Synthesis on the effects of grain legume insertion and cereal-grain legume intercrops in low input cropping systems in Southern France
Bibliographic record
Abstract
Six cropping systems (CS) of three 3-year rotations based on durum wheat and sunflower inserting 0, 1 (pea or fababean) and 2 grain legumes (GL) (pea and soybean), and with or without cover crops (CC) were compared at INRA Toulouse from 2004. This experiment is still on going for a twelfth year. We demonstrated that 6 key points. 1) Pea as a preceding crop increased durum wheat grain production by 8% compared to sunflower as a preceding crop with a mean reduction of N fertilization of 45 kg N ha-1. 2) Inserting GL in the rotations significantly affected the amount of C and N inputs to the soil that were lower than with cereals and consequently led to a decrease in soil organic-C (SOC) and –N contents. 3) N leaching simulated using the STICS model was higher when increasing the number of GL (from 22 to 52 kg N ha-1 after two rotation cycles of 6 years, for 0 to 2 GL respectively). 4) However, CC insertion i) reduced N leaching (from 15 to 18 kg N ha-1), ii) mitigated SOC loss, and iii) did not affect durum wheat grain protein concentration or yield. 5) Daily measured N2O emissions over the whole 3-year rotation were low but significantly higher under the CS including fababean than for the cereal-based CS (1.12 vs. 0.78 kg N2O-N ha-1 year- 1) despite a lower N fertilization. Then, in such conventionally-tilled systems, properly designed cropping systems that simultaneously insert grain legumes and cover crops reduce N requirements of the following durum wheat, stabilize SOC content but do not decrease N2O emissions at the rotation level.
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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 | 0.000 |
| 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) | 0.002 | 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".