Geothermal energy in agrifood systems
Bibliographic record
Abstract
The Caribbean Community (Caricom) region is vulnerable to climate change. Located beneath Mexico’s Yucatan Peninsula and adjacent to the warmest basin of water on planet earth – the Gulf of Mexico – Belize showcases and exemplifies climate variability in weather extreme terms - significant rainfall, flash floods, heatwaves, drought and flooding. Like other countries in the wider region, the blunt reality is that Belize is facing new climate change pressures and challenges. These corollary test existing capabilities of whole business sectors, energy infrastructures and food systems. Nowhere is this impact more acutely felt than in the agrifood sector – agrifood systems – for example in the production and processing of chilli peppers, bananas, citrus fruits, sugar beet, coconuts, nuts and seafoods. Persistent and frequent tropical storms and hurricanes impact business economics, growing conditions and the cost of energy and security. However, adapting to what is happening and understanding what possibilities are open to agrifood producers and processors, can place their destiny in their own hands. This adaptation is difficult. But it is essential. For us, co-organising Geothermal Week Belize afforded us the opportunity to better understand if geothermal energy resources (non-volcano) and technologies might be appropriate in small business conditions in agrifood systems in Belize. Toward this end, we aimed to combine local knowledge of ‘on the ground realities’ of what might work (or not) in climate variability conditions, while also collaborating with multiple agrifood stakeholders in Belize. Co-organising Geothermal Week Belize afforded us the opportunity to, (i) better understand the synergies of regional dynamics, (ii) embrace a listening-mode for understanding and learning, (iii) gain insights into the small to medium agricultural producer and processer communities, (iv) understand how communities can be sustainable from local resources for local needs and, (v) eschew large-scale systems change thinking. Our work nudges forward four questions with agrifood stakeholders in Belize: 1)What issues are faced by agrifood communities in Belize? 2)How might geothermal resources and technologies benefit the agrifood system in Belize? 3)How might agrifood businesses adapt to climate variability, become more energy efficient and sustainable? 4)How might R&D projects be realised ‘on the ground’ in Belize so that the agrifood communities can see and understand geothermal energy working? Informed and assisted by sector-specific insights by members of the Caribbean Agricultural Research and Development Institute (CARDI) and also by local knowledge from the University of Belize, this report reflects the work undertaken and that is captured in the title of ‘Exploring the Future of Geothermal Energy in the Agrifood Sector’ of our activities during the week of 4th and 7th of November 2024 at the University of Belize, Belmopan, Belize. A week of activities generated a range of conversations with agrifood production and processing stakeholders. These are reported on here in eight sections: (i) prefatory, (ii) prospectivity, (iii) potentiality, (iv) projectivity, (v) inter-country semiotic reflectivity, (vi) nature-inspired adaptation, (vii) formativity, and (viii) conclusions. Geothermal Week Belize establishes an initial baseline view of what is and what is not relevant in the specifics of agrifood system in Belize. A preliminary baseline assessment is that geothermal technologies, thus far, have had a negligible effect on how the energy and agrifood sector function in Belize. A baseline synopsis from our stakeholder engagement indicates that there is a perceived need for more understanding, more adaptation, with more measures to reduce the energy costs in production and processing in the Belize agrifood sector. Moreover, the baseline assessment points to a perceived need for understanding further how geothermal technologies can enhance controlled environment agriculture (CEA) systems and maximize efficiency, reduce costs and resource use, and increase crop yields by providing optimal growing conditions year-round. Also, there is a perceived need for geothermal cooling equipment solutions in controlled environment agriculture (CEA) systems and the built environment more generally. From technology coupling point of view, an important geothermal energy coupling is with optimisation technology, including artificial intelligence-led systems for controlled environment agriculture (CEA), smart agriculture building designs and synergistic solar-thermal technology interfaces. In sum, this baseline work brings into view the perceived need for small-scale, affordable, energy-efficient, environmentally sustainable, and locally autonomous geothermal R&D projects in Belize. Working towards developing an R&D project at the University of Belize’s Central Farm campus would provide practical small steps for agrifood community demonstration and which may one day be more portable across the agrifood system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".