Forage Management Considerations to Improve Animal Productivity and Feed Efficiency

Transcription

1 Forage Management Considerations to Improve Animal Productivity and Feed Efficiency Bill Mahanna, Ph.D., Dipl. ACAN Global Nutritional Sciences Manager DuPont Pioneer Johnston, Iowa Introduction Efficiency of feed utilization in livestock production is important given the desire of most societies to consume more meat/milk as their economic conditions improve. Efficiency also plays into global food security concerns from a decrease in the agricultural land base due to urbanization of expanding human populations. Feed efficiency further impacts the profitability of livestock enterprises given current feed cost and increased competition for forage/grain in biogas and ethanol production. Feed conversion expresses the ratio of feed input to body mass gain output. Livestock industries use feed conversion as a profitability benchmark in the production of poultry (<2:1), pork (< 3.5:1) and beef (~4.5-20:1 depending upon age and diet). The dairy industry inverts the feed conversion ratio and typically evaluates dairy feed efficiency (DFE) defined as the units of milk output per unit of dry matter intake (DMI). Assuming an average dairy herd consists of 25% early lactation cows, 25% late lactation cows with the remaining 50% in mid-lactation, the recommended minimum DFE across an entire dairy herd is (Hutjens, 2004). This presentation will focus primarily on applied aspects of forage management to help improve feed quality and consistency to optimize DMI, rumen health/function and nutrient utilization. DFE Calculation Considerations Before discussing forage management considerations to improve the digestible dry matter of the diet, a few more concepts impacting the calculation of DFE must be discussed. Firstly, DMI by animal group (e.g. transition cows, early lactation or late lactation) must be accurately determined so DFE by animal group can be evaluated. Feed efficiencies will normally vary across animal groups (Table 1) with lactation cows approaching a DFE of 1.8, while late lactation, pregnant cows, gaining back lost body condition, will naturally have a lower DFE. Table 1. Minimum dairy feed efficiency goals by animal grouping. Source: Hutjens Animal Group Dairy Feed Efficiency Minimum Goals Entire Herd ( DIM) Heifer Group (<90 DIM) Heifer Group (>200 DIM) Older Cows (<90 DIM) Older Cows (>200 DIM) Fresh Cow Group (<21 DIM) Problem Herds ( DIM) <1.3

2 The evolution of feed management software interfacing with feed mixers now allows producers to capture feed delivery data. Perhaps more important is the ability to capture weigh-backs (feed refusals, orts) so accurate feed intakes by pen can be determined. It is also important that accurate pen counts are captured in larger dairies with cows moving between multiple pens and to be sure intake data and production data are from exactly the same time period. While average DMI per cow is what is used to calculate DFE, it is also important to understand and manage the daily variation in DMI such that the mechanics of feed delivery are not causing undue problems with rumen health and function. Secondly, monitoring forage moisture is critical for determining the dry matter of the diet, especially in rations comprised of upwards of 60-70% forage. Harvest moisture can vary significantly in grass or legumes based on plant maturity and wilting time. Even with maize silage, which tends to have a more consistent DM, rain or snowfall on an open bunker face can very quickly change the DM of the forage. This can result in errors in calculating DFE or more importantly, alter the forage to grain ratio in the diet as the forage is allocated by weight into the mixer wagon. It may also make economic sense to think of DFE from a total enterprise perspective, rather than a cow group perspective. In other words, total milk shipped (not produced) against total feed produced or brought on to the farm. This would help account for important economic factors such as DM loss (shrink) in silages or commodities, feeder error/waste and feedbunk management. Finally, when calculating the milk yield (numerator) portion of DFE, it is important to correct for milk components as more nutrients are needed when milk fat and protein content increases. The most common approach used in the United States is to correct (standardize) by adjusting milk yield to a content of 3.5% fat (FCM, calculated as (lb milk) (lbs fat)). A better approach is to use energy corrected milk (ECM, calculated as (12.82 x lb fat) + (7.13 x lb protein) + (0.323 x lb milk)) to also account for the variation in protein content of milk. Both of FCM and ECM assume 5.0% lactose content. Many nutritionists are starting to standardize milk yield for income potential based on how milk components are priced in specific end-use markets (e.g. fluid market, cheese or yogurt). Revenue corrected milk (RCM) corrects milk yield based on United States Federal Milk Marketing Order Class III component pricing for fat, protein and milk solids used to price over 50% of the milk produced in the United States. There is a computer and smart-phone app developed by Adisseo (MilkPay, which automatically uploads current component pricing and calculates FCM, ECM and RCM (Figure 1). Using RCM as the numerator in calculating DFE helps move DFE beyond solely a biological metric of efficiency to one which encompasses producer profitability.

3 Figure 1. Screen shots from MilkPay.com showing differences in fat, energy and revenue corrected milk based on current component pricing in the Upper Midwest US Federal Milk Marketing Order. Source: RCM can also be used in the calculation of income over feed cost (IOFC). IOFC is calculated as milk revenue/cow/day minus feed cost/cow/day. Both DFE and IOFC are key metrics increasingly being tracked by progressive North American dairies. However, if IOFC shows improvement, it may not necessarily indicate herd improvement, but simply that milk price has increased or feed cost was lower. By fixing feed price, component prices and milk check assessments (quality premiums, hauling) changes in RCM-based IOFC reflects the true economic performance of a herd over time (Bethard, 2012). Importance of Forage Quality The concept of higher forage quality improving DFE is fairly straightforward. Cows that consume more DM will give more milk (generally 2 kg milk for every 1 kg increase in DMI). As intake and milk production increases, typically so does DFE. The reason for the improved DFE is that a larger and larger portion of the cow s feed intake is being used for productive purposes and a smaller proportion for maintenance (Erdman, 2011). Casper (2008) published a literature review of 422 trials with an averaged DFE of 1.52, but with a range of 0.86 to Casper reported that from his field experience, herds having higher milk production with lower than expected DM intakes tended to be feeding extremely highly digestible forages. A separate field study of six commercial herds by Casper (2004) demonstrated a reasonably good relationship (R 2 =0.59, P<0.01) between DFE and the DM digestibility of the diet. There was also an inverse relationship (R 2 =0.72, P<0.01) between DFE and DMI such that cows with a higher DFE, consumed less DM. Within this study, the range in digestibility of the forages explained most of the variation in the diet, given that forages comprised the largest portion of the diet and had significantly more variability in digestibility than the grain or commodity feeds. Forage Focus Areas Once forage genetics are chosen and planted, there are four major areas over which producers have some control in optimizing forage quality: 1) harvest maturity/moisture, 2) particle size, 3) storage/feedout and 4) nutritional profiling.

4 Forage Harvest Maturity/Moisture Harvest maturity and moisture recommendations vary with different forage crops, animal requirements and storage structures (Table 2). Maturity stage affects both yield and quality. With grass and legumes, this is an inverse relationship in that delaying harvest to increase yield significantly reduces quality by increasing fiber content and reducing fiber digestibility. Optimum harvest timing also varies with the nutritional demands of the animal (e.g. need for digestible fiber to drive intakes and energy density) and agronomic considerations (e.g. reduced stand longevity from aggressive cutting schedules). Table 2. Acceptable ranges for forage crop harvest maturity and moisture content Crop Maturity Moisture Maize Silage Milkline 1/2-7/8 down the kernel 62-70% Lucerne Silage Mid-to-late bud 55-68% Grass Silage Start of stem elongation 55-68% Cereal Silage Grain at boot to soft dough 55-68% Maize silage and cereal crops are different in that delayed harvest typically increases both yield and nutritional value due to the increase in starch deposition in the seed/kernel. In healthy maize silage plants from 1/3 kernel milkline to blacklayer (kernel physiological maturity), it is possible for the plant to lay down as much as 1% unit of starch every day that harvest is delayed. The counterbalancing effect is the decline in digestibility of the fiber portion of the plant. However, research shows that newer maize genetics with improved late-season plant health do not decline significantly in fiber digestibility as the plant matures to the ¾ milkline or slightly later stage of kernel maturity. Proper maturity (and wilting time for grass/legumes) has a tremendous impact on moisture needed to fuel bacterial silage fermentation and exclude oxygen from the silage mass by reducing porosity. As forage DM increases, porosity also increases across similar dry matter densities (Mahanna, 2009). Proper maturity (and minimal wilting time) also ensures adequate fermentable sugars to drive the fermentation process as well as increase nutritional value of the forage. Forage Particle Size It is difficult to offer generalized particle size (chop length) recommendations because proper length depends on several factors including the need for physically effective fiber (pendf) levels in the ration, the type of storage structure, silage compaction capabilities and unloading methods (e.g. tower silo unloaders, silo bunker facers). Other factors affecting chop length include the need to chop finer to damage maize kernels if onchopper processing is not available or chopping longer to compensate for particle reduction from bagging or feed mixing. In general, shorter chop tends to improve compaction in the storage structure and also increases surface area of fiber or kernels to improve rate of digestion by rumen bacteria or intestinal enzymes. Longer chop increases the pendf of the feed; however, excessive length can contribute to feed sorting. It is best to work with the harvesting crew and nutritionist to decide on the proper chop length of each feed recognizing that particle

5 length in the final ration is what is most important. It is best to begin at evaluating particle size at the feedbunk and work backwards as to the amount of each feedstuff in the ration and how much pendf each one of those feeds need to contribute to the entire diet. Europeans tend to chop maize silage much finer (7-10mm) compared North American producers (17-19mm) who want to increase the pendf of maize silage particularly when it is the primary forage in the diet. Depending upon chopper design and roller mill settings, longer chopped silage may not deliver the desired degree of kernel damage. Higher dietary maize silage inclusion rates coupled with higher dry matter silages to capture more starch have focused the need to assure aggressive kernel damage. There has been much debate about what level of kernel processing is acceptable. This was complicated by the lack of a lab method to quantify the extent of kernel damage and lack of accepted processing standards. This changed with the commercialization of a standard laboratory assay (Ro-Tap Silage Processing Score) developed at Pioneer in conjunction with the U.S. Dairy Forage Research Center and Dairyland Laboratories (Arcadia, Wisconsin). This lab method protocol was openly shared and is now available as a routine analysis in many forage laboratories (Mahanna, 2008). While it is helpful to have a post-harvest, standardized laboratory measurement of kernel damage, it is equally important to have a field method to make processing adjustments as the crop is being harvested. DuPont Pioneer has developed a simple field test using a l liter (32-ounce) cup. Producers are encouraged to sample several loads each hour by filling the cup level with silage; spreading the sample out and quickly picking out every whole and half kernel. If that number exceeds 2-3 kernels in 1 liter volume of silage, it is important to discuss with the chopper operator how to improve kernel processing. If left unattended, the result will be a loss in energy as unprocessed kernels escape ruminal and intestinal digestion. There are several factors that chopper operators can check to improve processing: length of chop (longer typically more difficult to damage kernels), roller mill wear and design (life of hours depending upon mill), roller mill gap (1-3mm depending upon chop length and kernel maturity), aggressiveness of the rolls, and roller mill differential (typically 20-40%). Shredlage ( is the name of a new type of kernel processor released in North America in It was designed to allow a longer length of chop for more effective fiber in high maize silage diets, while delivering aggressive kernel damage not typically possible with conventional rolls in longer-cut silage (Mahanna, 2012). Figure 2. Conventional kernel processing rolls (L) and shredlage (R) rolls (R). Photos courtesy of Randy Shaver, University of Wisconsin

6 This new technology has been relatively rapidly adopted by dairy producers not satisfied with their current level of kernel processing and also desiring to eliminate grasses, legumes or straw from the diet as a source of physically effective fiber. Preliminary lactation studies with shredlage have been encouraging. Maize silage from the same hybrid was harvested at the University of Wisconsin Arlington Research Dairy at approximately 35% dry matter and stored in 3-meter (10-ft) diameter silo bags (Ferraretto and Shaver, 2012). The conventional kernel processed silage (KP) was harvested with a John Deere 6910 at a theoretical chop length of 19 mm (0.74 in.) and processed with conventional rolls set at a 3-mm gap. The shredlage treatment (SHRD) was harvested with a Claas Jaguar with half of the knives removed to achieve a 30- mm (1.18-in.) chop length with novel cross-grooved rolls set at a 2.5-mm gap. The trial diets included very high maize silage inclusion rates (12.5 kg DM) typical of many large dairies desiring the convenience and nutritional value of maize silage over grass or legume forages. The cows fed the SHRD diet tended (P <0.08) to consume 0.64 kg (1.4 lb) per day more dry matter than the KP treatment cows. Milk yield averaged 43.6 kg (96.0 lb) for the SHRD cows and 42.8 kg (94.2 lb) for the KP cows but was not statistically different (P < 0.14) between treatments. Fat percentage, protein percentage and milk urea nitrogen did not differ between treatments. The researchers concluded that feeding shredlage maize silage tended to increase dry matter intake and that fat- (P < 0.07) and energycorrected (P< 0.10) milk yields also tended to be 1 kg (2.2 lb) per day greater for cows fed shredlage compared to traditionally harvested and processed maize silage. The cow response is theorized to be from a more normalized rumen environment due to the increased pendf of the maize silage as cows are increasing intake in early lactation. Forage Storage/Feedout Silage fermentation can be simplified into three phases. Silages experience aerobic conditions during harvest and filling, followed relatively quickly by anaerobic conditions which initiate lactic acid bacterial growth and ph decline, and finally, back to aerobic conditions during feedout. Dry matter loss (shrink) begins with plant cell respiration and aerobic microflora utilizing carbohydrate sources (primarily sugar) producing water, heat and carbon dioxide (C0 2 ). It is this carbon, lost to the atmosphere, which causes shrink loss. Wilting time and speed of harvest impact the extent of these aerobic field losses. These processes will continue until the oxygen in the silage mass is depleted (Mahanna, 2010b). Plant moisture and compaction play a role in reducing the length of the aerobic phase in the storage structure by reducing silage porosity. The subsequent anaerobic phase establishes an environment suitable for domination by homofermentative and heterofermentative lactic acid bacteria (LAB). There would be no shrink loss in this phase if only homofermentative LAB were active. However, less than 0.5% of epiphytic organisms naturally found on fresh crops are LAB and only a small proportion of these are homofermentative. To put the loss from heterofermentative LAB in perspective, there is a 24% loss of dry matter from the heterofermentative fermentation of glucose. These anaerobic fermentation losses can be reduced by 25% or more with the use of homofermentative strains found in reputable silage inoculants. The re-exposure of silage to aerobic conditions can be divided into two areas: 1) top and side exposure with upwards of 20% of silage contained in the top meter in most bunkers and drive-over piles, and 2) face exposure during feedout. The combination of these two

7 sources of shrink loss can vary significantly due to management level with estimates of greater than 20% loss in net energy (in starch equivalents) reported in the literature from aerobically unstable silages. The increased use of bunkers and piles with large exposed faces (as compared to the smaller face exposure in tower silos or bags) results in significantly more shrink in the aerobic, feedout phase than in the initial aerobic phase. Several technologies can be employed to reduce top and face spoilage including specialized packing equipment, oxygen-barrier film, silage facers and bacterial inoculants containing Lactobacillus buchneri. The fact that L. buchneri is a heterofermentative LAB may lead to questions as to why inoculant manufacturers would use a LAB known to be less efficient than homofermentative strains. They are used because the metabolites of their growth inhibit yeast growth during feedout, and it is silage yeast which initiates the cascade of events leading to aerobic losses. In addition, most products containing L. buchneri also contain homofermentative strains to facilitate a rapid, front-end ph decline. Silage shrink results in the loss of the most valuable silage nutrients. When silages ferment, sugars and starch are what the aerobic organisms and LAB utilize, and fiber levels are actually increased (concentrated). To understand the true cost of shrink, losses must be replaced with a nutritionally equivalent energy source, such as maize grain. For example, if management changes could reduce shrink by 20% (from 15% to 12.5%), it would equate to a value of approximately 2 per metric ton of as fed maize silage if that energy had to be replaced with maize grain priced at 219/metric ton (Mahanna, 2010b). Silage producers are keenly aware of the losses from top or side spoilage. However, they may need additional convincing as to the shrink loss being incurred in what may appear to be normal silage. What does not work very well for quantifying shrink is relying on truck weights into the bunker compared against TMR weights out of the bunker. There is just too much room for measurement error, and it does not account for the biological fact that silage comes out of the storage structure higher in moisture than when it was ensiled due to aerobic microbial activity generating moisture. However, there are several approaches that can be used to quantify the nutritional cost of shrink. One is the use of ash, ph and temperature measurements of silage on the bunker face compared to a deeper (0.5 m) probed sample. In a 2003 Idaho field study of 12 non-inoculated bunkers and piles conducted by DuPont Pioneer researchers, the average ash, ph and temperature were 0.27% units, 0.3% units and 8 o C (12.9 o F) higher for the face compared to the deep probe sample. When the ash data was entered into an organic matter recovery equation developed at Kansas State University (Holthaus et al., 1995), it estimated an increased 5.6% organic matter dry matter loss in the surface silage. Totally replacing the lost organic matter with maize starch would require more than 25 kg of maize grain for every metric ton of silage fed. Another approach used by Pioneer to help producers visualize the heating caused by aerobic microbial activity is the use of thermal sensitive cameras (Figure 3). Silages normally heat 6-8 o C (10-15 o F) above the ambient temperature at ensiling. The moisture in larger bunkers or piles retains this unavoidable (physiological) heat, which is slowly dissipated throughout the storage period. If silage is removed from the storage structure and continues to heat, this is problematic heating caused by aerobic organism growth leading to a loss in nutrient value and palatability.

8 Figure 3. Thermal image of well-managed, inoculated bunker of maize silage Proper silage feed-out management is essential to maintain consistent and high quality ensiled forages. Research shows that poor face management can easily double shrink losses. Besides the financial loss associated with shrink, feed quality and consistency can vary dramatically and may contribute to livestock production and rumen health problems. Porosity is the enemy of silage so proper moisture (to fill in the air spaces), particle size, compaction (density) and sealing methods are also keys to maintaining anaerobic conditions. It is also advisable to remove and dispose of visibly moldy feed from the sides or top of the storage structure and not to allow loose, aerated silage to pile up for extended periods of time before feeding. Proper feedout practices are especially important during warm periods of the year because the biological activity of aerobic bacteria and yeast organisms increases twofold for approximately every 6 o C (10 o F) increase in temperature. Consequently, it becomes challenging to stay ahead of aerobic instability during the spring and summer. It is also common to have bunklife problems with harvested forages that have been rained-on before chopping and ensiling. Rain can leach crop sugars and splash soil-borne bacteria and fungi (mold) onto the crop, effectively seeding the silage with spoilage organisms awaiting the chance to grow if provided the opportunity. Crops stressed by drought, insect or hail damage will generally possess elevated fungal counts dictating that proper management be followed when ensiling these stressed crops. The first criteria of stable silage is achieving a low terminal ph producing a hostile environment to inhibit the propagation of spoilage microorganisms such as aerobic bacteria, yeast and molds. Inoculants containing L. buchneri strains have been a tremendous benefit by inhibiting the growth of yeast. A second criteria for stable silage is the maintenance of an anaerobic, or oxygen free environment for as much of the silage as possible. Silages should be removed from bunker and pile faces by mechanically shaving the silage face from top to bottom or peeling the silage horizontally with a frontend loader bucket. This is preferred to lifting the bucket from the bottom to the top. Lifting creates fracture lines in the silage mass and allows oxygen to enter promoting aerobic activity. Even when removing the desirable 15cm daily from the silo face, oxygen can still penetrate upwards of a meter back into the stored mass. This facilitates heatgenerating aerobic activity which may not fully dissipate from the face. Use of inoculants containing L. buchneri do allow for reduced feedout rates while maintaining aerobic stability. Silage facers are becoming increasingly popular. They blend feed across the entire face and cleanly remove silage without disrupting compaction, which is often the result with improper use of front-end loaders.

9 Forage Nutritional Profiling Tremendous progress has been made in recent years in the analytical ability to accurately profile the nutritional value of forages and to understand the associative effects when combined with other dietary ingredients. The ultimate goal is to optimize and normalize rumen microbial populations so enhanced nutrient utilization leads to improved DFE. Some of these analytical advances include laboratory estimates of: 1) neutral detergent fiber digestibility (NDFD), 2) 7-hour ruminal starch digestibility, 3) maize silage processing scoring, 4) particle size screening of non-forage starch sources, 5) fecal starch analysis (quantify starch wastage), 6) soluble protein content ( a proxy measurement for increased ruminal starch digestion over time in fermented storage) and 7) Fermentrics TM which is a proprietary gas fermentation analysis allowing for quantification of digestion rates, associative effects and microbial biomass production (Mahanna, 2010). When producers implement rations containing a high inclusion rate of any single feedstuff, it is critical that the digestion parameters of this feed be clearly understood as quickly as possible to prevent the loss in production while determining how it fits into the ration of a commercial dairy. A good example is the high maize silage inclusion rate (exceeding 10 kg DM/cow/day) which has proven to be the foundation of economical rations on many North American dairies. However, some of these herds have reported loose manure, erratic intakes and lowered components among lactating cows fed longstored maize silage despite no readily apparent changes in nutrient content of the silage or other ration components. Research results are now shedding light that changes in starch digestibility over time in storage may be an underlying cause of these mysterious feeding problems (Mahanna, 2007). Newbold et al. (2007) reported research on changing starch digestibility in maize silages stored in bunker silos in the Netherlands. The proportion of starch degraded after three-hour in situ incubations increased significantly (P < 0.001) with storage time with a mean of 53.2% at two months and increasing to 69% at 10 months of storage. There is an abundance of maize silage fermentation research showing rapid changes in ph, soluble protein and starch digestibility (not fiber digestibility) in silages during the first two to three months of the ensiling process. This research lends credence to feeding recommendations of many nutritionists to wait to feed new-crop maize silage until early- December (if inventories allow). What was not understood until recently was what happens in longer-stored maize silage and fermented maize grain. Time-course studies with lab-scale research silos indicate that maize silage starch digestibility plateaus after about five to six months in storage. This finding is further supported by the gradual increase in protein solubility in maize silage samples submitted to commercial laboratories. It is thought that protein solubility is highly related to starch digestibility due to the solubilization of zein proteins thought to interfere with ruminal starch digestion (Mahanna, 2007). By waiting several months to feed new-crop maize silage, producers avoid the rapid changes that reduce feed consistency. It is also important that nutritionists continue to account for the upward drift in ruminal starch digestibility (about 2 percent units per month) that occurs, following the early dynamic period. Failure to account for changing ruminal starch digestion rates may explain some of the spring acidosis and milk fat depression seen on dairies feeding high levels of maize silage; especially when fed in conjunction with high rumen fermentable starch sources such as barley, wheat, and highmoisture or steamflaked maize.

10 Acidosis The potentially negative effect of sub-clinical ruminal acidosis is a genuine concern in diets containing highly digestible forages. Acidosis in these diets are typically cause by either the lack of effective fiber or not accounting for the changes in ruminal starch digestiblity discuss previously. Casper (2008) reported on a summary of the energy metabolism database from the Energy Metabolism Unit (EMU) of the United States Department of Agriculture-Agriculture Research Service (USDA-ARS). The EMU database represents more than 40 years and 1351 individual lactation trials of studies measuring the energy and protein digestibility of diets that varied in forage types, grain sources, protein sources, and fat supplements. In this database, diets that resulted in lactating dairy cows having inverted fat and protein ratios (indicative of sub-clinical acidosis) caused a negative effect on DFE. Acidosis dramatically reduced the relationship of DFE to absorbed DM (FE = * DM absorbed, kg/day; R 2 = 0.28, P < 0.01). Acidosis also was linked to reductions in the digestibility of ADF and cellulose. Casper and Mertens (2007) reported that acidosis increased the amount of heat produced per unit of digestible energy (51.4 vs. 54.6%), reducing the conversion of digestible energy into net energy available for productive purposes. Casper concluded that implementing forage management practices to produce the highest quality forages, while preventing acidosis, had significant potential for improving DFE. Fermentrics TM Adjusting rations to account for digestibility differences among new-crop forages can be a humbling experience. Single time point NDF digestibility values (e.g. 24 hr-ndfd, % of NDF) can provide some comparative direction for nutritionists, but have limited value in modern ration-balancing software requiring feed library inputs for digestion rates (K d ) rather than single time-point NDFD values. A new laboratory method called Fermentrics TM Gas Fermentation System (Figure 4) was released for commercial use by North American dairymen and their nutritionists in October, This method utilizes a rumen-fluid, batch culture, gas fermentation system to which mathematical curve-peeling techniques are applied to differentiate rapid from slowly fermenting carbohydrate pools. This allows for a more direct approach to measuring carbohydrate pool (B 1 (starch), B 2 (soluble fiber), B 3 (NDF) ) digestion rates as defined by the Cornell Model (Mahanna, 2010). Most laboratories that currently provide NDF digestion rates calculate them from NDF, lignin and single time point NDFD values inputted into what is commonly referred to as the Van Amburgh Rate Calculator (Van Amburgh et.al, 2003, CVAS, 2010). Alternatively, rate and extent of organic matter degradation, employing hundreds of data points, can be determined with in vitro gas production systems based on monitoring gaseous fermentation products (CO 2, CH 4 ) of microbial metabolism and additional CO 2 produced upon buffering of microbial produced short-chained fatty acids (primarily acetate and butyrate). In addition to measured digestion rates, Fermentrics also provides one of the best indicators of total ration energy density and associative effects between all dietary ingredients by quantifying microbial biomass production (MBP). MBP on Fermentrics reports is measured directly by analyzing the substrate that remains after a 48-hour incubation with a NDF analysis (without amylase or sodium sulfite). The difference between the weight of the substrate before and after NDF analysis is the microbial biomass yield of the rumen fluid incubated sample. To give an idea of the variability in

11 MBP among dairy diets, 275 lactation total mixed rations analyzed with Fermentrics in 2011 showed an MBP yield of 117 mg/g with a range of mg/g. The same level of variability exists within individual forages. In 123 maize silage samples analyzed by Pioneer in 2012, the average MBP was 113 mg/g with a range of mg/g. If DMI of the diet is known, it is possible to convert MBP to estimated grams of rumen microbial protein produced by using the equation: MBP x 0.41 x 1.3 x Kg of DMI. The 0.41 is the assumed amount of microbial protein contained in the biomass being measured, 1.3 is an adjustment factor accounting for about 30% of the rumen bacteria existing in the liquid phase thus not measured in the biomass value. Using an actual TMR example from Fig 4, mg/g MBP with an average cow DMI of 27 kg, equates to approximately 1,978 grams of microbial protein produced. According to the 2001 NRC Nutrient Requirement of Dairy Cattle, the microbial supply would be expected to support about 28 liters (1978/70) of milk production. However, the total protein supply utilized for milk production will be sum of microbial protein plus the contribution of rumen undegraded protein provided in the diet. In challenging field situations, Fermentrics TM provides consulting nutritionists with data that can be shown to producers to help them understand why they are experiencing production issues and also help convince the producer why the recommended course of action must be implemented. It has also proven helpful to conduct a TMR gas fermentation analysis when cows are performing to expectations and use this as a benchmark should future production falter (Mahanna, 2010). Figure 4: Example of a Fermentrics Report of a lactation total mixed ration Conclusion There are many factors that exert an effect on dairy feed efficiency including animal maintenance cost, cow health, housing/environmental conditions, stage of production, changes in body condition, and dietary additives proven to enhance rumen function or alter nutrient digestion. However, with forages being the backbone of the dairy ration, it makes economic sense to focus management attention on enhancing forage quality from harvest through feedout and then employing available technologies to better understand associative effects when forages are combined with other ration ingredients.

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