Status : Verified
| Personal Name | Gamboa, John Albert F. |
|---|---|
| Resource Title | Assessing the potential of smart agriculture technologies for enhancing climate resilience in Philippine agriculture: a technology foresight through scenario building |
| Date Issued | 10 May 2025 |
| Abstract | Philippine agriculture continues to be hindered by the ongoing risks of climate change such as progressive extreme weather, shifting rainfall, and pest outbreaks. The productivity and climate resilience in farming practices are offered solutions through the promising smart agriculture technologies encompassing the Internet of Things, Artificial Intelligence, and predictive analytics. This study used technology foresight to evaluate the potential of these technologies, generating three future scenarios: (1) SA-ming Pagsubok (Struggling for Stability), which captures the effects of erratic policy implementation and funding constraints; (2) SA-mantala (Slow but Steady Transformation), which reflects farmer resistance and institutional inertia; and (3) SA-nib Pwersa (Strength in Partnership), which examines how public-private collaboration can drive rapid adoption. A Grand Scenario was then synthesized to integrate key drivers—policy stability, financial accessibility, stakeholder engagement, and innovation—to support long-term adoption. Through collaborative integrated cross-sectoral policies and specific digital literacy campaigns, the technological gap in Philippine agriculture has to be bridged. This study proposes a strategic technology roadmap for responsive, accessible, and sustainable adoption aimed toward smallholder farmers to foster agri-food system transformation. Supporting the enabling environment strengthens agricultural resilience, national food security, and economic stability while advancing climate change adoption. |
| Degree Course | Master of Technology Management |
| Language | English |
| Keyword | Technology management; Technology foresight; Scenario building; Predictive analysis; Machine learning; IoT; Deep learning; Big data; AI; Climate resilience; Smart agriculture; Agriculture |
| Material Type | Thesis/Dissertation |
Preliminary Pages
44.71 Kb
Category : P - Author wishes to publish the work personally.
Access Permission : Limited Access