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Using data to solve problems in broiler breeder male management

Pieter Oosthuysen, senior manager, accounts and technical (Africa), Cobb Europe writes about the use of data in breeder management

Male 2Objectivity and experience are important when interpreting suboptimal performance data using regression graphs. (Image source: Cobb)

Modern technology is increasingly being used on breeder farms for data generation. This data can be analysed to make smarter decisions and improve flock performance and efficiency. 

As genetics improve yearly, so will efficiencies and production performance. Comprehensive data collection provides opportunities to predict future performance, supply chain demands and future results.

Many breeder farmers are only using paper records and little electronic data. Some operations generate so much data, that it becomes messy, while others are unreliable. Good data collection is crucial for many critical decisions, such as feed allocation, which is based on reliable body weight data.

There have been many missed opportunities, where farmers/workers have missed vital and negative behavioural signs that affect bird performance.

Interpretation of the data requires local knowledge such as seasonal effects or knowledge of breed specific behavioural traits. For example, a consultant could state that heavy hens produce less chicks. However, the relationship is multi-factorial, because as hens age, they become heavier and egg production declines. Moreover, chick production is a function of both fertility and hatch of fertile (incubation).  Furthermore, fertility is most often attributed to male management, and on rare occasions it is female related.

  Pieter

Objectivity and experience are important when interpreting suboptimal performance data using regression graphs. Comparing performance of a farm to industry standards is a basic first step when analysing data. It only shows how the farm compares to the industry. Data that can help to improve flock productivity and profitability is crucial, and with roosters it is reflected in weight, condition, feed intake and fertility.

In the production of hatching eggs or chicks, reproductive performance is always the main driver. But how do you measure a certain biological event that cannot be measured or weighed? First, find out if the males are getting enough feed. What is the cause behind low early hatchability or poor peak percentage of hatchability? The measurements are subjective but need to be quantified to produce data.

In the field case below, the breeder males were overweight and fertility was declining. The production graph indicated the males were heavy and considerably above their weight for target age.

The condition of the males was quantified based on a breast muscle scoring system (Figure 1).

Figure 1

  The male breast scores are explained in Figure 2. Figure 2

In Figure 1, 65% of the males had the desired fleshing score of three, while 15% of the males were too thin and 10% were emaciated. Only 10% were well developed with a fleshing score of four, leaving no males with fleshing scores of five, which would be deemed overweight and unfit for reproduction. 

This means that 75% of the males were in good reproductive fitness condition, indicating they were not over fed. The remaining 25% were off target for 36 weeks of age, indicating they may not be receiving enough feed, although they appeared overweight. Based on the body weight data alone, it appears the males were underfed to control the weight. This demonstrates the difference between weight and size, that the farmer experienced. Therefore, we increased the feed and fertility began improving. 

Figure 3 shows the fleshing target table based on ages.

Figure 3

In another case study of declining hatchability, the farmer collected and kept weekly breast scoring records for each house. There were 23 houses totaling 15,000 males represented in the data (Figure 4).

Figure 4 Figure 4 indicated that the males develop rather fast in their fleshing scores from 23 to 35 weeks of age. The score three and four increased too rapidly. The thinner males with scores one and two remained a small portion of the population. The remaining males with a score of three reached a point at 35 to 40 weeks, where they rapidly increased fleshing  scores of four and five, with a corresponding decline in score three males. This is the result of early and rapid increases in feed allocation through 32 weeks. Ideally, 70% of males should score three as long as possible for optimal fertility.

In Figure 4, hatchability data was graphed with the fleshing scores. It is interesting to note that the decline in hatchability began around the same time as the decline in score three, and corresponding increases in scores four and five. This indicated that the males became too heavy to continue mating, and overtime the hatchability declined as the males developed bigger breast muscles. Using the data, it was ascertained that males were overfed in early production (23 to 32 weeks). Therefore, future feed intakes can be adjusted to control early muscle development, and improve the hatchability through conditioning males after 40 weeks.

Another important performance indicator of males is their weekly weight gain. After 32 weeks, they should gain very little weight (20 to 25g per week) and even large males should continue to grow. In Figure 5, the males had good weekly weight gains after 30 weeks, for a few weeks, and at around 35 weeks the weight gains ceased abruptly. At that point, they started losing conditioning, so much so that by 40 weeks they were losing weight. The decline in growth or weekly gains after week 36 had a direct impact on percentage of fertility which dropped by 4%.

As seen in these examples, it is important to record measurable performance data. When there is a sudden change in production, the data can be used to identify the cause and prevent it from reoccurring in future flocks.


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