Application of Two Bioenergy Byproducts with Contrasting Carbon Availability to a Prairie Soil: Three-Year Crop Response and Changes in Soil Biological and Chemical Properties
Bibliographic record
Abstract
The bioenergy industry produces a wide range of byproducts varying in their chemical composition depending on type of technology employed. In particular, pyrolysis and transesterification conversion processes generate C-rich byproducts of biochar (BC) and glycerol (GL), respectively, which can be added to soil. These two byproducts vary in their carbon availability, and comparing their effects when added to agricultural soil deserves attention. This study investigated the immediate and residual effects of a single application of BC and GL to a cultivated Brown Chernozem soil from the semi-arid region of southwestern Saskatchewan, Canada. In the first season following addition of amendments, BC and GL alone had no significant impact on all measured parameters. However, when combined with 50 kg urea N·ha−1 (BC + UR), the yields obtained were similar to those with 100 kg urea N·ha−1 alone. The GL with urea N (GL + UR) treatment had reduced crop yield and N uptake compared to urea alone in the year of application attributed to N immobilization, but had a positive residual effect in the second year due to remineralization. Both GL and GL + UR treatments enhanced dehydrogenase activity compared to other treatments whereas BC + UR tended to decrease microbial biomass C. The crop and soil response to application of biochar was less than observed in previous studies conducted elsewhere. Direct and residual effects of glycerol addition on the crop were more evident. An application rate greater than 2.8 t·ha−1 and 3.5 t·ha−1 for BC and GL, respectively, may be required to induce larger responses.
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 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 | 0.000 |
| 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".