Evaluation of Nitrogen Fertilization in C4 Grasses Grown for Bioenergy
Bibliographic record
Abstract
Nitrogen fertilization has the potential to significantly affect yield, nutrient concentration and removal and overall stand longevity of C4 grasses grown for bioenergy production. While most studies report that these grasses need little to no fertilizer inputs to maximize yield, no specific recommendations have been given for fertilizer requirements across a wide range of C4 perennial grass species, cultivars, harvest timings and N application rates. Two field trials were established in the summer of 2008 in Ridgetown and Elora, Ontario to compare 4 Miscanthus (M. sinensis and M. sacchariflorus crosses; Nagara-116 and Amuri-114, and Miscanthus x giganteus; M1 Select and Polish), 2 switchgrass (Panicum virgatum L.; Cave-in-rock and Shelter) and 2 big bluestem varieties (Andropogon gerardii Vitman; Prairie view and Southlow), 4 nitrogen fertilization rates (0, 40, 80, and 160 kg N ha-1) and 2 harvest timings (fall vs. spring harvest). Plots were harvested in the fall of 2009 and spring and fall of 2010. Yield response to N fertilization varied by location, species and year. In general, increasing N fertilization rates generated an increased yield response in all tested species. Different rates of N fertilization resulted in variable responses to N, P and K tissue concentrations and removal in switchgrass and Miscanthus. Tissue N concentrations and removal both increased with added N fertilization at both locations. In most cases N removal was the highest at the highest N application rate. Nitrogen concentrations were highest in the leaves and seed heads across species, N rates and locations. P and K concentrations and removal with increasing N fertilization in the whole plant and in individual plant parts did not follow clear trends in either Miscanthus or switchgrass in both locations. Delaying harvest to spring reduced yield at both locations by 35-42% and 62-65% for Miscanthus and switchgrass, respectively. Yield losses by delaying harvesting to the spring were increased with higher rates of N fertilization. Delaying harvesting decreased N, P and K concentration and removal in most grasses and locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".