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affects machinery management decisions. ASAE Standards (2000a) defines field efficiency as the ratio of effective field capacity to theoretical field capacity.
Paper Number: 02-1008 An ASAE Meeting Presentation

Extracting Machinery Management Information from GPS Data Randal K. Taylor, Professor and Extension Engineer Biological and Agricultural Engineering, 237 Seaton Hall, Kansas State University, Manhattan, KS 66506 [email protected]

Mark D. Schrock, Professor Biological and Agricultural Engineering, 237 Seaton Hall, Kansas State University, Manhattan, KS 66506

Scott A. Staggenborg, Associate Professor and Extension Specialist Northeast Area Extension, Kansas State University, Manhattan, KS 66506

Written for presentation at the 2002 ASAE Annual International Meeting Sponsored by ASAE Hyatt Regency Chicago, Illinois, USA July 28-31, 2002 Abstract. GPS data obtained during yield mapping operations for 23 fields were used to glean machinery management information. Yield monitor data were exported into the Ag Leader advanced format and GPS time, logging interval, mass flow, and distance were used to help assess combine performance. Field efficiency and capacity were determined. Time required to harvest, turn, and unload the crop were estimated. The relationship between efficiency and crop yield or unloading time was weak. Turning and efficiency were moderately correlated. Optimizing field traffic patterns to minimize turns appeared more opportunity for increasing field efficiency than unloading on the go. Keywords. Yield monitor, precision agriculture, field efficiency, field capacity, combine harvester.

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 Engineers (ASAE), 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 ASAE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE meeting paper. EXAMPLE: Author’s Last Name, Initials. Title of Presentation. ASAE Meeting Paper No. xx-xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at [email protected] or 616.429.0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

Introduction Though machinery inputs are a significant portion of crop production expenses, machinery selection has long been a challenge for crop producers. Software and expert systems at various levels have been developed to aid machinery selection and evaluation (Kjelgaard and Wu, 1983; Kline et al., 1986; Doster and Parsons, 1990; Kotzabassis et al. 1990; and Reeder et al., 1991). Research has been conducted to provide information to assist in machinery management decisions (Pandey and Devnani, 1987 and Taylor, 1991). However this research is often quite specific to an environment and difficult to generalize. The ability of a machine to perform efficiently within an environment is an important criterion that affects machinery management decisions. ASAE Standards (2000a) defines field efficiency as the ratio of effective field capacity to theoretical field capacity. Field efficiency accounts for a failure to use the theoretical working width of a machine, operator habits, turning time, and field characteristics. For a given field operation, there are two major items that influence field efficiency, overlap and turning time. For combine harvesting, another major factor affecting field efficiency is unloading the grain. Overlap is not a major concern for a row-crop field with straight rows of equal length. However, due to slopes and terraces, many fields in Kansas do not have straight rows of equal length. Field operations are typically accomplished on the contour with rows of varying length. Harvest width is also often less than the actual header width. Peterson et al. (1981) found that field efficiency decreased with increasing implement width when field operations were conducted between terraces. Steichen and Powell (1985) presented a farmability index for terraced fields and concluded that field efficiency was a function of implement and terrace design. Pandey and Devnani (1987) evaluated two harvest patterns and concluded that field efficiency could be improved by optimizing harvest patterns. Grisso et al. (2002b) evaluated a steering adjustment index as a measure of field farmability and found only a moderate relationship between the index and field efficiency. During harvesting operations, the grain must be unloaded from the combine. Field efficiency can be increased if the crop is unloaded while the combine is still harvesting. With higher yielding crops, there is more grain thus increasing the time required to handle the crop. A grain cart and operator to facilitate on-the-go unloading are needed to maintain field efficiency. The competitive climate in production agriculture is requiring farm managers to become more productive, while labor restrictions continue to be a problem. It can be challenging to make grain handling decisions with limited information. Field efficiency and capacity data are typically collected through time-motion studies (Taylor, 1991). However with the increasing popularity of precision agriculture technologies the data collection process can be automated and in many instances is occurring without operator input. Grisso et al. (2002a) used spatial data obtained through DGPS to evaluate field efficiency of planting and harvesting operations and found that field efficiency was reduced by 10% and 20% for contour planting and harvesting operations. Taylor et al. (2001) found that field efficiency decreased with increasing planter width for fields in northeast Kansas. Though field capacity increased with planter width, the ratio was not oneto-one. A 30 ft planter did not have twice the field capacity of a 15 ft planter. Data used by Grisso et al. (2002a) and Taylor et al. (2001) were seamlessly collected by equipment operators without taxing their workload. However using the data for machinery management decisions was a secondary objective of the data collection process. The objective of this research was to use DGPS data gathered during harvest to evaluate the potential for improving harvest efficiency and capacity.

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Procedures Combine harvest information was obtained for 23 field/year combinations in Kansas over a span of six years (Table 1). Data were obtained for the four major crops grown in eastern Kansas (corn, soybeans, grain sorghum, and wheat). Combines were unloaded at the edge of the field for all cases. The number and location of unloading points was field specific and not measured. Fields ranged in size from 19 to 87 acres and had varying yields. Harvest patterns were also field specific and no attempt was made to quantify them in an effort to explain field efficiency. The yield monitor data were exported in the Ag Leader advanced format and imported into a MathCad program. The data were sorted with the GPS time values placed in ascending order. The data were then queried to find all lines with either a zero distance or mass flow. These lines were deleted from the file. The change in GPS time for each observation was calculated by subtracting the GPS time from the subsequent GPS time value. The final data point in the file was assigned a change in time equal to the recording interval. The total time spent in the field (ttl) was determined by subtracting the minimum GPS time value from the maximum GPS time value. The time spent harvesting (th), when the combine was moving and had grain flow, was determined by summing the sampling interval column. Four other time values, turning (tt ), unloading (tu), stopped (ts ), and overnight (to), were determined from the change in GPS time values using arbitrary thresholds. The time spent turning was estimated by summing the ∆t values the exceeded the sampling interval but were less than 90 s. The 90 s value was estimated based on unloading capacities and bin sizes for the combines used. This time was insufficient to completely unload a full bin and does not allow time to move to the edge of the field. Unloading time was estimated by summing the ∆t values that exceeded 90 s and were less than 10 minutes. The upper limit was deemed sufficient time to travel from the far side a field and unload a full bin. Stopped time was estimated by summing the ∆t values the exceed 10 minutes and were less than 5 h. This time could have been used for anything from repairs to waiting on trucks. The sum of all ∆t values that exceeded 5 h was considered overnight. The average harvest speed was determined from the recording interval and distance traveled per observation. This speed and the header width were used to calculate the theoretical field capacity. The theoretical time (tth) to harvest the field was determined by dividing the field area, as determined with a georeferenced boundary, by the theoretical field capacity. The effective field capacity was calculated by dividing the field area by the field time. The field time (tf ) was determined by subtracting the overnight time from the total time and was intended to represent the amount of time that the combine was at the field ready to work. Most daily maintenance occurs in the morning before harvest starts and this procedure would exclude time spent on this task from the harvest time per ASAE recommendations (ASAE 2001a). Field efficiency was determined by dividing the theoretical time by the field time. This includes all the time elements recommended by ASAE. Unfortunately it also possibly includes the operator’s personal time which should be excluded. However, without more information about the harvest operation, it is impossible to separate personal time from other time lapses. Harvest efficiency was the ratio of theoretical time to the sum of harvest, turning, and unloading times and was intended to be a more generalized representation of field efficiency. An operating efficiency, previously described by Taylor et al. (2001), was defined as the ratio of the theoretical time required for the field task to the working time used for the field task. Given the preprocessing of the data (removing zero distances and mass flows), the operating efficiency should indicate the amount of overlap for the field.

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Yield was calculated by summing the mass of grain harvested and dividing by the field area and standard test weight. Material capacity, the average material flow during combine operation, was calculated by dividing the total mass harvested by the harvest time. Table 1. Field, combine, and yield monitor information. Field AR AR DB DB HE HM JC KN KR LI LI LI LU NL PA PA PE PE PN PN PW PW WA

Year 99 00 97 99 01 01 97 99 01 97 98 99 98 97 99 00 01 02 01 02 01 02 99

Crop Wheat Wheat Beans Beans Beans Corn Corn Corn Milo Beans Corn Beans Milo Corn Wheat Wheat Beans Wheat Beans Wheat Beans Wheat Milo

Area, ac 41.3 41.3 56.1 56.1 32.7 55.2 87.1 70.2 69.1 80.7 80.7 80.7 47.4 38.6 55.2 55.2 32.7 32.7 24.4 24.4 19.1 19.1 33.8

Header Width, ft 25 25 25 25 21 15 15 15 20 20 15 20 15 15 25 25 21 21 21 21 21 21 25

Combine Case-IH 2166 Case-IH 1680 Case-IH 2166 Case-IH 1680 Case-IH 2188 Case-IH 2188 John Deere 9500 John Deere 9500 Case-IH 2188 John Deere 9500 John Deere 9500 John Deere 9500 John Deere 9500 John Deere 9500 Case-IH 2166 Case-IH 1680 Case-IH 2188 Case-IH 2188 Case-IH 2188 Case-IH 2188 Case-IH 2188 Case-IH 2188 Case-IH 1680

Yield Monitor AFS Ag Leader YM 2000 AFS Ag Leader YM 2000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader YM 2000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader YM 2000 Ag Leader YM 2000 Ag Leader YM 2000 Ag Leader YM 2000 Ag Leader YM 2000 AFS Ag Leader YM 2000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader PF 3000 Ag Leader YM 2000

Results The various time categories for each field are shown in Table 2. As expected the times within each category vary greatly. Fifteen of the fields were harvested on multiple days. Harvest times were generally similar for fields with multiple years of data, however turning, unloading, and stopped times were not. This could be expected for fields with different crops, but the AR, PA and DB fields had the same crop grown in the both years. The unloading time averaged 1.2 hours ranged from 0.2 to 2.8 hours. Unloading time was related to crop and is shown plotted versus yield in figure 1. The line in figure 1 was forced through the origin, since a crop that yields zero will not require time to unload. The cluster of points with yields less than 50 bu/ac and unloading times greater than 1 hour are mostly from larger fields fields. The data in the same yield range with lower unloading times are mostly from smaller fields. The larger fields would produce more grain at the same yield levels and would require more unloading time. However, when unloading time was plotted versus total grain mass these outliers were still present and were from larger fields (figure 2). This indicates that field size had some influence on unloading time that was independent of crop yield. The travel time to the edge of the field in the larger fields was potentially greater than that for smaller fields and was likely causing the outliers in figure 2.

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The average time spent turning was 1.2 h and the range was 0.2 to 2.8 h. As expected turning time was influenced by field size (figure 3) and 68 percent of the variation in turning time could be explained with field size. The other 48 percent could possibly be explained with field and operator characteristics. However, six fields that had two years of data (AR, DB, PA, PE, PN, and PW) had dissimilar turning times and field characteristics (shape and location of terraces) did not change between the two observations (Table 2). The differences for three fields (PE, PN, and PW) could be due to a different crop, but the same crop was grown in both years on three fields (AR, DB, and PA). There may have been differences in harvest pattern or unloading habits that could be due to operator tendencies. Table 2. Times within each category for each/year field in hours. Field AR AR DB DB HE HM JC KN KR LI LI LI LU NL PA PA PE PE PN PN PW PW WA

Year 99 00 97 99 01 01 97 99 01 97 98 99 98 97 99 00 01 02 01 02 01 02 99

Total

Harvest

Field

Turn

Unload

Stopped Overnight

5.2 27.1 97.8 23.8 172.8 695.4 894.7 30.7 20.0 556.1 170.2 25.3 8.5 6.9 9.8 25.6 4.8 2.9 3.2 2.7 2.6 1.8 25.3

3.1 3.0 6.3 5.0 4.1 5.6 9.1 8.3 6.1 8.2 8.4 7.5 4.8 4.1 4.2 4.2 2.7 2.3 2.0 1.9 1.7 1.4 3.1

5.2 7.5 12.0 8.9 8.8 9.6 19.7 15.2 8.7 14.5 19.2 10.2 8.5 6.9 9.8 7.5 4.8 2.9 3.2 2.7 2.6 1.8 9.6

0.6 0.3 2.1 1.2 1.3 2.0 2.1 2.3 1.3 2.2 2.2 1.6 1.1 1.2 1.4 0.8 0.6 0.3 0.7 0.4 0.5 0.2 0.8

1.5 0.6 2.2 1.5 0.7 1.8 2.4 2.2 1.4 1.6 2.8 0.9 2.1 1.4 1.2 0.3 0.4 0.3 0.2 0.3 0.2 0.2 1.7

0.0 3.6 1.3 1.1 2.7 0.3 6.1 2.4 0.0 2.6 5.8 0.2 0.5 0.2 2.9 2.1 1.1 0.0 0.3 0.0 0.2 0.0 4.1

0.0 19.7 85.8 14.9 164.0 685.8 875.0 15.5 11.3 541.6 151.0 15.0 0.0 0.0 0.0 18.1 0.0 0.0 0.0 0.0 0.0 0.0 15.7

5

3.0

2.5

Unloading Time, h

y = 0.017x R2 = 0.37 2.0

1.5

1.0

0.5

0.0 0

20

40

60

80

100

120

140

160

Crop Yield, bu/ac

Figure 1. Unloading time increased linearly with crop yield. 3.5

3.0 y = 0.0003x R2 = 0.44 Unloading Time, h

2.5

2.0

1.5

1.0

0.5

0.0 0

2000

4000

6000

8000

10000

12000

Total Mass, bu

Figure 2. Unloading time as a function of total mass harvested from each field.

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2.5

2.0

Turning Time, h

y = 0.0273x - 0.1591 2 R = 0.68 1.5

1.0

0.5

0.0 0

10

20

30

40

50

60

70

80

90

100

Field Size, ac

Figure 3. Turning time as a function of field size.

Average speed and field capacity for the harvest operation are shown in Table 3 for each field/year. Like the turning, unloading, and stopped times, there were some differences for the same fields in different years. This observation was found even when the same crop was grown. However there were no measurements of crop or field conditions specific to a year that could have caused the difference. Theoretical field capacity, based on the average operating speed and header width, and the actual field capacity, based on field size and harvest time are also shown in Table 3. The theoretical field capacities are substantially greater than the effective field capacities also shown in Table 3. This difference is also illustrated through the somewhat low field efficiencies. Field efficiencies are shown in Table 3 and ranged from 0.29 to 0.75 with an average of 0.54. These values are lower than those cited by ASAE (2001b). However these efficiencies include all time lapses or breaks up to 5 h. There could easily be time included in this calculation that should be omitted. The harvest efficiency included time lapses up to 10 minutes and ranged from 0.41 to 0.77 with an average of 0.61. While these values are only slightly greater than the field efficiencies, we believe they are a more accurate representation of the field efficiency attained during harvest. The operating efficiencies shown in Table 3 should be a representation of the amount of overlap and exceed 0.90 for all but four field/years. Soybeans were grown on those four fields and were harvested with flexible headers. Operating a flexible header in most fields would force the operator to harvest on the contour leading to more overlap and lower operating efficiencies. The other four soybean fields in the study were also harvested with flexible headers and the operating efficiency for these fields averaged 0.94.

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Field and harvest efficiencies were dissimilar for some fields with multiple years of data for the same crop (AR, LI 97 and 99, and PA) indicating that operators or seasonal environmental conditions have an impact on efficiency. Field and harvest efficiencies are plotted versus crop yield in figure 4. There is weak correlation between field efficiency and crop yield and only slightly better between harvest efficiency and crop yield. Only 10 percent of the variation in harvest efficiency could be explained with crop yield. As previously stated, we believe harvest efficiency is better than field efficiency for assessing combine field performance. There are certainly many factors other than crop yield affecting harvest efficiency. Table 3. Capacities and efficiencies for the field/years in this study

Field AR AR DB DB HE HM JC KN KR LI LI LI LU NL PA PA PE PE PN PN PW PW WA

Year 99 00 97 99 01 01 97 99 01 97 98 99 98 97 99 00 01 02 01 02 01 02 99

Speed 4.4 4.7 3.0 4.2 4.7 5.5 5.4 4.8 4.8 4.7 5.4 5.1 5.5 5.4 4.6 4.4 5.1 5.8 5.5 5.1 5.0 5.8 4.0

Theoretical Field Field Capacity, Capacity, Field Harvest Operating ac/h ac/h Efficiency Efficiency Efficiency 13.4 14.3 9.2 12.8 12.0 10.1 9.7 8.7 11.7 11.3 9.9 11.8 10.0 9.8 13.9 13.4 13.1 15.1 14.1 13.3 13.0 15.0 12.2

7.9 5.5 4.7 6.3 3.7 5.7 4.4 4.6 7.9 5.6 4.2 7.9 5.5 5.6 5.7 7.4 6.8 11.3 7.7 9.2 7.3 10.5 3.5

0.59 0.39 0.51 0.49 0.31 0.57 0.45 0.53 0.68 0.55 0.42 0.67 0.55 0.57 0.41 0.55 0.52 0.75 0.55 0.69 0.57 0.70 0.29

0.59 0.74 0.58 0.57 0.45 0.59 0.66 0.64 0.68 0.59 0.61 0.68 0.59 0.59 0.58 0.77 0.67 0.75 0.60 0.69 0.61 0.70 0.50

0.98 0.96 0.98 0.87 0.67 0.98 0.99 0.98 0.97 0.97 0.97 0.91 0.98 0.96 0.95 0.98 0.92 0.93 0.87 0.96 0.87 0.91 0.91

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0.90 0.80

y = -0.0006x + 0.669 R2 = 0.10

0.70

Efficiency

0.60 Harvest Field Linear (Harvest) Linear (Field)

0.50 y = -0.0006x + 0.5749 R2 = 0.04

0.40 0.30 0.20 0.10 0.00 0

20

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160

Crop Yield, bu/ac

Figure 4. Harvest and field efficiency versus crop yield.

Combine and crop performance data for the 23 field/years used in this study are shown in Table 4. Field and harvest efficiencies are shown plotted against unloading time in figure 5. The unloading time indicates the time that the combine is stopped to unload one bushel of grain. Again there is little relationship between these efficiencies and the time required to unload grain. Approximately 11 percent of the variability in harvest efficiency can be explained with the unloading time. The relationship is negative with harvest efficiency decreasing as the required unloading time increases. The relationship between turning time and field and harvest efficiencies is plotted in figure 6. Harvest efficiency shows a stronger relationship with turning time than field efficiency and both are negative. Efficiencies decrease with increasing turning time per acre. More than 60 percent of the variability in harvest efficiency was captured with turning time which is substantially better than that obtained with unloading time. The relationship between efficiency and time required to turn and unload appears to be influenced by crop, but there was insufficient data in this study to explore it further. Given the stronger relationship with turning time and harvest efficiency, it appears that farm managers should focus more effort on reducing the time spent turning during harvest rather than unloading on the go.

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Table 4. Crop and combine performance data. Year 99 00 97 99 01 01 97 99 01 97 98 99 98 97 99 00 01 02 01 02 01 02 99

Yield, bu/ac 39.62 39.51 31.52 30.88 74.33 130.3 98.69 130.9 102.6 47.7 140.7 21.47 138 105.7 35.43 33.83 44.09 39.05 39.71 45.29 38.93 40.88 102.2

Material Capacity, bu/h 521.3 543.7 282.2 343.2 593.0 1287.1 947.4 1111.9 1160.5 469.0 1350.4 231.5 1365.0 998.0 468.4 444.5 533.6 547.1 486.7 578.6 438.7 559.7 1132.2

Turning Time s/ac 50.5 22.7 137.6 77.8 205.8 182.1 88.8 116.9 108.5 98.2 97.9 70.0 85.3 110.9 93.0 55.1 111.6 33.5 147.3 59.9 140.2 43.9 85.9

Unloading Rate, s/bu 3.27 1.39 4.55 3.12 1.08 0.89 1.01 0.86 0.69 1.52 0.89 1.97 1.17 1.20 2.29 0.63 0.95 0.72 0.77 1.13 0.89 0.89 1.73

0.90 0.80 0.70

y = -0.0282x + 0.6669 R2 = 0.11

0.60 Efficiency

Field AR AR DB DB HE HM JC KN KR LI LI LI LU NL PA PA PE PE PN PN PW PW WA

Harvest Field Linear (Harvest) Linear (Field)

0.50 0.40

y = -0.0295x + 0.5734 R2 = 0.05

0.30 0.20 0.10 0.00 0.00

1.00

2.00

3.00

4.00

5.00

Unloading Rate, s/bu

Figure 5. Field and harvest efficiencies versus unloading time.

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0.90 0.80 y = -0.0019x + 0.795 2 R = 0.63

0.70

Efficiency

0.60 0.50

Harvest Field Linear (Harvest) Linear (Field)

y = -0.0014x + 0.6554 2 R = 0.14

0.40 0.30 0.20 0.10 0.00 0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

Turning Time, s/ac

Figure 6. Field and harvest efficiency versus turning time per acre.

Conclusions This data show the challenges associated with using precision ag data to assist machinery management decisions. The assumptions made about the data can possibly influence the results. Based on these assumptions and the procedures outlined in this study, harvest efficiency is more dependent upon turning time than unloading time. Farm managers could more quickly improve harvest efficiency by modifying harvest patterns to minimize turning than by unloading grain on-the-go. The challenges associated with processing this data were mostly based on the assumptions that were used regarding turning and unloading times. This was certainly an exercise in data mining, but the time required to gather this much information with a stop watch is prohibitive. Something as simple as knowing when the unloading auger was operating would greatly improve this process and it could easily be added to a yield mapping system for minimal cost.

Acknowledgments The authors would like to acknowledge the contribution of data for this study from Richard Little, Carbondale, Kansas and Kurt Staggenborg, Marysville, Kansas.

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References ASAE Standards, 48th Ed. 2001a. S495 Uniform Terminology for Agricultural Machinery Management. St. Joseph, Mich.: ASAE. ASAE Standards, 48th Ed. 2001b. D497.4 Agricultural Machinery Management Data. St. Joseph, Mich.: ASAE. Doster, D.H., and S.D. Parsons. 1990. Can you find a better machinery size? ASAE Meeting Paper No. 901554. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Grisso, R.D., P.J. Jasa, and S. Rolofson. 2002a. Analysis of traffic patterns and yield monitor data for field efficiency determination. Applied Engineering in Agriculture 18(2):171-178. Grisso, R.D., P.J. Jasa, M.A. Schroeder, M.E. Kocher, and V.I. Adamchuk. 2002b. Field efficiency influences from steering adjustments using analysis of traffic patterns. ASAE Meeting Paper No. 021009. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Kjelgaard, W.L., and Z. Wu. 1983. Micro-computer program for field machinery management. ASAE Meeting Paper No. 831536. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Kline, D.E., D.A. Bender, C.E. Van Donge, B.A. Carl, and J.K. Schueller. 1986. Machinery selection using farm-level intelligent decision support systems. ASAE Meeting Paper No. 864519. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Kotzabassis, C., B.A. Stout, and H.T. Wiedemann. 1990. Farm machinery selection and management expert system. ASAE Meeting Paper No. 907018. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Pandey, M.M. and R.S. Devnani. 1987. Analytical determination of an optimum mechanical harvesting pattern for high field efficiency and low cost of operation. J. agric Engng Res. 36(4):261-274 Peterson, C.L., C.L. Miller, J.H. Milligan, and R.V. Withers. 1981. Economic impact of terraces on dryland farming. TRANSACTIONS of the ASAE 24(4):951-956. Reeder, R.C., R. Leeds, R.K. Wood, and R.G. Holmes. 1991. Effect of machine size on total machinery costs for Ohio grain farms. ASAE Meeting Paper No. 911547. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA. Steichen, J.M. and G.M. Powell. 1985. Measuring farmability of terrace systems. TRANSACTIONS of the ASAE 28(4):1130-1134. Taylor, R.K. 1991. Effect of net wrap on large round baler efficiency. Applied Engineering in Agriculture 11(2):229-230. Taylor, R.K., M.D. Schrock, and S.A. Staggenborg. 2001. Using GPS technology to assist machinery management decisions. ASAE Meeting Paper No. MC01-204. ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659 USA.

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