Short‐Term Effects of Diverse Compost Products on Soil Quality in Potato Production
Bibliographic record
Abstract
Core Ideas Compost application increased soil organic matter content and improved soil quality. Compost products with greater C concentrations resulted in greater soil improvements. Particulate organic matter C was the index most responsive to compost addition. Mature composts with greater C concentrations and dry matter were most suitable. Soil quality has declined with intensive potato production practices in New Brunswick, Canada. Compost application may rapidly increase soil organic matter (SOM) and reverse declining productivity. This study assessed five diverse compost products for their short‐term effects on soil quality, and in particular SOM. Selected compost products derived from a range of forestry, marine, and municipal waste materials were compared with a non‐amended control. Treatments were applied to field plots at 45 Mg ha –1 dry weight in October of 2014 and 2015. Biological, chemical and physical soil properties under potato production in 2015 and 2016 (after one and after two consecutive applications) were used to evaluate soil quality. Compost application increased soil pH and concentrations of Mehlich‐3 extractable nutrients (K, Ca, Mg, and S). Compost reduced bulk density in the potato hill by 8% in both years. Particulate organic matter (POM) was the most sensitive indicator to compost‐application with twofold increases in POM‐C. Compost application increased soil organic carbon by 24% in 2016 and also increased permanganate oxidizable carbon, and soil respiration. Several soil properties were strongly correlated with compost composition, with better quality composts (i.e., more mature; greater in C, N, and other plant‐available nutrient concentrations) producing the greatest soil quality response. Overall, mature composts with greater C concentrations (i.e., low ash) and greater dry matter were most suitable for enhancing soil quality in New Brunswick potato production systems.
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 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 | 0.001 |
| 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) | 0.000 | 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 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".