Effect of biochar on soil quality and potato productivity in New Brunswick, Canada
Bibliographic record
Abstract
Soil degradation is a global issue that threatens the productivity and resilience of agroecosystems. Environmentally-sensitive technologies must be developed to improve soil quality for sustainable crop production. The use of biochar, a carbon-rich alternative end product for forestry residues, has been proposed to counteract soil degradation, improve soil quality and help the agricultural sector to mitigate climate change. The objective of this study was to determine the effects of biochar type and biochar application rate on potato crop response and on physico-chemical indicators of soil quality in a potato cropping system. It was hypothesized that biochar application would positively affect soil structure, soil moisture regimes and soil fertility. Further, it was hypothesized that these changes in soil quality would lead to higher potato yields. A fully phased rotational field experiment was established in October 2015. Five treatments were arranged in a Latinized block design with five replicates. The treatments were an untreated control and a factorial combination of two biochar products (Airex and Maple Leaf), amended at two application rates (10 and 20 t ha-1). Soil and plant tissue samples were analyzed during the growing season, and potato yields were measured. Eight months after they were applied, both biochar products improved soil aggregate stability and lowered soil bulk density, but biochar did not significantly affect the soil moisture regimes. Biochar raised soil pH, while increasing soil organic matter, soil potassium, and soil phosphorus contents. No difference was found between potato yields in the control plots and the biochar-amended plots. Results from this experiment suggest that wood-based biochars that are not manufactured to address specific soil quality issues will affect soil quality in a manner similar to other organic amendments (increase the pH, lower bulk density, increase aggregate stability, and improve the concentration of some soil nutrients). They also suggest that biochar will not necessarily increase yield in a well-managed potato cropping system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".