This quarter's benchmarking article will concentrate on two areas. We will begin by comparing production records from the previous quarter and previous year with values from a year ago. This review will help us see what progress we have made in key performance indicators (KPIs).

Secondly, we will examine various factors that contribute to the inefficiencies of the breeding herd. We'll take a closer look at how the abilities of various artificial insemination (AI) technicians can impact reproductive levels. In addition, we'll study the impact that time of day, day of the week and multiple inseminations have on various reproductive performance indicators.

The database used for this analysis is again provided by Swine Management Services LLC (SMS), Fremont, NE. As in the past, we will use pigs weaned/mated female/year (PW/MF/Y) to evaluate whether progress has been made by comparing the 52-week average and 13-week average performance of the first quarter 2007 (Table 2) to the same time period in 2008 (Table 1).

Pigs Weaned Edges Upward

Some interesting points arise when comparing the 2007 and 2008 data. Using the 52-week average KPI values, it is apparent that the top 10% of herds has changed very little. This demonstrates how difficult it is to improve productivity at the upper echelon of the industry.

For example, PW/MF/Y, arguably one of the most important indicators of herd performance, shows a slight drop from 26.8 in 2007 to 26.5 in 2008 in the top-10% farm category. However, when PW/MF/Y is compared for all farms contributing to the database, the 2008 average (22.8) was 0.3 pigs higher than the 2007 average (22.5).

While the best herds failed to improve this important KPI for the 52-week period, the average producer made some gains.

When the 13-week PW/MF/Y averages were compared, again the top 10% of farms remained relatively static. However, the all-farm average jumped over half a pig (22.21 vs. 22.75) for the quarter.

A comparison of other KPI averages shows relatively minor changes when the top-10% and all-farm categories are reviewed. For example, farrowing rate for the top 10% of farms was 87.1% in 2007 and 87.4% in the 52-week comparison for 2008. Likewise, farrowing rate for all farms tallied in at 82.4% and 82.5%, respectively. The difference between the top producers and average producers was maintained at about 5%.

Still, farrowing rate is important when the focus is on improving herd efficiency. Obviously, sows must farrow in order to have a saleable product — whether producing weaned pigs or market hogs. While gains in this KPI do not make up for the adverse feed costs and low market hog prices, it is important to note that the productivity gains made in this area will likely prevail as the market hog price cycles recover to more profitable levels.

Additionally, farrowing rate for the average producer improved from 80.2% in 2007 to 81.8% in 2008 in the 13-week comparison.

Since the average values for the top 10% of operations actually declined slightly (87.9% vs. 87.5%) from 2007 to 2008, the improvement shown by all farms in the farrowing rate category would suggest the improvement was made by those who needed it most. It is a bit discouraging to see the farrowing rate of the bottom 25% slip, however.

Finally, it is worth noting that average parity of culled sows in the 52-week summary has dropped in the top 10% of herds (3.07 in 2007 vs. 2.45 in 2008). The reverse is true when all-farms parity averages are compared in the 52-week data. The same trend is reflected in the 13-week summary.

A Closer Look at the AI Data

The second objective of this quarter's benchmarking review is to examine the factors that contribute to breeding herd inefficiency. In an effort to drill down to find some deficiencies related to AI, SMS provided data focusing on the effects of the AI technician, day-of-the-week comparisons and time-of-day effects. Farrowing rate is the primary KPI used for these comparisons.

Clearly, there are differences between technicians when it comes to a successful AI program. Figure 1 shows the farrowing rate by AI technician, which shows whether the technician made both the first and second matings for a given sow or only the first, second or the third mating, respectively.

Figure 1 reinforces that it is difficult to have the same technician mate an individual sow when she is in estrus for consecutive days. The data serves as a good illustration of the team effort to get sows successfully mated. Additionally, the data can help identify those technicians who are part of a team where poor farrowing rates are consistently found. It is likely that the same technicians in that team would be even poorer if individual sows mated by individual technicians could be identified.

In this case, technicians #8, #2 and #9 have consistently been on teams that have an 85% farrowing rate and above. Conversely, technicians #7 and #5 have consistently been part of teams with a farrowing rate of 80% and below. Again, it is likely that these technicians' farrowing rates would have been even poorer if they mated sows exclusively.

These poor performers are candidates for additional AI technique training and, perhaps, for estrous detection training, which could be provided by their more successful co-workers. External trainers could also be called in.

The day of the week on which inseminations occur can have a significant impact on an AI program. Figure 2 features eight AI technicians and the farrowing rates they achieved, listed by the day of the week.