economics of soil compaction due to machinery traffic and implications for machinery selection

13
Economics of Soil Compaction due to Machinery Traffic and Implications for Machinery Selection Kisan Gunjal,’ Gilbert Lavoie2 and G. S. V. Raghavan3 ’Assistantprofessor, Department of Agricultural Economics, McGill Universio, Macdonald College, 21111 Lakeshore Road, Ste. Anne de Bellevue, P.Q. H9X 1 CO. 2Researchassistant, Department of Agricultural Economics, McGill University, Macdonald College, 21111 Lakeshore Road, Ste. Anne de Bellevue, P.Q. H9X I CO. 3Professor,Department of Agricultural Engineering, McGill Universi 0, Macdonald College, 211 I1 Lakeshore Road, Ste. Anne de Bellevue, P.Q. H9X 1 CO. Received 12 May 1986, accepted I9 June 1987 To determine the impact of soil compaction, experiments were carried out on corn in 1976 (above-averagerainfall year) and in 1977 (below-average rainfall year) with various sizes of tractors and levels of intensities (number of passes). Contrary to the common belief, larger tractors were found to cause less economic loss. These losses ranged from $400/ha for 30 kW tractors to $63/ha for 180 kW tractors in 1976. The losses (or gains in 1977) are added to the fixed, operating, and timeliness costs of machinery, which is obtained by the engineeringmethod of finding the least-costmachinery complement.The optimum machin- ery sizes are then calculated and discussed for corn silage and grain corn crops, based on the typical six passedoperations under two weather conditions. Afin de dkterminerles implications de la compaction du sol, des exphiences ont 6t6 r6alisks sur le mais en 1976 (am& avec des pr6cipitations au-dessus de la moyenne) et en 1977 (annk avec des pr6cipitations en dessous de la moyenne). Pour rkaliser ces exphiences, des tracteurs de diffkrentesgrosseursont 6t6 utilists h diff6rents niveaux d’intensit6(nombre de passages). Contrairement h la croyance populaire, les plus gros tracteurs ont Ct6 ceux qui ont le moins affect6 le rendement des plantes. En 1976, les pertes de rendement ont vari6 entre $ m i h a avec un tracteur de 30 kW et $63/ha avec un tracteur de 180 kW. Ces pertes (ou gains c o m e ce fut le cas en 1977)ont 6t6 additionnks aux coats fixes, variables ainsi qu’aux collts dus aux retards d’ex6cution associ6s h la machinerie agricole. Finale- ment, les grosseurs optimales de tracteurs sont calculks et discubks pour le mais-fourrager et le mdis-grain en se basant sur une intensit6 d’utilisation de six passages/o@rationspour deux conditions mCtCorologiques diffkrentes. Canadian Journal of Agricultural Economics 35 (1987) 591-603 59 1

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Page 1: Economics of Soil Compaction due to Machinery Traffic and Implications for Machinery Selection

Economics of Soil Compaction due to Machinery Traffic and Implications for Machinery Selection

Kisan Gunjal,’ Gilbert Lavoie2 and G. S. V. Raghavan3

’Assistant professor, Department of Agricultural Economics, McGill Universio, Macdonald College, 21111 Lakeshore Road, Ste. Anne de Bellevue, P.Q.

H9X 1 CO. 2Research assistant, Department of Agricultural Economics, McGill University,

Macdonald College, 21111 Lakeshore Road, Ste. Anne de Bellevue, P.Q. H9X I CO.

3Professor, Department of Agricultural Engineering, McGill Universi 0, Macdonald College, 211 I 1 Lakeshore Road, Ste. Anne de Bellevue, P.Q.

H9X 1 CO.

Received 12 May 1986, accepted I9 June 1987

To determine the impact of soil compaction, experiments were carried out on corn in 1976 (above-average rainfall year) and in 1977 (below-average rainfall year) with various sizes of tractors and levels of intensities (number of passes). Contrary to the common belief, larger tractors were found to cause less economic loss. These losses ranged from $400/ha for 30 kW tractors to $63/ha for 180 kW tractors in 1976. The losses (or gains in 1977) are added to the fixed, operating, and timeliness costs of machinery, which is obtained by the engineering method of finding the least-cost machinery complement. The optimum machin- ery sizes are then calculated and discussed for corn silage and grain corn crops, based on the typical six passedoperations under two weather conditions.

Afin de dkterminer les implications de la compaction du sol, des exphiences ont 6t6 r6alisks sur le mais en 1976 (am& avec des pr6cipitations au-dessus de la moyenne) et en 1977 (annk avec des pr6cipitations en dessous de la moyenne). Pour rkaliser ces exphiences, des tracteurs de diffkrentes grosseurs ont 6t6 utilists h diff6rents niveaux d’intensit6 (nombre de passages). Contrairement h la croyance populaire, les plus gros tracteurs ont Ct6 ceux qui ont le moins affect6 le rendement des plantes. En 1976, les pertes de rendement ont vari6 entre $ m i h a avec un tracteur de 30 kW et $63/ha avec un tracteur de 180 kW. Ces pertes (ou gains c o m e ce fut le cas en 1977) ont 6t6 additionnks aux coats fixes, variables ainsi qu’aux collts dus aux retards d’ex6cution associ6s h la machinerie agricole. Finale- ment, les grosseurs optimales de tracteurs sont calculks et discubks pour le mais-fourrager et le mdis-grain en se basant sur une intensit6 d’utilisation de six passages/o@rations pour deux conditions mCtCorologiques diffkrentes.

Canadian Journal of Agricultural Economics 35 (1987) 591-603 59 1

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592 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

INTRODUCTION Soil degradation is a major problem in North America. Two important causes of this degradation are erosion and soil compaction. Historically, most studies have focused on the soil erosion problem. Very little attention has been paid to the economic consequences of soil compaction. A concern has been raised in Canada about the seriousness of the soil compaction problem in a report on soil conser- vation by the Standing Senate Committee on Agriculture, Fisheries and Forestry (Sparrow 1984). According to Mehuys (1984), soil compaction is the principal source of soil degradation in Quebec. The annual production losses due to soil compaction in the province have been estimated at about $30 million by Fox and Coote (1985) and at about $100 million by the Science Council of Canada (1986).

Machinery traffic influences crop yields by altering the soil structure. It affects the ease of root penetration into the soil and the availability of air and water to plants (Raghavan, Taylor, McKyes and Douglas 1978). Heavy compaction also increases the presence and impact of certain diseases (Vigier and Raghavan 1980). The economic impact of this is quite important; a recent study by Gunjal and Raghavan (1986) has shown that green pea yield losses due to machinery traffic in Southeastern Quebec, depending on the tractor size, are about 1.8 to 3.4 times the cost of the machinery itself. This suggests there is a major economic cost associated with machinery use, which should be included in the decision-making process of machinery selection and use.

This study attempts to analyze the economic implications of soil compaction caused by tractors of various sizes performing typical preemergence operations of cultivating, chiseling and seeding. The specific objectives of this paper are:

to convert the experimental plot level losses to actual farm-level economic losses;

0 to establish the most economical machinery size for corn-producing farms in Quebec by estimating the total economic costs of tractors and the cor- responding complement of implements; and

0 to assess the soil compaction impact and its implications for machinery selection under wet and dry weather conditions.

SOIL COMPACTION EXPERIMENTS To study the impact of machinery traffic on grain corn and corn silage, randomized complete block design experiments were conducted on the Macdonald College farm located in the Montreal area. The farm has a Ste. Rosalie clay soil (80% clay particles by weight) susceptible to compaction. Four replications of twelve treat- ments were used. These treatments consisted of four different numbers of passes (1,5, 10 and 15 preemergence passes with three two-wheel drive tractors having contact pressures of 31.4,41.2 and 61.8 kPa (kilopascals) leading to a cumulative contact pressure ranging from 3 1.4 kPa to 927 kPa in twelve different levels. In

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Table 1 . Models for estimating the relationship of yields to cumulative contact pressure

Variable'

Dry yield Dry yield

1976 1971 1976 1977 of corn silage of grain corn

Intercept In (NP)

NP

( N O 2

F R2

n

Otg/ha) 20988.3 10394.0 - 1634.1

(5.43)** 9.951 (3.35)**

-0.01 122 (3.88)**

29.5** 6.24** 0.38 0.19

48 56

1123 1.2 5456.6 -979.3

(6.33)** 3.399 (1.97)*

-0.004166 (2.05)*

a** 2.11 0.46 0.07

48 56

NP contact pressure)

In(NP) = natural log of NP R2 = coefficient of determination n = number of observations

** Figures in parenthesis are t values Source: Raghavan, McKyes, Taylor, Richard and Watson (1979) and Raghavan, McKyes, Gendron, Borglum and Le (1978).

= cumulative contact pressure in kilograms per hectare (N = number of passes and P =

* = 0.05 level of significance = 0.01 level of significance

addition to these twelve treatments, there was also a control plot (zero traffic treat- ment) in each replication.

The first experiment was conducted in 1976, a year with an above-average rainfall (henceforth called a wet year) and was repeated in 1977, a year with a below-average rainfall (henceforth called a dry year).' The plot sizes were 2.25 m by 10 m in 1976 and 1.5 m by 10 m in 1977. In both years seeding and harvesting was done by hand and the total plant yield and grain yield was standardized on a per-hectare basis. The details of the experiments are given in Raghavan and McKyes (1 977).

YIELD-COMPACTION MODELS

Experimental yield Based on field trial data for 1976 and 1977, mathematical models were found by Raghavan, McKyes, Taylor, Richard and Watson (1979) and Raghavan, McKyes, Gendron, Borglum and Le (1978) to estimate the relationship of the yields (on a dry-matter basis) of both, corn silage and grain corn, with cumulative contact pressures.* These models are presented in Table 1 .

The semilog models for 1976 indicate that, as contact pressure increases, the yield decreases at a decreasing rate (the first derivative is negative and the second

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594 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

derivative is positive). This reveals the detrimental effects of compaction under wet conditions. Whereas the quadratic models for 1977 imply beneficial effects of compaction up to a certain point and detrimental effects thereafter under dry conditions. This can be explained by the fact that, in a dry year, water availability to the plants is higher in a moderately compacted soil than in a loose, uncompacted soil. However, in heavily compacted soil, the growth of the plant is restricted because of the high root-penetration resistance of the soil and insufficient water storage. It should be mentioned here that the 1977 grain corn equation is not sig- nificant. Therefore, the results based on this specific equation may have relatively less confidence. Following Gunjal and Raghavan (1986), yield losses or gains are calculated by subtracting the control level (NP = 0) yield from the yields associated with the cumulative contact pressure, NP, calculated for the experimental plots.

Yield losses at farm level The above models estimate the yields and yield losses (gains) when the entire soil surface is subjected to contact pressure (experimental conditions). However, at the farm level, the yield losses would be much lower since only a part of the soil would be under machinery traffic. In order to estimate the yield losses (gains) at the farm level, the following steps were followed.

Contact pressure is calculated on the basis of tractor weight and rear-wheel width and diameter for different tractor sizes ranging from 24 kW to 127 kW. The data are taken from the Agricultural Engineers Yearbook of Standards (ASAE 1983-84). The following relationship is then estimated between contact pressure and tractor size to derive a smooth curve:

CP = 30.4832 + 0.276965 kW (15.1)** (8.66)** F = 74.98**,R2 = 0.69, n = 35

where

CP = contact pressure in kPa and kW = tractor size in kilowatts (other definitions are given in Table 1).

In general, the estimated contact pressure is found to increase linearly with the tractor size.

0 Cumulative contact pressures are calculated by multiplying the estimated contact pressures (from Fq. 1) by the number of passes.

0 The experimental yield losses are estimated for different tractor sizes using models presented in Table 1.

0 The actual compaction area percentage, CAP, is calculated by dividing the area under the rear tires by the average area covered in one standard implement pass. This average area is estimated for cultivating, chiseling and seeding (three typical preemergence operations in Quebec). The area under the tires is equal to

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ECONOMICS OF SOIL COMF’ACnON 595

the rim width times 1.2 (for 20% extra area) times the number of rear wheel tires. The average area covered by the above three selected implements is equal to the kilowattage of the tractor divided by 15.77. This 15.77 kW/m is the average power needed to pull one metre width of the selected three implements. The necessary data are derived from the Agricultural Engineers Yearbook of Standards (ASAE 1983-84) and are also adapted from the Comit6 de RCfkrences &onomiques en Agriculture du QuCbec (1985, Agdex 740).

To establish a smooth curve relationship between the actual compaction area percentage and the tractor size, the following equation is obtained:

(2) In (CAP) = 6.35466 - 0.732289 ln(kW) (68.6)* * (30.4)* * F = 915.84**, R2 = 0.97, n = 35

where In (CAP) = natural logarithm of compaction area percentage (other definitions are

The estimated CAP is found to decrease exponentially as tractor size increases. 0 Finally, the the farm level yield losses are obtained by multiplying the exper-

imental yield losses by the corresponding CAP f a ~ t o r . ~ The farm-level yield losses, in dollar terms, for a case of machinery-use

intensity equivalent to three passes of a range of tractor sizes are presented in Table 2. It is interesting to note that the farm-level yield losses decline continu- ously with an increase in tractor size. The losses start at $202/ha for corn silage and $400/ha for grain corn for a 30 kW tractor. For a 180 kW tractor, these losses are $63/ha and $124/ha for corn silage and grain corn, respectively. This can be explained by the fact that big tractors create less “effective” compaction than small tractors. This is contrary to common belief, but it follows from Eq. 1 and 2 that, as tractor size increases, contact pressure increases linearly, while the actual compaction area decreases loglinearly (Eq. 2) resulting in less and less “compac- tion effect” on yield within the selected range of tractor sizes. This, however, does not consider the long-term cumulative effect of subsoil compaction.

In the wet year, the yield losses for grain corn are heavier than for corn silage while, in the dry year, the yield gains are almost identical for both crops. This suggests that economically the grain yield is affected more by machinery traffic than the whole plant (corn silage) yield. However, in both crops the losses per hectare are substantial and should not be ignored when making machinery selection decisions.

given in Table 1 and Eq. 1).

MACHINERY COSTS

For the selection of optimum machinery size and machinery-use intensity, the costs of owning and operating machinery are important and are considered in this

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Table 2. Contact pressure, compaction area percentage, and estimated yield losses at farm level for three preemergence passes'

~~

Farm level vieldlossb for three asses Corn silage Grain corn Tractor

Size P CAP 1976 1977 1976 1977

OrW ocpa) ($ha) 30 38.8 47.7 202.1 - 26.1 400.1 - 29.1 40 41.6 38.6 166.0 - 22.4 328.8 -25.0 50 44.3 32.8 142.9 - 20.1 283.0 - 22.4 60 47.1 28.7 126.6 - 18.5 250.7 -20.5 70 49.9 25.6 114.4 - 17.3 226.5 - 19.2 80 52.6 23.3 104.9 - 16.4 207.6 - 18.1 90 55.4 21.3 97.2 - 15.6 192.4 - 17.3

100 58.2 19.7 90.8 - 15.0 179.8 - 16.6 110 61.0 18.4 85.4 - 14.5 169.2 - 16.0 120 63.8 17.3 80.9 - 14.0 160.1 - 15.5 130 66.5 16.3 76.9 - 13.7 152.2 - 15.0 140 69.3 15.4 73.4 - 13.3 145.3 - 14.6 150 72.0 14.7 70.3 - 13.0 139.1 - 14.3 160 74.8 14.0 67.5 - 12.7 133.7 - 13.9 170 77.6 13.4 65.0 - 12.5 128.7 - 13.6 180 80.3 12.8 62.7 - 12.2 124.2 - 13.4

'including cultivating, chiseling and seeding busing 1981-85 average prices in Quebec ($30/t at 55% dry matter, for corn silage and $155/t at 86% dry matter, for grain corn) Source: CREAQ, 1985.

section. The costs of total machinery complement are calculated following Brown and Schoney's (1985) method. They include:

0 fixed costs, which are the sum of the amortized investment charges (depre- ciation and interest) and a certain percentage of purchase price as insurance, housing and tax charges;

0 operating costs, which include charges toward repairs, maintenance, fuel, oil and labor. These are dependent on the area covered, speed of operation, field efficiency and width of machine; and

0 timeliness costs, which represent a value of risk and uncertainty as related to machinery use, measured by the yield losses both in quality and quantity due to delay or poor timing in machinery operations and inadequate machin- ery size.

Timeliness costs constitute a major component of machinery costs and in- fluence farm machinery selection (Oskoui 1983). Timeliness costs depend on such factors as speed of operation, field efficiency, width of machine and the crop losses per day of delay for a given farm size. The following equation can be used to approximate the timeliness costs (simplified from Hunt 1977 and Brown and Schoney 1985):

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ECONOMICS OF SOIL COMPACTION 597

Table 3. Timeless loss factors for seeding corn in Ontario

Seeding date k value (loss per day of delay)

(95) May 10 -May 20 0.5 May 20 - May 30 1.5 May 30 - June 10 1.7

1.2

Source: Estimated on the basis of the experimental data from Irwin (1981).

Average May 10 -June 10

T, = H, * (kV/uhs) (3) where

T, = H , =

k = V =

h = u =

s =

timeliness cost of implement i ($/year); the time required to complete a given operation and given area; it is equal to the total area covered divided by the area covered per hour by implement i (speed X width X efficiency); timeliness loss factor (fraction of crop yield lost per day of delay); value of crop (yield X hectarage X crop price); usable portion (fractional utilization) of work time available; total hours available for work per day; and 2 for premature or delayed schedule; 4 for balanced schedule; usually 2 for Quebec since the growing season is very short (these are unitless numbers as used in the above-cited sources of the equation).

Timeliness loss factor k and fractional utilization of time u are not easy to estimate. There is a lack of field experiment data to determine the k value for Quebec regions. For seeding, the value is estimated from an experiment on the effects of planting dates on crop quality and quantity, carried out in Ontario (Irwin 198 1). The results are shown in Table 3. The remaining data are taken from Brown and Schoney.

' Fractional utilization of time u for the Montreal area is estimated from a study by Dyer et a1 (1978) on spring field work day probabilities for selected sites across Canada. The u value is calculated by dividing the minimum expected working days between May I and June 29 at each given probability level (certainty level) by 60 days. At an assumed certainty level of 80%, u is estimated at 0.43 (25.6/60).

Cost calculation procedure In order to calculate the above-mentioned costs of a full machinery complement, one must first establish the most appropriate implement sizes corresponding to each size of tractor. This is accomplished by finding the least-cost width of each of the six selected implements (cultivator, chisel, seeder, sprayer, rotary hoe and plow) using an electronic spreadsheet on microcomputer. Then a series of tractor sizes and their corresponding implements are established to generate fixed, vari- able, and timeliness costs for a wide range of tractor sizes. This least-cost pro- cedure outlined by Hunt (1977) is also applied by Brown and Schoney (1985) for

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598 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

Table 4. Machinery costs based on six typical passes'

corn silige Grain corn Total Total

Tractor Fixed Operating Timeliness machinery Timeliness machinery size costsb costsc costsd costs costs costs ______

(kW) 30 40 50 60 70 80 90

100 110 1 20 I30 140 150 160 I70 180

~

52.1 69.5 86.9

103.6 118.7 125.9 133.0 140.2 147.3 153.4 159.6 165.7 171.9 178.0 184.2 190.3

~

75.3 65.3 59.3 55.5 53.6 54.5 55.3 56.7 58.1 59.9 61.7 63.5 65.4 67.2 68.9 70.7

154.4 115.8 92.6 77.6 67.8 66.2 65.0 64.0 63.3 63.3 63.3 63.3 63.3 63.3 63.3 63.3

~~ ~~

($ha) 281.4 250.6 238.8 236.7* 240.1 246.6 253.6 260.9 268.6 276.5 284.5 292.5 300.5 308.4 316.4 324.3

146.8 110.1 88.1 73.9 65.7 64.1 63.0 62.0 61.5 61.5 61.5 61.5 61.5 61.5 61.5 61.5

274.3 244.9 234.2 232.9* 236.9 243.5 250.0 258.0 265.7 273.7 281.7 289.7 297.7 305.7 313.7 321.6

'machinery costs include the costs of tractor and the Corresponding required implements for a farm of 160 ha; six passes include cultivating, chiseling, seeding, spraying, weeding and plowing bamortization is based on ten-year expected l i e for the tractor and eight-year expected life for the implements (Couture 1983 and ASAE 1983-84), and 12% interest (discount) rate; salvage value is estimated at 10% and the housing, insurance and tax charges at 2.75% of the purchase price (Hunt 1977); fixed costs are slightly different for the two crops 'operating costs are based on costs of fuel at $0.37/L; costs of oil. filters, grease, etc. estimated at 15% of the fuel cost (Schoney); labor costs at $8 an hour; also, repair and maintenance costs are a certain percentage of purchase price and vary by machine type; operating costs are slightly different for the two cmps dtimeliness costs are based on value of crop at $930/ha for grain corn and $977.70/ha for corn silage (CREAQ 1985) * = least-cost tractor size

the Saskatchewan fann situation (details are given in the respective studies). These costs are later combined with the yield losses associated with the corresponding tractor sizes.

The above model of machinery cost calculations is applied to a 160 ha farm growing grain corn and corn silage. The machinery costs per hectare associated with various sizes of tractors and the corresponding implements for a six-pass case are presented in Table 4.

Based only on the machinery costs, the most economical tractor size for both grain corn and corn silage is around 60 kW. As expected, the fixed costs contin- uously increase. Operating costs first decrease to reach a minimum around 70 kW and increase thereafter. This is because the increase in fuel and repair costs even- tually offsets the labor cost savings resulting from bigger tractors. Timeliness costs

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ECONOMICS OF SOIL COMPACTION 599

decrease as tractor size increases to the point (1 10 kW) where the tractor power required by the most critical least-cost implement size is below that of the given tractor. Beyond this point, the implement sizes are kept constant, as they are the least-cost ones. Hence, the time required to complete a given operation and thereby the timeliness costs remain constant for tractor sizes over 110 kW.

Similar to Brown and Schoney’s results, we observe that the increase in fixed costs is initially smaller than the decrease in operating and timeliness costs. How- ever, a further increase in tractor size eventually increases the fixed cost to a point where this increase outweighs the decrease in timeliness and operating costs. This results in a U-shaped cost curve, with an optimum size of machinery complement around a 60 kW tractor.

TOTAL ECONOMIC COSTS Total economic costs are obtained by adding the machinery costs (fixed, operating and timeliness costs) of six typical passes (cultivating, chiseling, seeding, spray- ing, weeding and plowing) to the compaction losses of three preernergence passes (cultivating, chiseling and seeding). These are presented in Table 5 and Figure 1. It is evident from the results that in general the total economic cost and the optimum tractor size is affected by type of crop and weather conditions. For corn silage, the optimum tractor size under wet conditions (1976) is obdned around 90 kW. For grain corn in a wet year, the optimum tractor size is about 120 kW for six passes. Under dry conditions (1 977) the minimum total economic cost tractor size is about 60 kW for corn silage and 50 kW for grain corn.

The least-cost tractor sizes are bigger under wet conditions than under dry conditions. This can be explained by the fact that soil compaction is more detri- mental under wet conditions than under dry conditions, and bigger tractors in general cause less effective compaction than smaller tractors at the farm level. As tractor size increases over 90 kW for corn silage and 120 kW for grain corn, the increase in machinery cost is more than the decrease in yield loss.

Farmers generally invest in machinery based on the expected rainfall. The last 20 years’ May-to-September rainfall data indicate that about 60% of the years are below average and 40% are above average. Using these fractions, a weighted average of the total economic costs of 1976 and 1977 is calculated. The minimum- cost tractor size for the expected year is about 70 kW for corn silage and 80 kW for grain corn. A bigger optimum tractor for grain corn is explained by the fact that, in monetary terms, soil compaction affects grain yield more than whole plant yield.

CONCLUSION Based on the results and the analysis, the following conclusions can be drawn.

0 Yield response to soil Compaction due to machinery traffic prior to plant emergence is not the same during a wet year as during a dry year. In 1976 (wet

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600 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

Table 5 . Total economic costs.

Corn silage Grain corn Tractor size 1976 1977 1976 1977 ___

(kW) 30 40 50 60 70 80 90

100 110 1 20 130 140 150 160 170 180

483.9 416.6 381.7 363.3 354.5 351.4 350.7* 351.7 354.0 357.4 361.4 365.9 370.7 375.9 381.4 387.0

255.7 228.1 218.7 218.2* 222.9 230.2 238.0 245.9 254.1 262.5 270.9 279.2 287.5 295.7 303.9 312.1

($ha) 674.8 573.7 517.2 483.6 463.4 451.1 442.9 437.8 434.9 433.8* 433.9 435.0 436.9 439.4 442.4 445.8

245.1 219.9 21 1.9* 212.4 217.7 225.3 233.3 241.4 249.7 258.2 266.7 275.1 283.5 291.8 300.0 308.3

%oil compaction losses due to three preemergence passes plus machinery costs of al l six passes * = least-cost tractor size

700

650

800

550

500 0

SSO

so0

250

200

Grain Corn 1976

30 50 70 DO 110 150 150 170

TRACTOR SIZE, kW

Figure 1. Relationship between total economic costs and selected tractor sizes (*indicates least-cost tractor size).

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ECONOMICS OF SOIL COMPACTION 601

year) soil compaction had a detrimental effect on yield, while yield response in 1977 (dry year) was beneficial. This is explained by a higher water availability during a dry year in a moderately compacted soil compared with a loose soil. The information on the extent of the loss/gain is useful for farmers in deciding their cultivation practices and the type of tractor to be used.

0 At the farm level, yield losses (during a wet year) and yield gains (during a dry year) both decrease as tractor size increases. This is contrary to the layman’s belief. However, it can be explained by the fact that bigger tractors create less “effective” compaction (up to a certain size). This information may help manu- facturers design tractors that have a lower compaction area percentage and/or lower contact pressure. It should be noted, however, that bigger tractors are more likely to cause long-term subsoil compaction than smaller tractors and therefore will have an impact on machinery selection in the long run. This is one of the weaknesses of this study; there is a need for more research on the long-term impact of soil compaction.

0 The yield losses for corn silage, based on the weighted average of the two years, range from 5.2% to 18.8% of the total economic cost of machinery for the corresponding tractor sizes of 180 kW to 30 kW. Similar values for grain corn are 11.5% and 34.2%. Thus yield losses due to soil compaction represent a substantial part of the total economic costs and therefore they should not be ignored in farm machinery selection. The optimum tractor size, a weighted average of the two years, is 70 kW for corn silage and 80 kW for grain corn, whereas the optimum tractor size based only on machinery costs is 60 kW.

To have a more complete picture, soil compaction due to machinery traffic after seeding should also be included. However, sufficient experimental data are not available at this time to combine both losses. The total economic costs esti- mated in this study include only the yield losses due to preemergence compaction. Therefore, this study represents only partial soil-compaction costs. However, assuming an upward parallel shift in the yield loss curves due to additional soil compaction, it can be concluded that the optimum tractor sizes are likely to remain the same. There is a need for further field research that will combine pre- and postseeding compaction.

It is acknowledged here that the results are based on several assumptions and are also constrained by the limitations of the experiments. In particular, the opti- mum tractor sizes are found to be very sensitive to the changes in the number of passes and the draft per metre used in the calculations. Nevertheless, it is hoped that they will add a new dimension to the economic implications of machinery use and selection.

NOTES ‘Total rainfall for May through September was 54.2 cm in 1976 and 36.2 cm in 1977. The average rainfall in the area for the same period is 43.2 cm.

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The first three models (corn silage 1976, 1977 and grain corn 1976) are as published in the cited references. To obtain the grain corn equation for 1977, the fourth model is derived by multiplying the ear yield equation (as published in the cited reference) by a shelling ratio (grain massltotal ear mass) of 0.855 based on the previous year’s data. This was necessary, as the experiment in 1977 was concluded after the ear yield stage. The above method used to calculate the farm level yield losses is based on the assumption that the same area is compacted over and over in multiple passes. Alternatively, it is also possible to calculate the yield losses with a method that would adjust the cumulative contact pressure, NP, by the CAP factor in the yield equations. For example, in a case where CAP is 20% and the number of passes is five, this alternate method implicitly assumes that the entire soil surface is covered only once, whereas the earlier method implicitly assumes that the same 20% area is compacted five times. Obviously, at the farm level, the compaction pattern is somewhere between these two methods. However, the yield losses found with the first method are smaller and seem more reasonable. Also there is no precise and easy way to combine the two methods. Therefore, in this study as in the previous study (Gunjal and Raghavan 1986), the first method is selected.

ACKNOWLEDGMENT The comments by John Henning, Laurie Baker, and anonymous reviewers are greatly appre- ciated. The authors gratefully acknowledge the funding support by the Comid de Rtfdrences Jkonomiques en Agriculture du Qudbec (Grant MCA 86 B 2073) of the Quebec Ministry of Agriculture.

REFERENCES American Society of Agricultural Engineers. 1983-84. Agricultural Engineers Yearbook of Standards. 13th ed. Brown, W. J. and R. A. Schoney. 1985. Calculating least-cost machinery size for grain farms using electronic spreadsheets and microcomputers. C a d i a n Journal of Agricultural Economics 33(March):47-65. ComitC de References Economiques en Agriculture du Quebec. 1985. Grande Culture. Agdex 1001854 and Agdex 740. Couture, M. J. 1983. Farm business management. Diploma in Agriculture Program. Ste. Anne de Bellevue, P.Q.: Macdonald College of McGill University. Dyer J. S., W. Baier, H. N. Hayhoe and G. Fisher. 1978. Spring field work day prob- abilities for selected sites across Canada. Technical Bulletin 86. Agrometeorology Section, Land Resource Research Branch. Ottawa: Agriculture Canada. Fox, M. G. and D. R. Coote. 1985. A preliminary economic assessment of agricultural land degradation in Atlantic and central Canada and southern British Columbia. Paper pre- pared for the Regional Development Branch, contribution 85-70. Ottawa: Agriculture Canada. Guqjal, K. R. and G. S. V. Raghavan. 1986. Economic analysis of soil compaction due to machinery traffic. Applied Engineering in Agriculture: Journal of the American Society of Agricultural Engineers 2(2):85-88. Hunt, Donnell. 1977. Farm Power and Machinery Management. 6th ed. Ames, Iowa. Iowa State University Press. Irwin, R. W. 198l.On-farm drainage benefit. Final report for the Cormgated Plastic Drain- age Tubing Association. Guelph: University of Guelph.

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Mehuys, G. R. 1984. Degradation of Agricultural Lond in Quebec: A Review and Impact Assessment. Ottawa: Science Council of Canada. Oskoui, E. K. 1983. The practical assessment of timeliness penalties of machinery selec- tion purposes. Agricultural Engineer 38(4):111-15. Raghavan, G. S. V. and E. McKyes. 1977. Study of traction and compaction problems on eastern Canadian agriculture soil. Report 3. Ste. Anne de Bellevue, P.Q.: Macdonald Campus of McGill University, Department of Agricultural Engineering. Raghavan, C. S. V., E. McKyes, G. Gendron, B. K. Borglum and H. H. Le. 1978. Effects of tire contact pressure on corn yield. Canadian Agricultural Engineering 20(1):34-37. Raghavan, G . S. V., E. McKyes, F. Taylor, P. Richard and A. Watson. 1979. The relationship between machinery traffic and corn yield reductions in successive years. Trans- actions of the American Society of Agricultural Engineers 22(6). 1256-59. Raghavan, C. S. V., F. Taylor, E. McKyes and E. Douglas. 1978. Machinery traffic effects on the production capability of agricultural soils. Ste Anne de Bellevue, P.Q.: Macdonald Campus of McGill University, Department of Agricultural Engineering. Science Council of Canada. 1986. A Growing Concern: Soil Degradation in Canada. Ottawa: Supply and Services Canada. Sparrow, H. 0. 1984. Soil at Risk: Canada’s Eroding Future. A report by the Standing Senate Committe on Agriculture, Fisheries and Forestry. Ottawa: Senate. Vigier, B. and G . S. V. Raghavan. 1980. Soil compaction effects in clay soils on common root rot canning peas. Canadian Plant Disease Survey 60(4):43-45.