C<sub>4</sub>bioenergy crops for cool climates, with special emphasis on perennial C<sub>4</sub>grasses
Bibliographic record
Abstract
There is much interest in cultivating C4 perennial plants in northern climates where there is an abundance of land and a potential large market for biofuels. C4 feedstocks can exhibit superior yields to C3 alternatives during the long warm days of summer at high latitude, but their summer success depends on an ability to tolerate deep winter cold, spring frosts, and early growth-season chill. Here, we review cold tolerance limits in C4 perennial grasses. Dozens of C4 species are known from high latitudes to 63 °N and elevations up to 5200 m, demonstrating that C4 plants can adapt to cold climates. Of the three leading C4 grasses being considered for bioenergy production in cold climates--Miscanthus spp., switchgrass (Panicum virgatum), and prairie cordgrass (Spartina pectinata)--all are tolerant of cool temperatures (10-15 °C), but only cordgrass tolerates hard spring frosts. All three species overwinter as dormant rhizomes. In the productive Miscanthus×giganteus hybrids, exposure to temperatures below -3 °C to -7 °C will kill overwintering rhizomes, while for upland switchgrass and cordgrass, rhizomes survive exposure to temperatures above -20 °C to -24 °C. Cordgrass emerges earlier than switchgrass and M. giganteus genotypes, but lacks the Miscanthus growth potential once warmer days of late spring arrive. To enable C4-based bioenergy production in colder climates, breeding priorities should emphasize improved cold tolerance of M.×giganteus, and enhanced productivity of switchgrass and cordgrass. This should be feasible in the near future, because wild populations of each species exhibit a diverse range of cold tolerance and growth capabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".