Soil Acidity Indicators for Liming in Tropical Acid Soils Cropped With Soybean Under Short- and Long-Term No-Tillage Systems
Bibliographic record
Abstract
Although the movement of liming materials under no-tillage (NT) systems can intensify stratification of soil chemical properties and be deleterious to soybean growth, little is known regarding the soil acidity indicators used to predict lime requirement (LR) in Brazilian soils under NT. Thus, we hypothesize that the recommendation criteria used for predicting LR in soils under NT must be different from those adopted in soils under conventional tillage (CT), and the reference values for such liming indicators may vary according to the phase following the adoption of the NT system. The study aimed to obtain soil acidity indicators for soybean (Glycine max (L.) Merill) in tropical acid soils under no-tillage (NT) systems and their respective critical levels according to the phase of NT. Sites under NT were commercial soybean crop areas located in the Cerrado region, Brazil. Systems analyzed were NTI (NT management from 0 to 5 years—initial phase), and NTTC (NT management from 5 to 20 years—transition-consolidation phase). Soil samples were collected at the 0-5, 0-10, and 0-20 cm layers, and analyzed for chemical characteristics. Relationships between crop yield response of soybean to lime application and various soil acidity-related characteristics led to establishing soil acidiy indicators for liming in tropical acid soils under no tillage. Critical levels were approximately similar in both phases of NT for exchangeable Ca and Mg, and potential acidity, but varied greatly depending on the soil layer and phase of NT management for soil pHCaCl2, CECpH7.0, and base saturation. In general, for both phases of NT, the critical levels of soil acidity indicators were lowest for the 0-20 cm layer, moderate for the 0-10 cm layer, and highest for the 0-5 cm layer. Lime applied with incorporation in the NTTC phase kept the soil with chemical attributes more favorable for plant growth than when surface liming was employed in the NTI phase, which was verified by the soybean yield response. Our results indicate the differences on the soil acidity indexes between the top and bottom depths that would not have been realized in a soil sampling for conventional tillage. Hence, recommendation criteria for lime application considering distinct soil depths and NT systems will be helpful when making lime decisions. Further research should focus on the development of reliable methods for predicting LR according to the NT phase and consequently maximize soybean production under NT systems in Brazil.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".