Predicting manure volatile solid output of lactating dairy cows
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».