Corn Forage Yield and Quality for Silage in Short Growing Season Areas of the Canadian Prairies
Why this work is in the frame
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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.
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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) | 0.000 | 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 it