Effect of Crowding Stress on Dry Matter Accumulation and Harvest Index in Maize
Bibliographic record
Abstract
Conflicting results have been reported on the effects of spacing and emergence variability on grain yield in maize (Zea mays L.). Effects of spacing and emergence variability on maize grain yield are the net result of the responses of all plants within the stand. The objective of this study was to quantify effects of spacing and emergence variability on crop yield in terms of increased or decreased crowding stress on resource capture (i.e., dry matter accumulation) and resource utilization (i.e., dry matter partitioning) of the individual plants within the crop canopy. Results of previously reported studies were analyzed in terms of plant dry matter accumulation, leaf area, plant growth rate during the critical period for kernel set bracketed by silking (PGRs), grain yield, and harvest index, that is, the proportion of dry matter partitioned to the grain at maturity. Results show that a moderate increase in plant‐spacing variability does not influence maize grain yield at the canopy level because reductions in grain yield of plants that experience enhanced crowding stress is compensated, in part, by increased yield of plants that experience reduced crowding stress; crowding stress affected dry matter accumulation but did not affect harvest index. In contrast, plant‐emergence variability reduced grain yield at the canopy level because the reduction in grain yield was attributable, in part, to a reduction in harvest index of plants with PGRs less than the threshold for kernel set. Hence, plants can compensate for factors that influence resource capture, but cannot compensate for a reduction in factors that influence resource utilization.
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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".