Yield and Nutrient Uptake of Soybean Cultivars Under Intensive Cropping Systems
Bibliographic record
Abstract
Sustainable agricultural systems are necessary to improve soybean [Glycine max (L.) Merr.] seed yield and to increase nutrient use efficiency. Intensification of agricultural systems is an important tool to increase farmers’ profitability in the Cerrado region (Brazil), where soybean is rotated with corn in the same growing season. However, this intensification requires soybean cultivar with short growing periods which is achieved by indeterminate soybean cultivars. There is a lack of information regarding the nutrient uptake by soybean cultivars under intensive agricultural systems in the Cerrado. We sought to investigate soybean biomass production and soybean seed yield of determinate and indeterminate soybean cultivars. We also aimed to quantify the amounts of nutrients taken up by soybean biomass and seeds. Field research was conducted to evaluate 17 soybean cultivars commonly grown by farmers, and we considered the determinate and indeterminate soybean growth habit. Nutrient uptake and aboveground soybean biomass were higher under shorter soybean growth and development cycles. Nitrogen, phosphorus and potassium extraction in modern cultivars was higher than in cultivars used in past decades. Nutrient use efficiency was higher in determinate soybean cultivars than in indeterminate soybean cultivars.
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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) | 0.000 | 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".