: . n ‘ { 0 l .. I:.‘ c y o\ «It. i . \Iflu‘l\|\l- fir I‘qllt‘clloc nu“ ‘3 d ~II‘L‘MN‘2 xvii-I‘ll. - ‘ i no ‘ (\HI hvnkixnhufllr. \NsfikhwlAn; - HI}- I’I. \oll!HN..|l|IunvoIl/H, $‘IWI.\UU:\IJ\].\I.I{I I ITIME = ITIME + 720 43 FIGURE 4.3 FLOII CHART (I: THE SUBROUTINE CLOCK. ITIME 8 ITI AH 8: AM 8 AM no _y I ITIME . ITIME - 720 ITIME < 720 " I AM - HM L l : IS ITIME >10 ITIME . ITIME - 60 lM . ITIME ENcoDE (7,901,ATIME) LL - LL + 1 IN . 0 LL, Aw, ITIME, AM ENCODE (7,902,ATIME) ‘I LL, Aw, IN, IM, AM II 44 total of the energy use was stored in an appropriate energy use array. Two separate arrays were used for equipment Operated by electricity. One array was a peak storage location for Operations during a power company's on-peak time and the other for Off-peak Operation times. When all systems had been checked and energy use determined and stored in the appropriate array, each piece of equipment was "turned Off.” The term ”turned Off” refers to the model's method of resetting the energy consumption equations to zero. At this point the simulation model advanced the simulated time one minute. The procedure was then repeated 2,880 times, representing the number of minutes required to s imulate two days . 4 . 2 .1 Electrical Lighting Lighting accounts for three to six percent of a typical dairy farm's total electric bill. The lighting system model, contained within the Milking System Simulator, consisted Of two fluorescent lights in the milkhouse, a fluorescent light for every two parlor stalls, and a mercury vapor light in the free stall area (Energy Facts: E-1273, .1979). The fluorescent lights in the milkhouse and parlor were modeled as eight foot fixtures with two tubes. Although the input variables can be changed, fluorescent fixtures were modeled rather than incandescent bulbs because they provide three to four times as much light (lumens) per unit of energy as incandescent bulbs (Energy Facts: E-1288, 1979). The lighting system uodel assumed the fluorescent lights would be on during milking and calculated the energy use for each minute of operation during this period. The following equation assumed each 45 fixture had a power consumption of 216 watts or an energy use per minute of 3.6 watt- minutes. WATT (I + 9) = WATT (I + 9) + (LITEMH * 3.6) (4.1) Where WATT (I + 9) = energy used by the lighting system (watt: min) LITEMH = total number of fluorescent fixture units (integer) The mercury vapor lamp located outside the milkhouse in the free stall area was modeled as a fixture with a lighting output of 11,000 lumens. The high intensity discharge lamp was chosen because it was intended for all night lighting of a large outdoor area: it provides more than twice as much light per watt as do standard incandescents; its efficiency is not affected by temperatures below 50°F as are fluorescent lights (Energy Facts: E-1288, 1979). The lighting system model assumed the mercury vapor lamp will be on from 7:00 p.m. in the evening to 7:00 a.m. in the morning and calculated the energy use for each minute of operation during this period. The following equation assumed the fixture had a power consumption of 312 watts or an energy use per minute of 5.2 watto minutes. WATT (I + 9) = WATT (I + 9) + 5.2 (4.2) Where WATT (I + 9) = total energy used by the lighting system (watt) 4.2.2 Electrical Power units An electric motor's efficiency rating determines how well it converts electrical energy into mechanical energy. The higher the rating, the more efficiently the motor uses energy. The energy that is not transmitted to the driven equipment is referred to as the motor 46 losses. These losses are equal to the power input minus the power output. Motor losses can be broken down into four categories (Anderson, 1978): 1) 12R losses result from the energy required to drive the current (I) through the resistance (R) of the conductors: 2) friction and windage losses are due to the energy required to overcome friction in the bearings and resistance that air presents to rotating parts such as cooling fans used to cool the motor: 3) core losses are attributed primarly to energy lost in the process of overcoming hystersis, a magnetic memory acquired in. the steel cores of the motor; and 4) stray losses which include several types of losses too difficult to measure and typically vary as the motor load varies. These four categories of losses represent the only energy that is consumed by the motor. The rest of the energy is transmitted to the equipment that is being driven. A motor rated at 100 percent efficiency uses 746 watt- hours of energy per horsepower (Gould, 1975). Since the efficiency of a motor will vary with its load, assigning an efficiency rating to a motor was rather complicated. The situation required the development of a load profile of electric motors using Michigan mergy Audit data. This consisted of a plot of motor loads versus time, and assigning efficiencies for each operating load. Analysis of the data indicated significant variations in motor loads and efficiencies. An average efficiency of 74.6 percent was assigned to all electric motors simulated by the Dairy Farm Simulation Model. However, allowances for adjustment were made when the motor's actual load and efficiency were known. Equation 4.3 was developed to predict the energy use of electric motors. The electrical energy consumption is 47 given in watt: minute, where E is the efficiency of the motor expressed as a decimal with a default value of .746. WATTS (I) = WATTS (I) + hpE (746 % E) % 60 (4.3) Where WATTS (I) - total electrical energy consumption (watt- min) hpE - advertised power of the electric motor (hp) E 3 percent efficiency of the electric motor (decimal) 4.2.3 Diesel Power Uhits A.S.A.E. D230.0 Agricultural Machinery Management Data (A.S.A.E., 1981) includes formulae to model fuel use of tractor and combine engines based on type of fuel and percent load. Concern over the accuracy of the predictions was expressed with the fuel use equations, after checking actual fuel use data collected during the a Michigan Energy Audit Study (M.S.U., 1981). The main concern was that the model predicted fuel consumption higher than was generally reported. An excerpt of paragraph 3.3 of A.S.A.E. 0230.3 shows the typical diesel fuel consumption given in gallons per horsepower: hour. gal/hp° hr = 0.52x + 0.77 - 0.04 /738x + 173 _ (4.4) The equation predicts fuel consumption at full speed control lever setting where x is the ratio of equivalent PTO power required by. an operation to the maximum available from the PTO. There is a conversion factor of 0.1970 gal/hp° hr per L/kWh to convert the English units to metric units. 48 The inverse of the fuel consumption equation from A.S.A.E. D230.3 indicates fuel efficiency in units of fuel economy hp- hr/gal in English units and of kWh/L in metric units. A closer examination of the diesel fuel economy equation of the present A.S.A.E. D230.3 reveals three notable aspects; 1) the full load fuel economy is about 12.18 hp- hr/gal which is below the 14.72 hp- hr/gal that may be considered to be a more typical full load fuel economy figure (Nebraska Tractor Test Laboratory, 1981); 2) the equation does not suitably predict fuel economy at very light lOads as evidenced by the fact at zero load the fuel economy, hp- hr/gal, does not equal zero; and 3) a plot of the fuel economy equation versus percent load reveals the curve "hooks down" at, high loads indicating' that typical fuel economy falls off above an 80 percent load._ In order to further evaluate the present fuel economy model and develop other models with current information, a data base was developed that contained selected information from Nebraska Tractor Test reports. All diesel tractors that were listed in the booklet ”Nebraska Tractor Test Data for 1981," were included in the data base (Nebraska Tractor Test Iaboratory, 1981). This was representative of new tractors on the market as of January 1, 1981. The fuel economy data for 206 diesel tractors with PTO were taken from the "Varying Power and Fuel Consumption" section of the Nebraska Tractor Test reports. These tests consist of six load settings equally spaced from zero power to rated power. The data were then used as input data for the Statistical Analysis System (SAS) programs. This versatile set of statistical programs was used to analyze the existing standard and develop and-analyze other models. 49 Statistical analysis with SAS revealed that the present diesel fuel economy model is substantially below the average data. Further research confirmed that the present fuel consumption standard has- a 15 percent allowance for fuel consumption of tractors in good condition but not in the new condition of the Nebraska test tractors. What this indicated was that the present diesel fuel economy model values must be multiplied by 1.15 to give typical present Nebraska Tractor Test fuel economy values. In terms of fuel consumption, this indicated that the present diesel fuel consumption model gives values 15 percent~ above. typical present Nebraska Tractor Test fuel consumption. Even when the model was adjusted, it still ”hooks down" at high loads and did not accurately handle very low load cases. Development of a new fuel economy model was pursued with the main objective being to develop as simple a model as possible that would accurately predict typical fuel economy for the entire load range. Several different forms of equations were considered as potential diesel fuel economy models. The final equation considered was a third order polynomial without the zero order term. The omission of the zero order term simplifies the equation by eliminating one coefficient and also forces the curve through the origin to make it more accurate at 2 + CX3 had the lowest error value and low loads. The equation Ax + Bx fit the average data. This particular equation and its coefficient used for power units in the Dairy Farm Simulation Model is shown at the top of the next page. Typical fuel consumption is given in gallons for each minute of operation, where hpx is the load expressed as a decimal. 50 (4.5) DF = DF + hpD[1.0 % (41.829 th - 42.692 thZ + 15.838 hpx3)] + 60 Where DF 8 total diesel fuel consumption (gal) hpD - advertised power for tractor and/or loader unit (hp) hpx a percent load on power unit (decimal) 4.3 Tabulation of Energy Use The total energy use determined by the model's simulation of two days of feeding and/or milking systems operation was multiplied by 15.208 to represent the total average monthly energy use of the Operations. The multiplier was based on 365 days per year divided by 12 months which was then divided by two to represent the two days of each month which were simulated. Figure 4.4 is a reproduction of a table generated by the Dairy Farm Simulation Model. The simulation model produced the table using the support routine BREAK and shows energy consumption for a 150 cow stationary feeding system utilizing a mobile mixer for feed delivery. Included in the table are the power supplier's on-peak times for their Time-of-Day Rate Schedule; the times when the morning and evening feeding processes. began: and the total average monthly energy use, by system, operation, and individual equipment. 51 FIGURE 4.4 A SAMPLE TABLE FROM THE DAIRY FARM SIMULATION MODEL FOR SIMULATED AVERAGE MONTHLY ENERGY CONSUMPTION OF A 150 COW STATIONARY FEEDING SYSTEM UTILIZING A MOBILE MIXER. Peak Period From 11:00 A.M. to 6:00 P.M. Morning Feeding Time: Evening Feeding Time: 4:30 P.M. 5: 00 A.M. On Peak Off Peak Tbtal Diesel Operation kWh kWh kWh Gal. Feeding System Corn Silage 56.97 102.59 159.55 0.00 Haylage 5.43 9.77 15.20 0.00 H. M. Corn 5.43 9.77 15.20 0.00 Feed Mixer 0.00 0.00 0.00 54.96 Conveyors 9.04 16.28 25.33 0.00 Sub Totals 76.87 138.41 215.28 54.96 Fanm Totals 76.87 138.41 215.28 54.96 52 4.4 Calculation of Energy Dee Charges Fuel price and different types of electrical energy rate schedules used in the model by the support routine PBILLS are explained in this section- Initially, the price of fuel and the electricity rates were set at.current 1981-82 prices. Diesel fuel was $0.264 per liter during this period, which is equivalent to $1.00 per gallon. The Michigan Public Service Commission governs the electrical rates and rules under which electricity is sold. The price of electricity varies from one power supplier to another. This particular model used current, Winter 1981, Detroit Edison Electric rates for economic comparisons. The amount of electrical energy used by a piece of equipment within a system simulator was calculated in kilowatt° hours(kWh). For example, a 100-watt light bulb burning for 10 hours uses one kWh of energy for each hour of operation. The electrical energy bills were based upon the number of kilowatt° hours used during an average simulated month. Typically, the electrical energy bill was calculated as the sum of the following: - Minimum Monthly Service Charge Electrical Energy USe Charge (cost per kWh times the' number of kWh used) - Fuel and Purchased Energy Adjustment Charge (varying cost per kWh times the number of kWh used) State Sales Tax (electrical energy use charge times 0.04) 53 4.4.1 Electrical Energy Use Charge The electrical energy rate schedules, including minimum monthly service charges used by the model, are four typical schedules used by most electrical power suppliers. The rates used were current Detroit Edison charges for the Inverted late, the Farm Flat Rate, the Commercial Rate and the Time-of-Day Rate Schedules. The rates were used in the model to compare the cost advantage of various rate schedules. Future cost comparisons will only be accurate when a specific power supplier's electrical energy rate schedule is used for a particular farm. The Inverted lute Schedule, developed several years ago by the Michigan Public Service Commission, was directed toward power suppliers with 200,000 or more customers. The so-called "life line rates" were designed to reward conservation by imposing a higher per unit charge on consumers who used large amounts of electricity. The Public Service Commission allowed adjustments in the rates for senior citizens and customers with special needs such as farmers. The following rate was used in the model: Minimum Monthly Service Charge: 3 2.65 Energy Charge: First 400 kWh @ $ 0.0488 per kWh Next 400 kWh @ $ 0.0548 per kWh Over 800 kWh @ $ 0.0618 per kWh The Farm Flat Rate Schedule for energy consumption was a special farm rate schedule approved by the Public Service Commission for farm enterprises consuming considerably more than 800 kWh per month, and thus, always paying the maximum rate when using the Inverted Rate 54 Schedule. The energy charge used in connection with the Farm Flat Rate Schedule was the same rate used for the second step of the Inverted Rate Schedule, $0.0548 per kWh. The minimum monthly service charge remained the same. Generally, it was not advantageous for a farm to be on this special farm rate schedule unless the monthly electrical energy consumption was greater than 1,142 kWh. The requirements. for consumption of 1,142 kWh or more was predicated by the lower rate charged by the Inverted Rate Schedule for the first 400 kWh and the higher charge for energy consumption greater than 800 kWh. The commercial Rate Schedule applied to commercial, industrial, and farm service customers who required in excess of 50 kW transformer capacity for all uses excluding residential, but including lighting, heating, and power. Commercial Rate-Schedules were generally based on a flat rate, while some others were based on a customer's load factor, therefore providing an incentive to control kW demand, and range from one half cent ($0.005) to one cent ($0.01) higher than domestic rates for the same power supplier. The energy charge assumed for the model when using the Commercial Rate Schedule was $0.0590 per kWh. .A customer on the Commercial Rate Schedule was also subject to a minimum monthly service charge of $5.55 as well as any fuel and purchased energy adjustment charges. The Time-of-Day Rate Schedule developed by power suppliers was designed to compensate customers who could voluntarily shift part of their load from the peak demand periods of the day to times when the electrical demand on the generators was lower. The philosophy behind this rate schedule, was to serve the customers' electrical loads with 55 fewer and more efficient generating plants, thus holding down the cost of production. Time-of-Day Rate Schedules varied from one power supplier to another. Generally, the rate schedule consisted of a two step rate for determining the energy charge (some suppliers had three) with each step based on a specific time period during the day. Energy consumption is first recorded using two meters or a meter with two registers which can. determine the time of day the electrical energy is consumed. The time of day when each meter or register records energy consumption is determined by the power companies' Time-of-Day Rate Schedule. The program simulated Detroit Edison's Time-of-Day Rate Schedule with the first period, the on-peak period, extending from 11:00 a.m. to 6:00 p.m.; with the second period, the off-peak period, extending from 6:00 p.m. to 11:00 a.m. The rate charged for electrical energy consumed during the on-peak period of the day was usually higher than the rate charged for the off-peak period. The difference in cost was usually large enough to offer an incentive for the farm customer to operate mch of the farm's electrical equipment during the off-peak period. The electrical energy use charges were calculated as follows: Minimum Monthly Service Charge: $ 4.00 Energy Charge: On-Peak kWh @ $ 0.0815 per kWh ‘Off-Peak kWh @ 3 0.0315 per kWh This rate schedule can produce a monetary savings for the farmer who is able to shift much of the electrical energy consumption to the off-peak period. Farmers anticipating a switch to the Time-of-Day Rate Schedule should carefully analyze their electrical use throughout the 56 entire day before switching. Cost savings must be evaluated with respect to any inconvenience resulting from the shift of electrical loads to the off-peak period. The shift to off-peak periods may result in a lower operating efficiency and even reduced productivity or, with an on-peak rate of $0.0815 per kWh, a much higher bill could occur if too much of the electrical energy was used during the on-peak period. 4.4.2 Energy Adjustment Charge The fuel and purchased energy adjustment charge was applied to all rate schedules to make adjustments for the often fluctuating cost of fuel used to generate electricity. This production cost adjustment provision was generally different from one month to another, and in some cases this charge became a credit. In accordance with this provision the model added a fuel and purchased energy adjustment rate of $0.0038 per kWh consumed to the monthly bill. 4.4.3 State Sales Tax A state sales tax exemption was available to farmers on the portion of their electrical energy bill for farm buildings utilized for direct production of a farm product. To be eligible for a sales tax exemption the farm Operator had to complete a sales tax exemption form and file it with the utility company. The State Government decided on the amount of electrical energy used in the farm residence. The balance of the electrical energy used in farm buildings could then be exempt from sales tax charges. The model assumed no sales tax exemption and calculated a four percent sales tax on the energy charge portion of the bill. The assumption was due to the inconsistancy within the state's application of policy and the inconsistancy of application within other states. 5. MILKING SYSTEM SIMULATOR Economic factors and a natural desire to lessen dairy labor loads has motivated dairymen to direct more attention to milking parlor automation. The milking process consists of up to 70 percent of total time spent in the milking parlor on an annual basis (Eabson, 1976). Actual time spent in the milking parlor involves: assembling and sanitizing equipment, moving cows, washing and stimulating cows, milking, and cleaning. Three basic types of milking parlor systems adapt well to extended automation (Babson, 1976): the Diagonal side-opening stall, the Sawtooth Herringbone, and the Rotary milking parlor (see Figure 2.1). Two other types, flat milking barns and conventional stanchion barns, have a limited capability for automation. Table 5.1 is a summary of -the different milking systems and milking units recorded by the Michigan Energy Audit Survey (Appendix A). The Sawtooth Herringbone design, using a single milk receiving jar and low-mounted milk lines, was the most common milking parlor reported. (The low-mounted milk lines allow milk to flow downward from the milking unit to the low- mounted lines thus reducing turbulence and obstruction to milking vacuum. While several farmers reported using weigh-jars incorporated into their low line systems, the performance of the system was not altered if the weigh-jars were installed so the milk entry equalled or was lower than the lowest point of the connecting milk hose. The 57 58 low-line milking system shown in Figure 5.1 was representative of most of the systems surveyed, regardless of parlor type or herd size. 5.1 Simulator Development The dairy farm milking system simulator was designed to simulate the low-line milking system in Figure 5.1 based on the results of the Michigan Energy Audit Sirvey. Many components make up the low-line milking system and they all must be compatible to provide the necessary cow milking capacity. Any reference to commercial products or trade names in this section does not imply discrimination or endorsement by the author. The Dairy Housing and Equipment Handbook (Midwest Plan Service, 1978) was used as a guide in the development of the simulation model, while the equipment performance data for various milking system combinations were furnished by Bow°Matic, 1979 and Surge, 1979 dairy equipment companies. All the information‘ was adjusted to be in agreement with real situations as described and recorded by dairy farmers participating in the Michigan Energy Audit. 5.1.1 Milking Time Performance of the shmulated milking system was based on the capacity of the system measured in cows per minute. To calculate milking system performance, the model. required the first five user inputs shown in Figure 5.2 and a previous input, the number of cows milked. The input parameters shown in Figure 5.2 include the beginning times for morning and evening milkings, the average yearly milk yield per cow, the estimated milking time per cow, and the number of milking units. The first calculation performed using these inputs, determines the variable EMILK which was the time when milking ends. 59 .CS. 02.2w0wm 3.83 5030 < :23 §w>w 0;; !.d|s>0._ $0.5 udgoo Ah v.0 unsor— 12¢» x :5. x430 Kw>CUmK k¢mh >¢<:Z(w / 17]. QJOu.Z4t13m 1320<> 60 FIGURE 5.2 OPERATIONAL FLOW CHART FOR MILKING SYSTEM SIMULATOR. IS MILKING SYSTEM MINFO = I I CALCULATE ENERGY USE FOR IPARLOR LIGHTING SYSTEM INPUT START TIME MMILK MORNING MILKING I INPUT START TIME NMILK EVENING MILKING . I INPUT AVERAGE YEARLY MILK YEILD HAVE I INPUT AVERAGE MILKING TIME TCOW I INPUT NUMBER OF PARLOR MILKING UNITS UNITS 61 FIGIRE 5.2 CONTINUED CALCULATE END OF MILKING TIME EMILK I CALCULATE TIME BETWEEN MILKINGS REST I INPUT VACUUM PUMP HORSEPOWER VHP I INPUT BULK TANK COMPRESSOR I HORSEPOWER CHP1 I INPUT BULK TANK COMPRESSOR 2 HORSEPOWER CHPZ , . I e L MINFO=1 J FIGURE 5.2 CONTINUED 62 SANATIZE TIME :qual To TIME STIMULATE COWS STIM 8 X DRINK 8 XX TIME > MILKING T N M P N] . TIME and < TIM , UR VACUU PU” 0 I I I MILKING 8 O l CALCULATE — POUNDS OF MILK RECEIVED MILKING = 0 BY THE RECEIVING JAR IS RECEIVING JAR > O TURN MILK PUMP ON I WASH TIME equal To TIME GS CALCULATE no POUNDS OF MILK RECEIVED BY THE RECEIVING JAR yes POST WASH TIME equal To TIME no 63 EMILK = [(33123 * TCOW) : UNITS] + SMILK ,(5.1) Where: EMILK - end of milking (minutes after midnight) HSIZE - number of cows milked (integer) TCOW - estimated milking time per cow (minutes) UNITS - number of milking units (integer) SMILK - beginning time for milking (minutes after midnight) The second calculation determined the variable REST which was the time between milkings. The combination of the two variables, EMILK and REST, determined the times when all parlor operations occurred. REST = NMILK - MMILK (5.2) Where: REST = time between milkings (minutes) NMILK = evening milking time (minutes after midnight) MMILK = morning milking time (minutes after midnight) 5.1.2 Milking Uhits The number of cows a system can milk in a given period of time (Table 5.2) was directly related to the fifth user input, the number of milking units. The milking units themselves did not require a direct energy input to function, however, the number of units operated determined the vacuum requirement of the system. The essential components of the milking units were the teat cup assembly, air and milk hoses, and the claw. The addition of automatic milking’ machine detaching units are intended to be a step-saving routine to protect the cow from injurious -milking and ‘permit the Operator to .be more efficient. Automatic detaching units do) not necessarily” improve labor efficiency' simply because they save time required for the operator to remove the milking 64 TABLE 5.1 MILKING PARLOR DESIGNS USED BY MICHIGAN DAIRY FARMERS PARTICIPATING IN MICHIGAN ENERGY AUDIT. Stanchion Barn1 Sawtooth Herringbone Parlor2 Milking Units Observations Milking Uhits Observations 3 1 3 1 4 2 4 3 6 1 8 5 12, 3 16 1 1 Around-the-barn pipeline. 2 Low-line, with and without weigh jars. TABLE 5.2 MILKING CAPACITY FOR MILKING FACILITIES WITH VARIOUS AMOUNTS OF AUTOMATION.1 Milking Facility Types“ Stanchion] Diagonaljl Rotary] 7 Herringbone] Number of Milking Units: 3 4 6 8 12 16 20 24 Type of Mechanization None3 28 34 49 37 60 752 862 1152 Crowd Gates 36 51 42 652 812 942 1262 Crowd Gates 5 Prep-Stalls 31 40 55 44 682 70 972 1322 Detacher units 44 54 41 59 72 78 972 Detacher units a Crowd Gates 45 64 78 85 106 Detacher units, Crowd Gates, and Prep_8ta118 48 58 47 67 82 89 111 1 Derived from Babson Brothers Dairy Research Publication, 1976. 2 Denotes the number of Operators milking. 3 A facility with base equipment including pipeline milking system. “ Steady-state throughputs, parlor set-up and clean up not included. 65 machine. The actual removal process represents only a small portion of the time that an operator spends with each cow. With the addition of automatic detachers, the amount of vacuum required per milking unit increases and thus the amount of energy required for the milking operation increases. Users of the program wishing to simulate automatic detaching units need only adjust the "milking time per cow" input and the horsepower requirement of the vacuum system. 5.1.3 vacuum System Two methods are used to rate the air flow of a vacuum pump. The New Zealand method measures air volume at half the vacuum the American Standard (15.0 inches of mercury) method does, so equivalent New Zealand cfm (cubic feet per minute) capacities are double the American. Standard cfm capacities at equal vacuums. The required air flow capacity of a vacuum pump is based on the size and design of the system. The milking system model allowed for variations in system design by permitting the vacuum pump(s) horsepower to be a user input. The horsepower requirement for a vacuum pump can best be determined by obtaining the machine manufacturer's specifications. 5.1.4 Sanitation Equipment The method used to clean any type of equipment depends on the nature of the material that needs to be removed. Circulation cleaning, used exclusively on large dairy farms, will adequately clean cold milk soil from milk handling equipment. The cleaning process is affected by time, temperature, turbulence, detergent concentration and composition, and water composition (U. of F., 1978). A standard (procedure for circulation cleaning consists of a rinse, wash, acid rinse, and sanitize schedule. 66_ The simulated cleaning cycle, CLEAN, shown in Figure 5.3 was designed for washing a pipeline system and bulk tank. Each cycle consisted of filling a wash vat with 40 gallons of water and then Operating the vacuum and milk pumps to circulate the water through the system. The prewash rinse cycle consisted of circulating 105-110°F water for five minutes. The rinse cycle, immediately following the milking process, was designed to remove 95 percent or more of the soil load from the equipment and reduced the amount of detergent required during washing. The wash cycle used water at 170°F along with an alkaline, nonfoaming detergent. This cycle began after the prewash rinse and lasted ten minutes. Residual detergent and soil that was loosened during washing was removed during the five minute postrinse. The post-wash rinse used cold water and acid to clean the equipment and control the development of milkstone. Sanitation of the equipment with a chlorine sanitizer was delayed until just prior to the next milking so that any bacterial contamination of the equipment that might have occurred between the washing time and the next use would be destroyed. Operation of _ the sanitation cycle was identical to the prewash rinse except for the chlorine sanitizer added to the 105-110°F water. The simulated cleaning of the the milk handling equipment also included a parlor wash-down using a booster pump and water at 105-110°F. The parlor washing began five minutes after the end of milking and continued for ten minutes. The simulated booster pump was a one horsepower surge water gun with a water delivery rate of two gallons per minute . 67 FIGURE 5.3 FLOW CHART OF THE SUBROUTINE CLEAN DRAW WARM WATER DRAW 8 5 VAT 8 VAT + DRAW II TLRN BOOSTER PWP ON] DRAW HOT WATER DRAWH = 5 VAT 3 VAT + DRAWH DRAW WARM WATER BOOST = 5 yes IS VAT > 40 DRAW COLD WATER DRAWC = 5 VAT 8 VAT + DRAWC l TIRN VACUUVI PIMP ON] I [TURN MILK PLMP ON 1 6 68 5.1.5 water Heating and Supply System Many of the operations within the milking system required an adequate water supply. The subroutine WATER, shown in Figure 5.4 simulated water use on an as-needed basis for the milking center and daily drinking water. The system modeled a two-inch deep well with a one horsepower submersible pump. The, well supplied water to a 42 gallon pressure tank at a rate of 20 gallons per minute. The pressure tank supplied water to the hot water heaters, cold water for the parlor washing and daily drinking water. The water supplied for drinking begins immediately after each cow is milked and was calculated at a rate of 30 gallons per day per cow. The two electric hot water heaters simulated in Figure 5.4 were for use in the milking parlor. Hot water heater one (HWAT 1) was modeled as a large hot water heater used by the major water heating loads required for washing the milking system and cleaning the parlor. Hot water heater two (HWAT 2) was modeled as a smaller hot water heater set at a lower temperature for prepping cows. The size and element wattage of the hot water heaters can be inputs to the program. In case of default the values assumed are 120 gallons and 6000-watts for HWAT 1 and 80 gallons and 4000-watts for HWAT 2. Water drawn from either hot water heater was replaced by 50°F water from the water supply system. The temperature of the water mixture in the hot water tank was then recalculated. Equation 5.3 determined the temperature of the water mixture in the tank by assuming ‘perfect mixing. 69 FIGIRE 5.4 FLOII CHART 07 THE SUBROUTINE WATER [ STORAGE 8 STORAGE - BOOST - DRAW - DRAWC - DRAWH - WINK - STIMJ TURN WELL PUMP ON CALCULATE STORAGE 8 STORAGE + 20 I HOT WATER HEATER I TEMPERATURE THERM 1 TURN HOT WATER HEATER 1 ON THERM I < I70°F I CALCULATE HOT WATER HEATER 2 TEMPERATURE THERM 2 TURN HOT WATER HEATER 2 ON MILKING equal To 1 70 HWATT = {[(HWATC - HWATD) * HWATt] + (HWATD * 50)} : HWATC (5.3) Where: HWATT 8 new temperature of the hot water heater (°F) HWATC 8 capacity of the hot water heater (gal) HWATD 8 amount of water drawn from the water heater (gal) HWATt 8 previous temperature of the hot water heater ‘ (°F) The result of Equation 5.4, THERM, was to turn on or off the heating elements of the hot water heaters. The thermostat for HWAT 1 was set to turn the element on at. 170°F‘ and off at 180°EH. The thermostat for HWAT 2 was set to turn the element on at 120°F and off at 130°F. The equation also included a heat transfer coefficient of .004 Btu/ft2°°F to determine loss or gain of heat to the environment. THERM = [(HWATT - AMB) * EXP(-0.0167 * cx)] + AMB (5.4) Where: THERM 8 temperature of the water at the thermostat (°F) HWATT 8 new temperature of the hot water heater (°F) AMB 8 ambient air temperature hot water heater room (°F) EXP 8 preceding term is in exponential form Cx 8 hot water heater heat transfer coefficient (Btu/ft2°°F) The hot water heater temperature was then recalculated using a specific heat coefficient of 0.0068 gal-°F/W- min. The equation assumed 100 percent heat transfer for one minute of heating. The new calculated temperature from Equation 5.5 was stored for use in the next minute of system operation. If no heating was required during the previous minute of system operation, HWATt was equated to THERM. 71 HWATt = THERM + [EEWAT * (0.0068 + HWATC)] . (5.5) Where: HWATt 8 temperature of the hot water heater after heating (°F) THERM 8 temperature of the water at the thermostat (°F) ELWAT 8 element wattage of the hot water heater (watt) HWATC 8 capacity of the hot water heater (gal) 5.1.6 washing and Stimulation Proper premilking stimulation was the first and most important step in the milking Operation. Thorough washing with warm water, massaging of the udder, drying, and stripping out foremilk was the most successful method of udder stimulation. Research indicated that udder stimulation done automatically using a prep-stall required a 45 second spray of 120°F water at a minimum pressure of 50 pounds per square inch (Babson, 1976). An alternate method to automated prep-stalls was a vigorous manual washing and massaging of the udder for 15 to 30 seconds. The milking capacity of a system with no mechanization and one using prep-stalls is shown in Table 5.2. A conservative estimate of the time saved per cow milking using prep-stalls was 30 seconds. The amount of water required for automated prep-stalls, however, was four times greater than manual requirements. Enuation 5.6 of the milking system simulator assumed manual prepping with a water requirement of 0.25 gallons per cow milking. Parlors using automated prep-stalls can be simulated by increasing the variable PGAL to one gallon per cow milking and making appropriate adjustments in the estimated milking time per cow. 72 PREP '8 (HSIZE * PGAL) % (EMILK - SMILK) (5.6) Where: PREP 8 water required for prepping (gal/min) PGAL 8 water required for prepping (gal/cow-milking) EMILK 8 end of milking (minutes after midnight) SMILK 8 beginning time for milking (minutes after midnight) 5.1.7 Milk Handling Equipment In the low-mounted milk line system, milk flows downward from the milking unit and into the low-mounted lines which slope to the milkroom. Once in the milkroom, the milk is collected in a glass receiver jar. When the milk level in the receiver jar reaches an established point a magnetic float switch starts the milk pump. The milk is pumped from the receiver jar to the milk tank for cooling. When the milk level in the receiver jar drops the milk pump stops. A time-lag control allows the milk pump to operate long enough to remove the remaining milk in the glass receiver at the end of milking. The milk handling section of the model assumed a 24 gallon receiving jar and a 0.5 horsepower milk pump with a capacity of 22 gallons per minute. The pounds of milk received by the receiving jar per minute was determined by Equation 5.7 while Equation 5.8 determined the total amount of milk contained in the receiving jar after each minute of pump operation. 73 JAR 8 (HSIZE * HAVG) * (DMILK * 2) (EMILK - SMILK) (5.7) Where: JAR 8 amount of milk in the receiving jar (lbs/min) HSIZE 8 number of cows milked (integer) HAVG 8 average annual cow milk production (lbs/cow) DMILK 8 average days in milk (integer, default 8 305) EMILK 8 end of milking (minutes after midnight) SMILK 8 beginning time for milking (minutes after midnight) JART = JART + JAR - DUMP (5.8) Where: JART 8 total pounds of milk in the receiving jar (lbs) JAR 8 amount of milk in the receiving jar (lbs/min) DUMP 8 JART, unless JART > 60 then DUMP 8 60 (lbs/min) 5.1.8 Milk Cooling Systems The most important single factor in maintaining milk quality is fast, prOper cooling and holding of milk. The milk cooling system subroutine modeled a bulk tank designed for every other day pickup with two refrigeration compressors. Cooling requirements for milk in a farm ' bulk milk tank designed for every other day pickup were formulated using A.S.A.E. Standards developed by the International Association of Milk, FoOd and Environmental Sanitarians, the United States Public Health Service, and the" Dairy Industry Committee (A.S.A.E., 1980). According to the requirements, a tank designed for every other day pickup shall cool 25 percent of the rated volume of the tank, containing raw mulk, from 90°F to 50°F within one hour after the tank has been filled to 25 percent of its rated capacity, with the cooling 74 system in operation during the filling period. Fer subsequent milkings the cooling capacity of the tanks must be capable of preventing the blend temperature of the milk from rising above 50°F at any time. The size of the bulk tank and the horsepower of each compressor were designated as user inputs as shown in Figure 5.2. This allowed examination of a wide range of herd sizes without extensive changes to the program. In the case of parlors with a single cooling compressor, the horsepower input for the second compressor could be set to zero. A flow chart of the subroutine COOL MILK, shows the operation of the simulated milk cooling system (see Figure 5.5). The total amount of milk in the bulk tank each minute was determined by Equation 5.9. 1 TANK = TANK + DUMP (5.9) Where: TANK 8 total amount of milk in the bulk tank (lbs) TANK1 8 amount of milk previusly in the bulk tank (lbs) DUMP 8 amount of milk pumped into the bulk tank I (lbs) Once the milk began to enter the bulk tank the temperature of the milk was checked continuously to determine when the compressor(s) would operate. The temperature of the mdlk in the bulk tank including any new milk pumped into the tank at 90°F was determined by Equation 5.10. The equation assumed a specific heat value of 1.0 Btu/lb- °F for milk (Bou0Matic, 1979). 75 FIGURE 5.5 FLOW CHART OF THE SIBROUTINE COOL MILK CALCULATE POUNDS OF MILK IN THE BULK TANK TANK '5 CALCULATE "0 Y°5 I MILK TEMPERATLRE TANK > o ' IN THE BULK TANK TBPZ I TLRN ON COMPRESSOR 2" I II I TURN ON COMPRESSOR 1 l 76 TEMP1 = [(TANK1 * TEMPB) + DUMP * 90] 4 (TAle + DUMP) (5.10) Where: TEMP1 8 milk temperature in the bulk tank before cooling (°F) TANK1 8 amount of milk previusly in the bulk tank (lbs) TEMP3 8 milk temperature in the bulk tank after a heat gain or loss due to the environment (°F) DUMP 8 amount of milk pumped into the bulk tank (lbs) If the temperature of the milk in the bulk tank was greater than 45°F both refrigeration compressors were designed to operate. Once below 45°F the first compressor was designed to cool the milk down to 36-38°F. The amount of heat removed per minute from the milk was determined by the number of compressors Operating, compressor horsepower, and design of the cooling system. A direct expansion cooling system, with the condensing unit as an integral part of the tank, has a cooling capacity of approximately 8000 Btu/hp- hr. The .change in milk temperature using this type of system was modeled by Equation 5.11. TEMP2 = [TANK * TEMPI) - (BTU 1 + BTU 2)] : TANK‘ (5.11) Where: ' TEMP2 8 milk temperature in the bulk tank after cooling (°F) TANK 8 total amount of milk in the bulk tank (lbs) TEMP1 8 milk temperature in the bulk tank before cooling (°F) BTU 1 8 heat removed by compressor 1 (Btu) BTU 2 8 heat removed by compressor 2 (Btu) Calculation of the milk temperature in both cases assumed perfect - mixing of the milk. This was accomplished by allowing the agitator to Operate whenever the refrigeration compressors were operating. The final calculation used a heat transfer coefficient Of 0.004 Btu/ft2°°F 77 to determine heat gain or loss to the bulk milk tank because of the environment. The result of Equation 5.12 usually increased the needed cooling capacity five percent. 2 TEMP 8 [(TEMP - AMB) * EXP(-0.0167 * cx)] + AME ' (5.12) Where: TEMP3 8 milk temperature in the bulk tank after a heat gain or loss due to the environment (°F) TEMP2 8 milk temperature in the bulk tank after cooling (°F) AMB 8 ambient temperature in the milk room . (°F) EXP 8 preceding term is in exponential form Cx 8 bulk tank heat transfer coefficient (Btu/ft2°°F) Energy saving devices, such as heat exchangers, especially plate or tube coolers, and heat recovery units for space heating or water heating, were not modeled. Validation of energy savings was not possible because an insufficient number of Michigan Energy Audit farms used such devices. Farms which reported the use of the heat recovery devices showed energy savings ranginga from nine ‘percent to' thirty-five percent when compared to similar farms without the devices. Observations, although limited, correlated with performance values reported by eighteen New YOrk farms (Koelsch, 1979). 5.2 validation of Milking System Simulator The milking system model was validated by simulating an actual milking system and comparing results obtained to actual requirements. A dairy farm, representative of the 21 dairy farms participating in the Michigan mergy Audit, provided the actual system and requirements needed for validation. 78 The farm used for validation was located in Caseville, Michigan. The farm milked 100 cows with a herd average of 15,914 pounds per cow. The milking system used was a basic Double-4 Sawtooth Herringbone parlor with eight milking units. The average milking time per cow, including manual washing and stimulation by one Operator, was six minutes. The vacuum unit needed for this system required a ten horsepower pump to maintain sufficient vacuum pressure. The milk was pumped to a 2,000 gallon bulk milk tank designed for every other day pick-up with milk cooling accomplished by two five horsepower refrigeration compressors. Water heating from 1978 to 1979 was done by two conventional electric hot water heaters with specifications similar to those described in Section 5.1.5. From 1979 to 1980 the main water heating load was assisted by the addition of a heat recovery unit. The unit, a Surge ARC cOndensing unit, utilized a special water cooled heat exchanger to condense the refrigerant from the milk tank, and at the same time heat water. The resulting hot water was transferred to a Special tank for later use with the main hot water heater. If the water in this special tank reached 140°F, a valve closed and the refrigeration system was switched over to an air-cooled system. 5.2.1 Milking Parlor validation The values represented by the bar graph in Figure 5.6 indicate the total electrical energy consumption by the milking parlor each month from 1978 to 1980. The solid line drawn horizontally across the graph shows the total electrical energy consumed per month as predicted by the computer simulation. The line represents 3,653.7 kWh. The values which should .be noted are the mean kWh and the standard deviations 79 new: 5.5 WY snow" OF SIMILATED AND ACTUAL ELECTRIC». EIERGY USE FOR THE CASEVILLE DAIRY MILKING PAmOR (Farm Number 32-00881. 3 ‘ 978-1979 2 S1 3 4000 — 19794930 SIMULATED ENERGY USE Na 3 N 2 O N V \ / \ z o 3 :5 § 3 S E b: L o 3.00 .I 7 ‘ \ \ N2 N N N N N N N N? N \/ N N N N N N N N? N N” \ N N N N N \V \r N? § N N N N N S N” S; 2 N; N N N N N N N N2 \5 "“ . N? N N N N N N N N? N; 8 S; s N N , N N N N Né N; 5 N/ N N N, I N. N N N N? N’ N? \ N N! I \’ N N N N/ N5 \/ \ N N/ I N/ N N N N/ / gem-v‘Ws SSSNZN \ / \ N \ I I I I N/ N N N N N N \/ N §¢ N N N5 5 N4 N N N N2 N? N s N A? a N? \ S N NE N4 . 2..., \ N \ \ 2 I 2 \ \ K a 5 6 7 8 9 IO 12 1 2 3 4 nmmm TABLE 5.3 mm” SW or SIMULATED AND ACTUAL ELECTRICAL EIERGY USE FOR THE CASEVILLE DAIRY MILKING PARLOR (Farm NUmber 32-0088). 1978 +5 1979 1979 +5 1980 MONTH COPUTER SIMULATION T + ' Slmula'l'lon TO‘l'al SImula‘l'Ion MEAN MONTH o a Difference DIfference klh kWh 1 kWh 1 5 3,553.7 3,703 -I.33 3,758 -3.03 5 3,553.7 3,590 1.77 3,203 14.07 _ 7 3,553.7 3,703 -1.33 2,982 22.53 8 3,553.7 3,703 -1.33 2,982 22.53 9 3,553.7 3,590 1.77 3,088 18.32 10 3,553.7 3,782 -3.39 2,980 22.51 11 3,553.7 3,525 0.79 . 3,143 15.25 12 3,553.7 3,838 -4.80 3,171 15.22 1 3,553.7 3,815 -4.25 3,513 4.01 2 3,553.7 3,814. -4.20 3,589 1.80 3 3,553.7 3,980 -8.20 4,219 -I3.40 4 3,553.7 3,782 -3.39 3,401 7.43 TOTALS 43,844.4 44,925.0 * 40,039.0 * MEAN 3,553.7 3,743.8 -2.41 3,335.5 9.50 Std. Mo * 11407 * 37908 T 80 calculated for each of the two years. The values for simulated and actual data are within one standard deviation both years despite the addition of the heat recovery unit. The other values given in the table indicate the percent difference between the simulated and actual results. The monthly values for the first year range from -8.20 percent to 1.77 percent with an average monthly difference of only -2.4 percent. The addition of the unmodeled heat recovery unit caused monthly values to range from -13.40 percent to 22.61 percent during the second year. The average monthly difference between the simulated and actual results was 9.50 percent. 5.2.2 Milking System Validation The milking system consists of the milking units, vacuum pump and milk pump. Submetering of the electricity consumed per month by the vacuum pump and milk pump enabled validation of this system. Figure 5.7 provides a bar graph of the electricity consumed by the system from 1978 to 1980. The predicted electrical energy consumption was 1,067.4 kWh. The horizontal line across the bar graph represents the simulated energy consumed. It is evident that a major difference exists between the real and simulated systems for the first year of data. The values for 1978-79 differed an average of 15.47 percent. A major contributor. to the difference between the simulated and the actual electricity consumed was the length of time the vacuum pump was allowed to operate. As a result of careful management of the system during the second year, the difference was decreased to less than one standard deviation. The monthly difference between the simulated and the actual electricity consumed for 1979-80 ranged from -24.72 percent 81 FIGIRE 5.7 MY 5m N SIMILATED AND ACTUAL ELECTRICAL EDERGY USE FOR TIE CAEVILLE MIRY NILKIIG SYSTEM (Farm Number 312-0088). I1978-1979 7 ,. Hoe/19791980 SIMULATED ENERGYUSE g .I g 1300 Q g Np N N N N N Z 8 N? N N N N N N N N? \/ N N \' N N N N N/ q 88 N/ N N N N N N N N a N/ N N; N N N N N N N N N N? N \/ N N N N N N N N N \/ N \/ N N N N N N N. N N \/ N N” N N N N N N N N N N2 N 8 “” S? N N N N N N N N NE \/ N N'x'TN N N N ‘ N N N N5 N? N 5 N2 s N N N N N? N N N5 N? N 1“” ~v4 \1 N N N NI N N \/ / N I a \/ N N N N, N N, N N \/ x N N/ N Nr Nr N! N? N! N? N N2 N2 N E 2 NI NI NI NI NI NI N N/ N/ N x N NI NI NI NI NI NI N N/ N/ N “a N/ N NI NI NI NI NI NI N N/ N/ N N2 N N4 N5 N4 N2 N2 N2 N N4 N; N \5 g \z N? N2 N2 N? N? \ N4 N2 N no I \I NI \( \( \z I \/ S 0 7 8 9 IO 11 12 1 2 3 4 Toma TAH.E 5.4 mNTIlY SW 01" SIMMTED AND ACTUAL ELECTRICAL EIERGY USE FOR THE CASEVILLE DAIRY MILKING SYSTEM (Farm Number 32-0088). 1978 +5 1979 1979 +5 1980 MONTH COMPUTER SIMULATION Tofa' SImulaTlon TOTaI SlmulaTIon MEAN MONTH leference leference 1.10. km S kWh S 5 1057.4 1,285 -15.93 1,279 -15.54 5 1057.4 1,250 -14.51 1,050 0.70 7 1057.4 1,285 ~15.93 954 10.73 8 1057.4 1,285 -15.93 954 10.73 9 1057.4 1,250 -14.51 987 8.15 10 1057.4 1,288 -17.13 977 9.25 11 1057.4 1,279 -15.54 1,042 2.44 12 1057.4 1,321 -19.20 978 9.14 1 1057.4 1,210 -11.79 1,080 -1.17 2 1057.4 1,209 -11.71 1,118 -4.53 3 1057.4 1,257 -15.75 1,418 -24.72 4 1057.4 1,225 -12.87 1,011 5.58 TOTALS 12,808.8 15,154.0 * 12,878.0 * MEAN 1,057.4 1,252.8 -15.47 1,073.2 -0.54 Std. Dev. * 34.7 * 140.7 * 82 to 10.73 percent with an average of one half of one percent while still milking the same number of cows with approximately the same milk production. 5.2.3 water Heating System validation This system was validated by comparing the simulated data to actual data obtained from submetering the two hot water heaters used in the parlor. The actual data obtained per month from 1978 to 1980 are shown by the bar graph in Figure 5.8. The water heating system was simulated using the appropriate input data for the real system during 1978-79. For the 1979-80 year, the input data were left unchanged even though a heat recovery unit was added to assist the main hot water heater. The horizontal line across the bar graph represents the simulated energy consumed by hot water electrically heated for this farm. The simulated value of 1,521.4 kWh did not compare closely to the real values. Water heating for the real system during 1978-79 was lower than that predicted by over three standard deviations or 10.81 percent. A probable cause for this difference is less hot water was used for cleaning the system than was predicted. The major difference between the real and simulated data for the second year was explained by the addition of the Surge ARC heat recovery unit. The Surge unit was a heat exchanger installed in the discharge line of the refrigeration compressor. Concentric tubes carry water and refrigerant gas in counter-current fashion. Equipped with a pump to circulate water through the heat exchanger, the Surge unit stored the water in a separate tank .for future use by the main hot water heater. It should be noted that the 40.18 percent difference between the real and simulated energy use was greater than was expected 83 Elm 5.8 IDNTILY SW OF SIMILATED AM) ACTU‘L ELECTRICAL EIERGY USE Fm TI-E CA$VILLE DAIRY I'DT WATER SYSTEM (Farm Mmber 32-0088). 1509 _ g i 5 1978-1979 \smm 11 Emma? 1181: $1455 ’1979-1980 TED “ Q i N‘ N N N N N 0 ‘3" _ N N N N N N N N ~ N4 N N N N N N N a: N4 N N N N N N ,, N =1 N’ N N N N N N r N D ‘ N4 N N N N N N 4 N 9 N4 N N N N N N 4 N = v N N N . N' N I N 01199 N4 N N N N N4 N4 4 N4 0 T N4 N N N N N4 N4 4 N4 N4 N N N N N4 N4 4 N4 6 N4 N N N N N4 N4 4 N4 1gmag ‘1” ‘1 ‘1?’ ‘Ir ‘1 ‘I" ‘II' I, ‘H’ 5 ‘ N4 N N4 N4 N4 N4 N4 4 N4 N4 N4 4 N4 4 N4 N4 N4 N4 4 N4 E N4 N4 N4 4 N4 N4 N4 N4 4 N4 see N4 N4 4 N4 4 N4 N4 N4 N4 4 N4 ‘ N4 N4 4 N4 4 N4 N4 N4 N4 4 N4 N4 N4 4 N4 4 N4 N4 N4 N4 4 N4 N4 N4 4 N4 4 N4 N4 N4 N4 4 N4 89,, N4I N41 [4 N41 14 N4 N4 N4 N4 4 N4 5 7 e 9 1a .11 12 1 2 3 4 MONTH TABLE 5.5 MONTHLY SUMHHRY OF SIMULATED AND ACTUML ELECTRICAL ENERGY USE FOR THE emu: 011m HOT 11411512 SYSTEM (Farm Mmber 32-00881. 1978 To 1979 1979 To 1980 MONTH COMPUTER SIMULATION T f ' Simulaflon beal Slmulafion MEAN NONTH o a Difference leference 1.151 1.1111 s 141111 I 5 1,514.4 1,364 11.03 1,305 16.05 6 1,514.4 1,320 .14.73 1,040 45.62 7 1,514.4 1,364 11.03 967 56.61 8 1,514.4 1,364 11.03 967 56.61 9 1,514.4 1,320 14.73 1,031 45.89 10 1,514.4 1,386 9.26 986 53.59 11 1,514.4 1,305 16.05 1,016 49.06 12 1,514.4 1,393 8.72 995 52.20 1 1,514.4 1,423 6.42 1,151 31.57 2 1,514.4 1,422 6.50 1,132 33.78 3 1,514.4 1,444 4.88 1,252 20.96 4 1,514.4 1,369 10.62 1,122 34.97 TOTALS 18,172.8 16,474.0 * 12,964.0 * MEAN 1,514.4 1,372.8 10.31 1,080.3 40.18 S‘I’de “V0 * 4304 T 112.6 * 84 by the addition of such a unit.. The 29.87 percent difference between the two years of real data was the approximate energy savings expected when adding this type of heat recovery (Koelsch, 1979). 5.2.4 Milk Cooling System Validation The validity of the milk cooling system was checked by comparing the simulated energy consumed with the actual requirement of two refrigeration compressors. The actual energy requirements for the two compressors were obtained from a single submeter of the two units. The results of the 1978 to 1980 audits are shown by the bar graph in Figure 5.9. The energy consumed by the milk cooling system is usually very consistent except during the spring months of February, March, and April, when milk production increases due to calving. The line drawn horizontally across the bar graph represents the simulated energy consumption for cooling the average monthly milk production. The computed value obtained for milk cooling was 1,064.9 kWh. The simulated value compared closely with the average monthly energy consumed. During the first year the difference was 3.92 percent while the difference rose to just under 10.0 percent the second year. Variations from month-to-month ranged from -16.22 percent to 4.40 percent for 1978-79, and -31.25 to 4.71 percent from 1979-80. The monthly variations between actual and simulated energy consumption were expected because of fluctuations in herd milk production, especially during the spring. The larger variation which occurred during the second year may be attributed not only to milk prOduction rates, which increased slightly the second year, but also to the addition of the Surge heat exchanger. The heat exchanger which uses water to assist 85 new: 5.9 1101111111 511mm 01- s1MULATEO ANO ACTUAL ELECTRICAL evaROY use FOR m: 0mm: DAIRY MILK MUG svsnsn (Farm Nunber 32-0088). 7 1509 .3 4: A s 1978-1979 4 .4 4 1979-1980 4 3 mm 3 4 ' 4 7 / 2 13m 3 4 4 Q SIMULATED ENERGY USE _ 7 4 4 § 4 4 N4 4 mm 1 4 / \4 4 8 7 4 4 N4 N4 m 7 4 N4 N4 / \/ = 4 .4 N4 N4 N4 N4 8 H”-- 4 r N N4 N4 N4 N4 N4. V1 ‘1 I V f 8 4 4 4 N 4 N4 N4 N4 N4 N4 5 '«°-+ 4 4 4 N4 4 N4 N4 N4 N4 N4 4 4 4 N; 4 N4 N4 N4 N4 N4 5 4 4 4 Ni 4 N4 N4 N4 N4 N4 I N / \ / we / 4 4 N, 4 N4 N4 N4 - 4 4 4 \4 4 N4 N4 N4 N4 N4 4 4 N, 4 N4 N4 N N4 N4 4 4 4 N/ 4 N4 N4 N4 N” N4 ”0 4 4 N; l4 N4 N4 N4 N4 N4 5 6 9 IO 11 12 1 2 3 4 thH TABLE 5.6 WY SW or SIMILATED AID ACTIML ELECTRICAL EIERGY USE FOR TIE CASEVILLE DAIRY MILK COOLING SYSTEM (Farm Number 32-0088). 1978 To 1979 1979 +0 1980 MONTH CONFUTER 5 'MULAT ION Tofal 51111111 aflon 1501.51 51 mu I aflon MEAN MONTH D1fference leference klh th S kin X 5 1,064.9 1,054 1.03 1,184 -10.06 6 1,064.9 1,020 4.40 1,103 -3.45 7 1,064.9 1,054 1.03 1,051 1.32 8 1,064.9 1,054 1.03 1,051 1.32 9 1,064.9 1,020 4.40 1,070 -0.48 10 1,064.9 1,108 -3.89 1,017 4.71 11 1,064.9 1,041 2.30 1,085 -1.85 12 1,064.9 1,124 -5.26 1,198 -11.11 1 1,064.9 1,183 -9.98 1,282 -16.93 2 1,064.9 1,183 -9.98 1,339 -20.47 3 1,064.9 1,271 -16.22 1,549 -31.25 4 1,064.9 1,188 -10.36 1,268 -16.02 TOTALS 12,778.8 13,300.0 * 14,197.0 * MEAN 1,064.9 1,108.3 -3.92 1,183.1 -9.99 Std. Dev. * 81.6 * 155.0 * 86 condensor cooling, can through mismanagement cause the condensing temperature to rise above 110°F. An increase in condensing temperatures results in an increase in discharge pressure which in turn decreases compressor efficiency. 5.3 Energy Requirements for Simulated Milking Systems Many alternative systems or techniques are available to reduce labor and energy requirements for milking a dairy herd. The validated Milking System Simulator was used ‘to predict energy requirements for milking six different herd sizes. The major emphasis was placed on scheduling milking times to take advantage of the Time-of-Day mate Schedule, .although both energy use and its costs were examined. The selected systems were designed to meet Bow-Matic high capacity milking parlor specifications. Assumptions used in the analysis of this work are described in the following sections. 5.3.1 Milking Time The major assumption which affected all other milking parlor specifications was milking capacity. The milking capacity' of the simulated farms was based on the average milking time of those farms surveyed. The total milking time for those farms was approximately two hours regardless of herd size. The average milking time per cow was six minutes with a range of five to ten minutes per cow. Most farms began their milking operations at 5:15 a.m. and 4:45 p.m. Some farms began as early as 5:00 a.m. and 4:30 p.m. or as late as 7:00 a.m. and 6:30 p.m. The simulated systems were designed to begin milking at 5:00 a.m. and 4:30 p.m. The optimizing routine, TDAY, 87 was then used to minimize electrical energy charges using the Time-of-Day Rate Schedule. 5 . 3 . 2 Milking Equipment The nulking equipment used to meet Bow-Matic specifications based on herd size are shown in Table 5.7. The number of milking units was determined by using an average milking time of six minutes per cow while maintaining the total milking time between 1.5 and two hours. The recommended horsepower for the vacumm pump was based on requirements for Bow-Matic rotary vacuum pumps assuming a New Zealand airflow rate of 20 cfm per milking unit. The requirements for the bulk tank and refrigeration units were dictated by standard available sizes. The bulk tank sizes were calculated for a maximum of five milkings plus a ten percent reserve. The assumed milk production was 55 pounds per day per cow. The refrigeration compressors designed for direct expansion cooling and every-other-day pickup met A.S.A.E. Standards described in 5.1.8. The horsepower required to operate the compressors met Bow- Matic specifications. TABLE 5.7 MILKING PARLOR EQUIPMENT INPUTS ASSUMED FOR SIMULATED MILKING PARLORS. MILKING PARLOR EQUIPMENT REQUIRED EQUIPMENT BY HERD SIZE 100 150 200 250 300 Number of Milking Units ( # ) 8 10 12 14 16 Bulk Tank Size (gal) 2000 2500 3000 4000 5000. Bulk Tank Compressor 1 (hp ) 5 5 8 10 10 Bulk Tank Compressor 2 (hp ) 5 ~ 5 8 10 10 Vacuum Pump (hp ) 10 10 12.5 15 17.5 88 5.3.3 Energy Requirements for Simulated Milking Parlors The energy requirements for the simulated. milking' parlors are listed in Table 5.8. The table expresses the electrical energy use for each parlor system in kWh by herd size. As expected, the results show the total average energy requirements increasing from 3,089.99 kWh to 7,945.13 kWh as herd size increased. The electrical energy requirement on a per cow basis indicated "economy's of size.” The small herd required 61.8 kWh per cow. While the simulated large herd needed only 26.5 kWh per cow. The difference in energy requirements per cow is explained by examination‘ of the simulated energy requirements by milking parlor system. The energy used by the milking and milk cooling systems, hot water for cow preparation, and the water supply system, will not explain the difference in simulated energy per cow. Each of these systems performed as expected. The simulated energy requirements for each system increase with herd size while maintaining approximately the same energy requirement per cow for each operation. The remaining two systems, the main hot water heater and the booster pump, are therefore responsible for the difference between the expected total energy consumption use and that shown in Table 5.8. The simulation results show little or no difference in the amount of energy consumed by these systems regardless of herd size. The two systems used the main hot water heater and the booster pump for cleaning and washing the parlor and its milking equipment. The modeling method used for both systems assumes the same amount of water, detergent, and equipment operating hours used for these parlor operations, independent of herd or parlor size. In reality this may or 89 TABLE 5.8 SIMULATED MONTHLY ELECTRICAL ENERGY REQUIREMENTS IN KlLOWATT'HOURS FOR MILKING PARLOR SYSTEMS BY HERD SIZE. MILKING PARLOR SYSTEMS Mi 1111119 System: Vacuun Pump Milk PUmp Sub-Tofal (1111111) M1 "1 Cooling: Corrpressor 1 Compressor 2 Agifafor Sub-Tofai (kWh) Matu- l-baflng: Wafer I-bafer - Main Wafer I-bafer - Prep Sub-Tofal 11.11111 Parlor Clean-Up: Wafer Pin - Hell: nghflng: Tofal (kWh) MONTHLY ELECTRICAL ENERGY USE BY HERD SIZE 100 1,053.50 13.75 1,067.36 697.80 273.55 93.51 1,054.85 1,521.39 312.02 1,833.41 11.14 129.29 312.15 4,418.89 150 1,205.57 19.75 1,225.32 1,045.15 495.22 140.18 1,681.55 1,521.42 435.55 1,957.07 11.15 188.08 391.22 5,454.39 200 1,533.53 25.51 1,659.24 1,388.07 495.48 115.25 2,000.80 1,521.42 549.14 2,070.56 11.15 231.76 468.10 5,441.51 250 2, 055. 75 29.30 2,096.06 1,505.88 448.38 107.25 2,151.85 1,521.42 649.46 2,170.88 11.15 263.67 543.24 7, 246.85 300 2,499.89 30.57 2,530.46‘ 1,579.35 471.19 112.52 2,263.06 1,521.42 737.62 2,259.04 11.15 254.81 515.52 7,945.13 90 may not be true as it is highly dependent on operator preference, temperature, hardness of water, and the amount and type of detergent used in the cleaning operation. While the model assumed a standard operating procedure for an average milking parlor, as recommended by dairy equipment manufacturers, adjustments in the model can be made for individual farms. The simulated energy requirements shown in Table 5.8 are not 'intended to be the actual energy requirements for the simulated herd sizes in all situations. Many factors affect the standard values presented. These standard values can be used as management tools to analyze specific operations and promote efficient energy use through conservation. For example, dairy operations using less energy than these standard values Could be. considered energy efficient while operations using more energy suggest a need for improvement in energy management. 5.3.4 Energy Cost The various electrical energy rates charged by power suppliers provided another area for analysis through the use of the computer model. The monthly energy charges by herd size and electrical rate schedule are summarized in Table 5.9. As expected, a savings resulted with the Farm Flat Rate as compared to the Inverted Rate. The savings realized was approximately 9.3 percent of the Inverted Rate for each herd size. In comparison to the Commercial Rate, a similar savings of 7.8 percent was realized. The energy charges listed in Table 5.10 were calculated by the subroutine TDAY. The optimizing routine is capable of shifting the starting time for morning and evening milkings in order to minimize 91 time-of-day electrical rate charges. The program maintained the 11 1/2 hour interval between morning and evening milkings while shifting' the starting' times at 15 minute intervals. The milking parlors on Time-of-Day metering with a nulking time of 5:15 a.m. and 4:45 p.m. realized a 27 percent reduction in their energy charges over the Farm Flat Rate, and 34 percent over the Inverted Rate. Parlors which gained the maximum benefit from time-of-day metering had I to change their starting time for milking. By delaying the start of milking by 1.5 hours, a savings of 24 percent was obtained. In comparison to the Farm Flat and Inverted Rates the savings amounted to 45 percent or more. Generally, dairy operations switching to time-of-day metering will reduce their electrical energy operating costs. The shift to off-peak periods may result in lower operating efficiency and reduced productivity. Uhless an appropriate milking time is selected and other electrical loads are controlled, the operating cost of a dairy operation could increase. Finally, time-of-day metering’ will not directly reduce the amount of energy consumed. Indirectly the amount of energy consumed may be reduced by virtue of. the awareness created while controlling the electrical loads. 92 TABLE 5.9 MONTHLY ELECTRIC ENERGY CHARGES IN DOLLARS FOR SIMULATED MILKING PARLORS UTILIZING VARIOUS RATE SCHEDULES. ELECTRICAL RATE SCHEDULE COMMERCIAL RATE IRVERTED RATE FARM PLAT RATE TIME or DAY RATBI TIME 0? DAY RATE2 ENERGY CHARGE IN DOLLARS BY HERD SIZE 100 277.68 279.35 255.41 191.60 144.10 150 341.45 346.14 314.64 227.24 173.29 200 402.25 409.83 371.11 269.48 207.03 250 451.84 461.77 417.17 301.81 232.62 300 494.84 506.81 457.11 328.88 255.17 ION PM 11:00 acme to 6:00 p.m., 5:15 acme MILKING "' 4:45 Pom. MILKING ON PEAK 11:00 a.m. to 6:00 p.m., 6:45 a.m. MILKING - 6:15 p.m. MILKING TABLE 5.10 MONTHLY ELECTRIC ENERGY CHARGES IN DOLLARS FOR SIMULATED MILKING PARLORS UTILIZING TIME-OF-DAY RATE1 SCHEDULES. MILKING START TIME AM 5:00 5:15 5:45 6:00 6:15 6:30 6:45 7:00 7:15 7:30 PM 4:30 4:45 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 ENERGY CHARGE IN DOLLARS BY HERD SIZE 100 197.63 191.60 178.97 174.79 160.93 153.04 144.10 137.56 139.43 141.29 200 277.71 ~ 269.48 252.63 247.31 231.92 223.10 207.03 207.67 209.42 211.27 300 339.82 328.88 310.17 302.82 285.52 266.34 255.17 256.82 258.52 260.16 1 ON PEAK 11:00 a.m. to 5:00 p.m. 6. FEEDING SYSTEM SIMULATOR Dairy farmers had primarily two feeding system options. They could feed their herds with either a stationary (tower silo and mechanical feed bunk) or a mobile (bunker silo, mobile unloading boxes, and open feed bunks) feeding system. The choice of system depended on farmstead' layout, barn layout, herd size, feed storage, feed type, investment cost, labor required, and operator preference. There was a third feeding system option, manual feeding, however, it was used only when the situation warranted and only if labor requirements could be satisfied. 6.1 Simulator Development The Feeding System Simulator, like the Milking System Simulator, was an interactive model run as one of two options to the Dairy Farm Simulation Model. The development of the feeding system segment of the model required acquisition of data on which equipment and labor requirements for feeding the milking herd could be based. Data in the literature pertaining to these requirements were insufficiently documented or detailed to be of use to this part of the model. A study which reported data with sufficient detailed discussion of system design and collection method was the Cooperative Regional Project NC-119 (Speicher, 1979). 93 94 The NC-119 project, conducted in 1979 by Michigan State university and University of Minnesota personnel, determined labor inputs for stationary and mobile feeding systems on dairy farms. The project was a time and motion study of twenty Michigan dairy farms. The farms surveyed had many similarities with farms participating in the Michiganv Energy Audit Study. Ten of the farms had stationary systems which were made up of upright silos, a series of flight conveyors, a stationary mixer and a mechanical feeder. The other ten farms had a mobile system consisting of a tractor drawn mixer wagon and a combination of upright and bunker silos. Data recorded on each farm included time required for: a) set up; b) setting the mixer load scale: c) loading various feeds: d) batch mixing; e) mixer wagon travel; and f) batch unloading. The results of the time and motion study, and information collected through the Michigan Energy Audit were utilized in developing the Feeding System Simulator shown in Figure 6.1. Any reference to commercial products or trade names in. this section does not imply discrimination or endorsement by the author. 6.1.1 USer Inputs Performance of the simulated feeding system was dependent on over fifteen user inputs. The first two inputs, morning and evening feeding times, sequence all of the feeding events shown in Figure 6.1. The three questions which follow pertained to the forage, grain, and supplements fed on a dairy farm. While there were several feed types and storage units on the market, the feed types and storage units simulated were based on current practices as determined by the Michigan Energy Audit Survey (Appendix A). Feed types selected included corn silage, haylage,_and high moisture corn. A user can select one or all 95 three of the feed types and an appropriate silo type. The subroutine SILO, (Figure 6.2), allowed for a choice of either a bunker silo or an upright silo for each feed type along with the necessary support equipment. The quantity of feed apportioned to each cow daily was also a user input to the subroutine SILO. The input was used in EQuation 6.1 and Equation 6.2 to calculate total amount of feed apportioned per feeding and the amount of feed to be unloaded from each silo. TFED 2 (F201 + FEDZ + FE03)(HSIZE) + 2 (6.1) Where: TFED - total pounds of feed fed per feeding (lbs) FED1 = pounds of corn fed per cow per‘day (lbs) F802 8 pounds of haylage fed per cow per day (lbs) FED3 = pounds of high moisture corn fed per cow per day (lbs) HSIZE = number of cows milked ' (integer) FEDU = FEDU1 - (100 + UNR) (6.2) Where: ' FEDU = pounds of feed remaining to be unloaded (lbs) FEDUl = pounds of feed to be unloaded (lbs) UNR = silo unloading rate (cwt/min) The next input allowed users a choice with respect to mixing the feed ration prior to its delivery to the herd. If a mixer was chosen, the desired mixer type, with or without a weigh scale and the horsepower required to operate the unit, was selected. 96 FIGURE 6.1 OPERATIONAL FLOW CHART FOR FEEDING SYSTEM SIMULATOR. IS FEEDING SYSTEM Yes FINFO = I INPUT START TIME MFEED MORNING FEEDING I INPUT START TIME NFEED EVENING FEEDING IS ORN SILAGE 95 :r FED no IS HAYLAGE Yes _ FED no IS HIGH MOISTURE COR ’95 :; FED no FIGURE 6.1 CONTINIED 97 CALCULATE as '5 y‘» AMOUNT OF FEED TFED - O “ FED PER FEEDING TFED no "0 es MOBILE MIXER no MOBILE MIXER no NITH SCALES ITHOUT SCALES yes yes I) II A v v V (A yes TATIONARY MIXER WITI-OUT SCALES I I V o CALCULATE ”PUT SILO UNLOADING RATES MIXER HORSEPOHER s STATIONARY MIXER FEDU MIXHP wITH SCALES IS FEED DELIVERY BY . DELIVERY BY .. DELIVERY BY .. MOBILE MIXE CONVEYORS FEED WAGON 65 yes Y yes U INPUT INPUT DEL'I VERED TO MECHANICAL TRACTG? HORSEPOWER USED TO PULL WAGON FHTI'P INPUT MECHANICAL BUNK . FEEDER HORSEPOVER HBFHP FIGURE 6.1 I FEED . O Y°s no es CONTINUED WITH SCALES ‘98 FINFO I I yes FEEDING TIME equal To TIME‘ Is I FFED < O E: :;E MIX FEED MIXED yes IS EED MIXED BY MIXER no yes es [TURN MIXER ONI FFED 8 TFED ITURN MIXER ON [MIX 8 MIX - I] SILOS UNLOADED II DELIVERED SILOS UNLOADED BY MOBIL ' 6 no FEED DELIVERED BY FEED WAGON TURN FEED WAGON TRACTOR ON I CALCULATE AMOUNT OF FEED REMAINING TO BE FED V FFED A ‘ U TURN FEED CONVEYOR ON DELIVERED SIMALTANEOUSLY INDIVIDUALLY “RN SRO HRNSHD V U UNLOADERS ON UNLOADER ON A! I I ' I ‘ CALCULATE CALCULATE V CALCULATE AWUNT 0F FEED AIDUNT 0F FEED ANDUNT (F FEED REMAINING REMAINING REMAINING TO BE UNLOADED TO BE UNLOADED TO BE FED FEDU FEDU G) (5 BY SHUTTLE FEEDER TURN SHUTTLE FEEDER ON 99 FIGURE 6.2 FLOW CHART OF THE SUBROUTINE SILO INPUT POUNDS 0F FEED FED PER COW FED INPUT SILO SILO LOADER HORSEPOWER UNLOADER LHP TYPE UM INPUT CONVEYOR LENGTH CONVL INPUT INPUT ' ‘I SILO UNLOADER HORSEPOWER SILO UNLOADER HORSEPOWER 100 The final set of user inputs related to the feed delivery method. A user analyzing a mobile feeding system could choose to deliver the feed by feed wagon or, a mobile mixer. A stationary feeding system allowed feed delivery by conveyor and/or mechanical bunk feeder or in rare cases, a mobile feed wagon. The time required to deliver the feed was based on the delivery method. Equation 6.3 assumed a feed mixer was used for feed delivery while, Equation 6.4 assumed a delivery rate ‘ adjusted for the system capacity of the delivery method. 1 . FFED -= FFED - (100 .- MIXUNR) (6.3) Where: FFED 3 pounds of feed remaining to be fed (lbs) FFED1 = pounds of feed to be fed (lbs) MIXUNR = mixer unloading rate (cwt/min) FFED = FFEDl - (100 : DELUNR) . (6.4) Where: FFED = pounds of feed remaining to be fed (lbs FFED1 = pounds of feed to be fed (lbs) DELUNR a system delivery rate (cwt/min) 6.1.2 Time Requirements for Feeding Events The variables used by the model to predict the daily and monthly labor and energy requirements for feed handling included: set up, feed loading, mixing, mixer unloading, travel time, and other miscellaneous chores associated with feed handling. Time required to set up the feeding system, before feeding could ‘ begin ranged from two to seven minutes for farms with mobile feeding 101 systems. Set up for the mobil system included starting two tractors and positioning them at a bunker silo. The set up time for stationary systems required only checking the system controls; a duration of less than one minute. Time required to set mixer load cell scale ranged from 0.1 to 0.2 minutes in stationary systems and 0.2 to 1.3 minutes with a mobile mixer. Longer times associated with mobile feeding systems were due to the operator having to dismount and remount the loader tractor or skid loader and recalibrate the load cell every time the mixer was moved. I Time regpired to unload a feed type from a silo was recorded in minutes per hundredweight (min/cwt). unloading rates for upright silos filled with high moisture corn, corn silage, and haylage are in Table 6.1. The tower silo unloading rates were influenced. by the moisture content of ensilage, length of ensilage cut, ensilage compaction, ambient temperature, depth of unloader cut, and position of unloader balance weights. The time to load bunker ensiled forages and high moisture corn with a tractor mounted loader or skid loader is shown in Table 6.2. Unloading rates for bunker silos included operator transit time from the mixer or feed wagon tractor to the loading deviCe, time to load the desired amount of feed and the time to dump any excess feed. Very little difference existed between tractor mounted loaders, and skid loaders in loading rates on a minutes per hundredweight basis. Batch mixing time varied greatly between farms and between feeding systems. Generally, feed mixing started when loading of the last feed was completed. The feed was mixed from two to six minutes before unloading started and lasted throughout the unloading process. TABLE 6.1 UNLOADING RATES FOR UPRIGHT SILOS STORING CORN SILAGE, 102 HAYLAGE, AND GROUND HIGH MOISTURE CORN. FEED TYPE SILO IOADER RATE RANGE DIAMETER TYPE ft min/cwt min/cwt Corn Silage 20 Top 0.41 0.36 to 0.62 24 Top 0.47 0.36 to 0.62 16 to 24 Bottom 0.21 [0.19 to 0.24 Haylage 20 Bottom 0.61 0.56 to 0.69 20 to 24 Top 1.10 0.73 to 1.80 Ground High 14 to 20 Top 0.61 0.50 to 0.75 Moisture Corn 16 to 24 Bottom 0.21 0.19 to 0.24 TABLE 6.2 UNLOADING RATES FOR BUNKER SILOS STORING CORN SILAGE, HAYLAGE, AND GROUND HIGH MOISTURE CORN. RATE RANGE FEED TYPE UNLOADER TYPE min/cwt min/cwt Corn Silage Tractor with loader 0.21 0.07 to 0.35 Skid Loader 0.17 0.06 to 0.23 Haylage Tractor with loader 0.21 0.07 to 0.35 Skid Loader 0.17 0.06 to 0.23 Ground High Tractor with loader 0.29 0.24 to 0.34 Moisture Corn , Skid Loader~ 0.31 0.24 to 0.38 103 Batch unloading time was defined as the amount of time required for all feed to be placed into the feed bunk. In the stationary system the unloading time included travel time through a series of flight conveyors and the mechanical feeder. The unloading rate for the mobile mixer included travel time from the last load station to the feed bunk and the time required to return the equipment to the storage area. Table 6.3 is a summary of mixer unloading rates. In general, mobile systems were able to unload a hundredweight of ration in 0.20 minutes per hundredweight faster than stationary systems. TABLE 6.3 STATIONARY AND MOBILE MIXER UNLOADING RATES (MIN/CWT). RANGE AVERAGE FEEDING SYSTEM min/cwt min/cwt Stationaryl 0.14 to 0.46 0.30 Mobile2 0.03 to 0.12 0.08 Mobile? 0.17 to 0.26 0.20 1. Time for all feed to reach the feed bunk by conveyor. ' 2. Time to unload mixer with tractor positioned at the bunk. 3. Time to unload mixer starting at last loading position and ending when equipment is parked. 6.2 validation of Feeding System Simulator In order to verify the results obtained from the computer modeling procedure, the Feeding System Simulator was validated by simulating actual systems. Two dairy farms participating in the Michigan Energy Audit from 1978 to 1980 provided the actual system requirements. The 104 simulated results were compared to the actual requirements of the real situation to form the validation. validation of the stationary feeding system is described in Section 6.2.1, and validation of the mobile feeding system is described in Section 6.2.2. 6.2.1 Stationary Feeding System A dairy farm in Caseville, Michigan, milking 100 cows, was used to validate the stationary feeding system options of the model. Feeding equipment used on the farm included four upright silos, a series of flight conveyors, and a stationary mixer which unloaded into two long flight conveyors connected to a mechanized bunk feeder. The two silos used for corn silage were 20 by 70 feet conventional . top unloading silos with ten horsepower motors. The haylage silo was an 18 by 70 feet sealed concrete silo with a five horsepower bottom unloader. The fourth silo, a steel 18 by 60 feet glass lined silo with a five horsepower bottom unloader was used to store high moisture corn. Silo flight conveyor length. was influenced. by the location. of the upright silos and the stationary mixer located in the feed loading center. The conveyor lengths and the horsepower required for the above silos were: 30 feet and one horsepower for each of the corn silage silos, 30 feet and one horsepower for the haylage silo, and five feet and one horsepower for the silo storing high moisture corn. The feed was loaded into a Model 1830 Oswalt mixer with scales and operated by a ten horsepower motor. Mixing started after the last feed was loaded and lasted for six minutes. The mixer continued to operate while unloading the feed ration-into two 30-foot conveyors Operated by one horsepower motors. The feed. was conveyed to the feed bunk and distributed by a two horsepower shuttle feeder. 105 A comparison between the data simulated and actual energy consumed is shown in. Figure 6.3. While each piece of equipment was not. sub- metered the total energy used each month by the feeding system was recorded. The values on the bar graph represent the total electrical energy used each month for feeding the dairy herd from 1978 to 1980. The solid line drawn horizontally across the graph represents 471 kWh, the total electrical energy consumed per month as predicted by the computer simulation. Values to note in Table 6.4 are the mean kWh and the standard deviation calculated for each of the two years. The variance between simulated and actual data were within one standard deviation in each case. The other values in the table indicate the percent difference between simulated and actual monthly data. These values ranged from -4.27 percent to 14.32 percent during 1978-79 and -21.50 percent to 14.32 percent during 1979-80. Although a wide variation existed monthly, the mean percent difference for each year was less than four percent. 6.2.2 Lbbile Feeding Systems A dairy farm in Stockbridge, Michigan was used to validate the mobile feeding system options of the model. In_ 1978 the milking herd included 39 cows, and in 1979 the herd size was increased to 50 cows. The feeding operation on this farm consisted of a wood and concrete bunker silo measuring 12 by 40 by 80 feet, and a 30 horsepower tractor mounted bucket loader. The silo was used to store a combination of corn and alfalfa silage. The silage was removed from the silo by the tractor mounted bucket loader and delivered by the bucket loader to a 106 FIGIRE 6.3 WY SWY (F SIMILATED AND ACTUAL ELECTRICAL EIERGY USE Fm TIE CASEVILLE DAIRY STATIONARY FEEDING SYSTEM (Farm Number 352-0088). SUKUHJTIBDIETUENSYWUSE “a ,8 I978-1979 3 - Iamnnmm a F 5 R 5 z 7 7 I / / / g see .. é I '1 g :3 /‘ /’ “Q "Q ,2 ./ ‘./ 5 er I e / / N, \ I / \/ a I“ I S? I S é .é 2 §4 §é 5 5 $5 8 \ I I \ / \ / I \ I \ / / / / \1 I \ \/ I \I \/ / / ‘/ . \/ I \ I \/ I \I \/ / / \/ \I I \ / \/ I \I \/ I / ‘/ E ‘I I \ \/ I \I \/ / / \/ \I I \ 5 ‘/ I \I \/ / / §/ 5 §I I § / §/ I \I \/ x / \/ see ,4 / / \ \ / / \ / a 32 a s a $2 a S? s? a a A? R2 2 S I N; 9 R5 R2 5 a N; 54 9 S 2 SF 2 R2 R2 2 2 S/ A. R} 5 R / R/ 4 \4 \/ / / x 5 6 7 8 9 IO 1 I 12 I I 2 3 4 Idcnrnu TABLE 6.4 TOMMY SUNIARY OF SIMULATED AND ACTUAL ELECTRICAL EI£RGY USE FOR THE CASEVILLE DAIRY STATIONARY FEEDING SYSTEM (Farm NUmber 32-0088). 1978 +0 1979 1979 To 1980 MONTH C(NPUTER SIMULATION T + ' SImulaTIon 70-1-5; SImuIa‘I'Ion MEAN MONTH ° ° leference DIfference XIII. 141m 7 kWh S 5 471.0 452 4.20 452 4.20 6 471.0 452 4.20 .452 4.20 7 471.0 452 4.20 412 14.32 8 471.0 452 4.20 452 4.20 9 471.0 452 4.20 532 -11.47 10 471.0 412 14.32 532 -II.47 11 471.0 452 4.20 440 7.05 12 471.0 492 -4.27 440 7.05 1 471.0 492 -4.27 480 -1.88 2 471.0 452 4.20 480 -1.88 3 471.0 452 4.20 600 -21.50 4 471.0 452 4.20 480 -1.88 TOTALS 5,652.0 5,454.0 * 5,752.0 * MEAN 471.0 455.3 3.45 479.3 -1.73 Sfdo “V0 ‘7 2006 7’ 52.2 ‘7 107 feed bunk located in the center of the free stall area. Concentrates were fed manually to the herd during the milking operation. A similar system was simulated with the use of the computer model. Input parameters were used which closely represented the real experimental system. A comparison of the actual and simulated data are shown in Figure 6.4. Values on the bar graph represent the total diesel fuel used each month for feeding the dairy herd in 1978 and '1979. Solid lines drawn horizontally across the graph represent the simulated diesel fuel use per month for each of the two herd sizes for 1978 and 1979. Values to note in Table 6.5 are the gallons of diesel fuel consumed and the standard deviations calculated for each of the two years and herd sizes. In each case, the difference between the actual and simulated values were within one standard deviation. The monthly percent difference ranged from -18.56 percent to 1.8 during 1978-79 and -25.43 percent to 4.40 percent during 1979-80. Each year the system required more diesel fuel than was predicted by the model. The mean percent difference indicated the model was 8.12 percent low during 1978-79 and 14.14 percent low during 1979-80. A major contributing factor related to this consistently low prediction was farm management. The operator' elected to use the tractor mounted bucket loader to deliver each bucket load of feed to the feed bunk. The excess travel time related to this delivery method was not accounted for in the model since this was the exception rather than the rule among feed handling systems. Another factor affecting the low prediction for diesel.fuel use was the variation in tractor engine fuel consumption due to environmental temperatures. In Figure 6.4 the increase in fuel 108 FIGRE 6.4 KIND-LY SMRY (F SIMILATED AID ACTUAL DIESEL FUEL USE Fm TI-E SWRIDE DAIRY KBILE FEEDIm SYSTEM (Farm Mmber 32-0270). 18 _ SIMULATED ENERGY us: 79-80 F’ 7‘ '1978-1979 [ 5 a a fl, 1979-1980 5 5 5 . ‘ / 7 / x 9 - g 2 2 2 2 SIMULATED ENERGY USE 78-79 / p I I I Z 5 555555557 5 Z / / / 2 4 2 6 M é / x x x / / / / / x x '21 5 5 5 5 5 5 5 q5 A5 A5 5 5 4 / x / / / x / S/ \/ r / / / / / / w/ \/ z x/ / / 8 5 A5 5 5 5 5 A5 A5 A5 A5 A5 5 ‘5 ‘o 4 E/ 4 c/ I Q4 §4 \1 E4 \ O/ 6 8 A5 A5 5 A5 A5 A5 A5 A5 A5 A5 A5 A5 x/ x/ / x x/ x/ x/ x/ x/ x/ x/ x/ 5 A5 A5 5 A5 A5 A5 A5 A5 A5 A5 A5 A5 a- x/ x/ / x/ x/ x/ x/ A/ x/ x/ x/ x/ x/ x/ / xw x/ x/ x/ p/ x/ x/ x/ x/ x/ x/ / x/ x/ xw x/ / x/ x/ x/ x/ x/ x/ / x/ xx x/ x/ x/ x/ A“ S/ \/ A5 A5 5 A5 A5 A5 A5 A5 A5 5 A5 A5 a xfl x/ x/ x / x/ x/ x N/ / x 5 O 7 O 9 IO 11 12 I 2 3 4 MONTH TABLE 6.5 ENTRY SW OF SIMULATED AND ACTUAL DIESEL FUEL USE FOR TI'E SMRIDE DAIRY MILE FEEDING SYSTEM (Farm Nunber 32-0270). 39 COVS 1978 To 1979 50 COWS 1979 To 1980 MONTH CWUTER SIMULATION To‘l'al SImula‘I’lon COIPUTER SIMULATION ToTaI Slmulaflon MEAN MONTH leference MEAN mm H leference gal gal 1 gal gal S 5 10.18 10.5 -3.05 13.05 12.5 4.40 6 10.18 11.0 -7.45 13.05 14.5 -10.00 7 10.18 10.0 1.80 13.05 14.0 -6.79 8 10.18 10.5 -3.05 13.05 14.5 -10.00 9 10.18 10.5 -3.05 13.05 14.5 -10.00 10 10.18 10.5 -3.05 13.05 15.5 -15.81 11 10.18 11.5 -11.48 13.05 16.5 -20.91 12 10.18 12.0 -15.17 13.05 16.0 -18.44 1 10.18 12.5 -18.56 13.05 17.5 -25.43 2 10.18 12.5 -18.56 13.05 17.5 -25.43 3 10.18 11.0 -7.45 13.05 15.2 -14.14 4 10.18 10.5 -3.05 13.05 14.2 -8.10 TOTALS 122.16 133.0 * 156.60 182.40 " BEAN 10.18 11.08 -8.12 13.05 15.20 -I4.14 Std. Dev. * . 0.85 * 1.47 * * 109 consumption during the winter months, NOvember, December, January, and February is graphically shown. Presently, the model does not have the requisite weather simulation package necessary to adjust the fuel consumption equation, for temperatures less than 75°F and a barometric pressure less than 28.60 inches of mercury (Equation 4.5). 6.3 Energy Reguirements for Simulated Feeding Systems There was considerable variation in daily feeding chore time and energy use with respect to feeding systems among the farms surveyed. The daily chore time for mobile systems averaged 0.58 minutes per cow, with a range of 0.20 to 1.80 minutes, and an average of 0.012 gallons of diesel fuel per cow 'was consumed. The daily chore times for stationary systems averaged. 0.70 minutes, with a range of 0.30 to 1.20 minutes per cow, and an average of 0.17 kWh per cow per day was consumed. Variations in chore time and energy consumption were explained by differences in: a) ration components: b)‘ number of cow groupings; c) number of feedings; d) silage unloading rates; e) travel time connected with mobile systems; f) batch unloading rates: and g) farmstead layout. To enable a fair comparison between both feeding systems these factors had to be controlled. The Feeding System Simulator allowed the user to input many of the variables which control Ithe feed handling system and subsequent variations in daily chore time and energy consumption. The ability of the simulator to analyze feeding systems_ was demonstrated by the simulation of two feeding system designs, a mobile and a stationary design, with similar feed rations. The basic design of each simulated feed handling system is shown in Figure 6.5 and Figure 6.6. 110 The analysis of the two systems included a complete capital investment and an operating cost breakdown. The equipment and practices selected for each system were based on comparable systems and equipment described in the Michigan Energy Audit Survey (see Appendix A). Prices for simulated feeding systems were determined from individual component prices. The prices reflected factory prices for new equipment, and varied. widely depending (x1 equipment size, options selected, and terms of sale. Prior to making a decision regarding any feeding system, dairymen will want to check with local suppliers. 6.3.1 Herd Size The simulated farms consisted of five specific herd sizes. The herd sizes selected, 100, 150, 200, 250, and 300 cows, represented. the average number of cows milked daily. Sizes were selected from those previously examined by the Michigan Energy Audit Study and represented those most commonly found in Michigan. 6.3.2 Feed Ration The feeding of dairy cattle for maximum production with a minimum feed cost per hundredweight (cwt) of milk required a balanced mixture of forage and grain (Bath, 1978). Although computer formulated dairy rations based on least cost, milk price, cow maintenance, and levels of milk production and fat content were available, the ration assumed for analysis was based on sample rations fed by Michigan Energy Audit cooperators. The balanced feed ration, shown in Table 6.6 and 6.7, consisted of dry hay, haylage, corn silage, high moisture corn, soybean meal, and minerals. 111 TABLE 6.6 RATION FORMULATED FOR A 1400 POUND COW PRODUCING 55 POUNDS OF MILK ( 3.5 PERCENT EAT ) DAILY. DRY MATTER BASIS AS FED BASIS FEED TYPE - AMOUNT AMOUNT TOTAL FED FED RATION lbs lbs % ALFALFA HAY (AVERAGE) 2.70 3.00 3.210 ALFALFA SILAGE (EARLY) 4.80 12.00 12.820 CORN SILAGE (AVERAGE) 21.00 60.00 64.100 ROUGEAGE SUBTOTAL 28.50 75.00 80.130 HIGH mISTURE CORN 10.40 14.00 14.960 SOYBEAN MEAL 3.90 4.40 4.700 DICALCIUM PHOSPHATE 0.19 0.20 0.210 CONCENTRATE SUBTOTAL 14.49 18.60 19.870 RATION TOTAL 42.99 93.60 100.000 RATION FORMULATED IN TABLE 6.6. TABLE 6.7 NUTRITIONAL CONTENT OF DRY MATTER BASIS FEED TYPE NE PROTEIN Ca P TDN DM Heal lbs lb lbs lbs % ALRALFA HAY (AVERAGE) 1.59 0.4644 0.0338 0.006 1.566 90 ALFALFA SILAGE (EARLY) 2.83 0.8592 0.0614 0.011 2.784 40 CORN SILAGE (AVERAGE) 15.14 1.7010 0.0567 0.042 14.700 35 ROUGHAGE SUBTOTAL 19.56 3.0246 0.1519 0.059 19.050 HIGH MOISTURE CORN 9.58 1.0400 0.0031 0.032 9.464 74 SOYBEAN MEAL 3.29 2.0202 0.0140 0.029 3.159 89 DICALCIUM PHOSPHATE 0.00 0.0000 0.0450 0.036 0.000 96 CONCENTRATB SUBTOTAL 12.87 3.0602 0.0621 0.097 12.623 RATION TOTAL 32.43 6.0848 0.2140 0.156 31.673 112 In order to insure accurate weighing of ingredients and a consistently balanced ration, both systems utilized an Oswalt mixer equipped with a four-point load cell scale. The Oswalt mixer used a three auger design which moved the feed from front-to-back as well as from side-to-side within the mixer. The design enabled the ration to be mixed in four to six minutes (Butler Manufacturing, 1979). An electrically powered mixer was selected for the stationary feed handling system, while a trailer mounted mixer powered by a diesel tractor was chosen for the mobile system. 6.3.3 Feed Storage There were several storage units on the market for both forage and grain. Using the same type of storage for all feed materials, while simplifying system mechanization, may not be appropriate (or expedient). Significant cost differences exist with respect to structure and unloader type so that a specific choice could ultimately affect the profitability of the investment. The storage units used for analysis were those most commonly utilized. The following assumptions were made concerning each feed material and type of storage used with each feeding system. Storage volume for each unit was determined by calculating the amount of different feed required to produce a balanced ration, for one year (see Table 6.6). The feed requirement was then multiplied by the number of cows milked plus one half cow for each young animal (Midwest Plan Service, 1978). An allowance was included for spoilage, seepage, and fermentation using the information in Table 6.8 and described in Section 6.3.4. 113 Alfalfa hay continued to be one of the best sources of both protein and energy for dairy cattle. Research showed that hay (90% DM) was important for stimulating normal muscle tone in the animal's rumen and maintaining normal digestive activity (U. of F., 1978). There was a tendency for farmers mechanizing their feeding systems to cut back on the amount of hay fed. The ,cut back is represented by the low amount of hay fed in the sample ration. It should be noted, however, that even this small amount would reduce the incidence of ketosis and twisted stomach. At the present time, the Feeding System Simulator does not calculate energy consumption for feeding systems electing to feed dry hay since a majority of the dairy farmers surveyed hand fed dry hay. An investigation of the types of haymaking~ equipment and the- accompanying storage and handling equipment currently on the market revealed that most of the equipment was compatible with both the mobile and the stationary feeding system. In an effort not to effect the final analysis of the two feed handling systems, similar hay‘ feeding methods were assumed. Alfalfa haylage is hay harvested and processed in the early bloom stage. Normally, haylage will contain about two percent more protein and a higher energy value than when harvested as field cured hay. Haylage usually contains 40 to 70 percent moisture and will usually retain 70 percent moisture and 30 percent dry matter, without seeping at normal silo pressures. Hay at 50 percent moisture or less before ensiling, is too dry for proper storage. Sufficient moisture is necessary in order to provide a top seal which would prevent air from entering the surface, and to provide the weight required for good 114 packing to prevent heat damage. While good packing procedures will prevent heat damage, providing an air tight container is the best way to prevent additional storage losses (Benson, 1978). Semisealed silos and bunker silos could be used for haylage storage with proper packing but they are not air tight since they must be opened for feeding and air can penetrate loosely packed forage. The economic comparison of stationary and mobile feeding systems assumed: a sealed upright concrete silo with bottom unloading for the stationary feeding system. and. a .precast concrete bunker silo for the mobile feeding system. The haylage assumed for comparison contains 60 percent moisture which was wet enough to provide adequate compaction in the bunker silo. Cbrn silage is made from well-eared corn plants harvested in the 'hard-dough or dent stage. Ensiled at a. moisture level. of 60 ‘to 70 percent corn silage has a relatively high ranking energy value. The main disadvantage to corn silage is its low protein level content. Additionally, it is lower than legume forages in most minerals, particularly calcium and manganese (Hillman, 1977). A conventional semisealed concrete silo or bunker silo cOuld be used for corn silage storage. The wetter material helps to form a top seal preventing air from entering the surface and provides the weight necessary for expressing the entrapped air from the silage stored below it (Maddex, 1977). The economic comparison of feeding systems assumed: a conventional upright concrete silo with top unloading for the stationary feeding system and a.pmecast concrete bunker silo for the mobile feeding system. 115 High moisture corn is whole shelled corn stored at moisture levels above 20 percent, thereby eliminating the drying energy expense. The corn should be ground so that it packs well. Proper harvest moistures are 25 to 30 percent for high moisture corn. If the corn in the field is dryer than 22 percent, some other type of storage should be used; adding water will not raise the moisture level (Harvestore, 1976). High moisture corn should be stored. in sealed silos. These are generally steel construction, but sealed concrete silos featuring bottom unloading of the grain are as reliable. Conventional silos with a good roof and stave silos with additional reinforcement can be used. Bunker silos are not recommended as higher storage losses are associated with these storage units (Benson, 1978). The economic comparison assumed identical storage units for both feeding ‘systems. Upright sealed concrete silos featuring bottom unloading were selected for the two systems. The silos were sized to unload three to four inches from the top per day, thus preventing additional spoilage losses. Concentrates include grains and many by-product feeds, including high protein and mineral supplements. Although mineral supplements do not contain energy or protein, they are included in the concentrate class because of their high density. Concentrate mixtures high in protein and minerals are required when low protein feeds, such as corn silage, are fed to dairy cattle. The minerals most commonly lacking in corn.silage dairy rations are calcium, phosphorus, sodium, and chlorine (Bath, 1978). 116 Storage uethods for concentrates included bottom unloading gravity bins and fifty pound sacks. Among the dairy farms surveyed, the most common method of storing and feeding concentrates incorporated a centrally located feed center and bin storage. Concentrates were added to the ration simultaneously with the unloading of an upright silo or directly to a mobile mixer or feed wagon by gravity. Since each of the methods used to add concentrations to the ration did not require an additional energy input to the feed handling system, they were not included as a feeding system simulation option. Similar methods for feeding concentrate were assumed in an effort to minimize any effect on, the economic analysis of the two feed handling systems. The principal high moisture protein supplement in the simulated. ration, was soybean meal with a 50 percent protein content (Table 6.6). The minerals, calcium and phosphate, were included in the protein supplement as dicalcium phosphate. Salt, fed free choice, provided the minerals sodium and chlorine. ,6.3.4 Storage Losses A key issue in an economic comparison of upright and bunker silos is the difference in storage losses due to spoilage, seepage, and fermentation (Benson, 1978). The losses vary from silo to silo and from farm to farm, depending upon the type and moisture content of the forage, physical condition of the silo, fineness of the chopped forage, and most importantly the level of management. The storage losses shown in Table 6.8 occurred with similar management practices. The difference in performance which existed between horizontal and upright silos was used in the analysis of the simulated feed handling systems 117 to determine the additional annual operating cost associated. with horizontal silos. The additional cost was based on the difference in silo losses shown in Table 6.8 and calculated on the feed prices shown in Table 6090 1 TABLE 6.8 DRY MATTER STORAGE LOSSES IN HAY CROP SILAGE ENSILED AT VARIOUS.MOISTURE LEVELS IN DIFFERENT TYPES OF SILOS.2 MOISTURE LEVEL OXYGEN LIMITING CONVENTIONAL HORIZONTAL % 8 8 8 71 + 19.10 21.20 13.40 61 - 70 8.80 10.10 14.00 Under 60 4.40 7.50 16.70 1. Includes losses from spoilage, seepage, and fermentation. Does not include field losses. 2. Source T. H. Patton "Silage and Silos," Pennsylvania State Uhiv. Special Cicular 223, Extension Service, College of Agriculture. TABLE 6.9 FEED COST VALUES ASSUMED FOR ECONOMIC ANALYSIS. FEED TYPE MOISTURE CONTENT A VALUE PER TON1 % $ CORN SILAGE 65 $25 HAYLAGE 60 $30 HIGH MOISTURE CORN 26 $94 1. On an as fed basis. 118 6.3.5 Capital Investment The economic analysis of the stationary and mobile feeding systems involved determining lifetime investment costs for five ‘herd. sizes associated with each simulated feeding system. In order to price complete feed handling systems, individual component prices were necessary. Prices reflected factory prices on new equipment, sized and equipped in a comparable manner to that which was described by dairymen in the farm survey. Equipment sizes selected for the simulated stationary feeding systems and mobile feeding systems are shown in Table 6.11 and Table 6.17, respectively. In January, of every year, Dairy Herd Management magazine publishes an extensive list of farm equipment manufacturers and deaLers according to equipment type. Using this listing, approximately 20 firms which sell feed handling eqUipment were identified. After the companies were identified, local distributors and dealers were visited in an effort to obtain cost and performance figures for the equipment selected. The information obtained was summarized according to category of use on the basis of cost, and in the case of powered equipment, on the basis of energy consumption. The total component investment costs for the equipment chosen for the simulated stationary and mobile feeding systems are shown in Tables 6.12 and 6.18. The equipment costs and performance figures were obtained from the following dealers. I Prices for silos and appropriate upright silo unloading equipment were supplied by Butler Manufacturing Company: Jamesway Division, Booms Silo Company, Harvestore Products Incorporated, and Northwest Ohio Silo Company. Bunker silo prices were obtained from A.D.L. Systems Inc. 119 Feed mixer performance and price data were obtained from Butler Manufacturing Company: Jamesway) Division for the Oswalt Ensilmixer. The Ensilmixer Models 1830, 320, 370, 380 were used with both feeding systems. Feed conveyors, associated with upright silos, and shuttle feeder performance and price data were obtained from Butler Manufacturing Company: Jamesway Division, Harvestore Products Incorporated and Van Dale Corporation. Design information for concrete bunks with a spacing of 26 to 30 inches per cow were obtained from Midwest Plan Service. Fenceline feed bunks which allow eating from one side only were used with mobile feeding systems, and mechanical bunks allowing cows to eat from both sides were used with stationary feeding systems. Prices for construction were obtained from A.D.L. Systems Inc. The Official Guide° Tractors and Farm Equipment - 1980, a quarterly issue, produced by the National Farm and Power Equipment Dealer's Association, contains cost figures on new and used equipment as well as fuel consumption and horsepower ratings of both later and earlier model tractors. This source provided the data required to derive a regression equation, relating purchase price to power take-off(PTO) horsepower (Equation 6.5). Bucket loaders for tractors in the 20 to 40 horsepower range added an additional $1,800 to the purChase price. T.C. = $240.00 * PTO° hp (6.5) Where: , T.C. = Tractor investment cost or purchase price (dollars) PTO°hp = Power take-off horsepower (hp) 120 6.3.6 Cost of Capital In view of the significant differences in capital requirements for different feeding systems, the capital required became important. If funds were borrowed, the cost was in the form of interest paid. If the money was owned, the cost was intangible, represented by returns foregone by not investing in the most profitable project. The latter cost, referred to as an opportunity cost, was generally higher than the prime interest rate. For economic comparisons the opportunity cost and interest paid were equated. The analysis assumed Production Credit Association's fourth quarter 1980 interest rate of 12.5 percent. 6.3.7 Ownership Cost The economic evaluation of the feeding systems included calculating the annual cost of owning and operating the various systems. The total monthly ownership costs shown in Tables 6.13 and 6.19 represent the annual costs spread over 12 months. The total monthly costs included the fixed costs (F.C.) of depreciation of the initial investment, interest, insurance, taxes and the operating costs for maintenance, repairs, and energy. Values for these costs, excluding energy, were determined by projecting the present day costs over the life of the equipment. Several assumptions were made to simplify the analysis. A general inflation rate for future costs was considered to be zero. Inflation rates for energy, diesel fuel, and electricity were considered equal and constant over the simulated time period, and were assumed relative to the general inflation rate. The estimated useful life and the annual percentage rates charged for depreciation, interest, insurance, taxes, maintenance and repairs are shown in Table 6.10. These annual 121 percentage rate figures were used in calculation of ownership and operation costs. The total monthly fixed cost of ownership was determined by multiplying the purchase price of a system component by the total fixed cost percentage which was then divided by 12 to give the monthly ownership cost. The fixed cost (F.C.) in Equation 6.6 included costs for depreciation, interest, taxes, insurance, and shelter. MFC = (SCC * FC) 4 12 - (6.6) Where: MFC a monthly fixed cost (dollars) SCC = intial system component investment cost (dollars) FC - annual fixed cost (decimal) Equation 6.7 was used to calculate the operating cost for repairs and maintenance. Repair and maintenance expenditures due to normal wear, part failure or accidents were inevitable. The size of the system as reflected by its investment cost and the amount of use the system received were factors affecting' the repair and. maintenance costs. The monthly repair and maintenance cost was calculated by multiplying the initial system investment cost by the annual percentage rate for repairs and maintenance then dividing the result by 12 months. MRM = (SCC * RCF) % 12 (6.7) Where: MRM = monthly repair and maintenance cost (dollars) SCC = initial system component investment cost (dollars) RCF = annual repair cost factor (decimal) 122 Monthly operating costs for labor and energy were determined by the Dairy Farm Simulator Model. Energy costs for diesel fuel and electricity were based on operating hours per month times the energy rates described in Section 4. The calculated labor Charge was based on the total operating time of the system multiplied by a labor rate of $4.50 per hour. It should be noted that tractors included in the simulated mobile feeding systems were used in other parts of the dairy Operation. This assumption required fixed costs and operating costs for tractors to be based on an hourly rate. These costs were determined by multiplying the tractors investment cost by the appropriate annual fixed cost and operating cost percentages from Table 6.10. The annual costs were divided by an annual hourly life of 1,000 hours per tractor and the result multiplied by the hours of tractor operation per month. The hourly charge did not include diesel fuel use as it was determined separately. mu: 6.10 Am OWNERSHIP (DST 123 1 EXPRESSED AS A PERCENTAGE OF THE PURCHASE PRICEZ° EQUIPMENT ITEM LIFE3 DEPRECIATION INTEREST4 T.I.S.5 gogA: YRS. i I S X CONCRETE SI LOS: UPRIGHT 15.0 6.60 6.25 1.75 14.60 HORIZONTAL 15.0 6.60 6.25 1.75 14.60 SILO UNLOADER - 7.5 13.30 6.25 1.25 20.80 TRACTOR/LOADER 11.0 9.10 6.25 2.25 17.60 FEED MIXER 10.0 10.00 6.25 1.75 18.00 CONNEYORS/FEEDERS 10.0 10.00 6.25 1.50 17.75 FEED BUNKS 15.0 6.65 6.25 1.25 14.15 FEED ALLEYS 20.0 5.00 6.25 1.25 12.50 RCF 1.0 1.0 5.0 9.0 3.0 5.0 3.0 1.0 1. 2. 3. 4. 5. 7. Operaflng cosf defermlned by compufer analysls "DaIry Farm Slmulaflon Model" w. Bowers, 1975. Modern Concepf of Farm Machinery Managemenf. Tracfor/Loader LIfe assumes 11,000 hours of operaflon. lnferesf Rafe assumed a+ 12.5 x. T.I.S. 2 Taxes, Insurance, and Shel+er. F.C. = leed Cos? Par Year. RCF 8 Repair and Ma1n+enance Cos? Facfor. 124 FEED ROOM O—~ FREE STALL CONFINEMENT HOUSING ‘ , MILKING PARLOR MILK ROOM I! . UPRIGHT SILOS CONCENTRATE STORAGE FEED MIXER .— SHUTTLE FEEDER \ L ] FIGURE 6.5 TYPICAL DESIGN OF A ROOFED FREE STALL SYSTEM UTILIZING UPRIGHT SILOS AND STATIONARY FEED HANDLING EQUIPMENT. 125 TABLE 6.11 SIMULATED FEEDING EQUIPMENT INPUTS FOR STATIONARY FEEDING SYSTEM. SIZE REQUIREMENTS1 BY HERD SIZE FEEDING SYSTEM'EQUIPMENT 100 150 200 250 300 Feed Storage (tn): Corn Silage 1200 1800 2400 3000 3600 Haylage 300 450 600 750 900 High Moisture Corn 300 450 600 750 900 Silo Size (ft): Corn Silage 30x70 . 28x602 30x702 28x603 30x703 Haylage 18X60 20X70 24X60 28XGO 30X60 High Moisture Corn 18x60 20X70 24X60 28x60 30x60 Silo Unloader Size (hp): Corn Silage 10 7.52 102 1o3 103 Haylage 5 7.5 7.5 10 20 High Moisture Corn 5 7.5 7.5 10 20 Conveyor-Mixer Length (ft): Corn Silage 30 172 202 153 203 Haylage 30 30 30 35 45 High Moisture Corn 5 5 5 5 5 Feed Mixer (hp): ’ Oswalt Mixer w/scale 7.5 30 30 35 40 Feed Delivery (hp): Feed Bunk Conveyor 2 2 2 2 2 Shuttle Feeder 2 3 3 3 4 1. Subscript indicates number of units required. 126 TABLE 6.12 SIMULATED INVESTMENT COST FOR STATIONARY FEEDING SYSTEM. FEEDING SYSTEM EQUIPMENT Corn Silage Silo: Conventional Silo Top Unloader1 Baylage Silo: Sealed Type Silo Bottom Uhloader1 High Moisture Corn Silo: Sealed Type Silo Bottom U’nloader1 Feed Mixer: Oswalt Mixer w/Scale Feed Delivery System: Conveyors Shuttle Feeder2 Feed Bunks3: TOTAL ($) INVESTMENT COST IN DOLLARS BY HERD SIZE 100 33,000 13,325 19,500 17,700 19,500 17,700 10,500 4,000 3,900 1,900 141,025 150 49,500 25,900 29,250 19,400 29,250 19,400 16,500 5,500 5,850 2,850 203,400 200 66,000 26,650 39,000 20,950 39,000 20,950 16,500 5,800 7,800 3,800 246,450 250 82,500 38,840 48,750 22,465 48,750 22,465 17,500 8,000 9,750 4,750 303,770 300 99,000 39,980 58,500 23,030 58,500 23,030 20,500 11,500 11,700 5,700 351,440 1. Cost represents two silo unloaders since silos were assumed to last 15 years while silo unloaders had a life expectancy of 7.5 years. 2. Cost assumed at $39.50 per cow. 3. Cost assumed at $19.00 per cow - with bunks feeding both sides. 127 TABLE 6.13 MONTHLY SDMULATED OWNERSHIP COST FOR.STATIONARY FEEDING SYSTEM EQUIPMENT. MONTHLY OWNERSHIP COST FEEDING SYSTEM EQUIPMENT IN DOLLARS BY HERD SIZE 100 150 200 250 300 Corn Silage Silo: Conventional Silo 401.50 602.20 803.00 1,003.70 1,204.50 Top Unloader 115.50 224.40 231.00 336.60 346.50 Haylage Silo: Sealed Type Silo 237.20 355.90 474.50 593.10 711.70 Bottom unloader 153.40 168.30 181.50 194.70 199.60 High Moisture Corn Silo: Sealed Type Silo 237.20 355.90 474.50 593.10 711.70 Bottom Unloader 153.40 168.30 181.50 194.70 199.60 Feed Mixer: Oswalt Mixer w/Scale 157.50 247.50 247.50 262.50 307.50 Feed Delivery System: Conveyors 59.20 81.40 85.80 118.30 170.10 Shuttle Feeder 57070 86050 115040 144020 173000 Feed Bunks: 22.40 33.60 44.80 56.00 67.30 TOTAL FIXED COST 1,595.00 2,324.00 2,839.50 3,496.90 4,091.50 Feeding System: Repair & Maintenance 230.60 327.30 377.40 434.60 501.30 Electricityl 24.30 54.80 70.10 85.10 107.90 TOTAL OPERATING COST 419.15 628.70 770.60 914.80 1,088.00 TOTAL OWNERSHIP COST 2,014.15 2,952.70 3,610.10 4,411.70 5,179.50 1. Electricity costs based on the Time-of-Day Rate Schedule, feeding time began at 5:30 a.m. and 5:00 p.m. 128 6.3.8 Results of Simulated Stationary Feed Handling Systems The different kinds of feed handling equipment required to simulate stationary feed handling systems for herd sizes of 100, 150, 200, 250, and 300 cows are shown in Table 6.11. The simulated values determined for the stationary feed handling system were for average conditions and should not be considered true for all applications. These values were obtained. by considering' all. previous assumptions and. computing' the average energy use per month based on a similar farm located in Michigan. The values may vary considerably when viewed with different assumptions. The investment cost required for each component, for each herd size examined, is shown in Table 6.12. The total investment cost increased as expected. with increases in milking cow herd size. The total investment ranged in price from $141,025 for the 100 milking cow herd to $351,440 for the 300 milking cow herd. The more expensive system cost $1,171 per cow, while the less expensive system was $1,410 per cow. The decrease in per cow investment cost which occurred with the increase in herd size was attributed to "economies of size." Monthly ownership cost for each herd size examined is shown in Table 6.13. The ownership cost was comprised of fixed and operating costs. The ownership cost ranged from $2,014.15 for the 100 milking cow herd to $5,179.50 for the 300 milking cow herd. The ownership cost on a per cow basis, also showed "economies of size." The smallest herd size examined required $20.14 per cow each month, while the 300 milking cow herd required $17.26 per cow each month. 129 Fixed costs, or cash / noncash costs were borne irregardless Of ‘whether the enterprise was currently Operative. ‘Fixed. cash costs consisted Of taxes, insurance, and shelter, while noncash costs included depreciation and interest on the investment. The fixed cost represented 79 percent of the monthly ownership cost and ranged from $15.95 per cow down to $13.64 per cow as herd size increased. The operating cost was a variable cost incurred when the enterprise was Operational. The operating cost included variable costs for repair and maintenance, labor, and energy. Repair and maintenance was based on a percentage of the original investment cost Of each component within the system. These costs ranged from $2.31 per cow for the smaller 100 milking cow herd down tO $1.67 per cow for the larger 300 milking cow herd. Labor requirements for stationary feed handling systems were generally high, because the laborer operating the feed handling equipment was assumed to be constantly observing the equipments' Operation. The assumption was valid for those systems surveyed, although the larger herd sizes Offered the laborer Opportunities to perform other tasks during the feeding process. Table 6.14 shows, the simulated monthly labor hours required for each Operation within the feeding system by herd size. The labor requirements increased with herd size, but averaged 0.35 minutes per cow per feeding regardless Of herd size examined. The labor cost shown in Table 6.13 ranged from $164.24 for the 100 cow herd to $478.80 per month for the 300 cow herd. These costs represented 41 percent Of the Operating costs. 130 TABLE 6.14 SIMULATED MONTHLY LABOR USE IN HOURS FOR STATIONARY FEEDING SYSTEM OPERATIONS. FEEDING SYSTEM MONTHLY LABOR USE BY HERD SIZE OPERATION 100 150 200 250 300 SILO UNLOADERS: Corn Silage 13.2 21.2 26.4 34.4 43.6 HaYIage 300 500 700 902 1102 High Moisture Corn 1.0 2.0 2.0 3.0 4.0 FEED MIXERl: 5.0 5.0 5.0 4.0 4.0 FEED CONVEYORSZ: * * * * * TOTAL (hrS) 36.5 58.6. 71.8 87.8 106.4 1. Feed mixer operated while unloading to conveyors and feed bunks, labor was divided proportionately. 2. NO labor associate with conveyor Operation Since Operated jointly with other feeding system equipment. Electricity was the exclusive energy source for the stationary feeding system. Table 6.15 shows the simulated monthly electrical energy use in kilowatt°hours (kWh), by herd Size, for each of the stationary feeding system operations. The kilowatt-hours required to operate the 100 cow herd each month was 471 kWh, and increased with corresponding increases in herd Size to 2,750.4 kWh per month for the 300 cow herd. The feed mixer, which Operated while unloading to the feed conveyor and subsequent Shuttle feeder, consumed over 50 percent of the electrical energy required by the stationary feed. handling system. The high energy use was due to the feed mixer's unloading capabilities being restricted by the capacity of the feed conveyors and subsequent shuttle feeder. The addition Of another feed conveyor and shuttle feeder would allow the feed mixer tO unload faster, thus reducing the mixer's energy requirement. 131 Energy consumption for the Simulated stationary feed handling systems shown in Table 6.16 was related to the number of barrels of Oil required to produce the needed electricity. The 100 milking cow herd required 0.81 barrels of oil per month to produce the 471 kWh of electrical energy. The number Of Oil barrel equivalents increased to 4.74 barrels for the 300 milking cow herd. The electrical energy conversion rate was based on the generating efficiency of electrical power suppliers and included transmission losses. The production of 580 kWh of electrical energy required one barrel of oil when an oil fired generating plant using #6 fuel Oil at 6.2 {106 Btu/bbl had an efficiency rating of 32 percent (C.S.W.C., 1979). Electricity costs based on the Farm Flat Rate Schedule ranged from $24.58 for the 100 milking cow herd to $157.33 for the 300 milking cow herd, with the 200 milking cow herd requiring $83.70 in order to operate. The electricity costs Shown in Table 6.13 were based on the Time-Of-Day Rate Schedule with feeding times at 5:30 a.m. and 5:00 p.m. I The monthly energy cost was only $24.30 to feed the 100 milking cow herd and $107.90 to feed the 300 cow herd. These cost were further reduced to $12.43 and $90.48 when feeding times were changed to 6:45 a.m. and 6:15 p.m. in order to take full advantage of the lower rates available with time-of-day metering. 132 TABLE 6.15 SIMULATED MONTHLY ELECTRICAL ENERGY USE IN KILOWATT'HOURS FOR STATIONARY FEEDING SYSTEM OPERATIONS. FEEDING SYSTEM MONTHLY ELECTRICAL ENERGY USE BY HERD SIZE OPERATION 100 150 200 250 300 SILO UNLOADERS: Corn Silage 151.9 159.6 283.7 354.6 425.5 Haylage 20.3 38.0 53.2 91.2 202.6 High MOiSture Corn 501 1502 1502 3004 8100 FEED MIXERl: 212.7 759.8 942.1 1,311.9 1,742.5 FEED CONVEYORSZ: 50.6 68.9 90.1 114.8 140.8 SHUTTLE BUNK FEEDER: 30.4 60.8 79.0 100.3 158.0 TOTAL (kWh) 471.0 1,102.3 1,463.3 2,003.2 2,750.4 1. Feed mixer Operated while unloading tO conveyors and feed bunks. 2. Total includes all conveyors Operating during the Operation of the feeding system. TABLE 6.16 SIMULATED MONTHLY ENERGY USE IN OIL BARREL EQUIVALENTS FOR STATIONARY FEEDING SYSTEM OPERATIONS. OIL BARREL EQUIVALENTS1 HERD SIZE ENERGY USE TOTAL ELECTRIC POWER GENERATION kWh bbl bbl 100 471.0‘ 0.81 0.81 150 1,102.3 . 1.90 1.90 200 1,463.3 2.52 2.52 250 2,003.2 A 3.45 3.45 300 2,750.4 4.74 4.74 1. Number 6 fuel Oil at 6.2 * 106 Btu/bbl = 580 kWh/bbl @ 32 % eff. 133 MOBILE FEED MIXER N K FREE STALL CONFINEMENT HOUSING , . V - ' I MILKING PARLOR MILK ROOM I’ CONCENTRATE FEED CENTER STORAGE UPRIGHT SILOS . BUNKER SILO :::1 O WIDE FEED ALLEY) FIGURE 6.6 TYPICAL DESIGN OF A MFED FREE STALL SYSTEM UTILIZING UPRIGHT AND BUNKER SILOS AND mBILE FEED HANDLING EQUIPMENT. 134 O TABLE 6.17 SIMULATED FEEDING EQUIPMENT INPUTS FOR MOBILE FEEDING SYSTEM. SIZE REQUIREMENTS BY HERD SIZE FEEDING SYSTEM EQUIPMENT 100 . 150 200 250 300 Feed Storage (tn): - Corn Silage 1200 1800 2400 3000 3600 Haylage 300 450 600 750 900 High Moisture Corn 300 450 600 750 900 Silo Size (ft): Corn Silage1 50x100 60x120 70x140 80x160 90x170 Haylagel 30x40 30x60 40x60 40x80 50x75 High Moisture Corn 18XGO 20X70- 24XGO 28X60 30X60 Silo unloader Size (hp): Corn Silaged2 30 30 30 30 30 Haylage2 30 30 30 30 30 High Moisture Corn 5 7.5 7.5 10 20 Conveyor-Mixer Length (ft): High Moisture Corn 5 5 5 5 5 Mobile Mixer (hp): Oswalt mixer w/Scale 60 70 70 90 110 Feed Deliverya: ' x x x x x 1. Silo sizes indicate width & length. All bunker silos were 12' high. 2. Bunker Silos were unloaded using a tractor mounted bucket loader. 3. Mobile mixer delivered the feed directly tO the feed bunk. 135 TABLE 6.18 SIMULATED INVESTMENT COST FOR MOBILE FEEDING SYSTEM. INVESTMENT COST IN DOLLARS BY HERD SIZE FEEDING SYSTEM EQUIPMENT 100 150 200 250 300 Corn Silage Silo: Concrete Bunker Silo 37,080 46,620 56,860 '67,810 75,680 Bucket Unloader1 x x x x x Haylage Silo: Concrete Bunker Silo 14,670 20,130 22,440 28,250 29,375 Bucket Unloader1 x x x x x High Moisture Corn Silo: Sealed Type Silo 19,500 29,250 39,000 48,750 58,500 Bottom Unloader2 17,700 19,400 20,950 22,465 23,030 Mobile Feed Mixer: Oswalt Mixer w/Scale 10,500 16,500 16,500 17,500 20,500 2 Wheel Diesel Tractor1 X X x X x Feed Bunks3: 3,200 4,800 6,400 8,000 9,600 Wider Feeding Alleys“: 1,600 2,400 3,200 4,000 4,800 TOTAL ($) 104,250 139,100 165,350 196,775 221,485 1. NO investment cost determined, as equipment was assumed to be used with other farm Operations. ‘ 2. Cost represents two Silo unloaders Since Silos were assumed to last 15 years while silo unloaders had a life expectancy of 7.5 years. 3. Cost assumed at $32.00 per cow. ‘ 4. Cost assumed at $16.00 per cow, with bunks feeding one side only. TABLE 6.19 mNTHLY SIMULATED OWNERSHIP COST FOR [OBILE FE-ING SYSTEM EQUIPMENT. 136 FEEDING SYSTEM EQUIPMENT 2,355.10 100 Corn Silage Silo: Concrete Bunker Silo 451.10 Bucket Unloader1 x Haylage Silo: .- ' Concrete Bunker Silo 178.50 Bucket Unloader1 x High Moisture Corn Silo: Sealed Type Silo 237.20 Bottom Uhloader 153.40 Mobile Feed Mixer: Oswalt Mixer w/Scale 157.50 Diesel Tractor1 x Feed Dunks: 37.80 Wider Feeding Alleys: 16.70 TOTAL FIXED COST 1,232.20 Feeding System: Tractor Operating Cost 36.40 Repair & Maintenance 151.60 Bunker Silo Spoilage2 70-90 Labor 3050 Electricity3 .30 Diesel FUel 64.00 TOTAL OPERATING COST 426.70 TOTAL OWNERSHIP COST 1,658.90 MONTHLY OWNERSHIP COST BY HERD SIZE IN DOLLARS 150 200 567.20 691.80 x 244.90 273.00 x 355.90 474.50 168.30 181.50 247.50 247.50 x 56.60 75.50 25.00 33.30 1,665.40 1,977.10 49.30 68.90. 201.80 238.00 106.30 141.80 139.10 172.80 .90 .90 84.50 99.50 581.90 721.90 2,247.30 250 825.00 343.70 593.10 194.70 262.50 94.40 41.70 79.00 275.40 177.20 211.10 1.70 134.70 879.10 2,699.00 3,234.20 300 920.80 357.40 711.70 199.60 307.50 113.30 50.00 2,660.30 104.20 317.00 212.70 249.30 4.20 179.30 1,066.70 3,727.00 X X X 1. NO fixed ownership cost determined, as equipment was assumed to be used with other farm operations, although an Operating cost was calculated. 2. Bunker Silo spoilage allowance was calculated for spoilage greater than the spoilage anticipated with a conventional tower 5110. 3. Electricity costs were based on the Time-Of-Day Rate Schedule, feeding time began at 5:30 a.m. and 5:00 p.m. 137 6.3.9 Results of Simulated Mobile Feed Handling Systems The various kinds of feed handling equipment required to Simulate mobile feed handling systems for herd Sizes of 100, 150, 200, 250, and 300 cows are Shown in Table 6.17. The simulated values determined for the mobile feed handling system were for average conditions and Should not be considered applicable to all situations. These values were derived by considering all previous assumptions and computing the average energy use per month based. on. a similar farm. located in Michigan. The values may vary considerably when viewed with different assumptions. The investment cost required for each component for each herd Size examined is shown in Table 6.18. The total investment cost increased as expected. with increases in milking cow herd Size. The total investment ranged in price from $104,250 for the 100 milking cow herd to $221,485 for the 300 milking cow herd. The more expensive system cost $738 per cow, while the lower cost system was $1,042 per cow. The decrease in per cow investment cost which occurred with the increase in herd Size was indicative Of the benefit Of larger mobile feed handling systems. Monthly ownership cost for each herd size examined is shown in Table 6.19. The ownership cost included fixed and operating costs. The ownership cost ranged from $1,658.90 for the 100 milking cow herd to $3,727.00 for the 300 milking cow herd. The ownership cost on a per cow basis revealed an advantage in utilizing larger herds with mobile feed handling systems. The smallest herd Size examined required $16.59 per cow each month, while the 300 milking cow herd required $12.42 per cow each.month. 138 Fixed costs, or cash / noncash costs were borne irregardless of whether the enterprise was currently Operative. iFixed. cash. costs consisted Of taxes, insurance and shelter, while noncash costs included depreciation and interest on the investment. The fixed cost represented 73 percent of the monthly ownership cost and ranged from $12.32 per cow down to $8.87 per cow as herd Size increased. The operating cost was a variable cost incurred when the enterprise was Operational. The Operating cost included variable costs for repair and maintenance, labor, energy, additional silo spoilage related to bunker silos and tractor operating costs. Repair and maintenance was based On a percentage Of the original investment cost Of each component within the. system. These costs ranged from $1.52 per cow for the smaller 100 milking cow herd down tO $1.06 per cow for the larger 300 milking cow herd. Labor requirements shown in Table 6.20 for mobile feed handling systems indicate labor requirements per cow decreased from 0.23 hours down to 0.18 hours per month. The decrease in labor requirements was related to the rate at which a tractor mounted bucket loader could load a mix wagon and the unloading rate Of the mobile mixer. The labor costs shown in Table 6.19 ranged from $103.50 for the 100 cow herd to $249.30 per month for the 300 cow herd and represented only 24 percent Of the total Operating costs. 139 TABLE 6.20 SIMULATED IONTHLY LABOR USE IN HOURS FOR MBILE FEEDING SYSTEM OPERATIONS BY HERD SIZE. FEEDING SYSTEM MONTHLY LABOR USE BY HERD SIZE OPERATION 100 150 200 250 300 SILO UNLOADERS: Corn Silage 600 902 1202 1502 1802 Haylage 1.0 1.0 2.0 3.0 3.0 High Moisture Corn 1.0 2.0 2.0 3.0 4.0 FEED CONVEYORS1 : * * * * * TOTAL (hrs) 23.0 30.9 38.4 46.9 ' 55.4 1. Labor required for conveyor operation was included in the unloading Of the High Moisture Corn Silo. Electricity and diesel fuel provided the energy to Operate the mobile feeding system. Table 6.21 shows the simulated monthly electrical energy use in kilowattohours (kWh), and the diesel fuel use in gallons (gal) by herd size for each Of the mobile feeding system Operations. ' The amount of electricity consumed was relatively insignificant compared to the amount Of dieSel fuel required to Operate the system. The monthly diesel fuel consumption ranged from 64 gallons for the small 100 cow herd to 179.3 gallons for the larger 300 cow herd. The tractor which operated the mobile mixer consumed the greatest amount Of diesel fuel. Energy consumption for the simulated mobile feed handling systems shown in Table 6.22 was related to the number of barrels of Oil required to produce the amount of diesel fuel and electricity necessary for operation. The 100 milking cow herd required 1.53 barrels Of Oil per month to produce the 64 gallons Of diesel fuel, and the 6.1 kWh 140 of electrical energy. The number Of Oil barrel equivalents increased tO 4.42 barrels for the 300 milking cow herd. The electrical energy conversion rate was based on the generating efficiency Of electrical power suppliers and included transmission losses. The pmoduction Of 580 kWh of electrical energy required one barrel Of Oil when an Oil fired generating plant ‘using #6 fuel oil at 6.2 * 106 Btu/bbl had an efficiency rating' of 32 percent (C.S.W.C., 1979). The diesel fuel refining process required one barrel Of crude Oil to produce 42.0 gallons Of #2 diesel fuel (Exxon Corp., 1980). The electricity costs and the diesel fuel costs for the mobile feed handling systems are Shown in Table 6.19. The combined monthly energy cost to feed the 100 milking cow herd was $64.30 and $183.50 to feed the 300 milking cow herd. The cost Of owning and Operating mobile feeding system tractors was listed as a variable cost Since tractors were used in other parts of the dairy Operation. This cost ranged from $36.40 per month for the 100 cow herd to $104.20 per month for the 300 cow herd. The variable cost charged for bunker silo spoilage was determined from Table 6.8 which indicates the anticipated silo spoilage losses Of various silo types, and Table 6.9 which indicates the as fed cost assumed for each feed type. The spoilage losses shown in Table 6.19 represents the spoilage associated with bunker silos which is greater than the spoilage anticipated with conventional tower silos. These additional spoilage costs ranged from $79.90 for the 100 milking cow herd to $212.70 for the 300 milking cow herd. 141 TABLE 6.21 SIMULATED MONTHLY ELECTRICAL ENERGY USE IN KILONATT’HOURS.ANO DIESEL FUEL USE FOR’MOBILE FEEDING SYSTEM OPERATIONS. FEEDING SYSTEM HERD SIZE OPERATION 100 150 200 250 300 kflh gal th gal th gal kWh gal kWh gal SILO UNLOADERS: Corn Silage * 14.4 * 21.6 * 28.7 * 35.9 * 43.1 Haylage * 2.4 * 2.4 * 4.8 * 7.2 * 7.2 High MolsTure Corn 5.1 * 15.2 * 15.2 * 30.1 * 81.0 * MOBIL FEED MIXERI: * 47.2 * 60.5 * 66.0 * 91.6 * 129.0 FEED CONVEYORSZ: 1.0 * 2.0 * 2.0 * 3.0 * 4.1 * TOTAL (klh) 6.1 * 17.2 * 17.2 * 33.1 * 83.1 * TOTAL (gal) * 64.0 * 84.5 * 99.5 * 134.7 * 179.3 1. Mobile feed mixer OperaTed afier The lasT feed was loaded and confinued ThroughouT The The Travel Time To The feed bunks. 2. TOTai includes conveyor used while unloading The High MoisTure Corn silo. TABLE 6.22 SIMULATED MONTHLY ENERGY USE IN OIL BARREL EQUIVALENTS FOR MOBILE FEEDING SYSTEMS. OIL BARREL EQUIVALENTS1 HERD SIZE ENERGY use ELECTRIC POWER DIESEL FUEL TOTAL GENERATION PRODUCT I ON kIh gal 661 bbl bbl 100 6.1 64.0 0.01 1.52 1.53 150 17.2 84.5 0.03 2.01 2.04 200 17.2 99.5 0.03 2.37 2.40 25) 33.1 134.7 0.06 3.20 3.27 300 85.1 179.3 0.15 4.27 4.42 1. Number 6 fuel Oil 6+ 6.2 * 105 BTu/bbl = 580 kWh/Dbl a 32 5 eff. Number 2 diesel fuel 61 5.88 * 106 BTu/bbl = 42.0 gal/bbl. 142 6.3.10 Comparison of Simulated Stationary and Mobile Feed Handling Systems Investment cost is likely to be the major criteria when dairymen compare the two feed handling systems. A factor to be considered prior to investing in change, expansion or remodeling of a feed handling system is the profitability of the investment relative to other components of the farm business. For example, many dairymen might pay as much as a 20 percent opportunity cost rather. than a 12.5 percent interest charge if they elected not to use the extra investment for more or better cows, more efficient housing or milking systems, or more land. The investment cost for the simulated mobile feeding systems ranged from 26 to 37 percent lower than a similar simulated stationary feeding system for the herd sizes examined (Figure 6.7). The analysis #1 of the-ownership costs for the two feed handling systems (Figure 6.8) revealed the cost advantages for the simulated mobile feeding systems were reduced an average of nine percent for the herd sizes examined, and twelve percent when electricity costs were based on the lowest cost available using the Time-of-Day Rate Schedule, analysis #2, (Figure 6.9). The reduction in cost advantage was explained by further examination of the individual components of the fixed and operating costs which made up the ownership cost. Fixed costs for the two feed handling systems accounted for slightly less than three percent of the mobile feeding systems' reduction in cost advantage. The reduction was related to differences in life expectancy, taxes, insurance and shelter shown in Table 6.10. These fixed ownership costs, expressed as a percentage of the purchase 143 price, were in themselves an assumption and any attempt to alter the current analysis in an effort to minimize differences was thought to be unwarranted. Operating costs shown in Figure 6.8 indicated the mobile feeding system tended to be slightly less expensive to operate when compared to stationary feeding systems with herd size greater than 100 milking cows. This tendency was reversed in Figure 6.9,, however, when the feeding times were changed in order to take full advantage of the lower rates available with time-of-day metering. The operating costs contributed slightly more than six percent to the mobile feeding systems reduction in cost advantage and approximately nine percent in the latter case. Approximately' 1.5 percent 40f the mobile feeding system's reduction in cost advantage could be related to the cost of energy. This was primarily due to the higher retail cost of diesel fuel compared to current prices for electricity. TWO percent of the reduction in cost advantage was attributed to the bunker silo unloading tractors and the tractor used to operate the mixer. The stationary feeding system's counterparts to this equipment are listed as fixed costs in Table 6.13 and become operating costs for the mobile system, .(Table 6.19). The change in cost categories was due to the use of the tractors in other parts of the dairy operation. The major reduction in cost advantage was related to the additional spoilage losses associated with bunker silos. The additional operating cost for bunker silos reduced the cost advantage ‘for mobile feeding systems .approximately four percent. 144 Energy consumption and costs were lower for stationary feed handling systems with herd sizes less than or near 150 milking cows. Figure 6.10 indicates that energy costs continued to be lower for herd size of 150 milking cows or more. Based on the Farm Flat Rate, the stationary feeding system saved an average of $25.00 per month. When compared to the Time-of-Day Fate with feeding times beginning at 6:45 a.m. and 6:15 p.m., the stationary feeding system realized an average savings of $63.00 per month for similar mobile feeding systems. The savings equaled a 53 percent reduction in energy costs. Based on barrels of oil consumed, the stationary feeding system required more energy to feed herd sizes greater than 150 milking cows (Figure 6.11). The seven percent increase in petroleum energy required to feed herd sizes greater than 150 milking cows could be turned around to approximately a 98 percent savings if electricity was produced by nonpetroleum sources. Electricity produced by coal, nuclear, and hydroelectric power plants requires virtually no petroleum input. Economically, coal and hydro generated electricity compared favorably with oil generated power even when all environmental control costs were included (W.O.C.O.L., 1980). The comparison of petroleum based power plants to nuclear power plants was not as simple, and the answers were dependent upon the assumption used in the analysis. Generally, however, nuclear power was as cheap or cheaper than oil generated power. The alternative for diesel fuel had not proved as promising (Wakefield, 1980). 145 Labor requirements for systems with "high" capital investments were generally expected to be less labor intensive than "low" capital investment systems. An analysis of dairy feeding systems graphically indicated the opposite to be true (Figure 6.12). The stationary and mobile feeding systems were competitive for herd sizes less than or near 100 milking cows. As herd size increased to over 100 milking cows, the mobile feeding system became more labor efficient. The difference in labor efficiency was related to an assumption made during the analysis of simulated stationary feeding systems. It was assumed the laborer operating the stationary feed handling equipment would oversee the equipment the entire time it operated. The assumption was valid in lieu of the information provided by the Michigan Energy Audit Survey and the NC-119 Regional Project. The larger herd sizes offered the Opportunity for the laborer to perform other tasks while the corn silage silo was unloading and while the conveyors were delivering the ration to the herd. Analysis of the labor requirements for each feeding on a minute per cow basis, revealed that little difference existed within the stationary feed handling system regardless of herd size. 'The stationary system averaged 0.35 minute per cow per feeding for.the herd size examined, while the mobile system decreased labor requirements from 0.23 to 0.18 minute per cow per feeding. Stationary feeding systems can be made more labor efficient by: a) reducing the number of feeds in the ration; b) properly maintaining and adjusting silo unloaders; c) simultaneously unloading .two silos containing the same feed; and d) computer controlled feeding systems. 146 The effect on labor requirements of simultaneously unloading two silos is shown in Figure 6.7 by line 81. The reduction in stationary feeding system labor requirements reduced the mobile feeding system's investment cost advantage from the original 34 percent to a final 19 percent on an annual ownership cost basis. The addition of computer controlled feeding to the stationary system.‘would. further increase system efficiency and lower the manpower hours to a competitive level with the mobile feeding system for all herd sizes. The additional manpower hours could then be utilized in performance of other farm tasks. Reduction in manpower requirements, would not affect the ownership cost analysis, however, because the savings in. operating' costs would be made up in fixed investment costs. 147 FIGIRE 6.7 SIMULATED INVESTIENT COST FOR STATIOMRY All) IQBILE FEEDING SYSTEIS. M nonmmmmmcsmm 35“ + s manouurm HANDLING 31131-3143 ,4 s 308 q 250 . i 8 3 200 . VI ‘8 , g 150 . 100 _ 50 . 1 1 r 1 l 108 150 200 250 300 Hard Size FIGURE 6.8 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 FIGURE 6.9 Dollars 5000 4500 4000 3500 3000 2500 2000 1500 I000 500 148 SIMULATED IDNTI'LY OWNERSHIP, FIXED, AND (PERATIIK; mSTS FOR STATIGIARY All) I‘DBILE FEEDIIB SYSTEMS (ANALYSIS I). <>l>0mz mom FEED HANDLING W J, 3 sunomnrm HANDLING SYSTEMS ownsmp cost a s I00 I50 200 250 300 Hard Siz. SIMULATED MONTHLY OWNERSHIP, FIXED, AND OPERATING COSTS FOR STATIONARY AND MOBILE FEEDING SYSTEMS (ANALYSIS 2). ' <>I>sz MOBILE FEED HANDLING SYSTEMS s STATIONARY FEED HANDLING srs'rms ”,0 om cos-r s . '3‘ mm cos'r ‘ I00 I50 200 250 300 Hard Siz- 149 FIGURE 6.10 SIMULATED MONTHLY ENERGY COSTS FOR STATIONARY AND MOBILE FEEDING SYSTEMS. Dallas 180 M MOBILEFEEDHANDLINGSYSTEMS M S STATIONARY FEED HANDLING SYSTEMS 150 .— 140 .. 120 .4 100 150 200 250 300 Herd Size FIGURE 6.11 SIMULATED MONTHLY ENERGY USE IN OIL BARREL EQUIVALENTS FOR STATIONARY AND MOBILE FEEDING SYSTEMS. Barrels of Oil M MOBILE FEED HANDLING SYSTEMS 5 ‘ S STATIONARY FEED HANDLING SYSTEMS S 4 -1 3 _ 2 1 1 .. 0 I I T l l 100 150 290 259 300 Herd Slze 150 FIGURE 6.12 SIMULATED MONTHLY LABOR REQUIREMENTS FOR STATIONARY AND MOBILE FEEDINGFSYSTEMS. ° laberHous . 129 Ml )flJan:FEEDHHAJHNJNEISYSTENEI 119 ‘_ S EHUEHKNEABS’FEEDIHUHWDEDNSERRNHDKS S A I O I I00 .. 8! STATIONARY FEED HANDLING SYSTEM ALTERNATIVE . .o' 90 - 80 - 70 r 80 - 50 . 40 - 30 . 20 . I0 - I I I *T r, 100 ISO 200 250 300 Herd Size 7. SUMMARY AND CONCLUSIONS The agricultural industry has had to make adaptations as a result of rising energy costs. [wiry farming, because of a relatively high energy requirement, is especially. susceptible to rising energy costs and energy availability. Dairymen who judge success as a difference between the costs Of inputs and the selling price Of a product are only capable of viewing energy in terms Of an input cost. A national energy policy designed to enforce conservation Of energy would be beneficial, however, there is a reluctance among dairy farmers to change from traditional methods to new energy efficient methods due to the financial risks involved. This research project was designed to assist dairymen, in analyzing the influence Of production technology and milking capacity on energy consumption in order that management decisions could be made regarding conservation. The research objectives were: 1) to determine farmstead task Options within each particular phase Of milking and feed handling on a dairy facility; and 2) to develOp an interactive computer model capable of simulating’ labor and. energy use based. on those options available within the milking and feed handling operations. The components and options available within the milking and feed handling Operations of a dairy facility have been identified. Approximately 1,000 individual systems can be assembled with the options presently available. The options identified were from the 151 152 twenty-one dairy facilities surveyed, however, there are many other Options. The surveyed facilities are representative Of 60 to 70 percent of the dairy facilities in Michigan. A simulation-computer model, capable Of interacting with an Operator via telephone lines, was developed. The Dairy Farm Simulation Model, a mathematical model of the milking and feed handling Operations of a dairy facility, can predict average monthly labor and energy use requirements for 60 to '70 percent Of the dairy facilities in Michigan with an accuracy of 110 percent, depending on the accuracy of the facility information provided. The simulation model can pinpoint where higher energy costs and shortages will be most severe, where more readily available forms of energy can be substituted, where economies can be made, and what adjustments can be made to increasing prices and decreasing supplies of energy. It is possible to make changes in the current options, to add new options, or to change many of the assumptions used in the calculation of energy consumption and cost without revamping the entire simulation model. This can be useful in the future as prices, equipment, and energy requirements change. The labor and energy data used in the simulation model were Obtained from two additional research projects. Labor information for the feed handling simulation was provided by a cooperative regional project between Michigan State University and the University of Minnesota (Speicher, 1979). Energy consumption data, for both the milking and feed handling simulation Of a dairy facility, were Obtained from the Michigan Farm Energy Audit project which was conducted by the Agricultural Engineering Department at Nfichigan State university, and is described in Section 3. II 153 Table 3.1 is an example Of an energy budget compiled for Michigan dairy farms. The table shows the predicted average yearly energy consumption for dairy farm Operations on a per cow basis. Energy use in Table 3.1 was based on data collected through the Michigan Energy Audit Study, and in some cases simulated by the Dairy Farm Simulation Model. Due to the small sample size, the data may not always be representative of typical energy use. Additional labor and energy use information was Obtained from 18 of 21 surveys distributed to dairy facilities participating in the Michigan Farm Energy Audit Study (Appendix A). Based on the specific assumptions described earlier, the following conclusions were drawn: 1. The result of modeling average yearly milk production and milk cow numbers indicated the model's accuracy' for' determining’ monthly electrical energy use varied from 99.5 percent to 68.7 percent for the Milking System Simulator when compared to actual energy consumption data. The difference between the actual energy consumed each year and the energy consumption predicted by the simulation model varied from -2.41 percent to 9.50 percent for all parlor Operations and from -15.47 percent to 10.31 percent for individual systems. A higher percent difference Of 40.18 percent was encountered for the hot water system for the second year when a heat recovery unit not modeled was installed on the farm used for model validation. 2. 154 The result of modeling average yearly milk cow numbers indicated the model's accuracy for determining monthly electrical energy use varied from 98.2 percent to 74.5 percent >for the Feed System Simulator when compared tO actual energy consumption data. The difference between the actual energy consumed each year and the energy consumption predicted by the simulation model varied from -1.73 percent to 3.45 percent for I the stationary feed handling system and from -14.14 percent to -8.12 percent for the mobile feed handling system. mergy requirements and costs were predicted with the Dairy Farm Simulation Model for two alternative feed handling systems and were consistent with trends reported by Michigan Energy Audit dairy farmers. Herd sizes included 100, 150,” 200, 250, and 300 cows. The following conclusions are based on the nmmber of barrels of Oil needed to produce the energy required. a. The electrically Operated stationary feed handling system required the least amount Of energy to Operate up to and including a herd size Of 150 cows. b. The mobile feed handling system, using only bunker silos and based on energy consumption, was preferred for herd sizes greater than 150 cows, as it used six to eight .percent less energy than. the stationary feed handling system. 155 c. The energy cost to Operate the two alternative feed handling systems was lower for the stationary feed handling system regardless Of herd size. This advantage *was dependent on the electrical rate schedule used for comparison. Based on the Farm Flat Rate, the stationary feeding system saved an average Of $25.00 per month. Compared to the Time-Of-Day Rate when feeding times were 6:45 a.m. and 6:15 p.m., the stationary feeding system realized an average savings Of $65.00 per month for similar mobile feeding systems. The savings equaled a 53 percent reduction in energy costs. The labor requirement predicted by the model for' the two alternative feed handling systems was based on the total Operating time Of the equipment. Assuming the Operator was present during all Operations, including the time required to unload an upright silo, the stationary feed handling systems were competitive for herd sizes less than or near 100 milking cows. The stationary system averaged 0.35 minutes per cow per feeding regardless of herd size, while the mobile feed handling system became more labor efficient as herd size increased over 100 milking cows. The mobile system decreased labor requirements from 0.23 minutes per cow per feeding for the 100 milking cow herd to 0.18 minutes per cow per feeding for the 300 cow herd. 156 The total cost analysis Of owning and operating the two alternative feed handling systems was based on the model's predicted performance of the two systems. However, where economics is involved, answers change when prices change. a. The system which‘ required the lowest monetary investment and incurred the lowest total ownership cost was the mobile feed handling system. The *investment cost advantage with a herd size of 300 cows was 37 percent but diminished to 26 percent when the herd size was reduced to 100 cows. The ownership cost advantage with a herd size of 300 cows was 28 percent but diminished to 25 percent when the herd size was reduced to 100 cows. b. The predicted operating cost of each system varied only slightly. The savings in energy cost of the stationary feed handling system was usually offset by the increase in the labor cost, except when the lowest Time-of-Day Rate(2) available was utilized. The results Of simulating a milk handling system for herd sizes .Of 100, 150, 200, 250 and‘ 300 cows, using the four electrical rate schedules included within the simulation model, indicated that the Time-Of-Day Rate Schedule provided an Operator with the lowest operating cost. In comparison to the Farm Flat and Inverted Rate Schedules, a savings Of 45 percent or more would be realized by dairy farmers willing to alter their milking schedule to gain the maximum benefit from time-Of-day metering. 157 It was concluded that no one system, milking or feed handling, was better than another. The "best” system remained the one designed to meet the needs of the Operator by incorporating personal preferences and limitations into the system. A national energy policy designed to lessen dependence on imported oil will likely identify conservation efforts which turn a greater factor Of energy needs to three domestic sources 8- coal, renewable sources, and nuclear power. These Options will provide energy economy as a Iwhole, a somewhat different balance among resources, and an increase in the generation of electricity and systems which use electrical power. Thus, dairymen and electrical power suppliers alike will find that this simulation model has the potential of becoming a powerful tool in providing assistance in the design and management Of energy efficient dairy facilities.‘ Further work is required, however, before the model can be applied to the actual design or redesign Of a particular dairy facility in Michigan. This model points directly to areas where additional research would be beneficial. 8. SUGGESTIONS FOR FUTURE RESEARCH The Dairy Farm Simulation Model predicts average energy and labor requirements for various management Options essential to future energy management decisions on dairy farms. Assumptions made while developing the model placed restrictions on its range of applicability. In addition, knowledge of the complex management scenarios. and interactions which occur between dairy farm. systems and equipment within each system was rudimentary at best. As new information relative to energy rates and energy or labor use “becomes available, improvements to the Dairy Farm Simulation Model will be possible. Research needs of the Dairy Farm Simulation Model outside of, mathematic or computer programming techniques fall into fOur areas: 1) improvement Of milking and feeding system equipment; 2) model expansion: 3) relaxation of restrictions on the models; and 4) application Of the model. Table 8.1 lists future topics for research in decreasing order of importance and shows the areas which each topic would fall. First, and of utmost importance, is research leading to better prediction Of energy use on dairy farms. At the present time there is a need for more accurate energy reporting and measuring of energy input to output per unit operation. Research efforts in the past have failed to provide an accurate measurement of energy input to work output per unit Operation on which. generally acceptable equations can be 158 159 TABLE 8.1 RECOMMENDED FUTURE RESEARCH TOPICS FOR DAIRY FARM ENERGY HANAGDIENT. TOPIC PRIORITY AREA Field Application RESTRICTED & APPLICATION Optimization of Energy Management APPLICATION Energy Accounting 1 IMPROVEMENT Energy Savings Devices 2 EXPANSION & IMPROVEMENT Waste Handling System 3 EXPANSION Cow-Calf HOusing System 4 EXPANSION Parameter Studies 5 APPLICATION Model validation 6 APPLICATION Input Parameters 7 RESTRICTION 8 9 0 an. Economic Considerations EXPANSION & IMPROVEMENT developed, i.e. rate and time of energy use by equipment within each Operation. New energy saving equipment and management Options will necessitate new energy accounting and reporting research. Research which will result in improvements for the model will need to provide more detailed information than that previously conducted. The desired results can best be Obtained through expert analysis of a limited number of dairy farms. The energy analysis should. provide: 1) a straightforward tabulation Of energy use by equipment and operation, including Operating times, energy units and prices paid per energy unit; 2) a comparison of the energy use per analog of farm activity, i.e. BTU per cow or BTU per pound of milk; 3) a survey to gain general information and information relative to the facility's management schemes; 4) a feasibility study of alternative energy conserving approaches adaptable to the Operation of the facility surveyed; and 160 5) a mass and energy balance comparing actual inputs to theoretical requirements. An energy analysis conducted using these procedures will permit a more thorough and useful energy accounting. Several energy saving devices are presently available for installation in milking parlors. Incorporating these options into the model is the second most important research area. Many investigations have already been conducted on this tOpic. The bulk of the research has not been concerned with recording actual energy use on production dairy farms, however, the addition of these devices to the model will allow users Of the program to compare the energy saving potential of each Option with the entire dairy Operation. Expansion Of the dairy model to include manure handling is the third research area listed in Table 8.1. This is listed third in order because the information is already available for development. Waste disposal is presently the third highest energy consuming dairy Operation, accounting for 4.9 to 12.6 gallons ’Of diesel fuel and 5.6 kWh of electricity per cow, (Table 3.1). Simulation Of the manure handling system would greatly extend the energy management capacity of 'the model. The fourth item, cow-calf housing, rates a relatively high research priority because it is one of the four major dairy Operations, and on some dairy farms uses as much energy as the milking and cooling systems combined. Unfortunately, an accurate modeling Of cow-calf housing will require extensive reworking of the model. Theoretically, to add housing tO the model means support routines. simulating weather, heating, cooling, and 'ventilation will also Abe required. The incorporation Of these support routines into the simulation model will necessitate a more sophisticated, and thus, costly model. 161 Several types Of parameter studies could be conducted on the dairy system model. such studies could reveal important infOrmation on the character of the model and the significance Of errors in the input data. A.1arge number Of parameters are involved, therefore, careful planning and sound experimental design should be exercised to minimize expense. The relative meortance of several parameters was shown earlier. Additional information would result from more formal parameter studies. The knowledge gained from each parameter study ‘ should justify the cost increases. Arguments to place a higher priority than sixth are easily made for continued validation of the model. Nevertheless, the result of such research is merely a more precise definition of the limits of the model. The simulation of additional farms with the existing model is less important than adding and evalidating the waste handling and housing models. Seventh in order of priority is the relaxation of restrictions on input parameters since many Of the inputs to the existing systems were severely restricted. This was due to the large number Of possible management schemes and the complex sequencing which would have resulted without these restrictions. Fumther research and programming techniques may provide an easing of these previously imposed restrictions. The eighth research priority is an alteration of the program for field use in workshops with farm operators or by the TELPLAN (MSU) and AGNET (NU) computer networks. In the model's present form, extensive reworking would be necessary to adapt it for field.use. The model does require periodic neintenance to keep the energy charges current with I 162 increasing energy cost. Presentation of the model in the field is needed if it is to benefit dairy farm operators. Its usefulness will be severely limited without the needed research to add the other model Options and further validate the model's results. Optimization is assigned a relatively low priority. Although an Optimization study is possible with the existent model, better results would be Obtained if the above projects were completed first. In the model's present form, unconstrained Optimization for minimum energy use .would result in dairy Operations which are not compatible. Penalties or restrictions associated with certain system combinations would need to be added before an Optimum dairy Operation or management strategy could be realized. Finally, additional work my be useful in developing an economic model for use in conjunction with the Dairy Farm Simulation Model. An economic model which utilizes different methods Of calculating equipment cost would help in determining. the final amount of energy saved. If the available energy supply is limited, equipment cost may have a limited impact on the decision. The current hand-method of calculating equipment costs is slow but exact, as it allows the Operator to adjust equipment cost for regional differences and personal preference. The ten tOpics listed in Table 8.1 are the "ten most wanted" research considerations for future development and application of the dairy system model. Several Of these research topics will require considerable time and research effort. The knowledge gained, however, will have application far beyond improvement of the Dairy Farm Simulation Model. APPENDICES APPENDIX A 163 APPENDIX A MICHIGAN ENERGY AUDIT SURVEY Agricultural Engineering Department East Lansing, Michigan April 28, 1980 Dear Cooperator : We would again like to thank you for your cooperation in this project. As indicated earlier, April 30, 1980 will conclude Phase II Of the Energy Audit project. While data will no longer be collected during Phase III Of the project, we are requesting your help in completing the enclosed survey. The information you provide will allow us to quickly analyze the data previously collected and will help develop conservation programs that will benefit you. Energy conservation is probably not one Of your most popular conversation topics, but if it can be translated into quick paybacks and significant cost reductions, I'm sure you will be willing to listen. Many dairy farmers are faced with decisions to reorganize and modernize their Operation in order to survive. The major problem encountered in making these decisions is receiving specific cost and data information before it becomes Obsolete due to fast changing economic conditions. In an attempt to keep our information current with your Operation, the survey may ask some questions which may have been asked earlier. We realize some Of the information we are requesting may be difficult to Obtain without wasting some Of your valuable time. If the information is not readily available, simply indicate with the letters N.A. and proceed to the next question. Please be assured that the information you provide will, as in the past, he held in strict confidence. If you have any questions or comments, please do not hesitate to contact us. Respectfully, es Hewett III On Campus Study Team EJH/slc Enclosures 164 MICHIGAN ENERGY AUDIT SURVEY (continued). . ' . ENERGY AUDIT SURVEY DAIRY FARM NAME TELFARM NUMBER DIRECTIONS Please read the survey carefully before completing. The survey is divided into three major Operations found on the dairy farm: Housing, Milking and Feed Handling. A variety of possible combinations are listed within each Operation. Complete the information, as accurately as possible, for only those items which you presently use within each Operation. If the information is unknown, unavailable or too difficult to Obtain place the letters N.A. in the space provided and continue. Please feel free to make additional comments on the survey when explanation is necessary. A. GENERAL INFORMATION 1. Herd Size: a. Milking Herd (#) b. Dry Cows (#i c. Young Stock (#) 2 e labor: a.' Employees (#) b. HOurly Wage (S) 165 MICHIGAN ENERGY AUDIT SURVEY (continued). . . 3. Power Company: a. Name b. Rate Group 4. Outdoor Lighting: a. Outdoor Light (#) b. Typels) c. wattage(s) II. HOUSING A. MILKING HERD 1. Design Type: a. Free Stall (l) Stanchion (J) b. warm (l) Cold (J) c. Enclosed (/) Covered (I) 2. Building Size: a. Length (ft) b. Width (ft) c. Stalls (l) (#) 3. Construction Material: a. Floor Slotted? (l) b. walls Insulation? (/) c. Roof Insulation? (l) d. Alleys Scrapers? (l) 166 MICHIGAN ENERGY AUDIT SURVEY (continued). . 4. Lighting: a. Type(s) b. Lightls) (#) b. wattage(s) 5. watering Systems: a. Type b. Mfr. c. Heated (/) (#) 6. ventilation: a. Fan (#) b. Motor Size(s) (Hp) 7. Heating System: a. Type of Heat b. Uhits (#) c. Output (Btu) d. Temp. Setting ( °F) B. Young Stock 1. Type: a. Free Stall(s) (/) _____ (#) b. Calf Hutches N) __ (#) c. Individual Pens (/1 __ (#1 d. Other MICHIGAN ENERGY AUDIT SURVEY 5. 6. 7. 167 (continued). Construction Material: a. Floor Insulation? (l) Insulation? (/) b. walls c. Roof Lighting: a. Type(s) b. Light(8) (#) b. wattage(s) watering Systems: (#i a. Type b. Mfr. c. Heated (/) ventilation: a. Fan (#) b. Motor Size(s) (Hp) Heating-System: a. Type of Heat b. Units (#l c. Output (Btu) d. Temp. Setting ( °F) 168 MICHIGAN ENERGY AUDIT SURVEY (continued). III. MILKING A. GENERAL INFORMATION 1. Milking Schedule: a. Milking(s) (#/Day) b. Milking Times A.M. Milk Production:i a. Herd Average (lbs/cow) b. Bulk Milk Pick-up (days) Labor Requirements: a. Set up minutes b. Collecting Cows minutes c. Milking minutes d. Clean-up, Misc. B. MILKHOUSE 1. person(s) person(s) person(s) person(s) Other per per per per milking milking milking milking minutes Hot Water Heater: a. Type(s) b. Capacity (gal) c. Temp. Setting ( °F) water Pump: 3. e 'IYpe b. Capacity (gal/hr) c. Horsepower MICHIGAN ENERGY AUDIT SURVEY IV. 169 C. MILKING SYSTEM 1. System Design: a. Diagonal (/) Polygon (/) Rotary (/) b. Crowd Gates c. Auto-Parlor Gates d. Auto-Feed Bowls e. Auto-Detachers f. Auto-Pipeline Wash 2. System Operation: a. Milking Units b. Booster Wash Pump c. Booster wash Heater FEEDING SYSTEM A. GENERAL INFORMATION 1. Feeding Schedule: a. Feedings (#/Day) b. Feeding Times A.M. (continued). Sawtooth Herringbone (J) Tie-Stall Pipeline Other (/1 (I) (I) (l) (I) (#) (Hp) (Btu) (I) Mfre Mfr. Mfr. Other 170 MICHIGAN ENERGY AUDIT SURVEY (continued). . 2. Labor Requirements: a. Set up per feeding minutes person(s) b. Load Mixer or per feeding Feed wagon minutes person(s) c. Feed Mixing per feeding minutes person(s) d. Unloading Feed 4 per feeding minutes person(s) d. Clean-up, Misc. per feeding minutes person(s) Feed Ration: a. High Moisture Corn Grain % moisture lbs/cow/feeding b. Grain % moisture lbs/cow/feeding c. Silage % moisture lbs/cow/feeding d. Haylage % moisture lbs/cow/feeding e. * Dry Hay % moisture lbs/cow/feeding f. Supplement % moisture lbs/cow/feeding g. % moisture lbs/cow/feeding *Indicate delivery method for dry hay 171 MICHIGAN ENERGY AUDIT SURVEY (continued). . . B. FEED STORAGE (if additional Space is needed use back of page) 1. Upright Silos: 1 2 3 a. Feed Type b. Silo Size (ft) x x x c. Unloader Type d. unloader (Hp) e. unloader (V/¢) f. Conveyor length (ft) g. Conveyor Motor (Hp) h. Silo Mfr. i. Silo Construction 2. Bunker Silos: 1 2 3 a. Feed Type b. Silo Size . (ft) x x x c. Uhloader Type d. Uhloader Mfr. e. unloader (Hp) f. Silo Mfr. g. Silo Construction 3. Dry Bins (i.e. hoppers): 1 2 3 a. Feed Type b. Bin Capacity (lbs) c. unloader Type d. unloader (Hp) ee Bin Mfre 172 MICHIGAN ENERGY AUDIT SURVEY (continued). . . C. DELIVERY METHOD TO STORAGE 1. Silo Blower: a. Tractor Mounted (I) Electric Blower (I) b. Capacity Required Horsepower c. Mfr. 2. Bunker Silo Loader: a. Tractor Mounted (I) I Skid Loader (I) b. Bucket Capacity (ft3) c. Mfr. Horsepower D. FEED MIXING 1. Mixer Type: a. Stationary (I) ______ Mobil (I) ______ NOne (I) 6. weigh Scale (I) c. Capacity (lbs) Mfr. 2. Mixer Power Unit: a. Tractor (I) Electric Motor (I) b. Hersepower Mfr. E. DELIVERY METHOD FROM STORAGE 1. System Design: a. Fence Line Bunk(I) One side (I) Both Sides(I) b. Stanchion Barn (I) Automated(I) ' Manual (I) c. Parlor Feeding (I) Automated(I) Manual (I) MICHIGAN ENERGY AUDIT SURVEY 2. 173 System Operation: a. Ce Conveyor Delivery (I) Total Length (ft). Shuttle Feeder (I) Total Length (ft) Shuttle Capacity (lbs) Drawn Feed Wagon (I ) Wagon Capacity (lbs) Tractor Mounted Loader(I) Bucket Capacity (ft3) Skid-Steer Loader (I) Bucket Capacity (ft3) Other (please Specify) (continued). % Herd Fed Horsepower % Herd Fed Horsepower Mfr. % Herd Fed Horsepower % Herd Fed Horsepower % Herd Fed Horsepower .Ij‘le'l.lv .IIC 3 B APPENDIX 174 APPENDIX B UNIT CONVERSIONS Description EEHJEi Equivalent Area £t2 0.092 m2 Crude Oil bbl 42.0 gal. Cooling Capacity Btu/hpOhr 1.414 kJ/kW° hr Corn Silage (35% DM). tn 50.0 ft3 14.15 m3 Flow cfm 1.699 m3/min Haylage (40% DM) tn 50.0 ft3 Heat Energy Btu 1.055 kJ Heat Transfer Coef. Btu/hr0ft2°°F 5.678 W/m2°°C High Moisture Corn tn. 54.0 ft3 15.28 m3 bu. 67.6 lb 30.6 kgf Length ft 0.305 m Light lm 1.0 cd/sr Mass lbm 0.454 kg Milk gal. 8.5 lbm Power hp 0.746 kW Pressure psi 6.895 kPa UNIT CONVERSIONS (continued). Description Specific Heat Coef. Temperature Volume Weight 175 Units gal'°F/W°min Btu/lb'°F °F ft3 gal e tn lb Equivalent 0.014 4.187 l°°C/W- min kJ/kg-°c [(°F-32)/1.8]°C 0.283 3.79 2000.0 0.907 907.2 0.454 m3 1 lb t kgf kgf APPENDIX C 176 APPENDIX C ENERGY EQUIVALENTS Description M Equivalent Crude Oil bbl. 6.50 x 106 Btu Diesel Fuel. 661. , 5.88 x 106 Btu gal. 1.40 x 105 Btu Electricity 661. 5.80 x 102 kWh* kWh 3.413 x 10“ Btu Fuel Oil #6 * bbl. 6.20 x 106 Btu gal. 1.47 x 105 Btu Gasoline bbl. '5.21 x 106 Btu gal. 1.24 x 105 Btu LPG 661 3.85 x 106 Btu gal. 9.16 x 10“ Btu Quad Btu 1.00 x 102“ Btu B IBLIOGRAPHY 177 BIBLIOGRAPHY A.D.L. Systems Inc., 1980. Unpublished Price. List and Equipment Information. Portland, MI. Anderson, Edwin P. and Rex Miller, 1978. Electric Motors. Theodore Audel and Company, Indianapolis, IN. Annual Report to Congress, 1979. Department Of Energy (Publication DOE/EIA - 0173/2). washington, DC. A.S.A.E., 1980. ”EP256.2 Refrigeration Equipment Capacity for Bulk Milk Cooling Systems." A.S.A.E. Standards,quricultural Engineers Yearbook, St. Joseph, MI. A.S.A.E., 1981. "A.S.A.E. EB30.3 Agricultural Machinery Management Data." Agricultural Engineers Yearbook, St. Joseph, MI. A.S.A.E., 1982. "MCFate Provides U.S. Senate Subcommittee Testimony on Energy in Agriculture." Agricultural Engineering, St. JOseph, MI. August, page 24. Babson, 1976. The way Cows Will bg_Milked‘gg_Your Dairy Tbmorrow - 8th Edition. Babson Brothers Dairy Research Service, Oak Brook, IL. Bath, Donald L., et al, 1978. Daigy Cattle: Principles, Practices, Problems, Profits. Lea and Febiger Publishing, Philadelphia, PA. Beach, Bennett H., et a1, 1980. The Energy Puzzle - How You Fit 22, Alliance to Save Energy, Washington, DC. Page 7. Benson, Fred J., 1978. Economic Comparison of Silage Systems. Department. Of .Agricultural and. Applied. Economics, University' of Minnesota, St. Paul, MN. Bodansky, David, 1980. "Electricity Generation Choices for the Near Term.” Science - VOlume 207, Number 4432. February, pages 721-727. Booms Silo Company, 1980. Unpublished Price List and Equipment Information. Harbor Beach, MI. Bou°Matic, 1979. Dair°I