Spreadsheet for Matching Tractors and Implements

An ASABE Meeting Presentation Paper Number: 061085

Spreadsheet for Matching Tractors and Implements Robert Grisso and John Perumpral Biological Systems Engineering, Virginia Tech Blacksburg, VA 24061-0303, USA 540-231-6538, email: [email protected]

Frank Zoz John Deere Product Engineering Center, Waterloo, IA, USA

Written for presentation at the 2006 ASABE Annual International Meeting Sponsored by ASABE Portland Convention Center Portland, Oregon 9 - 12 July 2006

Abstract. Well matched tractor-implement systems are important for maintaining high operating efficiency. The objective of this paper is to demonstrate the use of spreadsheet for matching tractors and implements. The spreadsheet based on the Brixius Model and ASAE Standards D497.5 to predict tractor performance and implement draft respectively is discussed. Two sets of cases selected to demonstrate the use of spreadsheet include matching either the tractors with the implements, or the implements with the tractors. Results of these cases considered are presented and discussed. Cases considered include three tractors of different power levels and configurations and three different implements and three different soil types. The results show that the spreadsheet can be used effectively to match implements with tractors or vice versa. Results also demonstrate that optimization of weight distribution for maximum power delivery efficiency, and computation of field capacity and fuel consumption are possible with the use of spreadsheet. Keywords. Tractor performance, draft force implement matching, modeling, spreadsheet. The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2006. Title of Presentation. ASABE Paper No. 06xxxx. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at [email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

Spreadsheet for Matching Tractors and Implements Robert Grisso, John Perumpral, Frank Zoz1 Introduction Agricultural sustainability depends on farm profitability. Thus, farmers are under constant pressure to produce more with less and to reduce production costs through improved operating efficiency. The operating efficiency depends heavily on how well the tractor and implement are matched. When they are ideally matched, one could expect reduced power loss, improved operating efficiency, reduced operating costs, and optimum utilization of capital on fixed costs (Taylor et al., 1991). In the past, farmers depended on their experience to match the tractors and implements. While this approach may enable the producer to carry out the intended operation, the system may not be operating at optimum operating efficiency. Therefore, for improving the operating efficiency, it is important that both units be selected in such a way that the power generated by the tractor is fully utilized. The process of matching tractor and implement may start at the implement or at the tractor end. In other words, a tractor may be selected to match the implement or vice versa. In either case, for proper matching of tractor and implement, the ability to accomplish the following are necessary: A. Predict the draft and power requirement of the implement taking into consideration factors such as depth and speed of operation, implement width and soil condition. B. Predict the tractive capability and the drawbar power that can be developed by the tractor taking into consideration the factors such as vehicle configuration, weight distribution, ballasting, tractive device type, and terrain conditions. Many studies have been conducted to determine the draft and power requirements of tillage implements in different soils (Gould et al., 1999; Grisso et al., 1996; Harrigan and Rotz, 1994; Upadhaya et al., 1984). Models to predict the draft and power requirements of implements have also been developed by many investigators. Gee Clough, et al. (1978) modeled the tractor-plow performance using empirical relations developed based on experimental data obtained from 14 different fields with sandy clay loam, clay loam, and sandy loam soils. Predicted values were within plus or minus 20% of the measured values for 86% of the cases. Grisso et al. (1996) reviewed several published reports and concluded that the implement draft is a function of implement width, and operating depth and speed. The effect of speed on draft force varied considerably based on the type of implement. It has also been widely reported that the draft force increases significantly with operating speed and the relationship ranges from linear to quadratic. 1

The authors are Robert “Bobby” Grisso, ASABE Member Engineer, Professor, and John V. Perumpral, ASABE Fellow Member Engineer, Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA; and Frank Zoz, ASABE Fellow Member Engineer, Retired Engineer, John Deere Product Engineering Center, Waterloo, IA. Corresponding author: Robert “Bobby” Grisso, 200 Seitz Hall (0303), Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061; phone: 540-231-6538; fax: 540-231-3199; e-mail: [email protected]. Mention of trade and company names are for the reader and do not infer endorsement or preferential treatment of the products by Virginia Tech.

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From a study dealing with the effect of tool depth on draft force, Upadhaya et al. (1984) concluded that both soil conditions and geometry of the tool also affect the draft force. Harrigan and Rotz (1994) proposed a simple function to predict the draft force of tillage and seeding implements. They also presented reference tables for soil and machine specific parameters. These tables and mathematical expressions were adopted by American Society of Agricultural Engineering to revise the ASABE standard for Agricultural Machinery Management data as part of ASABE D497.5 (ASABE, 2006) to predict the draft on tillage implements in different soil types. There also have been several attempts to predict the tractive performance of power units using graphical methods, templates, and software programs. The traction model developed by Brixius (1987) formed the basis for majority of these studies. Evans et al. (1989) used Brixius model to determine the ballasting and to predict the tractive performance of tractors using TK solver. Grisso et al. (1992) demonstrated the flexibility of templates introduced by Zoz (1987) to compare the performance of bias-ply versus radial tires, dual versus single tires and to observe the influence of variables such as speed, tire size, ballast, and soil condition on the performance of 2WD, 4WD or MFWD tractors. As the use of spreadsheet gained popularity, Al-Hamed et al. (1994) used it to predict the performance of 2WD, 4WD, and MFWD tractors equipped with bias-ply and radial tires in agricultural soils. Comparison of predicted and experimental results showed that the spreadsheet can be used effectively to study the performance of tractors. AlHamed and Al-Janobi (2001) took another step and demonstrated that the same can be accomplished with a Visual C++ program. Studies in the past have developed programs to assist with the decision making process for the selection and management of machinery and to make the different operations cost and energy efficient. Only a few researchers have directed their effort to develop appropriate procedure for matching of tractors and implements based on estimated power requirement and power availability taking into consideration the terrain and equipment factors (Downs et al., 1990; Downs and Harrison, 1998; Gould et al., 1999; Powell, 2001). White (1977) discussed matching of tractor and implement on the basis of available power minus a power reserve of 17% of total tractor power as a standard practice to overcome unexpected loads. More recently Sahu and Raheman (2005) have developed a decision support system using Visual Basic 6.0 programming language and found it suitable for matching of implements with 2WD tractor and to predict the field performance of the system. The overall goal of this paper is to demonstrate the use of spreadsheet for matching the tractors of different configurations with different implements or vice versa taking into consideration the terrain conditions, tool width, and operating depth and speed. Traction and Tillage Mechanics For matching the tractor with implements, one should be able to predict the tractive performance of the power unit and the draft requirement of the implement. The models available to predict the tractive performance of tractors and draft requirements of implements are available in Zoz and Grisso (2003) and ASABE (2006) respectively. In this section, only the models used to develop the spreadsheet are included.

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The spreadsheet for predicting the tractor performance is based on Brixius model (Brixius, 1987). He developed the relationships for net traction ratio (NTR), gross traction ratio (GTR), and motion resistance (MRR) as a function of mobility number and wheel slip. They are:

( ) ( )

δ ⎫ ⎧ ⎛ CI ⋅ b ⋅ d ⎞ ⎪ 1 + K 1 ⋅ h ⎪ B =⎜ ⎟⋅ ⎨ ⎬ n ⎝ W ⎠ ⎪1 + K ⋅ b ⎪ d ⎭ 2 ⎩ −C ⋅B ⎛ T = C ⋅ ⎜1 − e 2 n GTR = 1 ⎜ r ⋅W ⎝ C ⋅s M C5 MRR = = +C + 6 4 W B B n n NT NTR = = GTR − MRR W

(1)

−C ⋅s ⎞ ⎞ ⎛ ⎟ ⋅ ⎜1 − e 3 ⎟ + C ⎟ ⎜ ⎟ 4 ⎠ ⎝ ⎠

(2)

(3)

(4)

Where: Bn - the mobility numbers; b - the unloaded tire section width; r - the tire rolling radius; h - the tire section height; s - the wheel slip; NT - is the net traction or pull; T - the axle torque; CI - is the cone index; d - the unloaded tire diameter; d - the tire deflection; W - the dynamic load on the tractive devices; M - is the motion resistance and NTR - is the net traction ratio. Equations 1, 2 and 3 include six coefficients (C1…C6 and two constants (K1 and K2). Values of these coefficients and constants depend on the type of tires. For radial tires, values of C1, C2, C3, C4, C5, C6, K1, K2, are 0.88, 0.08, 9.5, 0.032, 0.90, 0.5, 5 and 3, respectively. The spreadsheet for the implement draft prediction is based on the equation published in ASABE Standard D497.5 (ASABE, 2006) and shown below: D = Fi [A + B (S) + C (S) ] W T

(5)

Where: D is the implement draft; Fi is a dimensionless soil texture adjustment where i is 1 for fine, 2 for medium, and 3 for fine textured soils; A, B, & C are machine specific parameters; S is operating speed; W is implement width; and T is operating depth for major tools (for minor tillage tools and seeding implements T=1). A table included in the standard provides the values for the machine and soil parameters for commonly used implements. For implements of interest in this study, parameter values were included in the spreadsheet. Spreadsheet The spreadsheet developed for matching tractors and implements have two major components. One predicts the tractive performance including the pull a particular tractor can develop under a given terrain condition. The second component predicts the draft force on a soil engaging tool

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taking into consideration factors such as soil texture, tool width and operating speed and depth of the tool. Both components are briefly described in this section. The spreadsheet, the database and the instructions to use are available at the website (http://filebox.vt.edu/users/rgrisso/Tractor.htm) for general use. Tractor Performance Sheets The spreadsheet component developed for predicting the tractor performance is designed to predict the performance of wheeled tractors of different configuration (2WD or 4WD/MFD) in different soils. This part of the spreadsheet include data on 700 tractors ( 101 AGCO, 146 John Deere, 127 Case, 123 New Holland, 122 MF, and 87 others ) extracted from the published Nebraska Tractor Test Reports (NTTR). The database also include data on 39-R1 bias tires, 51R1 radial tires, and 17-front F2 tires (bias). Thus when the tractor model and the tire sizes are specified, the database will provide the needed information ( weight distribution, power input, tire width and diameter etc. ) for predicting the performance of tractors. The tractor performance component of the spreadsheet has the provision to predict the tractor performance for a specified weight distribution on the front and rear axle (performance mode ) or to calculate the weight distribution and performance for a specified wheel slip (weight mode ). The weight mode can be used to calculate the required weight for desired performance. This mode together with an optimizing ballast scheme can predict the optimum wheel slip and maximum tractive efficiency. A Visual Basic macro uses a golden section search as the optimization algorithm when weight mode is in use. The algorithm reviews the results of changing the weight distribution and travel reduction and searches for the optimal power delivery efficiency. The tractor performance also include an optimization routine to determine the optimum weight distribution for maximum drawbar power. The details of this routine is available in Jones and Grisso (1992). Since fuel use is an important factor in machinery management, provisions to predict the fuel consumption was also included in the spreadsheet. The following equation developed by Grisso et al. (2004) was used to predict fuel consumption: Q = (0.22 X + 0.096) · (1 – (-0.0045 X NRed + 0.00877 NRed)) * Ppto

(6)

Where: Q - Fuel consumption at partial load and full/reduced throttle, l/h; X - Ratio of equivalent PTO power to rated PTO power, decimal; NRed - Percentage of reduced engine speed for a partial load from full throttle, %; and Ppto - Rated PTO power, kW. The input data needed for the spreadsheet use include some data common to both modes and some data that are mode specefic. A list of these input variables follow. Common inputs: • •

Model – Specify Tractor Model Rear tire size - Specify tire size to extract tire dimensions from the database. Default tire size will be of the tires used in NTTR tests.

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• • • • • • • • • •

Front tire size - Specify tire size to extract the tire dimensions from the data base. Default tire size will be of the tires used in NTTR tests Number of tires - Specify single, dual, or triple tires. Default number is the number of tires used in NTTR tests. Tractor wheelbase - Taken from the data base depending on the model specified. Hitch height - Drawbar height taken from the database when tractor model is specified. Draft angle - Specify the angle between the line of pull and the horizontal in degrees. Default draft angle is zero degrees. Hitch location behind the rear axle - The spreadsheet automatically assigns a nominal value to start the process. Actual distance is specified when the implemwnt is selected. Load Factor - Specify the percentage of PTO power available to do work. Input power - Enter the PTO power. The spreadsheet will compute the axle power using the formula PTO Power * Load Factor * Mechanical Efficiency. Travel speed, theoretical (Vt - Speed at Zero slip. The default value is 8.8 km/hr. Soil strength - Enter appropriate soil cone index. Approximate values for fine, medium, and coarse soils are 1380, 1140 and 550 MPa (200, 165, and 80 psi) respectively.

Input Specific to Performance Mode: • Tractor static front axle weight - Enter the actual weight on the front axle. When not specified, the default value is the static unballasted weight for the model specified • Tractor static rear axle weight - Enter the weight on the rear axle. When not specified, the default value is the static unballasted weight for the model specified. Input Specific to weight Mode: • Slip (travel reduction) - Percent wheel slip. • % Dynamic front weight desired - Enter the percentage of dynamic weight on the front axle. Rear weight is calculated to provide the desired travel reduction. Output of tractor performance calculations will include the following: • Drawbar pull. • Travel speed. • Drawbar power. • Tire pressures appropriate for static weight distribution (Higher preassure may be necessary for transporting heavy implements). • Power delivery efficiency. • Vehicle traction ratio. • Tractor weight to power ratios. • Fuel consumption. Prediction of Implement Draft The second set of sheets developed for implent draft calculations predicts the draft requirement of 12 major and 7 minor tillage implements and 12 seeding equipments. Certain outputs from the the tractor performance part are used as input for draft prediction and to determine the width of the implement. For example when an implement is matched, with a tractor, the speed and the

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predicted tractor pull are used as inputs to compute the force per soil engaging tool and then the implement width. In addition to these, other inputs needed for draft and implement width predictions include the following: • • • •

Tool depth.- Enter the desired operating depth. Spacing between soil engaging units - Enter the spacing between the the soil engaging tools to determine the width of the implement. Soil factor - Specify the soil texture. The texture selected for draft prediction should be compatible with the cone index specified for tractor performance prediction. Field Efficiency - Depends on downtime. Default value is 85%.

The output from the implement draft prediction will include the following: • • • • • •

The draft force/ soil engaging tool Number of soil engaging tools – Total number of tools that can be handled by the tractor. Implement width - Total implement width (number of tools times the spacing between the tools) Actual operating depth - Based on the predicted pull and implement width selected, the operating depth will be adjusted to fully utilize the pull developed. Thus the actual tool depth may be slightly higher or lower than the tool depth specified as input. Field capacity - Computed from the implement width and travel speed. Fuel consumption per unit area - Fuel consumption (l/ha)

When tractors and implements are matched depending on the situation, one may start with the tractor and select an implement to effectively utilize the drawbar power generated or start with the implement and select a tractor that can provide adequate pull to operate the implement. In the first case, the pull that the tractor can develope is predicted first and then the draft requirement/single soil engaging tool. Knowing the total pull available and the draft/single soil engaging tool, the number of soil engaging tools the tractor can handle with the available pull is calculated. Then knowing the spacing between the soil engaging units, the width of the implement is determined. In the second case, the draft requirement per soil engaging tool is predicted. Knowing the draft/ tool and the number of tools the total draft requirement for the implement is computed. Using this information, a tractor capable of developing the required pull is selected using the tractor performance component of the spreadsheet. Once the implement width and the tractor drawbar power are known, the spreadsheet will calculate the field capacity and fuel consumption/unit area using the fuel consumption computed using equation (6).

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Spreadsheet Applications The cases considered to demonstrate the use of the spreasheet for matching tractors and implements and the results are presented in this section. Matching the tractor with implement and vice versa are demonstrated. In the first set of cases considered, the objective is to select the implement to match the tractor. Three tractors of different power levels and configurations are selected and their specifications are summarized in Table 1. Three different implements (Moldboard plow with no coulter, Disc Harrow-Tandem, and Field Cultivator) are to be matched with each tractor to operate in three different soils: A) fine textured, loose soil (CI=450 kPa, Fi=3), B) medium textured (CI=860 kPa, Fi=2), and C) coarse textured, firm soil (CI=1725 kPa, Fi=1). Figures 1 and 2 are typical outputs from the analysis of tractor perfrformance and implement selection respectively. In the second set of cases considered, the objective is to select tractors to match with a set of implements to operate in fine, medium and coarse (Fi=3, 2, and 1, respectively) textured soils. For each implement, two widths were considered. The results of the first set of cases are presented in Table 2. For convenience in reading, this table is split into two. The first part covers the tractor data and the second part covers the data associated with the implements selected to match with the tractors The table includes the width of the implements selected, field capacity and fuel consumption for each implement with an unballasted tractor and with tractor weight optimized for maximum power delivery efficiency (PDE). The odd numbered rows represent the results for unballasted tractors and even numbered rows represent results for tractors with optimized weight distribution. Most results are as expected and the following are the selected observations: • •

• • • • •

As expected, the predicted draft the tractor is able to develop is higher in coarse, firm soil than in fine, loose soils. Optimization of weight distribution found to have very little influence on predicted draft developed by the tractor and thus on the width of the implements selected. The optimized weight distribution provided an increase in power delivery efficiency. However, the front axle weight became extremely light (for 2WD tractors) and practical value of weight optimization needs to be explored further. The implements selected in general are wider in fine, loose soils. This is resulting from lower draft requirement per width. For the same reason, the field capacities of the three implements were higher in fine, loose soil. As expected, field capacity increased greatly from high-draft moldboard plow to lowdraft field cultivator. The fuel consumption (l/ha) for each implement-soil combination are almost the same irrespective of ballasting used. Fuel consumption decreased greatly from high-draft to low-draft implement.

The second set of analysis were conducted to match the implements with the tractor. To accomplish this, first the draft requirement of the implement under consideration is predicted.

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Using this information, the drawbar and PTO power requirements are estimated. Once the PTO power requirement is known, from the database, the spreadsheet provides a list of tractors of different makes and configurations that can be matched with the implement under consideration. The implement data from the second set of analysis is summarized in Table 3. Tractor data is not included because choices are many as discussed earlier. Observations on draft force, field capacity, and fuel consumption are similar to the results discussed before. Conclusions • • •

The spreasheet developed can be used effectively to match tractors and implements. The spreasheet approach provides flexibility to either select an implement to match the tractor or to select a tractor to match the implement. The spreadsheet approach can also be used to predict the field capacity and fuel consumption and to optimize weight distribution for maximum PDE.

References Al-Hamed, S.A. and R.D. Grisso, F.M. Zoz and K. Von Bargen. 1994. Tractor performance spreadsheet for radial tires. Computers and Electronics in Agriculture 10:45-62 Al-Hamed, S.A. and A.A. Al-Janobi 2001. A program for predicting tractor performance in Visual C++. Computers and Electronics in Agriculture 31:137-149 ASABE. 1998. ASABE Standards, 49th Ed. 2002. D497.4 JAN98. Agricultural machinery management data. St. Joseph, Mich.: ASAE. Brixius, W.W. 1987. Traction prediction equations for bias ply tires. ASAE Paper No. 871622. St. Joseph, Mich.: ASAE. Downs, H.W., Taylor, R.K., and Al-Janobi, A. 1990. A decision aid for optimising tractor implement system. ASAE Paper No. 90-1569. St. Joseph, Mich.: ASAE. Downs, H.W., and Hansen, R.W. 1998. Equipment: Selecting energy efficient tractor. http://www.ext.colostate.edu/pubs/farmmgt/05007.pdf (Available on: 4/26/06) Evans, M.D., R.L. Clark, and G. Manor. 1989. A traction prediction and ballast selection model. ASAE Paper No. 89-1054. St. Joseph, Mich.: ASAE. Gee-Clough, D., M. McAllister, G. Pearson, and D.W. Everndern. 1978. The empirical prediction of tractor-implement field perfromance. Journal of Terramechanics 15(2):81-94 Gould Lund, R.D., and Hill, J. 1999. Matching tractors and implements-the economic way. http://www.computus.info/Machsem/Webroom1.htm (Available on: 4/26/06) Grisso, R.D., M. Yasin and M.F. Kocher. 1996. Tillage implement forces operating in silty clay loam. Transactions of the ASAE 39(6):1977-1982 Grisso, R.D., S.A. Al-Hamed, R.K. Taylor, and F.M. Zoz. 1992. Demonstrating tractor performance trends using Lotus templates. Applied Engineering in Agriculture 8(6):733738 Grisso, R.D., M.F. Kocher, and D.H. Vaughan. 2004. Predicting tractor fuel consumption. Applied Engineering in Agriculture 20(5):553-561 Harrigan, T.M. and C.A. Rotz. 1994. Draft of major tillage and seeding equipment. ASAE Paper No. 94-1533, St. Joseph, Mich.: ASAE. Jones D. and R.D. Grisso. 1992. Golden section search an optimization tool for spreadsheets. Computers and Electronics in Agriculture 7:323-335

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Powell, G. 2001. Selection and matching of tractor and implements. http://www2.dpi.qld.gov.au/fieldcrops/3492.html (Available on 4/27/06) Sahu, R.K. and H. Raheman. 2005. Decision support system for matching and performance prediction of 2-WD tractor – implement system. Ag Systems (in review). Taylor, R., M. Shrock, and K. Wertz. 1991. Getting the most from your tractor. http://www.oznet.ksu.edu/library/ageng2/mf588.pdf (Available on 4/27/06) Upadhyaya, S.K., T.H. Williams, L.J. Kemble and N.E. Collins 1984. Energy requirement for chiseling in coastal plain soils. Transactions of the ASAE 27(6), 1643-1649 White, R.G. 1977. Matching tractor horsepower and farm implement size. http://www.agf.gov.bc.ca/resmgmt/publist/200series/200200-2.pdf (Available on 4/27/06) Zoz, F.M. 1987. Predicting tractor field performance (update). ASAE Paper No. 87-1623, St. Joseph, MI: ASAE Zoz, F.M. and Grisso, R.D. 2003. Traction and tractor performance. ASAE Distinguished Lecture Series #27, ASAE Publication Number 913C0403, St. Joseph, MI:ASAE

Figure 1. Tractor Performance Output from Spreadsheet Analysis for Tractor 1 (Ford 5635).

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Figure 2. Output from Spreadsheet Analysis of Implement Selection for Tractor 1 (Ford 5635). Table 1. Tractors considered and their specifications. Tractor 1 2 3

Configuration 2WD MWFD 4WD

Tractor Model Ford 5635, 24 Speed Transmission John Deere 7810 Partial PowerShift Transmission White 4-270, 16 Speed Transmission

PTOPower (kW) 50 113 178

Front Tires 9.5F15.0 14.9R30 20.8R38, Dual

Rear Tires 480/70R 18.4R42 20.8R38, Dual

Wheel Base (mm) 2342 2799 3289

Table 2. Results of analysis conducted to match tractors with implements (Tractor Data). PREDICTED Tractor

Soil

A 1

B C A

2

B C A

3

B C

Front WT (kg) 1,220 610 1,220 630 1,220 645 2,454 3,968 2,454 4,066 2,454 4,665 7,817 6,855 7,817 7,510 7,817 8,322

Rear WT (kg) Slip (%) 2,275 19.0 3,140 12.0 2,275 18.4 3,464 10.0 2,275 18.3 4,099 8.0 4,522 28.9 4,205 20.0 4,522 19.5 4,203 14.0 4,522 17.9 5,069 10.0 6,084 11.7 5,126 15.0 6,084 10.1 5,688 11.0 6,084 9.9 6,418 9.0

Speed (km/h) 7.2 7.8 7.2 8.0 7.2 8.1 6.3 7.1 7.1 7.6 7.3 8.0 7.8 7.5 8.0 7.9 8.0 8.1

Draft (kN) 16.6 16.2 17.1 16.6 17.4 16.6 36.9 36.0 39.8 39.0 41.2 39.7 52.9 56.8 57.9 58.8 60.3 59.7

PDE 0.663 0.704 0.689 0.737 0.702 0.757 0.572 0.627 0.698 0.731 0.737 0.779 0.644 0.665 0.718 0.721 0.749 0.749

Drawbar wt/ptopower Power (kg/kW) (kW) 33.0 70 35.0 75 34.2 70 36.7 82 34.9 70 37.6 95 64.5 62 70.7 72 78.8 62 82.5 73 83.2 62 87.9 86 114.9 78 118.7 67 128.1 78 128.7 74 133.5 78 133.6 83

Fuel Cons (l/h)

15.8

35.7

56.5

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Table 2 (cont’d) Results of analysis conducted to match the tractors with implements (Implement Data). Moldboard Plow (no coulters) Tractor

B C A

2

B C A

3

Field Cultivator

Soil

A 1

Disk Harrow, Tandem

B C

Depth (mm) 180 194 175 161 164 200 219 216 206 216 189 198 219 217 200 204 208 205

Width (m) 1.83 1.22 1.22 1.22 0.91 0.61 3.96 3.66 2.74 2.44 2.13 1.83 5.18 5.79 2.74 3.96 2.74 2.74

Field Cap (ha/h) 1.11 0.81 0.75 0.83 0.56 0.42 2.12 2.20 1.66 1.58 1.32 1.24 3.44 3.70 1.86 2.65 1.86 1.88

Fuel (l/ha) 14.2 19.6 21.1 19.2 28.1 37.5 16.8 16.2 21.5 22.6 27.1 28.8 16.4 15.2 30.4 21.3 30.3 30.1

Depth (mm) 197 209 194 208 191 206 207 204 202 205 198 198 205 202 207 200 205 203

Width (m) 2.03 1.83 1.63 1.42 1.42 1.22 4.47 4.27 3.66 3.45 3.25 3.05 6.10 6.71 4.27 5.28 4.47 4.47

Field Cap (ha/h) 1.24 1.21 1.00 0.96 0.87 0.84 2.39 2.57 2.21 2.24 2.01 2.06 4.05 4.29 2.89 3.54 3.03 3.06

Fuel (l/ha) 12.8 13.1 15.9 16.4 18.1 18.8 14.9 13.9 16.1 16.0 17.8 17.3 13.9 13.2 19.6 16.0 18.6 18.4

Depth (mm) 126 129 128 126 127 129 128 127 127 126 126 126 127 127 127 128 126 127

Width (m) 5.91 5.52 4.57 4.38 4.00 3.62 13.53 12.76 10.86 10.48 9.53 8.95 18.29 19.81 12.95 15.43 13.53 13.34

Field Cap (ha/h) 3.60 3.66 2.81 2.97 2.46 2.51 7.23 7.68 6.57 6.78 5.88 6.06 12.15 12.67 8.76 10.33 9.17 9.13

Fuel (l/ha) 4.4 4.3 5.6 5.3 6.4 6.3 4.9 4.6 5.4 5.3 6.1 5.9 4.6 4.5 6.4 5.5 6.2 6.2

Table 3. Results of analysis conducted to match tractors and implements. INPUT Implement

Moldboard Plow (no coulters)

Disk Harrow, Tandem

Field Cultivator

Fi Soil Texture 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

Depth (mm) 203 203 203 203 203 203 152 152 152 152 152 152 127 127 127 127 127 127

Width (m) 1.8 1.8 1.8 3.7 3.7 3.7 6.4 6.4 6.4 9.1 9.1 9.1 5.5 5.5 5.5 14.3 14.3 14.3

PREDICTED Field Speed (km/h) 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0

Draft (kN) 36.4 25.5 16.4 72.8 51.0 32.8 42.6 37.5 33.2 60.8 53.5 47.5 23.1 19.7 15.0 60.4 51.3 39.2

Drawbar Power (kW) 81 57 37 163 114 73 95 84 74 136 120 106 52 44 34 135 115 88

PTOPower (kW) 108 76 49 217 152 98 127 112 99 181 160 141 69 59 45 180 153 117

Field Cap @ .85 (ha/h) 1.25 1.25 1.25 2.50 2.50 2.50 4.38 4.38 4.38 6.25 6.25 6.25 3.75 3.75 3.75 9.80 9.80 9.80

Fuel use (l/ha) 27.5 19.2 12.4 27.5 19.2 12.4 9.2 8.1 7.2 9.2 8.1 7.2 5.8 4.9 3.8 5.8 4.9 3.8

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