Seasonal biomass accumulation and nutrient uptake of wheat, barley and oat on a Black Chernozem Soil in Saskatchewan
Bibliographic record
Abstract
Dry matter and nutrient accumulation in the growing season are the main factors in the determination of seed yield and nutrient use efficiency. Field experiments were conducted with spring wheat (Triticum aestivum L.), barley (Hordeum vulgare L.) and oat (Avena sativa L.) in 1998 and 1999 at Melfort, Saskatchewan, Canada, to determine the biomass accumulation and plant nutrient uptake at different growth stages, and their relationship with days after emergence (DAE) and growing degree days (GDD). All crops generally followed a similar pattern of biomass and nutrient accumulation in the growing season, which increased continuously with growing time, with much faster increase at early growth stages than at late growth stages. Maximum biomass accumulation rate and amount usually occurred at late boot stage (46–47 DAE or 443–460 GDD) and ripening stage (89–90 DAE or 948–1050 GDD), respectively. Maximum rate of nutrient uptake occurred at tillering to stem elongation stages (22–36 DAE or 149–318 GDD). Maximum amount of nutrient uptake generally occurred at the beginning of flowering to medium milk stages (61–75 DAE or 612–831 GDD), except for P in 1998 when it occurred at late milk to ripening stages (80–90 DAE or 922–1050 GDD). In general, the maximum nutrient uptake rate and amount, respectively, occurred earlier than maximum biomass accumulation rate and amount. For various cereal species/cultivars, maximum biomass accumulation rate was 204–232 kg ha-1 d-1, and maximum uptake rate of N, P, K and S, respectively, was 3.2–5.7, 0.30–0.60, 3.85–7.05 and 0.45–0.60 kg ha-1 d-1. The findings suggest that a sufficient supply of nutrients from soil and fertilizers at early growth stages is of great importance for optimum crop yield. Key words: Barley, biomass accumulation, cereals, growth stages, nutrient uptake, oat, wheat
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".