Use, calibration and verification of agroecological models for boreal environments: A review
Bibliographic record
Abstract
Abstract Past assessments report negative impacts of the climate crisis in boreal areas; but milder and shorter winters and elevated atmospheric CO2may provide opportunities for agricultural productivity potentially playing a significant role in future food security. Arable cropping systems are expanding in boreal areas, but the regional mainstay will likely continue to be livestock production. Agroecological models can when appropriately calibrated and evaluated, facilitate improved productivity while minimising environmental impacts by identifying system interactions, and quantifying greenhouse gas emissions, soil carbon stocks and fertiliser use. While models designed for temperate and tropical zones abound, few are developed specifically for boreal zones, and there is uncertainty around the performance of existing models in boreal areas. We reviewed model performance across boreal environments and management systems. We identified a dearth of modelling studies in boreal regions, with the publication of three or less papers per year since the year 2000, constituting a significant research gap. Models IFSM and BASGRA_N performed best in grassland production, DNDC best in predicting soil N2O and NH3emissions. No model outperformed all others, strengthening the case for ensemble modelling. Existing agroecological models would be worthy of further evaluation, providing model improvements designed for boreal systems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".