Summary Objectives

Feed cost is the main production cost in poultry and rabbits, and represents 50 to 70% of the total production cost. Consequently, feed efficiency is the most important selection criterion in these species. For meat-type producing animals, feed efficiency is defined as the ratio between weight gain to feed intake during a given period. Weight gain is relatively easy to obtain at the individual scale, although it is labour intensive. On the opposite, feed intake measurements in individual animals are more difficult to obtain. In chickens, to measure feed intake, birds are housed in individual cages and the feeder has to be weighed at regular intervals. This method has several drawbacks:

-Animals are housed individually whereas they are usually reared in groups -Animals are housed in cages whereas they are usually reared on the floor -The measurement is available only for longer periods, usually one measurement per week or every two weeks. This provides a very limited indication of the changes in feed intake, considering that a broiler production cycle is lasting only 5 weeks.

The objective of this deliverable is to use automatic feeding stations to obtain individual data of feed intake in group-housed animals, and that are reared in conditions as close as possible to production conditions (e.g., on the floor for chickens, in collective cages for rabbits). Moreover, these feeding stations allow for the continuous collection of data of feed intake and to establish the dynamics of feed intake within a day, and all along the production cycle.

These new traits have been correlated to feed efficiency to propose new traits that can be used for both nutrition and genetics studies of feed efficiency in poultry and rabbits.

Rationale:

Feeding stations are developed for individual recording of feed intake in both rabbits and chickens. Animals are identified electronically using RFID tags that are detected automatically by the feeding station when animals enter the feeding station.

In chickens, animals are detected and identified by an antenna placed on the front access of the feeder. As animals climb on a plateau mounted on a scale and they are weighed at each visit. Feed intake is calculated at each visit as the difference between feeder weight at the beginning and at the end of the visit. To check whether feed intake patterns were generic or specific for a given type of production, the station was tested using rapidly growing broilers and using Label Rouge chickens with a lower growth rate, and with different types of diets. Consistency of visits collected by the feeding station and true visits were assessed by video recording.

In rabbits, measurements are done in collective cages with 4 to 7 rabbits. A circular antenna is placed at the entrance of a corridor giving access to the feeder. Feed intake is calculated at each visit as the difference between feed weight in the feeder at arrival and at departure of the rabbit. Experiments to determine the ideal size of the group of rabbits, to test the reliability of the measurements, and to estimate the effect of the feeding station on growth performance of rabbits have been carried out. Feed intake, feeding behaviour, and growth traits have been compared with different numbers of rabbits per cage and between different types of feeders.

Teams involved:

INRA: poultry experiments IRTA: rabbit experiments UNEW: behavioural assessment in poultry experiment

Species and production systems considered:

Poultry and rabbits were considered in this deliverable. Applicability of the results is worldwide.

Introduction

Feed costs represent the main part of production costs in many species, including poultry (50-70% of total cost depending on the production type) and rabbits. The efficiency to use feed is thus the first criterion of the economic pillar of sustainability. This is also an indicator of the environmental impact of production as it is highly correlated to nutrient excretion (de [START_REF] Verdal | Genetic variability of metabolic characteristics in chickens selected for their ability to digest wheat[END_REF]. In growing animals used for meat production, this criterion is often assessed through the feed conversion ratio, which is the ratio of feed intake to growth rate during a given period.

Measuring animal body weight is relatively easy, but can be laborious. On the opposite, measuring feed intake of individual animals is much more difficult.

Until recently, to measure feed intake in chickens, animals were reared in individual cages equipped with individual feeders, separated from the other animals (Figure 1). Animals and feeders were weighed at 1 or 2-week intervals, because feed intake measurements are not considered reliable when measured for shorter intervals. This method has several drawbacks. First, animals are reared in artificial conditions that are different from production conditions (isolated vs in groups, in cages vs on the floor), which can affect feed intake and feeding behaviour. Second, it has a negative impact on animal welfare due to the long period of cagerearing. Third, it gives only very partial information on feed intake, as a maximum of one value of cumulative feed intake in a week per bird is obtained. Therefore, the dynamics of feed intake during the production cycle and within a day were not available.

Figure 1. Individual feed intake measurement in cages.

The partners have developed feeding stations, which have been developed further in the Feeda-Gene project to solve these problems. For chickens, birds are equipped with a RFID chip (134 or 125 KHz) on the neck, which allows detection of the bird by an antenna placed on the top of the access to feed (Figure 2). Each feeder access has its own scale to measure remaining feed in the feeder. When a bird wants to eat, it climbs on a plateau mounted on a scale and the bird is then weighed. Both feed intake and the weight of the bird can thus be obtained at the individual level and in production conditions, without modification of the natural behavior of the animal.

Figure 2. Individual feed intake measurement using the feeding station in birds raised on the floor.

For rabbits, animals are equipped with a RFID ear tag at 125 KHz and are detected by a circular antenna placed at the entrance of a corridor of access to the feeder (Figure 3). A photoelectric cell is used to detect the presence of animals in the feeder. Feeder weight is recorded every second. A complete description of this system is provided in deliverable D2.2.

Results

Poultry data

To determine which criteria could be used to describe feed intake and feeding behaviour in chickens, a series of four experiments were performed.

 Experiment 1 (March-April 2017): 80 Cobb500 birds were reared during 5 weeks, using a standard diet, to test how fast birds get used to the feeding station and to test the reliability of the body weight data obtained from the station.  Experiment 2 (September-November 2018): 80 medium growing broilers were reared during 7 weeks, to define the algorithm of calculation of feed intake and to test reliability of feed intake data.

 Experiment 3 (May-June 2018): 80 Cobb500 birds were reared during 5 weeks, using a standard diet to validate and to adapt the algorithm used to calculate feed intake.  Experiment 4 (September 2018-March 2019): 80 Label Rouge birds and 80 Cobb500 birds were fed either a classical corn-soybean diet or an alternative diet including local feedstuffs and by-products of cereals. The aim is to compare feed intake, feeding behaviour, and feed efficiency data depending on genotype and diet.

System reliability

To test the familiarization of the birds to the station, the proportion of animals that were detected during the first days of the experiment was calculated. It appears that animals get used to the feeding station very fast. All birds started to use the feeding station in less than 3 days after their arrival (see Figure 3 for an example with Cobb500 birds). After this time, all birds are detected every day by the feeding station. Growth of birds was not affected by the feeding station, and the recorded weights were slightly above the reference growth curve for Cobb500 chickens. The difference between manually and automatically recorded weights was low (2%, Figure 5). To ensure that animals were well detected by the feeding station, we compared visits detected by the feeding station and visits detected by video recording. In an experiment performed on medium growing broilers, we recorded during 8 days a total of 1096 visits by video analysis. Among these visits, 95% were detected by the feeding station. The 5% missed visits included one-half of truly missed visits and one-half of very short visits (of less than 10 seconds) that could not be associated with feed intake (Kefi, 2018).

An algorithm was developed to calculate feed intake based on data collected on 80 Cobb birds during one day. It represented 593 feeding visits. Cook's distance [START_REF] Cook | Detection of influential observations in linear regression[END_REF] was used to remove feed weight data that had a large influence on the mean weight between two visits. After correction to zero of negative feed intake values (mainly due to very short visits leading to high fluctuations of the scales), the mean absolute value of the difference between calculated and measured feed intake was 2.8%.

Feed intake data in chickens

Based on data collected during the experiments, we combined second-by-second data of feed weight for each access to the feeding station and electronic chip detection at each feeding visit to identify new feed intake traits. The first step is to define the limits of a visit. Based on video observation and on data collected by the feeding station, a feeding visit is defined as:

starting when a new electronic chip is detected, ending when the same chip is no longer detected within 2 minutes between two consecutive readings of the same chip. The series of reading has to be continuous, i.e. no other chip is read during the sequence, extended before the first reading or after the last reading of the chip when variation of feed weights in the access indicates that animal has not been detected immediately or was no longer detected before departure.

When two visits are very close, the time can be insufficient for the feed scale to ensure a stable reading. In that case, the two successive visits were grouped and the total duration of the feeding block (DB) was calculated as the sum of duration of both visits (DV). Feed intake of each visit (FIV) is calculated as a proportion of feed intake of the block (FIB):

𝐹𝐼 𝑉 = 𝐷 𝑉 ×𝐹𝐼 𝐵 𝐷 𝐵
Once the amount of feed is calculated for each visit, we are able to determine new feed intake traits:

number of visits per day duration of each visit dynamics of the visits during the day (e.g., many visits throughout the day, visits at key moments during the day) feed intake at each visit total feed intake during the day feed intake rate (g/min) number of visits to different feeder accesses (1 to 8 per feeding station). With several feeding stations, we will also be able to see if different stations are visited by a given animal, giving information on its preferred feeding spots.

Some elements of social behaviour could also be obtained from the elementary data of the feeding stations. For example, social groups could be identified if several animals are recorded frequently at the same time. Moreover, if a given animal is frequently read in the middle of feeding sequences of many other animals, it could be a dominant animal disturbing the access to the feed of dominated animals.

Below is an example of these new feed intake traits during one day on several medium-growing broilers. The daily duration of feed intake and the duration of each visit is highly variable between animals, as well as the feed intake rate, but the total daily feed intake is similar. Within a day, bird 23 and bird 243 have frequent and short meals of 20-40 seconds, whereas bird 115 and bird 248 have both short and long meals (Table 1). Figure 6 is an example of dynamics of feed intake obtained during the same period (a dash represents a period of feeding). This now needs to be confirmed on a larger number of animals.

Figure 6. Dynamics of feed intake depending on bird.

Another element of feeding behaviour that we calculated was the preference of birds for a given access of the station. In Table 2, we can see that bird 243 does not use the eight accesses to the feeder independently, whereas bird 248 does not show any preference. Figure 7 shows the daily feed intake per chicken for the whole experiment for alternative (black curves) and classical (blue curves) diets. Classical diets are based on corn and soybean whereas alternative diets include less soybean meal (to reduce imports and increase protein autonomy) and more cereal by-products (to limit competition between human and animals).

Figure 8 shows the body weights for the same birds. Solid lines represent data collected automatically by the feeding station and dashed lines represent data coming from manual weighing. In both cases, automatic and manual data are relatively close. Animals consumed 12% more feed and were 3% heavier with the alternative diet than with the classical diet. Analysis of other feed efficiency and feeding behaviour traits is on-going. Data on standard chickens will be collected in February-March 2019.

Rabbit data

In rabbits, a feeding device has been developed as described in deliverable D2.2 of the Feeda-Gene project. Data have been collected at each visit and sent to a database. Daily information for each individual animal is then obtained and, at the end of the fattening period, individual information for the whole fattening period is saved in the database. Depending on the objective of the study, information can be retrieved at the level of the visit, per day, or for the whole fattening period.

In Figure 9, a timeline of the different experiments conducted during the project is given. All the activities conducted since the full availability of the device in the farm are indicated:

-Experiments indicated in yellow: batches used for the selection experiment (task 5.5).

-Experiments indicated in green: experimental batches. In some of these batches, feed intake at the cage level was recorded manually and compared with data provided by the electronic feeders. -Experiments indicated in red: batches in which feed intake was available only at the cage level. Information recorded during the first five batches of 2018 have been communicated as an oral presentation at the 2018 Symposium of the Spanish Association of Rabbit Production (ASESCU) meeting (Sanchez et al., 2018).

Growth and feed intake assessment using electronic feeders

The objectives are:

-To assess the influence of the electronic feeder on feed intake and growth.

-To explore the consequences of increasing the number of animals per cage equipped with an electronic feeder on growth and feed intake of the rabbits.

To accomplish these objectives, information and results from different experimental trials have been integrated. A brief description of the different experimental trials is given before reporting the results. This is done separately for each trial and overall conclusions will be given.

Trials A1 and A2

They are replicates of the same design. Within each trial, five different experimental treatments were compared:

-C-integrated (C stands for commercial): the feeders are integrated in the cage and connected to the automatic feed provision system (Figure 10A). With these feeders, it is not possible to measure feed intake. Between five and six animals are housed in each cage of this type. -C-not-integrated (C stands for commercial): the commercial feeders are not integrated in the cage (Figure 10B). As they are removable, it is possible to weigh leftovers of feed and cage feed intake can be measured. Between five and six animals are housed per cage. -E-5, E-6, and E-7 (E stands for electronic): three prototypes of electronic feeders were used. In trials A1 and A2, individual feed intake information recorded by the feeders was not reliable and not used for these trials. However, cage feed intake was recorded manually, by measuring the amount of feed provided to the electronic feeder.

Least-square means for the different treatments of trials A1 and A2 are given in Table 3. Using the electronic feeder devices penalised growth as growth rate was equal or higher in the commercial feeders than in those equipped with electronic feeders. This could be associated to the size of the group. In trial A1, the growth rate decreased with the number of animals per cage in cages with electronic feeders. However, in trial A2 the opposite was observed.

Although not significant, important differences (up to 47 g/d) were observed for average feed intake of animals in cage "C-not integrated" compared to those in cages with electronic feeders.

A) C-integrated B) C-not-integrated

Trials T7 and T6

These two trials correspond to batches from the selection experiment conducted in task 5.5.

The numbers behind T in the name of the trials stand for the number of animals raised per cage equipped with the electronic feeder (7 and 6, respectively). Within T6 and T7, three lines selected on different traits were compared. The characteristics of the three genetic lines under selection are the following:

-RFI: Selected for individual residual feed intake when feed is provided ad libitum.

-ADGR: Selected for growth corrected for feed intake when feed supply is restricted.

Restriction is done by limiting access to the feeder device to 12 h/d. -GRP: Selected for cage residual feed intake. The selection unit is the group (4 animals). In this line, feed intake is measured using the C-not-integrated system (Figure 10B).

For the RFI and ADGR selection trials, individual feed intake was measured by the electronic feeders, and cage feed intake was also recorded manually. Each line has a control group since part of the animals from each line were raised using the C-integrated system (Figure 10B), without measurement of feed intake. As observed in trials A1 and A2, the use of the electronic feeder penalised the growth of the animals. The ADG was consistently lower with the electronic feeder than in the control group for the lines selected for RFI and GRP. The contrast between feeders for the ADGR line is not informative since the control group is under ad libitum feeding while the animals with the electronic feeder are fed restrictedly. In the RFI and ADGR lines, growth was similar for groups of six or seven animals when using electronic feeders. Feed intake of ad libitum fed animals was 16-20% lower in the RFI line with the electronic feeder than in GRP line with commercial feeders, which is similar to the result observed in trials A1/A2. Individual feed intake obtained with the electronic feeder was 10-20% lower than records computed from cage feed intake measurements. Part of this difference can be explained by the raw data treatment from the electronic feeders are treated. The algorithm discards some visits considered as false visits. In some cases, these visits are real, which leads to an underestimation of feed intake.

Remarks:

-The electronic feeders penalize growth; this is probably associated with the fact that animals feel uncomfortable accessing the feeder through a tunnel. Another possibility could be that dominant animals occupy the tunnel not allowing others to access the feeder. -The lower growth rate observed with electronic feeders is associated with a lower feed intake. -Feed intake recorded by the electronic feeders is lower than the actual feed intake.

-Further research is still needed to :

-Improve the software of data treatment to reduce the bias in feed intake estimation.

-Propose alternative ways to use the electronic feeder aiming to minimize the reduction in growth rate when using this device. One possibility is to combine two cages, offering to each animal access to two feeders instead of one. We believe that this could partially avoid the problems associated to several individuals occupying a feeder for a very long time.

Feeding behaviour traits

The description of feeding behaviour data recorded by the electronic feeder is based on records obtained in trials T6 and T7, in cages equipped with the electronic feeders, i.e., with animals from lines RFI (fed ad libitum) and ADGR (restricted to 12 h/d of feeding).

Feeding behaviour traits were measured at two different levels:

-Cage level: hourly feed intake patterns within a day -Individual level: the variation across animals was aggregated for the whole fattening period and their correlation with feed efficiency traits was calculated.

Cage level

At the cage level, the hourly pattern of feed intake has been compared between ad libitum (line RFI) and feed-restricted animals (line ADGR). Figures 10 and11 show these patterns in T7 (seven animals per cage) and T6 (six animals per cage), respectively.

As expected for the ADGR line, there was an important reduction in the feed intake and occupation time during the period during which no feed was provided (i.e., from 6:00 to 18:00). During this period, feed intake was not exactly zero because in certain feeders, feed was distributed just before 6:00, allowing a feed provision that can last for up to 4-5 hours. The area under the curve shows that the total feed intake for the ADGR line is about 80% of that of the RFI line. However, during the period of access to feed, feeding rate is about 2.5 g/min in the ADGR line and only 1.75 g/min in the RFI line. The number of visits of feed-restricted animals is also higher than in ad libitum fed animals (50 vs 25 visits per hour). Moreover, there is less variability in feed intake and occupation time across days and cages in the RFI line (points in a given hour). During feeding periods, the difference in feeder occupation time between the RFI and ADGR lines is low. It seems a bit higher in cages with ad libitum feeding (60%) than in cages with feed restriction (50%). Under feed restriction, there are more visits and thus more spaces between visits that are counted as non-occupied time. This may result in competition between animals to access the feeder. Cage mates often drive the feeding animal out of the feeder. The similar occupation time between the RFI and ADGR lines could be an indication that seven animals in a cage is too many relative to the capacity of one feeder. A too large number of animals in a cage with individual feeder could create an undesired feed restriction. The fact that occupation time is constant throughout the day may support this hypothesis, which is reinforced by the observation that with six animals per cage, the occupation time throughout the day is not constant in ad libitum fed animals (Figure 11). In that case, although the number of visits per hour remains more or less constant (25-30), the feeding rate is reduced to 1.25 g/min.

Individual level

Individual feeding behaviour traits can be deduced from data of elementary visits to the feeder This was done by aggregating visits on a day-animal basis. We calculated the day-animal sum of occupation time, number of visits and feed intake, mean of feeding rate, and time between consecutive visits. The daily data were averaged within animal. Erreur ! Source du renvoi introuvable. presents the mean and standard deviation of these traits, jointly with performance traits. Results are presented separately for trials T7 and T6, while distinguishing between feeding regimes (ad libitum and restricted feeding). In Table 5, there are two traits representing daily feed intake of individual animals: "Feed intake" and "Average daily feed intake". "Feed intake" is computed by aggregating visits each day and averaging these across days during the fattening period. In this case, when information for a given day is not available, it is simply ignored. The "Average daily feed intake" is computed in a different way: before averaging across days, missing daily information is predicted using a three-parameter within-animal Legendre mixed linear model. Thus, when actual information is not available, a prediction of feed intake was used. For selection purposes, this second trait is the one used. Despite the difference in calculation, the correlation between both measurements is very high (>0.95), which can be seen in the dendograms given in Figures 12 and13, in which both traits always cluster together. The higher social pressure with seven instead of six animals per cage can also be seen in Table 5, as the individual daily occupation time is 20 minutes higher in the latter than in the former. Conversely, the time between consecutive visits of the same animal is about 35 seconds shorter in ad libitum fed animals with six animals per cage, compared with seven animals per cage This trait is highly variable, indicating that in some cases different blocks of the same visit were sometimes considered as different visits. When computing daily feed intake (to derive ADFI), this was taken into account by merging closely-spaced visits of the same animal. For animals under feed restriction, the interval between visits is increased as the time between the last visit before 6:00 and the first visit after 18:00 h was kept for calculations. No relevant difference was observed between T6 and T7 for individual feeding rate when animals were ad fed ad libitum (1.46 vs 1.49 g/min). Figures 12 and13 show images of the correlation matrix for each feeding regime group. This is a preliminary assessment of the correlation between individual feeding behaviour traits, and feed intake and feed efficiency traits. The first remarkable result is that the two defined feed efficiency traits (FCR and RFI) only cluster together when animals are ad libitum fed indicating that under feed restriction, other factors (e.g., example social interactions) might alter the biological and physiological meaning of these traits, relative to their interpretation under ad libitum feeding. Under ad libitum feeding, the FCR and RFI generally cluster together with feeding rate, while growth clusters with occupation time (OT).

Conclusions

It seems that the use of electronic feeders modifies production performance of rabbits compared to conventional feeders, whereas no effect of the use of an individual feeding station on the performance of broilers is observed. The effects in rabbits could be due to a too large number of animals per feeder. The presence of a tunnel to access the feeder could also modify the behaviour of the rabbits and influence their access to the feeder. This tunnel is not present in the chicken feeding station. Further experiments will be conducted in rabbits in the Feed-a-Gene project and in future projects to disentangle the nature of the interaction between the animals and the electronic feeder, to improve protocols for their use.

In rabbits, important differences among batches of animals were observed, both in relation to feeding behaviour and to performance traits, either when animals were fed ad libitum or under feed restriction. Despite this, some common patterns in performance and feeding behaviour traits were observed. In chickens, total feed intake per bird was shown to differ between diets provided. Investigations with birds kept in a setting with feeding stations will be extended to the use of rapidly growing broilers. The use of feeding stations allowing measurement of individual feed intake via electronic feeders seems a promising approach for research targeted at improving nutrition, management, feed efficiency, and overall sustainability of broiler production.

Finally, we identified similar traits in both rabbits and in chickens relevant to performance and feeding behaviour such as number of visits to the feeding station, daily feed intake, time between visits, feeding rate, daily feeding duration, meal duration, and dynamics feed intake within a day. These traits are under further investigation as potential selection criteria considered in WP5 of the Feed-a-Gene project.

  1. Summary ........................................................................................... 2. Introduction ........................................................................................ 3. Results .............................................................................................. 3.1 Poultry data ........................................................................................... 3.1.1. System reliability ........................................................................................................... 3.1.2. Feed intake data in chickens ...................................................................................... 3.1.3. Effect of diet and genotype on feed intake data. ................................................... 3.2 Rabbit data .......................................................................................... 3.2.1. Growth and feed intake assessment using electronic feeders ................................. 3.2.2. Feeding behaviour traits ................................................................................................. 4. Conclusions ..................................................................................... 5. Presentations and publications of results ......................................... 6. References ......................................................................................

Figure 3 .

 3 Figure 3. Feeding stations used in rabbits.

Figure 4 .

 4 Figure 4. Time at which birds are detected for the first time by the feeding station.

Figure 5 .

 5 Figure 5. Growth curve of Cobb500 birds using the feeding station (orange: automatic records, blue: theoretical growth curve; black: manually recorded weights).

Figure 7 .

 7 Figure 7. Comparison of daily feed intake per bird as recorded by the feeding station or by manual weighing, for a classical or an alternative diet.

Figure 8 .

 8 Figure 8. Comparison of body weight data collected by the feeding station of by manual weighing of birds, for a classical or an alternative diet.

Figure 9 .

 9 Figure 9. Timeline of the development of the electronic rabbit feeder and experimental trials conducted during the project.

Figure 10 .

 10 Figure 10. -Commercial feeders used in the different trials: A) C-integrated: the feeder is integrated in the cage and with the automatic feed provision system. B) C-not-integrated: the feeder is not integrated in the cage; it can be removed and allows manual recording of cage feed intake.

Figure 10 .

 10 Figure 10. Hourly patterns of feeding behaviour traits, for a rabbit line selected for residual feed intake (RFI in blue-pink) and a rabbit line selected for daily gain under feed restriction (ADGR in green-orange) with seven animals per cage (T7). Each line represents a different cage.

Figure 11 .

 11 Figure 11. Hourly patterns of feeding behaviour traits, for a rabbit line selected for residual feed intake (RFI in blue-pink) and a rabbit line selected for daily gain under feed restriction (ADGR in green-orange) with six animals per cage (T6). Each line represents a different cage.

Table of contents

 of

Table 1 .

 1 Feed intake and feeding behaviour data in four medium-growing broilers.

	Bird

N° of visits Mean duration (seconds) Min-Max Duration (seconds) Total Duration Intake rate (g/min)

							Total feed
							intake (g/d)
	23	47	34.9	0-156	27 min 21 sec	2.17	53.6
	115	31	86.8	2-203	44 min 51 sec	1.11	50.7
	243	32	63.4	0-158	33 min 50 sec	1.80	54.3
	248	48	42.9	0-231	34 min 20 s	2.44	53.5

Table 2 .

 2 Repartition of visits to the different accesses depending on the animal.

	Bird	1	2	3	4	Access	5	6	7	8	P (²)
	23	10.6	12.8	10.6	10.6	12.8	19.1	0.0	23.4	0.09
	115	6.5	22.6	9.7	6.5	29.0	6.5	9.7	9.7	0.09
	243	21.9	18.8	25.0	12.5	0.0	12.5	6.3	3.1	0.04
	248	14.6	12.5	14.6	14.6	18.8	12.5	6.3	6.3	0.60
	3.1.3.

Effect of diet and genotype on feed intake data.

Table 3 .

 3

Least-square means (+ SE) for average daily gain and average daily intake for the different experimental groups in trials A1 and A2.

Feeder type Animals per cage Body weight gain (g/d) Feed intake (g/d) A1

				A2	A1	A2
	C-not-integrated	4-6	48.4 (0.96) a 50.7 (1.02) ab	191 (15.8) a	175 (15.8) a
	C-integrated	5-6	50.8 (0.63) a	51.1 (0.68) a
	E-5	5	48.4 (1.25) a	45.7 (1.17) c	166 (19.8) a	131 (18.5) a
	E-6	6	44.3 (1.12) ab 46.1 (1.13) c	156 (18.5) a	128 (19.8) a
	E-7	7	43.3 (1.11) b 47.3 (1.12) ab	156 (19.8) a	129 (19.8)

a Different superscripts within column indicate that means are statistically different (P<0.05).

Table 4 .

 4 Mean values for average daily gain (ADG), individual feed intake, and cage feed intake recorded (ADFI) in trials T6 and T7 with electronic feeders.

			T7 (7 rabbits per cage)	T6 (6 rabbits per cage)
	Treatment group	Feeder type	ADG (g/d)	Indiv. ADFI (g/d)	Cage ADFI (g/d)	ADG (g/d)	Indiv. ADFI (g/d)	Cage ADFI (g/d)
	RFI-control*	C-integrated	59.6			51.7
	RFI	Electronic	46.6	142	178	47.0	151	166
	ADGR-control*	C-integrated	59.0			48.2
	ADGR	Electronic	34.1	115	137	34.1	117	133
	GRP-control*	C-integrated	59.4			51.9
	GRP**	C-not-integrated 64.8		212	53.2		206

* Control groups were formed by 5-6 rabbits per group. ** In treatment GRP 4 rabbits, per cage were raised.

Table 5 .

 5 Means (standard deviations), across individuals, within trial and feeding regime treatment for feeding behaviour and performance traits

	Batch

AD LIBITUM FEEDING (RFI) FEED RESTRICTION (ADGR)

Figure 12. Phenotypic correlation matrix between performance and feeding behaviour traits when six rabbits are raised in a cage (T6). INT=interval between visits, ADG=average daily gain, OT=occupation time, ADFI=average daily feed intake, RFI=residual feed intake, FI=feed intake, NV=number of visits, FR=feeding rate, FCR=feed conversion ratio.

AD LIBITUM FEEDING (RFI) FEED RESTRICTION (ADGR)

Figure 13. Phenotypic correlation matrix between performance and feeding behaviour traits when seven rabbits are raised in a cage (T7). INT=interval between visits, ADG=average daily gain, OT=occupation time, ADFI=average daily feed intake,, RFI=residual feed intake, FI=feed intake, NV=number of visits, FR=feeding rate, FCR=feed conversion ratio.


Manon Granger

Manon Granger-Delacroix

Crossflow

published or not. The documents may come

Crossflow microfiltration of skim milk : Toward optimization of design and conduct of the operation

Crossflow microfiltration of skim milk: Toward optimization of design and conduct of the operation

Expected results

Research prospects

Question

Socio-economic context

Since 90's, crossflow microfiltration with 0.1 μm pore size membrane (MF0.1 μm) is used in industry to separate casein micelles and serum proteins. The great value of both protein fractions results into an increasing demand for MF0.1 μm equipment.

Scientific context

Today, various MF designs and operating conditions can be proposed leading to a large offer of MF systems. However, the performances of these existing systems, essential for both dairy industry and equipment manufacturers are not always investigated. In particular, some key operating parameters (e.g. thermal history of milk) are not studied. Moreover the interactions between operating variables have not always been considered. The lack of data on process performances makes the optimization of MF0.1 μm a complex issue.

• Increase the added value of the MF0.1 μm fractions of skim milk

••

  Develop optimized processes to produce new products from MF0.1 μm fractions of skim milk • Participate in the development of an electronic knowledge book on crossflow microfiltration of skim milk with 0.1 μm pore size membranes • Participate in the development of a computer aided system to optimize membrane processes for separation of milk proteins links between operating parameters, products characteristics and performance indicators for the separation of milk proteins using MF0.1 µm ? Study filtration performances by identifying the role of some key parameters (process, products) • Sort parameters according to their impact on MF0.1 μm performances manon.granger-delacroix@inra.


Summary

Objectives:

Feed-a-gene aims to better adapt different components of monogastric livestock production systems to improve the overall efficiency and to reduce the environmental impact. This involves among others the development of new and alternative feed resources and feed technologies, the identification and selection of robust animals that are better adapted to fluctuating conditions and the development of feeding techniques that allow optimizing the potential of the feed and the animal. In the project, one objective of WP2 "New traits for feed efficiency" was to evaluate the short and long-term consequences of environmental perturbations on feed efficiency. The data generated in three trials performed in Feed-a-Gene can be used for modelling the response of pigs to changes in environmental conditions. These dynamic models will help to better account for environmental disturbances and robustness in pig production. These data will be also used for validating models dealing with the detection of individual responses of pigs to perturbations in feed intake implemented in the WP3 of the Feed-a-Gene project.

The databases presented here contain data on individual growth performance recorded daily in finishing pigs submitted to short-term environmental challenges. These challenges included temporary provisions of a low-energy-density diet or a diet contaminated with deoxynivalenol (DON), or short exposures to an acute heat stress (HS) challenge. Part of results related to these databases have been published [START_REF] Renaudeau | Impact of a short-term dietary challenge on growth performance and feeding behavior in finishing pig[END_REF][START_REF] Serviento | An acute challenge with a deoxynivalenolcontaminated diet has short-and long-term effects on performance and feeding behavior in finishing pigs[END_REF] or will be published soon (Renaudeau, in preparation).

Teams involved:

INRA PEGASE Data and results of three experiments with pigs carried out by INRA are included in the databases.

Species and production systems considered:

Growing finishing pigs

Introduction

In pig production, 60 to 70% of the total production costs are due to feed expenses. The reduction of feeding costs remains a major preoccupation for maintaining or increasing the competitiveness of the pig industry. Small increments/deteriorations in feed efficiency can have major impact on farm profitability. Feed efficiency is a complex trait which depends on a large number of factors such as animal factors (e.g., breed, gender, and age), nutritional factors (e.g., the quantity and the quality of feed) and environmental factors (e.g., management, health and climate). In commercial conditions, pigs are frequently facing environmental perturbations with subsequent short and long-term effects on their health and performance. However, quantitative information describing impacts of short-term environmental challenge is lacking.

The aim of the present deliverable is first to provide original data on short and long-term responses to single or repeated acute dietary or thermal challenges. As initially planned in the project, data had to be obtained both on growing pigs and on sows. One of the objectives of the project was to build databases for having the possibility to access the inter-individual variability in the responses to environmental perturbations. To reach this goal, we decided to focus only on a single animal model (i.e., growing-finishing pigs) to be able to perform measurements on a larger number of animals using designs with a greater statistical power than originally planned. In comparison to what was planned, 365 pigs were used instead of 200 and three different environmental challenges were tested instead of two.

A first experiment was carried out with a DON contaminated diet. Pigs are highly sensitive to mycotoxins including DON because they have limited metabolic ability to transform DON into less toxic products. They are generally fed with cereal-based diets, which are sensitive to mycotoxin contamination. Among the broad family of mycotoxins, DON is often associated to a reduced performance in livestock, with symptoms including feed refusals and digestive disorders. Our hypothesis is that the reduced performance observed during a 5-d challenge period of feeding a DON contaminated diet would have both short and long-term consequences on feed efficiency. A second experiment was carried out with a high-fibre diet for simulating a challenge imposed to pigs by an abrupt diet transition from a normal diet to a diluted, low-nutrient fibrous diet. A third experiment was designed for evaluating the impact of an acute heat stress challenge on pig performance and feed efficiency.

Description of the research

Experiment 1: Animals and Treatments: A total of 160 Pietrain × (Large White × Landrace) pigs were used in the experiment during two replicates, with each pig individually measured for live body weight (BW) and individual feed consumption using an automatic weighing station and electronic feeding stations. The two replicates of the experiment were conducted at the INRA experimental facilities at the Unité Expérimentale Porcs de Rennes (UEPR) located in Saint Gilles, France. Each replicate had an equal number of castrated males and females. Within each replicate, pigs were blocked according to sex and litter origin, and were allotted to one of four experimental treatments in a randomized complete block design. From 91 to 98 d of age, the pigs started the transition from a commercial grower diet to the control finisher diet. The measurement period of the experiment started and ended at 99 and 154 d of age, respectively, and all pigs were slaughtered at 161 d of age.

The two diets used were based on corn and soybean meal and were formulated to contain the same amount of standardized ileal digestible lysine per MJ of NE (0.8 g/MJ NE) and to meet the ideal protein profile for essential amino acids (Table 1). The control diet used corn with a very low DON concentration. The DON-contaminated diet was obtained by using a naturally-contaminated corn containing an initial amount of 4.8 mg DON/kg. The analysed DON content in the control and DON-contaminated diets were 0.14 and 3.02 mg DON/kg diet, respectively (Table 2). During the pre-and the post-challenge periods, pigs were fed with the control diet. Before moving to the experimental room, pigs were tagged in the right ear with a serial number and a RFID chip for identification in the sorting station (which also served to weigh the pigs) and in the automatic feeders. The layout of the experimental room is shown in Figure 2. A more detailed description of the automatic and intelligent precision feeders (AIPF) used in the experiment is given by Pomar et al. ( 2011). In summary, the experimental room had two feeding zones (zones A and B) which could be accessed through an automatic sorter. Each feeding zone was equipped with four automatic feeders. During the non-challenge periods, the sorting station was programmed in a random way so that pigs could access either zone.

During challenge periods, feeders in zone A were filled with the DON-contaminated diet and feeders in zone B with the control diet. During these periods, animals of the challenged groups fed with the DON-contaminated diet only had access to zone A (via the sorting station) while animals fed with the control diet only had access to zone B. During the challenge period, the resting areas of the control and DON-challenged groups were also separated to avoid crosscontamination of mycotoxins via the faeces. Feed and water were provided ad libitum. After the end of the trial period (or end of P5), all pigs remained in the experimental room for another 7 d and were fed the control diet before they were slaughtered at 161 d of age.

Figure 2. Design of the experimental room.

Measurements and calculations: Five pigs were removed from the data because of poor performance, leg injury, and/or death with causes unrelated to the experimental treatments.

Live body weight was measured automatically when the pigs passed through the automatic sorting station. The average daily body weight was calculated as of all body weight recordings each day. Daily feed intake was calculated based on the recordings of the AIPF on the number of feed servings the pig requested (in theory, one serving equals 25 g) and a calibration factor (CF). Calibration measurements were done weekly on all feeders to correct for the actual amount of feed delivered per serving. From these measurements, the CF was calculated to be the ratio of the actual amount delivered to the theoretical value. To evaluate the adaptive ability of the pigs during challenge periods, the daily marginal change in daily feed intake of the challenged pigs (or the feed intake retrieval rate) was calculated as the feed intake difference between d 0 and d 7 of challenge divided by 7 d of the challenge.

All pigs were fasted 24 h before slaughter and body weight at slaughter was measured by passing the pigs through the automatic sorter before they were transported to the slaughterhouse. Ultrasound backfat thickness (BFT) measurements were taken behind the last rib (i.e., at the boundary of thoracic and lumbar vertebrae), 3 cm off the midline (P2 points) at the beginning of the experiment, at the end of periods P2, P4, and P5. Dressing yield and lean meat percentage were also measured in a commercial slaughterhouse.

For the feeding behaviour traits, data obtained during the first day of the experiment (99 d of age) and from the other days of BFT measurements were removed from the database. Number of feeder visits and meals per day were recorded by the AIPF. A visit was recorded each time a pig was detected by the AIPF. The duration of each visit was calculated based on the time the pig entered and was detected by the feeder and the time the pig left. Two consecutive visits separated by a time interval not longer than a given meal criterion were considered to belong to one meal. When the time at the feeder was set to exceed 5 min, the meal criterion duration no longer affected the number of meals. From this result, the adopted meal criterion was set at 5 minutes and this value was chosen for further calculation of daily components of feeding behaviour traits. These components were the meal frequency (meals/day), daily feed intake (g/d), and average rate of feed intake (total feed intake / total consumption time, g/min).

Experiment 2:

Animals and Treatments: A total of 160 Pietrain × (Large White × Landrace) pigs (average initial body weight of 69.1 ± 6.9 kg, 115 d of age) were used in the experiment with two replicates, with each pig individually measured for live body weight and fed individually using an electronic feeding station. In the control group (CC), pigs were fed a standard diet (7.6 g of digestible lysine/kg and 9.9 MJ NE/kg) during the whole experimental period (55 days). The DC, CD, and DD groups were challenged with a "diluted" fibrous diet for a 7-d period at 130 d, at 153 d, and at 130 and 153 d of age, respectively. Digestible lysine and NE contents were reduced by 20% in the diluted diet compared with the control diet, using wheat bran and sunflower meal as diluters. In both diets, essential amino acids were kept constant relative to lysine (Table 3).

Within each replicate, all pigs were equipped with RFID ear-tag and housed in a same room.

The room consisted of one resting area and two feeding areas separated by a weighing-sorting station (Figure 2). This sorter allowed for continuous measurements of individual live body weight. The two feeding areas were equipped with four feeding stations each. These electronic feeder systems distributed automatically the chosen diet to each pig and recorded the visits to the feeder, with their time and the amount of feed consumed.

Experiment 3:

Animals and Treatments: this study was designed to evaluate the effect of an acute heat challenge on performance and thermoregulatory responses of finishing pigs. The experiment was conducted at the experimental facilities of INRA in Saint-Gilles (INRA-UEPR). The study included a total 45 crossbreed castrated males and was carried out in three successive replicates of 15 pigs each. For each replicate, three blocks of five littermates were selected at 95 d of age and moved to an experimental building with two similar climatic-controlled rooms with nine and six individual pens, respectively. Each individual pen (0.70 × 2.30 m) was equipped with a metal-slatted pen floor, a feed dispenser, and a nipple drinker designed to avoid spillage of feed and water. Pigs remained in the climatic-controlled rooms during 60 days, which consisted of an adaptation period (15 days) and a subsequent experimental period (45 days). This 45 day period was split into three successive sub-periods of 15 days (P1, P2, and P3). In the first room (T room), pigs were kept in thermoneutral conditions (22°C) for the whole experimental period. The second room (H room) was used to challenge the animals.

The challenge was repeated during the three consecutive 15 day-periods, each of which consisted of a 3-day pre-challenge period (22°C), a 5-day heat challenge period (32°C), and a 7-day recovery period (22°C). During the first day of the heat stress period, ambient temperature was gradually increased from 22 to 32°C at a constant rate of 2°C/h starting at 09:00h. On the first day of the recovery period, the ambient temperature was decreased from 32 to 22°C at the constant rate of 4°C/h starting at 08:00h. Within each litter, one pig was assigned to one of the five experimental groups. Animals of groups TTT and HHH were housed in the T and H rooms, respectively for the 45 days of the experiment (Table 4). Animals of group HTT were housed in the H room on P1 and in the T room on P2 and P3. Animals of group THT were housed in the T room on P1 and P3 and in the H room on P2. Finally, animals of group TTH were housed in the T room on P1 and P2 and in the H room on P3. Measurements: Feed refusals were manually collected each morning at 0800 h, weighed and sampled for dry matter determination. A sample of feed offered to the animals was also taken weekly for dry matter determination and samples were pooled at the end of each replicate for further chemical analysis. The live body weight was determined at the beginning and the end of periods P1, P2, and P3 on d3, d0, and d5 at 0830 h. The weighing station was located in the corridor between the two experimental rooms and the transfer from one room to another was therefore operated immediately after each weighing at the end of periods P1 and P2. During the adaptation period, pigs were accustomed to the weighing station and the transfer between each room to avoid excessive stress.

Databases

The parameters of which values are reported in the database are shown in Table 5 (Experiment 1), Table 6 (Experiment 2), and Tables 7,8, and 9 (Experiment 3).

  1. Summary.......................................................................................................................................... 2. Introduction ..................................................................................................................................... 3. Description of the research ............................................................................................................. 4. Databases ...................................................................................................................................... 5. Access to the database .................................................................................................................. 6. Conclusions .................................................................................................................................... 7. Publications generated from these databases ..............................................................................

Figure 1 .

 1 Figure 1. Design of experiment 1. The pigs were given two diets during the 56-day experiment: control and DON-contaminated. Pigs from group CC (n=39) were fed the control diet throughout the experimental period. Pigs from groups DC (n=39), CD (n=38), and DD (n=39) were challenged on during 2, during 4, or during both periods 2 and 4, respectively with the DON-contaminated diet. Pigs were slaughtered 7 d after the end of the experimental period.

Table of contents

 of

Table 1 .

 1 Composition of experimental diets (Exp. 1) 1 As-fed-basis. Values are calculated for the same dry matter content (87.0%).5 As-fed-basis. Values are calculated based on equation ofNoblet et al. (1994) for calculating NE in growing pigs (Eq.11).

	Diet

4

Table 2 .

 2 Analysed mycotoxin composition of the experimental diets (Exp.1). The pigs of group CD were given the DON-contaminated diet also for 7 d starting at 134 d of age (i.e., from 134 until 141 d of age). The pigs of group DD were challenged with the DON-contaminated diet for 7 d starting at 113 d of age and for another 7 d starting at 134 d of age. After each challenge, a 14-d recovery period was applied.

	1,2

1 Dietary mycotoxin concentrations were analysed by a commercial laboratory (GIP Labocea, 22440 Ploufragan, FR). 2 All values are expressed on an as-fed basis and are calculated based on the same dry matter content of 87%. 3 Sum of Aflatoxins B1, B2, G1, and G2.

The design of the study is presented in Figure

1

. During the experimental period, the pigs from group CC received the control diet throughout the finishing period (i.e., from 99 to 154 d of age). The pigs of group DC were given the DON-contaminated diet for 7 d starting at 113 d of age (i.e., from 113 until 120 d of age).

  The two replicates of the experiment were conducted at the INRA experimental facilities at the Unité Expérimentale Porcs de Rennes (UEPR) located in Saint Gilles, France. Each replicate had an equal number of castrated males and females. Within each replicate, pigs were blocked according to sex and origin of litter, and were allotted to one of four experimental treatments in a randomized complete block design. From 91 to 98 d of age, pigs started the transition from a commercial grower diet to the control finisher diet. The experiment started and ended at 99 and 153 d of age, respectively, and all pigs were slaughtered at 161 d of age.

Table 3 .

 3 Composition of experimental diets (Exp. 2) 1 As-fed basis. Values are calculated for the same dry matter content (87.0%). SID = standardized ileal digestible.4 As-fed-basis. Values are calculated based on equation set byNoblet et al. (1994) for calculating NE in growing pigs (Eq.11).

	Diet

3

Table 4 :

 4 Distribution of the different experimental groups among the two climate-controlled rooms during the three consecutives periods of the trial.Pigs had free access to water and were fed ad libitum with a cereals and soybean meal-based diet containing 176 g/kg and 9.70 MJ/kg of crude protein and net energy, respectively. Feed was offered twice daily at 0830 and 1630 h. Photoperiod was fixed to 12 h of artificial light (from 0730 to 1930 h) and relative humidity was not controlled.

	Experimental groups

Table 5 :

 5 Parameters in the database related to Exp. 1 (FaG_INRA_D2-7_exp1.xlsx; 17 columns, 8520 lines).

	Parameter	Description	Values/units
	Obs.	No. of observations	1-8519
	Rep	Replicate	1 or 2
	Pig_N	Pig identification
	Sex	Gender type	1 = female, 3 = castrated male
	Group	Experimental group	1 = CC, 2 = DC, 3 = CD, 4 = DD
	Sire	Sire identification
	Sow	Sow identification
	Date	Date	dd/mm/yyyy
	Age	Age of the pig	days
	BW	live Body Weight	kg
	ADFI	Average Daily Feed Intake	g/d
	NVD	Number of Visits per Day	nb/d
	NMD	Number of Meals per Day	nb/df
	TDI	Total Duration of Ingestion	min/d
	RFI	Rate of Feed Intake	g/min
	TMI	Time of Ingestion per Meal	min/meal
	FIM	Feed Intake per Meal	g/meal

Table 6 :

 6 Parameters in the database related to Exp. 2 (FaG_INRA_D2-7_exp2.xlsx; 17 columns, 8765 lines).

	Parameter	Description	Values/units
	Obs.	No. of observations	1-8764
	Rep	Replicate	1 or 2
	Pig_N	Pig identification
	Sex	Gender type	1 = female, 3 = castrated male
	Group	Experimental group	1 = CC, 2 = DC, 3 = CD, 4 = DD
	Sire	Sire identification
	Sow	Sow identification
	Date	Date	dd/mm/yyyy
	Age	Age of the pig	days
	BW	live Body Weight	kg
	ADFI	Average Daily Feed Intake	g/d
	NVD	Number of Visits per Day	nb/d
	NMD	Number of Meals per Day	nb/df
	TDI	Total Duration of Ingestion	min/d
	RFI	Rate of Feed Intake	g/min
	TMI	Time of Ingestion per Meal	min/meal
	FIM	Feed Intake per Meal	g/meal

Table 7: Parameters in the database including the information on the pigs and their experimental treatments of the 8 columns,136 lines).

Access to the database

The database is available (free access) for stakeholders (e.g., academia, animal production sector, and feed industry) active in the domain of feed efficiency in swine production.

The database is available as an Excel file.

The database has been published at the Zenodo platform (DOI: 10.5281/zenodo10.5281). Further information on the database is available upon request from David Renaudeau (david.renaudeau@inra.fr ).

Conclusions

The present database contains individual records on feed intake traits and development of body weight of a total of 356 pigs exposed to single or repeated challenges similar to those encountered in commercial conditions, affecting the performance of pigs.

This information is available in Excel files deposed in an open access repository to improve and maximize access to this very original research data to a large community of researchers with interest in performance and feed and nutrient efficiency of pigs.

The data could be very useful for further studying or modelling time-related aspects of robustness of animals to various environmental perturbations.