Soil and Crop Response to Varying Levels of Compaction, Nitrogen Fertilization, and Clay Content
Bibliographic record
Abstract
Compaction affects nearly all soil properties and functions, thereby affecting the growth, distribution, and function of roots, and crop productivity. Our objectives in this research were to evaluate the effects of compaction, N fertilization, and soil texture on corn ( Zea mays L.) growth and yield and to determine whether additional N fertilization could compensate for lower yields caused by compaction. Soil was differentially compacted for 3 yr to create two compaction treatments in Year 1 and four in Years 2 and 3; N fertilizer was applied at two rates in Year 1 and four different rates to each compaction treatment in Years 2 and 3; yield was measured in each of the 3 yr, and soil and crop growth properties were measured after 2 and 3 yr. Variation in soil texture across the experimental plots allowed us to partly assess the effects of clay content on the soil and crop response to compaction. Compaction substantially reduced plant growth and productivity; yields were reduced by up to 2.7 Mg ha −1 (33%) and dry matter by 4.1 Mg ha −1 (26%) in compacted soils. Avoiding compaction for a year appeared to allow crop growth and yields to recover. For example, grain yield after two consecutive years of compaction (5.21 Mg ha −1 ) was significantly lower than after 1 yr of compaction followed by no compaction (7.59 Mg ha −1 ). Compaction increased soil strength near the bottom of the plow layer, where a carryover effect of compaction was apparent. Compaction‐induced yield reductions were greatest on soils with more clay and were attributed to adverse soil physical conditions rather than limited N fertility.
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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 | 0.000 |
| 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) | 0.000 | 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".