Interação genótipo x ambiente para a eficiência alimentar em suínos durante as fases de crescimento e terminação no Brasil versus Holanda
Notice bibliographique
Résumé
One of the main goals of modem pig breeding 1s to improve feed efficiency of crossbred (CB) pigs across the diverse, and often challenging, environments in commercial farms. The main aim of this thesis was to investigate the existence and magnitude of genotype by environment interaction for feed efficiency im CB pigs kept under Brazilian commercial production circumstances and purebred (PB) pigs kept under Dutch circumstances. In pig breeding programs, PB boars are selected 1n a nucleus, and mated with crossbred dams to produce CB growing-finishing pigs used for pork production in commercial farms. In this thesis, I investigate the possible causes of a lower than 1 genetic correlation for feed efficiency between the PB performance in the nucleus level and the CB performance mn the commercial level (rpc), and compare the properties of different traits to represent feed efficiency and the implications of their adoption by pig breeding programs. In Chapter 2, I estimated the genetic correlations between feed efficiency traits, growth performance, and carcass traits in PB and CB pigs, and compared three different traits representing feed efficiency: feed conversion rate (FCR), residual energy intake (RED), and residual feed intake (RFI). The results show that the inclusion of phenotypes recorded on CB pigs mn commercial farms mn the prediction of breeding values for PB, has the potential to increase genetic progress for the performance of CB. Given the genetic correlations with growth performance traits and the rpc, REI is an attractive feed efficiency parameter for a pig breeding program.In Chapter 3, I investigated the presence of a genotype by feed interaction (GxF) for feed efficiency and growth performance traits 1n different growth phases (starter, grower and finisher) of CB pigs fed one of two diets. The diets were based on corn/soy or wheat/barley co-products. I found that GxF was absent for average daily feed intake, growth, and FCR, but present for lipid deposition, REI, and RFI. The magnitudes of GxF for REI and RFI depended on the phase of the pigs” growth. Breeding pigs for feed efficiency under lower-input diets such as wheat/barley/co-products 1s recommended as feed efficiency will become more important, and lower-mput diets will become more widespread 1n the near future. In Chapter 4, I fitted feed intake and growth curves of CB pigs fed two diets, investigated the presence of GxF, and estimated genetic parameters for both curves. I found that GxF was absent for the curves” parameters. Given their medium to high heritabilities, these traits are a feasible alternative for pig breeding programs that are aiming to change the shape of feed intake and growth curves in CB pigs. Selection for feed efficiency by changing the trajectory of curves that describe feed intake as a function of body weight seems to be a good alternative to selecting for average feed intake parameters. I recommend selecting pigs with flatter curves (as they will have better feed efficiency) and selecting pigs with higher feed intake precocity. Higher feed intake precocity means a higher feed intake m early stages of growth associated with a higher growth maturation rate and a consequently lower feed intake later in the finishing period. In Chapter 5, I estimated the genetic correlations between growth performance and carcass traits of both PB and CB pigs in a temperate climate (the Netherlands, France and Canada) and a tropical climate (Brazil). To improve these traits m a tropical climate, higher genetic progress will be made by including phenotypes collected locallyin CB pigs. This 1s true even though the high rc would not require combined crossbred- purebred selection (CCPS) schemes. Ih Chapter 6, I placed my work m a broader context, discussed the implications and formulated recommendations for future breeding for feed efficiency im growing- finishing pigs, with special attention to feed efficiency 1n the tropics, and recommended future research. I concluded that mn the future, the biggest challenge facing pig breeding programs would be to routinely generate data on pigs” feed efficiency that allows the improvement of feed efficiency across the diverse and often challenging environments where CB pigs are farmed around the globe.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».