Variability among maize hybrids differing in canopy architecture for above-ground dry matter and grain yield
Bibliographic record
Abstract
Dry matter and grain yields are among the traits most commonly used to evaluate maize (Zea mays L.) hybrid performance. Production of both dry matter and grain yield is often influenced by hybrid size. The efficiency with which a hybrid allocates accumulated dry matter into economic grain yield has a large influence on potential grain production. The objective of this work was to quantify dry matter, grain yield and harvest index of 17 hybrids representing a range of canopy architectures. A field experiment was conducted on clay loam soil at the E. A. Lods Agronomy Research Center. Ste. Anne de Bellevue, Quebec in 1997 and 1998. Hybrids were arranged in a randomised complete block design and included 11 newly developed leafy reduced-stature (LRS). four non-leafy reduced-stature (NLRS), one conventional (Pioneer Brand 3979). and one late-maturing big leaf (LMBL) hybrids. In both years, generally above-ground dry matter was greater for the taller LMBL and Pioneer Brand 3979 than for the shorter hybrids, but greater grain yields were measured for both the taller and five of the 11 LRS hybrids. Moreover grain yields averaged over canopy groups were not different. The shorter hybrids had greater assimilate allocation to the grain than the taller (especially LMBL) hybrids, and this was evident in their harvest index values. However, within the LRS group, hybrids differed for both dry matter and grain yield with some being similar to the NLRS hybrids while others were similar to the taller Pioneer Brand 3979 hybrid. The hypothesis that the changes in dry matter allocation seen in two LRS hybrids evaluated in previous studies (BEGNA et al., 1997a,b) is a condition of all LRS hybrids was rejected. While these hybrids show considerable potential in this regard some careful selection for the production of commercial hybrids is required.
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 | 0.000 | 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) | 0.002 | 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 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".