Significance of land management practices under haskap orchards to mitigate the degradations of soil organic carbon stocks and soil health because of land use changes from forest and grassland
Bibliographic record
Abstract
Abstract Managing the impact of land use change (LUC) in soil organic carbon (SOC) and soil health is imperative while climate change (CC) and food security are in concern. Land management practices in major land uses play a vital role in maintaining SOC concentrations and soil health. Haskap is one of the fastest growing berry orchards in Canada, covering around 0.1 Mha. Therefore, the present study has been conducted in two different haskap orchards established after replacing forest and grassland, respectively. The shallow (S) and furrow (F) tilling as tillage management and control (Cn) and compost + fertilizer (CF) as nutrient management have been incorporated in the present study to explore the effect of different land use management practices on soil properties. The estimated loss of SOC stocks because of LUC from forest to haskap for 0–10 and 10–30 cm soil depth is around 40% and 50%, respectively. However, around a 20% increase in SOC stocks has been estimated while the grassland was replaced by the haskap orchard. Moreover, under Cn nutrient management, the F tillage showed a 50% gain in SOC stocks over S tillage, but under CF nutrient management, the S tillage showed at least 25% higher SOC stocks over F tillage management. The CF nutrient management showed a significant effect on AC stocks estimated to be 31% higher than that under Cn management. The C:N ratio for 0–10 and 10–30 cm soil depths is significantly influenced by the LUC. However, the management induced in the present study does not have any significant influences on the C:N ratio. The present study suggests that the conversion of grassland soils into haskap orchards could play a significant role in SOC stocks management. Moreover, S tillage and F tillage with nutrient management under haskap orchards could be viable management options to improve soil carbon sequestration in upper and lower soil depths, respectively, by maintaining the N content in the soil.
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".