Dietary cation–anion difference of Timothy (<i>Phleum pratense</i>L.) as influenced by application of chloride and nitrogen fertilizer
Bibliographic record
Abstract
Abstract The effectiveness of forages to prevent post‐calving hypocalcaemia, when used as a feed source for non‐lactating dairy cows, can be predicted by the dietary cation–anion difference (DCAD). Three to four weeks before calving, the ration of non‐lactating dairy cows should have a DCAD around −50 mmolc kg−1DM. In an experiment, swards, based on Timothy (Phleum pratenseL.), were used to (i) evaluate the impact of two types (CaCl2and NH4Cl) and four application rates of chloride fertilizer per season (0, 80, 160 and 240 kg Cl ha−1) in combination with two N application rates (70 and 140 kg N ha−1) on mineral concentrations and DCAD in the herbage, and (ii) determine the economically optimal rate of chloride fertilizer (Clop) for DCAD in herbage. Chloride and N fertilizers were applied in the spring and, after the first harvest in 2003 and 2004 at four locations that differed in K content of their soils. Two harvests were taken during each year. Averaged across N‐fertilizer application rates, harvests and locations, the highest rate of chloride fertilizer increased chloride concentration in herbage by 8·5 g kg−1dry matter (DM) and decreased DCAD in herbage by 190 mmolc kg−1DM to values as low as −9 mmolc kg−1DM. Both types of chloride fertilizer had the same effect on chloride concentration and DCAD in herbage and had no effect on DM yield. When no chloride fertilizer was applied on soils with a high content of available K, application of N fertilizer increased DCAD in herbage by 47 mmolc kg−1DM at both harvests. Herbage DCAD was lower in summer than in spring by 47–121 mmolc kg−1DM depending on the location. Application of chloride fertilizer can effectively lower the DCAD of Timothy‐based herbages; the economically optimal rate of chloride fertilizer in the spring varied from 78 to 123 kg Cl ha−1, depending on soil K and chloride contents and expected DM yield.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".