Modelling the Effects of Soil Conditions on Olive Productivity in Mediterranean Hilly Areas
Bibliographic record
Abstract
The majority of olive (Olea europaeaL.) production in Mediterranean environments is characterized by low external inputs and is practiced in hilly areas with shallow soils. This study aimed to study the yield and nutritional status for olive (cv. “Zeiti”) trees in northwestern Syria and establish correlations between yield, on the one hand, and soil/land factors and tree nutrition, on the other hand, to determine the most yield-affecting factors. Land and soil fertility parameters (field slope, soil depth, and soil nutrients) and concentrations of leaf minerals were determined. As olive roots can go deep in the soil profile to extract nutrients, the total available nutrients per tree (over the whole profile) were estimated. Multiple regression analyses were performed to determine the model that best accounts for yield variability. Total available soil potassium amount ( R2=0.68 ), soil total N amount ( R2=0.59 ), and soil depth ( R2=0.56 ) had the highest correlations with olive fruit yields. Available soil potassium amount and soil depth explained together 77% of the yield variability observed. In addition to these two factors, adding leaf B and Fe concentrations to the model increased the variability explained to 83%.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".