Predicting manure volatile solid output of lactating dairy cows
Bibliographic record
Abstract
Organic matter (OM) in livestock manure consisting of biodegradable and nonbiodegradable fractions is known as volatile solids (VS). According to the Intergovernmental Panel on Climate Change (IPCC) Tier 2 guidelines, methane produced by stored manure is determined based on VS. However, only biodegradable OM generates methane production. Therefore, estimates of biodegradable VS (dVS; dVS = VS - lignin) would yield better estimates of methane emissions from manure. The objective of the study was to develop mathematical models for estimating VS and dVS outputs of lactating dairy cows. Dry matter intake, dietary nutrient contents, milk yield and composition, body weight, and days in milk were used as potential predictor variables. Multicollinearity, model simplicity, and random study effects were taken into account during model development that used 857 VS and dVS measurements made on individual cows (kg/cow per day) from 43 metabolic trials conducted at the USDA Energy and Metabolism laboratory in Beltsville, Maryland. The new models and the IPCC Tier 2 model were evaluated with an independent data set including 209 VS and dVS measurements (kg/cow per day) from 2 metabolic trials conducted at the University of California, Davis. Organic matter intake (kg/d) and dietary crude protein and neutral detergent fiber contents (% of dry matter) were significantly associated with VS. A new model including these variables fitted best to data. When evaluated with independent data, the new model had a root mean squared prediction error as a percentage of average observed value (RMSPE) of 12.5%. Mean and slope biases were negligible at <1% of total prediction bias. When energy digestibility of the diet was assumed to be 67%, the IPCC Tier 2 model had a RMSPE of 13.7% and a notable mean bias for VS to be overpredicted by 0.4 kg/cow per day. A separate model including OM intake as well as dietary crude protein and neutral detergent fiber contents as predictor variables fitted best to dVS data and performed well on independent data (RMSPE = 12.7%). The Cornell Net Carbohydrate and Protein System model relying on fat-corrected milk yield and body weight more successfully predicted dry matter intake (DMI; RMSPE = 14.1%) than the simplified (RMSPE = 16.9%) and comprehensive (RMSPE = 23.4%) models to predict DMI in IPCC Tier 2 methodology. New models and the IPCC Tier 2 model using DMI from the Cornell Net Carbohydrate and Protein System model predicted VS (RMSPE = 17.7-19.4%) and dVS (RMSPE = 20%) well with small systematic bias (<10% of total bias). The present study offers empirical models that can accurately predict VS and dVS of dairy cows using routinely available data in dairy farms and thereby assist in efficiently determining methane emissions from stored manure.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".