Conifer wood biochar as an amendment for agricultural soils in South-Tyrol: impact on greenhouse gases emissions and soil carbon stocks
Bibliographic record
Abstract
Biochar is a carbonaceous material produced through the pyro-gasification of biomass. In the last decade, biochar has been proposed as a soil amendment because it can improve soil physico-chemical properties and carbon stocks, contributing to climate change mitigation. In the framework of the Wood-Up project (Optimization of WOOD gasification chain in South Tyrol to prodUce bioenergy and other high-value green Products to enhance soil fertility and mitigate climate change, FESR1028), we studied the impact of conifer wood biochar on the emissions of the main greenhouse gases (GHGs) from the soil: carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4), as well as on the soil carbon stock of agricultural fields in South Tyrol. In May 2017, 25 and 50 t ha-1 of pure biochar and biochar mixed with compost (45 t ha-1), were applied to the soil of a vineyard near Merano (South-Tyrol, northern Italy) following a randomized block experimental design with four replicates per treatment. Soil GHGs fluxes were monitored from June 2017 until December 2019. Fluxes were measured, in real time, with a high-resolution portable multi-gas analyzer based on cavity ring-down spectroscopy technology (Picarro inc., Santa Clara, CA, USA) connected to an automated dynamic chambers system (Eosense Inc., Dartmouth, NS, Canada). Gas emissions were measured monthly and were related to soil temperature and moisture to evaluate the impact of treatments on the sensitivity of GHGs fluxes to environmental parameters. The stability of conifer wood biochar in soil was assessed through the quantification of the Benzene PolyCarboxylic Acids (BPCA), specific biomarkers of black carbon, over time. The BPCA content in the soil was measured before the application of biochar and compost, three weeks after the application and two years later. During the first year of experiment, in biochar-amended soils, we observed a reduction of the temperature sensitivity of all GHGs fluxes in comparison to treatments without biochar (control and compost alone). In the second and third year an opposite trend was observed, with an increase of temperature sensitivity of GHGs fluxes in biochar-treated soil. The change of biochar effect over time might be linked to biochar ageing in soil. However, a role of soil moisture cannot be excluded, as it was higher in the first year of experiment. The experimental results will be presented in the broader context of the Wood-Up project.
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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 | 0.001 | 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".