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«University of California Division of Agriculture and Natural Resources Committee of Experts on Dairy Manure Management September 2003 February 2004, ...»

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National Center for Manure and Animal Waste Management. 2001. White Papers on Animal Agriculture and the Environment. CD copy distributed by MWPS (MidWest Plan Service, A Foundation of Knowledge), http://mwpshq.org/ National Research Council. 2001. Nutrient Requirements of Dairy Cattle: Seventh Revised Edition. Subcommittee on Dairy Cattle Nutrition, Committee on Animal Nutrition, National Research Council. The National Academy Press, Washington D.C. 408pp. (ISBN 0-309-06997National Research Council 2003. Air Emissions from Animal Feeding Operations; Current Knowledge, Future Needs. The National Academy Press, Washington D.C. 263pp. (ISBN 0Nelson, D.W. 1982. Gaseous losses of nitrogen other than through denitrification. p. 327-363. In F.J. Stevenson (ed.) Nitrogen in agricultural soils. Amer. Soc. Agron. Madison, WI.

Poudel, D.D., W.R. Horwath, J.P. Mitchell, and S.R. Temple. 2001. Impacts of cropping systems on soil nitrogen storage and loss. Agric. Systems 68:253-268.

Pratt, P.F. 1979. Integration, discussion and conclusions. p. 719-769 In Pratt, P.F. Nitrate in effluents from irrigated lands. Final report to the National Science Foundation. PB-300582, US Dept. of Commerce, National Technical Information Service, Springfield, VA.

Rible, J.M., P.F. Pratt, L.J. Lund, and K.M. Holtzclaw. 1979. Nitrates in the unsaturated zone of freely drained fields. p. 297-320. In Pratt, P.F. Nitrate in effluents from irrigated lands. Final report to the National Science Foundation. PB-300582, US Dept. of Commerce, National Technical Information Service, Springfield, VA.

Robbins, C. W., D. L. Carter, 1983. Selectivity coefficients for calcium-magnesium-sodiumpotassium exchange in 8 soils. J. Irrigation Science 4(2): 95-102.

Robbins, C. W., 1984. Sodium adsorption ratio-exchangeable sodium percentage relationships in a high potassium saline-sodic soils, J. of Irrigation Science 5 (3): 173-179.

Robbins, C. W., H. F. Maylan, 19993 Calcium, magnesium, and potassium uptake by crested wheatgrass grown on calcareous soils, Communications in Soil Science and Plant Analysis 24 (9-10): 915-926.

Robbins C.W., Hansen CL, Roginske MF, et al., 1996. Extractable potassium and soluble calcium, magnesium, sodium, and potassium in two whey-treated calcareous soils Journal of Environmental Quality 25 (4): 791-795.

Robinson, P. H. 2000. SHIELD (excel) Dairy Ration Evaluator. University of California, Cooperative Extension. Department of Animal Science, University of California, Davis, CA 95616-8521. 62 pp.

Ryden, J.C. and L.J. Lund. 1980. Nature and extent of directly measured denitrification losses from some irrigated vegetable crop production units. Soil Sci. Soc. Am. J. 44:505-511.

Shaffer, M.J. and J.A. Delgado. 2002. Essentials of a national nitrate leaching index assessment tool. J. Soil Water Conserv. 57(6):327-335.

Shultz, T., 1989. Computerized corral feed stations for dairy cows. California Agriculture, Oakland, Calif.,Division of Agriculture and Natural Resources, University of California.

Sept/Oct 1989. 43 (5), p. 26-27.

Van Horn, H. H., G. L. Newton, R. A. Nordstedt, E. C. French, G. Kidder, D. A. Graetz, and C.

F. Chambliss. 1998. Dairy Manure Management: Strategies for Recycling Nutrients to Recover Fertilizer Value and Avoid Environmental Pollution. University of Florida, Cooperative Extension Service Circular 1016.

(http://edis.ifas.ufl.edu/scripts/htmlgen.exe?DOCUMENT_DS096)

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The manure nutrient output of any dairy herd can be evaluated based upon knowledge of the ration fed and based on animal production records. Using the ‘UC Dairy Animal Waste Model’, developed as a part of this project, estimated manure nutrients generated by an example dairy in the Central Valley of California were calculated. Input data and other information used in this computation can be extracted from production records of well managed commercial dairy operations. This example dairy is not a single operation, but rather a composite of several dairies, and is presented to demonstrate what can be done, rather than to create average manure nutrient output values.

A 1,750 animal dairy (about the California average) was divided into milking, dry and replacement animals, as this is the way that dairy animals are almost always considered on commercial operations. Within each of these categories, animals were further sub-divided into several homogeneous performance groups that possess similar physiological attributes and would be fed the same (or very similar) rations in the same physical groups (i.e., pens) on a commercial dairy (Table A-1). In this example, mature milking cows and dry stock body weights vary between 1,323 to 1,543 lbs (600 to 700 kg). The feed intake of lactating cows varies between 46 to 55 lbs/head/ day (21 to 25 kg/head/day) depending on their milk production and stage of lactation. Dry stock are fed 50% to 60% of what is fed to the lactating cows. For replacement heifers, the body weights are proportional to their ages, from 132 to 1190 lbs (50 to 540 kg) and the feed intakes increase incrementally with body weight.

Based on the specific rations fed (i.e., their ingredients and analyzed or estimated nutrient composition), the rations fed are entered into the program, by animal group as defined in Table A-1, and calculations are made to allocate feed components to body gain or loss and milk production. An example for lactating cows is shown in Table A-2. The daily per animal production of nutrients in manure and urine is then calculated. Summation of input and output of nutrients, by defined animal group in Table A-1, is arithmetically combined to create a whole farm balance (Table A-3) for all nutrients defined in the rations fed to the cattle.





In a well managed dairy, manure and N outputs of milking cows are generally proportional to their feed intake and the manure/feed ratios remain fairly constant regardless of the productive performance of the cows and replacement heifers. However these ratios are impacted dramatically by the level of nutrients in the diet. As nutrient levels increase, manure/feed ratios increase. This site specific approach captures that variation thereby allowing efficiency of nutrient use to be estimated among commercial dairy operations.

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Characterization of Dairy Wastewater Lagoons During spring, summer, and fall of 2002, Deanne Meyer collected samples from the wastewater lagoons of seven representative dairies in the Central Valley. For the sampling, temperature measurements were taken at the surface and below in 1.5 m or 3.0 m increments until reaching the bottom at four locations in each pond. At the same sampling points, liquid manure samples were recovered for measurements of pH (on site) and ammonia (laboratory determination).

During sampling, the contents of the ponds are dynamically “boiling” which may have affected the consistency of the sampling protocol. The field data illustrate that the chemical conditions of dairy wastewater lagoon vary with the dairy operations (Table B-1). However, at a given installation, the pH and the total ammonia N concentrations, two of the deciding chemical factors of ammonia volatilization, do not vary much with the season, leaving the climate (temperature and wind velocity) as the critical parameters in deciding the volatilization rates.

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B-2 Ammonia Emission Model In our opinion, a process-based mechanistic model is needed to evaluate the interactive nature of various factors that contribute to the volatilization of ammonia from dairy manure. We have investigated the possibility of developing an ammonia mass transfer model that, when the rate constants are appropriately calibrated, will be able to compute the NH3-N volatilization from liquid manure storage lagoons, corral surfaces, and paved surfaces of flush lanes and free stalls.

Using this model, we may evaluate the roles of environmental (climatic) factors, N loading rates (or animal density), chemical/biological conditions of water, and physical dimensions of the storage lagoon on ammonia volatilization.

We have developed a mechanistic model for ammonia volatilization that takes into account the chemistry of ammonia (pH, concentration, Henry’s law constant), climatic conditions (temperature and wind velocity), and physics of mass transfer (diffusion and mass transfer coefficients). The mathematical equations used to define the ammonia volatilization process are

outlined as follows:

NH3-N emission from lagoon water, mg m-2 day-1 is:

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where [TAN] is total NH3-N concentration in the surface layer of lagoon water, K is the overall mass transfer coefficient, and F is the fraction of TAN that is in the free ammonia form.

The fraction of free NH3 may be expressed as

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KGNH3 and KLNH3 are mass transfer coefficients (m/s) through gaseous and liquid films at the interface of water and air, respectively, and are related to the diffusivities (m2/s) of ammonia in air (DairNH3) and ammonia in water (DairH2O), and ammonia in water (DwaterNH3) and oxygen in water (DwaterO2)

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where M is the molecular weight (g/mol), v is the molecular volume (m2/s), Ta is the air temperature (K), and Taq is the lagoon water temperature (K), and v is the atomic diffusion volume (cm3/mol).

The input parameters for the emission ammonia model include TAN, pH, Taq, Ta, and 8. The TAN and pH are specific to the wastewater characteristics in the storage and Taq, Ta, and 8 are specific to climatic conditions at the farm. All these parameters are dynamic and change over time, therefore, the emission rate changes as well. If feasible, TAN and pH in the storage should be monitored on site so that specific values can be used as input for the models. However, appropriate wastewater sampling and analysis procedures and schedules should be used.

Frequency of measurement depends on the variability of TAN and pH in the storage and the time scale used for emission calculation. The Taq, Ta and 8 should also be measured at the lagoon site. If onsite measurement is difficult, the values for Ta and 8 can be obtained from local weather stations and Taq can be estimated by using an empirical equation shown below for Taq (Stefan and Prued’homme, 1993).

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To demonstrate the ammonia emission model, ammonia emission rate from a dairy wastewater lagoon for a 1000 hd dairy was calculated with assumed levels for pH (7.0, 7.4, 7.8), surface temperature (ambient temperature), ammonia concentration (300, 450, 600 mg/L), and lagoon depth (10, 25 ft). The wastewater volume generated by the dairy was assumed to be 100 gal/hd/day. Using an average nitrogen excretion of 460 g head-1 day-1, total nitrogen concentration in fresh wastewater is about 1200 mg L-1. Assuming three-month storage, this lagoon has a volume of about 9 million gallons. Results of the sample computations for the dairy waste water lagoon in Fresno are summarized in the following Table B-2.

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The graphs in the following pages (Figures B-1 to B-5) illustrate that factors, such as depth of the lagoon, pH, concentration of ammonia in the lagoon water, and location (Fresno vs. San Joaquin) significantly affect the monthly ammonia volatilization from wastewater storage lagoons.

Generally, low temperature, low pH (pH = 7 or less), high water level (i.e. 25 ft), and low ammonia concentration (i.e. 300 mg L-1) reduce the ammonia volatilization.

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2.0 1.5 1.0 0.5 0.0

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Figure B-5: Simulated monthly ammonia losses from a dairy wastewater storage lagoon:

comparison for Fresno vs. San Joaquin climate data (pH = 7.4, TAN = 450 mg l-1, and depth = 10 ft) The graphs of Figures B-1 through B-5 may be integrated to determine the annual ammonia volatilization loss from the lagoons. To illustrate the range at which the ammonia volatilization may take place, the best and the worst possible scenarios calculate out to be 11 and 63 kg of ammonia volatilization head-1 yr-1 (Figure B-5). The estimated values are similar in magnitude to those derived from field measurements as reported in the literature.

Ammonia Emission Model Validation

The mass transfer model has been validated by conducting field ammonia volatilization experiments under controlled conditions (Figure B-6). The volatilization of ammonia from clean water and dairy wastewater were measured over a one-week period of time. The environmental conditions (water temperature, air temperature, relative humidity, and wind velocity) were continuously recorded throughout the experimental period (Figures B-7 and B-8).

In general, there were diurnal variations in the environmental conditions. The relative humidity rose continuously over the evening hours and reached a peak between 6 a.m. and 7 a.m. The temperatures of clean and wastewater were similar and were at their height between 1 p.m. and 8 p.m. They gradually cooled off during the evening hours and then started rising around 7 a.m.

The wind velocity varied considerably. The wind was calm in the morning hours, started to pick up speed in the mid-afternoon, and reached a peak at approximately 6 p.m. to 7 p.m. There were considerably fewer day-to-day changes in the environmental conditions.

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90 4.50

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70 3.50

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30 1.50 20 1.00 10 0.50 0 0.00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00

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60.0 2.00 50.0

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20.0 0.50 10.0

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Figure B-8: Average daily measurements of the environmental conditions during the ammonia volatilization experiments The recorded measure\ments of the environmental conditions were used as inputs for the model to simulate the ammonia volatilizations from the clean and from the wastewater. The simulated and the measured daily ammonia volatilization rates were compared (Table B-3). The discrepancies between the measured and predicted values are within 20% of measured values.



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