Crop response to soil management practices is driven by interactions among practices, crop species and soil type
Bibliographic record
Abstract
The impact of management practices on crop productivity has often been assessed. However, crop response to specific practices may vary as a function of soil type and other associated management practices. The objective of this study was to determine how interactions between tillage, fertilizer source, crop residue management, and pedoclimatic factors affected yield and N use efficiency (NUE) in a corn (Zea mays) - soybean (Glycine max) - spring wheat (Triticum aestivum) rotation in two contrasting soil types. A factorial experiment was conducted from 2009 to 2017, comparing moldboard plow [MB] vs minimum tillage [MT], five fertilizer sources (mineral control without nitrogen [PK], complete mineral fertilizer [NPK], liquid swine manure [LSM], liquid dairy manure [LDM] and solid poultry manure [SPM]), and two crop residue managements (retained [RR] vs harvested [RH]) on a silty clay and a sandy loam. In the silty clay, a positive effect of MT developed over time for all crop species, whereas MT had little effect on wheat yield, generally decreased corn yield and increased soybean yield in the sandy loam. When wheat residues were returned to the soil, corn grain yield in the next year was (i) decreased when combined with MT in both soils and (ii) when combined with liquid manures in the silty clay. In soybean, a positive residual effect of manures was observed under MT in the sandy loam only. For all crop species, yields were more responsive to fertilizer treatments in the sandy loam than in the silty clay, and manures generally performed better with wheat than with corn. Among the manures, LSM was the best fertilizer source. A regression tree indicated that corn grain yield was mainly driven by nitrogen supply (higher yield with > 110 kg available N ha−1) but that a lower response to N is expected in years with drier and colder conditions in July-August. Our results demonstrated that interactions among management practices is determinant for crop productivity, and that yield responses are modulated by crop species, soil type and sometimes, yearly weather conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".