Digital Competence as a Predictor of Smart Agricultural Technology Utilization among Agricultural Education Lecturers in Colleges of Education in South-South Nigeria

Abstract This study determined digital competence as a predictor of smart agricultural technology utilization among Agricultural Education lecturers in Colleges of Education in South-South Nigeria. Specifically, the study determined the level of digital competence, identified the availability and extent of utilization of smart agricultural technologies, examined factors influencing technology utilization, and determined the relationship between digital competence and technology utilization. A descriptive survey research design was adopted for the study. The population comprised all Agricultural Education lecturers in Colleges of Education in South-South Nigeria, while an illustrative sample of 220 lecturers was used for demonstration purposes. Data were collected using the Digital Competence and Utilization of Smart Agricultural Technologies Questionnaire (DCUSATQ). The instrument was validated by experts in Agricultural Education, Educational Technology, and Measurement and Evaluation, while Cronbach's Alpha reliability coefficients of 0.87, 0.84, and 0.81 were obtained for the Digital Competence, Technology Utilization, and Influencing Factors scales, respectively, with an overall reliability coefficient of 0.86. Data were analyzed using frequency, percentage, mean, standard deviation, Pearson Product Moment Correlation, and Multiple Regression at the 0.05 level of significance. The findings revealed that Agricultural Education lecturers possessed a high level of digital competence. Smart agricultural technologies were moderately utilized, with online agricultural databases and mobile agricultural applications being the most frequently used, while drone technology and precision agriculture tools recorded relatively low utilization. Institutional support, digital infrastructure, internet connectivity, and training opportunities were identified as major factors influencing technology utilization. The study further revealed a significant positive relationship between digital competence and smart agricultural technology utilization. Multiple regression analysis showed that digital competence significantly predicted smart agricultural technology utilization, while the combined influence of digital competence, institutional support, ICT infrastructure, and training exposure explained the variance in technology utilization. Based on the findings, it was recommended that there should be continuous professional development in digital agriculture, improved institutional support, enhanced ICT infrastructure, and increased investment in smart agricultural technologies to strengthen technology integration in Agricultural Education programmes.