Dry matter production, nutrient accumulation, and nutrient partitioning of barley
Bibliographic record
Abstract
Abstract Total dry matter (TDM) and nutrient accumulation, nutrient partitioning, and cumulative growing degree days at the time of maximum nutrient accumulation for two‐row spring barley ( Hordeum vulgare L.) are not well quantified under high‐yielding irrigated conditions common in the semi‐arid western United States. Thus, five cultivars of barley were grown under irrigated conditions on a loam soil in the 2015 and 2016 growth seasons to determine these factors. Total nutrient accumulation was greatest at either the soft dough or maturity stage where specific nutrients were greater at one stage as compared to the other. Mean N accumulation was greatest at the soft dough stage (256 kg ha −1 ) where the regression model accounted for 80% of the variation in the data. Additionally, spike N increased from 91 to 105 kg ha −1 from soft dough to maturity. Specific nutrients ( e.g ., K) had significantly greater plant ( i.e ., culms plus leaves) accumulation between soft dough and maturity, 253 and 172 kg ha −1 , respectively, where the spike at the same growth stages had an accumulation of 37 and 42 kg ha −1 , respectively. In contrast, other nutrients ( e.g ., P) were remobilized to the spike as noted by the increase from 14 kg ha −1 at soft dough to 26 kg ha −1 at maturity. In addition to nutrient partitioning, linear regressions resulted in well‐correlated models between TDM and total nutrient accumulation (R 2 = 0.35–0.88) for measured nutrients. Results from the current study provide critical data on nutrient accumulation as well as regression models for two‐row barley under high‐yielding conditions. This information can be used to improve harvest decisions as well as more accurately predict nutrient cycling in barley cropping systems.
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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 | 0.001 | 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 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".