Soil Chemical Properties and Production of Physic Nut Intercropped With Forage Plants and Grain Crops
Bibliographic record
Abstract
Intercropping cover plants with physic nut (Jatropha curcas L.) may be a viable strategy for improving soil quality and sustaining the yield of this oilseed crop. However, one of the main challenges facing prolonged cropping of physic nut is the lack of information regarding the agronomic practices of the crop in intercropping systems. The aim of this study was to evaluate the effect of cropping systems with cover plants and grain crops on the soil chemical properties and cumulative production of physic nut grain and oil. Eleven cropping systems and two evaluations were conducted in a split-plot arrangement on a dystrophic red latosol (Latossolo Vermelho Distrófico) in the municipality of Dourados. Growing cover plants or grain crops between the rows of physic nut did not provide significant increases in the cumulative production of grain and of oil over growing physic nut alone. There was reduction in the availability of nutrients, especially P and K, through growing Campo Grande Stylosanthes, U. humidicola, and Crotalaria. However, the beneficial effects of intercropping related to maintaining soil cover and the possibility of increasing the profitability of cropping physic nut from the production of forage crops and grains should be considered. Although the results did not show a significant increase in physic nut production in intercropping systems, the approach still offers opportunities to improve agricultural sustainability, crop diversification, and long-term profitability.
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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".