Modern technology is increasingly being used on breeder farms, creating ever more data. This data can be analysed by the farmer to make smarter decisions to improve flock performance and efficiency. These case studies show how sound datasets can help in broiler breeder male management.
As genetics continue to improve every year, so will efficiencies and production performance.
Comprehensive data collection provides opportunities to predict future performance, supply chain demand and future results. If the expected outcome is not achieved, then datasets are available to help understand the issues.
Many breeder farmers only use paper records and little electronic data. Conversely, some operations generate so much data that it becomes messy. Some data is unreliable due to staff completing tasks hastily, such as weighing birds while also collecting eggs. Good data collection is important since many critical decisions, including feed allocation, are based on reliable bodyweight data. Technology will never replace the stockman’s skills needed for successful flock management. However, there are many missed opportunities where farmers and workers have overlooked vital or negative behavioural signs that affect flock performance.
Many consultants offer 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 effects or knowledge of breed-specific behavioural traits. For example, a consultant could state that heavy hens produce fewer 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 eggs (incubation). Fertility is also most often attributed to male management, but on rare occasions, it can be female-related.
Objectivity and experience are important when interpreting suboptimum performance data using regression graphs. Comparing the performance of a farm to industry standards is a basic first step in data analysis but only shows how the farm compares to the industry. Data to help improve flock productivity and profitability is key. 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. The old adage ‘If you can measure it, you can manage it’ 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 do the measurements, and finally, collect the data. For example, how do you know if the males are getting enough feed? How do you identify the cause of low early hatchability or poor peak percentage of hatchability? This is where stockman skills are very important because the measurements are subjective but need to be quantified to produce data.
In the field study below, the breeder males were overweight and fertility was declining. The production graph indicated the males were heavy and considerably above their weight for the target age. On the farm, the condition of the males was quantified based on a breast muscle scoring system (Figure 1). The male breast scores are explained in Figure 2.
In Figure 1, 65% of the males had a desired fleshing score of 3, while 15% of the males were too thin and 10% were emaciated. Only 10% were well developed with a fleshing score of 4, leaving no males with fleshing scores of 5, which would be deemed overweight and unfit for reproduction. This means that 75% of the males showed good reproductive fitness, indicating they were not overfed. The remaining 25% were off target for 36 weeks of age, indicating that they may not be receiving enough feed, although they appeared to be ‘overweight’. Based on the bodyweight data alone, it appeared that the males were underfed to control their weight. This demonstrates the difference between weight and size as perceived by the farmer. Therefore, we increased the feed and fertility began to improve. Table 1 shows the fleshing target table based on age.
In another case study of declining hatchability, the farmer collected and kept weekly breast scoring records for each house. There were 23 houses totalling 15,000 males represented in the data (Figure 3). Figure 3 indicates that the males’ fleshing scores developed rather quickly from 23 to 35 weeks of age. Scores 3 and 4 increased too rapidly. The thinner males with scores 1 and 2 were well managed as their numbers declined and they remained a small proportion 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 fleshing scores rapidly increased to 4 and 5, with a corresponding decline in score 3 males. This was the result of early and rapid increases in feed allocation over 32 weeks. Ideally, 70% of males should score 3 for as long as possible for optimum fertility.
Hatchability data was plotted against the fleshing scores as a graph in Figure 3. It is interesting to note that the decline in hatchability began at around the same time as the decline in score 3 and corresponding increases in scores 4 and 5. This indicated that the males became too heavy to continue mating, and over time the hatchability declined as the males developed bigger breast muscles. Based on the data, it was determined that males were being overfed in early production (23 to 32 weeks). Therefore, future feed intakes could be adjusted to control early muscle development and improve hatchability by conditioning males after 40 weeks.
Another important key performance indicator for males is their weekly weight gain. After 32 weeks, they should gain very little weight (20 to 25 grammes per week) and even large males should continue to grow. In Figure 4 it can be seen that the males had good weekly weight gains for a few weeks after 30 weeks but that at around 35 weeks, the weight gains ceased abruptly. At that point, they started losing condition, 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 the fertility percentage which dropped by 4%.
As 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 flocks.