Duration: 10/01/2027 to 09/30/2032

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Although many colleges of agriculture have experienced rising enrollment over the past decade, fewer students maintain their agricultural focus through successful placement in an agriculturally based scientific or technical position after graduation (Dyer et al., 1996; NRC, 2009; Wilson et al., 2025). This multistate project builds on ten years of work under the S1057 and S1071 projects to revitalize interest in agriculture as a career path and to ensure secondary school students have the competencies to succeed in college and in careers that increasingly depend on both scientific knowledge and digital tools. Modern agriculture is now undergoing a transition toward what Bampasidou et al. (2024) called Agriculture 4.0, defined by the integration of artificial intelligence (AI), the Internet of Things (IoT), robotics, sensors, and data-driven decision tools into everyday production and business operations. What this multistate team operationalizes as Digital Agriculture. This transition is unfolding against a backdrop of significant global demand: Armanda et al. (2019) reported that the world will need to produce almost 50 percent more food by 2050 to meet the needs of a projected 9.73 billion people. Compounded with climate variability and natural resource constraints (Ahns et al., 2026), this intensifies pressure on the agriculture sector to produce more with greater precision and fewer resources. Digital agriculture offers tools to help meet these demands.

 

The Need as Indicated by Stakeholders

The workforce needed to sustain this transition towards Digital Agriculture is not keeping pace. According to the most recent employment projections, an estimated 104,766 annual job openings will occur nationally in food, agriculture, renewable natural resources, and the environment (FARNRE) fields between 2025 and 2030 (Wilson et al., 2025). There are not enough students earning FARNRE-specific degrees to meet this demand, which will require 28,907 (27.6%) of these jobs to be filled by graduates from outside FARNRE degree programs (Wilson et al., 2025). This gap is particularly acute in science and engineering roles, where growth in automation, robotics, and AI adoption is accelerating employer demand for technically skilled graduates.

 

School-based agricultural education (SBAE) sits at the forefront of this pipeline. Smith et al. (2025) report that over 15,000 SBAE teachers work nationally, and their efforts reach well over one million students (National FFA Organization, n.d.). Enrollment in a high school agricultural education program has been shown to be one of the most influential factors in whether students complete a degree in a college of agriculture (Dyer et al., 2002), and that enrollment correlates with more positive perceptions of agriculture as a career path (Smith, 2010). Motivation to learn science in agriculture has also been positively associated with academic achievement (Chumbley et al., 2015). These agriculture teachers are positioned to introduce students to digital and precision agriculture concepts well before students reach postsecondary study or the workforce. However, a persistent gap exists between the science and technologies reshaping the industry and what is actually taught in secondary classrooms.

 

Agricultural education has emphasized foundational STEM competencies since the S1057 and S1071 multistate projects established a framework for embedding science and mathematics content in agriscience curricula. Digital agriculture and precision agriculture competencies are a natural evolution of that same STEM foundation, not a separate body of content. Secondary SBAE programs have been slow to adopt digital agriculture technologies and other STEM topics (Morey et al., 2023; Pehrson et al., 2025). Teachers report that content knowledge, not interest or motivation, is their most significant barrier to integrating STEM into agriculture curricula (Myers & Washburn, 2008; Stubbs & Myers, 2015; Thompson & Warnick, 2007; Thoron & Myers, 2010), a barrier that will only grow as the technical content teachers are expected to teach expands to include data systems, automation, and emerging technologies such as the artificial intelligence tools used in precision agriculture.

 

Importance of the Work and Consequences of Inaction

The cost of failing to close this gap is already visible in industry hiring outcomes. Erickson et al. (2018) surveyed agricultural retailers and found that filling precision agriculture positions typically takes 60 to 90 days or longer, and that most applicants interviewed for these roles were rated low or deficient across core knowledge, skills, and abilities, including statistical reasoning and general precision agriculture knowledge. Without earlier and more consistent exposure at the secondary level, this mismatch between industry needs and applicant preparedness is likely to persist or worsen as digital agriculture technologies become more deeply embedded across the industry.

 

If this work is not undertaken, STEM integration and digital agriculture instruction may continue to develop unevenly across states and school systems. Teachers with access to equipment, industry partners, and specialized training may move ahead, while educators in smaller or less-resourced programs may be left to develop materials independently or omit the content altogether. The result could be widening differences in student opportunity, continued inconsistency in preparation for postsecondary programs, and a workforce pipeline that remains poorly aligned with the technologies used in agricultural production and management.

 

This project aligns with the U.S. Department of Agriculture’s National Institute of Food and Agriculture (NIFA) priority areas for Education and Workforce Development and Strengthening Agricultural Systems. These priority areas call for integrated research, education, and extension efforts that prepare the next generation of agricultural professionals and strengthen the food and agricultural systems on which farmers, ranchers, and foresters depend (NIFA, n.d.). The projects emphasis on digital agriculture also responds directly to NIFA’s Data Science for Food and Agricultural Systems crosscutting priority, which calls for research that helps educational systems and communities integrate data science and artificial intelligence into agricultural practice (NIFA, n.d.). This work is further consistent with the American Association for Agricultural Education’s Research Values; specifically, the increasing prosperity through innovation in AFNR systems, which includes developing a workforce with relevant skills for AFNR advancement (AAAE, 2023).

 

Technical Feasibility

Existing research points to a workable path forward. Nickeson (2013) found a significant positive relationship between Oklahoma agriculture teachers' precision agriculture training and their likelihood of teaching precision agriculture content in the classroom, suggesting that targeted professional development, rather than curriculum mandates alone, can move teachers toward adoption. This finding aligns with the Concerns-Based Adoption Model (Hall & Hord, 2006), which describes the environment, user system culture, resource system, change facilitator team, interventions, and users and nonusers involved in adopting an innovation, along with three diagnostic measures: stages of concern, levels of use, and innovation configurations. This model frames technology adoption as a gradual process shaped by teachers' stages of concern and levels of use, and it has guided this project's approach to STEM integration since its inception. Applying that same model to digital agriculture adoption gives the project a research-based framework for diagnosing where teacher support is most needed and for measuring progress as new content is introduced.

 

The project also inherits an existing infrastructure for this work. The three-dimensional science learning model outlined in A Framework for K-12 Science Education (National Research Council [NRC], 2012), built around science and engineering practices, crosscutting concepts, and disciplinary core ideas, and the Innovation Configuration mapping process this team has used to define high-quality agriscience practice, both provide established methodological tools that extend naturally to digital agriculture content. Any curriculum model produced by this work will continue to align with the Next Generation Science Standards and the Principles and Standards for School Mathematics (National Council of Teachers of Mathematics, 2000), consistent with the standard this project has held since inception. All participating institutions have the research lab and office facilities, computing infrastructure, high-speed internet access, and personnel needed to carry out the proposed work.

 

Advantages of a Multistate Effort

A continuation of this multistate effort allows researchers from multiple institutions to keep building the collaborative relationships established over the S1057 and S1071 cycles, as new experts continue to join the project. No single institution can address a challenge of this scope alone. Precision agriculture technologies and their relevance vary substantially by region and commodity system, from irrigation and remote sensing in Western row-crop production to variable-rate application in Midwestern grain systems to spatial data tools in Southern specialty crop and forestry operations. A multistate team spanning these production contexts can test and validate digital agriculture curricula across a far broader range of conditions than any individual state could achieve, producing findings that generalize nationally rather than reflecting a single region's technology adoption patterns.

 

The scope of this work also requires expertise that no single department typically holds in full: agricultural educators, precision agriculture specialists, and researchers versed in technology adoption theory must collaborate to translate fast-moving industry technologies into validated secondary instructional models. A coordinated multistate structure distributes this research burden across institutions, allowing the team to pool capacity for large-scale data collection, instrument development, and curriculum validation at a scale that would be unsustainable for individual faculty members working independently.

 

Anticipated Impacts of Successful Completion

The prior S1057 and S1071 cycles produced a completed alignment of the National AFNR standards with the Next Generation Science Standards, work that continues to help agricultural education programs demonstrate how their curriculum meets science and engineering learning outcomes. Continuing this project will extend that foundation by helping agricultural educators build awareness of the practices, crosscutting concepts, and disciplinary core ideas embedded in a modern agriscience program, one that now includes the data and technology tools reshaping the industry. Successful completion of this project would produce a validated framework and set of instructional resources that directly address the barriers teachers report today: a shared, research-based curriculum resource to reduce reliance on self-created materials; professional development models informed by the training-adoption relationship Nickeson (2013) identified; an industry-validated set of essential digital agriculture competencies used to anchor curriculum content in current workforce demand; and a structure for evaluating digital agriculture integration comparable to the Innovation Configuration approach this team has used for STEM content generally. As teachers gain the knowledge, materials, and confidence to integrate digital agriculture concepts across the general curriculum rather than a narrow subset of advanced students, the project anticipates measurable gains in student exposure to digital agriculture competencies, narrowing the gap between the technically skilled workforce agricultural employers need and the graduates currently available to fill it.

 

 

Related, Current and Previous Work

Objectives

Methods

Measurement of Progress and Results

Outputs

Outcomes or Projected Impacts

Milestones

Projected Participation

View Participation Form/Appendix E: Participation

Outreach Plan

Organization/Governance

Literature Cited

References

 

“AFRI Crosscutting Programs | NIFA.” Accessed September 24, 2026. https://www.nifa.usda.gov/grants/programs/agriculture-food-research-initiative/afri-crosscutting-programs.

Ahn, Jaehyun, Brian E. Myers, Laura A. Warner, John M. Diaz, and Pablo Lamino. “Revisiting Everett M. Rogers: A Systematic Review and Random Forest Analysis of Technology Adoption in Modern Agriculture.” Advancements in Agricultural Development 7, no. 2 (2026): 8–22. https://doi.org/10.37433/aad.v7i2.629.

Akwah, Henry, Donald M. Johnson, George Wardlow, Cengiz Koparan, and Aurelie Poncet. “Inservice Needs of Selected Arkansas Agriculture Teachers Related to Precision Agriculture.” Advancements in Agricultural Development 5, no. 4 (2024): 28–41. https://doi.org/10.37433/aad.v5i4.509.

Bampasidou, Maria, Deborah Goldgaber, Thanos Gentimis, and Anurag Mandalika. “Overcoming ‘Digital Divides’: Leveraging Higher Education to Develop next Generation Digital Agriculture Professionals.” Computers and Electronics in Agriculture 224 (September 2024): 109181. https://doi.org/10.1016/j.compag.2024.109181.

Erickson, Bruce, Scott Fausti, David Clay, and Sharon Clay. “Knowledge, Skills, and Abilities in the Precision Agriculture Workforce: An Industry Survey.” Natural Sciences Education 47, no. 1 (2018): 1–11. https://doi.org/10.4195/nse2018.04.0010.

FFA. “Agricultural Education.” National FFA Organization, n.d. Accessed September 24, 2026. https://www.ffa.org/agricultural-education/.

Harder, Amy. “Responsible Use of Artificial Intelligence and Digital Technologies in Agricultural Development.” Advancements in Agricultural Development 7, no. 2 (2026): 136–39. https://doi.org/10.37433/aad.v7i2.697.

King, Loren G., Aaron J. McKim, Matt R. Raven, and Catlin M. Pauley. “New and Emerging Technologies: Teacher Needs, Adoption, Methods, and Student Engagement.” Journal of Agricultural Education 60, no. 3 (2019): 277–90. https://doi.org/10.5032/jae.2019.03277.

Lindner, James R., Christopher A. Clemons, and Jason D. McKibben. “Artificial Intelligence in Education: Perspectives of Secondary Teachers.” Advancements in Agricultural Development 7, no. 2 (2026): 109–24. https://doi.org/10.37433/aad.v7i2.637.

Morey, Tiffany, Daniel Foster, and John Ewing. “Virtual Mentoring in Agricultural Education: Describing Digital Literacy, Technology Self-Efficacy, and Attitudes Toward Technology of Secondary Agricultural Educators.” Journal of Agricultural Education 64, no. 1 (2023): 12–27. https://doi.org/10.5032/jae.v64i1.27.

Pehrson, Sidney, Tyson J. Sorensen, Matthew J. Wood, Michelle S. Burrows, and Joseph Furse. “Examining AI Perceptions and Utilization Among Agricultural Educators in Utah.” Journal of Agricultural Education 66, no. 3 (2025): 2–2. https://doi.org/10.5032/jae.v66i3.2910.

Roberts, T. Grady. “Artificial Intelligence and Digital Technologies in Agricultural Development Research and Practice.” Advancements in Agricultural Development 7, no. 2 (2026): 1–7. https://doi.org/10.37433/aad.v7i2.701.

Scherer, Hannah H., Aaron J. McKim, Hui-Hui Wang, Catherine A. DiBenedetto, and Kelly Robinson. “Making Sense of the Buzz: A Systematic Review of ‘STEM’ in Agriculture, Food, and Natural Resources Education Literature.” Journal of Agricultural Education 60, no. 2 (2019): 28–53. https://doi.org/10.5032/jae.2019.02028.

Smith, A. R., M. Spiess, D. D. Foster, A. R. Rogers, and R. G. Lawver. National Agricultural Education Supply & Demand Study, 2025 Executive Summary. Executive Summary. n.d. Accessed September 14, 2026. https://www.naae.org/naae/document-server/?cfp=/naae/assets/file/public/advocacy/nsd-data/2025%20nsd%20executive%20summary.pdf.

Tummons, John D., Rebecca Mott, and Chad Reynolds. “Barriers to Adoption of Precision Agriculture Competencies in Secondary Agriculture Education Programs: A Case Study.” Precision Agriculture 27, no. 3 (2026): 78. https://doi.org/10.1007/s11119-026-10385-y.

Wilson, C. A., J. T. Akridge, B. E. Brewer, S. S. Martin, and J. L. Strine. Employment Opportunities for College Graduates in Food, Agriculture, Renewable Natural Resources and the Environment — United States, 2025-2030. Purdue University, 2025. https://www.purdue.edu/usda/employment/full-report/.

 

Attachments

Land Grant Participating States/Institutions

Non Land Grant Participating States/Institutions