Influence of soil texture on fertilizer and soil phosphorus transformations in Gleysolic soils
Bibliographic record
Abstract
The P dynamics in soils should be quantified in agricultural soils to improve fertilizer P (FP) efficiency while limiting the risk of P transfer from soils to water bodies. This study assessed P transformations following FP addition to Gleysolic soils. A pot experiment was conducted with five soils varying in texture from sandy loam to heavy clay, and receiving four FP rates under barley (Hordeum vulgare L.)-soybean (Glycine max L.) rotations. A modified Hedley procedure was used for soil P fractionation. Soil resin-P and NaHCO 3 -P i contents were interactively affected by texture and FP. The NaHCO 3 -P o , NaOH-P o , HCl-P and H 2 SO 4 -P were only affected by soil texture. Proportions of 78 and 90% of the variation in labile and total P were, respectively, related to soil clay content. The FP addition increased resin-P, NaHCO 3 -P i and NaOH-P i and -P o contents in coarse-textured soils, but the amount added was not sufficient to mask the initial influence of soil texture on the sizes of soil P pools. Plant P uptake was proportional to FP rate but less closely linked to clay content. The average increase in labile P per unit of total FP added in excess of plant exports was 0.85, 0.8 2 , 0.73, 0.55 and 0.24 for the sandy loam, loam, clay loam, clay and heavy clay soil, respectively. The results of this study stress the important of considering soil texture in Gleysolic soils when assessing P accumulation and transformations in soils, due to commercial fertilizers applied in excess of crop removal. Key words: P fractions, clay content, fertilizer P, plant P uptake, soil texture
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".