ALABAMA AGRICULTURAL EXPERIMENT STATION AUBURN UNIVERSITY GALE A BUCHANAN, DIRECTOR AUBURN UNIVERSITY. ALABAMA CIRCULAR 258 DECEMBER 1981 4 /fq " b si -~ ~ -b~ F- 'hp r * L w. Met ""' m I i M'Ylq Y a.. # 'fly.- ? Predicting Machine Performance Rates for Specific Field and Operating Conditions CONTENTS Page ACKNOWLEDGMENT INTRODUCTION ........... ............................ .............. .. ............... 2 3 4 ............................. SOME INITIAL PERFORMANCE RATE CONCEPTS Approximate Machine Performance Rates .............. Performance Rates for Average Conditions ............. PRECISE MACHINE PERFORMANCE RATES ..................... The Concept ............ ........................ Application Example .............................. Benefits From Its Use.............................11 SUMMARY ... ......... . ....... ........... ........ ............ ....... LITERATURE CITED ............. 4 5 6 7 10 13 13 ACKNOWLEDGMENT: The author wishes to express thanks to W. T. Dumas and the staff at the Marvyn Agricultural Engineering Research Unit for their help in providing machines and crops. The assistance of agricultural engineering students Wayne Fowler and Lee Coon with data collection and analysis is gratefully acknowledged. FIRST PRINTING 4M, DECEMBER 1981 Information contained herein is available to all persons without regard to race, color, sex, or national origin. PREDICTING MACHINE PERFORMANCE RATES for SPECIFIC FIELD and OPERATING CONDITIONS ELMO RENOLL1 INTRODUCTION FARM MACHINERY becomes larger, more complex, and more expensive it is important that it be used effectively in order to make maximum contribution to agricultural production. No piece of farm machinery is used for productive work 100 percent of the time. Time spent making field adjustments and repairs, adding seed, fertilizer, chemicals, water, and turning at row ends is lost production time and reduces machine capacity. Agricultural engineers and other scientists have long been interested in concepts and procedures for more efficient farm machinery use and have used many approaches in these studies. A systems approach for machinery budgeting and programming has been used by several researchers. Von Bargen (8) used it in his hay harvest work. Stapleton and Barnes (7) also applied the systems analysis concept in their work with cotton harvesting machines. Renoll (3,4) at Auburn University used systems analysis to study machine operations inthe field in an attempt to gain insight into the interaction between machine use and the physical and geometrical characteristics of the field. This work indicates that field machine efficiency is related to row length, turn condition, and terrace system among other things. A 1 Professor, Department of Agricultural Engineering. Field machine efficiency is defined as the ratio of the productive machine time to the sum of productive machine time plus the row-end turning time. Productive time is the actual time a machine is doing its specific job. For a planting operation, this would be the time actually spent placing seed in the ground. Some additional approaches have been used by other engineers. Sowell and Link (6) used a network analysis concept in their machinery selection studies. A mixed integer programming model was used by I. Amir et al. (1) for selection and economic evaluation of hay drying systems. Bowers (2) used a "rule of thumb" along with many years of experience when developing his procedure for efficiently matching machines to large tractors. The key to this approach was to avoid oversizing the equipment for a specific tractor. Information in this publication came from a study designed to identify and analyze some of the factors that influence machine performance rates. They include machine and field factors as well as managerial ability. The study included 4-row, 6-row, 8-row, and 12-row machines operating in fields up to 200 acres in size having row lengths ranging from 400 to 2,500 feet in length. Machine operators were typical of those found on Alabama farms. Data were obtained by time-record methods including manual observations and self-recording clocks. SOME INITIAL PERFORMANCE RATE CONCEPTS Scientists as well as farm operators have long been interested in methods and procedures for determining or estimating machine capacity. Such information has numerous obvious benefits. Approximate Machine Performance Rates For some conditions, extremely accurate machine capacity values are not needed. For such conditions, the following formula is commonly used. D=Wx where: S D = capacity (acres/day) W = machine width (feet) machine speed (miles/hour) S - Two assumptions are made in this approach. Nonproductive time such as adjustments, adding seed, and stops is assumed to [4] r~ s. M .. 2 - a FIG. 1. Field size and row length are important factors in determining machine capacity. Rows less than 400 feet greatly decrease performance rates. percenit. '11w assiiumed wxork day is 1() bours. Tphis is and( eaLsy V to ge~t a r()tlgh estiiiate (d1 machine c~acity but it also0 has '()nic{ ob1)X l)11 Iiuiiitatioi is. FIr fit1(d Opciatioios that hjave vecry little ilii-wroiclti\ e Itie, such as hiarr )\\ iiit nfiicchiauical cuilti\ atinig, the cajxi&itx 1iriiii tlhe firiiil a is toi) It,\\ h r p( ruti io, suchl as plIanting th at ha~\ largce aliiiits ( f ilhpro(liicti\ c tiiii the fhriiuia mild11( gr(t1\ ()\ (1(stiiiiatc thelcalpacit\ . Auithier 1)1o)lheml \ith th fi lriioia is that miachinie wxidith is uisedh rathier than tihe actual (fleoti\ c \\irkiiit xx icth heV abouiit IS a yniik ( Performance Rates for Average Conditions Ininiili situiatijons niiue accuii'acx is dhesiredh thani can he Obied(l withi the 1)rcx 10115 t)V-lhuia. Suich accuiracx cani be (ii d (h\ b uisinh Ie fn ) 11lxovinig for-nii la: f. x S \v here: 8.25 cap~acity (acres/hour) spee (1 (iiilos/hooir) W\ iiaciie \X idth (feet) 1:1. field( efficienci a (dcciii ial (' S 151 1i-fo)t ifti( it itnit (t t) itI 5f)ii( it~t d((iiv (I ((t(( to \\'( ias its J( )tI) ftliv t tr It in11t1(1 ii tlit if'titti i tt 1t St intltI iitii till is ( ihil it f ttIhi l sn l t i )i(i ii iii it ii t'akillit ist iih(iiis i ' , ,i ,nl at rO ~ 4)nls a~~i 's' n if i al FHI.2. Phy v ic codtIn an Idt of0 row-nd turing ce i have grea iunfluence ontui maci caaity ras. Reasonabtly smtoth tuar spaltc aboisdsied j (i FIG. 3. Farm machinery that is well serviced and correctly adjusted can reduce field delays and excessive down time thus increasing acre per hour capacity. MI ich of this e ffort has b~een spen t in examiltig in detail the relativeC impjortailce of the iniiduillal items irncluded in Ef. Some research efforts at Atiuirn hav e b~een along this line. a Th'is effort has resulted ini a pr~oposedl machine cap~acity predliction formula ha ing imore accuracy than those discussed earlier in this circular. The Concept The~ time re(luiire(I for a farm machine to cov er an acre 1 ~es the do)wn-the-row ti me pl1s the adldlitional time nede f(I(hr tmrn ing, adljustmnts , delay s, and1 other nonproducI tive& a('tivities. Ini this concept, time for each of these is dletermined ini~id ~ually and then totalled for the acre. From this infornation mach ine cap~acity can he (determind. Some 14 inpu)It itemts, includiing 1)oth fieldl andl machine conditions, are uisedl and expIressed as follows : ii Ild 171 where: T=A+B 1 C- T GT A time spent actually performing the specific operation. Sometimes called down-the-row time. (hours/acre). B time used for support functions including row-end turning, adding seed, etc. (hours/acre). T total time (hours/acre) C =performance rate (acres/hour) Values for A are determined as follows: 8.25 A 82(hours/acre) SW where: S = machine ground speed (miles/hour) W = effective machine width (feet) Effective machine width is the actual width the operator covers in one machine pass. Item B covers row-end turning, adjustments and other necessary time delays commonly associated with support functions. The B values are obtained from the following: 12 P 8.25 B WL + [ (fl + f2+f3 + f4+f5)x SW (hours/acre) The expression 12 P is used to determine the total turning WL time in hours per acre where: P =average time per turn (seconds) L -average or representative row length for the field (feet) The term [(fx + f2 + f3+ f4 + f 8.25 SW) X ] indicates the support functions time in hours per acre. Each f value is an input coefficient representing a specific support function. They are as follows: = coefficient for adding seed f2 -=coefficientfor adding fertilizer fl [8] f3 = coefficient for adding water and chemicals f 4 = coefficient for adjustments f5 - coefficient for other field delays Numerical coefficients for individual support functions are expressed as a percentage of the down-the-row time. Some suggested values are found in table 1. In many farming operations some fields are located away from the farm headquarters. While this does not influence the capacity of a machine on a specific field it does have influence on the number of acres a machine can cover in a growing season or other time period and would be very important in some situations. The effective capacity of a machine on a field remote from the headquarters can be determined by adding VU to B 60D above. V = time for round trip, barn to field and return (minutes) U - number of round trips, barn to field and return required to complete the field operation D = field size (acres) If the fields involved are in close proximity to the farm headquarters this item can be omitted. As suggested earlier T - A + B is used to calculate the hours per acre. If the component items for A and B are substituted in the above expression it becomes: 8.25 SW (hours/acre) T + 12 P WL +- [(f + f2 +f3+f4 + f5) x 825 ] SW 1 By substitution of T from above in C = 1 the capacity in T acres per hour can be determined. Application Example Using the formula to predict machine performance rates requires two kinds of input information. The first is "firm data" and includes all information about the operation that is known or has been measured. The other is "estimation data" and includes those items not actually measured. A specific input item might fall in the firm data category for one field and in estimation data for another. Obtaining the "firm data" information about the machine operation is not difficult. Most of these items are common [9] knowledge to the farmer or can easily be measured. Included would be such things as row spacing, ground speed, row lengths, turning time, and available labor. Coefficients for the "estimation data" are not so easily obtained. Estimates for many of these coefficients can be obtained from previous machinery-use data and from personal experience. If reliable coefficients material is not available from other sources, the information in table 1 can be used. Values in the table are from field research data obtained on efficient and well managed farms. The following example illustrates how to estimate the capacity for a 4-row planter. Specific operating conditions are as follows: Firm data 1. Planter-tractor mounted 4-row, 13.3 feet (W) 2. Row spacing-40 inches 3. Planter speed-4.2 miles/hour (S) 4. Seeding rate-16 pounds/acre 5. Fertilizer rate-300 pounds/acre 6. Chemical spray rate-8 gallons chemical and water/acre 7. Time per turn-12 seconds, average (P) 8. Row length-1,017 feet, average (L) 9. Field size-37 acres, adjacent to headquarters Estimation data 1. Coefficient for 2. Coefficient for 3. Coefficient for 4. Coefficient for 5. Coefficient for adding seed, fl -. 04 adding fertilizer, f 2 -. 12 adding chemicals, fa 08 -. 3 adjustments, f 4 04 -. stops and other delays, f 5-. 04 A + B and C = 1 are used to determine T The formulas T machine capacity. A A SW 8.25 82 = 0.15 hours/acre 4.2 x 13.3 8.25 and by direct substitution becomes 8.25 12 P B = WL + [(f + f2 substitution becomes: B f3 + f 4 + f5 ) x 8.25 ] and by direct SW 12x1 + [(.04 + .12 + .08 + .04+ .04) x ] 13.3 x 1017 4.2 x 13.3 = 0.06 hours/acre 82 [10] From the abo (,A -0.15 vs/acre ad li lthuis lecom(s TI + B~ SulbstitiitilL- in TI lnhoui S/ac(. B\ acres/hot!r. 5! i 1 urn in = (O 0 or11 or. U.15 + 0.060 (.2 1 ols/act1 )titlitioll (: = 11 1.5 l' 0.2 secto. Benfict From sonic 1)tl(flic Uset ( SItstol ALt Al l l as1( in 5cCv ed n11 Atabl i 1.~t r nts NNpeca It diat FIG. 4.rEffectivemieyse plnnn aonith instrucel maximie maxcine HNcapacity. ~ operat proper dishitfl and or traeinig n [ 11] TABLE 1. SOME TYPICAL COEFFICIENTS FOR PREDICTING FARM MACHINERY CAPACITY' Machine operation Plant (4-row) Adjusments Adjusments .03-.07 .05-.07 .05-.09 .06-.09 .03-.05 .01-.03 .02-.04 .02-.05 .03-.06 Other delays 2 .03-.04 .03-.04 .03-.06 .03-.06 .02-.03 .00-.01 .00-.01 .01-.03 .01-.03 ................................... Coefficient values Add seed .03-.05 .04-.06 Add fertilizer .10-.14 .12-.16 Add chemicals 3 .07-.09 .08-.11 .06-. 10 .10-.12 Cultivate (4-row) ............................... Plant (6-row) ................................... Cultivate (6-row) ............................... Spray (12-row) ................................. Disk harrow ................................... Harrow and apply chemicals ...................... ....... Plow (4-bottom) ........................ Plow (6-bottom) ................................ ~rr n . r~ P 1 Expressed as decimal percent of time the machine actually spent performing its function. 2 Includes such items as: idle field travel, field obstructions, operator instruction, and short rest stops. 3 Can vary considerably depending on the amount of chemical and water applied per acre. The concept is valuable to economists and others interested in estimating machine cost per hour prior to machine purchase or in comparing per hour costs for two different size machines. It will be helpful to more accurately determine correct size and machines needed for a specific cropping system. SUMMARY A method to predict machine capacity for row-crop machines under specific field and operating conditions is presented. It uses individual input coefficients to represent such things as row length, adjustment time, breakdown time, and other conditions that influence capacity. Fourteen such input coefficients are used. This report also includes a table of typical coefficient values that can be used if actual values are not available. The capacity concept was applied to various row-crop machines during its development. The predicted capacity values were compared with actual field measured capacity values and were found to vary less than 5 percent. The formula uses some input coefficients which are derived from in-the-field measurements as well as by estimation and thus is subject to these limitations. The prediction formula as presented here has application to row-crop machines. Modification for use with nonrow-crop machines should be possible. [13] LITERATURE CITED (1) AMIR, I., J. B. ARNOLD AND W. K. BILANSKI. 1978. Mixed Integer Linear (2) (3) (4) (5) (6) (7) Programming Model for Dry Hay System Selection-Part II. Application. TRANSACTIONS OF THE ASAE 21 (1): 45-51, 54. BOWERS, WENDELL. 1978. Matching Equipment to Big Tractors for Efficient Field Operations. ASAE Paper No. 78-1031, ASAE, St. Joseph, Michigan 49085. RENOLL, E. S. 1965. Row-Crop Machine Capacity in Terraced Fields. Highlights of Agr. Res., Alabama, Agr. Exp. Sta. RENOLL, E. S. 1970. Using Operation Analysis to Improve Row-Crop Machinery Efficiency. Alabama, Agr. Exp. Sta. Cir. 180. RENOLL, ELMO. 1972. Concept for Predicting Capacity of Row-Crop Machines. TRANSACTIONS OF THE ASAE 15 (6): 1028-1030. SOWELL, R. S. AND D. A. LINK. 1967. Network Analysis and Mathematical 1967. Data Needs for Agricultural Programming. TRANSACTIONS OF THE ASAE 10: (6): 820-828. STAPLETON, H. N. AND K. K. BARNES. (8) Systems Analysis. TRANSACTIONS OF THE ASAE 10: (3): 303-309. VON BARGEN, KENNETH. 1967. A Systems Approach to Harvesting Alfalfa Hay. TRANSACTIONS OF THE ASAE 10: (3): 318-319. [14] .Xabnis AgrICuLural ILxjprimlicit Station1 Sy)stei AUBUJRN UNIVERSITY turaI reseji h unit in \tthurn serves ti-I tttrestrX t'niX Crsit\ need~s of ;ll1(i hor-I \Ialh~lr. I vCINc itistakeC in this rcarch2 hp1 raim, since iflX ( ica \i