Targeted Feeding to Save Nutrients

Published on: 8/24/2017
Author/s : Charles J. Sniffen 1, William Chalupa 2. / 1 Ph.D. Fencrest, LLC; 2 Ph.D. University of Pennsylvania.
Introduction 
 
There is increasing pressure at federal, state and local levels about nutrient waste and pollution relative to N, P, CH4 and CO2 discharges from the animal industry. Large animal operations have come under more intense scrutiny than small animal operations, with CAFO regulations being put in place.   
 
This has put increasing pressure on producers and us as nutritionists to provide some of the solutions to the problem. There has been considerable research conducted in the last decade in this area; the focus of this paper will be to examine what can be done in the formulation of rations to save nutrients and as a result to increase efficiency, profitability and sustainability for the producer. 
 
 
Farm Nutrition Management 
 
As nutritionists, we assess farms as to animal grouping, degree of over stocking, management of the groups and the environment surrounding each group. We then look at the quality of the forages being offered and most importantly the variability of the quality of the forages and other on-farm feeds. We then make decisions as to how much safety we need to build into the rations being developed; basically the more variability in the management and the feeds the higher are the safety factors that are built into a ration resulting in a lower efficiency and a greater loss of nutrients. There are several approaches that can be taken to increase efficiency; the first is to have more groups; when we have few replacement groups and few lactating groups, by definition we will need to overfeed some of the animals within that group as well as underfeeding other animals within that group. We will, in developing rations, not feed for the average cow in the group but for the top 20 to 30% of the animals in the group, which will result in inefficiencies. Having multiple groups for many dairies is not possible and so we will need to develop strategies to improve efficiencies within these constraints as well.
 
There is also an opportunity to increase the efficiency of nutrient use with the use of robotic systems which will allow the ability to target the requirements of the cow more closely; this might mean the development of more than one parlor mix to more properly target each cow‘s requirements. 
 
 
Nutrition Advancements 
 
Saving nutrients has been the focus of many studies in recent years; admittedly not always from an environmental viewpoint but to increase efficiency and to reduce costs in feeding the cows. We have fed cows based on the Weende feed analyses system since the late 1800‘s; this was a significant advance at the time that was so powerful that it has continued to this day! We still use CP, EE, ash, and calculated NFC routinely in our evaluations of rations.  Crude fiber is still being used in parts of the industry.  A significant change was made with the change from crude fiber to initially ADF and then NDF and with the latest models aNDFom.  We then incorporated the measurement of lignin.  With the use of NDF, this resulted in a reduction in the calculated NFC. As we know, N has been a big issue in the environment resulting in many regulations. The fact is we, in the dairy industry, have been overfeeding N for a long time; our standard for many years has been to have a ration for high producing cows with 17 to 18 % CP.  We used to be concerned when the MUN went below 14 to 15 or when it went above 18 mg/dl. We now know that we can feed rations for high groups in the 14 to 15 %CP range; This started with the work of Broderick (Colmenero & Broderick, 2006) who did some classic work many years ago showing that milk production could be maintained with a lower CP and further that MUN‘s and more importantly that there was significantly less wasted N being excreted as urinary N.   
 
Nitrogen intake and excretion from rations varying in CP levels
 
 
There have been many other studies since then that corroborated this work. The models used at the time were the early NRC models to formulate these rations. With the initial release of the CNCPS system and NRC 2001, there was a significant step forward in our understanding of N utilization by the cow.

With the release of the latest versions of CNCPS there have been the developments of several platforms incorporating this model. Additionally, there are available other platforms based on either CNCPS or NRC. We will use only a two platforms to demonstrate the opportunities to formulate rations to increase efficiency.   
 
Below is an example from NDS of the different things we need to think about monitoring going forward.
 
 
The focus is on N and P excretion and CH4 & CO2 emissions. We need to become comfortable with monitoring N not only the excretion of N but also the NH3 potential.   
 
Our challenge is to formulate rations to reduce excess N & P excretion as well as to control CH4, CO2 & NH3 and nitrous oxide (N2O). The use of the new technologies in the NRC & CNCPS models allows us to make a step forward in achieving a reduction in excess nutrients and emissions. The above figure does not address N2O but does the others. For N the correlation between excess N intake, N in urine and the MUN are highly correlated. We do not measure the N excreted in the urine or the manure routinely but we all monitor MUN‘s now.  We use to accept MUN‘s in the 14 to 16 mg/dl area but now we routinely expect to achieve MUN‘s below 10 mg/dl. Below is an example from AMTS for predicted MUN.
 
 
Reports now provide information like above; if using CPM (recently the UPENN Model), Dalex, NDS or AMTS, they all have predictions; with the predictions in Dalex, NDS & AMTS being a little more accurate when using the 6.1 or 6.5 models.

 
Ration Formulation 
 
Ration formulation starts with a critical evaluation of the groups that are to be fed a ration. This starts with an evaluation of first the mature frame size weight and then the weighted average weight of the cows in the group that are being fed; this is very important in the CNCPS based models because it impacts not only animal requirements but the prediction of the rate of passage and CHO & protein digestibility. Next we want to define things like the days in milk, the amount of milk and the milk components; milk and milk components is a two-step process; first we need to be in the evaluation mode to determine if the model predicts the current milk and components accurately and if the model is predicting within a few lbs. we can then formulate for the milk and components desired. Additional information is important as well; we need to know about the environment that surrounds the group we are feeding – temperature, humidity, air flow, degree of overcrowding, bunk space and water space, to name a few. With this information, we can more accurately assess the performance and to formulate a ration more accurately. We will be using the CNCPS model using AMTS 6.1(AMTS 6.5 will be released in the first quarter 2015) and NDS 6.5 for the demonstration of the concepts in targeted ration formulation.   
 
In order to achieve a high efficiency of N utilization, there needs to first be a balance of not exceeding the rumen NH3 and peptide requirements. This is many times difficult to achieve when one is locked into an on-farm inventory of feeds that need to be fed out at certain rates.  In the long term, working with the nutritionist and agronomist the forages in the rations can be planned to reduce N & P excesses.
 
 
Above is a ration in AMTS 6.1 with a mixture of ingredients that might be used in the Western part of the US. It will be noted that urea and a fat source are put into the ration for consideration when the ration is optimized; again the forages are on farm forages and there may be minimum and/or maximum constraints placed on these forages due to inventory considerations. Note also that blood meal was a consideration but when the ration was optimized it was not used; this, of course, can change depending on prices.
 
Below are the results of the optimized ration in AMTS 6.1. For those used to using CPM, this is the familiar default screen.
 
 
The ration CP is at 15.4% which is up a little bit from the original formulation of 14.8% but there was an indication of a potential NH3 deficit so urea came into the solution. Note that the approach used was to use an optimization procedure rather than by substitution. With this approach, one can first establish the prices of the feeds to be considered, next the minimum and maximum amounts to be considered in the ration and then the nutritional constraints such as the minimum MP and ME in the ration.  With the advancement of the models, we now have the capability to refine rations so that we are not overfeeding N or P as we have in the past.  Understand though, nutritionists will overfeed nutrients when they observe day to day variability in the on-farm feeds offered to the different animal groups so as to maintain productivity.  
 
One of the challenges is the number of groups we should have.  It is advantageous from a nutrient management standpoint to have homogenous groups based on a physiological requirements basis as well as being able to fine tune the rations; we can further fine tune this with robotic systems.  However, having many groups adds challenges relative to increased labor costs in feeding as well as increased challenges in animals adapting to pen & ration changes.  With a one group system we will be overfeeding many of the cows which will potentially decrease efficiencies.   
 
Refinement with the advanced models starts on the carbohydrate side with improved prediction of the ruminal digestibility of the NDF in the ration. We first need a better prediction of the potentially available NDF which we now have with the use of what we now call uNDF240. This is an estimate of the indigestible NDF which replaces lignin*2.4. With this and the NDF digestibility at 30 and 120 hours we can improve our estimates of fiber digestion in the rumen.  We now have a much improved understanding of the ruminal starch digestibility with the use of the 7hr Invitro measurement, which at this point is a good ranking tool and has allowed us to refine our estimates of the rate of digestion of the starch being fed. These two sources of fermentable CHO make up the biggest percentage of rumen degradable CHO. With the improved estimates we have an improved prediction of microbial protein yield and also the ability to better predict the ruminal N needs so that we can minimize excess NH3 being absorbed into the blood and being excreted in the urine. The model continues to use the NRC 2001 mineral submodel with the estimates of the bioavailability‘s; very important as we consider over formulating minerals and the impact to the environment. It is suggested that we need to now consider formulating for fermentable CHO fractions rather than total fractions. Below is the fermentable CHO profile from NDS of the above ration.  We try to achieve a total CHO fermentability of over 40% DM and a fermentable NDF of over 10% DM.  Due to the cost of corn grain in the not too distant past, we started to reduce the amount of purchased fermentable starch which has moved us to higher amounts of fermentable soluble fiber and sugar. Additionally, it should be pointed out that when we have less than optimum management situations and/or excessive heat and humidity we can put a maximum on the fermentable starch and increase the fermentable soluble fiber; our goal, in this case, is to drop the fermentable starch as a % of the total fermentable CHO down closer to 42 to 43% DM.
 
 
With the optimization of the CHO fermentation, balancing for meeting the ME & MP the CP in the ration will be in the 14 to 15+ %DM; this will increase the risk of certain amino acids being a deficit. With high amounts of the proteins coming from corn and soybean meal the first limiting AA are Lys & Met. However, with rations which have significant amounts of barley and canola then His can also be limiting. Further, it is also possible for the branch chain AA‘s to become limiting. A review by Robinson (2010) suggests that considering only Met & Lys may not lead to increases in the efficiencies desired; this thinking is corroborated by Arriola Apelo, et al (2014) suggesting a need to refine the models even though they showed positive results from lowering the CP down to 15%. Below are the AA supplies and balances shown from NDS. It needs to be pointed out that in CNCPS 6.5 as shown in NDS the AA supplies are different than in 6.1 because of the updated CNCPS database. This points out that even though the 6.5 database is improved; assuming that the AA profile of each feedstuff is constant is not true; we will hopefully move to receiving AA analyses as a routine assay sometime in the future.  The key points from the table below (NDS) is that when you look at duodenal flow you will see that a significant % of the total AA flow comes from bacterial AA; this points out the importance of good estimates of ruminal CHO fermentation and rumen available N to match the potential CHO fermentation.
 
 
The next area is minerals. Our labs give us routinely NIR predictions and they are not quantitative estimates but qualitative; NIR prediction reliability is low. Given that P is a concern it is probably wise to develop good regional mineral analyses for the feeds fed that are regional for the area being served that reflects the soils and the soil management of the regions.
 
 
In the ration above (AMTS) the minerals were balanced with a little di-calcium phosphate and salt as well as inorganic trace minerals and vitamins. The P was just met as well as the Na & S, but according to the book value analyses, other macro minerals were exceeded relative to requirements. This points out the importance of developing a robust mineral analyses in the regions that are served and then going forward having good chemistry for the on the farm supply of forages plus the off-farm ingredient suppliers. Note that the ration was balanced to meet the P requirement (Wu et al, 2000, Cerosaletti et al 2004). It has been a concern for several years that we do not over feed the other minerals. Too often the trace minerals are fed to requirement without taking into account the trace mineral contributions from the forages and grains which are organic sources of the trace minerals. We have, for example, seen excess amounts of Cu leading to reduced yields of corn silage. It will also be noticed that there is a column for water analyses. We recommend that it is important to have water analyses done for each source of water on every farm; too often we have excess of minerals that we need to take into account, especially for dry cows where DCAD balances can become crucial. 
 
 
Nutrient Balances 
 
The goal is to achieve good balances to achieve nutrient savings. The optimized ration shown above resulted in the N & P losses for this group is shown below (NDS).
 
 
The key numbers that we are trying to influence are the ratios of Productive N/Total N and then the manure and urine ratios.  The P is primarily excreted in the feces and the two ratios we need to monitor. We need to increase the productive P for the total P intake and decrease the manure P per the total P intake.   
 
Below (NDS) are the two major areas that are very much on the global warming radar screen now, methane and carbon dioxide which ironically, we have made great strides in improving over the last 50 years.
 
 
There has been considerable research in the last few years looking at both CH4 and CO2 emissions not only from individual cows but also from farms.
 
We focused on the high group in this presentation but the more pertinent numbers are the whole farm balances. This means that we need to carefully balance the rations for replacements and dry cows as well. Too often we might do a good job with the lactating herd and then over-feed the replacements, contributing significantly to upsetting the nutrient balances on the farm. As we move forward there will be improvements in the models that predict the outcomes of feeding management and the rations that are being fed.  Chase (2010, 2011, 2014), as well as Chase et al (2009) discussed at length the relationships, from controlled and field research as well as field experiences and the use of the CNCPS model and the predictions of excretions of N & P as well as gaseous emissions. Higgs et al (2012) in two papers discussed both controlled studies as well as a case study in with the use of the CNCPS system relative to N excretion. Below is an Excretion Report from AMTS which depicts the annual whole farm N & P excretions.
 
Example of an annual whole farm excretion of N & P.
 
 
Cela et al (2014) did an in-depth analysis of whole farm N, P and K balances using detailed data from many NY farms, concluding when 70% of the feeds fed the cows are produced on the farm and the rations are balanced to meet requirements then the farms will be in nutrient balance. Given the concerns about NH3, N20, CH4 & CO2 emissions we need to include these in the reports in the future as well.   
 
 
Summary 
 
With rapid and affordable access to forage and feed analyses, improved nutrition models and the platforms that they are in, we have the opportunity to improve our capabilities in targeting feeding in a manner to increase the amount of nutrients fed into productive nutrients with a reduction in nutrient losses.   
 
It is important for a farm to not only just look at the potential losses from inefficiencies but also when looking at N & P consider what % of these losses are recycled back through the soil and retained by forages grown either on the farm or through neighbors‘ farms who are using the nutrients to grow crops; in this scenario where less than 70% of the feeds are grown on the farm, a regional balance. Also, it is important for a farm to consider how to reduce the variability of the on-farm feeds as well as the purchased feeds; this will allow the nutritionist to formulate closer to the needs of each group of animals on the farm.  Additionally, there can be opportunities to regroup animals and to improve the environment surrounding the cows as well as the management that will reduce nutrient losses.   
 
We have made significant progress over the last several decades in reducing nutrient wastage and our carbon footprint but we still have some challenges ahead.
 
 
Presented at the Western Dairy Management Conference 2015, Reno, NV, USA. 
 
 
References 
 
Arriola Apelo, S. I., A. L. Bell, K. Estes, J. Ropelewski, M. J. de Veth, and M. D. Hanigan. 2014. Effects of reduced dietary protein and supplemental rumen-protected essential amino acids on the nitrogen efficiency of dairy cows. J. Dairy Sci. 97:5688   
 
Cela, Sebastian, Quirine M. Ketterings, Karl Czymmek, Melanie Soberon, and Caroline Rasmussen. 2014. Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State. J. Dairy Sci. 97:7614 
 
Cerosaletti, P. E., D. G. Fox, and L. E. Chase. 2004. Phosphorus reduction through precision feeding of dairy cattle. J. Dairy Sci. 87:2314 
 
Chase, L. E. 2010. How much gas do cows produce? Proc. Cornell Nutr. Conf. pg. 186 
 
Chase, L. E. 2011. Ammonia emissions from dairy operations – what do we know? Proc. Cornell Nutr. Conf. pg. 173 
 
Chase L. E.  2014.  Using Models on Dairy Farms – How Well Do They Work? Mid-South Nutr. Conf.   
 
Chase, L.E., R.J. Higgs and M. E. Van Amburgh. 2009. Feeding low crude protein rations to dairy cows – opportunities and challenges. Proc. Cornell Nutr. Conf. pg. 220 
 
Higgs R. J., L. E. Chase, and M. E. Van Amburgh. 2012. Development and evaluation of equations in the Cornell Net Carbohydrate and Protein System to predict nitrogen excretion in lactating dairy cows. J. Dairy Sci. 95:2004 
 
Higgs R. J., L. E. Chase, and M. E. Van Amburgh. 2012. CASE STUDY: Application and evaluation of the Cornell Net Carbohydrate and Protein System as a tool to improve nitrogen utilization in commercial dairy herds. The Professional Animal Scientist 28:370 
 
Olmos Colmenero, J. J. and G. A. Broderick. 2006. Effect of Dietary Crude Protein Concentration on Milk Production and Nitrogen Utilization in Lactating Dairy Cows. J. Dairy Sci. 89:1704  
 
Robinson, P.H. 2010.  Impacts of manipulating ration metabolizable lysine and methionine levels on the performance of lactating dairy cows: A systematic review of the literature. Livestock Science 127:115  
 
Wu Z., L. D. Satter, and R. Sojo. 2000. Milk Production, Reproductive Performance, and Fecal Excretion of Phosphorus by Dairy Cows Fed Three Amounts of Phosphorus. J Dairy Sci. 83:1028.
 
 
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