The number and diversity of additives available for animal feed has dramatically increased during the last decades, particularly since the ban of antibiotic and hormonal growth promoters. Evaluating the efficacy of feed additives is an art in itself.
The European livestock industry, and more particularly the poultry production sector are continuously looking at improving their techno-economic performance. In the meanwhile they are trying to take into account regulatory affairs and society requests such as animal welfare or the environment. The use of appropriate additives can generate added value for different players of the sector.
The feed additives market has evolved a lot since the 50s, with first the use of antibiotics as growth promoters, then enzymes were proposed in the 80-90s, followed by prebiotics, probiotics, acidifiers and then vegetal extracts in the following decades. This alternative feed additives range has boomed and diversified due to the rational use of antibiotics in the 70s (positive list), followed by the reduction of their use in the 90s and finally their ban in Europe in 2006. Nowadays, feed additives from various origins are on the market, with or without registration file, scientific evaluation, or guaranteed composition and with a wide price range.
Despite this, potential users, whether it be farmers, integrators or feed formulators, have to decide whether they want to use an additive or not and to choose it properly.Providing reliable information therefore enabling evaluation of the efficacy of the marketed feed additives is the goal.
Use of additives
Assessing feed additive efficacy requires precisely identifying factors that limit technical and economical performances of the farms and therefore being able to choose the most adequate product which is able to target these points. If the request is not precise enough, the choice of the additive can lead to a non-adapted product. For example, improving FCR is a general aim. It is important to know why the FCR is poor. If it is due to bad digestibility of raw materials, the choice will be orientated to the use of enzymes. If linked to bad absorption of nutrients, products acting on gut mucosa protection or gut flora regulation would be more suitable.
Moreover, according to users, the main problem regarding the use of additives is variability of the results. However this variability is not specific to additives currently on the market. Antibiotic additives are facing the same problem.
Could part of this variability be linked to the fact that additives are tested with too general expectations or in conditions that do not correspond to their mode of action? To correctly evaluate an additive, it is essential to know for which objective it is being tested for. This is obvious for additives whose nature and function are the same: thus we expect from a coccidiostat a control of coccidiosis. Users expect from an enzyme a better digestibility of some raw materials or nutrients. However, when talking about vegetal extracts, it refers only to the nature of the additive and does not give any indication on its function. Consequently, it is necessary to know the exact composition in vegetal active molecules to conclude on the function of the product and on the adapted context to test it to get optimum effects.
How to conduct a trail
Once the additive is chosen in relation to its function and its precise target, it is necessary to assess its efficacy in a suitable and objective trial. First and foremost a protocol has to be established in order to avoid false positive and false negative results. False negative results, or an under evaluation of the potential of the additive, can be obtained when the additive is used under conditions where the product cannot express its full potential, when the targeted problem is not present or on contrary when the problem's intensity is too high. For example, it is possible to get false negative results when an anticoccidian product is tested on animals that are not infected with coccidiosis or when an enzyme is used with a raw material that is not its substrate. Similarly, evaluation of a gut flora regulator, whether it is an essential oil, a live bacteria or another product from different origin, has to be used on animals with unbalanced gut flora. False negatives are often obtained when gut flora regulators are tested in optimal sanitary conditions. To illustrate, Benzoni and et al. (2010) have tested, in the same trial, a gut flora regulator based on activated copper additive (B-Safe) both on broilers in good sanitary conditions and on broilers with non-optimal conditions aiming at increasing sanitary conditions. Presented in Figure 1 are the results of different conditions in the study on efficacy of the products.
In this type of trial, the challenge had to fit the desired objective. For example, generate an unbalanced gut flora through a bacterial inoculation or through breeding stress will not allow evaluating the same thing. Moreover the intensity of the challenge on reduction of zootechnical performances had to remain realistic when compared to technical results obtained on the field. Evaluating efficacy of an additive in extreme conditions is likely to lead to false results. Good criteria to assess admissibility of a trial is the proximity of performance of control batch without any additive with the average performance obtained by farmers. If performances are too far from the average field, trial results have to be considered cautiously. The use of a positive control group, receiving a recognised efficient product for the concerned criterion, can also attest for the admissibility of the trial. If the reference product does not lead to expected results, trial conditions and especially intensity of the challenge can be questioned. A contemporary negative control group, raised in same conditions, except the addition of the product provide also a guarantee against false positive results or against over evaluation of the efficacy of product. Indeed, authors sometimes conclude on the efficacy of a product by comparing its effect with a reference product. However if there is no negative control group, it is not possible to affirm that the positive control group (reference product) has improved performance. Thus it is risky to assume that the product is efficient. Negative control group is thus an essential admissibility condition for a trial. Ideal situation is to have a negative control group raised at the same time and in the same house than trial group. This is quite easy in experimental farms where animals receiving the various treatments are raised in different pens in the same house.
On field, it is possible to isolate animals through the set-up of pens among the rest of the flock or though selection of cages for animals raised in cages. Those isolated animals will be raised in same conditions than the rest of the batch but can be fed with different feed. Thus it will be possible to conclude on the effect of the additive. When it is possible to have a contemporary negative control but only in another house, the trial will be conclusive only if it is repeated on two successive batches with an inversion of houses between the two batches.
A period effect is possible in this type of trial but can be reduced by repeating the trial on numerous batches. When it is impossible to get a contemporary negative control, it is still possible to compare, in the same period of the year, farms performances receiving the control feed and on farms receiving the feed including the additive. In this case, farms have to be as similar as possible (same genetic, type of house, average performance). The more the breeding conditions differ, the more farms are needed in the trial in order to compensate for the variability of the biological response and to be able to see significant differences among performances.
Statistical representativity
Recording performance of a control and a test group provides average performance that can seem to be different. However this means comparison cannot lead to a reliable conclusion on effects of an additive. As illustrated in Figure 2, variability of data has to be taken into account to conclude. Indeed, in figures 2a and 2b, the difference of average weight between diet A and B are the same but variability's are different. As a result, in figure 2a, diet B achieves significantly higher live weight than diet A, while in figure 2b the diet has no significant effect on broiler's live weight.
Variability is estimated through standard deviation, or through coefficient of variation (CV expressed as %, CV = 100 x standard deviation / mean). By definition, several data are necessary to evaluate population variability and a single data per group is not enough. For example, to evaluate population growth, the most reliable process is to individually weigh all animals because no bias is created by animal selection. When setting up a trial, the number of animal to include can be calculated. It depends on the expected trial accuracy (F), on the population variability (CV) and on the least significant difference that we want to be able to detect (d, expressed as %).This number is calculated thanks to following formula: N = F x CV² / d². F is a constant that depends on α risk (risk to conclude that means are different when they are not) and on β risk (risk to conclude that means are not different when they are). Parameters currently used are: F=8, corresponding to α=0,05 and β=0,5. For more reliability, we can use F=21, corresponding to α=0,05 and β=0,1.
When there is a high number of animals, weighing in small groups represents a suitable alternative to individual weighing. Size of the group is calculated according to the total number of animals and to the number of data necessary originating from the above formula. When weighing of the whole flock is not possible, even in groups, individual we
The number and diversity of additives available for animal feed has dramatically increased during the last decades, particularly since the ban of antibiotic and hormonal growth promoters. Evaluating the efficacy of feed additives is an art in itself.The European livestock industry, and more particularly the poultry production sector are continuously looking at improving their techno-economic performance. In the meanwhile they are trying to take into account regulatory affairs and society requests such as animal welfare or the environment. The use of appropriate additives can generate added value for different players of the sector.The feed additives market has evolved a lot since the 50s, with first the use of antibiotics as growth promoters, then enzymes were proposed in the 80-90s, followed by prebiotics, probiotics, acidifiers and then vegetal extracts in the following decades. This alternative feed additives range has boomed and diversified due to the rational use of antibiotics in the 70s (positive list), followed by the reduction of their use in the 90s and finally their ban in Europe in 2006. Nowadays, feed additives from various origins are on the market, with or without registration file, scientific evaluation, or guaranteed composition and with a wide price range.Despite this, potential users, whether it be farmers, integrators or feed formulators, have to decide whether they want to use an additive or not and to choose it properly.Providing reliable information therefore enabling evaluation of the efficacy of the marketed feed additives is the goal.Use of additives Assessing feed additive efficacy requires precisely identifying factors that limit technical and economical performances of the farms and therefore being able to choose the most adequate product which is able to target these points. If the request is not precise enough, the choice of the additive can lead to a non-adapted product. For example, improving FCR is a general aim. It is important to know why the FCR is poor. If it is due to bad digestibility of raw materials, the choice will be orientated to the use of enzymes. If linked to bad absorption of nutrients, products acting on gut mucosa protection or gut flora regulation would be more suitable.Moreover, according to users, the main problem regarding the use of additives is variability of the results. However this variability is not specific to additives currently on the market. Antibiotic additives are facing the same problem.Could part of this variability be linked to the fact that additives are tested with too general expectations or in conditions that do not correspond to their mode of action? To correctly evaluate an additive, it is essential to know for which objective it is being tested for. This is obvious for additives whose nature and function are the same: thus we expect from a coccidiostat a control of coccidiosis. Users expect from an enzyme a better digestibility of some raw materials or nutrients. However, when talking about vegetal extracts, it refers only to the nature of the additive and does not give any indication on its function. Consequently, it is necessary to know the exact composition in vegetal active molecules to conclude on the function of the product and on the adapted context to test it to get optimum effects.How to conduct a trail เมื่อเติมที่เกี่ยวกับหน้าที่และเป้าหมายชัดเจน ได้เพื่อประเมินประสิทธิภาพในความเหมาะสมความประสงค์ทดลอง แรก และสำคัญโพรโทคอลที่มีการจัดตั้งเพื่อหลีกเลี่ยงการเท็จเท็จ และค่าบวกค่าลบผลการ ผลลบเท็จ หรือภายใต้การประเมินศักยภาพของเสริม ได้เมื่อเติมใช้ภายใต้เงื่อนไขที่ผลิตภัณฑ์ไม่สามารถแสดงศักยภาพเต็ม เมื่อไม่มีปัญหาเป็นเป้าหมาย หรือตรงกันข้ามเมื่อความรุนแรงของปัญหามีมากเกินไปได้ ตัวอย่าง มันได้รับผลลบเท็จเมื่อผลิตภัณฑ์ anticoccidian จะทดสอบในสัตว์ที่ไม่ติดเชื้อ coccidiosis หรือ เมื่อมีใช้เอนไซม์ที่ มีวัตถุดิบที่ไม่ใช่พื้นผิวของ ในทำนองเดียวกัน เครื่องปรับลมพืชลำไส้ การประเมินไม่ว่าจะเป็นน้ำมันหอมระเหย แบคทีเรียสด หรือผลิตภัณฑ์อื่นจากแหล่งกำเนิดต่าง ๆ ได้ที่จะใช้ในสัตว์ที่มีลำไส้สมดุลพืช สิ่งเท็จมักจะได้รับเมื่อไส้ฟลอร่าเร็คกูเลเตอร์จะมีทดสอบในสภาวะสุขาภิบาล การแสดง Benzoni และ al. et (2010) ได้ทดสอบ ทดลอง เดียวที่ลำไส้พืชควบคุมตามทองสามารถเปิด (บีเซฟ) ทั้งออกขโมยสุขาภิบาล และออกด้วยเงื่อนไขที่ไม่เหมาะสมที่มุ่งที่การเพิ่มเงื่อนไขสุขาภิบาล แสดงในรูปที่ 1 ได้ผลลัพธ์ของเงื่อนไขต่าง ๆ ในการศึกษาประสิทธิภาพของผลิตภัณฑ์In this type of trial, the challenge had to fit the desired objective. For example, generate an unbalanced gut flora through a bacterial inoculation or through breeding stress will not allow evaluating the same thing. Moreover the intensity of the challenge on reduction of zootechnical performances had to remain realistic when compared to technical results obtained on the field. Evaluating efficacy of an additive in extreme conditions is likely to lead to false results. Good criteria to assess admissibility of a trial is the proximity of performance of control batch without any additive with the average performance obtained by farmers. If performances are too far from the average field, trial results have to be considered cautiously. The use of a positive control group, receiving a recognised efficient product for the concerned criterion, can also attest for the admissibility of the trial. If the reference product does not lead to expected results, trial conditions and especially intensity of the challenge can be questioned. A contemporary negative control group, raised in same conditions, except the addition of the product provide also a guarantee against false positive results or against over evaluation of the efficacy of product. Indeed, authors sometimes conclude on the efficacy of a product by comparing its effect with a reference product. However if there is no negative control group, it is not possible to affirm that the positive control group (reference product) has improved performance. Thus it is risky to assume that the product is efficient. Negative control group is thus an essential admissibility condition for a trial. Ideal situation is to have a negative control group raised at the same time and in the same house than trial group. This is quite easy in experimental farms where animals receiving the various treatments are raised in different pens in the same house.On field, it is possible to isolate animals through the set-up of pens among the rest of the flock or though selection of cages for animals raised in cages. Those isolated animals will be raised in same conditions than the rest of the batch but can be fed with different feed. Thus it will be possible to conclude on the effect of the additive. When it is possible to have a contemporary negative control but only in another house, the trial will be conclusive only if it is repeated on two successive batches with an inversion of houses between the two batches.A period effect is possible in this type of trial but can be reduced by repeating the trial on numerous batches. When it is impossible to get a contemporary negative control, it is still possible to compare, in the same period of the year, farms performances receiving the control feed and on farms receiving the feed including the additive. In this case, farms have to be as similar as possible (same genetic, type of house, average performance). The more the breeding conditions differ, the more farms are needed in the trial in order to compensate for the variability of the biological response and to be able to see significant differences among performances.Statistical representativity Recording performance of a control and a test group provides average performance that can seem to be different. However this means comparison cannot lead to a reliable conclusion on effects of an additive. As illustrated in Figure 2, variability of data has to be taken into account to conclude. Indeed, in figures 2a and 2b, the difference of average weight between diet A and B are the same but variability's are different. As a result, in figure 2a, diet B achieves significantly higher live weight than diet A, while in figure 2b the diet has no significant effect on broiler's live weight.Variability is estimated through standard deviation, or through coefficient of variation (CV expressed as %, CV = 100 x standard deviation / mean). By definition, several data are necessary to evaluate population variability and a single data per group is not enough. For example, to evaluate population growth, the most reliable process is to individually weigh all animals because no bias is created by animal selection. When setting up a trial, the number of animal to include can be calculated. It depends on the expected trial accuracy (F), on the population variability (CV) and on the least significant difference that we want to be able to detect (d, expressed as %).This number is calculated thanks to following formula: N = F x CV² / d². F is a constant that depends on α risk (risk to conclude that means are different when they are not) and on β risk (risk to conclude that means are not different when they are). Parameters currently used are: F=8, corresponding to α=0,05 and β=0,5. For more reliability, we can use F=21, corresponding to α=0,05 and β=0,1.
When there is a high number of animals, weighing in small groups represents a suitable alternative to individual weighing. Size of the group is calculated according to the total number of animals and to the number of data necessary originating from the above formula. When weighing of the whole flock is not possible, even in groups, individual we
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