Numerical exploration of the impact of hydrological connectivity on rainfed annual crops in Mediterranean hilly landscapes
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.
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 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".