Furrow Irrigation for Corn Cultivation in Hydromorphic Soils
Bibliographic record
Abstract
The use of crop rotation in hydromorphic soils has been intensified in the state of Rio Grande do Sul, Brazil. Due to the difficult management of these soils, the use of irrigation is fundamental to increase the reliability of these production ecosystems. The present study aimed to evaluate the growth and yield components of corn under different managements of furrow irrigation. The study was conducted in Alegrete/RS in the experimental area of the Farroupilha Federal Institute during the 2017/2018 season. Two factors were evaluated: five managements of furrow irrigation, with a control (not irrigated) and 0, 25, 50 and 100% of the time required to replace the irrigation depth up to field capacity, and the influence of plant position relative to the total length of the furrow, at 0, 25 and 50 meters from its beginning. During the growth stage of corn, its LAI showed better performance for the three collections, at 34, 54 and 76 DAS, and plant height and shoot dry matter showed differences at 76 DAS in treatments that received irrigation. Yield components such as number of grains per ear, harvest index and grain yield were influenced by the use of irrigation, whereas water use efficiency did not differ between the use of irrigation and the control treatment. Lastly, best performances of application efficiency were found in treatments with 0% and 25% of the time required to replace the irrigation depth.
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 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 | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".