Is phosphorus fertilization necessary for watermelon production on high phosphorus soils?
Bibliographic record
Abstract
Nutrient loading has created water quality problems in the Chesapeake Bay watershed of the United States. This study was designed to examine if a high yield of quality watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] can be produced with reduced phosphorus (P) fertilizer inputs and the use of a preceding cover crop on high P soils of the Eastern Shore of Maryland. Watermelon was planted on a Norfolk soil (fine loamy, siliceous, thermic type kandiudults) in a split plot design, with four replications. The main plot treatments were cover crops (rye versus no cover) and the sub-plot treatments were five different P-fertilizer rates ranging from 0 to 60 kg P ha-1, at 15 kg ha-1 increments. Following harvest, all P fertility regimes left behind “excessive” P levels based on soil tests. The addition of P-fertilizer to these soils was unnecessary for the production of a high yield of marketable quality watermelons. In two of three sites, the use of cover crops preceding the watermelon crop increased yields and fruit size. Use of a rye cover crop and reduced P-fertilizer inputs could have a positive environmental impact by reducing the risk of P over-loading without negatively impacting watermelon yield and quality. Key words: Watermelon quality, phosphorus-reduction, rye cover crop, phosphorus loading
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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".