Parity Distribution Affects the Economic Performance of Swine Herds
Pius B. Mwansa Ph.D.
Parity distribution affects both the economic and biological performance of a breeding swine herd. Young sows’ performance is generally “lower” than that of older sows in key performance indicators (KPIs) up to the optimum parity or parities. For example, Stalder (2007 and 2008) showed results from a benchmarking service that indicated that the sows in the bottom 25% for most KPIs also had the lowest average parity, average parity of farrowed sows and average parity of culled sows. Young animals (gilts and parity 1 and 2 sows) are still growing and need to consume enough feed to maintain their growth as well as nursing their litters. Some aspects of the importance of sow removal to swine breeding and commercial undertakings have been covered in the article presented here http://www.genesus.com/global-tech-report/sow-removal
. In the current article, focus is on the management of parity profiles or distributions for better economic efficiency of swine enterprises. This task brings into play, both genetic and management factors.
The National Swine Improvement Federation (NSIF) Guidelines for Uniform Swine Improvement Programs (http://www.nsif.com/guidel/guidelines.htm
), in acknowledging the differential productivity (number born alive/litter size) by parity, suggests bigger litter size adjustments for parities 1 and 2 and relatively smaller adjustments to parity 3 sows when comparing litter size to mature sows (parities 4 and 5, see table below). The increasing and positive adjustment factors for parities 6 and above points to reduced litter size for those parities as well. First parity sows not only exhibit smaller litters and longer weaning-to-service intervals but are also prone to showing lower farrowing, piglet survival and weaning rates (Stalder, 2007).
Table 1. Recommended NSIF Parity Adjustment Factors for Number Born Alive
|Number born alive (L)
|4 and 5
With this in mind, a breeding or commercial herd with a higher proportion of very young and very old sows, will lead to average depressed productivity in KPIs such as total number of pigs born alive per female farrowed as well as number of pigs weaned per mated female per year. Therefore, an optimum parity profile in terms of profitability of the herd becomes inevitable. While geneticist have emphasized the importance of maintaining an optimal parity profile for economic efficiency of swine units, little agreement can be found as to what that profile actually is. The graphs below are depictions or renditions of a suggested optimum parity profile and a profile that is sub-optimal due to heavy reliance on older sows for productivity.
Figure 1. Examples of optimal and sub-optimal breeding herd parity profiles; Values used in the plots were adapted from Stalder et al. (2003).
It appears from the plots (above) that on average, pig units should aim to have only about 14-20% of the sow herd in parity 1. Clearly a parity profile with a descending “staircase” look is considered generally optimal as it has a higher proportion of sows in lower parities (1 to 4 or 5) while profile with a “normal distribution” look (bottom right) is suboptimal as it relies heavily on older sows. The profile on the right will tend to have reduced productivity in KPI values. Stalder et al (2003) reported recommendations for the ideal parity profile/distribution of the sow breeding herd to include 15% first parity sows, 14% second parity sows and 13% third parity sows. This suggests that a significant portion of the sow herd should be producing below the “mature parities”; NSIF suggests mature parities to be parity 4 and 5. Undoubtedly, it is important for operators to have an understanding of their own profitable parity distribution.
Genetically, parity structure can be managed by focusing on traits related to estrus, structural soundness and longevity. Estrus related traits such as weaning-to-service interval have a moderate to low heritability (below 20%) and are linked to higher pregnancy rates and lower non-return to estrus rates. These traits enable a sow to have a long and productive lifespan in the herd. There are also other direct measures of longevity which can be targeted for genetic selection. Additional physical inspection and phenotypic selection (over and above genetic selection) for traits such as conformation, structural soundness and feet and legs can go a long way in improving the average sows’ productive lifespan in a herd. The essence is to create a herd whose sows allow for more voluntary than involuntary culling. Examples of voluntary and involuntary culling reasons are shown in the table below.
Table 2. Examples of voluntary and involuntary culling reasons
|Voluntary Reasons (economic)
|Involuntary Reasons (biologic)
|ü Farrowing difficulties
ü Poor litter size
ü Poor milking and rearing ability
ü Poor maternal behavior
ü Poor index ranking relative to the herd average
|ü Anoestrus (lack of sexual activity)
ü Conception problems
When this is achieved, opportunities for adopting culling policies amenable to optimizing herd parity profiles for better economic performance can be adopted. Culling decisions should be focused on an optimal parity structure and removing sows that are unlikely to compete with an average replacement gilt. Abell et al. (2010) reported increased genetic lag with increased parity and went on to suggest that when the cost of the genetic lag exceeds the gilt development variable cost, it is the optimal time to cull the sow and replace it with the gilt in the breeding herd. Genetic lag can be defined as the time required for genetic improvement to pass from the nucleus (its source), through multiplication, to the commercial level of production.
Reducing genetic lag, through minimization of the number of levels from nucleus to commercial is a focus of the Genesus genetic improvement program. The Genesus genetic evaluation program and its attendant R&D undertakings have been retooled and redesigned into modular and more adaptable systems. There is a greater degree of consideration of traits such as sow longevity, reproduction, and sow efficiency over and above that of traditional production, carcass and meat quality and litter size traits. Furthermore, the Genesus R&D program is focused on genetic evaluation of economically important traits using genomic approaches.
Abell C.E, G. F. Jones, K. J. Stalder, and A. K. Johnson. 2010. Using the genetic lag value to determine the optimal maximum parity for culling in commercial swine breeding herds. The Professional Animal Scientist, 26:404-411.
Stalder, K. J. 2007. Parity distribution will affect your bottom line. http://nationalhogfarmer.com/mag/farming_parity_distribution_affect
Stalder, K. J. 2008. Parity’s impact on productivity. http://nationalhogfarmer.com/genetics-reproduction/farming_paritys_impact_productivity.
Stalder, K. J, Lacy C., CrossT, I., and Conatser M, S. 2003. Financial impact of average parity of culled females in a breed-to-wean swine operation using replacement gilt net present value analysis. J Swine Health Prod. 11(2):69-74.
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