Forage sorghum grown in a conventional wheat–grain sorghum–fallow rotation increased cropping system productivity and profitability
Bibliographic record
Abstract
Intensifying winter wheat ( Triticum aestivum)–grain sorghum [ Sorghum bicolor (L.) Moench]–fallow (W–GS–FL) crop rotation with annual forages can increase productivity and resource use efficiency. The objective of this research was to quantify the impact of increasing crop intensity by growing forages in a traditional W–GS–FL rotation on cropping system productivity, water use, precipitation use efficiency, and net income. The study was conducted at the Southwest Research-Extension Center near Garden City, Kansas, from 2013 through 2020. Winter wheat (W), grain sorghum (GS), forage sorghum (FS), and forage oats (FO, Avena sativa L.) were used to generate six crop rotation treatments. These rotation treatments interspersed with fallow periods (FL) were W–GS–FL, W–FS–FL, W/FS–GS–FO, W/FS–FS–FO, W/FS–GS–FL, and W/FS–FS–FL. A W/FS indicates winter wheat double crop FS planted in the same year. The yield of FS was 45%–56% more with W/FS–FS–FO and W/FS–FS–FL compared with W–FS–FL. Available soil water at GS planting was 23%–30% less, and GS yield was 52%–60% smaller with W/FS–GS–FL compared to W–GS–FL. Water productivity and pre-season soil water storage were greatest with W/FS–FS–FL and W/FS–FS–FO. Inclusion of W/FS increased cost of production compared with W–GS(FS)–FL rotations. Gross return was greatest with W/FS–FS–FO and W/FS–FS–FL. The W/FS–FS–FO increased cropping intensity, productivity, resource use, and gross margin relative to other rotations in the semi-arid Great Plains. Producers should consider double-cropping of FS after wheat harvest, followed by a second year of FS in dryland cropping systems if there is sufficient forage demand.
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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".