Yield and Water Use in Almond under Deficit Irrigation
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Bibliographic record
Abstract
Core Ideas Maximum yields were obtained with wireless real‐time tensiometers initiating irrigation at –45 kPa. A significant 16% reduction in water use relative to the grower control was achieved by initiating irrigation at –45 kPa with no yield reduction. Almond crop is sensitive to water management, as being too wet (initiation at ‐35 kPa) or too dry (initiation at –55 kPa) reduced yield by about 11.0 and 11.3%, respectively. ABSTRACT In North America, almond [ Prunus dulcis (Mill.) D.A. Webb] trees are grown almost exclusively in the Central Valley of California. Research on deficit irrigation is needed to improve water productivity. Real‐time technology assessing soil water potential to manage irrigation initiation has led to significant improvements in water productivity in other crops. The objective of this study was to examine the possibility of using real‐time tensiometry for irrigation to trigger irrigation events and to generate water savings without affecting crop yield. The yield responses and water consumption of mature almond trees were quantified from 2012 to 2015 for four different irrigation strategies in a commercial orchard located in the San Joaquin Valley in California. Three of the treatments were based on soil water potential threshold (SWPT) measurements and the fourth on the grower’s current management practices, which used estimated crop evapotranspiration (ET c ). The SWPT treatments were based on three different stress levels: wet (–35 kPa), medium (−45 kPa), and dry (−55 kPa). There was no significant difference in marketable yield between the grower irrigation strategy and the medium treatment, although the latter used 139 mm less water as a yearly average. In the dry treatment, there was 10% less water applied relative to the medium treatments and 30% less than the grower treatment but a 10% yield reduction compared with the medium and grower treatments. These results indicate that irrigation management for almond could be optimized by initiating irrigation at –45 kPa.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it