Productivity and Sensory Quality of Arabica Coffee in Response to Pruning Type ‘Esqueletamento’
Bibliographic record
Abstract
Through the pruning type ‘esqueletamento’ it was developed a technology for the management of trees called “Zero Yield” in order to keep the size of the crop and eliminate the harvesting in a low yield season, which usually compromises the earnings of the coffee grower. With this technology, the harvest takes place every two years, always in years of high yield season. This technology is highly dependent on climate, and genotype. Given this, the objective of the study was to select genotypes of Coffea arabica L. responsive to pruning type ‘esqueletamento’, with high productivity and high potential for quality of drink for specialty coffee production. The experiment was deployed in the Coffee Sector at the Universidade Federal de Lavras (UFLA) in December 2005 and after six crops, it has undergone the pruning type ‘esqueletamento’ in August 2014. 20 genotypes were evaluated, being 18 progenies in F5 generation, derived from the cross between cultivars of the Catuaí Group with coffees of germplasm Icatu and germplasm “Hibrido de Timor” and two commercial cultivars as witnesses. (Tupi IAC 1669-33 and Obatã IAC 1669-20). The following characteristics were evaluated: productivity (before and after pruning), vegetative vigor, the incidence of leaf rust, dull fruits, sieve No.16 and above, grain type mocha, aspect, and quality of drink. The genotypes 9 (H516-2-1-1-18-1-1), 12 (H516-2-1-1-18-1-4), 16 (H419-3-4-5-2-1-3), 18 (H419-3-4-5-2-1-5) and the cultivar Tupi IAC 1669-33 were responsive to the ‘esqueletamento’, with productivity after pruning higher than the maximum reached before the adoption of the pruning. All genotypes with the exception of Catucaí Amarelo 24/137 showed potential for the production of specialty coffees.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".