In situ Field Capacity in Brazilian Soils and a Derived Irrigation Management Practice Based on Water Suction
Bibliographic record
Abstract
Field capacity (FC) is a fundamental parameter in soil and water engineering and hydrologic modeling. Despite its relevance, the in situ determination of this parameter is not standardized and its determination by indirect methods is dubious. This study presents a method of calculation of in situ FC and its corresponding water suction (hFC), using the van Genuchten equation for water retention and the pedotransfer function by Ottoni Filho et al. (2016) for standardized in situ determination of FC. The methodology was applied to HYBRAS, a database of hydrophysical data for Brazilian soils with 1,075 soil samples from 15 Brazilian states. FC and hFC were confirmed to depend on textural class and pedogenetic origin (weathered and unweathered soils). Our analysis justified why FC must not be determined based only on a single predetermined water suction value. A simplified method is proposed for the management of irrigated soils through the determination of water suction in the root zone and the mode and confidence interval values of hFC corresponding to soil groups formed from textural classes and pedological nature. Various statistical calculations of FC and hFC are presented for these groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".