Predicted Yield and Nutritive Value of an Alfalfa–Timothy Mixture under Climate Change and Elevated Atmospheric Carbon Dioxide
Bibliographic record
Abstract
Climate change studies have often focused on individual forage species although legume‐grass mixtures are predominant on dairy farms in northern areas of North America. We assessed the effect of (i) future climate conditions (temperature and precipitation) and elevated atmospheric CO2 concentration ([CO2]), separately and together, on yield of alfalfa (Medicago sativa L.) and timothy (Phleum pratense L.), grown alone or in mixture, and (ii) an adaptation strategy (timing and number of harvests) on future yield and nutritive value of an alfalfa–timothy mixture. Forage dry matter (DM) yield and nutritive value for two contrasting climate areas in eastern Canada were simulated with the Integrated Farm System Model over two future periods (2020–2049 and 2050–2079) using three climate models and two representative concentration pathways (RCP 4.5 and 8.5) of greenhouse gas emissions. Under projected future climate and without adaptation, annual forage yield of both species and the mixture increased in the colder area and decreased in the warmer area. In both areas, first‐cut yield increased due to faster growing degree‐day accumulation, while regrowth yield decreased due to greater water and temperature stresses. Under elevated [CO2], annual yield and the alfalfa percentage in the mixture increased. When combining climate change and elevated [CO2], yield increased, except with the more drastic scenario (RCP 8.5, 2050–2079) in the warmer area, and forage nutritive value was reduced. With adaptation, the mixture yield was increased from 5 to 35%, while nutritive value was generally maintained under all future scenarios, mostly because of additional cuts. Core Ideas In eastern Canada, colder areas will benefit the most from climate change. In future climate, water and temperature stresses will reduce forage summer regrowth. Elevated CO2 will result in a higher yield increase in alfalfa than in timothy. When adapting harvest timing and number, annual forage mixture yield will increase. When adapting harvest timing and number, forage nutritive value will be maintained.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".