Impact of Cereal–Legume Intercropping on Changes in Soil Nutrients Contents under Semi–Arid Conditions
Bibliographic record
Abstract
Cereal–legume intercropping systems are not well studied under the semi–arid conditions of Southern Tunisia. Therefore, the present study aimed to investigate the effect of intercropping durum wheat (Triticum turgidum ssp. durum L.) with chickpea (Cicer arietinum L.) on crop grain yield and soil physicochemical proprieties such as carbon (C) and nitrogen (N) availability, microbial biomass nutrients (C and N) and plant nutrient content (N) in comparison to their monocultures. Field experiments were conducted during the 2020–2021 (EXP–A) and 2021–2022 (EXP–B) seasons in Medenine, Tunisia. The results revealed a significant augmentation (p < 0.05) in the total nitrogen proportions (Ntot) within the soil of intercropped durum wheat (DuWh–IR) compared to its monoculture (DuWh–MC). The observed variations amounted to 32% and 29% during the two growing seasons, identified as EXP–A and EXP–B. Additionally, the soil of intercropped durum wheat (DuWh–IR) significantly (p < 0.05) accumulated more total carbon (Ctot) than the monocrop (DuWh–MC) for both experiments, showing an increase of 27% in EXP–A and 24% in EXP–B. Simultaneously, the N− uptake of durum wheat significantly increased under the effect of intercropping, showing a rise of 26% in the EXP–A season and 21% in the EXP–B season. Similarly, the yield of durum wheat crops was comparatively greater in the intercropped plots as opposed to the monoculture crops, with variances of 23% in EXP–A and 20% in EXP–B. Intercropping cereals and legumes has the potential to enhance the soil fertility and crop production in the semi–arid regions of Southern Tunisia and contribute to environmental sustainability by reducing reliance on nitrogen fertilizers.
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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 | 0.000 | 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 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".