Spatial Variability of the Organic Matter in the Soil in Cassava Cultivation Under Differentiated Management
Bibliographic record
Abstract
The management of soil organic matter (SOM) is fundamental in agriculture for soil conservation and crop yields. However, in addition the soil being dynamics, it is heterogeneous, therefore, understanding the spatial variability of SOM is essential. The main objective of this study was to evaluate the spatial variability of SOM in a cassava cultivation under different management, seeking to classify its spatial dependence by geostatistics. A filed experiment was conducted out on soil classified as oxisol with four different management systems: irrigation (micro sprinkler, drip and no irrigation), spacing (1.0 × 0.8 m, 1.0 × 1.0 m and 1.0 × 1.5 m), weed control (manual control and no control) and acidity correction (limestone and withouth limestone), totaling 36 experimental plots. To determine the SOM, the wet oxidation method was used, and the semivariograms were generated by the GS+® software. The effect of the different management systems on the spatial variability of the SOM was evaluated at a depth of 0.0-0.2 m. The theoretical semivariogram model that best fitted the study was the Gaussian model, with a well-defined level, also expressing the condition of data stationarity. The spatial dependence was classified as strong and, through the thematic map generated from the kriging, it was possible to observe the variability in the SOM content for different management zones. The use of geostatistics techniques provided important information for understanding the spatial distribution of soil organic matter.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".