Balanced Nutrition and Crop Production Practices for Closing Grain Sorghum Yield Gaps—East Central Kansas Experiment Field
Bibliographic record
Abstract
A field experiment was conducted at the East Central Kansas Experiment Field near Ottawa, KS, and at the Kansas River Valley Experiment Field near Rossville, KS, in the summer of 2014 to evaluate diverse cropping systems approaches on closing sorghum yield gaps. Yield gaps can be understood as the difference between maximum yield and attainable on-farm yields. The factors that were tested include narrow row spacing; plant population; balanced nutrition practices, including various timings of nitrogen, phosphorus, and potassium (NPK) and micronutrient applications; crop protection with fungicide and insecticide applications; plant growth regulator effects; and the use of precision ag technology for maximizing yields, including a GreenSeeker meter (Trimble Navigation, Westminster, CO) for more precisely determining fertilizer nitrogen needs for sorghum. In addition, this project seeks to quantify the comparison between corn and grain sorghum grown side by side at two production input levels (low vs. high). Only sorghum grain yields are presented in this report. Grain sorghum yields were 115 to 135 bu/a in Rossville (under irrigation) and 60 to 80 bu/a in Ottawa (dryland). Rainfall was limited in Ottawa during the flowering and reproductive stages of growth, which drastically limited yield potential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".