Surface application of mulches and biosolids affect orchard soil properties after 7 years
Bibliographic record
Abstract
Changes in selected soil chemical and physical properties of a coarse-textured orchard soil were measured 7 yr after establishing different orchard soil management strategies which were deemed to be environmentally sound in a high-density apple orchard. Several locally available mulches and organic wastes applied to the soil surface were maintained and assessed as to their effect on tree performance and soil properties relative to a standard commercial practice involving maintenance of a weed-free herbicide strip (known as the “check” practice). The soil chemical status of these sandy soils was readily altered when large amounts of nutrients were contained in applied mulches and biosolids. Thus, elevated soil C [and cation exchange capacity (CEC)], N, P, Zn, Fe, Mn and Cu occurred after cumulative application of 90 t ha -1 of biosolids; soil pH and Ca content increased beneath a high-Ca shredded paper mulch, and soil was enriched in P and K beneath a high-PK alfalfa mulch. Small but statistically significant decreases in soil bulk density, and increases in wet aggregate stability and infiltration rate were measured for some non-check soil management treatments. Of most practical importance among the physical property changes was increased surface soil moisture retention capacity associated with shredded paper mulches applied over biosolids. Cumulative yield and long-term vigour exceeded control trees for all soil management treatments and were best for treatments that included application of a shredded paper mulch. Few measured soil properties were correlated with long-term tree performance except for several soil pH-related parameters. However, surface application of mulches and biosolids generally improved soil nutrient status and physical properties while maintaining or improving crop yield. Key words: Biosolids, Malus domestica Borkh., mulches, soil fertility, water retention capacity
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".