Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract Accurate prediction of potato yield is essential for informed agricultural decision-making, ensuring food security, and supporting farmers’ livelihoods. This is particularly critical in regions like Prince Edward Island (PEI), where potato production is not only a staple of local agriculture but also a cornerstone of the regional economy, accounting for a significant proportion of agricultural revenue and employment. Although machine learning algorithms have been extensively applied in agricultural yield prediction, previous studies have not fully leveraged the potential of capturing both short- and long-term dependencies. This research highlights the efficacy of integrating these temporal dependencies into machine learning models to enhance the accuracy of potato yield predictions. The methodology adopted in this research, including data collection, model selection, and scenario-based projections, can be applied to other regions and crops. Our projections for PEI toward the end of the century indicate a substantial decline in potato yields across different climate scenarios. Under the high-emission SSP5-8.5 scenario, our models predict a potential potato yield reduction of up to 70%. In contrast, the SSP1 and SSP2 scenarios suggest a more moderate decline in potato yield, ranging from 4% to 15%. These findings underscore the urgent need for reducing greenhouse gas emissions to mitigate the adverse impacts on potato production. Furthermore, they highlight the importance of implementing adaptive farming practices to sustain potato yield in the face of climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".