Conservation Agriculture to increase water productivity of durum wheat under semi-arid Mediterranean conditions
Bibliographic record
Abstract
Tunisia is facing twin challenges, namely, food and water security, which are pressing now and likely to increase in the future mainly due to climate change. To face this alarming situation, the implementation of conservation agriculture (CA) remains crucial for facing interannual variability in climatic conditions that impact durum wheat production. The current study aims to assess the effect of tillage systems on grain yield (YLD), above-ground biomass (AGB), and crop water productivity. The experiment was conducted at the Bourabia experimental station of the National Institute of Agricultural Research of Tunisia, located in a semi-arid zone of Tunisia, during cropping seasons (2013-2014 and 2014-2015). At harvest, above-ground biomass, yield, and yield components of durum wheat (Maali cultivar) were determined. Tillage practices included no-tillage (CA) and conventional tillage (CV). Preceding crops were either common vetch or bread wheat. The N rates applied were: 0, 75, 100, 120, and 140 kg N ha−1. The experiments were laid out in a ‘Split-Plot’ design with three replications. The results show that the relationship between water productivity (quantity of water used to produce a ton of grain) and grain yield illustrated a better water valorization in CA system. For yields lower than 2 t ha-1, more water was needed in AC than in CV to produce the same amount of grain; Whereas for yields greater than 2 t ha-1, the opposite was revealed. On the other hand, grain yield and above-ground biomass were higher under CA compared to CV (+806 and +2468 kg ha−1 for YLD and AGB respectively) in the dry growing season (year2), while in the favorable growing season (year1), the opposite was observed (-315 and -604 kg ha−1 for YLD and AGB respectively). This feature illustrates the positive effect of CA in low-rainfall growing season due to good soil infiltration and reduction of evapotranspiration. Therefore, these findings provide evidence of the positive impact of CA on rainfed durum wheat under semi-arid Mediterranean conditions.
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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".