Corn Forage Yield and Quality for Silage in Short Growing Season Areas of the Canadian Prairies
Bibliographic record
Abstract
The development of short-season hybrids has made corn (Zea mays L.) silage (CS) production possible in cooler areas. This work aimed at determining biomass yield and nutritive quality of short-season corn CS hybrids. Six corn hybrids were grown in three years at four locations within the Canadian prairies with four field replications. Hybrids were harvested before occurrence of frost at a target dry matter (DM) content of 300 to 400 g kg−1. Corn heat units (CHU) from seeding to harvesting (CHUseed-harv) and water supply were recorded. Samples were analysed for nutrient content; i.e., DM, neutral detergent fiber (NDF), crude protein (CP), starch, and in vitro DM and NDF digestibilities (48 h incubation). Then, CHUseed-harv, water supply, whole plant DM, CHU rating of the hybrid, and cob percentage were assessed as predictors of nutrient content. Location, hybrid, and year affected nutrient composition and yield. Overall, CP and NDF were positively correlated (r = 0.48, p < 0.01), but both were negatively correlated with DM yield (r = −0.63, −0.28, p < 0.01) and starch (both r = 0.71, p < 0.01). Within and among locations, CHUseed-harv differently affected nutrient composition and DM yield. However, DM yield was the most predictable factor (R2 = 0.86) with CHUseed-harv being the strongest contributor (48%) to the overall variability, followed by water supply (23%). Whole plant DM and CHUseed-harv were also good predictors of starch (R2 = 0.54). This work showed the high variability of biomass yield and nutritive quality of short-season CS hybrids grown in Northern areas.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 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".