Management Practices Affect Soil Organic Carbon Stocks and Soil Fertility in Cactus Orchards
Bibliographic record
Abstract
Management practices might alter soil chemical properties. This study evaluated soil chemical properties in a forage cactus Opuntia stricta (Haw.) Haw. (‘Orelha de Elefante Mexicana’) (OEM) production system in the Brazilian semiarid region. The experiment was established in June 2011, and the design was a split-split-plot in randomized complete blocks, in which the main plots were formed by distinct levels of organic fertilizer (cattle manure) (0, 10, 20, and 30 Mg ha−1 year−1), the subplots were formed by different levels of N inorganic fertilizer applied as urea (0, 120, 240, and 360 kg N ha−1 year−1), and the sub-subplots were distinguished by the distinct OEM harvesting frequency (annual or biennial). Soil samples were collected for chemical analysis, C and N contents analysis, and stocks analysis at 0 to 10 and 10 to 20 cm depths in August 2019. Organic fertilizer contributed to a linear increase in soil pH, Ca2+, Na+, sum of bases (SB), cation exchange capacity (CEC), and base saturation (V) at both depths (p < 0.05). With the application of 30 Mg ha−1 year−1 of cattle manure, there was storage of approximately 126 Mg C ha−1 and 13 Mg N ha−1 at 0 to 20 cm depths. Managing OEM with organic fertilizer and a biennial frequency of harvesting affects the soil’s chemical characteristics in cactus orchards, and it is a sustainable alternative for semiarid regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".