Effects of the combined application of biomaterial amendments and polyacrylamide on soil water and maize growth under deficit irrigation
Bibliographic record
Abstract
Combined applications of mixed biomaterial amendments and polyacrylamide (MBAP) to maize in semiarid areas have the potential to improve soil physical properties such that improved crop performance may be obtained under deficient irrigation management. In this study, three MBAP applications were C0 (conventional N fertilization application) and C2 and C4 (MBAP applied at rates of 2 and 4 t ha−1, respectively); three irrigation levels were W3 (nearly full irrigation, 85%–100% of field capacity), W2 (light deficit irrigation, 65%–75% of field capacity), and W1 (medium deficit irrigation, 55%–65% of field capacity). Under the same irrigation level, the MBAP significantly decreased soil bulk densities and increased soil hydraulic conductivities and soil water contents. The effects of irrigation levels on soil bulk densities and soil saturated hydraulic conductivities were not significant. Consequently, MBAP improved soil conditions for maize growth and increased grain and biomass yields, especially at the two deficit irrigation levels. Compared with that of C0, grain yields for C2 and C4 were increased by 52.8% and 39.3% under W2, and by 23.5% and 13.7% under W1, respectively. The MBAP and irrigation had significant interaction effects on evapotranspiration during sowing to jointing and on plant heights at 32 d after sowing. The incorporation of MBAP (2 t ha−1) and chemical fertilizer (111.8 kg N ha−1) resulted in the greatest yields under light deficit irrigation and seemed the best approach to improve soil physical properties and sustain maize productivity using limited water resources in dryland regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".