Interação genótipo x ambiente para a eficiência alimentar em suínos durante as fases de crescimento e terminação no Brasil versus Holanda
Bibliographic record
Abstract
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".