Estimating amino acid requirements through dose-response experiments in pigs and poultry - Protocol and results interpretation

Page created by Lester Butler
 
CONTINUE READING
Estimating amino acid requirements through dose-response experiments in pigs and poultry - Protocol and results interpretation
TECHNICAL NOTE                                         FEBRUARY 2012

  Estimating amino acid requirements through
          dose-response experiments
              in pigs and poultry

          - Protocol and results interpretation -

                      AJINOMOTO   ANIMAL   NUTRITION

                    AJINOMOTO EUROLYSINE S.A.S.
Introduction                                                                                     3

  A. Experimental protocols and requirement expression                                             4

   1. Expressing amino acid requirements                                                           4
      1.1. Expressing the requirement as a % of lysine                                             4
      1.2. Expressing the requirement as a % of the feed                                           5
      1.3. Conclusions on the way of expressing an amino acid requirement                          6

   2. The number and the levels of the dietary treatments have to be well-considered and controlled 7
      2.1. The number of treatments to test                                                         7
      2.2. Positioning the dose levels and choosing the growing infrequency                         8

  B. Interpreting data from dose-response studies                                                  9

   1. What the experimental data tell to us                                                        9

   2. Basis about statistics                                                                      10
      2.1. Means comparisons are not adapted to interpret dose-response studies                   10
      2.2. Modelling the response curve to estimate a requirement                                 10

   3. Criteria to take into account to estimate a nutrient requirement for a growing population   12
      3.1. Taking into account the variability of population                                      12
      3.2. Taking into account the dynamic aspect                                                 13
      3.3. Representing experimental data                                                         14
      3.4. Including a safety margin for the estimation of a requirement                          15
      3.5. Summary of the comparison between the linear- and the curvilinear-plateau models       17

  Conclusion                                                                                      18

  References                                                                                      19

2 | Technical note | Ajinomoto Eurolysine S.A.S.
INTRODUCTION

I   n animal nutrition, it is usual to carry out dose-response experiments to estimate amino acid requirements. Because
    a major part of the variability in the reported amino acid requirements is due to the protocol itself and the results
    interpretation (Dawkins, 1983), this document aims at clarifying why a dose-response study is performed, how to
design its protocol and how to interpret the results.

Dose-response studies are used in lots of different fields to determine the dose which corresponds to a specific response.
For example, in toxicology, the Lethal Dose 50 which corresponds to the dose needed to obtain the death of 50% of
the population is estimated thanks to a dose-response. In animal nutrition, the requirement for a specific nutrient, and
particularly amino acids, can be defined as the minimal amount of this nutrient (DOSE) needed to reach maximum
performance (RESPONSE) assuming that all the other nutrients are provided in adequate amounts. The objective of
determining amino acid requirements is to use these values to feed populations of animals. The aim of a dose-response
experiment is therefore to estimate a single value applicable to the whole population; the individual variation that exists
within the population has therefore to be taken into account in the estimation. In this document, only growing animals are
considered (adult animals have a specific metabolism and the practical objectives are not the same); the term “growing”
implying a dynamic aspect.

Different methods exist to estimate a nutrient requirement (Pomar et al., 2003):
     - The factorial approach: daily requirements are obtained for an individual animal at a specific point in time by
combining the estimated requirements for maintenance and production (hypothetical growth).
     - The empirical approach: nutritional requirements are defined as the minimal amount of nutrients needed to maximize
or minimize population responses for one or several performance criteria during a given period.

The definition of the term “requirement” for growing animals is thus definitely in the scope of the empirical method and
dose-response studies allow estimating amino acid requirements by depicting the response of a growing population
to increased levels of an amino acid. This method consists indeed of testing different concentrations of a nutrient and
then determining through statistical methods which level gives the best performance; but reported results can be quite
variable. A part of this variability comes from the fact that the method used to interpret the results are not always in
line with the objective and the protocol of the trial. The first step to perform a dose-response experiment is therefore to
define a very precise objective because the protocol and the result interpretation will depend on it.

                                                                        Ajinomoto Eurolysine S.A.S. | Technical note | 3
A. EXPERIMENTAL PROTOCOLS AND REQUIREMENT EXPRESSION

1. EXPRESSING AMINO ACID REQUIREMENTS

For any nutrient, requirements can be expressed in               pigs and poultry fed with cereal-based diets, the first and
different units or within different nutritional systems. Amino   the second limiting amino acid for growth.
acid requirements in the literature are often reported in a        To design a protocol for a dose-response trial which
wide range of units: amount in the feed (in % of the feed        aims at estimating an amino acid requirement expressed
or in g per unit of energy), ingested quantity relative to       relative to lysine, the basal diet has to be formulated by
weight gain (mg per g of daily gain), relative to lysine         following specific conditions.
(% of lysine), … These different ways of expressing the
requirement contribute to the variability that is reported       The lysine content in the experimental basal diet has to
concerning the amino acid requirements.                          be the second limiting factor for growth performance
  All of these units have their strengths and weaknesses
but the important point is that the desired mode of              In a dose-response experiment, one of the most important
expression will determine the protocol that has to be            criteria which must be taken into account when the amino
used in the dose-response experiment: the basal diet must        acid requirement is wanted to be expressed relative to
indeed be designed according to the way in which the             lysine is that in the basal diet, lysine must be the second
requirement will be expressed. That is why it is of the          limiting amino acid after the studied amino acid while
highest importance to precisely define the objective of the      the supply of the other amino acids should meet or
dose-response experiment with the unit of expressing the         slightly exceed animals’ requirements (Boisen, 2003). It is
requirement.                                                     important to choose enough low lysine level to ensure that
  However, whatever the way of expressing the                    lysine is really the second limiting amino acid during the
requirement which is chosen, the first rule to respect is that   whole period of the experiment. For the treatment diets,
the first limiting factor in the experimental basal diet has     different levels of the studied amino acid are provided,
to be the nutrient for which the requirement is wanted to        while the dietary lysine content remains constant.
be estimated. It is thus important to have a rough idea             When the studied amino acid is the first limiting factor
of the requirement before starting the elaboration of            for performance (weight gain, feed efficiency…) in the
the protocol. To achieve this objective, a literature review     basal diet, an increase in its level will result in an increase
and/or a pre-trial might be performed.                           in the response criterion up to the point where its supply is
                                                                 no longer limiting. A further increase in the tested amino
1.1. Expressing the requirement as a % of lysine                 acid supply would not result in a change in the response
                                                                 criterion and leads to the achievement of a plateau-value
For growing animals, amino acid requirements are                 which is determined by the second limiting factor in the
often expressed based on the concept of ideal protein.           diet. At this point the two first limiting factors are equally
This concept defines the amino acids profile (i.e. the           limiting.
composition of ideal protein as g of amino acid per 100             If lysine is actually the second limiting factor, the
g of lysine) which exactly meets animals’ requirements           plateau-value will be determined by the lysine supply and
for protein deposition and maintenance (Fuller et al.,           at the breakpoint (Figure 1 (b)), the tested amino acid
1989). The composition of ideal protein is such that all         and lysine are equally limiting; this point can therefore be
amino acids are equally limiting for performance and             considered as the requirement of the tested amino acid
corresponds to the minimum supply of amino acids that            expressed relative to lysine. It is assumed that theoretically
is required to maximize nitrogen retention, growth or            the optimal amino acid:lysine ratio is not affected by the
another response criterion. Lysine has traditionally been        lysine content in the diet as soon as lysine is actually the
used as the reference because it is mainly used for muscle       second-limiting factor (Boisen, 2003; Figure 1).
protein deposition and also because it is, respectively for

4 | Technical note | Ajinomoto Eurolysine S.A.S.
utilization; Baker, 2000). That is why it is important to
Response
                                                                               estimate the amino acid requirements for a given growing
                                                          Lys > requirement    stage.
                                                          Lys < requirement
                                                                                  For a given stage of production, estimating amino acid
                                                          Lys
be limited by another factor which is unknown (dietary                          on the specific objective of the experiment, different ways
factor, environment, feed intake capacity…). There is no                        of expressing the requirement can be chosen considering
way to know what this second limiting factor responsible                        the strengths and weaknesses of each of them (Table 1).
for the achievement of the plateau-value is.                                       At the time of making decisions regarding nutritional
                                                                                recommendations, the nutritionist should use the most
Weakness of expressing an amino acid requirement as                             advanced nutritional concepts and express requirements
an amount of the feed                                                           on the same basis to formulate consistent feeds; for
                                                                                instance: standardized ileal digestible (SID) system, net
The scope of an amino acid requirement determined as                            energy system, ideal amino acid profile concept.
an amount of feed is generally limited to the particular
conditions of the trial. Moreover, due to the difficulties
to control the limiting factors, the published requirements
often vary over a wide range.

1.3. Conclusions on the way of expressing an
amino acid requirement

Because the protocol is dependent on the way of
expressing the requirement, it should not be possible to
use another way of expressing the requirement than the
one chosen a priori. It is especially true when an amino
acid (other than lysine) requirement is estimated in %
of the diet; as lysine is oversupplied, it is not possible to
express this requirement as a ratio to lysine. Depending

  Table 1: Protocol to apply, strengths and weaknesses for different ways of expressing an amino acid requirement

      Mode of expression                                           Amount in the feed                         Ideal amino acids profile

      Unit                                                            % of the feed                                  % of lysine

      Objective                                         To determine the amino acid concentration     To determine the ratio at which the tested
                                                            in the feed which leads to the best        amino acid and lysine are equally limiting
                                                                       performance                                 for performance

      Characteristics of the basal experimental diets

         Studied amino acid                                            1st limiting                                    1st limiting
         Lysine                                                        Not limiting                          2nd limiting and sub-limiting
         Other amino acids                                             Not limiting                          Not limiting (ideal protein)
         Net Energy                                                    Not limiting                                   Not limiting

      Strengths                                             Allows to optimize a specific diet                 Easy to apply in practice

                                                          Good way to produce lysine reference                  Homogeneous results

                                                                                                           Not specific to the studied diet

      Weaknesses                                         Difficulties to control the limiting factors
                                                                                                      Assumes that the requirement is the same
                                                         Specific to the studied diet (lysine level,    whatever the stage of life of the animal
                                                                     energy level...)

                                                                                                        Requires great number of trials (lysine
                                                                   Very variable results
                                                                                                           level, different weight ranges)

6 | Technical note | Ajinomoto Eurolysine S.A.S.
2. THE NUMBER AND THE LEVELS OF THE DIETARY TREATMENTS HAVE TO BE WELL-CONSIDERED AND
CONTROLLED

2.1. The number of treatments to test                              of parameters that will be estimated (including the error
                                                                   parameter) in the model used to analyse the results (Mead,
The number of treatments has to be chosen according to             1988). By increasing the degrees of freedom in the
the objectives of the experiment and also according to             regression tests, using higher number of treatments allows
the method that will be used to interpret the results. The         to further improve model fitting to the data (Hernandez-
question concerning the modelling step is discussed in the         Llamas, 2009; Figure 2).
second part of this document (Part B) but the experimenter
has to know which type of model will be used to interpret          Accuracy: Increasing the number of replicates per
the results to be able to choose the number of treatments.         treatment will improve the accuracy of the model and
                                                                   thus reduce the confidence intervals on the parameters
There are some rules that can help to make a decision:             estimates. However, the number of treatments has to be
                                                                   given more importance than the number of replicates to
Robustness: The number of tested levels has to be at               ensure that only one solution will exist to estimate each
least equal to, or preferably one more than, the number            parameter in the model.
                             RESPONSE

                                                                 DOSE

 Figure 2: A low number of treatments decreases the model fitting to the data and increases the uncertainty about the best model
 to use: with only three tested doses, different models can be considered, the linear (  ), the quadratic (       ) and the linear-
 plateau (     ) models

                                                                             Ajinomoto Eurolysine S.A.S. | Technical note | 7
2.2. Positioning the dose levels and choosing the
growing infrequency                                                       (a)

Positioning the dose levels is also of great importance for

                                                                           RESPONSE
the estimation of a requirement. If the tested levels are
not positioned around an a priori requirement, a large
proportion of the work will indeed be wasted even with
the best statistical analyses (Pesti et al., 2009). However,
optimum placement of the dose levels is impossible without                                    DOSE
prior knowledge about the shape of the dose-response
                                                                          (b)
curve and the possible range of the requirement. The first
step is thus to perform a literature review and/or a pre-

                                                                           RESPONSE
trial to have a rough idea about the requirement and then
to be able to estimate it more precisely. The objective
of a dose-response experiment being the estimation
of a requirement, the tested levels should be allocated
¼ in the ascending portion of the curve, ½ near the a                                         DOSE
priori requirement and ¼ at high enough levels so that
                                                                          (c)
the plateau-value can be defined (Shearer, 2000). For
example, with 7 treatments, 2 levels have to be in the
ascending portion, 3 levels around the requirement and 2                   RESPONSE
levels on the plateau-value (Figure 3 (a)).

The important points to specifically pay attention are:
                                                                                              DOSE
1) the highest dose has to be above the level capable of
producing the maximum response; the failure to include           Figure 3: The choice of the position of the tested levels
                                                                 depends on the a priori shape of the response and on the
sufficiently high nutrient levels is a common design flaw        requirement
which results in a trial which can not be salvaged even by
the best statistical test. If all the tested levels are in the
ascending portion of the response curve, it will indeed not
be possible to estimate a requirement (Figure 3 (b)); in
the same way if all the tested levels are on the plateau-
value, it will not be possible to estimate a requirement
(Figure 3 (c)),
2) the space between the input levels has not to be too large
to perform accurate determination of the requirement but
sufficiently to avoid manufacturing problems.

8 | Technical note | Ajinomoto Eurolysine S.A.S.
B. INTERPRETING DATA FROM DOSE-RESPONSE STUDIES

1. WHAT THE EXPERIMENTAL DATA TELL TO US

As explained before, the dose-response observations,            Three main characteristics can be noticed:
and therefore the estimated requirement, are very
dependent on the study design.                                  1) There is a variability between individuals (or pens,
  Flaws in experimental design are responsible for a part       depending on the experimental unit) in the response for
of the variability that exists in the literature. Due to this   each tested dose,
variability, it is not so easy to evaluate which estimates      2) The different treatments have structure; meaning that
requirement is the correct one.                                 the doses are linked together since they can be classified
  After having chosen the protocol to put in place, the         in increasing order (contrary to unstructured treatments;
dose-response study will supply experimental data. These        for example “castrated” vs. “entire males” or “with” vs.
data have been obtained with a specific protocol, they          “without a specific product” are treatments without any
have therefore to be analysed with the proper method.           structure),
  To well understand how experimental data from                 3) The searched dose is a continuous variable: it could
dose-response experiments have to be interpreted, it is         take any value between 0 and +∞ (and not only one
necessary to understand what the specificities of these         among the tested doses).
data are.
  Figure 4 presents the type of graph that dose-response           To interpret these experimental data, specific statistical
experiments can provide.                                        methods which are adapted to biological studies, have
                                                                to be used.
                                                                 RESPONSE
   RESPONSE

                                                                                                                 1. Variability

                                                                                                      2. Structured treatments

                                                                                   3. The searched dose is a continuous variable

                               DOSE                                                            DOSE

                                  Figure 4: Experimental data from dose-response experiment

                                                                            Ajinomoto Eurolysine S.A.S. | Technical note | 9
2. BASIS ABOUT STATISTICS

2.1. Means comparisons are not adapted to                       Non-linear models: Non-linear models such as the linear-
interpret dose-response studies                                 plateau (LP or broken-line), curvilinear-plateau (CLP or
                                                                quadratic-plateau) or asymptotic (ASY) models (Table 2)
In a dose-response experiment, statistical analyses and         are frequently used to estimate amino acid requirements.
requirement determination have to be done using an              For the ASY model, because the plateau is attained at an
adapted model. Finding the best model to depict the             infinite level of the nutrient, the requirement is estimated as
animal’s response to an increase in a nutrient level is not a   the level of nutrient required to reach an arbitrary chosen
new topic. The point which was sure since at least 30 years     percentage of the plateau-value (e.g., 95%; Table 2).
ago is that the means comparisons (ANOVA) are not an            When the number of levels tested is not higher than four,
adequate method to analyse data from doses-response             the ASY model is particularly adapted because it is difficult
experiments but they are still misused by biologists            to estimate a plateau-value for the performance. In the
(Dawkins, 1983). This method has indeed to be used to           other cases, the choice between LP and CLP models can be
compare a set of unstructured treatments and qualitative        discussed. Both of them give indeed a clear definition of
treatments (i.e., discrete variables; for example “male”        what the requirement is (i.e., the dose needed to reach the
vs. “female”); it is thus not adapted to interpret dose-        plateau; Table 2), but the LP model has been discredited
response studies (Nelson and Rawlings, 1983) because it         by lots of scientists (Fisher et al., 1973; Curnow, 1973;
does not take into account that response variables are          Robbins et al., 1979 and 2006; Morris, 1989 and 1999;
continuous rather than discrete (Shearer, 2000), and does       Shearer, 2000; Wellock et al., 2004). The objective of
not consider that the different treatments are logically        the following section is to understand why the CLP model
structured (Morris, 1983). When means comparisons               is better adapted to estimate a nutrient requirement for
are used, there is no function defined and thus it is not       growing population compared to the LP model.
possible to precisely estimate the requirement (Pesti et
al., 2009). That is why a response-curve that represents
animal response to nutrient has to be chosen to determine
a nutritional requirement.

2.2. Modelling the response curve to estimate a
requirement

The choice of the statistical model depends on the shape
of the data (Table 2); however in some studies it is very
difficult to discern a pattern in the data and no curve
adequately fits the data points. In this case, it is possible
that an additional source of variation was influencing the
response. There are two types of models:

Linear models: They include the linear functions which
are used to describe a linear relationship between
two variables but which do not allow the estimation of
a requirement (Table 2). Requirements are sometimes
estimated using quadratic functions (which are also linear
models) but these models may not be appropriate if the
response criterion does not further respond to a high level
of nutrient.

10 | Technical note | Ajinomoto Eurolysine S.A.S.
Table 2: Statistical models*

         Shape of the response         Type of model                                    Graphic representation

         Increases linearly            Linear model:

                                                                                 RESPONSE
                                       linear function
                                       Y = aX + b

                                                                                                  DOSE       R?

         Increases, then               Linear model:
                                                                                Max
         decreases after reaching      quadratic function

                                                                                 RESPONSE
         a maximum response
                                       Y = aX2 + bX + c
                                       R = -b/2a
                                                                                                   R     DOSE

         Increases and be stable       Non-linear models:
         after
                                                                                Max

                                       • linear-plateau                          RESPONSE
                                         Y = Ymax + U·(R - X) for X < R
                                         Y = Ymax for X ≥ R
                                                                                               DOSE R

                                                                                Max
                                       • curvilinear-plateau
                                                                                 RESPONSE

                                         Y = Ymax + U·(R - X)2 for X < R
                                         Y = Ymax for X ≥ R

                                                                                                  DOSE   R

                                       • asymptotic                         95% Max
                                                                                 RESPONSE

                                       Y = Ymax - a·exp(-bX)

                                                                                                  DOSE       R

Y represents the response, Ymax, the maximum response and X, the dose; R represents therequirement, a, b, c and U,
parameters of the models to be estimated
* This table gives some examples of simple models but numerous others models exist that are more complicated with higher
  number of parameters to estimate.

                                                                      Ajinomoto Eurolysine S.A.S. | Technical note | 11
3. CRITERIA TO TAKE INTO ACCOUNT TO ESTIMATE A NUTRIENT REQUIREMENT FOR A GROWING
POPULATION

To depict the response of a growing population to
increased levels of a nutrient, even if a straight line         % of animals adequately fed
can be used to depict the ascending portion, it is never                 100
possible to tell if there is a sharp break between the lines              90
                                                                          80
or a smooth transition. However, if the response of an
                                                                          70
individual animal is given by a LP model, the response                    60
                                                                   (a)
of the population of this animal will resemble that of the                50

CLP model (Curnow, 1973; Morris, 1983; Leclercq and                       40
                                                                          30
Beaumont, 2000; Pomar et al., 2003; Pomar, 2005).
                                                                          20
Because the objective here is to define a requirement                     10
for a growing population, it seems that the CLP model is                    0
                                                                                70          80            90          100          110           120          130
the best-adapted model compared to the LP model. The
                                                                                                      % of mean requirement                            (b)
following section develops which criteria have to be taken
into account to correctly depict the response of a growing     Figure 5 (adapted from Brossard et al., 2009): Effect of a nutrient supply (as %
                                                               of the mean requirement of the population) on the percentage of pigs for which
population to an increase in the levels of a nutrient and      the requirement was met
to estimate the nutritional requirement for this population.   (a) “If 100% of the mean requirement is applied to a population, half of this population
                                                                    will be underfed.”
                                                               (b) “The requirement to feed a population is higher than 100% of the mean requirement.”
3.1. Taking into account the variability of
population

Requirements have to be estimated for the whole
population

Because a LP response is predicted for an average
pig, the requirement defined with the LP model can be             CV ADG (%)
considered as the requirement for a theoretical average                         15
pig (Morris, 1983; Leclercq and Beaumont, 2000; Pomar
                                                                                14
et al., 2003). This requirement corresponds therefore to
an average value, without considering the variation within                      13

the population. If this “average requirement” is applied to                     12
a population, half of this population will be underfed and
                                                                                11
so the average performance of the population will be
lower than expected (Figure 5 (a); Brossard et al., 2009).                      10
This implies that the requirement to feed a population
                                                                                 9
is higher than the requirement to feed an average                                    70        80          90          100         110         120           130
animal, to allow every individual in the population to                                                     % of mean requirement
reach its potential performance (Figure 5 (b)). Moreover
                                                               Figure 6 (adapted from Brossard et al., 2009): Effect of a nutrient supply (as %
when the number of animals fed adequately increases,           of the mean requirement of the population) on the coefficient of variation (CV)
the variability of the population will decrease (Figure        of average daily gain (ADG) of pigs
6, Brossard et al., 2009). To feed adequately every             “When the number of animals fed adequately increases, the variability of the population will decrease.”
individual in the population, the requirement has therefore
to be estimated for the whole population.

12 | Technical note | Ajinomoto Eurolysine S.A.S.
The curvilinear-plateau model is the one which                                          3.2. Taking into account the dynamic aspect
describes the best the response of a population
                                                                                        To estimate the requirement of a nutrient for a growing
The CLP response of the population can be explained                                     population, the dynamic aspect (for example, the growing
by between-animal variation (Wellock et al., 2004):                                     period of piglets between 12 and 25 kg) has to be taken
the length and degree of the curvature of the response                                  into account. An animal’s response to nutrient intake
increase with the population variability (Figure 7,                                     indeed changes over the interval during which data are
Pomar et al., 2003). Therefore to predict adequately                                    collected (Pomar, 1995; Haushild et al., 2010). If the
the response of a population in a given environment, it                                 duration of an experiment is too short, the requirement
is necessary to take the between-animal variation into                                  will be underestimated; and this is probably most true
account (Curnow, 1973; Fisher et al., 1973; Emmans                                      when growth is used as the response variable, since a
and Fisher, 1986; Pomar, 1995; Wellock et al., 2004).                                   deficiency of some nutrients will have an effect on growth
Models designed to simulate population responses need                                   only after a certain period.
to integrate the effect of population variation on growth                                  It has been demonstrated that increasing the time
performance and need to represent the population itself                                 over which animal responses are measured increases the
and not an individual animal even if it is representative                               curvilinearity of the responses (Figure 8, Pomar et al.,
of this population (Wellock et al., 2004; Hauschild                                     2003). The CLP model is the one that depicts a response
et al., 2010). The CLP model is the one that takes into                                 obtained on a period of time of several days, so it is
account the population as a whole so, compared to the                                   particularly adapted to estimate the requirement of a
LP model, it is preferred to estimate the requirement for                               growing population.
a population (Curnow, 1973; Morris, 1983; Baker et al.,
2002, Hauschild et al., 2010).

                                                                                                                                                  1 d of collection
                                                                                                                                                  28 d of collection

                          Individual = no variability
                          Population = between-animal variability

 Figure 7 (adapted from Pomar et al., 2003): Effect of between-animal                   Figure 8 (adapted from Pomar et al., 2003): Effect of data collection length
 variation and balanced protein intake on average daily protein deposition              and balanced protein intake on average protein deposition rate of pigs
 rate of pig populations
 “If the response of an individual animal is given by a LP model, the response of the
                                                                                        “The CLP model is the one that takes into account the duration of the collection period.”
 population of this animal will resemble that of the CLP model.”

                                                                                                   Ajinomoto Eurolysine S.A.S. | Technical note | 13
3.3. Representing experimental data

The adequacy of the LP model is not often supported
by experimental results (Baker, 1986; Moughan, 1999)                                                  (a)
contrary to the CLP model. This can be explained by the                                                                 100

shape per se of each curve. The LP model depicts indeed a

                                                                                         ADG (% the best performance)
                                                                                                                        95

constant marginal efficiency (slope) up to the requirement,                                                             90

to become zero thereafter; whereas in the CLP model, the                                                                85

marginal efficiency is diminishing linearly with increasing                                                             80

nutrient supply until zero when the requirement is reached                                                              75

(Figure 9).                                                                                                             70

                                                                                                                        65

                                                                                                                        60
                                                                                                                          11       13        15        17       19       21        23        25    27

                                                                                                                                                     SID Trp:Lys (%)
 Animal's
 response
                           Constant marginal efficiency                                             (b)
                                                                                                                   100
                                           Diminishing marginal efficiency

                                                                                         ADG (% the best performance)
                                                                                                                        95

                                                                                                                        90

                                                                                                                        85

                                                                                                                        80
                                                         Curvilinear-plateau

                                                         Linear-plateau                                                 75

                                                                                                                        70
                                     Nutrient dose
                                                                                                                        65

                                                                                                                        60
  Figure 9: Biological interpretation of the shape of the linear- and                                                     11       13        15       17        19       21        23       25     27
  curvilinear-plateau models
                                                                                                                                                    SID Trp:Lys (%)
  “The variability that exists among the animals contributes significantly to the
   decrease in nutrient efficiency over varying nutrient levels. The CLP model is
   the one that takes into account this biological dynamics.”                                                                  Kluge et al., 2010                    Ma et al., 2010
                                                                                                                               Naatjes et al., 2010 (Wheat-Barley)   Naatjes et al., 2010 (Corn)

It has been well demonstrated that the variability that
                                                                                                         Linear-plateau model                        Curvilinear-plateau model           Asymptotic model
exists among the animals contributes significantly to the
decrease in nutrient efficiency over varying nutrient levels
                                                                                    Figure 10: External validation of the response of average daily gain
(Curnow, 1973; Bikker et al., 1994; Pomar et al., 2003;                             (ADG) to increasing levels of standardized ileal digestible (SID) Trp:Lys
Wellock et al., 2004; O’Connell et al., 2005; Brossard                              supply in piglets. The figure combines the determined response curves
                                                                                    thanks to a meta-analysis with data not used in this analysis. External
et al., 2009; Haushild et al., 2010). Therefore, to take                            data concern results of trials using only two levels of Trp:Lys (a) or results
into account the between-animal variation, a model that                              of dose-responses to Trp (b) (from Simongiovanni et al., 2012)

depicts a diminishing marginal efficiency (e.g., the CLP
model) has to be used. Recently, response-curves of the
piglet population to the increase in the level of SID Trp:Lys
ratio has been estimated thanks to the LP, CLP and ASY
models (Simongiovanni et al., 2012). When these curves
were compared with external data to validate the models,
it shows that the CLP model was the best-adapted model
to depict the external data compared to the LP and ASY
models (Figure 10).

14 | Technical note | Ajinomoto Eurolysine S.A.S.
3.4. Including a safety margin for the estimation of                                                                   for corn for instance (Relandeau and Eudaimon, 2008).
a requirement                                                                                                          Another more precise method is based on regression
                                                                                                                       equations; the nutritional values are there estimated with
The choice of the statistical model is a preponderant factor                                                           prediction intervals of 7.8% in average for wheat and
of variation of reported requirements (Baker, 1986;                                                                    9.4% in average for corn for instance (Relandeau and
Barea et al., 2009; Simongiovanni et al., 2012), and any                                                               Eudaimon, 2008). Finally the most accurate method for
published requirement or recommendation should be done                                                                 feedstuffs evaluation is amino acid analyses for which
with the indication of the method used. This is much more                                                              the nutritional values are analyzed with coefficients of
important for the feed industry because the choice of a                                                                variation; for instance, in the range 2-8% for sulphur amino
statistical model is directly linked to the risk management.                                                           acids, 1-4% for all the others and even more depending
The delivery of an efficient feed to the farm is indeed a                                                              on the laboratory (Eudaimon M., personal communication).
result of numerous risk estimations.                                                                                   Thus the choice of the method to estimate nutritional values
Concerning the feedstuffs evaluation, one of the methods                                                               of the feedstuffs is linked to an uncertainty that has to
which is commonly used to estimate amino acid contents                                                                 be considered when making the choice of nutritional
is based on table values but with a confidence interval                                                                constraints.
of 26.3% in average for wheat and 14.1% in average

           (a)                                                                                                                   (b)
                                           Deviation to expected CP values vs. expected CP values                                                                   Deviation to expected Lysine values vs. expected Lysine value
                                     4                                                                                                                      0,4
                                                                                                                       Diff. Lys analyzed - expected, Pts
 Diff. CP analyzed - expected, Pts

                                     3                                                                                                                      0,3

                                     2                                                                                                                      0,2

                                     1                                                                                                                      0,1

                                     0                                                                                                                       0
                                                                                                                                                              0,8      0,9      1,0     1,1        1,2    1,3         1,4           1,5          1,6
                                      15      16         17         18         19               20                21
                                                                                                                                                        -0,1
                                     -1

                                                                                                                                                        -0,2
                                     -2

                                                                                                                                                        -0,3
                                     -3
                                                                                                                                                                                                                AEL, Survey starter piglets (2008)
                                                                                 AEL, Survey starter piglets (2008)                                     -0,4
                                     -4

                                                              CP expected, %                                                                                                                  Lysine expected, %

 Figure 11: Difference between the analysed and expected values as a function of the expected values for CP (a) and Lysine (b) dietary
 contents (from AEL survey, starter piglet diets, 2008)

                                                                                                                                                                    Ajinomoto Eurolysine S.A.S. | Technical note | 15
The nutritional values in the final diet have theoretically,                  The choice of a statistical model on a practical point of
a fifty-fifty chance to be lower than the targeted value                      view is also directly linked to the risk management. The LP
(nutritional constraint) due to the normal law around the                     and CLP models indeed differ in the way they take into
targeted value. In practice, we can easily observe that in                    account the risks: the risk taken with animals’ performance
most of the cases, the targeted value is not reached and                      is greater using the estimates from LP model than using
the analysed value is lower or higher than the expected                       the estimates from the CLP model (Figure 12). The
one depending on the nutrient measured (Figure 11). That                      requirement estimates of the CLP model are therefore
is why; the choice of the target is of great importance.                      more adapted for practical issues.
Making a choice on a nutritional constraint must therefore
not be done only on performance and/or feed costs
objectives but also on this associated risk to lower supply
than the expected value.

   (a)                                                               (b)
   ANIMAL’ S RESPONSE

                                                               ANIMAL’ S RESPONSE

                        NUTRIENT DOSE                                                           NUTRIENT DOSE

                                                Confidence interval:

                                    Analyses            Regression                         Table

   Figure 12: Risk management concept: The risk taken with animal’s performance is due to the model used to choose
   the nutrient constraint ((a) linear-plateau; (b) curvilinear- plateau) but is also due to the method used to obtain the
   estimated value of the dietary content (analyses, regression equations or table values)

16 | Technical note | Ajinomoto Eurolysine S.A.S.
3.5. Summary of the comparison between the                     some are useful” (George Box quoted by Ryan, 1997).
linear- and the curvilinear-plateau models                     The final choice of the statistical model is dependent on
                                                               the objectives that have to be reached (Table 3), taking
Unfortunately, absolute confirmation about the choice of       into account that all requirements that are determined
the model is impossible since all statistical models are       empirically should be considered as estimates.
approximation to the truth: “All models are wrong, but

                      Table 3: Comparaison between the linear- and the curvilinear-plateau models

                                                           Linear-plateau                 Curvilinear-plateau

  1. Taking into account the between-
     animal variability in a population                              -                                 ++
  2. Taking into account the dynamic
     aspect                                                          -                                 ++
  3. Representing experimental data                                 +                                  ++
  4. Including a safety margin for the
     estimation of a requirement                                     -                                 ++

  Practical issue : Confidence in the
  estimation of nutritional requirements                       Very low                             Very high
  for growing populations

                                                                      Ajinomoto Eurolysine S.A.S. | Technical note | 17
CONCLUSION

   Experimental findings are essential for the feed industry to design efficient formulas and to face the challenges of
   economy, health and environment in animal production.

           When dose-response studies are performed, statistical analyses and results interpretation have to be
           done in accord with the experimental design and objectives. This allows to avoid any misinterpretation
           and to well estimate nutrient requirements which are intended to be applied in practice.

           The adequacy between the way of expressing the requirement and the experimental design is a
           guarantee of accurate estimation.

           However, the choice of the statistical model contributes also a lot to the variability in estimated
           requirements; dose-response interpretations have therefore to be done in the respect of some statistical
           and biological rules.

           The comparison of models clearly demonstrates that the CLP model is the best compromise for most
           response. It is indeed the best descriptor of the effect of a nutrient on growth performance for growing
           populations and so, gives an adequate estimate of amino acid requirements for practical applications.

   A more accurate description of the response of pigs and poultry, accompanied by better methods for estimating
   amino acids requirements, will contribute to improved diets for pigs and poultry.

18 | Technical note | Ajinomoto Eurolysine S.A.S.
REFERENCES

Baker, D.H. 1986. Problems of pitfalls in animal                Fisher, C., T.R. Morris, and R.G. Jennings. 1973. A model
experiments designed to establish dietary requirements          for the description and prediction of the response of
for essential nutrients. J. Nutr. 116:2339-2349.                laying hens to amino acid intake. Br. J. Poult. Sci. 14:469-
Baker, D.H. 2000. Recent advances in use of the ideal           484.
protein concept for swine feed formulation. As-Aust. J. of      Fuller, M. F., R. McWilliam, T. C. Wang, and L. R. Giles.
Anim. Sci. 13:294-301.                                          1989. The optimum dietary amino acid pattern for
Baker, D.H., A.B. Batal, T.M. Parr, N.R. Augspurger, and        growing pigs. 2. Requirements for maintenance and for
C.M. Parsons. 2002. Ideal Ratio (Relative to Lysine) of         tissue protein accretion. Br. J. Nutr. 62: 255-267.
Tryptophan, Threonine, Isoleucine, and Valine for Chicks        Hauschild, L., C. Pomar, and P.A. Lovatto. 2010. Systematic
during the Second and Third Weeks Posthatch. Poult. Sci.        comparison of the empirical and factorial methods used
81:485-494.                                                     to estimate the nutrient requirements of growing pigs.
Barea, R., L. Brossard, N. Le Floc’h, Y. Primot, D. Melchior,   Animal. 4:714-723.
and J. van Milgen. 2009. The standardized ileal digestible      Hernandez-Llamas, A. 2009. Conventional and
valine-to-lysine requirement ratio is at least seventy          alternative dose–response models to estimate nutrient
percent in post weaned piglets. J. Anim. Sci. 87:935-947.       requirements of aquaculture species. Aquaculture. 292
Bikker, P., M.W. Verstegen, R.G. Campbell, and B. Kemp.         (3-4):207-213.
1994. Digestible lysine requirement of gilts with high          Kluge, H., J. Bartelt, and G. Stangl. 2010. Studies on
genetic potential for lean gain, in relation to the level of    the tryptophan requirements of piglets. In: 9. BOKU-
energy intake. J. Anim. Sci. 72:1744-1753.                      Symposium Tierernährung, Vienna, Austria, April 15,
Boisen, S. 2003. Ideal dietary amino acid profiles for          2010, poster.
pigs. In: Amino Acids in Animal Nutrition (eds JPF D’Mello),    Leclercq, B., and C. Beaumont. 2000. Etude par simulation
CABI Publishing, Wallingford, UK, pp. 157-168.                  de la réponse des troupeaux de volailles aux apports
Brossard, L., J.Y. Dourmad, J. Rivest, and J. van Milgen.       d’acides aminés et de protéines. INRA Prod. Anim. 13:47-
2009. Modelling the variation in performance of a               59.
population of growing pig as affected by lysine supply          Ma, L., Z.P. Zhu, R.B. Hinson, G.L. Allee, J.D. Less, D.D. Hall,
and feeding strategy. Animal. 3(8):1114-1123.                   H. Yang, and D.P. Holzgraefe. 2010. Determination of
Curnow, R.N. 1973. A smooth population response curve           SID Trp:Lys ratio requirement of 11- to 22-kg pigs fed
based on an abrupt threshold and plateau model for              diets containing 30% DDGS. ASAS Des Moines, Iowa,
individuals. Biometrics. 29:1-10.                               March 15-17, 2010, Abstract 151.

Dawkins, H.C. 1983. Multiple Comparisons Misused: Why           Mead, R. 1988. The design of experiments: statistical
so Frequently in Response-Curve Studies? Biometrics.            principles for practical application. Cambridge University
39(3):789-790.                                                  Press, Cambridge, UK.

Emmans, G.C., and C. Fisher. 1986. Problems in                  Morris, T.R. 1983. The interpretation of response data
nutritional theory. In: C. Fisher and K.N. Boorman. Nutrient    from animal feeding trials. In: Haresign, W. (ed.) Recent
requirements of poultry and nutritional research. ed.           Advances in Animal Nutrition, ed. Butterworths, London,
Butterworths, London, pp. 9-39.                                 pp. 2-23.

                                                                       Ajinomoto Eurolysine S.A.S. | Technical note | 19
Morris, T.R. 1989. The interpretation of response data           Pomar, C. 2005. Nos modèles de simulation actuels sont-
from animal feeding trials. In: D.J.A. Cole and W. Haresign.     ils adéquats pour prédire la croissance corporelle et
Recent Developments in Poultry Nutrition, ed. Buttersworth,      les besoins nutritionnels des porcs charcutiers ? 37ème
London, pp. 1-11.                                                Journées de la Recherche Porcine. 37:307-316.
Morris, T.R. 1999. Experimental Design and Analysis in           Pomar, C., I. Kyriazakis, G.C. Emmans, and P.W. Knap.
Animal Sciences. CABI Publishing, New York.                      2003. Modeling stochasticity: Dealing with populations
Moughan, P. J. 1999. Protein metabolism in the growing           rather than individual pigs. J. Anim. Sci. 81(E.
pig. In: I. Kyriazakis. A Quantitative Biology of the Pig, ed.   Suppl.):E178-E186.
CAB International, Wallingford, Oxon, UK. pp. 299-331.           Relandeau, C., and Eudaimon M. 2008. Measuring
Naadjes, M., J.K. Htoo, K.H. Tölle, and A. Susenbeth.            and predicting amino acid contents in feedingstuffs.
2010. Optimales Tryptophan-Lysin-Verhältnis in Weizen-           AJINOMOTO EUROLYSINE Technical Information N°32.
oder Mais-basierten Diäten bei wachsenden Schweinen.             Robbins, K.R., H.W. Norton, and D.H. Baker. 1979.
In: 9. BOKU-Symposium Tierernährung, Vienna, Austria,            Estimation of Nutrient Requirements from Growth Data. J.
April 15, 2010, pp. 72-77.                                       Nutr. 109:1710-1714.
Nelson, L.A. and J.O. Rawlings. 1983. Ten common misuses         Robbins, K.R., A.M. Saxton, and L.L. Southern. 2006.
of statistics in agronomic research and reporting. J. Agron.     Estimation of nutrient requirements using broken-line
Educ. 12:100-105.                                                regression analysis. J. Anim. Sci. 84(E. Suppl.):E155-E165.
O’Connell, M.K., P.B. Lynch, and J.V. O’Doherty. 2005.           Ryan, T.P. 1997. Modern Regression Methods. John Wiley
Determination of the optimum dietary lysine concentration        and Sons, New York, NY.
for growing pigs housed in pairs and in groups. Animal           Shearer, K.D. 2000. Experimental design, statistical
Science. 81:249-255.                                             analysis and modeling of dietary nutrient requirement
Pesti, G.M., D. Vedenov, J.A. Cason, and L. Billard.             studies for fish: a critical review. Aquaculture Nutrition.
2009. A comparison of methods to estimate nutritional            6:91-102.
requirements from experimental data. Br. Poult. Sci.             Simongiovanni, A., E. Corrent, N. Le Floc’h, and J. van
50:16-32.                                                        Milgen. 2012. Estimation of the tryptophan requirement in
Pomar, C. 1995. A systematic approach to interpret the           piglets by meta-analysis. Animal. In press. doi:10.1017/
relationship between protein intake and deposition and           S1751731111001960.
to evaluate the role of variation on production efficiency       Wellock, I.J., G.C. Emmans, and I. Kyriazakis. 2004.
in swine. In: Proceedings of the Symposium on Determinants       Modeling the effects of stressors on the performance of
of Production Efficiency in Swine. Can. Soc. Anim. Sci.,         populations of pigs. J. Anim. Sci. 82:2442-2450.
Ottawa, Ont. Canada, pp 361-375.

20 | Technical note | Ajinomoto Eurolysine S.A.S.
Notes

          Ajinomoto Eurolysine S.A.S. | Technical note | 21
SIMONGIOVANNI Aude, LE GALL Eric, PRIMOT Yvan and CORRENT Etienne

                    AJINOMOTO    ANIMAL   NUTRITION

                 AJINOMOTO EUROLYSINE S.A.S.
               153, rue de Courcelles 75817 Paris Cedex 17
                        Tel: +33 (0)1 44 40 12 12
                        Fax: +33 (0)1 44 40 12 13
                    www.ajinomoto-eurolysine.com
You can also read