Final Report for SW99-024A
Average whole-farm N balance for dairies in the West was consistent with other studies in that imported feed was the single largest N input and animal products, milk and meat, were the largest source of output. The most important subsystem was how efficiently feed N was converted to product N. Manure handling should not overshadow herd and crop N efficiencies in attempts to improve N balances. Milk urea nitrogen was not a good predictor of whole-farm nitrogen balance because of the inclusion of nonlactating animals in the summary.
1. Verify the accuracy of MUN analysis for predicting nitrogen (N) and the N (protein) metabolism of lactating cows in independent digestion studies.
2. Determine the potential use of MUN analysis and a recently developed computer worksheet for predicting N losses and whole-farm efficiencies on commercial dairies in UT and ID.
3. Disseminate all results to agricultural scientists and consultants as well as dairy producers.
Sub-objectives of the University of Maryland as they relate to the overall project are:
1) to improve computer worksheets for calculating whole farm balances by adapting them to the Western region and by implementing changes identified by using the worksheets in this region,
2) to assist with evaluation and improvement in MUN analysis based on research conducted at Utah State University, and
3) to disseminate information from this project to farmers via extension publications and the World Wide Web.
Using figures from the 1996 state Agricultural Statistics reports from the coterminous states in the Western SARE region, receipts from milk sales totaled $6.86 billion, or 14% of all agricultural receipts (total of $49.6 billion) in the region. During the 1980’s and 90’s, milk production has shifted away from the traditional dairy states in the upper Midwest and Northeast toward the Western U.S. In the 1980’s 8 of the top 10 states, based on the propensity to produce milk (a composite index reflecting the relative competitiveness of dairy operation), were located in the Western SARE region (Fallert et al., 1994). Moreover, from 1980 to 1990 milk production rose faster than the U.S. average in each of the coterminous states in the Western SARE region with the exception of MT and WY (Fallert et al., 1994). Lastly, from 1992 to 1997 milk production in the Western U.S. increased 28%; the Western U.S. was the only region in the country to show an increase in milk production during this time (Anonymous, 1998c). Thus, the dairy industry in the region is large and is increasing faster than in any other region of the country.
Historically, livestock production in the U.S. has focused on maximal production with little or no consideration given to the efficiency of nutrient conversion (i.e. mass of farm nutrient output ÷ mass of nutrient inputs to the farm). Assessment of whole-farm nutrient budgets on dairy farms reveals that N inputs (e.g., feed, fertilizer, manure) are vastly greater than the N in farm outputs (e.g., milk, animals, feed sold) (Aarts et al., 1992; Bacon et al., 1990; Halberg et al., 1995). Consequently, substantial amounts of N are lost to the environment. The consequences of this environmental nutrient loading by dairy farms are receiving increasing attention (Lanyon, 1994, Tamminga, 1992). Concern lies primarily with N and P and their effects on groundwater NO3- contamination (Canter, 1997) and surface water eutrophication (Sharpley et al., 1994), respectively.
There has been much recent negative publicity regarding the environmental impact of N and P losses from dairy farms throughout the Western SARE region including CA (Diringer, 1997), UT (Los, 1997), and WA (Postman, 1998). New environmental legislation in the region includes the passage of Dairy Nutrient Management Plans in ID (Snyder, 1998) and WA (Anonymous, 1998b) and the formation of the Dairy Waste Enforcement Task Force in CA (Anonymous, 1998a). As the dairy industry continues to grow in the region, public concern and legislative regulation will increase.
The dairy industry, as well as other livestock enterprises, has been identified as a potential contributor to point and non-point source environmental pollution. It has been reported for the U.S. (Howarth et al., 2002) and Europe (van Egmond et al., 2002) that the main sources of reactive nitrogen (N) in the environment are fertilizers and manure. The N in urine (more than half of the total N excreted) is quickly converted into ammonia by urease in the environment (Tamminga, 1992). Ammonia may be volatilized to the atmosphere and subsequently transferred through several environmental reservoirs contributing to reduced atmospheric visibility and particle matter, soil acidity, nitrate pollution of ground water, and eutrophication of streams, lakes, and estuaries (Galloway, et al., 2002). Ammonia in soil is converted into nitrate (NO3-N) that may leach into the groundwater or run off into surface water as well as provide fertilizer for crops (Tamminga, 1992; Nelson, 1999). Concern arises regarding the N content in manure and its effects on groundwater, surface water, and air as it leaves the farm (Koelsch and Lesoing, 1999).
In an effort to curb the detrimental effects of these potential environmental contaminates, the USDA and EPA have established that most animal feeding operations must develop a comprehensive nutrient management plan (CNMP)(U.S. Department of Agriculture, 1999). It is difficult to measure the various N losses from farms on a routine basis, and strategies to control one type of loss (e.g. volatilization) often lead to increases in a different loss (e.g. leaching). Thus, whole-farm N balance (total farm N inputs minus N exports in products) is often considered as a means to estimate unaccounted for N that can become a hazard to the environment (Kohn et al., 1997). When this method effectively reduces whole-farm N balance, it not only benefits the producer from an environmental standpoint, but may also help producers comply with new environmental regulations.
A spreadsheet model called the Maryland Nutrient Balancer (Kohn, 2001), based on the same principles as the worksheets developed by Dou et al. (1996, 1998), was developed to account for whole-farm N and phosphorus (P) balances by quantifying all forms of N and P coming onto the farm as well as leaving. By tracking N from the time it comes onto the farm to when it leaves, producers gain a better understanding of whole-farm N dynamics. This spreadsheet allows producers to determine the nutrient balance and also to look at “what if” scenarios for making management decisions.
Most of the research conducted on whole-farm N balance has been done with either computer models (Aarts et al., 1992; Kohn et al., 1997; Kuipers et al., 1999; Wang et al., 2000) or case studies involving 1 (Bacon et al., 1990; Dou et al., 1996; Van Horn et al., 1996; Klausner et al., 1998) or 2 dairies (Dou et al., 1998) and none have been carried out in the western United States. Koelsch and Lesoing (1999) reported whole-farm balances for 33 beef and swine confinement livestock operations in Nebraska, but no dairy. There is a need to better understand factors that affect N balance on dairy farms, especially under Western conditions.
Several researchers have examined ways of lowering overall farm N balance (Aarts et al., 1992; Dou et al., 1996; and Kohn et al., 1997). One method of lowering excess N produced on the farm is by ration manipulation so that protein in the ration is utilized more efficiently, thereby reducing potential N loss (Van Vuuren et al., 1993; Van Horn et al., 1994; Vagnoni and Broderick, 1997). Kohn et al. (1997) conducted a computer sensitivity analysis of the N flows in dairy farms associated with: 1) the animal herd, 2) crop uptake, and 3) fertilizer or manure availability. The conversion of feed N to milk N was the single most important factor affecting the environmental impact (through N losses) of dairy operation. If this method could effectively reduce whole-farm N balance, it would not only benefit the producer from an environmental standpoint, but could also favorably improve dairy economics because protein is an expensive nutrient in dairy diets.
The protein (N) efficiency (milk protein )protein consumed) of dairy cattle can be improved by dietary manipulations with the result that N output in the milk is increased and N excretion in the urine is reduced. Because protein is an expensive dietary nutrient, these improvements are beneficial not only environmentally, but economically as well. Mathematical models of dairy farm enterprises indicate a tremendous opportunity to reduce whole-farm N losses by increasing the protein efficiency of the herd. A tool for assessing protein efficiency, the milk urea nitrogen test (MUN), is currently available through DHIA. The MUN test is fast, cheap, noninvasive, and strongly correlated with urinary nitrogen levels (r2 = 0.72) (Jonker et al., 1997). Optimizing MUN could reduce nitrogen loss into the environment. Testing under field conditions in the West is needed. Also, four equations have been developed (Jonker et al., 1997) that pertain to N excretion of dairy cows as it relates to the MUN test and need to be verified under Western conditions:
Urinary N, grams/day = 12.54 x MUN
N intake, grams/day = (urinary N + milk N + 97) ) 0.83
Fecal N, grams/day = predicted N intake – predicted urinary N – milk N
N efficiency, % = 100 x (milk N ) predicted N intake)
The MUN test and Maryland Nutrient Balancer are tools that could help determine N balances under field conditions easier and cheaper.
Objective 1. Verify the accuracy of MUN analysis for predicting nitrogen (N) and the N (protein) metabolism of lactating cows in independent digestion studies.
Sixteen multiparous Holstein cows from the Caine Dairy Research and Teaching Center at Utah State University (four with rumen cannulae) were assigned to four replicated 4 x 4 Latin squares with 21-d periods (107 ± 27 DIM). Two low protein diets were formulated to be 17% CP and contained either 20% corn (diet LP) or 13.5% corn plus 3.2% sucrose (diet LP sucrose). Additionally, two high protein diets were formulated to be 18.5% CP and contained a similar level of corn and sucrose as diet LP sucrose, but contained additional urea (diet HP urea) or soybean meal (diet HP SBM) as supplemental sources of RDP. Diets were formulated to be isoenergetic and equalized for RUP content within CP level. Percent alfalfa hay and corn silage were kept constant for all treatments. Days 1 to 14 served as the adaptation period and all samples and data were collected from d 15 to 21. Diets were fed for ad libitum intake twice daily as TMR to allow for 10% orts. Milk yield was recorded at both the a.m. and p.m. milkings.
Total urine collections were made with indwelling Foley catheters, inserted on d 18 of each experimental period. Urinary output was measured every 24 h for 3 d. After the weight of urine was recorded, its specific gravity was determined and urinary volume computed as the quotient of urinary weight and specific gravity.
Weekly composites of the feed ingredients, forage mixture, concentrates, TMR, and orts were dried at 60oC for 48 h, ground, and then analyzed for N, DM, NDF and ADF. Milk samples were analyzed for protein, fat, SNF, lactose, SCC and MUN at the Rocky Mountain DHIA Laboratory (Logan, UT). Urine was analyzed for total N by adding 100 ml of 1/10 diluted urine to 8- x 5-mm tin capsules containing a small quantity of Chromasorb absorbent. Samples were dried overnight at 40oC and then analyzed for total N.
Data were analyzed as a 4 x 4 Latin square, replicated once for ruminal data and replicated four times for all other responses, using the general linear models procedure of SAS. It should be noted that the experimental design is a little different that originally proposed for Objective 1; however, combining two experiments into one and revising the treatments achieved the same end results as what was proposed.
Objective 2: Determine the potential use of MUN analysis and a recently developed computer worksheet for predicting N losses and whole-farm efficiencies on commercial dairies in UT and ID.
Data were collected from 41 dairy farms (34 farms with Holstein and 7 farms with Jersey cows as the predominant breed) selected from seven counties in Utah and six counties in Idaho. Farms were selected from counties that had at least 5,000 dairy cows (Idaho Agricultural Statistics Service, 1999; Utah Agricultural Statistics Service, 1999). Within each county, producers were identified who were members of the DHI and willing to cooperate in the study.
Producers were initially contacted by phone, and then sent a letter outlining their involvement in the project. Producers were subsequently interviewed face-to-face to collect the information needed for the Maryland Nutrient Balancer (Kohn, 2001). The program was used to create a whole-farm N balance for each farm (also a P balance). Feed data were gathered from producer records which included actual receipts, ration sheets, and nutritional information directly obtained from their consultant. Tons of feed and fertilizer purchased were converted to tons of N using actual feed crude protein content and fertilizer N analysis provided by the farmer, when available, or using National Research Council (2001) values for feed when no analysis was available. Tons of crops grown and sold were also collected from the dairy farmer and converted to tons of N. All analysis of data was done on a DM basis. Required inputs were: tons of purchased feed and fertilizer, tons of crops grown and sold, number of cows purchased and sold, tons of manure exported and its N and P content, and annual milk weights sold. Production characteristics such as milk protein percent (crude protein), rolling herd average milk, average DHIA test day MUN concentration (Rocky Mountain DHIA laboratory), and cow and heifer numbers were also required inputs. An average number of animals for the whole year was used to determine the number of cows and heifers. Only one dairy contracted all heifer raising off-farm.
Nutrient inputs and outputs were reported for all farms for January through December 1999 except for one farm that underwent a major change of focus in 1999. The time period of June 1999 through May 2000 was reported for this farm. This farm was left in the analysis because the data included a complete year, overlapped six months of the collection period for the other farms, and analysis could not detect it as an outlier. All other farms changed little during the data collection period. Farm information and manure samples were collected in the summer and fall of 2000 in order to have complete information for the 1999 year.
Five to 10 sub-samples of stored manure (the number varying depending on farm size) were randomly collected from each storage facility on each farm and combined into a composite sample. Samples of manure in piles were taken from 6-8″ into the pile at various sites (n = 3 – 5 samples per pile) in order to obtain a representative sample. Samples were immediately frozen and stored for later analysis of N content. Total tons of manure coming out of storage were estimated from Natural Resources Conservation Service values (U.S. Department of Agriculture-NRCS, 1992) and combined with the N concentration of the manure so that total tonnes of N coming out of storage could be compared with the amount going into storage.
Stored manure samples were analyzed for DM by drying in a forced air oven at 60oC, ground through a 1 mm mesh screen in a Wiley Mill, then further dried at 105oC. The DM content was then determined based on final weight of the sample (AOAC, 1984). Samples dried at 60oC were analyzed for N by the combustion method (AOAC, 1996) using a Carlo Erba Elantech NA 2100 Protein Nitrogen Analyzer (ThermoQuest, Italia S.p.A., Milan, Italy). Nitrogen content on an absolute DM basis was used in data analysis.
Calculations within the Maryland Nutrient Balancer
The Maryland Nutrient Balancer (Kohn, 2001) calculated summaries and several efficiencies that were used in later analysis. These efficiencies and the methods used to calculate them are shown in Table 1.
The data were analyzed as a completely randomized design with individual farm as the experimental unit. Descriptive statistics were analyzed from participating herds. All whole-farm N balances were converted to a per kg of milk and meat N basis (balance per product) prior to analysis to eliminate the effect of farm size on farm balance. A multivariate regression model was developed with whole-farm N balance per product as the dependent variable and HNUE, MNSE, and crop N utilization efficiencies as independent variables. Univariate regression models with whole-farm N balance per product regressed on HNUE, MNSE, whole-farm P balance per product (Spears et al., 2003), imported feed per product, milk production, herd size, manure exported per product, imported fertilizer per product, total N available to crop, total crop N, availability of manure N applied, and uptake of available N were analyzed to determine the relative importance of the components of each subsystem. Herd N utilization efficiency had the greatest impact on whole-farm N balance per product, therefore, factors influencing HNUE were analyzed further. Univariate regression of the dependent variable of HNUE was performed on the independent variables of herd P utilization efficiency (Spears et al., 2003), milk production per cow, Jonker et al. model efficiency, Kauffman and St.-Pierre model efficiency, MUN, herd size, and imported feed per product. Breed of cow did not significantly affect dependent variables; therefore, herds from both breeds were combined in the analyses. Regression analysis and ANOVA were performed in SAS (1996) using Proc GLM. Variables considered to be significantly different at the P < 0.05 level.
1. Verify the accuracy of MUN analysis for predicting N and the N (protein) metabolism of lactating cows in independent digestion studies.
Selected results are shown in Table 2. High protein diets increased DMI, but did not increase milk production. Higher protein diets resulted in greater N level in urine without an increase in N output in milk. Gross N efficiency was decreased in higher N diets.
Several methods were used to determine MUN, with the values derived from Rocky Mountain DHIA (which uses a wet chemistry system) giving predictable responses to changes in diets and was more correlated with BUN (blood urea nitrogen) concentrations than values from the other methods (Table 2). Urinary nitrogen concentrations from this experiment were used to compare with urinary nitrogen concentrations computed from MUN concentrations based upon the equations of Jonkers (Table 3) and Kauffman and St.-Pierre (Table 4) and a modified equation of Kohn (Table 5). The equations derived by Jonkers underestimated urinary nitrogen by 55 g/d, the equations developed by Kauffman and St.-Pierre overestimated urinary nitrogen 25 g/d, and the modified equation of Kohn overestimated by 33 g/d. Nitrogen efficiency was underestimated by 7.5% and overestimated by 3.2% and 2.9%. The magnitude of the error of these predictions using the latter equations (10% and 14% for urinary N excretion and N efficiency, respectively) suggests that MUN could be a useful tool for evaluating N losses and N efficiency in lactating Holsteins.
Experiments we conducted at Utah State University were used to verify a model developed at the University of Maryland that uses milk urea nitrogen (MUN) and other variables to predict aspects of nitrogen utilization efficiency by dairy cows. We conducted a series of investigations at the University of Maryland to better understand the differences. Using retrospective analysis of previous experiments conducted at the University of Maryland, we found that MUN results reported before and after a change was made in the way standards were used were lower by 4 mg/dl. We also compared 14 DHIA laboratories across the U.S. by sending similar milk samples to all labs and noting the results reported. The laboratories that use various wet chemistry methodologies yielded similar results, but laboratories using NIRS for MUN analysis resulted in different results depending on the laboratory. Predicting urinary nitrogen excretion from milk urea nitrogen requires use of a wet chemistry procedure for MUN analysis.
2. Determine the potential use of MUN analysis and a recently developed computer worksheet for predicting N losses and whole-farm efficiencies on commercial dairies in UT and ID.
Note: Due to space constraints, only a few tables/figures can be shown. A complete listing of all data can be found in the M.S. thesis of Rhonda Swain, Utah State University, Logan, 2001 or in the Journal of Dairy Science articles listed under the Publications section.
Whole-farm N and P balance information such as sources coming onto the farm (animals, feed, and fertilizer) and sources leaving the farm (milk, animals, manure, and feed) were collected during face-to-face interviews and entered into the Maryland Nutrient Balancer. All calculations were made by the Balancer (see Table 1).
Prior to this grant, the Maryland researchers had developed a software application to calculate phosphorus and nitrogen flows on livestock farms. In the first year, the software was tested in the Western region of the U.S. and refined. In the second year, the software was implemented to assist farmers with calculating nutrient balances, and to collect data from farms. In the final year, data were collected and summarized to improve our understanding of current practices affecting nutrient flows so as to improve incentive, regulatory, or technology transfer programs.
We made numerous changes to the software as a result of difficulties found in adapting it to the region. For example, we needed the capability to enter more feeds per farm. In addition, the practice of applying manure to legumes was added. The software refined by the project is available for download from the University of Maryland web site, and more than 141 sites in 27 countries and 25 states have downloaded it.
Of the 41 farms, 23 grew any crops, while 18 purchased no feedstuffs. Farms that grew crops ranged from using a small amount to all of their manure on-farm, while farms that did not grow crops exported all their manure. Close to half of the farms in this study (44%) imported all feed. Milk production ranged from 6554 kg/yr to 13,835 kg/yr with an average of 10,344 kg/yr, well above the year 2000 national average (Natl. Agric. Stat. Srv, 2001) of 8276 kg/yr, and consistent with the average (10,143 kg/yr) for herds in this region (Rocky Mt. DHIA Annual Summary, 2000). There was a large range in the size of herds involved in the study (57 cows to 1960 cows). Average herd N and P utilization efficiencies (HNUE and HPUE), calculated as N or P in meat and milk per N or P in feed, were 21.3% and 31.7%, respectively.
Whole-farm N balance for dairies in the West was consistent with other studies in that imported feed was the single largest N input and animal products, milk and meat, were the largest source of output (Table 6). Farms that grew crops had inputs from fertilizer for crop application and legume fixation from alfalfa that were not present for those farms that grew no crops (Table 7). Those farms that grew no crops had 98% of their inputs as imported feed because they had no need for fertilizer and had no crops for legume fixation (Table 7). These farms also exported a greater amount of manure and compost because they had little or no land for application. Herd size was significantly larger for those farms that grew no crops, but on a per animal basis, there was little difference in whole-farm N balance between the two groups.
Average whole-farm N balance for all farms, expressed as tonnes of N per year, is shown in Table 6. Our results show a positive whole-farm N balance, indicating that more N is coming onto farms than is being accounted for in the sale of product (mostly milk, some meat), exported crops, and exported manure. Previously reported whole-farm N efficiencies (N in total outputs divided by N in total inputs x 100) ranged from 15% (Aarts et al., 1992) to 46% (Bacon et al., 1990). The average whole-farm N utilization efficiency (output/input) for the farms in this study was 35.8%. This means that 64.2% of the N coming onto the farm could not be accounted for in product, cash crops, or compost and manure. A large portion of this N was probably lost through volatilization and represents a major portion of the N flow on these dairies. Loss of N through volatilization needs to be controlled as more states begin to enact laws regarding odor from dairies. In addition, a source of N loss could be the drying step used to analyze manure samples. Heating the sample would volatilize any remaining ammonia N and underestimate the N content of the sample. This loss may or may not be significant because most of the ammonia may have all ready volatized before the sample was taken (R. Kohn, personal communication). Also, none of the dairies incorporated manure in less than seven days. This would have allowed any remaining ammonia to no longer be available for crops. The values in this paper are based on the assumption that minimal amounts of N were lost due to drying the sample prior to analysis.
One problem with comparing efficiencies between studies is that not all studies use the same categories for inputs and outputs. For example, the farm analyzed by Bacon et al. (1990) showed a high whole-farm N utilization efficiency, but they did not make allowance for N fixation by legumes.
Average farm MUN was used in the model of Jonker et al. (1997) with modifications by Kauffman and St-Pierre (1999) to calculate lactating cow N efficiency and was significantly correlated when regressed on herd nitrogen utilization efficiency (HNUE) as calculated by the Maryland Nutrient Balancer (P < 0.05). However, the model accounted for only 11.9% of the variation in HNUE (Figure 1) and was not a good predictor of herd N utilization. This may be because this model was intended to predict lactating cow N utilization efficiency, while the N utilization calculated by the Maryland Nutrient Balancer represents N brought onto the farm from all animals (lactating and dry cows plus heifers) as well as all animal products leaving the farm (milk and meat). Further research in this area may be warranted in order to allow better prediction of herd N utilization.
Herd P utilization efficiency and herd N utilization efficiency were closely related (Figure 2). The management of N appears to be linked to the management of P. This could be because those who are properly balancing rations for N, or protein, are also balancing for P. It may also be a reflection of feeds high in N typically being high in P (e.g., soybean meal). If more attention was paid to improving the efficiency with which N and P were utilized, ration price may decrease, milk production may increase, and environmental liability would decrease. Even though whole-farm P balance was not part of the study, the Balancer calculated those values and the results are shown in Tables 8 and 9.
Whole-farm N balance was evaluated separately for those that grew crops and those that did not because this had a significant affect on farm N balance (P = 0.05)(Table 7) and because the amount of land base for crops can affect interpretation of whole-farm N balances. Most of the farms reported in the literature were small dairies with sufficient land to have an outlet for manure produced on the farm (Dou et al., 1996; Van Horn et al., 1996; Klausner et al., 1998). Dairies in the Western U.S. are different because of larger herd size and the fact that many dairies include only enough land base to house the animals plus a buffer around the dairy (i.e., less than 0.4 ha per cow). On farms where crops were grown, total tonnes/year of unaccounted for N was less than on those farms where no crops were grown (Table 7), primarily due to differences in herd size. Increased inputs of N associated with crop production increased the total N balance on farms that grew crops and made the output/input percent on farms that grew no crops better by 11%. The average herd size for farms that grew no crops was nearly 2.5-fold higher than for farms that grew crops, but when the data were corrected for number of animals per farm (Table 7), the amount of unaccounted for N was slightly higher per animal for farms that grew crops compared with farms that grew no crops. On a whole-farm basis, the higher positive N balance makes these farms a greater potential threat to the environment (especially odor) than those that grew crops. In this study, farms without crops typically had a larger herd size, higher milk production per cow, greater tonnes of manure exported, and no imported fertilizer or legume fixation.
On farms where crops were not grown, almost all manure was exported and those farms showed lower farm N balances. The N and P in the manure do not simply disappear when taken off farm. Caution must be taken not to see this as a “quick fix.”
A multiple regression analysis was used to determine the relative impact of the subsystems (herd, crops, and manure management) on whole-farm balance per product (tonnes unaccounted for N per tonne milk and meat N). The multiple regression analysis for farms that grew crops is shown in Table 10. A total of 90% of the variation in farm nitrogen balance was explained by the efficiencies of the farm components. Herd N utilization efficiency explained 54.2% and crop uptake of available N explained 33.4% of the observed variation. Estimated availability of manure N applied to crops and manure nitrogen storage efficiency (MNSE) explained about 12% of the variation among farms. Even though MNSE was the least important of the subsystems in this model, it is still a significant factor when determining how to decrease whole-farm N balance per product (analysis not shown). These results are in agreement with those of a sensitivity analysis on modeled dairy farms reported by Kohn et al. (1997). Aarts et al. (1992) also found improving HNUE to be important in decreasing overall farm N balance. Wang et al. (2000) estimated that increasing forage yield by 50%, compared with yields from their base herd, would decrease whole-farm N mass balance by 29%. Paul et al. (1998) suggested that increasing HNUE not only decreased N in the manure, but the N might also be less susceptible to volatilization and more likely to be incorporated by crops. Again, results from this study support the concept that the most important method to improve whole-farm N balance is improved HNUE.
The multiple regression analysis of whole-farm N balance per product on farm subsystems for farms that did not grow crops is shown in Table 11. The model accounted for 84% of the variation in whole-farm N balance per product. Herd N utilization efficiency accounted for 50.6% and MNSE accounted for 49.4% of the observed variation. For farms that grew no crops, MNSE became more significant than for farms that grew crops because with no land base there were no sources of variability except for the N in manure and compost. In both farm groupings, HNUE accounted for a significant portion of the variation in whole-farm N balance per product.
On farms where crops were grown (data not shown), univariate regression analysis of whole-farm N balance per product with crop N utilization efficiency was correlated (R2 = 0.324, P = 0.005, negative slope). It was also interesting that on some farms, crop N utilization efficiency was greater than 1.0, meaning N in the crop was greater than the N available for the crop as calculated by the program. We do not have enough data to determine the reason for this, but some possibilities are that N fixation by legumes was underestimated (alfalfa is the principal crop in this region), or there was carry over of N in the soil from previous years that was unaccounted for. The program only looked at the balance for a single time period, not multiple years.
On farms where crops were not grown, exported manure per product was negatively correlated with whole-farm N balance (R2 = 0.24, P = 0.039). This makes sense because exported manure or compost is one of the few options available to these farms for improvement of whole-farm N balance. Koelsch and Lesoing (1999) proposed exporting manure as an efficient means of lowering farm N balance. Although this has been shown to lower whole-farm N balance, the N in the manure does not simply disappear when taken off farm. As noted by Kohn et al. (1997), caution must be taken not to see this as a “quick fix.” For a single herd that does not grow crops, this could be an effective way to balance a CNMP, but from a watershed perspective, it could be a problem. Univariate regression analysis of MNSE with whole-farm N balance per product for farms that grew no crops was negatively correlated (R2 = 0.304, P = 0.018). Again, manure handling becomes important because they have fewer options and must make better use of the subsystems available to them.
Whole-farm P balance per product was correlated with whole-farm N balance per product for farms that grew no crops (R2 = 0.254, P = 0.033) and showed a trend (P = 0.076) for farms that grew crops. Many states are passing regulations based on soil P levels, rather than N levels, and this study suggests that improvement in the whole-farm balance of one nutrient is associated with improvement of the other.
One of the largest areas of potential discrepancy in this study was the possible inaccuracy of N and P input and output estimates. Examining 41 farms and using averages avoided some of this discrepancy, but will not be possible in developing a comprehensive nutrient management plan (CNMP) for a single farm. Larger farms appeared to keep better records than smaller farms. In addition, sampling techniques may be an issue in developing an accurate CNMP. It is difficult to obtain manure samples from a pen of animals or from a storage facility that are representative of the whole farm. Issues of record keeping must be resolved before accurate evaluation of farm nutrient balances can be accomplished.
Herd N utilization efficiency was significantly related to whole-farm N balance per product for both farms that did and did not grow crops (Tables 10 and 11); therefore, factors influencing HNUE were analyzed. When all dairies in this study were analyzed together, herd size was positively correlated with the HNUE (R2 = 0.11, P = 0.036). Farms that had more than 500 cows (n = 14) had a significantly higher HNUE (23.5% SD = 4.2) than farms that had less than 500 cows (20.2% SD = 4.9; n = 27). However, when the farms were analyzed based on whether a farm did or did not grow crops, there was no relationship between HNUE and herd size, probably due to sample size. Larger farms appeared to be more consistent in converting feed N into product than smaller farms. Increased cow numbers may allow for better grouping strategies to be employed in herd management. St-Pierre and Thraen (1999) showed that optimal grouping strategy would increase herd efficiency by providing nutrients where they were most needed. The larger farms in this study did have increased numbers of groupings of cows compared with smaller farms. Interestingly, the number of heifers per farm was not related to whole-herd N balance or HNUE for farms that grew or did not grow crops (data not shown). However, for farms that did not grow crops, the ratio of heifers to cows per farm showed a trend relative to either whole-farm N balance per product (P = 0.059) or HNUE (P = 0.051, negative slope. This is interesting because many dairies with little land base are considering contracting the raising of their heifers off-farm. Those data suggest that reducing the number of heifers relative to the number of cows on a dairy would increase HNUE efficiency and decrease whole-farm N balance per product; both desirable goals.
There was also a positive correlation between HNUE and milk production (R2 = 0.281, P = 0.024) for farms that grew no crops, but no relationship for farms that grew crops. This may be a function of the slightly higher HNUE and milk per cow for farms that grew no crops compared to those farms that grew crops. It may also mean that for farms that grew no crops, maximizing conversion of feed N to product N left less N to be handled in manure and compost. Other studies have shown that increased HNUE was associated with a slightly negative relationship with milk yield (Dinn et al., 1998) or no relationship (Dhiman and Satter, 1997). Results from this study show that all herds that produced 13,000 kg/yr or more had HNUEs of close to 25% suggesting that a greater proportion of consumed N is being used for milk and meat and less for maintenance of the animal.
Herd P utilization efficiency (Spears et al., 2003) was correlated with HNUE (R2 = 0.539, P = <0.0001) for farms that grew crops, while farms that grew no crops showed a trend (P = 0.074). Although we cannot determine the reason for the difference between farms based on whether they did or did not grow crops, we suggest two possible explanations for why HPUE and HNUE might be related. The first may be related to the composition of milk. Wu et al. (2001) reported a positive relationship between milk protein and P concentrations. It was suggested that because phosphate bridges hold casein micelles in milk together, increased milk protein would require an increase in milk phosphorus, even though this form of P comprises only a small fraction of the total milk P. The second may be that rations properly balanced for P are likely balanced for protein. It is interesting to note that in a study where adjustments were made to lower whole-farm N balance (Bacon et al., 1990), whole-farm P balance was also decreased.
Imported feed N per product was not correlated with HNUE (P = 0.147) for farms that grew crops, but was correlated (R2 = .98, P = <0.0001) with farms that did not grow crops. This is likely because farms that grew crops only bought enough feed to supplement the amount grown on-farm while farms that grew no crops bought all of their feed. For farms that grew feed, legume fixation/product and imported N fertilizer/product were more important than imported feed/product.
3. To disseminate information from this project to farmers via extension publications and the World Wide Web.
Publications and presentations are listed below. Numerous journal and popular press articles resulted from this study. In addition, a web site was developed at the University of Maryland to disseminate this information to dairy farmers. The World Wide Web site (http://www.inform.umd.edu/ManureNet/) receives 2000 to 5000 visits per month. Extensive information on using MUN and improving herd N utilization efficiency is available on the site. This information was updated this year to reflect changes in measurements made by DHIA laboratories. The USU Dairy Extension web site (http://www.extension.usu.edu/dairy) also contains most of the popular press articles on MUN and N balances.
Rebecca A. Sannes, M.S., Dairy Nutrition, 2000, The influence of ruminally degradable carbohydrates and nitrogen on microbial protein synthesis and protein efficiency of lactating Holstein cows, David Vagnoni, major professor.
Rhonda Swain, M.S., Dairy Nutrition, 2001, Whole-farm nitrogen efficiency and balance compared with the milk urea nitrogen analysis, Allen Young, major professor.
Rachael Johnson, M.S. Dairy Management, 2001, The relationship of milk urea nitrogen and DHIA production variables in western commercial dairy herds, Allen Young, major professor.
Refereed Journal Articles
Spears, R.A., A. J. Young, R.A. Kohn. 2003. Whole-farm nitrogen balance on dairies in the western United States. J. Dairy Sci. 86(12):XX.
Johnson, R.G. and A.J. Young. 2003. Relationship of milk urea nitrogen and DHI production variables in western commercial dairy herds. J. Dairy Sci. 86(9):3008-3015.
Spears, R.A., A. J. Young, R.A. Kohn. 2003. Whole-farm phosphorus balance on dairies in the western United States. J. Dairy Sci. 86:688-695.
Sannes, R.A., M.A. Messman, and D.B. Vagnoni. 2002. Form of rumen-degradable carbohydrate and nitrogen on microbial protein synthesis and protein efficiency of dairy cows. J. Dairy Sci. 85:900-908.
Jonker J. S. and R. A. Kohn. 2002. Using milk urea nitrogen to evaluate diet formulation and environmental impact on dairy farms. In: Optimizing Nitrogen Management in Food and Energy Production and Environmental Protection: Proceedings of the 2nd International Nitrogen Conference on Science and Policy, The Scientific World 1:852-859.
Kohn, R. A., K. E. Kalscheur, and E. Russek-Cohen. 2002. Evaluation of models to estimate urinary nitrogen excretion and expected milk urea nitrogen. J. Dairy Sci. 85:227-233.
Jonker, J. S., R. A. Kohn, and J. High. 2002. Dairy herd management practices that impact nitrogen utilization efficiency. J. Dairy Sci. 85:1218-1226.
Jonker, J. S., R. A. Kohn, and J. High. 2002. Use of milk urea nitrogen to improve dairy cow diets. J. Dairy Sci. 85:939-946.
Jonker, J.S. and R.A. Kohn. 1998. MUN testing: useful but ambiguous. Hoard’s Dairyman, Mar. 25, p. 252.
Jonker, J. S. and R.A. Kohn. 1998. Measure MUN and evaluate dairy cow nutrition. Dairy, Food and Environmental Sanitation: a Publication of the International Association of Milk, Food and Environmental Sanitarians. Vol. 18, p. 89.
Kohn, R. A. 1999. Improving animal efficiency. Dairy Online Connection, Sep., Monsanto Inc.
Kohn, R. A. 1999. The impact of herd management on nutrient losses to water resources. Dairy Online Connection, Sep, Monsanto Inc.
Kohn, R. A. 1999. Using milk urea nitrogen to evaluate dairy cow nutrition. Michigan DHIA Annual Performance Summary 1999, p 26.
Kohn, R. A. 2000. Caution needed when interpreting MUNs. Hoard’s Dairyman, Jan. 25, p. 58.
Young, A.J. 2001. Milk Urea Nitrogen Test (MUN). USU On-line Extension Publication AG/Dairy-01.
Young, A.J. 2002. Nitrogen Budgets for Your Dairy. USU On-line Extension Publication AG/Dairy-02.
Young, A.J. 2002. Phosphorus Budgets for Your Dairy. USU On-line Extension Publication AG/Dairy-03.
Kohn, R. A. 2002. Research helps refine use of milk urea nitrogen. Hoard’s Dairyman, August 10, p. 530.
Jonker, J. S., R. A. Kohn, J. High, and A. Grove. 2000. A pilot project to introduce the routine use of milk urea N analysis for diet evaluation. J. Dairy Sci. Suppl. 83.
Swain, R.A., J.L. Walters, R.A. Kohn, and A. J. Young. 2001. Whole-farm nitrogen efficiency and balance compared with the milk urea nitrogen test. J. Dairy Sci. 84 (Suppl. 1): 271 (Abst.).
Johnson, R.G., J.W. Walters and A.J. Young. 2001. Relationship of milk urea nitrogen and DHIA production variables in western commercial dairy herds. J. Dairy Sci. 84 (Suppl. 1): 267 (Abst.).
Young, A.J. 2002. Nitrogen and Phosphorus Balances for Dairies in Utah and Idaho, 2002 Utah Nonpoint Source Water Quality Conference, September 24-26, 2002.
Young, A.J. and R.S. Spears. 2002. Fate of nitrogen and phosphorus on dairy farms. Proc. Intermountain Nutrition Conference, January 22-23, 2002, pp. 145-154.
Young, A.J. 2002. Nitrogen and Phosphorus Balances for Dairies in Utah and Idaho, 2002 Utah Nonpoint Source Water Quality Conference, September 24-26, 2002.
Young, A.J. and R.S. Spears. 2002. Fate of nitrogen and phosphorus on dairy farms. Proc. Intermountain Nutrition Conference, January 22-23, 2002, pp. 145-154.
Kohn, R. A. 2001. Nitrogen: the other nutrient. Maryland Dairy Talk vol. 4 (3) pp 1-3.
Young, A.J. “Biosecurity and the Technician and Update on MUN Testing.” Annual Technician Conference for Rocky Mt. DHIA, Provo, UT (June 2001).
Kohn, R. A. 2000. Current research in nutritional management. Dairy Environmental Summit. Consolidated Nutrition, Decatur, IL, Nov. 8, 2000.
Kohn, R. A. 2000. Integrated nutrient management. Dairy Environmental Summit. Consolidated Nutrition, Decatur, IL, Nov. 8, 2000.
Kohn, R. A. 2000. Determining the efficiency of nitrogen utilization on dairy farms. Nutrition Seminar, Dept. of Animal Science, Virginia Tech, Blacksburg, VA.
Young, A.J. “True Protein and its Relationship with MUN.” Annual Technician Conference for Rocky Mt. DHIA, Provo, UT (June 2000).
Young, A.J. “MUN Update and Research At USU,” Utah Dairy Convention, Ogden, UT (February 2000).
Kohn, R. A. 1999. Nutrient management update. Maryland Dairy Talk vol. 2 (2), pp. 4-5, Maryland Cooperative Extension, College Park.
Kohn, R.A. 1999. Pilot project saves farmers money. Maryland Dairy Talk vol. 2 (3), pp. 4-5, Maryland Cooperative Extension, College Park.
Kohn, R. A. 1999. Opportunities to reduce nutrient losses from animal agriculture. Colloquium: Large Scale Animal Production and Human Health. Johns Hopkins School of Hygiene and Public Health, May 25, Baltimore.
Kohn, R. A. 1999. The impact of new technology in dairy cattle management and feeding on reducing nutrient losses to water resources. Department of Dairy and Animal Science Seminar, The Pennsylvania State University, November 3, 1999, State College, PA.
Kohn, R.A. 1999. Improving nutrient utilization within the animal and its effect on nutrient losses from a farm. Ohio Composting and Manure Management Program, November 16, 1999, The Ohio State University, Columbus.
Kohn, R. A. 1999. Strategies for whole farm nutritional modeling. Purina Mills Dairy Unit Seminar, Purina Mills Inc., St. Louis, MO, Dec. 16, 1999.
Young, A.J. “MUN Update,” “Accessing Information in the Future (Internet)” and “Y2K Update.” Joint Annual Technician Conference for Rocky Mt. DHIA and Idaho DHIA, Provo, UT (June 1999).
Young, A.J. “MUN Update” and “Accessing Information in the Future (Internet).” Montana DHIA Technician Conference (Rocky Mt. DHIA), Bozeman, MT (March 1999).
Young, A.J. “Producer Comments On the Use of MUN.” Rocky Mt. DHIA Producer Meeting, Utah State Dairy Convention, Salt Lake City, UT (February 1999).
Young, A.J. “The Milk Urea Nitrogen (MUN) test.” Rocky Mt. DHIA Producers Meeting, Utah Dairy Convention, Salt Lake City, UT (Feb. 1998).
Young, A.J. “How to Use Your DHIA Records to Troubleshoot Nutritional Problems and the Milk Urea Nitrogen (MUN) test.” Meetings for Montana dairy producers (5 locations, 5 days) (Mar./Apr. 1998).
Young, A.J. “How to Use and Interpret the DHI Herd Summary and MUN test.” Dairy producer meeting for Duchesne and Uintah Counties (April 1998).
Young, A.J. “What is the MUN test.” Joint Annual Technician Conference for Rocky Mt. DHIA and Idaho DHIA, Provo, UT (June 1998).
Young, A.J. “MUN Update.” Utah Dairy Seminars (3 locations: Richfield, Provo, and Wellsville, UT (December 1998).
Evaluated and summarized Milk Urea Nitrogen (MUN) values for approximately 1254 monthly herd DHIA tests for dairy producers in Utah, Idaho, and Montana and sent a printout in the form of summary graphs of the result to all of the dairy producers. Feedback from producers has been extremely supportive of this project. In addition, several research projects dealing with MUN are currently in progress.
There is a need for information on quantifying and reducing environmental nutrient losses from dairy farms at a regional level. Enhancing protein efficiency of a dairy herd offers a unique opportunity to simultaneously reduce environmental N level and improve profitability of dairy farmers by monitoring protein efficiency of dairy rations.
Dairy farmers can use MUN to fine-tune diets and prevent overfeeding or underfeeding of protein to their cows. The work we conducted will make it possible for farmers to use this technology more effectively. If they reduce overfeeding of cows, it will reduce their cost of feed because protein is an expensive ingredient. In addition, cows use energy to excrete the excess nitrogen. Thus, by not overfeeding, energy costs or lost production can be reduced. In addition, with less protein being fed, less N will be excreted in manure. This effect will make it easier to comply with field-by-field nutrient management plans. Many farmers in the west export their manure to the nearest available land. Reducing N in manure reduces the distance the manure must be shipped. Finally, reducing N excreted to manure will reduce ammonia-N volatilization, N runoff, and N leaching proportionally. The software for calculating nutrient balances can be used to help farmers and consultants understand the balance of nutrients on their farm and to quantify the unaccounted for N and P. These unaccounted for nutrients may accumulate in soils or be lost to the environment.
Educational & Outreach Activities
See Objective 3 for a complete listing.
Participants in a recent cooperative extension project introducing use of milk urea nitrogen to fine tune-dairy diets were able to reduce feed costs by an average of $6.00 per cow per year by using information on bulk-tank MUN analysis once per month. There were 450 participating farms averaging 108 cows per farm for a total impact of $292,000 per year.
Farmers used MUN to reduce feed expenses and improve management. One farmer estimated that MUN allowed him to determine the source of a feeding problem that cost him over $50,000 in impaired reproduction and loss of animals.
Farmer adoption rate of whole-farm N balances has been slow. Based on reactions to the project, there will be some that will adopt these ideas reluctantly and others who have adopted these ideas, in principle and fact, and will probably be the survivors. The information from this study may add management and economic information that can be used to convince farmers that adoption of these practices would be a benefit to them. It seems that some of the farmers still do not grasp what is involved in putting together a nutrient management plan and its importance (i.e. they do not maintain the necessary records to put together a plan), while others are current in their records and had no problems giving the needed information. Farmer use of the monthly MUN graphs has been very positive, and many have used these to make management decisions that have been positive for their dairies.
The University of Maryland has introduced the use of MUN to over 450 farmers in Maryland, Pennsylvania, Virginia, West Virginia, and Delaware through a project in which we paid for sampling and sent interpretive results. Participants in the project decreased MUN by 0.5 mg/dl compared to non-participants. Participants with high MUN decreased MUN by 1 mg/dl compared with non-participants, and participants with low MUN increased it by 3 mg/dl compared with non-participants. Thus, it appears that participants in the program increased or decreased protein feeding when recommended. The typical farm would have saved $595 per annum in feed costs by reducing protein feeding.
Of 454 participants in the MUN study, 190 returned surveys about their participations and 70% of these respondents reported using results from the program. Thirty percent indicated that they planned to use MUN analysis again. While most farmers were unsure of the economic impact the program had, several reported positive returns up to $1500 from the program due to reduced feeding costs.
– We had 42 dairies from Utah and Idaho participate in Objective 2 (Appendix 1). Their role has been to provide information from their own dairy so that a nutrient balance can be calculated using the Maryland Nutrient Balancer program developed by Dr. Richard Kohn at the University of Maryland.
– Over 450 dairy farmers participated in the University of Maryland MUN study.
– Over 1254 monthly DHIA MUN reports have been prepared for dairy farmers in Utah, Idaho, Montana, and Wyoming.
Areas needing additional study
The University of Maryland is currently introducing research results on commercial dairy farms in Maryland. They are satisfied that the development of models to predict nitrogen digestibility, intake, and N utilization efficiency of a herd. These models enable rapid estimation of the probability of overfeeding or underfeeding protein based on production and milk composition. They would like to develop models to make similar measurements related to phosphorus. They would also like to develop inexpensive systems to evaluate ruminal degradability of protein in dairy cow diets. Finally, they would also like to increase the functionality of the software to include diet formulation as well as management of inventories of feeds and nutrient balances.
Utah State University is currently implementing a study similar to the MUN study from the University of Maryland to see if there are regional differences that might require changing the MUN models to fit this area. They are also interested in determining if different housing systems contribute to greater or lesser ammonia losses.