Altering electrolyte balance of diets for lactating dairy cows to reduce phosphorus excretion to the environment
Bibliographic record
Abstract
Four early-Iactating dairy cows were randomly allocated to four diets with calculated dietary cation-anion balance (DCAB; Na + K -cr -5 2 -) of +50, +100, +200 and +400 mEql kgDM.Diets were formulated to be isoenergetic and isonitrogenous, and su pp lied similar levels of P (0.46%) and Ca (0.77%).Salts of MgCI 2 , MgS0 4 , K 2 C0 3 and NaHCO s were used to alter the DCAB.The study was designed to test the hypothesis that P excretion in manure of dairy cattle could be reduced by manipulating electrolyte balance of the diet.The experiment was conducted as a 4 x 4 Latin Square design with 21-d periods.During the last five days, diets were offered at a restricted level and samples of blood, milk, feces and urine were collected.Actual DCAB levels, based on the chemical analysis of diets, were: +139, +176, +242 and +454 meqlkg of DM.DMI showed a tendency (P<0.10) to have a positive and quadratic relationship with DCAB.Milk yield also showed a quadratic response explained by the equation: MY (kg/d) = 25,25 (2.45) + 0.043 (0.012) x -0.00007 (0.000019) x2 (P<0.05).Measures of blood and urinary acid-base status of the cows revealed that DCAB altered acid-base balance, but the animais did not experience metabolic acid stress.Feces was the main route of P excretion, but fecal P was not affected by DCAB (P>0.05).In contrast, urinary P excretion decreased quadratically with DCAB: Urinary P g/d = 6.35 (2.09) -0.037 (0.013) x + 0.00006 (0.00002) x2 (P<0.05)being minimised at levels of +300 mEql kg DM.Plasma concentration of P tended (P<0.10) to be higher at lower DCAB levels, implying that DCAB may have influenced P homeostasis.The overall P balance was not affected by the different DCAB levels.The range of DCAB where both P excretion and animal performance could be optimised is very narrow (+250 to +350 mEqlkg DM), so using DCAB to control P excretion in dairy cattle requires caution.support, his motivation and expert advice during the course of this research and the preparation of this manuscript.My appreciation to Dr. Arif Mustafa for serving on the advisory committee and for his willingness and preparedness to always listen and assist in every way possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.007 | 0.001 |
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".