Economic weights for feed intake in the growing pig derived from a growth model and an economic model

Economic weights are obtained for feed intake using a growth model and an economic model. The underlying concept of the growth model is the linear plateau model. Parameters of this model are the marginal ratio (MR) of extra fat and extra protein deposition with increasing feed intake (FI) and the maximum protein deposition (Pdmax). The optimum feed intake (FI0) is defined as the minimum feed intake that meets energy requirements for Pdmax. The effect of varying FI and MR on performance traits was determined.An increase in FI results in a larger increase in growth rate with lower MR. For a given MR, feed conversion ratio is lowest when FI equals FI0. Lean meat percentage (LMP) is largest for a low MR in combination with a low FI. The decrease in LMP with higher FI is largest when FI exceeds FI0.Economic weights for FI, MR and Pdmax depend on FI in relation to FI0.Economic weights for FI are positive when FI is less than FI0 and negative when FI is larger than FI0. The MR has only then a negative economic weight, when FI is below FI0.Economic weights of FI and MR have a larger magnitude with lower MR and lower FI. In contrast, economic weights for growth rate and FI derived from the economic model only change in magnitude and not in sign with different levels of these traits. The economic model always puts a negative economic weight on FI since it expresses profit due to a decrease in FI with constant growth rate and LMP. This holds the risk of continuous decrease in FI in pig breeding programs. In contrast, the use of growth models for genetic improvement allows direct selection for an optimum feed intake which maximizes feed efficiency in combination with maximum lean meat growth. It is concluded that recording procedures have to be adapted to collect the data necessary to implement growth models in practical pig breeding applications

Feed intake and feeding behavior traits for gestating sows recorded using electronic sow feeders

Electronic sow feeding (ESF) systems are used to control feed delivery to individual sows that are group-housed. Feeding levels for gestating sows are typically restricted to prevent excessive body weight gain. Any alteration of intake from the allocated feeding curve or unusual feeding behavior could indicate potential health issues. The objective of this study was to use data recorded by ESF to establish and characterize novel feed intake and feeding behavior traits and to estimate their heritabilities. Raw data were available from two farms with in-house manufactured (Farm A) or commercial (Farm B) ESF. The traits derived included feed intake, time spent eating, and rate of feed consumption, averaged across or within specific time periods of gestation. Additional phenotypes included average daily number of feeding events (AFE), along with the cumulative numbers of days where sows spent longer than 30 min in the ESF (ABOVE30), missed their daily intake (MISSF), or consumed below 1 kg of feed (BELOW1). The appetite of sows was represented by averages of score (APPETITE), a binary value for allocation eaten or not (DA_bin), or the standard deviation of the difference between feed intake and allocation (SDA-I). Gilts took longer to eat than sows (15.5 ± 0.13 vs. 14.1 ± 0.11 min/d) despite a lower feed allocation (2.13 ± 0.00 vs. 2.36 ± 0.01 kg/d). The lowest heritability estimates (below 0.10) occurred for feed intake traits, due to the restriction in feed allocation, although heritabilities were slightly higher for Farm B, with restriction in the eating time. The low heritability for AFE (0.05 ± 0.02) may have reflected the lack of recording of nonfeeding visits, but repeatability was moderate (0.26 ± 0.03, Farm A). Time-related traits were moderately to highly heritable and repeatable, demonstrating genetic variation between individuals in their feeding behaviors. Heritabilities for BELOW1 (Farm A: 0.16 ± 0.04 and Farm B: 0.15 ± 0.09) and SDA-I (Farm A: 0.17 ± 0.04 and Farm B: 0.10 ± 0.08) were similar across farms. In contrast, MISSF was moderately heritable in Farm A (0.19 ± 0.04) but lowly heritable in Farm B (0.05 ± 0.07). Heritabilities for DA_bin were dissimilar between farms (Farm A: 0.02 ± 0.02 and Farm B: 0.23 ± 0.10) despite similar incidence. Individual phenotypes constructed from ESF data could be useful for genetic evaluation purposes, but equivalent capabilities to generate phenotypes were not available for both ESF systems

Improving sow welfare and outcomes in the farrowing house by identifying early indicators from pre-farrowing assessment

Poor outcomes reflect low performance during the farrowing and lactation periods and unanticipated sow removals. Since the period around farrowing has the highest risk for sow health issues, monitoring of sows in that time-period will improve both welfare and productivity. The aim of this study was to identify the most relevant risk factors for predicting poor outcomes and the implication for sow welfare. Identifying these factors could potentially enable management interventions to decrease incidences of compromised welfare or poor performance. Data from 1,103 sows sourced from two nucleus herds were recorded for a range of variables investigated as potential predictors of poor outcomes in the farrowing house. Poor outcomes (scored as binary traits) reflected three categories in a sow's lifecycle: farrowing, lactation, and removals. Univariate logistic regression was used to identify predictors in the first instance. Predictors from univariate analyses were subsequently considered together in multi-variate models. The least square means representing predicted probabilities of poor outcomes were then reported on the observed scale. Several predictors were significant across two different environments (farms) and for all three categories. These predictors included feed refusal (lack of appetite), crate fit, locomotion score, and respiration rate. Normal appetite compared to feed refusals reduced the risk of farrowing failure (13.5 vs. 22.2%, P = 0.025) and removals (10.4 vs. 20.4%, P P P = 0.025) and reduced piglet mortality (P P P = 0.014). Sows with higher respiration rates had a significantly (

Outline of R&D directions for Australian pig genetics

Staff at the Animal Genetics and Breeding Unit (AGBU) have contributed to better use of genetically superior pigs in the Australian pig industry through R&D projects conducted in cooperation with breeders, the development of genetic evaluations tools (PIGBLUP, National Pig Improvement Program (NPIP), PBSELECT), training of industry personnel in genetic principles and provision of fee-based consultancy services to breeders. Often these activities are inter-linked and a wide range of expertise is required to be able to respond effectively to the needs of the industry in regard to genetic services. ... Infra-structure has been put in place to facilitate the uptake of PIGBLUP, NPIP and PBSELECT and to provide extension material to the industry. Overall, the awareness of Estimated Breeding Values (EBVs) has increased in the industry as a whole and communication with breeders, producers and industry extension personnel has improved over the last 5 years. These achievements enable the development of a number of new projects that are proposed to accelerate genetic gain and adoption of improved genetics in the Australian pig industry. Each project has a R&D component which will be conducted in cooperation with breeders who are expected to provide in-kind contributions. This cooperation fosters adoption of new technologies by breeders. The relevant modules in AGBU's genetic evaluation systems (PIGBLUP, NPIP, PBSELECT) will be modified to accommodate R&D results. In addition, information about these projects and genetic principles in general will be disseminated to industry

Breeding for improved welfare of growing pigs

Welfare should be considered in pig breeding programs. A number of welfare traits related to pork quality, health and survival of pigs have already been included in pig breeding programs and this list of welfare traits should be extended further. It is important to provide the best-possible environment to pigs on farms. Animal breeding can contribute to this aim indirectly by providing descriptors of environmental conditions from genetic analyses of performance traits which can be used for assessment and optimisation of husbandry practices. Further, selection for improved disease resistance reduces pathogen load in the environment. Maintaining good welfare for all pigs on farms all the time requires a detailed monitoring system which has been provided by the Welfare Quality® (2009) protocol. The 12 welfare criteria defined by the Welfare Quality® (2009) protocol provide guidance for the genetic improvement of welfare in pigs. Genetic variation exists for numerous traits related to these 12 welfare criteria. For example, genetic variation was found for the number of shoulder ulcers in sows which is an important welfare trait of sows. Selecting pigs with less skin ulcers may also offer opportunities to improve comfort of growing pigs. Growth is an important performance trait which is affected by the health status of animals. Therefore, growth has been used as a proxy for health which affects the welfare of pigs. For this purpose, it is important to record growth of all animals including sick pigs to better identify pigs with health and welfare problems. This will also enhance estimates of indirect genetic effects for growth which may be a selection strategy to improve behaviour of group-housed pigs and reduce the incidence of tail biting. Indirect genetic effects quantify the heritable component of the social effects a pig has on performance of its group mates. Multiple factors and traits affect and describe welfare of pigs and numerous avenues are open for pig breeding to further improve welfare of pigs on farms

Breeding pigs with improved disease resilience

The "ability to maintain a relatively undepressed production level while infected" was defined as resilience by Albers et al. (1984). In sheep breeding, selection for resistance versus resilience to nematode challenge has been well investigated. A third avenue is breeding for disease tolerance. Differences between disease resistance and disease tolerance have been outlined by Guy et al. (2012) in this workshop. Specific definitions and measurements of resilience used by various authors were discussed by Bisset and Morris (1996), who pointed out that both resistance and tolerance mechanisms may contribute to the expression of resilience, when it is defined in terms of productivity relative to a standard challenge level. This definition of disease resilience also implies that information is required about productivity, health and physiology of pigs, as well as measures of the environment to quantify the challenge level that pigs experienced

From genetic to phenotypic trends

The performance of pigs is influenced by genetic and environmental factors. Both the genetic merit of the pigs and the environment they are raised in have to be improved continuously to optimise progress in performance. In addition, the requirements of genotypes available today may have changed due to selection and the management of pigs has to be adopted accordingly to maximise phenotypic performance. This document has two main components. Firstly, genetic and phenotypic trends are summarised for Australian pig populations and genetic trends are compared with those achieved in the Australian sheep and beef industries. Secondly, data were available from an on-farm trial to evaluate the effect of feeding a high energy diet on performance. This information may be a starting point to explore avenues to improve pig performance in Australia by matching management practices to the specific requirements of different genotypes available in the industry

Development of selection criteria to improve carcase quality and use of haemoglobin levels in sows and piglets to improve piglet survival, performance and pork quality: Final Report APL Project 1025

Additional return per carcase can be achieved by having more weight in the more valuable primal cuts for carcases with the same total weight and fat depth. Australian data were required to quantify the economic benefits of higher saleable meat yield for a given carcase weight and fat depth for the Australian market and to develop selection strategies for improved carcase market value. Primal cut weights were obtained for 2,311 carcases which were combined with 23,210 pedigree and 16,875 performance records. A proportion of these pigs had fat and muscle depth measures available from the PorkScan™ system. It was not possible to obtain light-stripping information from the PorkScan™ system and images of the standing pig were collected instead. These images were analysed with a freely available image-analysis program (ImageJ) to obtain 14 linear or area measurements for each pig to describe conformation of live pigs three weeks prior to slaughter. In total, over 36,000 image-analysis records were obtained. Genetic analyses of data provided information about genetic and phenotypic associations between primal cut weights and other traits

Mean and variation in back fat influence profit of pig production

Profit of pig production is influenced by several traits. Selection decisions should therefore be based on an economic index ($Index), which is the sum of estimated breeding values (EBVs) of individual traits multiplied by their economic value. The economic value of a trait represents the change in profit when the trait is increased by one unit, keeping all other traits constant. Cameron and Crump (2001) presented economic values for several performance traits, and noted that the economic value for back fat (BF) depends on the mean BF. Payment systems in Australia often set a base price that is reduced for pigs exceeding a certain limit in BF. Therefore, the proportion of pigs that do not achieve the base price affects the average return per pig and depends not only on the mean value but also on the variation in BF. The aim of this study was to derive economic values for BF assuming different means and standard deviations (sd) in BF

Considering seasonal effects on farrowing rate and litter size in sow breeding objectives

Introduction: Seasonal effects on fertility in gilts and sows are characterised by reduced reproductive performance during the summer and autumn period (Love et al., 1993). Both the number born alive (NBA) and farrowing rate (FR) are affected by seasonal infertility. Economic values (EV) for these traits depend on assumed production parameters, cost parameters (Amer et al., 2014) and mean performance in FR (Hermesch, 2021), which consequently are influenced by seasonal effects. Both NBA and FR were genetically different traits between seasons or environments based on temperature groupings (Lewis and Bunter, 2011; Bunz et al., 2019). Both may be considered as different traits in the most challenging season (summer) versus the other seasons. The hypothesis of this study was that by considering the economic implications of seasonal effects in sow breeding objectives (BO), the relative emphasis placed on traits changes