Numerical exploration of the impact of hydrological connectivity on rainfed annual crops in Mediterranean hilly landscapes
Why this work is in the frame
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Bibliographic record
Abstract
Within hilly agricultural landscapes, topography induces lateral transfers of runoff water, so-called interplot hydrological connectivity. Runoff water from upstream plots can infiltrate downstream plots, thus influencing the water content in the root zone that drives crop functioning. The impact of runoff on crop functioning can be crucial for optimizing agricultural landscape management strategies. However, to our knowledge, no study has specifically focused on the impact on crop yield. The current study aims to comprehensively investigate the impact of runoff on crop functioning in the context of Mediterranean rainfed annual crops. To quantify this impact, we conduct a numerical experiment using the AquaCrop model and consider two hydrologically connected plots. The experiment explores a range of upstream and downstream agro-pedo-climatic conditions: crop type, soil texture and depth, climate forcing, and the area of the upstream plot. The experiment relies on data collected over the last 25 years in OMERE, an environment research observatory in northeastern Tunisia, and data from literature. A key finding in the results is that water supply through hydrological connectivity can enhance annual crop production under semiarid and subhumid climate conditions. Specifically, the results show that the downstream infiltration of upstream runoff has a positive impact on crop functioning in a moderate number of situations, ranging from 16% (wheat) to 33% (faba bean) as the average across above ground biomass and yield. Positive impact is mostly found for higher soil available water capacity and under semiarid and dry subhumid climate conditions, with a significant impact of rainfall intra-annual distribution in relation to crop phenology. These research needs to be expanded by considering both a wider range of crops and future climate conditions.
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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 | 0.001 |
| 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 it