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Smart Agriculture in Ecuador: Adoption of IoT Technologies by Farmers in Guayas to Improve Agricultural Yields

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  • Ruth Rubí Peña-Holguín

    (Centro de Estudios Estadísticos, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador)

  • Carlos Andrés Vaca-Coronel

    (Centro de Estudios Estadísticos, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador)

  • Ruth María Farías-Lema

    (Centro de Estudios Estadísticos, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador)

  • Sonnia Valeria Zapatier-Castro

    (Centro de Estudios Estadísticos, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador)

  • Juan Diego Valenzuela-Cobos

    (Centro de Estudios Estadísticos, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador)

Abstract

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant ( p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R 2 = 0.771) and performance (R 2 = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture.

Suggested Citation

  • Ruth Rubí Peña-Holguín & Carlos Andrés Vaca-Coronel & Ruth María Farías-Lema & Sonnia Valeria Zapatier-Castro & Juan Diego Valenzuela-Cobos, 2025. "Smart Agriculture in Ecuador: Adoption of IoT Technologies by Farmers in Guayas to Improve Agricultural Yields," Agriculture, MDPI, vol. 15(15), pages 1-24, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1679-:d:1716415
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    References listed on IDEAS

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    2. Abdoh Jabbari & Abdulmalik Humayed & Faheem Ahmad Reegu & Mueen Uddin & Yonis Gulzar & Muneer Majid, 2023. "Smart Farming Revolution: Farmer’s Perception and Adoption of Smart IoT Technologies for Crop Health Monitoring and Yield Prediction in Jizan, Saudi Arabia," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    3. Hanson, Erik D. & Cossette, Max K. & Roberts, David C., 2022. "The adoption and usage of precision agriculture technologies in North Dakota," Technology in Society, Elsevier, vol. 71(C).
    4. Nicole McDonald & Eloise S. Fogarty & Amy Cosby & Peter McIlveen, 2022. "Technology Acceptance, Adoption and Workforce on Australian Cotton Farms," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
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    6. Jennypher Pacheco & Wilman-Santiago Ochoa-Moreno & Jenny Ordoñez & Leonardo Izquierdo-Montoya, 2018. "Agricultural Diversification and Economic Growth in Ecuador," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
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