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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Techno Agriculturae Studium of Research Journal of Multidisciplinary Sustainability Asean Journal Emerging Technologies in Education
Carlos Chavarria
University of Belize
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Other

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THE APPLICATION OF DRONE TECHNOLOGY AND IMAGE ANALYSIS FOR MONITORING GRAZING PATTERNS AND RANGELAND CAPACITY IN CATTLE FARMING Ricardo Figueroa; Carlos Chavarria; Luis Ramirez
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2963

Abstract

The increasing demand for sustainable cattle farming and the pressure on rangeland resources have highlighted the need for efficient monitoring of grazing patterns and land carrying capacity. Traditional methods of monitoring rely on manual field surveys, which are labor-intensive and have limited coverage. Recent advancements in drone technology and image analysis present new opportunities for data-driven decision-making in livestock and rangeland management. This study explores the use of drone technology combined with image analysis techniques to monitor grazing patterns and assess rangeland capacity. A research and development design was employed, with drones capturing high-resolution aerial imagery of grazing areas at regular intervals. Image analysis techniques, including vegetation index extraction and spatial pattern analysis, were used to assess grazing intensity, vegetation cover, and biomass distribution. Data from the drone-based imagery were validated through ground observations and rangeland productivity records. The results show that drone-derived imagery accurately captured spatial variations in grazing behavior and vegetation condition, allowing for precise mapping of grazing zones and reliable estimates of rangeland carrying capacity. Compared to traditional methods, the drone-based approach was more efficient, offered greater spatial accuracy, and reduced the need for field surveys. In conclusion, integrating drone technology and image analysis offers a scalable solution for sustainable rangeland and livestock management.
Food Security and Climate Change Impacts on Nutrition: Exploring Vulnerabilities in Food Systems and Their Health Consequences Muh. Aniar Hari Swasono; Carlos Chavarria; Olivia Jimenez
Journal of Multidisciplinary Sustainability Asean Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v3i2.3641

Abstract

Background. Climate change has emerged as a major threat to global food security, profoundly affecting food availability, accessibility, utilization, and stability, with direct consequences for human nutrition and health. Disruptions in food systems caused by rising temperatures, extreme weather events, and environmental degradation have intensified nutritional vulnerabilities, particularly among low-income and climate-sensitive populations. Purpose. This study aims to explore how climate change impacts food systems and to assess the resulting nutritional and health consequences across different socio-economic contexts. Method. A mixed-methods approach was employed, combining secondary data analysis from global food security, climate, and nutrition databases with a systematic review of peer-reviewed literature and international reports. Descriptive and comparative analyses were used to identify patterns of vulnerability, while thematic analysis was applied to examine pathways linking climate stressors, food systems, and nutritional outcomes. Results. The results indicate that climate change significantly undermines food system resilience, leading to reduced dietary diversity, increased micronutrient deficiencies, and heightened risks of malnutrition. The impacts are most pronounced in regions with limited adaptive capacity and weak food governance structures. Conclusion. The study concludes that strengthening food system resilience through climate-adaptive policies, sustainable agricultural practices, and integrated nutrition strategies is essential to mitigate health risks and ensure long-term food security in a changing climate.
From Resistance to Resilience Psychological Factors Influencing Teachers' Adoption of AI Tools Amie Primarni; Carlos Chavarria; Olivia Jimenez
Journal Emerging Technologies in Education Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v4i1.3518

Abstract

Background. Rapid advancements in artificial intelligence (AI) technologies are transforming educational environments and reshaping teaching practices worldwide. Educational institutions increasingly encourage teachers to integrate AI-powered tools such as generative systems, intelligent tutoring platforms, and automated feedback technologies into classroom activities. Despite the potential benefits of AI for improving instructional efficiency and personalized learning, many teachers initially demonstrate hesitation or resistance toward adopting these technologies. Psychological factors such as technological self-efficacy, perceived usefulness, technological anxiety, and professional identity concerns play a critical role in shaping teachers’ responses to AI-driven educational innovation. Purpose. This study aims to examine the psychological factors influencing teachers’ adoption of AI tools and to explore how educators transition from resistance toward resilience when interacting with emerging educational technologies. Method. A mixed-methods research design was employed involving 120 in-service teachers from various subject areas. Data were collected through structured questionnaires measuring psychological constructs related to AI adoption and through semi-structured interviews exploring teachers’ experiences with AI tools. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were examined through thematic analysis. Results. The findings indicate that technological self-efficacy, perceived usefulness of AI tools, and psychological resilience significantly influence teachers’ intention to adopt AI technologies. Initial resistance tends to decrease as teachers gain practical experience, receive institutional support, and develop greater confidence in using AI tools. Conclusion. Psychological readiness is a key determinant of successful AI integration in education. Strengthening teachers’ self-efficacy, reducing technological anxiety, and fostering resilience through training and institutional support can facilitate more effective adoption of AI-driven teaching practices.
Co-Authors Luis Ramirez Muh. Aniar Hari Swasono Olivia Jimenez Ricardo Figueroa