful participatory design research in agricultural contexts.
So-called ‘agriculture 4.0’ technologies, such as robotics, AI, drones etc., are apparently set to revolutionise farming, helping us to produce more, with less. However, a growing literature from social science disciplines, such as Science and Technology Studies (STS), Sociology, and Transition Studies, illustrates that new technologies have both positive and negative consequences. For the future of farming to be responsible, the consequences of adopting different technologies and practices need to be anticipated. Students at university, who are studying courses related to agri-food systems, are a key cohort that will shape the future of farming. This paper describes the use and refinement of creative teaching methodologies that help to expose students to literature from Science and Technology Studies (STS), particularly on ‘responsible innovation,’ which many agri-food students rarely study. The concept of responsible innovation is important for agri-food students to understand because it enables them to consider the opportunities and risks of different future farming systems, helping to make future trade-offs more tangible. With one main learning objective in mind, to enable students to interrogate the opportunities and risks of agricultural technologies, we shared student-led stories of future agricultural utopias and dystopias, using them as a tool for critical discussion.
Pollinators are vital for agricultural sustainability, yet their populations face increasing threats from habitat loss, pesticide use, and climate change. This study aimed to design and evaluate a five-day instructional unit for fifth-grade students focused on pollinator conservation and the human impacts affecting pollinator populations. Grounded in Vygotsky’s Sociocultural Theory, the curriculum incorporated scaffolded, inquiry-based strategies to foster both scientific understanding and environmental stewardship. Using a one-group pretest–posttest design, data were collected from 104 students across three classrooms in Georgia. A researcher-developed assessment measured content knowledge and self-reported confidence. Results indicated significant gains in student comprehension with mean scores improving by over four points. Students also reported higher confidence in their understanding of pollinator conservation. Findings suggest that scaffolded, real-world instruction effectively enhances elementary learners’ knowledge and engagement with environmental issues. Recommendations include integrating pollinator concepts into state science standards, expanding professional development for teachers, and conducting longitudinal studies to assess retention and behavior change. This study demonstrates the potential of early agricultural literacy initiatives to build student capacity for addressing sustainability challenges.
This is the annual state of the journal report for Advancements in Agricultural Development. 2025 was another great year for AAD. First, we experimented with novel methods to elevate the digital presence of research published in AAD. Second, we held our second symposium. Third, we refined several policies to provide greater clarity for our authors. Fourth, we have implemented a new way of recognizing the best articles published each year.
Traumatic events occurring during childhood (0-17 Years), or Adverse Childhood Experiences (ACEs), can have a negative influence on the life of the afflicted individual, including a substantially higher risk of addiction, poverty, cardiac issues, diabetes, and early death. These negative repercussions of ACEs can be mitigated through appropriate teacher-student relationships. Agricultural educators report supporting students with ACEs regularly, but are not confident in their abilities to emotionally support students in these unique situations. This ambiguity in the role of educators in supporting students with ACEs can cause compassion fatigue, secondary traumatic stress (STS), and, ultimately, burnout, which has proven to be one of the leading causes of agricultural educator attrition. This study sought to compare the effects of STS on the burnout of agricultural educators. The author utilized the Professional Quality of Life (ProQOL) survey to measure STS and burnout and gathered 59 usable responses. The author used linear regression to compare STS’s effect on agricultural educator burnout, resulting in a statistically significant interaction that suggests STS contributes to teacher burnout. The findings lead the author to recommend providing mental health services to educators experiencing STS and offering training on strategies to support students facing ACEs.
This study investigates how educational attainment shaped the motivations and experiences of Extension Master Gardeners (MGs) and Master Watershed Stewards (MWSs) during the initial phase of the COVID-19 pandemic. The pandemic’s disruptions provided a unique context for examining how volunteers with differing educational backgrounds engaged with Extension programming and adapted their roles. This research is part of a larger project that examined the relationships between volunteer leadership competencies and stewardship behaviors within Penn State Extension's MG and MWS programs. An online questionnaire was distributed to 3,000 volunteers, yielding 1,196 responses (39.9% response rate). Of these, 331 participants (27.7%) provided open-ended responses describing their motivations and experiences, which were analyzed in this study. A mixed-methods design integrated thematic analysis, Latent Dirichlet Allocation (LDA) topic modeling, natural language processing, and principal component analysis (PCA). Results indicated that volunteers with graduate degrees frequently assumed strategic and leadership-oriented roles emphasizing virtual engagement and innovation, while those without graduate degrees focused on hands-on service, community outreach, and applied learning. Despite these variations, both groups demonstrated resilience and a strong commitment to service. Findings underscore the importance of aligning volunteer roles with educational backgrounds to enhance engagement, satisfaction, and program effectiveness. Implications for Extension programming and future research are discussed.
In Honduras, sustainable livestock practices [SLPs] are gaining attention as strategies to address environmental degradation and improve rural livelihoods. However, their implementation and scaling remain uneven. This qualitative case study explores the perspectives of ten development specialists engaged in national livestock projects, examining how human and social capital influence program success. Guided by the Sustainable Livelihoods Framework [SLF], data were collected using a combination of semi-structured interviews, document reviews, and field observations. Data analysis highlighted the challenges, strategies, and contextual factors shaping the outcomes of sustainable livestock interventions. Findings reveal persistent barriers to adoption, including limited producer engagement, resource constraints, and market-related challenges. Despite these obstacles, participants described effective approaches such as Farmer Field Schools (FFS), participatory training methods, and strong institutional partnerships. Technical expertise and collaborative networks were key factors contributing to positive outcomes. Producer organizations also played a vital role in facilitating market access and strengthening collective action. The study concludes that sustainable livestock development must go beyond technical training to institutional strengthening, value chain integration, and local adaptation. Recommendations include expanding participatory learning methods, enhancing access to financial and technical support, and fostering inclusive decision-making for long-term sustainability.
