Multivariate Analysis of Physical and Chemical Soil Attributes Under Forage Palm Cultivation and Agriculture Reuse in the Semiarid Region
Bibliographic record
Abstract
The use of treated effluents (UTE) for irrigation has grown considerably in recent years, especially in arid and semi-arid regions. The objective of this research was to evaluate the effect of soil fertility under cultivation with forage cactus intercropped with different legumes. The experiment was conducted over a period of two years in a research unit that uses irrigation with reuse water (RW) in consortia of cactus species with wood and forage legumes. The soil layers were analyzed in the layers of 0-10 and 10-20 cm in three seasons: T0: without reuse water (RW); T1: dry season + reuse water (RW); T2: wet season + reuse water (RW) under cultivation of three palm varieties or cochineal cactus (mexican elephant ear palm, small palm and baiana). The soil attributes evaluated were granulometry, soil density, pH, salinity, sodicity, macronutrients, organic matter, Mineral N and total organic carbon stock (O.C.S). The plants were periodically irrigated with domestic effluent treated with constant irrigation 2 L h-1. The data were subjected to multivariate statistical analysis, using correlation matrix, cluster analysis, and factorial analysis considering the factors as principal components. According to the factorial analysis, Factor 1 (F1) and Factor 2 (F2)—F1 consisting of Sand, Ca2+, pHs, stock of. K and base saturation (V), and F2 consisting of Soil Organic Matter (SOM), stock of. Mg, CEC, stock of Ca2+, clay and soil density—were essential to differentiate the environments. The cluster analysis formed four groups. The structural groups showed greater similarity, denoting the relationship between source material and land use, followed by the chemical groups pHs, Ac. Pot., V+, CEC, Na+, stock of Na+, PST, and Ds; the structural weighted total porosity, stock of P.; Ksoil, stock of K, and Mg, SB, Te, stock of . Mg, and finally stock of Total, stock of Nitrog, Nit. Inorg.; SOM, TOC, Ca2+ and Tp of soil. This is a local peculiarity due to the climatic pattern of the Brazilian semiarid region in use of domestic sewage treated in agriculture as a forage index and as a social factor. The multivariate statistical analysis through principal component analysis and clustering made it possible to form groups according to soil attributes, which can help in decision making regarding soil fertility and fertilization management in this region with agricultural reuse application mainly in semiarid regions of Brazil.
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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 | 0.001 |
| 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".