Drip-fertigation of apple trees affects root distribution and development of K deficiency
Bibliographic record
Abstract
The locations of root intersections were mapped relative to micro-emitters in spring, 1996 for Gala apple (Malus domestica Borkh) on M.26 rootstock, which were established and grown for 8 yr under micro-irrigation with fertigation. Root distribution was mapped for trees that had been irrigated daily between May and October using drip or microjet systems and fertigated each year during May. Drip-irrigated trees had nearly half of their total root intersections within 30 cm depth and lateral distance from the emitter. Roots of microjet-irrigated trees were more widely distributed over a soil volume extending to 60 cm depth and 90 cm lateral distance from the trees. Leaf K concentrations had decreased to near deficient concentrations for drip-irrigated trees after 5 yr.Root distribution mapping and soil sampling were undertaken in spring 1997 for N-fertigated Gala on M.9 apples, planted in 1992 on the same soil, with or without annual K applications during 1992–1996. Fertigation of 15 g K tree−1 each year in July–August did not affect root distribution, which remained laterally and vertically restricted but prevented the decline of exchangeable soil K below 50 mg kg−1, exchangeable K/Ca + Mg + K ratios below 0.04 and the appearance of K deficiency symptoms in the trees. There was no evidence of long-term soil-K enrichment after 5 yr of K fertigation. Key words: Leaf and soil K, micro-irrigation, Malus domestica Borkh, root restriction
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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 | 0.000 |
| 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) | 0.001 | 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".