Use of the Dairy Feed Management Plan Checklist in Feed Management Plan Development

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 Conservation Service (NRCS) Feed Management Practice Standard 592. Please check in your own state for a state-specific version of the standard.

The national Feed Management Education team has developed a systematic 5-step development and implementation process for the Feed Management Practice Standard. A complete description of the 5-steps can be found in a companion fact sheet entitled Five Steps to the Development and Implementation of a Feed Management Plan.

The fourth step of this systematic process focuses on the development of the Feed Management Plan. Key participants at step four are the producer and their nutritionist. The key tools to be used at step four are the Feed Management Plan (FMP) Checklistand the Feed Management Plan Template. This fact sheet will concentrate on using the checklist. The next fact sheet in this series A National Template for Preparing a Dairy Feed Management Plan will discuss the template.

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Using the Feed Management Plan Checklist

The FMP checklist is designed to assist dairy operators and their nutrient management advisor to determine feeding management factors that affect nutrient management. The checklist is meant to be used as an on-farm assessment tool. The factors contained in this assessment can be used as a guide to document and identify feeding management practices that will impact whole farm nutrient management.

The FMP checklist is designed to systematically gather information that can be used to develop the feed management plan. The organization of the checklist is divided into six management categories of:

targeting nutrient requirements

ration balancing

ration management practices

production aids/enhancers

monitoring tools

forage management practices

To use this checklist, each practice should be discussed with the operator: Are they already implementing the practice? If Yes, indicate so and skip to the next question. If No, discuss whether or not the practice could be implemented and consider the economic implications. In many cases the economic implications will be a “best professional” judgment by the consulting nutritionist or producer.

It is important to address the question “Will it be considered in the future?” as this can provide guidance for reviewing and updating the FMP in the future.

The ‘Benefit to the Environment’ column provides the possible impact the practice could have on whole farm nutrient management. It is meant to be informative and should not be answered for each farm.

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

The next step in the process is to write the Feed Management Plan. A fact sheet on developing the FMP is available at A National Template for Preparing a Dairy Feed Management Plan.

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 in the article.
Feed Management Plan Checklist
Example Feed Management Plan Checklist(Dairy).

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.

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

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 jhharrison@wsu.edu, and Becca White, Lynn Johnson-VanWieringen, and Ron Kincaid, Washington State University. Mike Gamroth, Oregon State University Tamilee Nennich, Texas A&M University.

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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

“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.”

March 5, 2019March 8, 2021

Whole Farm Nutrient Management – A Dairy Example

Introduction

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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.

1^st 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.

2^nd 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.

3^rd 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:

formulation of diets to meet animal requirements,
grouping animals according to nutrient needs,
determining dry matter routinely and adjusting rations accordingly, and
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:

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.

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.

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

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

Partners

March 5, 2019March 8, 2021

Evaluating Corn Silage Quality for Dairy Cattle

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1 Introduction
2 Model NE_L-3x and DMI
3 Non-fiber Carbohydrates and Their Digestibility
4 Fiber and Its Digestibility
5 Model Comparisons
6 References

Introduction

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.

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Model NE_L-3x and DMI

We (Schwab et al., 2003) modified the NRC (2001) TDN_maintenance 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 TDN_maintenance using the NRC (1989) empirical equation in MILK2000 (Schwab et al., 2003). In MILK2006, the NE_L-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 NE_L intake through effects on both NE_L-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 NE_L-3x content but impacts NE_L intake primarily through effects on DMI. In MILK2006, the IVNDFD value used for calculating NE_L-3x is adjusted for differences in DMI predicted from IVNDFD using an equation adapted from Oba and Allen (1999a). Thus, IVNDFD impacts NE_L 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 NE_L 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 TDN_maintenance, NE_L-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 TDN_maintenance differences between MILK2006 and MILK2000 are minimal. The NE_L-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 TDN_1x (%), NE_L-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 TDN_1x	MILK 2006 NE_L-3x	MILK 2006 Milk/ton	MILK 2000 TDN_1x	MILK 2000 NE_L-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 TDN_1x (%), NE_L-3x (Mcal/lb.) and milk (lb.) per ton using MILK2006 or MILK2000.
Quality	MILK 2006 TDN_1x	MILK 2006 NE_L-3x	MILK 2006 Milk/ton	MILK 2000 TDN_1x	MILK 2000 NE_L-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.

References

Allen, M., and M. Oba. 1996. Fiber digestibility of forages. Pages 151-171 in Proc. MN Nutr. Conf. Bloomington, MN.
Ballard, C. S., E. D. Thomas, D. S. Tsang, P. Mandebvu, C. J. Sniffen, M. I. Endres, and M. P. Carter. 2001. Effect of corn silage hybrid on dry matter yield, nutrient composition, in vitro digestion, intake by dairy heifers, and milk production by dairy cows. J. Dairy Sci. 84:442–452.
Blasel, H.M., P. C. Hoffman, and R. D. Shaver. 2006. Degree of starch access: An enzymatic method to determine starch degradation potential of corn grain and corn silage. J. Anim. Feed Sci. Technol. 128:96-107.
Crocker, L.M., E. J. DePeters, J. G. Fadel, H. Perez-Monti, S. J. Taylor, J. A. Wyckoff, and R. A. Zinn. 1998. Influence of processed corn grain in diets of dairy cows on digestion of nutrients and milk composition. J. Dairy Sci. 81: 2394-2407.
Doggett, C. G., Hunt, C. W., Andrae, J. G., Pritchard, G. T., Kezar, W., and J. H. Harrison. 1998. Effect of hybrid and processing on digestive characteristics of corn silage. J. Dairy Sci. 81(Suppl.1):196(Abstr.)
Ebling, T. L., and L. Kung, Jr. 2004. A comparison of processed conventional corn silage to unprocessed and processed brown midrib corn silage on intake, digestion, and milk production by dairy cows. J. Dairy Sci. 87:2519–2527.
Ferreira, G., and D. R. Mertens. 2005. Chemical and physical characteristics of corn silages and their effects on in vitro disappearance. J. Dairy Sci. 88:4414-4425.
Firkins, J. L., M. L. Eastridge, N. R. St-Pierre, and S. M. Noftsger. 2001. Effects of grain variability and processing on starch utilization by lactating dairy cattle. J. Anim. Sci. 79(E. Suppl.): E218-E238.
Hoffman, P. C., Lundberg, K. L., L. M. Bauman, and R. Shaver. 2003. In vitro NDF digestibility of forages: The 30 vs. 48 hour debate. Univ. of WI Extension Focus on Forage Series. Vol. 5, No. 16. http://www.uwex.edu/ces/crops/uwforage/30vs48-FOF.htm.
Hunt, C. W., W. Kezar, and R. Vinande. 1989. Yield, chemical composition and ruminal fermentability of corn whole plant, ear, and stover as affected by maturity. J. Prod. Agric. 2:357-361.
Ivan, S. K., R. J. Grant, D. Weakley, and J. Beck. 2005. Comparison of a Corn Silage Hybrid with High Cell-Wall Content and Digestibility with a Hybrid of Lower Cell-Wall Content on Performance of Holstein Cows. J. Dairy Sci. 2005 88:244-254.
Joy, M. T., E. J. DePeters, J. G. Fadel, and R. A. Zinn. 1997. Effects of corn processing on the site and extent of digestion in lactating cows. J. Dairy Sci. 80: 2087-2097.
Jung, H.G., M., Raeth-Knight, and J. G. Linn. 2004. Forage fiber digestibility: Measurement, variability, and impact. Pages 105-125 in Proc. 65th MN Nutr. Conf. Bloomington, MN.
Justen, B. A. L. 2004. Digestion kinetics and vitreousness in breeding maize (Zea Mays L.) for silage yield and nutritional quality. M. S. Thesis. Plant Breeding and Genetics. Univ. of Wisconsin – Madison.
Knowlton, K.F., M. S. Allen, and P. S. Erickson. 1996. Lasalocid and particle size of corn grain for dairy cows in early lactation. 1. Effect on performance, serum metabolites, and nutrient digestibility. J. Dairy Sci. 79: 557-564.
Knowlton, K. F., B. P. Glenn, and R. A. Erdman. 1998. Performance, ruminal fermentation, and site of starch digestion in early lactation cows fed corn grain harvested and processed differently. J. Dairy Sci. 81:1972-1984.
Lauer, J., K. Kohn, and P. Flannery. 2005. Wisconsin Corn Hybrid Performance Trials Grain and Silage. Univ. of WI Ext. Publ. A3653. http://corn.agronomy.wisc.edu/HT/2005/2005Text.aspx.
Lundberg, K. L., P. C. Hoffman, L. M. Bauman, and P. Berzaghi. 2004. Prediction of forage energy content by near infrared reflectance spectroscopy and summative equations. Prof. Anim. Sci. 20:262-269.
Mertens, D. R. 2005. Particle size, fragmentation index, and effective fiber: Tools for evaluating the physical attributes of corn silages. Pages 211-220 in Proc. Four-State Dairy Nutr. & Mgmt. Conf. MWPS-4SD18. Dubuque, IA.
Mertens, D. R. 1987. Predicting intake and digestibility using mathematical models of ruminal function. J. Anim. Sci. 64:1548-1558.
National Research Council. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. Natl. Acad. Sci., Washington, DC.
National Research Council. 1989. Nutrient Requirements of Dairy Cattle. 6th rev. ed. Natl. Acad, Sci., Washington, DC.
Neylon, J. M., and L. Kung, Jr. 2003. Effects of cutting height and maturity on the nutritive value of corn silage for lactating cows. J. Dairy Sci. 86:2163–2169.
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Oba, M. and M. Allen. 2005. In vitro digestibility of forages. Pages 81-91 in Proc. Tri-State Dairy Nutr. Conf. Ft. Wayne, IN.
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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.

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

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.

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

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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Estimating Manure Nutrient Excretion

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Introduction

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.

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

Estimating Manure Nutrient Excretion

The front and back end of an animal is connected. While this principle seems obvious, it has historically been ignored in nutrient planning procedures. This fact sheet describes tools that allow integration of feed management and animal performance into nutrient planning processes for animal feeding operations.

A new standard published by the American Society of Agricultural and Biological Engineers (D384.2, Manure Production and Characteristics) is a tool for developing farm specific Comprehensive Nutrient Management Plans (CNMP). This standard allows accurate estimates of nutrient and solids excretion reflective of feed programs and animal performance. Accurate estimates of manure excretion are critical to estimating land requirements and land application costs, sizing manure storage, and planning treatment technologies. This fact sheet will introduce the new manure excretion standard and its application.

Contents of the Manure Production Standard

An ASABE committee of animal scientists and agricultural engineers developed predictive equations for estimating manure excretion for five species (beef, dairy, horse, poultry, and swine) and “typical” characteristics for excreted and as-removed manure. The standard is found at ASABEfollowed by a search of “Standards” and “Title” options for “Manure Production”. The ASABE standard includes seven sections.

Section 1 lists a new “typical” characteristics tabular summary for individual species and groupings of animals. See Tables 1 and 2.(PDF file) These values should provide a reasonable estimate of excretion for animals in the year 2000. As time passes, published typical values become less accurate and should be used with caution for individual herds or flocks. Differences in genetics, feed program, and animal performance between individual farms create a potential for errors when typical values are applied. They may have value for preliminary nutrient planning estimates but should NOT be used in final farm-specific nutrient management plans.

Sections 2 through 7 define the equations for cattle, dairy cattle, horses, poultry (separate sections for meat birds and layers), and swine, respectively. Equation based estimates are provided for all species groups for dry matter, N and P excretion. Equations for estimating additional characteristics are available for some species.

Section 8 of the new standard summarizes As-Removed manure characteristics. The work group summarized a wide range of data sets for inclusion in this section. These values can be beneficial for estimating storage volumes and manure application rates when no other farm-specific information is available. However, when farm specific manure samples are available, they are preferred.

Two Approaches for Estimating Excretion

Two distinctly different approaches were used equation based estimates of excretion. The beef, swine, and poultry work groups used an animal mass balance approach (Figure 1). Excretion is estimated as a difference between feed nutrient intake and retention in body mass or animal products (eggs or milk). intake and retention in body mass or animal products (eggs or milk). The dairy and horse work groups used existing data sets as a basis for multi-variable regression analysis. The dairy work group proposed equations for lactating cows, dry cows and heifers. The horse work group chose to publish separate equations for exercised and sedentary horses. See Table 1. Estimated typical manure (urine and feces combined) characteristics as excreted by meat-producing livestock and poultry.(PDF file) Diet based numbers are in BOLD. Source ASAE D384.2 March 2005, Manure Production and Characteristics.

Figure 1. Mass balance approach was used for estimating excretion haracteristics for beef cattle, swine and poultry.

Factors Affecting Nutrient Excretion

The new standard defines the relationship between feed inputs and animal performance and manure excretion characteristics. For example, the quantity of solids excreted is directly tied to the dry matter digestibility of the diet. Since dry matter digestibility for many species is often 80 to 85% (15 to 20% of solids in feed excreted in feces), small changes in dry matter digestibility produce large differences in solids excreted. A dietary modification that changes dry matter digestibility change from 85% to 80% results in 33% more solids in the feces. Similarly, dietary intake of protein and phosphorus is directly related to excreted N and P.

Historically, manure excretion estimates have been based upon standards published by the ASABE, USDA Natural Resources Conservation Service, and Midwest Plan Service. These previous standards varied excretion estimates with species and animal weight only. A linear relationship was assumed between excretion and body weight. However, this approach provides a poor explanation of important biological factors that influence manure excretion. In addition, these standards become dated with time because they do not recognize changes in genetics, animal performance, and feeding options. Current and past excretion estimates based upon species and body weight alone often produce inaccurate estimates of manure excretion for individual farms.

The standard for manure excretion released by ASABE in 2005 was designed to provide farm-specific estimates of excretion reflective of individual farm feed programs and animal performance. In addition, this standard will better adapt to changes in excretion that occur over time due to factors such as improved animal genetics. Thus, the equation based standard for manure excretion released in 2005 should remain accurate well into the future.

Is This Important?

Tables 3, 4, and 5(PDF file) illustrate excretion estimates for beef, swine, and dairy calculated from the new equations. Some of the more dramatic differences between the current ASABE and other standards are associated with P and total solids excretion. These differences tend to become larger as emerging feed technologies reduce nutrient excretion and as feeding of by-products of corn processing and other food processing industries become increasingly popular. To illustrate the importance of the new ASABE standard for farm specific estimates, comparisons are illustrated below for three species.

Beef

A comparison of excretion characteristics estimated by the new ASABE standard with past standards (Table 3, Rows A-C) suggests that previous estimates are in reasonable agreement for N excretion but in poor agreement with P excretion. A significant effort to better match beef cattle rations with phosphorus requirements has reduced P excretion substantially.

Considerable variation exists between individual cattle feedlots relative to performance and feed program strategies. Substantial variation in N and P excretion is anticipated when comparing a corn based ration (Table 3, Row C) and a ration with 40% distillers grains (Table 3, Row D). Combining feed program variation with typical ranges in animal performance can produce a 2-fold range in N excretion and a 3-fold range in P excretion (Table 3, Rows F and G). Large errors in beef cattle excretion estimates are common unless performance and feed program are considered in estimating excretion.

Swine

Typical nitrogen excretion estimates for swine for the new standard have changed little from the past ASAE standard (Table 4, Rows A-B). However, phosphorus excretion is substantially lower than other standards. Total solids excretion is also generally lower than previously accepted values.

Table 4 illustrates the importance of a standard that responds to emerging feeding strategies (Table 4, Row C). Diets based on use of crystalline amino acids and phytase have the potential for lowering dietary CP and P levels and N and P excretion. A low CP diet would produce N excretion levels up to 40% less than new standard typical value. Low P diets would reduce P excretions levels by 33 to 40% from new typical values.

Dairy Cattle

Generally the new ASABE standard predicts greater excretion of nutrients and solids as compared to the past ASAE standard and other existing accepted values for lactating cattle (Table 5, Rows A and B. Steadily increasing milk production will create an even larger disparity between predicted excretion by the new ASABE standard and other past values.

Tools for Applying ASABE Standard

The proposed ASABE equations complicate the process of estimating nutrient and solid excretion. Software tools based upon these equations provides one option for improving the utility of equations and their application to farm specific CNMPs. Two spreadsheet tools use the ASABE estimate of excreted nutrients as a basis for estimating land requirements for managing manure nutrients. A Nutrient Inventory comes with instructions and a one-hour video discussing its application (available at University of Nebraska). A second tool nearing completion (FNMP$) will estimate land requirements, cost and time required for land applying manure, and potential economic benefits of manure nutrients (will be available at the same web site).

A simplified hand calculator of nutrient excretion was introduced in a MWPS publication, Manure Characteristics (Table 6). It uses a mass nutrient balance procedure for estimating excretion for beef, dairy, poultry and swine. It provides a simplified approach that produces similar answers to procedures used in the ASABE standard.

Information Requirements for Using New Standard

The information requirements of the new standard are greater than with past standards. Farm specific information is needed for animal performance ( e.g. weight gain or milk production) and feed program (dry matter intake and nutrient concentration). Those input requirements are summarized in [media:Table7excretion.pdf | Table 7]].

Applications of New ASABE Standard

Most nutrient planning processes follow a step-wise procedure similar to that illustrated in Figure 2. At this time, the equation-based estimates of solids and nutrients will have their greatest utility in the strategic or long-term planning. These strategic plans are of greatest value to a new or expanded facility or when a regulatory permit is being assembled.

Figure 2 illustrates a second critical planning phase, the Tactical or Annual Plan. For decisions such as manure application rates, timing, and location, constantly changing conditions such as weather and residual soil nutrients must be considered. On-farm data such as manure samples will likely be of greater value to annual planning processes than the predictions made by the new ASABE equations

Figure 2. Common planning procedure used for nutrient management planning.

Improvements in nutrient excretion estimates offered by the new equations should improve the accuracy of farm-specific planning for:

Land requirements for managing N and P. The equations provide a more accurate estimate of nutrient driven land requirements for manure application when on-farm data on manure production is not available. Nitrogen volatilization and availability estimates remain a weak point for this planning process.
Cost of manure application. The ASABE equations are being used to estimate manure nutrient value as well as time, equipment, and labor requirements for handling manure (Kissinger et al., 2005).
Ammonia emissions. Ammonia emissions from animal facilities are of increasingly interest to the regulatory community. The equations should provide a mechanism for adjusting farm emission estimates based upon several farm-specific factors.

The equations also allow a prediction of dry matter excretion and possibly volatile solids excretion if feed digestibility values are known. This approach will allow farm specific estimates of solids excretion that will benefit planning estimates of:

Anaerobic and aerobic lagoon sizing,
Anaerobic digester sizing and gas production,
Storage sizing if solids estimates are combined with known moisture contents resulting from specific manure handling systems

Summary

The new ASABE standard for manure excretion provides an important tool for key strategic planning activities important to a comprehensive nutrient management plans. In addition, the new standard provides an important tool for integrating feed management decisions into CNMPs and deciding the environmental and economic benefits and costs of feed program options.

Related Files

To follow the references in this article, it is recommended that you print these four PDF files and refer to them at the appropriate places in the article.
Tables 1 and 2
Tables 3, 4 and 5
Table 6
Table 7

Disclaimer

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

Acknowledgements

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.

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

R.K. Koelsch, University of Nebraska-Lincoln

Images: CC 2.5 Rick Koelsch

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NRC Recommendations for Dairy Cows

Introduction

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 (K_d) of diet components in the rumen, and passage rate (K_p) 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 × [K_d/(K_d + K_p)]

RUP = B × [K_p/(K_d + K_p)] + 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.

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.

This project is affiliated with the LPE Learning Center.

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

Partners

March 5, 2019July 6, 2020

Potential Routes for Pathogen Transport to Water

The movement of pathogens to water is dependent upon multiple environmental and transport factors.

Ground Water Contamination by Manure Pathogens

Thomas Harter, Groundwater Hydrologist at University of California-Davis discusses potential for ground water contamination: “While invisible to the human eye, most pathogens are giants of the micro-world … A typical bacterial pathogen is … much too large to fit between the clay or silt particles of many clay, silt, or loam soils…Only in sandy soils, the pore space is indeed large enough to provide ample traveling space for pathogens. Even there, pathogens frequently collide onto grain surfaces where they tend to become permanently attached. Ultimately, most pathogens are strained or filtered out of the water cycle long before reaching groundwater or a stream. Even if pathogens reach an aquifer, the aquifer itself will filter most remaining pathogens over relatively short distances (100 ft – 300 ft)…”

Dan Shelton, Environmental Microbial Safety Lab Research Leader, USDA Agricultural Research Service identifies some important exceptions including: “sandy or rocky soils, which generally allow for greater infiltration, …heavy soils (e.g., clay) containing significant cracks or fissures, or channels created by decayed plant roots or burrowing worms, (creating potential contamination of shallow ground water or tile drains)… and soils/subsoils throughout the Appalachian region derived from limestone geological formations (known as karst). Finally, improper installation of wells can allow for direct contamination of groundwater via the leaching of organisms along the well casing.”

Protozoa and bacterial pathogens are commonly too large to fit between the particles in most soils.
Source: Thomas Harter, University of California-Davis.

Surface Water Contamination by Manure Pathogens

Pathogen contamination of surface water is more common than contamination of groundwater. Direct contact of animals with surface water or runoff from animal housing is a significant risk. Land application sites with high runoff and erosion potential provide an additional potential pathogen connection to surface water. Thus, soil and nutrient conservation practices that minimize runoff and erosion are key BMPs for pathogen risk reduction.

Environmental Factors That Influence Pathogen Survival

Jeanette Thurston-Enriquez, USDA Agricultural Research Service scientist, summarizes environmental factors that reduce the survival of pathogens:

High temperatures. Each pathogen has a different susceptibility but generally high temperatures are very effective in reducing populations.
Time. Bacteria are living organisms, so they can’t live forever…
Sunlight. Has a couple of effects on pathogens. It desiccates (reduces moisture levels) them and the UV light also inactivates pathogens…
Desiccation. Is one of the best ways to inactivate pathogens in the environment.

Macropores, caused by earthworms, roots and cracks, allow pathogens to travel unfiltered through some soil.
Source: Cornell University http://soilandwater.bee.cornell.edu/Research/pfweb/index.htm

Recommended Resources on Pathogen Transport to Water

Pathogen transport in the environment is summarized in Dr. Jane Frankenberger’s web cast presentation found in the pathogen webcast archive. Additional information on survival time can be found starting on page 25 of USDA NRCS technical note, Waterborne Pathogens in Agricultural Watersheds.

Page Developers: Rick Koelsch, University of Nebraska, and Janice Ward, US Geological Survey
Reviewed by: Dan Shelton, USDA ARS, Sheridan Haack, USGS

Page Updated & Maintained by: John Brooks (john.brooks@ars.usda.gov)

March 5, 2019March 10, 2022

Market Based Conservation

Market-based conservation is an evolving concept that can mean different things to different people. Market-oriented approaches to conservation can include:

Using economic approaches, such as auctions and trading of credits, niche marketing, and a variety of payment for ecosystem services strategies
Encouraging competitions, such as bidding for grants or offers to pay for a greater share of the cost
Providing data to inform the conservation investment decisions of others
Focusing on monetary and non-monetary incentives
Fostering knowledge-based conservation

Webcast Presentation

The LPE Learning Center hosted a webcast on Market Based Conservation: Implications for Manure Management in May, 2008.

Market Based Conservation as a Policy

At the White House Conference on Cooperative Conservation in 2005, Agriculture Secretary Johanns announced a new U.S. Department of Agriculture Policy on Market-Based Environmental Stewardship. The goal of this policy is to broaden the use of markets for environmental and ecosystem services through voluntary market mechanisms. These mechanisms may include environmental credit trading, insurance, mitigation banking, competitive offer-based auctioning, eco-labeling—and more. The intent of this new policy is to make a deliberate, determined effort to help bring producers and consumers together and to develop innovative tools to quantify environmental impacts. In December of 2008, the USDA announced the creation of the Office of Environmental Markets to catalyze the development of markets for ecosystem services.

Until the last few years, in the U.S., most of the incentives for conservation have been provided by government through sharing the cost of conservation practices on private lands because these practices also have public environmental benefits. Trading is a market approach that is gaining acceptance through the cap and trade system. The Environmental Protection Agency policy on water quality trading is an example of the market approach. With trading, regulated industries have the flexibility to find the least cost avenue to comply with emissions, or at times, to trade with others to improve environmental quality. That is, when regulated industries must reduce emissions it may be cheaper to pay other firms or farms to reduce emissions than to do it themselves. Trading has the potential to accelerate air and water quality improvement and reduce compliance costs. The key to market-based incentives is that they are voluntary, verifiable, and transparent.

Examples of Market Based Conservation or Trading Programs

The New York City Watershed Agricultural Program is a great example of market based trading with a complementary municipal and agricultural partnership. Local farmers and agribusiness worked with the city to protect drinking water quality on nearly 500,000 acres of farmland in the watershed that supplies New York with drinking water. This saved the city millions of dollars in the development of advanced treatment systems and helped the rural community maintain its character.

One of the best manure based examples that is currently available is the Environmental Credit Corporation Lagoon Cover Program. Through this program, they will design, finance, and install lagoon covers to capture methane and other emissions at no cost to the farmer. They use the results to sell the carbon credits and can provide additional income to producers in some cases. Companies that buy and sell credits like ECC are called aggregators of credits. While national carbon legislation in the US has still not passed, there are still voluntary opportunities that exist for those in the agricultural sector as outlined in this webcast on opportunities for pork producers.

A final example is Vermont’s Cow Power program. Central Vermont Public Service, a utility, created a surcharge/premium people can pay to purchase green power generated by anaerobic digesters on dairy farms. This premium goes back to the farmer, generating a marketplace incentive and reward for farmers who are generating renewable, green energy from manure.

Research Summaries on Market Based Conservation

An economic analysis of nutrient trading in the Chesapeake Bay Region: A study looks into nutrient credit trading as a means to improve the quality of water in the Chesapeake Bay.

Water Quality Trading in the United States provides a great overview of water quality trading programs implemented in the U.S. The primary source of information for this overview is a detailed database, collected and compiled by a team of researchers at Dartmouth College.

Paying For Environmental Performance: Using Reverse Auctions To Allocate Funding For Conservation Since demand for funding in conservation programs usually exceeds the available funds, allocating funding in a way that achieves the greatest environmental outcomes is essential. Reverse auctions are one way to efficiently allocate funding. This paper examines two reverse auctions conducted in Pennsylvania, designed to fund best management practices that reduced phosphorus pollution. It explains how reverse auctions can be used to maximize environmentally desirable outcomes, and outlines lessons learned from the Conestoga Reverse Auction Project within Pennsylvania’s Susquehanna River Watershed.

The Florida Ranchlands Environmental Services Project: Field Testing a Pay-for-Environmental-Services Program This paper examines a project in Florida that will field test a program that pays cattle ranchers to provide environmental services that will benefit the lake. The program came about after a 2004 study conducted by World Wildlife Fund (WWF) with several cattle ranchers concluded that a program to promote changes in water management practices on 850,000 acres of improved and unimproved pasture could moderate water flows to the lake, reduce phosphorus loads, and add to wetlands habitat. The study concluded that the agencies could buy these environmental services from cattle ranchers at a lower cost than producing the services by building new public works projects.

Doug Parker at the University of Maryland has written a report on Creating Markets for Manure: Basin-wide Management in the Chesapeake Bay Region. This report summarizes various methods for creating manure based markets. Other reports and programs from Georgia and Arkansas have focused on improving markets for poultry litter.

Author: Mark Risse, University of Georgia
Reviewers: John Lawrence, Iowa State University and Suzy Friedman, Environmental Defense Fund

March 5, 2019March 27, 2019

Pathogens and Potential Risks Related to Livestock or Poultry Manure

Links to PEDv (Porcine Epidemic Diarrhea Virus).

Microorganisms

Microorganisms (e.g. virus, bacteria, protozoa, and fungi) surround us, on us, and in us; they are ubiquitous and everything in the world is governed by them. They are part of our everyday lives. They influence the the quality of our soil, food grown on that soil, and how our body reacts to that food. They are diverse, ranging from a simple mix of protein and DNA to complex multi-cellular small “animals”. Most environmental microorganisms spend their entire lives as quiet members of their ecological society, but some reach a level of infamy. Pathogens may only represent a very small portion of all microorganisms, but they are often the most visible, thanks to readily reported outbreaks, food recalls, and proliferation of internet news blogs and sites.

What is a Pathogen?

A pathogen is a biological agent that causes disease or illness; this disease can occur in humans, animals, or crops. Zoonotic pathogens refers to pathogens naturally transmitted from animals to humans and are often heard about on news sites or involved in food recalls.

All animals including pets, livestock, wildlife and humans, are possible hosts of potential human pathogens. We will focus on pathogens originating from livestock and poultry that might be transported to humans via air, water, soil, crop, and fomites (inanimate objects) contacted directly or indirectly by manure.

Zoonotic Pathogens

There are four general classes of zoonotic pathogens:

viruses
bacteria
protozoan parasites
helminth parasites

Zoonotic viruses are those found mainly in animals that cause disease in people who come into contact with the animal or share a vector (transmitter of disease) like a mosquito (West Nile Virus is a virus of birds which mosquitoes carry and can transmit to people). Viruses can only multiply when they are inside a host cell.

Until very recently, it was considered that most fecal or urine transmitted viruses of livestock were not zoonotic, but things have changed somewhat in recent years, and we are now in a steep learning curve as to how important ruminants and poultry are as reservoirs of these zoonotic viral agents.

Zoonotic bacterial pathogens are, like all bacteria, single celled microorganisms that can survive and, under favorable conditions, reproduce in terrestrial and aquatic environments. The zoonotic bacteria are those that typically cycle in domestic animals without causing disease in their typical hosts. However, when they get transmitted into people, the disease that is produced can be severe.

Examples of zoonotic bacteria are Salmonella spp., strains of Escherichia coli such as E. coli 0157:H7, Listeria monocytogenes, and Campylobacter spp.

Zoonotic protozoan parasites, are protozoa that are found in other animals and which can infect people. There are basically two roles that humans can play in this scenario. They can be accidental hosts in the life cycle of the protozoan, where the protozoan undergoes the same development in the human as it does in its normal reservoir host. Or, the human may be an intermediate host in the life cycle of the parasites, just like any other vertebrate; in this case, the reservoir host shed many stages into the environment with the goal of infecting as many intermediate hosts as possible.

In the case of zoonotic protozoa relative to domestic farm animals, only a few have proven to be of significant concern relative to the infection of people.
- Species of Cryptosporidium found in horses, cattle, pigs, and sheep can accidentally infect people, with C. parvum of young ruminants being the most common offender.
- Giardia of livestock typically does not seem to occur in people, but it does seem that they might be infected with the human form and could then serve as a source of stages that might be passed to humans.

Zoonotic helminth parasites are worms, nematodes (roundworms), cestodes (tapeworms), or trematodes (flukes), that have cycles similar to protozoa. Again, people can be infected accidentally by the worm in the same manner as a reservoir host or they can be serving as just another vertebrate intermediate host in the life cycle of the parasite.

Fortunately, in the case of domestic farm animals, the helminth parasites are for the most part not zoonotic with respect to people. The only forms with stages that might be infectious to people from manure would be the egg of the pig roundworm, Ascaris suum.

Photo source: Jeanette Thurston-Enriquez webcast presentation.

Detailed discussion of protozoan parasites, bacteria, and viruses can be found on pages 5, 12, and 18, respectively, of the USDA NRCS technical note

Waterborne Pathogens in Agricultural Watersheds

Several outbreaks of human illness and death have been attributed to drinking water contaminated with livestock manure. Of 66 drinking water outbreaks in affluent nations, the probable cause of 12 of the outbreaks was livestock manure (see Hrudey and Hrudey, 2004 in Research Summaries. These included:

An outbreak at the 1999 Washington County Fair, New York (E. coli O157:H7; of 781 confirmed cases, 71 people were hospitalized, and 2 died);
An outbreak in Walkerton, Ontario, Canada in 2000 (E. coli O157:H7 and Campylobacter jejuni; 2,300 people were ill, 65 were hospitalized and 7 died).

These outbreaks were indicative of the capability of the pathogens to survive and be leached through soil to groundwater sources of drinking water.

2008 distribution of confirmed zoonotic diseases. Data source:[ http://www.cdc.gov/mmwr/preview/mmwrhtml/ss5512a4.htm Centers for Disease Control MMWR Surveillance Summaries].

Not all illness outbreaks are livestock related. For example, animal manure was initially suggested as the source of the largest drinking water outbreak in U.S. history – the Cryptosporidium outbreak in Milwaukee, WI in 1993. Several years later following advances in microbiology and genetics, human sewage was identified as the likely contributor.

Antibiotic Resistant Bacteria in Agricultural Manures

An antibiotic-resistant bacterial population is one in which resistance is either intrinsic or has been acquired from exposure either to antibiotics or to other antibiotic resistant bacterial populations. The increased frequency of antibiotic resistant pathogens has become a serious public health concern as demonstrated with outbreaks of methicillin-resistant Staphylococcus aureus (MRSA) and antibiotic resistant Salmonella such as Salmonella DT104. Little research and information is available on the presence of antibiotic resistant bacteria originating in manure and manure land applied environments, and, thus, little is known about their fate and transport in soil, water, crops, and agronomic systems.

A listing of possible zoonotic pathogens can be found on pages 6 – 10 of an EPA literature review.

Authored by: Michael Jenkins and John Brooks, USDA ARS, Dwight Bowman and Janice Liotta, Cornell University.

Question or concerns, contact John Brooks (john.brooks@ars.usda.gov)

March 5, 2019March 27, 2019

Livestock and Poultry Environmental Stewardship Curriculum

LPES Curriculum Lessons

The lessons are divided into six modules: Introduction, Dietary Strategies, Manure Storage and Treatment, Land Application and Nutrient Management, Outdoor Air Quality, and Related Issues.

Go to the Lessons

Small Farm Fact Sheets

The small farm fact sheet series were developed to assist smaller-scale livestock and poultry producers with questions about regulations and environmental stewardship.

Go To the Small Farm Fact Sheets

Agricultural Environmental Management Systems (EMS) Series

The Ag EMS series is based on the ISO 14001 international standard for environmental management systems (EMS). The series is targeted toward educators and producers and assists with integrating environmental considerations into a systematic approach to day-to-day farm management.

Go To the Ag EMS Series

Introduction

Using the Feed Management Plan Checklist

Related Files

Disclaimer

Project Information

Author Information

Partners

Acknowledgments

Introduction

Introduction to Whole Farm Nutrient Management

Nutrient Utilization by the Dairy Cow

Whole Farm Nutrient Balance

Checklist Tools

Spreadsheet Based Whole Farm Nutrient Management Tools

Summary

Disclaimer

Project Information

Author Information

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Contents

Introduction

Model NEL-3x and DMI

Non-fiber Carbohydrates and Their Digestibility

Fiber and Its Digestibility

Model Comparisons

References

Disclaimer

Project Information

Author Information

Reviewer Information

Partners

Introduction

Using the Feed Management Plan Template

Estimate of Manure Nutrient Excretion

Feed Management Practices

Guidance Sections

Related Files

Disclaimer

Project Information

Author Information

Partners

Acknowledgments

Introduction

Estimating Manure Nutrient Excretion

Contents of the Manure Production Standard

Two Approaches for Estimating Excretion

Factors Affecting Nutrient Excretion

Is This Important?

Beef

Swine

Dairy Cattle

Tools for Applying ASABE Standard

Information Requirements for Using New Standard

Applications of New ASABE Standard

Summary

Related Files

Disclaimer

Acknowledgements

Project Information

Author Information

Partners

Introduction

Feed Management

Nutrients

Nitrogen (Protein)

Phosphorus

Potassium

References

Disclaimer

Project Information

Author Information

Reviewer Information

Partners

Ground Water Contamination by Manure Pathogens

Surface Water Contamination by Manure Pathogens

Environmental Factors That Influence Pathogen Survival

Recommended Resources on Pathogen Transport to Water

Webcast Presentation

Market Based Conservation as a Policy

Examples of Market Based Conservation or Trading Programs

Model NE_L-3x and DMI