Effect of Different Sources of Fertilization on Chemical Properties of Soil Under Cassava Cultivation in Western Pará, Brazil
Bibliographic record
Abstract
Nutrient management becomes essential in soils with low natural fertility, so the use of organic fertilizers can improve chemical properties of soil and reduce production costs. The objective of this study was to evaluate the effect of different fertilization sources on the chemical properties of soil under cassava cultivation. The experiment was conducted between 2017/2018 in the community of Boa Esperança, Santarém-PA using the cultivar Bem-te-vi, in the randomized block design with 4 treatments: T1 (without fertilization), T2 (fertilization NPK), T3 (organic fertilization with poultry manure), T4 (fertilization with cattle manure), with 5 replicates. The soil samples were analyzed for the contents K+, Ca²+, pH in H2O and KCl, Al³+ and soil organic carbon content (TOC). The data were submitted to analysis of variance and the means were compared by the Tukey test at 5% probability, using the software MINITAB© version 18. There were statistical differences between the treatments for all the chemical properties analyzed. The findings of the analysis were that fertilization with catle manure and NPK were the same and differed from the other treatments for the contents of K+, pH in H2O and KCl, for Ca²+ and TOC. The treatment without fertilization obtained the lowest values differing from the others, for Al³+. The source from NPK obtained the lowest content in the soil. The treatments with organic fertilizers had higher levels of the above nutrients in the soil. The chemical properties of the soil were altered from the different management systems for the cultivation of cassava.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".