Correlations Between Pratylenchus and Meloidogyne Populations, Soil Chemical Properties, Soil Texture, and Nutritional Status of Soybean Crops in Paraguay
Bibliographic record
Abstract
Nematodes cause great damage to soybean crops in Paraguay. Studies have investigated correlations between phytonematodes and soil chemical and physical properties, but little is known about correlations with the nutritional status of soybean crops. This study aimed to assess correlations between Pratylenchus, Meloidogyne, soil chemical properties, soil texture, and the nutritional status of soybean. The experiment was carried out in Paraguay in areas of commercial soybean cultivation infested with nematodes, totaling 83 collection sites. Analyses of nematodes in soil and root samples, chemical characterization of soil acidity, fertility, and texture, and chemical characterization of soybean leaves were performed, totaling 36 variables. Data were subjected to principal component analysis. Soil Al3+ favored the development of Pratylenchus populations. Organic carbon negatively influenced Meloidogyne. K+ and Mg2+ negatively affected Pratylenchus and Meloidogyne, respectively. Pratylenchus and Meloidogyne correlated negatively with clay contents. In sandy soils, there was a negative correlation between Pratylenchus and sand content. Pratylenchus and Meloidogyne led to an increase in foliar Ca and a decrease in foliar P. Soil fertility management can be used as part of the integrated management of Pratylenchus and Meloidogyne. It is worth mentioning that, in field studies, the complexity of biotic and abiotic factors in the crop system may contribute to diverging results, making it difficult to establish a single response pattern, especially when some factors affect others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".