Productivity, Technological Attributes and Water Use Efficiency of Sugarcane Cultivars Under Regulated Deficit Irrigation
Bibliographic record
Abstract
Irrigation systems with high water application uniformity, adapted cultivars, and management of regulated deficit irrigation (RDI) are some ways to increase water use efficiency in agriculture. RDI is a practice that aims to provide a smaller amount of water than that consumed by crops without significantly affecting agricultural yield. Objectives of this study were to evaluate the technological characteristics (Bx, Juice POL, Fiber, TRS and Cane POL), water use efficiency (WUE), number of stalks, and sugar and stalk yield of five sugarcane cultivars subjected to RDI and non-irrigation. The experiment was conducted at the School of Agricultural and Veterinatian Sciences, São Paulo State, Brazil. The treatments were distributed in a partially balanced incomplete-block design. The RDI provided 50% of the evapotranspiration water by the crop. At each 30 mm water deficit a 15 mm depth was applied. The evaluated sugarcane cultivars were ‘CTC 4’, ‘IACSP 93-3046’, ‘RB 86-7515’, ‘IACSP 95-5000’, and ‘IAC 91-1099’. The total irrigation depth applied during the cycle was 180 mm. The RDI reduced the technological characteristics of sugarcane. However, it increased the productivity of the stalks and sugar, and did not change the number of stalks per hectare, nor the water use efficiency. Among the cultivars, ‘IAC91-1099’ showed the highest sugar yield (21.81 t ha-1), stalk yield (146.5 t ha-1), and water use efficiency (146.7 kg ha-1 mm-1). The cultivar ‘CTC4’ showed little responsiveness to RDI, presenting a lower number of stalks per hectare and water use efficiency in relation to its growth under non-irrigation conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".