Response of ‘Honeycrisp®’ Apple Trees to Combinations of Pre-plant Fumigation, Deep Ripping, and Hog Manure Compost Incorporation in a Soil with Replant Disease
Why this work is in the frame
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Bibliographic record
Abstract
This study evaluated the effects of pre-plant treatments: deep ripping (DR), fumigation (F), deep ripping plus fumigation (DRF), deep ripping plus hog manure compost (DRC), and deep ripping plus fumigation plus hog manure compost (DRFC) in comparison with a non-treated control (NTC) on shoot and root performance of ‘Honeycrisp’ apple trees on M.4 rootstocks in an old orchard site with apple replant disease (ARD). Cylindrocarpon spp., Pythium spp., and Pratylenchus penetrans Cobb, all potential agents of ARD, were present in the orchard soil. Fine-root numbers (1 to 2.9 mm diameter) were significantly greater in the DRC and DRFC treatments than the DR treatment. After 6 years, trunk cross-sectional area (TCSA) and yield were largest for the DRFC treatment followed closely by F. The DR treatment had no effect on TCSA, yield, or yield efficiency when applied alone compared with the NTC. Contrast analysis demonstrated that F was significantly better than non-F for yield in all years and TCSA and yield efficiency in 2007. Also, there was a significant interaction between DR and F treatment in 2005 that significantly reduced yield in the DRF treatment. Contrast analysis showed that compost had a significant positive effect on yield in all three production years and TCSA and yield efficiency in 2007. Yield efficiency in the third production year was largest for F, DRC, and the DRFC treatments. Nutrient analysis revealed that soil phosphorus concentrations in compost-treated plots were double those in other treatments. High phosphorus content of compost may have contributed to the amelioration of ARD symptoms. This study found that in 2007, soil fumigation alone, as conventionally used for ARD control, and composted hog manure were equally effective in increasing yield and yield efficiency of apple trees planted in an ARD soil. The DRFC treatment was the overall best treatment in all years.
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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 it