Soil properties and fenugreek (Trigonella foenum-graecum L) growth, yield and quality as affected by tillage systems, crop rotation and nitrogen fertilization under semi-arid climatic conditions
Bibliographic record
Abstract
Abstract Adopting sustainable agricultural practices is considered as an effective strategy to mitigate climate change and to improve soil health and crop production.This study aims to assess the impact of tillage systems (no-tillage (NT) and conventional tillage (CT)), crop rotation (faba beans - oat and faba beans - durum wheat) and nitrogen fertilization rates (0, 20 and 40 kg N.ha −1 ) on soil properties and growth and yield of fenugreek. Soil samples were collected at two stages: at the 50% blossoming stage and after harvest and fenugreek quality and yield and its components were measured.The results showed that tillage systems and crop rotation treatments had great influence on soil properties. In general, the highest soil organic carbon (SOC) (13.4 and 14.2 g.kg −1 ), total nitrogen (1.0 and 1.1 g.kg −1 ), nitric nitrogen (75.73 and 62.42 mg.kg −1 ), ammonium-nitrogen (14.90 and 19.08 mg.kg −1 ). were recorded in no tillage practice with durum as a previous crop at 50% blossoming and harvest stage respectively. Exception for SOC, the highest nitrogen fertilization rate improved most of soil variables at both growth stages. Greater biomass and grain protein content were obtained under NT system and 40 kg N.ha −1 . Previous crops had no effect on grain protein content. Also, there was a significant synergy among soil fertility and fenugreek production. Conservation tillage, durum wheat previous crop and highest nitrogen fertilization rate gave the best aboveground biomass, yield and yield attributes performances. Overall, NT associated with durum wheat as a previous crop and nitrogen fertilization (40 kg N.ha −1 ) are the suitable combination to improve both soil quality, and fenugreek yield.
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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".