Hay to Reduce Dietary Cation-Anion Difference for Dry Dairy Cows
Bibliographic record
Abstract
Timothy grass has a lower dietary cation-anion difference [DCAD = (Na + K) - (Cl + S)] than other cool-season grass species. Growing timothy on low-K soils and fertilizing it with CaCl2 could further decrease its DCAD. The objective of this study was to evaluate the effects of feeding low-DCAD timothy hay on dry dairy cows. Six nonpregnant and nonlactating cows were used in a replicated 3 x 3 Latin square. Treatments were as follows: 1) control diet (control; DCAD = 296 mEq/kg of dry matter); 2) low-DCAD diet based on low-DCAD timothy hay (L-HAY; DCAD = - 24 mEq/kg of dry matter); and 3) low-DCAD diet using HCl (L-HCl; DCAD = - 19 mEq/kg of dry matter). Decreasing DCAD with L-HAY had no effect on dry matter intake (11.8 kg/d) or dry matter digestibility (71.5%). Urine pH decreased from 8.21 to 5.89 when L-HAY was fed instead of the control. Blood parameters that decreased with L-HAY were base excess (- 0.4 vs. 3.8 mM) and HCO3- (23 vs. 27 mM), and blood parameters that increased were Ca2+ (5.3 vs. 5.1 mg/dL), Cl- (30.5 vs. 29.5 mg/dL), and Na+ (60.8 vs. 60.1 mg/dL). Compared with the control, L-HAY resulted in more Ca in urine (13.4 vs. 1.2 g/d). Comparing L-HAY with L-HCl, cow dry matter intake tended to be higher (11.5 vs. 9.8 kg/d), and blood pH was higher (7.37 vs. 7.31). Urine pH; total dry matter; Ca, K, P, and Mg apparent absorption; and Ca, K, Na, Cl, S, P, and Mg apparent retention were similar. Absorption as a percentage of intake of Na and Cl was lower for L-HAY as compared with L-HCl. In an EDTA-challenge test, cows fed L-HAY regained their initial level of blood Ca2+ twice as quickly as the control treatment (339 vs. 708 min); there were no differences between L-HAY and L-HCl. This experiment confirms that feeding low-DCAD hay is an effective means of decreasing the DCAD of rations and obtaining a metabolic response in dry dairy cows.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".