Limitations to adopting regulated deficit irrigation in stone fruit orchards: a case study
Bibliographic record
Abstract
Fruit production development is resulting in large commercial orchards with improved water management standards. While the agronomic and economic benefits of regulated deficit irrigation (RDI) have long been established, the local variability in soils and climate and the irrigation system design limits its practical applications. This paper uses a case study approach (a 225 ha stone fruit orchard) to unveil limitations derived from environmental spatial variability and irrigation performance. The spatial variability of soil physical parameters and meteorology in the orchard was characterized, and its implication on crop water requirements was established. Irrigation depths applied during 2004-2009 were analysed and compared with crop water requirements under standard and RDI strategies. Plant water status was also measured during two irrigation seasons using stem water potential measurements. On-farm wind speed variability amounted to 55%, representing differences of 17% in reference evapotranspiration. During the study seasons, irrigation scheduling evolved towards deficit irrigation; however, the specific traits of RDI in stone fruits were not implemented. RDI implementation was limited by: 1) poor correspondence between environmental variability and irrigation system design; 2) insufficient information on RDI crop water requirements and its on-farm spatial variability within the farm; and 3) low control of the water distribution network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".