Induced Defoliation and Corn Productivity Performance
Bibliographic record
Abstract
Due to the change in the environment, diseases and insects can reduce the leaf area of agricultural crops. The objective of this study was to determine the impact of induced defoliation on the vegetative and reproductive phases of corn on its agronomic characteristics. The experiment was carried out in the experimental area of the Mato Grosso Federal Institute of Education, Science and Technology, Campo Novo do Parecis campus, in a second crop system in the 2017/2018 agricultural year, with sowing in March. This study used a randomized complete block design, consisting of 24 treatments and three replications, comprising eight phenological stages. The treatments were performed from the fourth leaf stage (V4) to physiological maturation (R5), with defoliation in the lower, middle and upper thirds of the plant. The characteristics evaluated were: plant height, stem diameter, ears insertion height, ears length, ears diameter, ears weight, number of rows per ears, number of grains per row, weight of grain per ears, prolificity; harvest index, weight of one thousand grains and grain yield. Defoliation between the fourth (V4) and twelfth leaves (V12) does not interfere in the evaluated characteristics. Grain and ear weight are interfered when defoliation occurs in the middle and upper third of plants in V12. Defoliation between flowering (R1) and beginning of grain filling (R2) negatively affects the weight of one thousand grains, grain and ear weight, and may reduce grain yield by up to 30%. Defoliation in the upper third of the plant significantly reduces the diameter and length of ears, grain and corn ear of the corn crop.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".