Implications of Agricultural Gypsum Doses in Physical-Hydric Attributes of a Typic Haplortox and on Root Growth and Soybean Productivity
Bibliographic record
Abstract
The response to agricultural gypsum, as a conditioner of the root environment in depth, has been observed for most annual crops. These responses are attributed to the better distribution of roots of the crops in depth in the soil by the reduction of chemical impediments, caused by the exchangeable aluminum and calcium deficiency in these layers, which allows to the plants the use of greater volume of water when they occur summer. In this way, the objective of this study was to evaluate the effects of gypsum doses on physical-hydric attributes, root growth and soybean productivity. The experiment was conducted at the Agronomic Institute of Paraná (IAPAR) in Santa Tereza do Oeste-PR. The soil was classified as Typic Haplortox. Five doses of agricultural gypsum were evaluated: 0; 3; 6; 9 and 12 t ha-1, in outline randomized block design with six repetitions. Soil density, total porosity, macroporosity, microporosity and saturated hydraulic conductivity were evaluated at layers of 0.0-0.1; 0.1-0.2 and 0.2-0.3 m. Soybean productivity and root growth were also evaluated. Data were submitted to regression analysis. The physical attributes soil density, macroporosity and saturated hydraulic conductivity did not differ significantly with the application of the gypsum doses in the 0.0-0.1 and 0.2-0.3 m layers. However, in the 0.1-0.2 m layer, due to pressures imposed by the machines and agricultural implements deforming the soil, there were significant differences in the physical attributes of the density, macroporosity and saturated hydraulic conductivity. There was no significant difference in grain productivity and root growth of soybean.
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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 | 0.000 |
| 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".