170 Do you Have the Energy to Grind Feed Costs and Maximize Net Income in Demanding Economic Times?
Notice bibliographique
Résumé
Abstract Energy is not only the most expensive component of the diet, but also among the most complex. Energy impacts all aspects of the growth and metabolism of the pig. Therefore, when feed costs escalate, energy is a predictable though challenging target for increased attention. To achieve greatest success, a two-pronged approach is warranted. The first focuses on our understanding of the fundamentals of energy science and the second logically follows with applying this knowledge in practice. This presentation will address both the science and the application. Formulating energy in pig diets is a complicated process. For example, one can achieve equal dietary energy values with different proportions of protein, starch, fat and fiber. Each of these nutrients is used with a different level of efficiency by the pig, leading one to question whether equal performance should be expected just because ME or NE is the same. We will dig a bit more deeply into how energy values of ingredients are generated, what assumptions are made in this process and the limitations of these values in predicting pig performance. Beyond ME and NE, we will note that changing energy sources may also alter the microbiota, oxidative load, gut structure and function, and even the digestibility of dietary nutrients. This, in turn, can impact not only the performance of the pig, but also resistance to disease and susceptibility to environmental stress. We will also discuss why feed efficiency is sometimes highly correlated to dietary energy but in other cases, this relationship is quite weak; how can this be? On a positive note, the ME or NE of ingredients has been found to be highly correlated with certain dietary constituents. This infers that the prediction of the energy content of ingredients should be quite effective, and such predictions will improve the precision of diet formulation. Moving forward, it makes sense that wherever possible, energy response curves should be part of the toolkit available to swine nutritionists, but these curves should involve diverse diet composition, unlike the energy titration studies commonly used in our industry. When energy is the most expensive, and production margins are weakest, this is the very time to invest strategically in diet analysis to ensure that we are extracting every bit of energy we can from available ingredients. In other words, are we achieving our formulation targets? Because the topic of dietary energy is so large and complex, we will remind ourselves where we can have the greatest impact on net income by managing energy. In conclusion, energy drives feed costs and drives performance. As complex a topic as it is, we will be rewarded by understanding dietary energy more deeply, and applying that knowledge as effectively as possible in commercial practice.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,047 | 0,014 |
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 ».