Prospects of Forage Production in Northern Regions under Climate and Land-Use Changes: A Case-Study of a Dairy Farm in Newfoundland, Canada
Bibliographic record
Abstract
Forage production in northern latitudes is challenging and uncertain in the future. In this case-study, the integrated farm system model (IFSM) was used to assess the impact of climate change and cropland expansion scenarios on forage production in a dairy farm in Newfoundland, Canada. Climatic projections indicated increases in temperature in the recent past (1990–2016) and under any future climate (2020–2079), thus enhancing agronomic performance. Temperature increases ranged from 2.8 °C to 5.4 °C in winter and from 3.2 °C to 6.4 °C in spring. Small precipitation increases (<10%) create narrower time windows to perform farm operations in the already stringent condition of excess moisture in the region. Results of land use scenarios including expansions of 20, 30, and 40% in cropland area, out of which 5% was dedicated to corn silage and the remainder to grass-legume mixtures, indicated increased yield and total production. Improvements in grass-legume yield ranged from 8% to 52%. The full range of production increases ranged from 11% to 105%. Increments in corn silage yield ranged from 28% to 69%. Total farm corn silage production increases ranged from 29% to 77%. An attainable cropland expansion of 20% would enable the farm to become self-sufficient in forage production under any climate scenario.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".