Influence of Irrigation and Fertigation on Fruit Composition, Vine Performance, and Water Relations of Concord and Niagara Grapevines
Bibliographic record
Abstract
A study was conducted between 1998 and 2002 to investigate the impact of different durations of irrigation and fertigation upon vine performance, fruit composition, and water relations of Concord and Niagara (<i>Vitis labruscana</i>) grapes in the Niagara Peninsula in Ontario and to quantify the degree of water stress that vineyards in the region typically experience. The six Concord treatments were a nonirrigated control, irrigation from budburst to veraison, and four fertigation treatments which applied 80 kg N/ha as urea. The nine Niagara treatments were a nonirrigated control, two irrigated treatments (ceasing at veraison and harvest, respectively), and six fertigation treatments of various durations. The modified FAO Penman-Monteith evapotranspiration formula was used in the final season to calculate water budgets and schedule irrigations. Transpiration rate and soil moisture data suggested that water stress was present in these vineyard blocks in 3 of 5 years of the study. The small transpiration differences between control and irrigated or fertigated treatments may have been due to early season irrigation increases in canopy size that led to later season water stress. Irrigation and fertigation led to enhanced berry set, larger berry size, increased vine size, and small increases in yield. Slight yield increases (~10% in Concord; 29% in Niagara) in irrigated and fertigated treatments were attributable to increased cluster numbers, cluster weights, and berry weights. In most seasons, yield increases were accompanied by small decreases in soluble solids (1.5 to 3.0 Brix) and methyl anthranilate concentrations. Timing of fertilizer application did not play a major role in any of these attributes. Use of the Penman-Monteith for irrigation scheduling needs to be evaluated over several seasons and validated using both plant and soil moisture monitoring.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".