Use of a New Deep Vertical Gypsum Placement Practice on Corn and Soybean Production in Conservation Tillage Systems
Bibliographic record
Abstract
Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) production in claypan soils in the north central U.S. may be constrained by the presence of acidic subsoils. Subsoil acidity can inhibit root growth leading to decreased drought tolerance and grain yields. In conservation tillage systems, management options to incorporate gypsum applications may be limited; thereby reducing available practices to lower subsoil acidity. The objective of this study was to determine the effects of surface placement of gypsum compared to a new practice for deep vertical placement of gypsum on corn and soybean plant growth and yields in a conservation tillage system. Field trials were conducted from 2012 to 2016 in northeast Missouri (USA) with treatments of gypsum (0, 2.9, and 5.2 Mg ha-1) broadcast on the soil surface or applied in a deep vertical band to a depth of 51 cm. Surface and deep banding of gypsum had inconsistent effects on corn and soybean plant heights, plant population and yields. However, deep banding of gypsum resulted in a 6.4 to 9.8% decrease in corn yields and a 9.9 to 13.0% decrease in soybean yields depending on the time after application. These results indicate that further research is warranted in conservation tillage systems in claypan soils to examine modification to the deep vertical placement practice or combining applications of surface-applied gypsum and deep placement of lime in order to develop a practice that will be more effective in overcoming subsoil acidity.
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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 | 0.001 | 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 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".