Using data to improve the management of male broiler mothers

Extensive data collection provides opportunities to predict the performance and outcomes of the male breeder in the future.  Photo: cup
Extensive data collection provides opportunities to predict the performance and outcomes of the male breeder in the future. Photo: cup

Modern technology is increasingly being used in breeding farms, which leads to the creation of more data. The farmer can analyze this data to make smarter decisions to improve herd performance and efficiency. These case studies demonstrate how sound data sets can assist in the management of male broiler mothers.

As genetics continue to improve each year, so will efficiencies and production performance.

  • Are the improvements in line with expectations?
  • How does the process compare itself to the best performing processes?

Comprehensive data collection provides opportunities to forecast future performance, supply chain demand, and future outcomes. If the expected outcome is not achieved, data sets are available to help understand the problems.

Combine technology skills and inventory workers

Example of a male who gets 3 points.  Photo: cup
Example of a male who gets 3 points. Photo: cup

Many breeder farmers use only paper records and little electronic data. On the contrary, some processes generate so much data that it becomes chaotic. Some of the data is unreliable due to staff completing tasks in a hurry, such as weighing birds while also collecting eggs. Good data collection is important because many critical decisions, including feed allocation, are based on reliable body weight data. Technology will never replace the stock man skills needed for successful herd management. However, there are many missed opportunities where farmers and workers have overlooked vital or negative behavioral signs that affect herd performance.

Many consultants provide data analysis services but do not have the experience and skills of a stockman in terms of breeder management. Interpretation of the data requires local knowledge, such as seasonal influences or knowledge of breed-specific behavioral traits. For example, the consultant can mention that heavy hens produce fewer chicks. However, the relationship is multifactorial because as chickens age, they become heavier and egg production decreases. Moreover, the production of chicks is a function of both fecundity and fertilized egg hatching (incubation). Fertility is also often attributed to male management, but in rare cases, it can be related to females.

Interpretation is the key

Objectivity and experience are important when interpreting suboptimal performance data using regression graphs. Comparing a farm’s performance to industry standards is an essential first step in analyzing the data but it only shows how the farm compares to the industry. Data to help improve herd productivity and profitability is key. With roosters this is reflected in weight, condition, amount of forage and fertility.

In the production of hatching eggs or chicks, reproductive performance is always the main driver. The old adage “if you can measure it, you can manage” is very true, but how do you measure a biological event that cannot be measured or weighed? First, find a way to quantify it, then make the measurements, and finally collect the data. For example, how do you know that males are getting enough food? How do you determine the reason for the low rate of hatching or poor hatchability? This is where Stockman skills are very important because measurements are subjective but need quantitative measurement to produce data.

Figure 1 – Meaty grades of males in a flock considered to be overweight.

In the field study below, bred males were more overweight and less fertile. The production graph indicated that the males were heavy and well overweight for the target age. On farm, male status was determined based on the breast muscle scoring system (shape 1). Male breast scores are explained in Figure 2.

Figure 2 – Explanation of the intrinsic scores.

in shape 1, 65% of the males had a desirable physical score of 3, while 15% of the males were extremely thin and 10% were emaciated. Only 10% are well developed with a score of 4, and no males are left with a score of 5, which are considered to be overweight and unfit for breeding. This means that 75% of the males showed good reproductive fitness, indicating that they were not overfeeding. The remaining 25% were off target by 36 weeks of age, indicating that they may not be receiving adequate forage, even though they appeared to be ‘overweight’. Based on body weight data alone, it turns out that males are undernourished to control their weight. This shows the difference between weight and volume as seen by the farmer. Therefore, we increased the feed and fertility began to improve. Table 1 The table shows the target body based on age.

Different situation, different dynamics

In another case study of declining hatchability, the farmer collected and kept weekly breast-registration records for each household. There were 23 homes totaling 15,000 males represented in the data (Figure 3). Figure 3 It indicates that the flesh grades of males developed very rapidly from 23 to 35 weeks of age. Grades 3 and 4 increased very quickly. The thinner males with grades 1 and 2 were well managed as their numbers declined and they remained a small percentage of the population. This would indicate that these males were removed. The remaining males with a score of 3 reached a point at 35 to 40 weeks where their physical scores rapidly increased to 4 and 5, with a similar decrease in males with a score of 3. This was a result of early and rapid increases in feed allocation over a 32-week period. Ideally, 70% of males should get a score of 3 for as long as possible in order to have optimal fertility.

Figure 3 – Hatching and meat conditions of 23 flocks and 15,000 males.

Hatching data versus meat grades are plotted as a graph in Figure 3. It is interesting to note that the decrease in hatchability began around the same time as the decrease in grade 3 and corresponding increases in grade 4 and 5. This indicates that the males became too heavy to continue mating, and over time the hatchability decreased. Males have developed larger breast muscles. Based on the data, it was determined that the males were overfeeding in early production (23 to 32 weeks). Therefore, future forage intakes can be modified to control early muscle growth and improve hatchability by conditioning males after 40 weeks.

Figure 4 – Insufficient weight gain has a negative effect on the fertility of 6 flocks and 3000 males.

Another key performance indicator for males is weekly weight gain. After 32 weeks, they should gain very little weight (20-25 grams per week) and even large males should continue to grow. in Figure 4 It can be seen that the males made good weekly weight gains for a few weeks after 30 weeks but at around 35 weeks the weight gain stopped abruptly. At that point, they started losing the condition, to the point where by 40 weeks they were losing weight. The decline in growth or weekly gains after week 36 had a direct impact on the fertility rate, which decreased by 4%.

Implementation in the herds of the future

As can be seen from 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 recurring in future herds.

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