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Whole Farm Nutrient Management – A Dairy Example

Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

Please check this link first if you are interested in organic or specialty dairy production

Introduction to Whole Farm Nutrient Management

Whole farm nutrient management (WFNM) includes the consideration of import of nutrients to the farm, movement and transformation (including losses) of nutrients within the farm operation, and export of milk, meat, crops, or manure.

In order to understand WFNM, it is necessary to consider all sources of nutrients, their movement within the farm, and how they might move to the environment. On most dairies, feed represents the largest import of nutrients, with fertilizer as the second largest import of nutrients. Feed Management practices currently exist to reduce imports of nutrients (particularly nitrogen and phosphorus) or decrease their excretion. Many of these specific practices and management considerations will be outlined in two assessment tools (see fact sheets- Opportunity Checklist and Feed Management Plan Checklist) as part of the implementation process of the Feed Management 592 Practice Standard.

Nutrient Utilization by the Dairy Cow

Nitrogen (N) is used for milk production in the dairy cow with an efficiency of ~ 25 to 35%. The remaining 65 to 75% of nitrogen consumed by the dairy cow remains in the initial manure (feces and urine). However, N is lost to the atmosphere via volatilization.

Phosphorus utilization by species varies from approximately 20 to 50%. The 50 to 80% not utilized is excreted in manure. A dairy cow uses approximately 27% of dietary P for milk production and thus approximately 73% of dietary P is not exported as milk from the farm.

“A dairy cow uses approximately 27% of dietary P for milk production and thus approximately 73% of dietary P is not exported as milk from the farm.”

Whole Farm Nutrient Balance

The goal of whole farm nutrient management is to achieve “zero farm balance” through the adoption of a variety of management practices, including Feed Management (see Figure 1). The practices and the relative positive or negative balance (balance = anything that remains or is left over) will be unique to each farm.

It is important to acknowledge that due to biological processes, there will be losses to the environment even when all the best management practices are adopted. Therefore, “zero balance” is difficult to achieve while maintaining high crop productivity.

The concept of Whole Farm Nutrient Balance has been described in different ways progressing from simple to more complex approaches. First, consider various approaches using nitrogen as the nutrient of interest.

1st Approach -The first approach is to estimate Mass-Balance uses the concepts of import and export of managed resources (see figure 2) at the farm boundary. This approach measures only those nutrients that cross the boundary of the farm and does not directly track nutrients flows within the farm or nutrient losses from the farm. The difference between inputs and managed outputs can be used to calculate a positive or negative balance. This positive balance represents nutrients that will be lost to the environment by both air and water pathways as well as those nutrients that accumulate on the farm (e.g. increased soil nitrogen levels). The positive balance provides an estimate of environmental risk.

2nd Approach – The second approach takes into consideration the import-export of nutrients as well as losses due to volatilization of nitrogen from manure during collection, handling, storage, and application (see figure 3). This approach would include the Mass-Balance approach, plus estimates of volatile nitrogen losses. This approach is commonly used for development of Nutrient Management Plans (NMP) and Comprehensive Nutrient Management Plans (CNMP) in many states.

3rd Approach – The third approach takes into consideration the losses of volatile nitrogen as well as leached nitrogen (see figure 4). This approach is also common to NMPs and CNMPs when leaching index tools and soil nitrogen indices are utilized in NM planning.

In contrast to nitrogen, phosphorus (P) is not lost to the atmosphere and therefore, what is not exported from the farm remains within the farmstead or possibly lost due to transport. Thus, the 1st approach (mass-balance) and 3rd approach (mass-balance plus surface and leaching loss) are the approaches that are more common for P based nutrient management planning.

Checklist Tools

The “Opportunity Checklist and Feed Management Plan Checklist” summarize the common Feed Management practices that can be adopted to assist with reducing the import of nutrients to the farm in the form of feedstuffs or reduce the excretion of nutrients in manure (see Figure 4). The opportunity checklist includes Feed Management practices or concepts that usually have the greatest initial impact. These include but are not limited to:

  1. formulation of diets to meet animal requirements,
  2. grouping animals according to nutrient needs,
  3. determining dry matter routinely and adjusting rations accordingly, and
  4. analyzing diet ingredients routinely.

Additional Feed Management practices and strategies that can further assist with reducing the importation of nutrients to the farm are outlined in the Feed Management Plan Checklist.

Spreadsheet Based Whole Farm Nutrient Management Tools

Several spreadsheet based tools are available to estimate the nutrient balance at the whole farm level. The name of these tools and where a copy can be obtained are:

  1. Whole Farm Balance Nutrient Education Tool – Washington State University
  2. Whole Farm Nutrient Balance – University of Nebraska
  3. Cornell Whole Farm Nutrient Balance Assessment Program

Summary

Whole farm nutrient management should include the consideration of import of nutrients to the farm, movement and transformation (including losses) of nutrients within the farm operation, and export of milk, meat, crops, or manure.

Whole Farm Fig 1.jpg

 

Whole Farm Fig 2.jpg

 

Whole Farm Fig 3.jpg

 

Whole Farm Fig 4.jpg

 

“Extension programs and policies are consistent with federal and state laws and regulations on nondiscrimination regarding race, sex, religion, age, color, creed, national or ethnic origin; physical, mental or sensory disability; marital status, sexual orientation, or status as a Vietnam-era or disabled veteran. Evidence of noncompliance may be reported through your local Extension office.”

Disclaimer

This fact sheet reflects the best available information on the topic as of the publication date. Date 5-30-2007

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

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This project is affiliated with the Livestock and Poultry Environmental Learning Center.

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Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, Project Leader, jhharrison@wsu.edu, or Becca White, Project Manager, rawhite@wsu.edu.

Author Information

Joe Harrison
Nutrient Management Specialist
WSU-Puyallup
jhharrison@wsu.edu
253-445-4638

Rebecca White
Feed Management Educator
rawhite@wsu.edu

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Evaluating Corn Silage Quality for Dairy Cattle

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Contents


Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

The Natural Resources Conservation Service has adopted a practice standard called Feed Management (592) and is defined as “managing the quantity of available nutrients fed to livestock and poultry for their intended purpose”. The national version of the practice standard can be found in a companion fact sheet entitled “An Introduction to Natural Resources Feed Management Practice Standard 592”. Please check in your own state for a state-specific version of the standard.

An index of forage quality, milk per ton of forage DM (Undersander et al., 1993), was developed using an energy value of forage predicted from ADF content and DMI potential of forage predicted from NDF content as its basis. The milk per ton quality index was later modified for corn silage (Schwab et al., 2003) using an energy value derived from summative equations (Schwab et al., 2003; NRC, 2001) and DMI predicted from both NDF content (Mertens, 1987) and in vitro NDF digestibility (IVNDFD, % of NDF; Oba and Allen, 1999b) as its basis. This milk per ton quality index (MILK2000; Schwab et al., 2003) has become a focal point for corn silage hybrid-performance trials and hybrid-breeding programs in academia and the seed-corn industry (Lauer et al., 2005). An update, MILK2006, will be discussed herein.

Please check this link first if you are interested in organic or specialty dairy production

Model NEL-3x and DMI

We (Schwab et al., 2003) modified the NRC (2001) TDNmaintenance summative energy equation for corn silage to include starch and non-starch NF C components with a variable predicted starch digestibility coefficient, and a direct laboratory measure of the NDF digestibility coefficient rather
energy value was derived from TDNmaintenance using the NRC (1989) empirical equation in MILK2000 (Schwab et al., 2003). In MILK2006, the NEL-3x energy value is derived using an adaptation of the TDN-DE-ME-NE conversion equations provided in NRC (2001).

Neutral detergent fiber content and IVNDFD are used to predict DMI (Schwab et al., 2003) in both MILK2000 and MILK2006. However, a one %-unit change in IVNDFD (% of NDF) from lab-average IVNDFD changes DMI 0.26 lb. per day (Oba and Allen, 2005; Jung et al., 2004) in MILK2006 versus the 0.37 lb. per day value (Oba and Allen, 1999b) that was used in MILK20

In MILK2000, variation in IVNDFD impacts NEL intake through effects on both NEL-3x content and DMI (Schwab et al., 2003). However, Tine et al. (2001) and Oba and Allen (1999a) reported that at production levels of intake, IVNDFD has minimal impact on NEL-3x content but impacts NEL intake primarily through effects on DMI. In MILK2006, the IVNDFD value used for calculating NEL-3x is adjusted for differences in DMI predicted from IVNDFD using an equation adapted from Oba and Allen (1999a). Thus, IVNDFD impacts NEL intake and hence the milk per ton quality index mainly through its impact on predicted DMI in MILK2006.

Non-fiber Carbohydrates and Their Digestibility

Dairy cattle nutritionists have long used non-fiber carbohydrate (NFC) as a quasi-nutrient rather than starch specifically. However, NFC is a calculated value (100-NDF-CP+NDFCP-Fat-Ash; NRC, 2001) comprised of varying proportions of starch, sugar, soluble fiber, and organic acids, and is subject to errors associated with analyzing the five nutrients used to calculate NFC. Although the NRC 2001 summative energy equation was based on NFC, starch rather than NFC is being used in summative energy equations (Schwab et al., 2003) by many commercial feed testing laboratories especially for corn silage which they have long been analyzing for starch content and have developed NIRS calibrations for starch determination. However, determining digestion coefficients for starch to use in summative energy equations has been difficult. The NRC 2001 model uses an NFC true digestibility coefficient of 98% and arbitrary processing adjustment factors. The MILK2000 model uses a non-starch NFC (NFC minus starch) true digestibility coefficient of 98% (NRC, 2001) and varies the starch true digestibility coefficient from a minimum of 76% (Firkins et al., 2001) to a maximum of 98% (NRC, 2001) using whole-plant DM and kernel processing as regression equation variables to predict apparent total tract starch digestibility (Schwab et al., 2003). Both approaches though are limited in their ability for detecting potential variation in starch digestibility across a wide array of samples, and novel lab assays are needed.

Starch, supplied in Midwestern and Northeastern diets primarily from dry or high-moisture corn grain and whole-plant corn silage, is an important source of energy for dairy cattle. However, the digestibility of corn starch can be highly variable (Nocek and Tamminga, 1991; Orskov, 1986; Owens et al., 1986; Rooney and Pflugfelder, 1986; Theuer, 1986). Various factors, particle size (fine vs. coarse grind), grain processing (steam flaked vs. dry rolled), storage method (dry vs. high-moisture corn), moisture content of high-moisture corn, type of corn endosperm, and corn silage maturity at harvest, chop length, and kernel processing, influence starch digestibility in lactating dairy cows. Because both physical and chemical properties of starch influence starch digestion, assessment of starch digestibility in the laboratory has been challenging.

In an attempt to address variation in starch digestibility, NRC (2001) suggested empirical processing adjustment factors (PAF) to adjust NFC digestion coefficients for high-starch feeds. However, since no system to measure variation in PAF for feedstuffs is available the PAF’s are subjective book values with minimal practical utility. For corn silage, U.S. Dairy Forage Research Center workers developed a kernel processing score (KPS; Ferreira and Mertens, 2005; Mertens, 2005) to assess adequacy of kernel processing in corn silage. But, the relationship between KPS values and in vivo starch digestibility measurements is not well defined. Ruminal in-vitro or in-situ degradation, either alone or in combination with in vitro post-ruminal enzymatic digestion of the ruminal residues, have been explored by some groups (Sapienza, 2002). Some commercial laboratories are attempting to employ in situ or in vitro systems to evaluate starch digestibility, but to date methods are highly variable between laboratories. Regardless of the method it is doubtful that samples can be fine ground as fine grinding of samples may mask differences among samples (Doggett et al., 1998). Relationships between in situ/in vitro measurements and in vivo starch digestibility are often not well defined. We recently developed an enzymatic lab assay, Degree of Starch Access (DSA), which is sensitive to differences in particle size, moisture content, and vitreousness of corn-based feeds (Blasel et al., 2006).

The DSA assay was found to be quite sensitive (Blasel et al., 2006) to particle size (R2 = 0.99) and moderately sensitive to DM content (R2 = 0.76) and endosperm type (R2 = 0.59), which are three primary factors that influence starch digestibility in corn grain. However, The DSA assay is a laboratory starch recovery procedure that does not result in a direct estimate of starch digestibility and only reveals differences in starch recoveries. For example, the DSA procedure would recover 95 percent of the starch in finely ground corn but only 5 percent of the starch in whole shelled corn. Thus, the DSA values provide an index of the variation in degree of starch access among feeds. We (Shaver and Hoffman, 2006) reviewed eight trials in the scientific literature (Taylor and Allen, 2005a; Remond et al., 2004; Oba and Allen, 2003; Crocker et al., 1998; Knowlton et al., 1998; Yu et al., 1998; Joy et al., 1997; Knowlton et al., 1996) with lactating dairy cows that reported total tract starch digestibility and particle size, moisture content, and endosperm type of the corns tested. From these data, we estimated their DSA values and evaluated the relationship between DSA and their measures of total tract starch digestibility. The resultant regression equation is applied to starch recovery values generated from the DSA assay to provide an estimate of total tract starch digestibility (termed Starch DigestibilityDSA; Shaver and Hoffman, 2006) which can be used in summative energy equations (Schwab et al., 2003; NRC, 2001) directly to calculate energy values for corn-based feeds on a standardized basis.

More field and in vivo evaluations of these laboratory assays related to starch digestibility (KPS, DSA, and in situ/in vitro) are needed. Therefore, the MILK2006 model continues to use the regression approach of MILK2000 (Schwab et al., 2003) as the default method for determining starch digestibility. But, user-defined options are available within the MILK2006 spreadsheet for determining starch digestibility from available KPS, DSA, or in situ/in vitro data. For hybrid performance trials where an objective is to assess true hybrid differences for kernel endosperm properties, the harvest maturity, whole-plant DM content, and sample particle size should be kept as similar as possible since these factors all influence the starch digestibility determinations.

Fiber and Its Digestibility

The NRC (2001) summative energy equation is based on fiber digestibility calculated using lignin. Whole-plant lignin content was found to have a strong negative relationship with IVNDFD within comparisons of brown midrib (bm3) hybrids to isogenic counterparts (Oba and Allen, 1999b). However, stover NDF and lignin contents increase while NDFD decreases with progressive maturity, but whole-plant NDF and lignin contents are constant or decline as grain proportion increases (Russell et al., 1992; Hunt et al., 1989). This may partially explain why for 534 corn silage samples, NDFD calculated using lignin according to NRC (2001) accounted for only 14% (P < 0.001) of IVNDFD variation (Schwab and Shaver, unpublished). Michigan State workers (Oba and Allen, 2005; Allen and Oba, 1996; M. S. Allen, personal communication, 2003 Tri-State Nutr. Conf. Pre-Symp.) reported that lignin (% of NDF) explained only half or less of the variation for corn silage IVNDFD. These observations coupled with the NRC (2001) suggestion that IVNDFD measurements could be used directly in the NRC model led us to implement IVNDFD rather than lignin-calculated NDF digestibility in the corn silage milk per ton models (Schwab et al., 2003). Use of NDF and IVNDFD in the corn silage milk per ton models has been discussed above.

Several commercial testing laboratories offer wet chemistry IVNDFD measurements. NIRS calibrations for predicting IVNDFD on corn silage samples are available at some commercial forage testing laboratories. However, Lundberg et al. (2004) found poor prediction by NIRS of corn silage IVNDFD. It is hoped that NIRS calibration equations can be improved upon in the future. The NRC (2001) recommended a 48-h IVNDFD for use in the NRC (2001) model, and for that reason we used 48-h IVNDFD measurements in MILK2000 (Schwab et al., 2003). However, debate continues within the industry about the appropriateness of 48-h vs. 30-h IVNDFD measurements. Some argue that the 30-h incubation better reflects ruminal retention time in dairy cows (Oba and Allen, 1999a) and that most of the in vivo trials that have evaluated effects of varying IVNDFD on animal performance also performed 30-h IVNDFD measurements (Oba and Allen, 2005). Labs and their customers also like the faster sample turn around that is afforded by the 30-h incubation time point. For that reason, and also for improved lab operation efficiency, a 24-h incubation time point is being employed by some labs. However, some argue that the 48-h incubation time-point is less influenced by lag time and rate of digestion, and thus is more repeatable in the laboratory (Hoffman et al., 2003). Hoffman et al. (2003) provided data on the relationship between 30- and 48-h IVNDFD measurements that showed a strong positive relationship (r-square = 0.84). But, the lab average at a specific incubation time point and the relationship between incubation time points within a lab can be highly variable among labs making the development of a universal incubation time point adjustment equation difficult. The average lignin-calculated corn silage NDF digestibility in the NRC (2001) is 59%. This reference point is important for adjustment of IVNDFD values from different labs and varying incubation time points so that the resultant TDN and NEL values are comparable to NRC (2001) values.

User-defined flexibility is available within the MILK2006 spreadsheet for entry of 48-, 30-, or 24-h IVNDFD incubation time point measurements. But, the labs incubation time point and average results for corn silage at that time point must also be entered into the spreadsheet along with the sample data. The 48-h IVNDFD incubation time point continues to serve as the default in the milk per ton spreadsheets. The Wisconsin Corn Silage Hybrid Performance Trials (Lauer et al., 2005) will continue to use the 48-h IVNDFD incubation time point because NIRS calibrations for this time point have been developed from corn silage samples obtained in this evaluation program over several years by locations and Justen (2004) did not find the earlier incubation time points to provide any benefit over the 48-h time point for hybrid selection.

Model Comparisons

Values for TDNmaintenance, NEL-3x, and milk per ton calculated using MILK2006 and MILK2000 across a wide range of whole-plant corn IVNDFD values and extreme quality differences are presented in Tables 1 and 2, respectively. The TDNmaintenance differences between MILK2006 and MILK2000 are minimal. The NEL-3x and milk per ton values are lower and the range in these values is compressed for MILK2006 relative to MILK2000 according to the equation differences between the two models that were described above.

Analysis of correlations between corn silage NDF, IVNDFD, starch, and starch digestibility and milk per ton estimates from MILK 2006, 2000, 1995, and 1991 models (n = 3727 treatment means; Shaver and Lauer, 2006) is presented in Table 3. Results show that the MILK2000 model was revolutionary relative to the earlier models (milk per ton hybrid rank correlation between MILK2000 and MILK1991 was only 0.68), because of its recognition of IVNDFD as an important quality parameter while the earlier models were influenced mostly by whole-plant starch and grain contents. The MILK2006 model relative to MILK2000 appears to be more evolutionary reflecting the relatively minor fine-tuning of equations (milk per ton hybrid rank correlation between MILK2006 and MILK2000 was 0.95), but the spreadsheet will allow for more user-defined flexibility. Future developments in laboratory methods for determining starch digestibility may influence its relationship to milk per ton estimates relative to the other quality measures.

Ivan et al. (2005) evaluated “low-fiber” (26% starch, 49% NDF, 58% IVNDFD) versus “high-fiber” (22% starch, 53% NDF, 67% IVNDFD) corn silages in 30% NDF diets fed to lactating dairy cows. Reported per cow per day milk yields were converted to milk per ton of corn silage DM basis using their corn silage DMI data. Actual milk per ton was 168 lb. higher for high-fiber than low fiber corn silage. Model-predicted milk per ton estimates were 132 lb. and 297 lb. higher for high-fiber than low-fiber corn silage from MILK2006 and MILK2000 models, respectively. This suggests reasonable agreement with in vivo data for MILK2006 and better agreement with in vivo data for MILK2006 than MILK2000. Presented in Figure 1 is model-predicted milk per ton minus milk per ton calculated using in vivo data from 13 treatment comparisons in 10 JDS papers (Ballard et al., 2001; Ebling and Kung, 2004; Ivan et al., 2005; Neylon and Kung, 2003; Oba and Allen, 2000; Oba and Allen, 1999a; Qiu et al., 2003;Taylor and Allen, 2005b; Thomas et al., 2001; Weiss and Wyatt, 2002) for MILK2006 versus MILK2000. There was less model over-predictive bias for MILK2006 than MILK2000. The model-predicted milk per ton minus in vivo-calculated milk per ton difference exceeded 100 lb. (approximately 1 lb. per cow per day) for only 2 of 13 treatment comparisons with MILK2006 versus 8 of 13 treatment comparisons with MILK2000.

While these observations with MILK2006 are encouraging, more model validations relative to in vivo data are needed. The MILK2006 Excel Workbook can be downloaded at the University of Wisconsin’s Extension website.

Table 1. Impact of IVNDFD (average lab IVNDFD 58% of NDF) in whole-plant corn harvested at 35% DM content with kernel processing on TDN1x (%), NEL-3x (Mcal/lb.) and milk (lb.) per ton using MILK2006 or MILK2000 with nutrient composition adapted from NRC (2001) for “normal” corn silage (8.8% CP, 45% NDF, 27% starch, 4.3% ash, and 3.2% fat).
IVNDFD% MILK
2006
TDN1x 		MILK
2006
NEL-3x 		MILK
2006
Milk/ton 		MILK
2000
TDN1x 		MILK
2000
NEL-3x 		MILK
2000
Milk/ton
46 65.3 0.66 2936 66.4 0.69 3074
50 67.0 0.67 3037 68.2 0.71 3244
54 68.8 0.68 3138 70.0 0.73 3413
58 70.5 0.69 3237 71.8 0.75 3579
62 72.3 0.70 3336 73.6 0.77 3743
66 74.0 0.72 3434 75.4 0.79 3905
70 75.8 0.73 3530 77.2 0.81 4065

Table 2. Impact of “low” (45% DM, unprocessed, 8.8% CP, 54% NDF, 46% IVNDFD, 20% starch, 4.3% ash, and 3.2% fat) versus “high” (30% DM, processed, 8.8% CP, 36% NDF, 70% IVNDFD, 34% starch, 4.3% ash, and 3.2% fat) quality extremes in whole-plant corn on TDN1x (%), NEL-3x (Mcal/lb.) and milk (lb.) per ton using MILK2006 or MILK2000.
Quality MILK
2006
TDN1x 		MILK
2006
NEL-3x 		MILK
2006
Milk/ton 		MILK
2000
TDN1x 		MILK
2000
NEL-3x 		MILK
2000
Milk/ton
“Low” 56.2 0.55 2242 57.3 0.58 2418
“High” 76.3 0.74 3617 79.9 0.84 4256

Table 3. Analysis of correlations for selected corn silage nutrients and their digestibility coefficients with milk per ton estimates from MILK2006, 2000, 1995, and 1991 models (n = 3727 treatment means; Shaver and Lauer, 2006).
r-values MILK
2006
Milk/ton1 		MILK
2000
Milk/ton2 		MILK
1995
Milk/ton3 		MILK
1991
Milk/ton4
NDF% -0.46 -0.40 -0.94 -0.99
Starch% 0.48 0.44 0.75 0.74
IVNDFD, % of NDF 0.49 0.70 0.16 -0.10
StarchD, % of Starch 0.30 0.21 -0.25 -0.27
1Calculated as per Schwab et al. (2003) except for modifications discussed herein.
2Calculated as per Schwab et al. (2003).
3Calculated as per Undersander et al. (1993) except for in vitro DM digestibility adjustment.
4Calculated as per Undersander et al. (1993) using ADF and NDF.

Corn Silage Fig 1.jpg


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  • Oba, M, and M. S. Allen. 2003. Effects of corn grain conservation method on ruminal digestion kinetics for lactating dairy cows at two dietary starch concentrations. J. Dairy Sci. 86:184-194.
  • Oba, M. and M. S. Allen. 2000. Effects of brown midrib 3 mutation in corn silage on productivity of dairy cows fed two concentrations of dietary neutral detergent fiber: 1. Feeding behavior and nutrient utilization. J. Dairy Sci. 83:1333-1341.
  • Oba, M. and M. S. Allen. 1999a. Effects of brown midrib 3 mutation in corn silage on dry matter intake and productivity of high yielding dairy cows. J. Dairy Sci. 82:135-142.
  • Oba, M. and M. S. Allen. 1999b. Evaluation of the importance of the digestibility of neutral detergent fiber from forage: effects on dry matter intake and milk yield of dairy cows. J. Dairy Sci. 82:589-596.
  • Orskov, E. R. 1986. Starch digestion and utilization in ruminants. J. Anim. Sci. 63:1624-1633.
  • Owens, F. N., R. A. Zinn, and Y. K. Kim. 1986. Limits to starch digestion in the ruminant small intestine. J. Anim. Sci. 63:1634-1648.
  • Qiu, X., M. L. Eastridge, and Z. Wang. 2003. Effects of corn silage hybrid and dietary concentration of forage NDF on digestibility and performance by dairy cows. J. Dairy Sci. 86:3667–3674.
  • Remond, D., Cabrer-Estrada, J. I., Chapion M., Chauveau B., Coudure R., Poncet C. 2004. Effect of corn particle size on site and extent of starch digestion in lactating dairy cows. J. Dairy Sci. 87:1389-1399.
  • Rooney, L. W., and R. L. Pflugfelder. 1986. Factors affecting starch digestibility with special emphasis on sorghum and corn. J. Anim. Sci. 63:1607-1623.
  • Russell, J. R., N. A. Irlbeck, A. R. Hallauer, and D. R. Buxton. 1992. Nutritive value and ensiling characteristics of maize herbage as influenced by agronomic factors. J. Anim. Feed Sci. Technol. 38:11-24.
  • Sapienza, D. 2002. Pioneer tripartite method: Linking nutrient content to availability. Pages 27-40 in Proc. 64th Cornell Nutr. Conf. East Syracuse, NY.
  • Schwab, E. C., R. D. Shaver. J. G. Lauer, and J. G. Coors. 2003. Estimating silage energy value and milk yield to rank corn hybrids. J. Anim. Feed Sci. Technol. 109:1-18.
  • Shaver, R. D., and P. C. Hoffman. 2006. Corn silage starch digestibility: What’s new? In Proc. NRAES Silage for Dairy Farms Conf. Camp Hill, PA.
  • Shaver, R. D., and J. G. Lauer. 2006. Review of Wisconsin corn silage milk per ton models. J. Dairy Sci. 89(Suppl. 1):282(Abstr.)
  • Taylor, C. C. and M. S. Allen. 2005a. Corn grain endosperm type and brown midrib 3 corn silage: Site of digestion and ruminal digestion kinetics in lactating cows. J. Dairy Sci. 2005 88: 1413-1424.
  • Taylor, C. C. and M. S. Allen. 2005b. Corn grain endosperm type and brown midrib 3 corn silage: Feeding Behavior and Milk Yield of Lactating Cows. J. Dairy Sci. 88: 1425-1433.
  • Theurer, C. B. 1986. Grain processing effects on starch utilization by ruminants. J. Anim. Sci. 63:1649-1662.
  • Thomas, E. D., P. Mandebvu, C. S. Ballard, C. J. Sniffen, M. P. Carter, and J. Beck. 2001. Comparison of corn silage hybrids for yield, nutrient composition, in vitro digestibility, and milk yield by dairy cows. J. Dairy Sci. 84:2217–2226.
  • Undersander, D.J., W.T. Howard, and R.D. Shaver. 1993. Milk per acre spreadsheet for combining yield and quality into a single term. J. Prod. Ag. 6:231 235.
  • Weiss, W. P., and D. J. Wyatt. 2002. Effects of feeding diets based on silage from corn hybrids that differed in concentration and in vitro digestibility of neutral detergent fiber to dairy cows. J. Dairy Sci. 85:3462–3469.
  • Yu, P., J. T. Huber, F.A.P. Santos, J. M. Simas, and C. B. Theurer. 1998. Effects of ground, steam-flaked, and steam-rolled corn grains on performance of lactating cows. J. Dairy Sci. 81: 777-783.

“Extension programs and policies are consistent with federal and state laws and regulations on nondiscrimination regarding race, sex, religion, age, color, creed, national or ethnic origin; physical, mental or sensory disability; marital status, sexual orientation, or status as a Vietnam-era or disabled veteran. Evidence of noncompliance may be reported through your local Extension office.”

Disclaimer

This fact sheet reflects the best available information on the topic as of the publication date. Date 5-25-2007

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

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This project is affiliated with the Livestock and Poultry Environmental Learning Center.

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Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, Project Leader, jhharrison@wsu.edu, or Becca White, Project Manager, rawhite@wsu.edu.

Author Information

Randy Shaver
Professor and Extension Dairy Nutritionist
Department of Dairy Science
College of Agricultural and Life Sciences
University of Wisconsin – Madison
University of Wisconsin – Extension

Reviewer Information

Pat Hoffman – University of Wisconsin
Jim Barmore – Nutrition Consultant

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A National Template for Preparing a Dairy Feed Management Plan

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Introduction

This factsheet has been developed to support the implementation of the Natural Resources Conservation Service (NRCS) Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

The Feed Management 592 Practice Standard adopted by NRCS is defined as “managing the quantity of available nutrients fed to livestock and poultry for their intended purpose.” The national version of the practice standard can be found in a companion factsheet entitled An Introduction to Natural Resources Conservation Service (NRCS) Feed Management Practice Standard 592. Please check your state-specific version of the standard.

The national Feed Management Education Team has developed a systematic five-step development and implementation process for the Feed Management Practice Standard. A complete description of the five steps can be found in a companion factsheet entitled Five Steps to the Development and Implementation of a Feed Management Plan.

The fourth step of this process focuses on the development of the Feed Management Plan. Key participants at Step 4 are the producer and his nutritionist. The key tools to be used at Step 4 are the Feed Management Plan (FMP)Checklist and the Feed Management Plan Template.

Please check this link first if you are interested in organic or specialty dairy production

Using the Feed Management Plan Template

The Feed Management Plan, or FMP, is intended to assist the producer with documentation of those practices that affect whole-farm nutrient management and contribute toward achieving nutrient balance at a whole-farm level. Nitrogen and phosphorus are the two nutrients that are required to be managed as part of the FMP in a Comprehensive Nutrient Management Plan.

When nitrogen and phosphorus imports exceed nitrogen and phosphorus exports, there is an imbalance at a whole-farm level. These imbalances can lead to impaired water quality in nearby water bodies due to both surface runoff and leaching of nutrients to groundwater. Excess nitrogen can also be volatilized and contribute to impaired air quality. Potassium is a nutrient that can lead to production and health problems if it is not monitored in dairy rations, therefore, it is also included as a nutrient to monitor.

The FMP template is designed to provide a common format to address all areas noted in the Feed Management 592 Practice Standard. It is organized with the following sections:

  • Contact information
  • General purpose and background information about the 592 standard
  • Specific purpose selection for the operation
  • When the plan was written
  • When the plan will be reviewed
  • Specific farm information for use with the electronic manure excretion estimator tool
  • Summary of feeding practices and equipment/technologies utilized on the farm
  • Record keeping
  • Recommendations

Estimate of Manure Nutrient Excretion

As part of the FMP, the impact that feed management will have on manure volume and nutrient content is estimated. The specific farm information section has been included to collect farm-specific descriptive information for use with the electronic manure excretion estimator tool. This tool is described in a companion factsheet entitled Estimating Manure Nutrient Excretion.

Feed Management Practices

This section should include a list and narrative of those practices that have been adopted. One way to document practices is to insert a copy of the completed Feed Management Plan Checklist. Proprietary information or specific ration formulations need not be included.

Guidance Sections

There are two important sections of the FMP that should contain specific guidance about sampling and analysis procedures, these are:

  • Record of feed sampling and feed analysis
  • Final recommendations

By following this link you will find a blank copy of the Feed Management Plan Template (PDF file). Additionally, a Completed Feed Management Plan (PDF file) is available as an example.

Related Files

To follow the references in this article, it is recommended that you print these PDF files and refer to them at the appropriate places.
Feed Management Plan Template
Example Feed Management Plan (Dairy).

Disclaimer

This factsheet reflects the best available information on the topic as of the publication date. Date 5-25-2007

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

Image:Feed mgt logo4.JPG

This project is affiliated with the Livestock and Poultry Environmental Learning Center.

Image:usda,nrcs,feed_mgt_logo.JPG

Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, project leader, jhharrison@wsu.edu, or Becca White, project manager, rawhite@wsu.edu.

Author Information

Joe Harrison, Becca White, Lynn Johnson-VanWieringen, and Ron Kincaid, Washington State University
Mike Gamroth, Oregon State University
Tamilee Nennich, Texas A&M University
Deb Wilks, Standard Nutrition

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Acknowledgments

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.
This project is affiliated with the Livestock and Poultry Environmental Learning Center

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“Extension programs and policies are consistent with federal and state laws and regulations on nondiscrimination regarding race, sex, religion, age, color, creed, national or ethnic origin; physical, mental or sensory disability; marital status, sexual orientation, or status as a Vietnam-era or disabled veteran. Evidence of noncompliance may be reported through your local Extension office.”

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NRC Recommendations for Dairy Cows

Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

Please check this link first if you are interested in organic or specialty dairy production

Feed Management

Feed Management is one of six components in a Comprehensive Nutrient Management Plan (CNMP). Feeds represent a costly fraction on a dairy farm budget and feed inputs are among the largest sources of nutrients imported to the operation. Feed management depends on adequate feed acquisition and allocation, in quantity and quality sufficient to supply the herd’s nutrient demands for a given period of time. Knowledge of animal nutrient requirements is paramount for a successful Feed Management.

Nutrient requirement standards for most economically important farm animal species have been reported by the National Research Council (NRC) since the early 20th century . NRC’s seventh revised edition of the Nutrient Requirements of Dairy Cattle, issued in 2001, included significant alterations over its previous edition (1989). Calculations of nutrient requirements and their interactions are integrated by the 2001 Dairy NRC in a computer model that allows for estimates of nutrient requirements and dynamic ration evaluation.

Better comprehension of the processes used to determine a dairy cow’s nutrient requirements in the NRC (2001) model is essential for the success of the nutrient management plan. A few aspects of nutrients that are relevant to Nutrient Management (nitrogen, phosphorus, and potassium) are discussed below. For more detailed information, please refer to the publication indicated above.

Nutrients

Nitrogen (Protein)

Two aspects must be considered to evaluate a ration’s adequacy: the nutrients supplied by the diet (nutrients contained in feeds) and the cows’ nutrient demand for body maintenance, reproduction, production and growth in cows that have not reached mature body weight.

Protein content in feedstuffs is usually referred to as crude protein (CP). In the laboratory, feed samples are actually analyzed for nitrogen (N) content, and CP is calculated as:

CP = N × 6.25

This equation is based on the assumption that dietary protein contains an average of 16% N.

“Nitrogen is of primary environmental concern because of losses of ammonia in the air and because of nitrate contamination of surface water and groundwater.” NRC (2001)

In the 2001 dairy NRC, feed protein supply is divided into two fractions: rumen degraded protein (RDP) and rumen undegraded protein (RUP). Rumen degraded protein supplies microbial needs. However, rumen microbes require non-protein N (ammonia, amino acids, peptides,) as “building blocks” of microbial protein (MCP). The extent of MCP synthesis in the rumen depends on a number of factors including level of feed intake, digestion rate (Kd) of diet components in the rumen, and passage rate (Kp) of digesta from the rumen. In the absence of a more reliable analytical method, the NRC subcommittee chose to use three fractions (A, B, C) derived indirectly from rumen incubation of in situ bags to derive RDP and RUP supplied by feed ingredients (kg/d):

RDP = A + B × [Kd/(Kd + Kp)]

RUP = B × [Kp/(Kd + Kp)] + C

Where A is the amount (kg/d) of N presumably readily available to microbes, B is the amount of N that is available by degradation (at a rate = Kd) and C is the amount of N unavailable for microbial growth.

Ruminants also recycle N to the rumen as salivary urea that can be used by rumen microbes, especially when dietary N is below optimal. That N source, along with enzymes and sloughed cells released in the gut are called endogenous CP because they come from within the body of the cow.

Finally, three sources of protein leave the cow’s stomachs and reach the small intestine:
MCP;
RUP; and
Endogenous CP.

Digestible protein will be hydrolyzed in the small intestine essentially into amino acids, which can be absorbed and used for body maintenance, growth, reproduction, and lactation. The absorbable amino acids, defined in NRC (2001) as metabolizable protein (MP), can be converted into milk protein with an average efficiency of 67%. Considering an average intestinal digestibility of 0.65, one can estimate the theoretical maximum milk N efficiency of utilization as:

0.67 × 0.65 = 0.44 or 44 %

After more than half a decade of its publication, the NRC (2001) protein requirement model withstood a number of comparisons and validations against measured data and other models. Some criticism has been observed. Those include the need for accurate feedstuff characterization, extent and complexity of inputs required by the model, overestimation of RDP requirements because nitrogen recycling is not taken into consideration, over-prediction of milk response to RUP supplementation, and over-evaluated energy value of proteins. However, if default values are replaced by more accurate feed and animal characterization, the NRC (2001) model has accurately predicted milk and protein production.

Finally, because the NRC (2001) is a dynamic model that incorporates animal-feed interactions, and feed-feed interactions. Thus, the computer model should be used rather than the tabulated values. In general, the NRC (2001) predicts that dietary CP contents between 16.5 and 17.5 % of the DM supply the protein requirements of early-lactation dairy cows under most conditions. Dietary CP should be equal to or below 16.5% as cows advance into the second half of the lactation.

Phosphorus

In the NRC (2001), phosphorus (P) in feed and microbes were given absorption coefficients (AC).

“Of all dietary essential mineral elements for dairy animals, phosphorus represents the greatest potential risk if excess is released into the environment contaminating surface waters and causing eutrophication.” NRC (2001)

Phosphorus AC is the efficiency with which P from a source is absorbed in the cow’s small intestine. The AC is variable, depending on a number of animal and feed characteristics. For instance, decreasing P content of the diet increases the AC and P efficiency of utilization from feed to milk. The NRC (2001) adopted fixed absorption coefficients for forages (0.64) and concentrates (0.70). Only mineral sources were given specific ACs. For instance, dicalcium phosphate AC is 0.75, while higher ACs were applied to monosodium phosphate and phosphoric acid (0.90). Those AC values were higher than the 0.50 value used previously (NRC, 1989). Endogenous P sources, a major recycling route in ruminants, have an AC above 0.70.

Phosphorus available for absorption is defined as absorbable P and is calculated as feed P (in grams) multiplied by its AC and summed for all feeds in the diet:

Absorbable P = ∑(feed P × feed P AC)

The NRC (2001) estimates dairy cows’ demand for absorbed P based on a factorial approach. The factorial determination of requirements accounts for the absorbed P necessary for maintenance, growth, reproduction and lactation.

Milk P averages at 0.090%, but may range from 0.083% to 0.100%. Given the milk volume produced by modern dairy cows, milk P makes up for the largest proportion of the requirements for a lactating dairy cow, followed by body maintenance, and only a small fraction needed for growth and reproduction. Phosphorus demand for fetal growth is relevant only in the last third of gestation.

Phosphorus supply adequacy is estimated as dietary absorbable P minus the sum of requirements (maintenance + growth + reproduction + lactation).

Current NRC P recommendations for lactating dairy cows range from 0.30 to 0.40 % of the diet DM, depending particularly on milk production. A number of studies have shown no production or reproduction benefits from feeding P above NRC dietary recommendations, and that most excess P is excreted in feces.

Using dicalcium phosphate ($400/ton = $0.82/lb P, discounted Ca value), one can estimate that it costs $1.50/cow/year for every one hundredth of a percentage unit (0.01) P increased above NRC recommended level for a cow eating 50 lb/d of dry matter. Overfeeding P to lactating dairy cows is uneconomical, wasteful and may harm the environment.

Potassium

The NRC subcommittee adopted a single AC of 0.90 for potassium (K) in all feeds. Potassium requirements are calculated similarly to P requirements.

Lactating dairy cows have high demand for K. As much as an ounce of K will be secreted with every 42 lbs of milk, but even larger quantities are lost with sweat, feces and particularly in urine. Those requirements must be supplied on a daily basis because K is not stored in the body.

Despite recognition that the requirement increases with higher temperatures (sweating), NRC (2001) K model does not take into consideration ambient temperature to calculate K requirements. Furthermore, K is an important element influencing the DCAD (Dietary Cation-Anion Difference) of a ration (in addition to sodium (Na) and chloride (Cl)). There has been increased interest in how DCAD affects acid-base balance of dairy cows. Whereas a low DCAD (in general lower dietary K and Na, high Cl) has been recommended for periparturient cows to prevent milk fever, higher postpartum DCAD (~+200 meq/kg) is suggested to maximize milk production. This dichotomy raises concerns and complicates K balance in a nutrient management plan.

NRC (2001) recommended dietary K levels ranging from 1.0 to 1.2% of the dry matter.

“Application of manures of fertilizers rich in potassium to crop land can result in excess potassium in the environment and very high potassium content of forages. This can cause problems with calcium and magnesium metabolism particularly for periparturient cows, and may cause udder edema.” NRC (2001)

Table 1. Nutrient requirements of lactating dairy cows estimated with the NRC (2001) model using sample diets varying feeds, stages of the lactation and milk production levels.1
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5
Animal description:
Age, months 52 55 53 55 59
Parity 3 3 3 3 3
Body weight, lb 1432 1432 1432 1432 1432
Body weight change, lb/d -0.88 0.00 -1.10 -1.10 1.50
Days in milk 45 120 60 120 250
Days pregnant 0 50 0 50 170
Body condition score 2.75 2.75 2.75 2.75 3.50
Production inputs:
Milk production, lb/d 98.0 98.0 130 130 45.0
Milk fat, % 3.50 3.50 3.50 3.50 3.70
Milk true protein, % 3.00 3.00 3.00 3.00 3.00
Milk lactose, % 4.78 4.78 4.78 4.78 4.78
Intake estimated by NRC (2001) model:
Dry matter intake, lb/d 51.8 59.5 64.7 70.3 42.7
Sample diet used in the NRC (2001) model, lb dry matter/d:
Corn silage, normal 23.50 28.20 24.07 32.00 19.40
Legume forage hay, mid-mat. 4.25 7.25 8.41 5.48 6.60
Bermudagrass hay, Tifton-85 — 2.38 — — 4.40
Grass hay, C-3, mid-mat. 1.98 — — 2.69 6.60
Whole cottonseed — — 4.54 — —
Soybean, meal, solv. 48% CP 6.72 6.41 3.68 9.49 0.46
Soybean, meal, expellers 2.33 1.01 — 1.83 —
Corn gluten meal — — 4.21 — —
Urea — — — — 0.18
Corn grain, steam-flaked — — — — 4.10
Corn grain, ground, hi moist. 10.37 — 17.80 15.46 —
Corn grain, ground, dry — 12.78 — — —
Tallow 0.99 — — 1.37 —
Calcium soaps of fatty acids 0.26 — 0.26 0.35 —
Calcium carbonate 0.20 0.20 0.29 0.22 0.10
Monosodium phosphate (1 H2O) 0.11 0.09 0.18 0.15 0.04
Salt 0.30 0.29 0.32 0.25 0.20
Vitamin and mineral premix 0.77 0.90 0.95 1.00 0.62
Diet nutrient contents:
% RDP 10.2 9.7 9.7 9.6 9.6
% RUP 6.9 6.1 7.8 7 3.8
% CP(%RDP + %RUP) 17.1 15.8 17.5 16.6 13.4
% phosphorus (P) 0.38 0.36 0.40 0.38 0.29
% potassium (K) 1.32 1.31 1.13 1.29 1.46
1Feeds were chosen from NRC (2001) feed library for example purposes. For accurate diet evaluation, the NRC (2001) model requires animal description and feed analyses for every specific situation.

References

National Research Council. 1989. Nutrient Requirements of Dairy Cattle. 6th rev. ed. Natl. Acad. Sci., Washington, D.C.

National Research Council. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. Natl. Acad. Sci., Washington, D.C.

“Extension programs and policies are consistent with federal and state laws and regulations on nondiscrimination regarding race, sex, religion, age, color, creed, national or ethnic origin; physical, mental or sensory disability; marital status, sexual orientation, or status as a Vietnam-era or disabled veteran. Evidence of noncompliance may be reported through your local Extension office.”

Disclaimer

This fact sheet reflects the best available information on the topic as of the publication date. Date 10-15-2006

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

Image:Feed mgt logo4.JPG

This project is affiliated with the LPE Learning Center.

Image:usda,nrcs,feed_mgt_logo.JPG

Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, Project Leader, jhharrison@wsu.edu, or Becca White, Project Manager, rawhite@wsu.edu.

Author Information

Vinicius Moreira
LSU AgCenter Southeast Research Station
VMoreira@agcenter.lsu.edu

Reviewer Information

Fred Moore – EPA Region 6 Liason
Michael Wattiaux – University of Wisconsin

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Costs of Slurry Manure Application and Transport

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Manure Value and Economics

Livestock such as dairy and swine often have slurry type manure. The manure is liquid but does not flow easily. It is either stored directly below the animal pens, or scraped or pumped periodically into a holding pen outside of the building.

Loading Slurry Manure

Loading slurry manure is accomplished with a pump powered by a tractor or stationary engine. The slurry can be loaded into tractor-pulled or truck-mounted tankers, or pumped through a hose attached to a tractor that applies it as it is being pumped from the pit. The cost of loading slurry is usually low because the pump can do it quickly and the volume per animal is not usually high.

Slurry Manure Transport

Transportation of slurry by tanker can be expensive because a lot of water is being transported and the same equipment that is hauling the slurry is usually land applying the slurry. When tankers are used, the number of hours spent transporting the slurry is frequently the limiting cost. The land may become unavailable to receive the slurry, due to crop planting times or soil conditions, before all of the slurry can be land applied. Often, the distance transported is limited so that the time constraints can be met.

If the slurry is pumped through a hose to the field, the transport time is negligible. As the slurry is pumped, it is simultaneously injected or surface applied to the land. The important cost becomes the cost of purchasing pipe and hose that is sufficient for this method of land application.

Land Application of Slurry Manure

The cost of land application of slurry varies with the type of equipment used. Tankers can be expensive to own unless they are used for many animals on many acres. There is a definite economy of scale with tankers. Additionally, the tankers usually require fairly large tractors or trucks. If the livestock owner does not have a cropping enterprise that requires the large tractor, ownership of the tractor for manure distribution alone becomes expensive.

Tankers are economical for large-scale operations with slurry manure.

When slurries are applied via hoses (called dragline hoses), a tractor pulled distributor is used to move the hose around the field so that the slurry is evenly distributed. The cost of the equipment can be very expensive, but the amount of time is decreased considerably compared to using tankers because most of the time is spent in applying the slurry. Very little time is spent getting into and out of the field, as is the case when using tankers.

Authors: Ray Massey, University of Missouri and Josh Payne, Oklahoma State University

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Dairy Feed Nutrient Management Fact Sheets

Introduction to Feed Management and Developing a Feed Management Plan

It is strongly recommended that you read these introductory fact sheets before the dairy-specific ones.

Developing A Dairy-Specific Feed Management Plan

Managing Feed Nutrients on a Dairy Farm

Tools and Resources for Developing a Feed Management Plan

These fact sheets were developed as part of the National Feed Management Education Project.

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Making Sense of Treatment Technology Options for Livestock Farms

Have you ever wondered whether manure should be treated on your livestock operation? What technology will work best in your situation? This webinar discusses strategies for selecting the right technology to meet your farm’s needs and reviews some proven and emerging technologies that are showing promise for the dairy industry. This presentation was originally broadcast on February 16, 2018. More… Continue reading “Making Sense of Treatment Technology Options for Livestock Farms”

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