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\nwheat and sunflower inserting 0, 1 (pea or fababean) and 2 grain\nlegumes (GL) (pea and soybean), and with or without cover crops (CC)\nwere compared at INRA Toulouse from 2004. This experiment is still on\ngoing for a twelfth year. We demonstrated that 6 key points. 1) Pea as a\npreceding crop increased durum wheat grain production by 8%\ncompared to sunflower as a preceding crop with a mean reduction of N\nfertilization of 45 kg N ha-1. 2) Inserting GL in the rotations significantly\naffected the amount of C and N inputs to the soil that were lower than\nwith cereals and consequently led to a decrease in soil organic-C (SOC)\nand –N contents. 3) N leaching simulated using the STICS model was\nhigher when increasing the number of GL (from 22 to 52 kg N ha-1 after\ntwo rotation cycles of 6 years, for 0 to 2 GL respectively). 4) However,\nCC insertion i) reduced N leaching (from 15 to 18 kg N ha-1), ii) mitigated\nSOC loss, and iii) did not affect durum wheat grain protein concentration\nor yield. 5) Daily measured N2O emissions over the whole 3-year\nrotation were low but significantly higher under the CS including\nfababean than for the cereal-based CS (1.12 vs. 0.78 kg N2O-N ha-1 year-\n1) despite a lower N fertilization. Then, in such conventionally-tilled\nsystems, properly designed cropping systems that simultaneously insert\ngrain legumes and cover crops reduce N requirements of the following\ndurum wheat, stabilize SOC content but do not decrease N2O emissions\nat the rotation level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".