Carbon dynamics in a biochar-amended loamy soil under switchgrass
Bibliographic record
Abstract
Allaire, S. E., Baril, B., Vanasse, A., Lange, S. F., MacKay, J. and Smith, D. L. 2015. Carbon dynamics in a biochar-amended loamy soil under switchgrass. Can. J. Soil Sci. 95: 1–13. The environmental impacts of switchgrass production for bioenergy could be reduced through the use of biofertilizers rather than mineral fertilizers and through soil amendment with biochar. The objectives of this study were: (1) to assess the impact of biochar and biofertilizer on switchgrass (Panicum virgatum L.) yield and parameters related to carbon dynamics, (2) to correlate carbon parameters with soil physico-chemical properties over the first two growing seasons, and (3) to develop a C budget. A complete randomized block design was installed in a sandy loam with split plot treatment design, the main plots receiving 0 or 10 t ha-1 of biochar and the sub - plots receiving no fertilization, mineral N fertilization, or biofertilizers. Biofertilizers had no significant impact on plant and soil. Biochar increased yield relative to the control treatment by about 10% during the first year and root biomass by up to 50% after 2 yr (P>0.1). Mineral N fertilization also increased yield resulting in higher plant C sequestration after 2 yr. Biochar increased CO2 soil concentration (CO2-soil) by up to 50% but its impact on CO2 emission flux (CO2-flux) changed over time. The impact of mineral fertilization on CO2-flux also varied with time. Soil CO2 dynamics was mostly influenced by temperature, N and water content. Biochar and fertilization treatments showed interactions on some plant and soil parameters. The highest C sequestration budget was obtained with a combination of biochar and mineral N fertilization. The equivalent of about one-third of the increase in soil C content associated with biochar treatments was respired away by soil microorganisms. Nearly one-fourth of C sequestered by plants remained in or at the soil surface (root and crop residues).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".