Agricultural extension curriculum relevance for preparing youth for modern technology-driven farming

Introduction

Modern farming bears little resemblance to the agriculture most extension curricula were designed to serve. Satelliteguided tractors, drone-based scouting, sensor-driven irrigation, and data analytics have transformed food production [1] . Yet extension education has not kept pace, leaving young people ill-prepared for careers contemporary agriculture offers [2,3] . In the United States, the average farm operator age has risen to 57.5 years [4] . Young people interested in agriculture expect technology-intensive pathways but encounter programs focused on conventional knowledge [5] . Texas, generating over $25 billion in agricultural output annually, offers a useful context with both high-tech operations and traditional systems [6] . This research examined: (a) how students, educators, and industry representatives rated the relevance of current extension curriculum content areas for technology-driven farming careers; (b) the magnitude of alignment gaps between curriculum content and industry skill demands; and (c) whether experiential learning exposure was associated with higher technology readiness among youth [7,8] .

Theoretical Framework

The curriculum relevance framework posits that educational effectiveness depends on alignment between content delivered, learner needs, and labor market requirements [9] . Complementing this, experiential learning theory argues that knowledge is constructed through concrete experience, reflection, conceptualization, and experimentation [10] . Together they predict that curricula incorporating hands-on technology interaction will outperform lecture-based approaches.

Materials and Methods

Materials

A convergent mixed-methods design collected data between September 2022 and June 2023 from three groups: students in final undergraduate years (n = 194), extension educators (n = 38), and industry representatives (n = 27) from four Texas universities. The 42-item questionnaire measured curriculum relevance, technology readiness, experiential learning exposure, and career intention. Ethics approval: Texas A&M IRB (Protocol TAMU-IRB-2022-0847, August 2022).

Instrument Development

Content areas were identified from curriculum standards and industry job posting analysis. Expert panel review reduced 48 items to 42 (CVI = 0.88). Pilot testing with 34 students yielded satisfactory reliability (Cronbach alpha = 0.85; subscale range 0.78-0.89). The technology readiness subscale was adapted from Parasuraman and Colby.

Methods

Curriculum relevance ratings were analyzed through weighted scoring across six content areas. The curriculumindustry alignment gap was computed as the absolute difference between educator ratings of current content coverage and industry ratings of content importance. Between-group comparisons (students with vs. without experiential learning) used independent t-tests. One-way 123 www.extensionjournal.com ANOVA compared relevance perceptions across respondent groups. Focus group data (four groups of 8-10 participants) were analyzed thematically. All analyses used SPSS 28.0 at p<0.05. Industry representatives placed substantially more weight on precision agriculture and data analytics than educators, while educators rated traditional content areas like soil science and communication higher. ANOVA confirmed significant between-group differences for precision agriculture (F = 8.41, p<0.001) and data analytics (F = 6.73, p = 0.002). Students with experiential learning exposure scored significantly higher on all technology readiness dimensions (p<0.001 for each). The largest gap appeared in practical technology competence (3.74 vs. 2.61), which is the dimension most directly linked to hands-on equipment interaction.

Results

Comprehensive Interpretation

The curriculum-industry alignment gap averaged 1.84 on a 5-point scale, with the widest gaps in precision agriculture technology (2.31) and data analytics (2.17). Focus group discussions revealed that educators acknowledged these gaps but cited resource constraints, institutional inertia, and their own limited training in newer technologies as barriers to curriculum updating. Industry participants expressed frustration that graduates often lacked basic data handling skills that they considered entry-level requirements.

Discussion

The curriculum-industry alignment gap confirms that extension curricula have not kept pace with agricultural technology transformation [2,3] . Industry representatives weighted precision agriculture and data analytics more heavily than educators, a disconnect attributed to generational technology differences and slow institutional revision [11,12] . The strong experiential learning-readiness association supports the prediction that hands-on technology engagement outperforms classroom instruction [10] . The 34.7% readiness advantage translates to reduced employer training costs and faster productive contribution [13] . The low relevance rating for traditional extension communication content was surprising and potentially concerning. While digital tools are clearly central to modern farming, the interpersonal and community engagement skills that extension communication develops remain important for careers that involve working with diverse farming communities [14,15] . A balanced curriculum would integrate technology skills without abandoning the relational competencies that distinguish extension professionals from pure technicians.

Practical Applications

References

1. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming: a review. Agricultural Systems. 2017;153:69-80.
2. Spielman DJ, Birner R. How innovative is your agriculture? Using innovation indicators and benchmarks to strengthen national agricultural innovation systems. Washington: World Bank; 2008. 47 p.
3. Rivera WM. Agricultural extension in transition worldwide: structural, financial and managerial reform www.extensionjournal.com strategies. Public Administration and Development. 2001;21(2):151-161.
4. USDA. 2017 Census of Agriculture: summary and state data. Washington: National Agricultural Statistics Service; 2019. 820 p.
5. Doerfert DL. National research agenda: American Association for Agricultural Education research priority areas for 2011-2015. Journal of Agricultural Education. 2011;52(1):13-21.
6. Texas Department of Agriculture. Texas agricultural statistics 2022. Austin: TDA; 2023. 156 p.
7. Roberts TG, Harder A, Brashears MT. American Association for Agricultural Education national research agenda 2016-2020. Gainesville: Department of Agricultural Education and Communication; 2016. 72 p.
8. Stripling CT, Ricketts JC. Research priority 3: sufficient scientific and professional workforce that addresses the challenges of the 21st century. Journal of Agricultural Education. 2016;57(4):95-105.
9. Tyler RW. Basic principles of curriculum and instruction. Chicago: University of Chicago Press; 1949. 128 p.
10. Kolb DA. Experiential learning: experience as the source of learning and development. Englewood Cliffs: Prentice Hall; 1984. 256 p.
11. Easterly RG, Warner AJ, Myers BE, Lamm AJ, Telg RW. Skills students need in the real world: competencies desired by agricultural and natural resources industry leaders. Journal of Agricultural Education. 2017;58(4):225-239.
12. Shoulders CW, Myers BE. Teachers use of agricultural laboratories in secondary agricultural education. Journal of Agricultural Education. 2012;53(2):124-138.
13. Knobloch NA. Is experiential learning authentic? Journal of Agricultural Education. 2003;44(4):22-34.
14. King DL, Rucker KJ. Preparing agricultural communication graduates for the profession: a profile of needed competencies. Journal of Applied Communications. 2013;97(3):69-83.
15. Edgar DW, Roberts TG, Murphy TH. Structured communication: effects on teaching and learning in a college of agriculture course. Journal of Agricultural Education. 2009;50(1):33-44.
16. Scherer HH, Holder B. Agricultural technology and implications for extension education. Journal of Extension. 2019;57(3):1-8.
17. McKim AJ, Velez JJ, Lambert MD, Barkley AP. A philosophical review of science and society within agricultural education. Journal of Agricultural Education. 2017;58(2):98-110.
18. Lamm AJ, Lamm KW, Rodriguez MT, Owens CT. Examining leadership style influence on engagement in a national change process: implications for leadership education. Journal of Leadership Education. 2016;15(4):1-14.
19. Martin MJ, Enns KJ, Garton BL. A qualitative examination of the personal characteristics needed for the ideal professor of agricultural education. Journal of Agricultural Education. 2019;60(3):178-196.
20. Phipps LJ, Osborne EW, Dyer JE, Ball A. Handbook on agricultural education in public schools. Clifton Park: Thomson Delmar Learning; 2008. 576 p.