Effect of Soil Coverage on Dual Crop Coefficient of Maize in a Region of Mato Grosso, Brazil
Bibliographic record
Abstract
With the objective of determining maize’s specific water requirements in different soil cover conditions in a Cerrado region of Mato Grosso, Brazil, this study used the dual’s crop coefficient (Kc dual) approach, according to FAO methods. An experiment was carried out in 2016, with three treatments: without vegetation soil cover; soil cover of 4 t ha-1 and soil cover of 8 t ha-1 dry matter of brachiaria grass. The used methodology accounts for crop’s transpiration component (through its basal coefficient, Kcb) and soil evaporation component (through its coefficient, Ke), which were determined for initial, intermediate and final phases of crop development. Experiments were carried in lysimeters to determine crop’s evapotranspiration, and in microlysimeters to determine soil evaporation. Crop’s transpiration, on three soil coverage treatments, showed overall highest values for the treatment with greater coverage (Kcb maximum values of 0.88, 1.00 and 1.03 from the lowest to greater soil coverage), while between crop’s phases, coefficient values were always higher at the intermediate stage, presenting decreases with crop senescence. Soil evaporation was highest on treatment without coverage in all crop’s stages (Ke = 0.37-0.78) and lowest in the treatment with greater coverage (Ke = 0.11-0.35). Yields were higher on treatments with coverage (9929.18 and 9939.52 kg ha-1 for treatments with 4 and 8 t ha-1) and lower when cultivated in soil without cover (8264.67 kg ha-1). Despite relatively higher crop’s transpiration with greater soil coverage, this treatment was identified as the best management option in the assessed tropical region of Brazilian Cerrado, in terms of rational use of water, due to lowest losses through evaporation, as also providing the highest grain yields.
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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 | 0.001 |
| 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".