I will present you the INRA system which is a fairly recent system as you know we have written two books to read books one is in English you have this book it is fairly thick but I can advise you to read it but not in one day because it is very thick and this morning I will just present you a brief summary of what is important to keep in mind when you want to use this system first of all I will recall briefly the story of the recent system at INRA in 78 when Robert was a student we have published the first red book remotest on the ravine on and after 10 years we have published an updating of this book here and after we had to spend about 20 years to have an updating of our systems between this boss system we had a publication of tables of multi species with several translation in English in Spanish and in Chinese the mossad magic target that we have in this systole project the name of the project was history is to try to be more precise in the prediction of supplies of energy and protein first I record use that for energy we use a net energy system for milk from it which is packaged in feed unit system and also we use a system of metabolizable protein which is called PDI and we also have include in our system amino acid digestible amino acid in scientist we wanted also to be able to try to predict the flows of absorbing nutrients I mean vfa glucose fatty acid amino acid obviously and also production of gays like methane we wanted also to be more precise in the prediction of the requirements and also mainly in the prediction of the responses to UFL to pretty high our two nutrients such as concentrate and also we would like to be able to enlarge the field of application because we knew that was our system was not accurate when we wanted to use it in the context of warm countries France and also in the Khans in the context of very intensive diets this is a team of Earth's history my collegues my fellow colleagues piano the idea and this morning I will review rapidly and briefly 17 major novelties that we have proposed in this book and I will stress only some of these novelties the first one concerns the data creation and statistical treatments to be able to update our feed unit system we are first put databases about 20 databases from that of the researcher and after that we have applied specific encoding of the experiment of the type of experiments objective etcetra etcetra after that we did treatments by meta-analysis and the outcome of that is a curve of about 9 500 equations which is for us the skeleton of our system the second novelty is about the transit fractional outflow rate because until now we use a constant value of 6% per hour and we knew that it was in fact false this is a reason why we are first tried to improve our land our modeling of this aspect by taking into account the type of feed fridge versus concentrate the liquid phase and also the factors such as dry matter intake and the proportion of concentrate have you here you have for instance the equations that we use to predict the transit outflow rate for concentrate Fred the value of 6% per hour is here that means that for instance in our new system the fractional outflow rate of particle is less than 6% except for high level of dry matter intake okay and we also have negative influence of the proportion of concentrate and this fractional outflow rates a major consequence of that is that from now the new TT nutritive values of feed in the table is only an indicative value I will go back on this aspect after we have used the result of the transit fractional outflow rate in combination with in-situ kinetics of protein and starch as it was already done in several others as a system such as the kernel one we have used this equation which is a classical one and with all this equation we have tried to predict the duodenal flow from experimental data and also from in-situ data this is for example the results we have obtained for protein the y-axis is a in vivo duodenal protein Flo and the x-axis is a predicted flow from the in situ and the kinetics of particle at flow rate in function of the dry matter intake and proportion of concentrate and interestingly the slope that we have is not different from one and we were able to find here and the intercept an estimation of the endogenous protein out flowing at the level at this level at the tottenham of the animal so that means that we have a prediction without bias of this flow of protein at the ordinal at the dénouement level which is first very important we did the same thing for starch and also fortunately for starch we obtained a slope of one with no intercept T which is fairly logical that means that now we are able also to predict not only the partition of protein digestion in the gut but also the partition of starch digestion in the girth I mentioned before the fact that now the nutritive value of feeds is variable for instance if we consider the bypass metabolism all protein which is called PDE in French you have here the values here you have the values that we had in the previous system okay now here you have the values that we have in the new system we have systemic decrease of the bypassed value for the field rich in protein which is logical because we have decreased the outflow rate from the women okay but this is a tabulated value if you use a diet with a higher level of dry matter intake you will have another value if you use diet with a lower level of matter intake you will have another value that means that from now the value the nutritive value that you have in the tables for protein are variable according to the type of diet this is the same thing for energy for instance here you have the UFL value of the previous system and here you have the the difference between the new system and the older one for this type of feed for instant here one UF in the previous system now a little bit more in the table but if you give this feed with a higher level of dry matter intake three and a proportion of concentrate of point two you will have lower value and if you use it in a more intense ration will have still a lower value and if you use this feed in aeration with a level of dry matter intake of four percent of the body weight you will have still lower value this is important to keep that in mind that now we have variable nutritive value for the fields University number four which is very important one for us is index that we used to try to assess nutrition of microbes particularly the protein energy ration and ratio and also the exchanges of ammonia and urea through the wall of the women this index is called remain protein balance and this is correlated with the transfer ammonia and urea through the rumen wall and a particularity is that we have used this item to predict some nutritive aspect digestive protein efficiency urinary night and excretion digestive integration microbial synthesis etc just to show you briefly with some figure what we have done here you have a view of the ribbon with microbes energy part the protein part here the degradation so by a pass of protein and here you have the exchanges of ammonia and urea by recycling here and until now we are we were not satisfied by the way we have used to model this aspect so this is resin the reason why we have created the rumen put in balance which is a difference between crude protein intake and crude protein at the duodenum that means that for instance if you have a positive value of that you have an outflow of ammonia from the women and if you we look at relationships that were friends and between the women put in balance and the ammonia in the rumen there is logically a positive relationship same thing for yurina urea and in the blood also in the milk and another outcome of that is that the efficiency of protein digestion which is a ratio between metabolizable protein to crude protein is heavily influenced by the women put in balance with a value of 67 for the value of zero here we can say that it is a people at this level we have this level of the address Shiva T but you see that we have large decrease of the efficiency of digestion of protein when you increase you increase remain put in balance and also a logically when you increase the women put in balance expressing nitrogen intake per kilogram of body weight clearly you have unique of the excretion of urinary n and if you look at the slope of this relationship the slope is between 50 to 80 percent of nitrogen coming directly directly from the women to the urinary excretion flow which is fairly important the consequence of that is that there is close intra experiment relationship between the women put in balance and the global protein efficiency of a derecho global protein efficiency is milk protein in percentage of protein intake if we are at zero here we are about close to 30 percent but you can have a large decrease of this global efficiency when you increase the women put in balance and this is a contrary if we decrease the women protein balance the novelty number five is also for us very important what you see as there is a star this is the issue of the digestive interactions as you know the value of a diet is not exactly the weighted sum of the values of the feed because there is terms of interaction and for us the target was to be able to model this interaction we have chosen two modules interaction at the level of the organic matter digestibility and we have tried we have checked several potential factors which could explain these digital digestive interactions at the end who have found that three major feed factors could have a significant influence so feeding liver here the proportion of concentrate and the women put in balance that I have just before to be able to model the influences of these three parameters we have built specific databases dealing only on the effect of dry matter intake for instance here dealing only on the effect of preserve shortage of concentrate and the effect of women protein balance you can see that for instant when you increase the proportion of concentrate the true values are less than the predicted value from the tables this is the contrary for high values of women put in balance for instance we have checked that these three effects were additive it was an important issue for us the outcome of that is that if we consider that the impact of digestive interaction can be considered as a non productive requirements you see that we have behind that hundreds of of experiments you have here the milk here one point is one group of animals you have the maintenance requirements in terms of energy and here you have the maintenance requirement plus the non productive requirements due to the digestive integration and you can see that in this part with very high yielding animals this part is about the same than the maintenance requirements of the animal which is not at all negligible after that another novelty is was the prediction of the methane production and the major problem that we had for that is that methane production is not a linear function of the feeding level and of the proportion of concentrative diet so we have to to use this type of equation in the computer programs that we have to formulate the diet and also we have objected the values that we had to try to conceive I'd loss of energy through the urine outflow and we also had to check the relationship that we can have between the loss of energy by methane and urinary energy and the loss of energy from the fickle way when we are in a context of digestive interaction here you have diets which were only for cues and the influence of the feeding level and clearly you can see that if you increase from one point the fickle energy you decrease from point five points this outflow of energy there is clearly a compensation between the both and for us it is very important to be able also to take this compensation into account because for instance if we compare our civil systems in terms of the impact of the digestive interactions and the net energy value according to the milk here our system is here the finished system is also at the same level they have done different approach but with the same factors of variation and here you can see the old nioc system with a higher value of the impact of the digestive interaction mainly due to the fact that they use a constant ratio between net energy and today n with a constant ratio you are not able to take all these substitutions that we have between the two losses of energy between feces and urine plus methane but it was the old system I presume that in the next system of Aniyah they will correct that novelty number seven just to be able to calculate fair multiple organic matter in the rumen it was for four s important to do that to be able to predict the microbial and the Viper production in the in the rumen we have used this equation it is in the book and an important consequence of that for us is that for instance if we consider our tables in terms of the PDA value of concentrate on byproducts you can see that here you have the PD I value coming from microbes and here's a PDI value coming from the bypass protein that means that clearly the range of variation between fields is very narrow from for the microbes and if you want to have a very high level of metabolizable protein in a feed you have to increase this PD fraction which is a PD fraction coming from the bypass and if we look at the context of feeding at the Ricoh fence and here you can see that beyond a certain level a PDI intake if you want to increase more supply of PDI you are obliged to use bypass protein and the outcome of that is that you have to be more aware on the issues of the profile of amino acids and clearly you see that there is a maximum of capacity of the women to project microbial protein this view which was published in a new paper and owner of the items is first very important in terms of applications the prediction of the flows of absorber nutrients I will go quickly through the pif a glucose fatty acid and also the profiles of amino acid of a PHA and also of fatty acid Sunna the following novelty number of 10 I will pass also quickly is the evolution of the risk of acidosis we have based this evolution 8 parameters which are more or less linked with rumen acidosis the following novelty is dealing with the fact that we add as I said before one read of equation for instance we have globally 100 of equation dealing with what happened in the gut and first of all we had to check the consistency of all this equation we have checked that with a mechanistic model of the rumen and terms of the guide and if we focus only of the equation that we used to predict the energy and the protein value of the diet here you have about 50 equation and all this equation were packaged into a software which is called sis to lab which is which allowed to calculate diets according to the ingredients the level of intake the proportion of concentrate and the remain protein balance and after that all that was also packaged further in a global computer program which is called in ratio which which will be available at the end of this year at the beginning of the next year somewhat on the following novelty which which is a valuation of the maintenance and non-productive protein requirements for us it was a new aspect for instance particularly at the level of the metabolic cycle protein we have used an equation which clearly clearly shows that when we decrease the energy content of the diet we increase the outflow of metabolizable fickle protein per kilogram of dry matter of this feed that means that if we compare the previous system and the present system for instance for this type of animal with a fairly high level of Michel we have an increase of more than 500 grams of requirements due to the non-productive requirements but by this way we are not very original because if we compare our situation to the other as a system before we were there and we were wrong now we are here and we are in the group here you are the other European system here you have the CNC paste and here you as in Iasi we are now at the same level of the taking into account metabolic fickle aspect another important novelty for us is was the efficiency of PDI because until now we use a constant value which was 64% now we have used a new approach I don't have time to describe but the basic assumptions that we did is that we had variation of the efficiency of the PDI and these variations are the same for all the post of protein synthesis in the animal I mean for milk oscar for body protein and for fecal endogenous protein and by this way we obtain relationships between the PDI content and the protein efficiency this is the equation that we have for dairy cows with exactly the same equation for the dairy goats for growing animals we have also search equations but with a pivot which is at the level of fifty percent which is different from here we are at the level of 67% and what seems to us important is that we have a clear relationship between the efficiency of the PDI under global efficiency of the dairy cow there is a positive relationship between the both showing that when we increase its efficiency we increase the global efficiency of the animal and what seems from important to stress is that we have to do a distinction between the mean efficiency and the marginal efficiency here you have a set of data with supply of metabolizable protein here the pivot value for this animal here you have the curve of the efficiency of PDI here you have the simultaneous response of milk protein yield any other simultaneous response of the urinary loss of nitrogen expressed on the protein basis to have a common scale here and you see that when we are at the pivot values I mean a mean efficiency of 67% the marginal efficiency the slope here is only point two and here the marginal efficiency is point hate showing that when we are at this level of the pivot we have if we want to change the level of supply of Padilla we have to keep in mind that the marginal efficiency is fairly low only 20 percent that means that the rest of the variation is recovered at the level of urinal Ranieri nitrogen so we were able now to predict fairly precisely the loss of nitrogen through urine with the possibility of doing a distinction between these laws coming from the pti inefficiency which mean a drawback in the metabolization of protein and coming from the women put in balance which is a drawback of the use of nitrogen at the level of the women so we are able now to do differential diagnosis of nutrition in ruminant by this way I will go quickly through this this novelty which is dealing with energy we did some new things but I don't have time the following novelty is de taking into account and the modeling of the mobilization and accretion of body reserves at the potential for us it was important to take that into account to take into account that at the beginning of a lactation the reserve behaves as a supply of energy and on the contrary in the following phase of the lactation the reserved as act as a requirement obligatory requirement and that means that now when we want to formulate a diet we based the first approach and potential trajectories of milk L of milk content of fat and protein and also on the status of the body reserves we take that into account within the calculation of the diet the last novelty is dealing with multiple responses to surprise I will focus on the example of the concentrate supply first of all had to use that to predict the response in terms of dry matter intake so we use this type relationship and now we have taken into account the influence of protein and the capacity of in take of the animal and also on the substitution and by this way it is possible for us when we are at the pivot the pivot is the potential that means that you cut potential trajectory at one time you have the p12 the pivot value here and for instance if you modify the supply of concentrate you have modification alteration of the lactose secretion lipid secretion and proteins the secretion same thing between goats and cows and also we are able to predict the response of protein production not only from concentrate but also from surprise of PDI and energy here you have the loads that we use the low of the response that we use and the pivot situation here it is important now to consider that we start from a pivot situation to predict the responses of animals same thing here if you want to predict submit yield milk fat prediction mill protein production and mix lactose prediction this equation are also in the in the red book and also the last not released for the amino acid we consider that amino acid in the indispensable amino acids such as listen methylene can be limiting factors it's not new and we consider that the response to this amino acid is mainly at the level of the milk protein content and we use a combination for instance here of listen and methylene to predict the Sentinels response of the milk protein content to this two level of surprise starting also from the pivot value which is requirements the covering of the requirements of the animal what I would like to stress as the conclusion is that we we have tried to do deeper dating it was necessary if we wanted to do a new scientifically based system we have seen a set of novelties here seventeen novelties they allowed to take into account some new aspect of biology underlying biology we are able now to increase a precision of our system which is for us very important we have checked the consistency of all the equations that we have used and also we have tried to have prediction of the absorbable nutrients flows in the gut but have also to classify that the price of a better precision is an increase of the level of complexity of the system which is more or less compensated thanks to the computer program thank you very much for this [Music]