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Online Growth Control as an Advance in Broiler Farm Management

Faculty of Bioscience Engineering, Division Measure, Model and Manage Bioresponses, Biosystems Department, Katholieke Universiteit Leuven, B-3001 Heverlee, Belgium

¹ Corresponding author: daniel.berckmans{at}biw.kuleuven.be

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Growth control in broiler chickens has been proven to be an efficient method to ensure broiler performance and yield and to lessen health problems. The growth control procedure has been tested in literature using a limited amount of animals in experimental facilities. Under these "ideal" circumstances, the birds could follow predefined growth trajectories with accuracies ranging from 3.7 to 6% (mean relative error). The objective of this research was to test the above growth control procedure in the field in a real broiler farm, evaluate its accuracy, and explore its benefits for the broiler farmer. In this procedure, a model-based control algorithm was used to calculate the feed supply to the broilers with the intention of following a target growth trajectory as close as possible. A simultaneous small-scale experiment was performed to have an idea about the order of magnitude of the accuracy of the same procedure under "ideal" laboratory conditions. In farm conditions, the mean relative error between the target trajectory and the weight of the controlled birds was 7.3%. Higher than that under laboratory conditions (2.7%), it was indicative of the challenges of transferring the growth control procedure to real scale. On the other hand, the growth control procedure has been proved to be beneficial under the farm conditions in the feed conversion ratio. The feed conversion ratio of the controlled group was better (1.64) than the ad libitum-fed ones (1.68).

Key Words: model-based control • broiler growth • growth trajectory

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Fast growth rate in the modern broiler industry has been proven to cause immense welfare problems such as cardiovascular diseases, sudden death syndrome (flip-over), and pulmonary hypertension syndrome that results in ascites (Julian, 1998). For welfare considerations, these growth-related diseases, and as a consequence, high mortality rates, cause a lot of pressure on modern broiler industry management. Solutions for this problem have been sought comprehensively by researchers for years. Feed restriction has been proven to give good results in decreasing health problems (Parks, 1982). Feed restriction in the early stage of the broiler growth period has been proven to reduce mortality from all causes while maintaining an optimum feed conversion ratio (Shlosberg et al., 1991; Mcgovern et al., 1999; Urdaneta-Rincon and Leeson, 2002, Demir et al., 2004, Salinas-Garcia et al., 2004). Less leg problems were confirmed by increased activity levels of the restricted birds (Savory and Maros, 1993; Nielsen et al. 2003). Economic advantages of mild restriction have been proven through reduced mortality (Lippens et al., 2000).

In addition to health-related problems, it has been confirmed that the end weights of restricted animals are higher than those of ad libitum-fed ones. Broiler chickens that undergo compensatory growth exhibit a feed intake greater than normal, and some associated digestive adaptation is observed. Food conversion efficiency and meat yield are improved by early-age food restriction (Plavnik and Hurwitz, 1991; Plavnik, and Balnave, 1992; Zubair and Leeson, 1996a,b).

In their study, Aerts et al. (2003a) introduced a procedure that modeled and predicted the dynamic growth response of broiler chickens to feed intake online. This approach allowed the prediction of broiler growth without any prior knowledge of the system and took into account the time-variant nature of the growth process of each flock. Subsequently, they integrated this procedure in a control strategy in which the growth of broiler chickens during the production process was monitored and controlled online. Every day during the growth process of chickens, the input (feed intake) and the output (weight) of the last 24 h were registered to the control algorithm, where it calculated online what the feed intake should be for the next 24 h. Instead of restricting the birds by using a fixed feed quantity during a fixed amount of time, this new strategy was able to alter the growth of birds by making them follow a previously defined target growth trajectory (Aerts et al., 2003b).

The modern process control techniques in livestock production have been extended in an integrated management system in which not only growth but pollutant emissions and heat production could be controlled as well (Aerts et al., 2003c; Frost et al., 2003).

Being highly advantageous in favor of broiler welfare, this paper aimed to test the online growth control strategy, which was developed by Aerts et al. (2003b) in laboratory conditions and in field conditions in a broiler farm and evaluate the results in the light of its competence in contemporary broiler management. To comprehend the variation in the performance of the growth control procedure in different scales, simultaneous experiments were done under laboratory and field conditions.

	MATERIALS AND METHODS

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Farm-Scale Experiments

Experimental Design. Experiments in field conditions were conducted at the Provincial Center for Applied Poultry Research, Antwerp (situated in Geel, Belgium). The growth period was carried out for 41 d during February and March 2006. The installation had 4 compartments with 1,500 Ross-type birds with a stocking density of 20 birds/m². In 2 compartments, the birds were fed ad libitum, whereas in the other 2 compartments, the growth was controlled to follow a predefined restricted growth trajectory. The target trajectory aimed to have 90% of the standard ad libitum growth during d 12 to 16, in which the restriction started on d 10 and increased linearly during time until d 12. Keeping constant from d 12 to 16, the restriction decreased linearly until it intercepted with the standard ad libitum curve on d 21. The standard ad libitum growth trajectory was calculated using a large data set of ad libitum-fed growth trajectories of the Ross bird that have been performed in the same experimental installation.

In 2 of the compartments, 1 of the ad libitum-fed and 1 of the controlled birds were kept on floor pens of wood shavings, and in the other 2, they were kept on peat. Each compartment was equipped with 1 weighing platform (747, version A1, Fancom B.V., Panningen, The Netherlands), and all weighing platforms were connected to an automatic weighing computer (F47, Fancom B.V.). All compartments were connected to an automatic feeding system (Minimax, Roxell N.V., Maldegem, Belgium) in which the daily feed delivered in the different compartments was recorded. Water was freely available by nipple watering system to all compartments.

Desired air temperature for the controller was set at 34°C during d 1, and it was decreased until it reached 18.4°C at the end of the growth period. Except for the first 5 and the last 4 d, birds were subjected to 18L:6D, in which the intensity of light changed from 5 to 20 lx. One hour of light was applied to the birds for the first 5 and the last 4 d.

Birds and Diets. The birds were Ross 308 broilers, mixed sex, obtained from a local hatchery (Belgabroed N.V., Merksplas, Belgium). The female breeders were 37 wk old. The birds were vaccinated against Newcastle disease (Poulvac NDW, Fort Dodge Animal Health Ltd., Southhampton, UK) and infectious bronchitis (Poulvac IB Primer, Fort Dodge Animal Health Ltd.) in the hatchery using a spray-enting technique. On d 23, they were vaccinated additionally against Gumboro (Poulvac Bursine 2, Fort Dodge Animal Health Ltd.) and Newcastle disease (Hipraviar-NDV-clon, Codifar NV, Wommelgem, Antwerp, Belgium) in the stables using the drinking water vaccination technique.

For the first 9 d, a prestarter diet with 23% protein and 2,890 kcal of AME_n/kg was given. From d 10 until 13, a starter diet with 22% protein and 2,794 kcal of AME_n/kg was given, and from d 14 to 35, a grower diet with 20% protein and 2,899 kcal of AME_n/kg was offered. The finisher diet, from d 36 to 42, consisted of 19.01% protein and 2,963 kcal of AME_n/kg.

Laboratory-Scale Experiments

The laboratory test installation had 3 compartments. In the first compartment with 6 birds, a step experiment in feed was performed in which the feed intake was restricted to 80% of ad libitum feed intake from d 10 to 17. In the second compartment with 6 birds, a growth control procedure was run. The same target trajectory as in the farm experiments with 90% of ad libitum growth from d 12 to 16 was used. Each compartment had the same number of female and male birds. The same growth trajectory as the one in the farm was used to be able to compare the results. Eight birds in the third compartment were fed ad libitum. Birds (Ross 308) were taken from the same hatchery as the farm experiments at the same day. The stocking density in the laboratory (5.3 birds/m²) was significantly lower than in the farm (20 birds/m²) due to the limitations in the laboratory test installation. The lightning schema and the feed composition were identical and simultaneous between the compartments (in the laboratory and in the farm) as well as between the laboratory and the farm-rearing conditions. Because there was no light-intensity control in the laboratory, lighting intensity was kept constant at a higher value (approximately 400 lx) than the farm (5 to 20 lx) during the light periods. The stocking densities from compartment 1 to 3 were 4, 4, and 5.3 birds/m², respectively.

Every day at 1200 h birds were weighed manually and individually using a regular weighing scale (±1 g; Soehnle, Weegtechniek Holland B.V., Zeewolde, The Netherlands). Feed supply of the last 24 h was also weighed, and the amount of feed not eaten was taken away.

Growth Control Algorithm

A model-based predictive control algorithm was used to control the growth response of the broiler chickens to the input and feed supply with the intention that it follow the predefined target trajectory. The dynamic response of the chicken was modeled using an online recursive estimation of the parameters. For precautionary reasons, in both experiments some upper and lower limits have been introduced to the feed advice output of the control algorithm. These were namely 10% more and 50% less than the standard ad libitum feed intake.

For the experiments in the farm, a graphical user interface was prepared for the user to register the weight of the birds, the feed supply of the last 24 h, and the mortality of the birds every day at the same hour during the growth period. Mortality was not used for control purposes but was only displayed for extra information to the user. On the other hand, the weight of the birds, which was recorded by the automatic weighing scale, and the feed supply measured via the feeding system were given as inputs to the control algorithm, where it calculated the amount of feed that should be given to the birds for the next 24 h to be able to follow the target trajectory. The objective function of the model predictive control was as follows:

where (t + F | t) = the predicted weight of the birds; CF = the cumulative feed supply; r = the target weight trajectory; N₁ = the minimum cost horizon (1 d); N₂ = the maximum cost horizon (4 d); and N_u = the control horizon (4 d).

The control algorithm, minimizing the above equation, calculated the feed advice for the following 24 h. This amount was registered to the feeding computer and offered to the birds for the next 24 h. The number of birds, feed conversion ratio, and cumulative feed supply were calculated and displayed daily in the interface as additional information. More information about the model predictive control used in the experiment can be obtained in the work of Aerts et al. (2003b).

For the experiments in the laboratory, each individual bird, as well as the exact feed intake per group, was weighed manually every day at the same hour. Daily average weight of the birds in compartment 2 and the feed intake of the last 24 h were used as inputs to the control algorithm, and the calculated feed amount was given to the birds for the following day. Until the day the control started in the controlled groups of laboratory and farm experiments, all the birds were fed ad libitum.

In the farm experiments, 75 birds randomly chosen from each compartment were weighed manually at the end of every week. The results were compared with the readings of the automatic weighing scale. The mortality rates of all experiments were evaluated starting from d 10 to measure the effects of applied feeding regimes on mortality.

Statistical Analysis

The error between the reference trajectory and the measured weight was calculated by using mean relative error (MRE), defined as

where MRE = a percentage; w(t) = the weight measured with the automatic weighing systems at time t (g); r(t) = the desired weight at time t (g); and N = the number of times the weight data was retrieved (Aerts et al., 2003a,b). Because the control algorithm needed 1 weight value every day, N added up to 42 for the whole growth period.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
As can be seen in Figure 1 and Table 1, the laboratory experiments reached higher end weights than the farm experiments. The ad libitum-fed group reached in average of 2,885 g in end weight at d 41. Four of the 6 birds in this compartment weighed more than 3,000 g at d 41. The difference in the environmental rearing conditions between the 2 experiments was the stocking density and the lightning intensity. It has been demonstrated in literature that the light intensity does not have a significant effect on weight or on the feed conversion ratio of broiler chickens (Buyse et al., 1996). On the other hand, stocking density inversely affects the growth of the birds (Dozier et al., 2005). Because the stocking density of the laboratory experiments (5.3 birds/m²) was significantly lower than the one in farm (20 birds/m²), it could be stated that lower stocking density stimulated the growth of the birds fed ad libitum in the laboratory experiments.

View larger version (11K): [in this window] [in a new window]   Figure 1. Weight response (panel A) and feed intake (panel B) of laboratory experiments. The step experiment consisted of feed restriction to 80% of ad libitum feed intake from d 10 to 17. The reference trajectory in the control experiment aimed to have 90% of the standard ad libitum growth during d 12 to 16, in which the restriction started on d 10 and increased gradually. The intercept with the standard ad libitum curve was on d 21.

View larger version (14K): [in this window] [in a new window]   Figure 2. Measured weight and reference trajectory together with the measured and advised feed intake of 1 of the 2 farm experiments (controlled group).

View larger version (11K): [in this window] [in a new window]   Figure 3. Comparison of the average weight responses of the controlled groups in the laboratory and in the farm experiments together with the reference trajectory.

There was no difference in the mortality between the ad libitum and the controlled group in the farm experiments. Hence, it could be concluded that there was no significant effect of growth control on the mortality of the birds.

In the laboratory experiments, the step (2,640 g) and the control experiment (2,434 g) resulted in a lower-end weight than ad libitum-fed birds. The step experiment (1.67), on the other hand, resulted in a worse feed conversion ratio than both the ad libitum-fed (1.62) and controlled group (1.62). This may suggest that growth control was more advantageous than reducing the feed intake a certain amount during the first 2 wk of growth.

It was difficult to draw conclusions on the mortality in the lab experiments due to the low number of birds.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The growth control procedure in broiler chickens was tested simultaneously at 2 different scales, one in the farm and the other under laboratory conditions. Because the input (feed intake) and the output (weight) of the system is precisely known under laboratory conditions due to manual weighing, the aim of this study was to investigate the effectiveness and accuracy of the control procedure under farm conditions with a large number of birds.

It was proven that the growth control experiment in the farm, even though giving a higher MRE (7.3 vs. 2.7%) in following the target trajectory than the laboratory experiments, was more accurate in satisfying the desired end weight. The feed conversion ratio of the controlled birds was equal to the ad libitum-fed ones in the farm (1.62), but the laboratory-controlled group gave a better feed conversion ratio than the ad libitum-fed birds (1.64 vs. 1.68).

The controlled group following a predefined weight trajectory gave better results in feed conversion ratio (1.62 vs. 1.67) than the step experiment, which consisted of restricting the feed quantity of the birds to 80% of ad libitum feed intake from d 10 until 17. More experiments have to be done to explore further the advantages of the growth control compared with the traditional restriction methods in achieving a better performance in broiler growth.

In addition to favorable results in growth control procedure, experiments demonstrated that the laboratory ad libitum-fed group was advantageous in the end weight as well as feed efficiency compared with the farm experiments. The reason for that was assumed to be the lower stocking density during the growth period.

It was suggested that the higher MRE and deviation from the target trajectory in the farm experiments was due to the inaccuracy of the sensor (weighing scale) and the actuator (feeding system).

In conclusion, experiments proved the benefits of growth control in the feed conversion ratio in field conditions with large amounts of birds. However, it was concluded that when applying the growth control procedure in the field, one should pay attention to the accuracy of the automatic registration of the feed intake (input) and the weight (output) of the birds.

Received for publication May 17, 2006. Accepted for publication October 13, 2006.

	REFERENCES

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