Advantages and limitations of dairy efficiency measures and the ...

The Professional Animal Scientist 33:393–400 https://doi.org/10.15232/pas.2017-01624 ©2017 THE AUTHORS. Published by Elsevier Inc. on behalf of the American Registry of Professional Animal Scientists. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Review: Advantages and limitations of dairy efficiency measures and the effects of nutrition and feeding management interventions M. B. de Ondarza* and J. M. Tricarico†1 *Paradox Nutrition LLC, 413 Lake Shore Rd., West Chazy, NY 12992; and †Innovation Center for US Dairy, Rosemont, IL 60018

ABSTRACT Economists, nutritionists, and geneticists have attempted to describe dairy cattle efficiency in simple, quantifiable terms. On-farm measures of dairy efficiency include physical feed efficiency, efficiency of nutrient usage, economic feed efficiency, total dairy enterprise efficiency, and lifetime efficiency. Each calculated measure of dairy efficiency has its own advantages and limitations. Each measure has merit for describing a segment of dairy efficiency, yet no one measure can sufficiently describe dairy efficiency or be applicable across all farms. Use of multiple dairy efficiency metrics, each with a moving target specific to the individual dairy enterprise, should be considered. Nutrition and nutrient management interventions can improve the use of dairy resources, increasing both economic and environmental sustainability. With greater DMI and milk yield, a smaller proportion of dietary nutrients are used for maintenance functions, improving productive efficiency and reducing the environmental impact of the dairy cow. Nutritional factors independent of cow genetic merit affect energetic losses in the form of feces, heat of digestion and metabolism, or methane. Improvements in nutrient retention can occur with increases in rate of digestion and decreases in rate of passage of feed ingredients. Forage and grain losses, feed ingredient options, and forage and feed ingredient targeting according to cow potential need to be considered. Consistency of delivery and consumption of the formulated ration without high feed refusal rates typically improves dairy efficiency. Cow grouping affects social behavior, cow well-being, nutrient wastage, milk yield, and expenses, with optimum strategies being farm specific. Key words: sustainability, milk production, nutrient waste, economic viability

INTRODUCTION Sustainable dairy production must return a profit for the dairy enterprise and produce quality milk for consumCorresponding author: [email protected]

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ers while maintaining optimal cow well-being and practicing environmental stewardship (von Keyserlingk et al., 2013). Feed typically accounts for 50 to 60% of the operating expenses on a dairy farm, making it a logical focal point when trying to increase efficiency (Knoblauch et al., 2012). Yet, high milk production, which requires proper nutrition, typically generates more profit than low feed cost (Dunklee et al., 1994; VandeHaar and St-Pierre, 2006). The economic objective of the farm is generally to maximize net economic returns while converting a greater percentage of feed nutrients into milk with little nutrient wastage. Fortunately, Place and Mitloehner (2010) concluded that increasing productive efficiency also results in fewer air emissions per unit of milk. In today’s marketplace, sustainability is a new indicator of quality. It can be tempting to use dairy efficiency metrics to address consumer and retailer questions about sustainability. However, although each measure has merit for describing a segment of dairy efficiency, no one measure can entirely describe a dairy’s efficiency or be applicable across all farms. Each calculated measure of dairy efficiency has its own advantages and limitations. Dairy efficiency goals should be considered to be moving targets that are specific for the current situation of individual dairy enterprises with the focus placed on continuous progress. The objectives of this review are to discuss the advantages and limitations of current measures of dairy efficiency and to describe the effects of nutrition and feeding management on dairy efficiency regardless of genotype. Actions that herd managers and nutritionists can immediately implement to increase dairy efficiency in their operations are discussed.

REVIEW AND DISCUSSION Dairy Efficiency Measures—Description, Advantages, and Limitations Physical Feed Efficiency. The most well-known and used measure of dairy efficiency is the amount of milk produced, expressed as 3.5% FCM, 4% FCM, or energy-corrected milk, per unit of DMI or “physical feed efficiency” (physical FE). This is a measure of gross feeding efficiency

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Table 1. Advantages and limitations of various dairy and feed efficiency measures Measure

Abbreviation Calculation

Advantages

Physical feed efficiency

Physical FE

FCM or energy-corrected milk/DMI

Indicates digestibility

Energy conversion efficiency Residual energy intake Ration cost efficiency

ECE

Milk energy/ME intake

REI

ME intake − Energy requirement Fiscal milk value/Fiscal DMI value Farm feed cost/45.4 kg of milk shipped

Considers diverse nutrient efficiencies Less influence of body reserves Reflects profits

Feed cost per hundredweight (45.4 kg) Milk income over feed cost Lifetime efficiency

FEvc Feed cost/cwt IOFC

Milk income − Feed costs

 

Energy in lifetime milk, conceptus, and body/ Lifetime GE intake

calculated as the ratio of total outputs divided by total inputs (Table 1). Physical FE indicates whether cows are digesting their ration according to expectations (St-Pierre, 2008) and influences both environmental and economic outcomes. The simplicity of calculating physical FE as FCM/DMI incurs numerous limitations (Table 1). First, physical FE does not consider body tissue accretion and mobilization, the implication being that physical FE changes with DIM. Maximum physical FE occurs in early lactation when cows are in negative energy balance and mobilizing body tissue to support milk production. As lactation progresses, physical FE declines exponentially over the first 3 mo and eventually linearly until lactation finishes (St-Pierre, 2008). Based on field experience, Hutjens (2005) suggested goals for physical FE (3.5% FCM/DMI) as 1.6 to 1.8 for multiparous cows 200 DIM, and 1.4 to 1.6 as a mean for all cows between 150 to 225 DIM. Erdman (2011) suggested assessment of 150-d physical FE to correct for the effects of DIM and evaluate nutrition and management changes on a dairy in year-on-year comparisons. It is evident that physical FE as a benchmark for sustainable milk production has numerous limitations, making it necessary to evaluate other economic and efficiency measures concurrently. Primiparous heifers still using nutrients for growth will present lower physical FE values than mature cows (Hutjens, 2005). Physical FE also ignores environmental stressors such as heat or cold that depress efficiency (Britt et al., 2003; Hutjens, 2005). Physical FE gives no consideration to nutrient density and nutrient profile. For example, increasing dietary fat increases dietary energy density, also increasing physical FE by 0.03 to 0.10 units per percentage unit of fat addition (Erdman,

Includes cost of dry period and reproductive efficiency Helpful for short-term feeding decisions Includes heifer, reproductive, and longevity efficiencies

Limitations Ignores nutrient density, cost, and body reserves Ignores body reserves Relies on prediction of energy requirements Ignores body reserves Ignores heifer costs and fiscal value of milk Dependent on feed costs and milk value Difficult to calculate for individual farms

2011). Typically, supplemental dietary fat is more costly than other energy sources. Protein quality and cost play a role in dairy efficiency but are not considered with FCM/ DMI. With ideal rumen function, digestion, and microbial protein synthesis, RDP can make up a greater proportion of dietary protein, reducing the need for RUP, which is typically more expensive. Grain and forage lost from shrink and feeding refusals are not considered in physical FE either but greatly influence environmental and economic outcomes. Efficiency of Nutrient Usage. Efficiency of use of individual dietary nutrients may not be similar (Armentano and Weigel, 2013), and calculation of separate nutrient efficiencies such as energetic efficiency and N efficiency can be valuable. Gross nutrient efficiencies, based on the amount of nutrient consumed, are typically calculated. Digestive efficiencies can be informative for comparing genotypes but can also be useful for nutritionists and environmentalists if fecal nutrient losses are separately accounted (Owens et al., 2016). Differences in metabolic efficiency suggest divergence in nutrient partitioning between milk production and other nutrient uses such as body tissue accretion. Thus, metabolic efficiency is used more by geneticists rather than by nutritionists or environmentalists (Phuong et al., 2013). Energy conversion efficiency is calculated as milk energy output divided by ME intake (Table 1). Unfortunately, as with physical FE, energy conversion efficiency will be improved with greater mobilization of body reserves (early lactation) and reduced during body tissue accretion (late lactation). Because of the negative effects of body reserve loss on reproduction and health, greater energy conversion efficiency is not always desirable. Residual energy intake (REI) is actual ME intake minus the predicted

On-farm measures of dairy efficiency

energy requirement of the cow based on production, BW, BW change, and gestational energy needs (Mantysaari et al., 2012; Table 1). Because BW changes are predicted and accounted for, REI is influenced less by body reserve loss and gain. A reduced REI indicates that less energy is wasted after accounting for the energy in milk, maintenance, and growth and that efficiency of energy use is improved. Mantysaari et al. (2012) concluded that stage of lactation affected REI among Nordic Red cows. This relationship could be due to true energetic efficiency differences during the lactation or to inadequate assessment of changes in body reserves affecting calculated REI. Economic Feed Efficiency. As measures of physical FE increase, economic profitability typically increases, but this positive association is not always true (St-Pierre, 1998). Robinson and Erasmus (2010) demonstrated that greater DMI and milk yield are typically more profitable than a similar physical FE ratio with reduced DMI and milk yield. Because dairy sustainability also requires dairy profitability, calculation and evaluation of economic efficiency is prudent. Ration cost efficiency is the fiscal value of milk divided by the fiscal cost of consumed DM (Robinson and Erasmus, 2010; Table 1). Unfortunately, ration cost efficiency does not account for BW changes, heifer growth, health, longevity, forage DM losses, feed refusals, and feed shrink. Feed cost per hundredweight (45.4 kg) is calculated as the accumulated feed cost for lactating and dry cows divided by the amount of milk (hundredweight) shipped (Table 1). Feed price, feed refusals, feed shrink, dry period length, reproduction, and herd health all affect feed cost per hundredweight; however, the heifer enterprise and milk composition are not considered (Bethard, 2013). StPierre (1998) argued against the objective of minimizing feed cost per hundredweight of milk but rather supported a system of accurate nutrient value estimation based on market prices of many feed ingredients and optimization of feed resources and production. Milk income over feed cost (IOFC) is a calculated margin that has been used for decades (Table 1). Use of IOFC is helpful for short-term feeding and management decisions, but it is not recommended for long-term herd performance assessment because it is dependent on fluctuating milk and feed prices (Bethard, 2013). Calculated IOFC may or may not include costs of feed shrink, feed refusals, cow health, dry cow management, or heifers. A survey of 95 Pennsylvania dairy herds from 2009 to 2012 determined a mean IOFC of $7.71, ranging from −$0.33 to $16.60 (Buza et al., 2014). Improved nutrition and milk yield positively affected IOFC more than reduced feed cost. Money Corrected Milk (Bethard, 2013) calculates milk prices in the same way as milk processors, with fixed feed and milk component prices over time, to more accurately reflect herd performance over time. This measure is an improvement over IOFC yet still does not include costs of feed shrink, feed refusals, cow health, dry cow management, or heifers.

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Lifetime Efficiency. Lifetime efficiency is the percentage of lifetime feed energy (GE) intake converted into milk, conceptus, and body tissues (VandeHaar and St-Pierre, 2006; Table 1). Obviously, earlier and more efficient calf and heifer growth and greater longevity generally equate to improved lifetime efficiency. It was calculated that a cow producing 9,000 kg of milk/yr at maturity would have a lifetime efficiency of 17% after the first lactation and 20.5% after the third lactation, only increasing to 21.4% after the fifth lactation (VandeHaar and St-Pierre, 2006). Total Dairy Enterprise Efficiency. To accurately describe the efficiency of a dairy enterprise, all nutrient losses and gains need to be accounted for. This includes nutrient losses associated with crops, manure, feeding management and reproductive inefficiency, feed nutrients required for replacement heifers and dry cows, and the value of animals sold for beef or other purposes. Integration of accurate farm data including actual DM and nutrient intakes with advanced nutrition models such as the NRC (2001), Molly model (Baldwin et al., 1987), or Cornell Net Carbohydrate and Protein System (Sniffen et al., 1992; Higgs et al., 2015; Van Amburgh et al., 2015) and whole-farm dairy models such as DairyWise (Schils et al., 2007) and the Integrated Farm Systems Model (Rotz et al., 2013) could help to more accurately calculate actual total dairy enterprise efficiencies.

Nutritional Factors Related to Dairy Efficiency With greater milk yield, a smaller proportion of dietary nutrients are used for maintenance functions. This “dilution of maintenance” has been the primary source of increased productive efficiency on commercial dairies for the last century. For example, a Holstein cow producing 45 kg of milk/d needs 4 times as much energy as that needed for maintenance, whereas a Holstein cow producing 90 kg/d requires 7 times as much (VandeHaar and St-Pierre, 2006). Improved milk production also has been a major contributor to the reduced environmental impact of the dairy industry over the last century (Capper and Bauman, 2013). Physiological state, physical and chemical aspects of the diet, psychogenic factors, and environment influence dairy cow meal size and frequency (Allen et al., 2009). If cows consume less DM and maintain milk yield, physical FE will improve. However, with greater on-farm DMI, diets can be reformulated so that required nutrients can be provided with reduced diet nutrient density, often promoting rumen health, reducing supplemental fat needs, and increasing economic FE if ration cost per kilogram is reduced. It must be recognized, however, that increased DMI can also promote feed passage, increasing fecal losses and reducing digestive efficiency to some degree (Tyrrell and Moe, 1975). Maximum energetic efficiency in the dairy cow equates to minimal energy loss. Opportunities exist on many dairies for improving dairy efficiency with dietary changes

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Table 2. Effect of nutrition and feeding management factors on dairy efficiency measures with considerations for nutritionists and dairy managers Factor DMI Fiber digestibility Starch digestibility Added dietary fat Feed additives By-product feeds Rumen function Precision feeding Feed storage losses and refusals Methane losses Cow comfort and feeding behavior Milk composition Heifer growth Dry period length

Dairy efficiency measures affected and considerations1 Greater DMI with increased milk yield may improve all efficiency measures. Reduced DMI with no change in milk yield may improve all efficiency measures. Greater DMI with reduced cost per unit of DMI can decrease physical FE but improve FEvc, feed cost/cwt, and IOFC. Greater fiber digestibility may increase milk yield, reduce fecal losses, and improve all efficiency measures, but a portion of the effect can be negated by increased DMI. Greater starch digestibility may increase milk yield, reduce fecal losses, and improve all efficiency measures; however, economic costs of grain processing and silage storage need consideration. Increased dietary fat may improve physical FE while reducing FEvc and IOFC and increasing feed cost/cwt. Feed additives may improve rumen health and digestion, reduce methane losses, increase milk yield, and improve all efficiency measures. Purchased commodity by-product feeds can reduce feed expenses, improving FEvc, feed cost/cwt, and IOFC but not physical FE, ECE, or lifetime efficiency. Improved rumen function may increase digestibility, reduce fecal loss, increase rumen microbial protein yield, and improve all efficiency measures. Provision of optimal diets to all cows based on DIM, potential production, ingredient prices, and predicted variation in parameters may improve all efficiency measures. Fewer feed storage losses and refusals can decrease feed cost/cwt and increase IOFC without affecting FEvc, physical FE, ECE, or lifetime efficiency. Reduced methane losses can improve all efficiency measures. Enhanced cow comfort and feeding behavior can optimize rumen health and digestion, increase milk yield, and improve all efficiency measures. Greater milk component percentage can improve physical FE, ECE, FEvc, IOFC, and lifetime efficiency without affecting feed cost/cwt. Improved rates of gain in heifers resulting in reduced age at first calving can increase lifetime efficiency without affecting other efficiency measures. Reduced days dry can decrease feed cost/cwt, increase IOFC, and increase lifetime efficiency without affecting physical FE, ECE, or FEvc.

FE = feed efficiency; FEvc = ration cost efficiency; cwt = hundredweight (45.4 kg); IOFC = milk income over feed cost; ECE = energy conversion efficiency.

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that reduce energy losses (Table 2). Only about 24 to 33% of GE consumed by the cow is actually used for productive purposes (NEl; Moe et al., 1972; Weiss, 2010). Energy losses as methane have been estimated at 3.7 to 10.1% of GE intake (Yan et al., 2000). An evaluation of 20 energy metabolism studies with 579 lactating dairy cows concluded that high milk yield and high energetic efficiency reduce methane energy losses as a proportion of energy intake (Yan et al., 2010). High efficiency of feed usage in the dairy cow requires maximum fiber and starch digestibility and minimal fecal excretion of energetic nutrients. Improved fiber digestibility potentially reduces fecal losses and increases milk yield (Oba and Allen, 2000; Kendall et al., 2009). Yet, depending on other dietary ingredients, improvements in forage fiber digestibility may increase DMI and rate of passage, negating a portion of the benefits provided by increased digestibility (Tine et al., 2001). Grain type, endosperm type, maturity, DM, and particle size influence total-tract starch digestion, ranging from 70

to 100% (Firkins et al., 2001). Degradation of zein proteins during ensiling increased starch digestibility in highmoisture corn (Hoffman et al., 2011). Kernel processing of corn silage improves starch digestibility, especially with more mature corn plants (two-thirds milkline or black layer; Johnson et al., 2003). Rumen starch digestibility of semiflint corn increased from 36% at a mean particle size of 3,668 μm to 59% at a mean particle size of 730 μm (Remond et al., 2004). Extrusion of wheat dried distillers grains with solubles improved OM and starch digestibility and increased 3.5% FCM yield, without changing DMI, indicating greater physical FE (Claassen et al., 2016). Enhancements in rumen function can improve digestibility, reduce fecal loss, and maximize microbial protein yield, lessening the need for expensive RUP and fat supplementation and potentially improving economic FE (Krause and Oetzel, 2005; Roman-Garcia et al., 2016; Strobel and Russell, 1986). Dietary factors including total dietary starch and fiber, digestion rates, and rumen effective fiber all affect rumen pH and retention of feed parti-

On-farm measures of dairy efficiency

cles. Cow comfort, cow management, feeding method, and forage management influence feeding behavior and feed intake patterns that also contribute to rumen health (Nocek, 1997; Stone, 2004). Proven feed additives can improve rumen pH, digestibility, milk yield, and physical FE and reduce methane losses (Desnoyers et al., 2009; Poppy et al., 2012). Ionophores such as monensin can be fed to inhibit growth of gram-positive bacteria, increase propionate yield, reduce acetate and butyrate, and decrease energy loss in the form of methane, and potential negative effects of ionophores are reduced milkfat concentration and decreased fiber digestion (McGuffey et al., 2001; Ipharraguerre and Clark, 2003). In a meta-analysis with 22 studies with dairy and beef cattle, monensin decreased the percentage of GE lost as methane from 5.97 to 5.43% (Appuhamy et al., 2013). Physical FE improvements with monensin may be diet dependent. Akins et al. (2014) increased physical FE with monensin regardless of diet, but the response was greater with a diet containing 27 versus 20% starch. Formulation of efficient rations requires accurate laboratory evaluation of rumen fiber and starch digestion rates and dynamic nutrition models that optimize and predict DMI and reveal dietary factors limiting efficiency of feed usage. Sophisticated techniques should be used to simulate responses of cow groups to diets and predict optimum diet nutrient densities based on DIM, potential production, ingredient prices, and predicted variation in parameters (St-Pierre and Thraen, 1999).

Feeding Management Factors Related to Dairy Efficiency Total potential DM losses from harvest to feed out range from 17 to 64% for hay-crop silage and 12 to 23% for corn silage (Holmes, 2013). Reducing these nutrient losses can significantly improve total dairy enterprise efficiency. Forage decisions including crop and storage types should be based on land availability, soil characteristics, manure management needs, climate, and feeding method. Dynamic models such as the Integrated Farm System Model (Rotz et al., 2013) can help to evaluate many of these factors, aiding the decision process. Once the crop is harvested and stored, DM losses continue. Typical silage storage losses range from 10 to 16% (Rotz and Muck, 1994). Numerous factors influence silage preservation, including extent of plant respiration, the activity of plant enzymes, aerobic microbes and Clostridia, and the rate of pH decline (Muck, 1988). Strategies for reducing silage DM and nutrient losses from cutting to feed out include practicing wide swath mowing to reduce hay-crop respiration and mechanical losses, excellent silage management (rapid filling, packing, sealing, minimal air infiltration at silage face; Ruppel et al., 1995), use of oxygen-barrier film (Borreani et al., 2007), and use of proven silage inoculants for rapid pH decline and longer aerobic stability.

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Dairies can reduce feed expenses and improve economic feed efficiency and total dairy enterprise efficiency by purchasing by-product feeds as commodities (Buza et al., 2014) but must address their inherent variability in quality and nutrient profile (St-Pierre and Weiss, 2007; Bradford and Mullins, 2012). Also, the cost of feed shrink and spoilage on the farm should be assessed (Kertz, 1998). Often, dairies purchase 1 or 2 mixed grain and mineral supplements, which are used in multiple rations. If this strategy results in better quality ingredients and additives being fed to lower-producing cows in late lactation, a dairy efficiency loss will be incurred. For highest production and efficiency, consumed rations should be similar to the formulated ration with little daily variation. In a survey of 22 commercial freestall herds, the delivered TMR contained more NEl (+0.05 Mcal/kg) and NFC (+1.2%) but less CP (−0.4%) and NDF (−0.6%) than the formulated ration. Every 0.5-percentage-point reduction in daily NEl variation (CV) was positively related to 3.2 kg/d milk yield, 1.0 kg/d DMI, and 4.3% physical FE (kg of milk/kg of DMI; Sova et al., 2014). Controlling variation in the delivery of nutrients to the animal by appropriate feed and forage selection, feed analysis, and mixing is essential. Separating forages by hybrid or cutting and sorting feed commodities by source can decrease the amount of fixed variation that would otherwise be assumed to be random (St-Pierre and Weiss, 2007). Increasing TMR moisture (Shaver, 2002) and increasing feeding frequency from 1 to 2 times/d (DeVries et al., 2005; Sova et al., 2013) can reduce TMR sorting. Low TMR refusal rates can equate to reduced feed wastage and improved dairy efficiency. However, time without available feed may limit DMI as well as increase slug feeding and subacute rumen acidosis (Stone, 2004). Experience on commercial dairies indicates that ad libitum intake and productivity can be maintained with low feed refusals (2–3%; Barmore, 2002). However, consistent feeding management is essential, including on-site forage DM analysis, accurate cows per pen counts, and a routine daily feeding schedule. Efficiency of feed usage is affected by the number of cow feeding groups and the criteria for grouping on the dairy (VandeHaar et al., 2012). With precision feeding, lactating cows are separated into multiple feeding groups and fed diets closer to their requirements. According to Maltz et al. (2013), cows that were individually precision fed according to energy balance estimates had increased milk and 3.5% FCM yield as well as greater physical FE compared with control cows fed a ration designed to provide nutrients for 40 kg of milk per cow per day. When cow data from 30 Wisconsin dairy farms was analyzed using a nonlinear optimization approach for grouping, increases in IOFC of $161 to $580/yr per cow were predicted with a change from 1 to 3 lactating feeding groups (Cabrera, 2012). Talebi et al. (2014) concluded that reduced pen stocking density minimized regrouping issues, which typi-

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cally result in more competitive behavior and decreased lying time. Best grouping strategies for dairy efficiency and optimal cow well-being are likely to vary with farm design, management, productivity, bST usage, feed expenses, labor, and equipment. Regardless, separation of cows into multiple feeding groups and provision of dietary nutrients closer to requirements generally improves dairy efficiency. Economic and welfare concerns with overcrowding, cow comfort, and transition cow management need to be respected both for long-term viability of the individual dairy as well as for best public image of the dairy industry (Grant, 2009). Incorporating the effects of feeding management and behavior into dynamic dairy nutrition models offers potential for improving nutrition strategies and the prediction of milk yield and dairy efficiency (Grant and Tylutki, 2011).

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IMPLICATIONS

Bradford, B. J., and C. R. Mullins. 2012. Strategies for promoting productivity and health of dairy cattle by feeding nonforage fiber sources. J. Dairy Sci. 95:4735–4746.

On-farm dairy efficiency measures can be useful instruments to evaluate changes in nutrition and management on the dairy. Each on-farm dairy efficiency measure has both advantages and limitations. Each can be informative, but they provide more appropriate guidance when examined simultaneously rather than in isolation. Dairy efficiency goals should not be viewed as fixed but as moving targets specific to the current conditions of each dairy enterprise. For these reasons, individual farm dairy efficiency measures are not recommended to directly address consumer and retailer questions about sustainability. Nutrition and feeding management have major effects on dairy efficiency. Dairy managers and nutritionists need to carefully consider diet digestibility, rumen function, feed analyses, nutrient requirement estimates for various animal groups, forage selection and associated agronomic considerations, forage preservation, as well as TMR preparation, delivery, and intake to define reasonable dairy efficiency targets and production goals for individual farms that will lead to greater economic and environmental sustainability.

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ACKNOWLEDGMENTS

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