comparison of costs of wheat production in saskatchewan and the u.s. northern plains

Comparison of Costs of Wheat Production in Saskatchewan and the U.S. Northern Plains

Dargan Glaze1 and Richard Schoney2

‘Agricultural Economist, Economic Research Service, USDA 2Professor, Agricultural Economics, University of Saskatchewan,

Saskatoon, Saskatchewan.

Received 27 April 2 993, accepted 24 April 1995

llis paper compares I989 average variable costs for U.S. northern plains and Saskatchewan spring wheat producers. Producers are divided into three cost groups. There are more statistically significant diferences between cost groups within a country than between the two countries. Relative to other producers within the same country, low-cost producers in both countries had higher yields per bushel, most bud lower variable inputs both per acre and per bushel, and most had lower debt loads. In terms of intercountry comparisons, low-cost producers in Saskatchewan and the U.S. northern plains had nearly identical average variable cash costs. While mid- and high-cost Saskatchewan producers had signijcantly lower average variable cash costs, they were almost entirely ofset by higher_tired costs.

Nous comparons les cot& de production variables moyens de 1989 entre les producteurs de bit! de printemps des Plaines du nord des I&s- Unis (PN) et ceux de la Saskatchewan. Les producteurs etaient rtcpartis en trois groupes selon les co&s de production. Les diflerences significatives au plan statistique entre les &ux groupes etaient plus nombreuses c3 1 ‘interieur d’un m&me pays que d kn pays ct 1 ‘autre. Compares h leurs concitoyens, les producteurs h faible cot2 par boisseau a2.zn.s les dew: pays afichaient des rendements plus t!levt!s et, &s co&s d ‘intrants variables generalement plus bas par acre et par boisseau et un fardeau d’endettement plus leger. Cornpart% par pays, les producteurs cf faible cot& de la province & Saskatchewan et dans les Plaines du nord (U. S.) afichaient des moyennes quasi iden- tiques pour les co&s en espbces variables. iU&ne si les producteurs b cot&s de production &eves et interrnediaires de la Saskatchewan avaient des cot&s en esptces variables significativement plus has, cet avantage etait presque entritrement eflace par le niveau plus &eve des co&s &es.

INTRODUCTION

Production competitiveness has become a popular area of concern as policy makers seek to monitor sector health, evaluate the conse- quences of free trade agreements, and assess the impacts of agricultural and tax policies. The recent North American Free Trade Agreement (NAFTA) between the U.S., Canada and Mexico has focused attention on the competitiveness of many Canadian and U.S. commodities including corn, wheat and

livestock products. Neighboring producers express concern when they feel that they are placed at a competitive disadvantage because of higher input prices as in Ontario and Michigan (OMAF 1989) or because of differing taxation polices as in Alberta and Montana (Perry, Nixon and &mnage 1992). Policy makers on both sides of the border are concerned about the ability of their farmers to compete in both the short and long run. Like- wise, Saskatchewan tarrners have concerns

Gmadian Joumal of Agricultural Economics 43 (1995) 367-385

367

368 CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

about U.S. farm programs, and U.S. producers have concerns about Canadian policies and institutions such as the Canadian Wheat Board.

Cost of production (COP) studies have long been a familiar tool in the study of inter- or cross-country agricultural commodity competitiveness (Brown 1992; Le Stum and Camaret 1990; Ortman, Stulp and Rask 1986; Parks, Rockeman and Walker 1980; Seecharan 1988; Sharples 1990; Stanton 1986). Yet intercountry COP comparisons are complex and fraught with estimation and interpretation problems (Aheam, Culver and Schoney 1990). This paper has three major objectives. The first objective is to identify and assess the problems associated with comparing costs of producing wheat in the U.S. northern plains (NP) states of Minnesota, Montana, North Dakota and South Dakota with those of Saskatchewan. The second objective is to compare and contrast U.S. northern plains and Saskatchewan costs of spring wheat production. Finally, the last objective is to identify and assess factors, if any, that influence inter- and intra-country differences in costs of production.

INTERCOUNTRY COST COMPARISON PROBLEMS

In general, intercountry comparison problems can be divided into four basic areas: data, cost estimation methodology, cost reporting format, and data adjustment. The following sections describe the problems encountered and their resolution.

Data Problems Data problems result from data collected at differing times or in a dissimilar manner. Both the U.S. and Saskatchewan cost of production programs vary considerably in their original intent, design and scope. The USDA has collected and evaluated COP data since the early 1900s. Over time, a sophisticated set of survey design, collection and evalua- tion tools have evolved. Weighted average cost estimates of producing major U. S . farm program crops were first required by the

Congress in the Agricultural and Consumer Protection Act of 1973. The Economic Research Service (ERS) and the National Agricultural Statistics Service (NASS) of the USDA collect and publish detailed production practices and cost data. The U.S. northern plains production costs are based on the 1989 wheat version of the Farm Costs and Returns Survey (FCRS). ’ Since the intent of the U.S. COP survey is to represent the entire national commodity sector, considerable care is used in survey design and the corresponding aggre- gation procedures: the FCRS is a complex multi-frame, probability-based survey. The FCRS data are used in combination with input price data collected by NASS to estimate offi- cial U.S. commodity costs of production.

Saskatchewan COP estimates are based on the 1989 Top Management Workshop survey of actual farm costs. In sharp contrast to the FCRS, the Top Management Work- shops and accompanying software were originally designed for an extension forward planning and financial monitoring program. This workshop setting is similar in context and participation to that of many other farm record keeping and management associations. Unlike the FCRS, the Top Management participants are not obtained through a random sample. Participation in the Top Management Program is voluntary and open to ail Saskatchewan grain and oilseed farmers, without direct monetary cost to the participants. A trained workshop staff assists in decision making and makes summary statistics available to farmers.

Two problems are associated with extension workshop data:

l data representativeness and l staff-farmer interaction.

Extension workshop data may not represent the whole population because of the selectivity bias associated with the nature of extension workshops. Daytime meetings tend to exclude farmers with full-time day jobs. In addition, the technical, production and cost data requirements and intensiveness of the workshops may tend to select for larger and better- managed farms. The potential size bias is assessed by comparing the Saskatchewan size

COMPARISON OF COSTS OF WHEAT PRODUCTION 369

distribution against Census Canada data. Using Census Canada data, only 22% of Saskatchewan farms and 25% of western Canadian farms have gross farm sales greater than $92,866, compared with about 75% of the Workshop participants (Table 1). Since there are only six observations in the group having $20,9X)-47,955 gross farm income, there is a relative paucity of data in this size grouping. Hence, the Top Management producers are mostly full-time, commercial operations. However, the degree of cost bias within size groupings is more important. In comparisons with similar-sized grain and oilseed farms of the National Farm Survey and Taxfiler data, the 1991 Top Management farms have very similar total farm gross returns, variable cash costs and assets, giving some confidence in their ability to represent class variable cost structure (Koroluk and Culver 1993). 2

Finally, there is the question of feedback between the interviewer and the interviewee. The U.S. study is based on farmer’s responses to a paid enumerator, while the Workshops encourage interaction between staff and farmers. This may not be a problem for several reasons. First, the Workshops were relatively new in 1989, and there was little previous interaction between the staff and farmers. Secondly, the staff concentrate on financial planning and devote few resources to agronomic production practices.

In order to correct for possible Saskatch- ewan sampling biases, the U.S. northern

plains data set is screened to a common profile of general cropping patterns, size and farm type This method has been used in several other studies with similar data comparability limitations (Gustafson, Nielsen and Morehart 1989; Koenigstein and Lins 1989; Mueller 1954). However, care must be taken not to overly reduce the data, in effect homogenizing both groups, and losing data uniqueness. Hence, the screening criteria should be as general as possible. The U.S. northern plains farm selection criteria are:

l nonirrigated hard red spring or durum wheat,

l more than 68 acres of wheat, l farm operators who considered their

operations to be primarily cash grain farms and

l producers achieving more than 2 bu/ acre yields.

Of the original 26 1 observations, 103 U.S. northern plains farms met the selection criteria. The corresponding Saskatchewan data set included 124 farms.

Cost Estimation Differences One problem associated with many inter- country comparisons is differing cost estimation procedures. Initially, this was thought not to be a major problem, as the original methodologies in estimating variable cash costs were very similar. However, there were more differences in fixed cost estimation. Differ- ences are reconciled by recompiling the data to a common set of underlying assumptions

Table 1. Comparison of 1991 Top Management Saskatchewan and western Canada grain and oilseed farms

Gross farm sales

Top Management Saskatchewan

Number Proportion Number Proportion

Western Canada

Number Proportion

$2,500-20,949 0 0.00% 9,734 22.1% 17,476 23.03% $20,950-47,955 6 4.76% 11,850 27.0% 19,101 25.18% $47,95692,865 25 19.84% 12,580 28.6% 20,146 26.55 % $92,866-224,999 78 61.90% 8,472 19.3% 15,721 20.72% $225,000+ 17 13.49% 1,324 3.0% 3,424 4.51% Total 126 100.00% 43,960 100.0% 75,868 100.00%

Source: Koroluk and Culver (1993).


and mathematical equations. The following section outlines the cost equations and initial differences.

Costs are divided into cash expenses and economic costs. Total cash expenses (or COP,) are short-run cash requirements associated with producing a crop and maintaining the farm business and are defined as:

COP, = h C'& + k M,(Rep, + i=l m=l

Fuel, + WL,) + S(H + Tax + Zp) (1)

where COP, = total cash expenses,

j = total number of inputs, v = total number of machine and

building systems directly employed,

Ci = price of direct cash input i, Xi = quantity of direct cash input i,

Mm = direct use share of machine and building,

Rep, = repairs of operating machine system m,

Fuel, = fuel cost of operating machine system M,

W = paid wage, Ll = amount of paid labor associated

with system m, S = indirect use share value assigned

to crop c, H = general overhead costs,

Ip = paid interest expenses for operating and real estate loans and

Tcu = property and real estate taxes. Cash expenses are delineated into the

traditional variable, AK,, and fixed cash expense categories. Most direct variable cash expenses (seed, fertilizer, chemicals, custom operations and technical services) are directly obtained from the farmer and are the simple product of farm unit price and quantity used per acre. 3 Direct fuel and repair costs (excluding truck expenses) are estimated by applying engineering specifications to actual data for farm machine and equipment operations. Saskatchewan employs very little hired

labor and thus differences are not a major concern. However, these differences carry over to unpaid labor and will be discussed later.

Fixed cash expenses (general farm overhead, taxes and interest charges) are allocated costs. Originally, general farm overhead expenses were defined and allocated somewhat differently between the two countries. To ensure comparability, the Saskatchewan definition is used and U.S. costs are recalcu- lated. 4 General farm overhead includes variable and fixed truck costs, insurance costs (excluding crop insurance), and general farm expenses. General farm overhead and interest expenses are allocated based on the enterprise share of total direct cash costs but excluding paid labor. Since Saskatchewan farm equipment is not included as a taxable item in property tax assessment, Saskatchewan taxes consist solely of real estate taxes. However, U . S. tax expenses include both real estate and equipment taxes. Actual interest expenses (operating and real estate) are allocated based on relative asset shares.5

Total economic cost, COP,, long-run opportunity costs that account fbr all production inputs, without regard to the ownership or equity positions of f&n operators (Morehart, Dismukes and Shapouri 1992). In general, COP, is those costs needed to maintain the productive resources over the long run. COP, includes both variable and fixed cash expenses (excluding interest payments), capital replacement and imputed costs of land, unpaid labor and capital invested in production inputs and machinery. COP, is defined as:

COP, = (COP, - Z*) + 0 + i m=l

M,(UF, + D, + Z,) + k S,(Rep, + m=l

Fuel, + Dm + Z,) + R (2)

where COP, = economic opportunity cost of

production, COP, = total cash exDenses (Ea.


0 = operating capital charge, &I = direct use share of machine m, S,,, = indirect use share of machine m,

D,,, = machine and building capital replacement (depmciation) charge,

Im = machine and building (nonland) interest charge,

U = oppmhmity wage rate, F,,, = amount of unpaid labor used and R = cash equivalent land cost.

Unpaid labor calculations differ between the two countries. The Saskatchewan unpaid hours are a result of field performance estimates based on farmer-specified machines, machine complements and machine performance parameters such as speed, width and field efficiency. This differs somewhat from the U.S. system where unpaid hours are a result of “generic” machine systems with prespecified field performance parameters. Unpaid labor cost is calculated by multiplying the actual unpaid labor hours used by a provincial or state-level farm wage rate.

Invested capital is divided into operating and fixed capital. Operating capital cost is determined by multiplying the interest rate associated with six-month Bank of Canada bonds or U.S. Treasury bills (12.40% for Canada and 8.03% for the U.S.) times the total variable cash expenses. 6

Fixed investment is divided into two components:

l nonland and l farmland.

Nonland investment has two associated opportunity charges:

l depreciation/depletion charges (“capital replacement’ ’ charges) and

l interest (‘ ‘nonland capital’ ’ charges). Capital replacement costs pose problems in that the original calculation procedures differed significantly. ’ Enterprise machine charges are calculated by multiplying the unit charge times the actual units used. Annual use of U. S . northern plains machines is based on engineering estimates, while the Saskatch- ewan estimates are based on calculated hours from field performance equations or based on actual acreages used. Nonland capital cost is calculated by applying the interest rate to the

current fair market value of machinery, equipment and buildings investment used in wheat production.

U.S. net land expense is estimated as a composite charge of cash and share rent values. An imputed net land charge is used for farms with owned land. Saskatchewan owned land expenses are based on a cash equivalent of 5 % of the 1989 fair market value. ’

Cost Reporting Format Problems U.S. and Saskatchewan cost reporting formats also differed. For example, in the U. S . , fuel and repair costs associated with pickup trucks are allocated by the farmer to the wheat enterprise and are included as part of total direct cash costs. However, in Saskatchewan, pickup truck fuel and repair costs are included in overhead costs. Accordingly, the U.S. data are recompiled and these costs are combined with general farm overhead to ensure con- sistency with Canadian data. As a result, budgets presented in this paper are not con- sistent with the format and underlying assumptions of previously published USDA COP budgets and therefore are not com- parable with previously published estimates.

Other Adjustment Problems The final problem is associated with conversion to a common currency. In times of fluc- tuating exchange rates, the choice of a single exchange rate can be troublesome, as farm inputs can be priced or purchased over a number of months. Likewise, input suppliers set their prices in the late fall-early winter and offer discounts to farmers for ordering early. For example, the Canada/U. S. exchange rate dropped by 6.9% in 1988 and by another 2.5% in 1989. Fortunately, the exchange rate was relatively stable from November 1988 to October 1989. For this study, the conversion rate is based on an April 1989 Bank of Canada exchange rate of C$l .189 = US$l, which is very close to the November 1988 to October 1989 average exchange rate.

COST COMPARISON PROCEDURE

While many studies tend to concentrate on the comparison of cost means, the profile of costs

w

Table 2. Saskatchewan and U.S. northern plains wheat production economic costs and returns per bushel by cost group, 1989 2

Cost group Mean LOW Middle High all groups

Item Sask NP Sask NP Sask NP Sask NP

Average area planted (acres) 588 511 675 768 581 755 630 700

Gross value of production: Yield (bushels per planted acre)

Primary crop ($/bu) Secondary crop ($/bu)

Total gross value of productiona ($/bu)

32.2 34.6 25.2 23.9 18.1 17.1 25.2 4.14 4.31 4.14 4.31 4.14 4.31 4.14 0.00 0.02 0.00 0.01 0.00 0.05 0.00 4.14 4.33 4.14 4.32 4.14 4.36 4.14

Cash expenses ($/bu): Seed Fertilizer Chemicals

F

24.9 5

4.31 E 0.02 5 4.33

2 F

Custom operations and technical services Fuel, lube, and electricity Repairs Hired labor

Subtotal, variable cash expenses (AK,)

0.20 0.26 0.26 0.42 0.36 0.66 0.27 0.36 0.26 0.39 0.51 0.59 0.91 0.43 0.25 0.22 0.39 0.33 0.56 0.55 0.40 0.01 0.07 0.01 0.19 0.01 0.08 0.01 0.17 0.18 0.23 0.29 0.39 0.48 0.25 0.18 0.17 0.27 0.22 0.46 0.40 0.30 0.01 0.06 0.00 0.14 0.01 0.49 0.01 1.16 1.22 1.56 2.10 2.39 3.58 1.67

General farm overhead (includes insurance) Taxes Operating interest Real estate interest

Subtotal, fixed cash expenses Total, cash expenses (COP,)

0.39 0.44 0.73 0.78 1.42 1.53 0.82 0.10 0.12 0.14 0.17 0.26 0.30 0.16 0.01 0.12 0.04 0.24 0.15 0.55 0.06 0.13 0.15 0.39 0.39 0.81 1.13 0.43 0.62 0.83 1.29 1.59 2.63 3.50 1.47 1.78 2.05 2.85 3.69 5.02 7.07 3.14

0.44 %

zi 0.55 E 0.36 2 0.13

2 0.31 c 0.25 r 0.21 8 2.25 3

$

0.88 E

0.19 0.29 0.52 1.88 4.13

Gross value of production less cash expenses ($/bu) 2.36 2.28 1.29 0.63 -0.88 -2.71 1.00 0.20


with each country is important, as it indicates future performance potential. Accordingly, the first step is to examine the differences in intra-country costs. Three cost groups are defined for each country based on the lowest 25 % (low-cost), mid 50% (mid-cost), and highest 25 % (high-cost) producers. Next, costs are compared across countries using pairs of the same relative cost efficiency.

All of the following cost efficiency comparisons are based on average variable cash costs per bushel, FIVC~. While average total costs may be a more intuitive choice in that all opportunity costs are included, single-period economic or opportunity costs represent only a snapshot of costs, and machine-related opportunity costs can vary over time because of the lumpiness associated with relatively infrequent machine replacement.

Additional pmblems axe associated with the choice of output units. Costs expressed as per- bushel costs are a concern because of the stochastic nature of yields, as Aheam and Msavada (1992) note. However, costs per acre present problems in comparing areas with differing soil types and would be misleading in comparing the black and brown soils of Saskatchewanbecauseofthedi.&ingacminput levels and the amounts of fallow. IWtunately, themeancountryyieldsamverysimilar-25.2 bu/ac and 24.9 bu/ac, mspectively, fbr Saskatch- ewan and U.S. northern plains &rms. Hence, many of the same per-bushel cost patterns will also exist on a per-acre basis.

Potential discriminating variables are divided into two groups: direct and indirect cost determinants. Cost group direct determinants are the various average variable cost components, which are listed in Table 2. Indirect cost group determinants include:

l fixed and opportunity costs (Table 2), l farm size, l farm financial structure and l land tenure (Table 3).

Indirect determinants may be important because, while Saskatchewan and the U.S. northemplainsfarmshadsimilarnetworthand debt/asset ratios, they differed in terms of total farm acres, leasing arrangements, income from other crops and the number of wheat acres.

Table 3. Indirect determinan ts: Tenure, income and asset/liability variables, Saskatchewan (Sask) and U.S. northern plains (NP) 3

Indirect Low-cost Mid-cost High-cost All farms

determinant Sask NP Sask NP Sask NP Sask NP

Proportion of land in summerfallow (%): Total acres 24 16 26 14

Proportion of land in wheat, by land tenure classifiition and all acres (%): Owned 32 29 37 29 29 25 34 Cash rented 28 36 32 28 25 37 29 Share rented 40 32 50 39 46 44 46 Total acres 33 32 39 32 31 30 35

Land tenure classification of land in wheat production (%I): Owned 52 38 62 Cash rented 15 40 15 Share rented 34 21 23 Total 100 100 100

42 56 56 58 24 17 24 15 34 26 20 26

100 100 100 100

Land tenure classification of all land (Z): Owned 55 Cash rented 17 Share rented 28 Total farm 100

43 65 46 61 67 61 36 18 27 21 19 19 21 17 27 18 14 20

100 100 100 100 100 100

Source of Income ($): Spring wheat Other crops Livestock Othera Total farm income

77,835 73,930 67,707 85,680 46,086 56,909 37,230 16,462 17,013

9gOo 9,089 210,545 136,478 150,718

77,365 36,995 14,263

38,125 47,592

4,039 12,455

102,212

50,377 19,164 17,214

128,623 86,754

62,843 61,772 18,824 10,109

153,548

27 17 26 15

2 28 3 33 g

39 ; 32

z! F

45 %

27 $ 28

100 2

2 F r

51 R 26 g 23 P

loo D

69,685 34,788 15,563

120,037


A simple F-test is used in the following analysis to identify individually significantly different variables. However, because variables may work together, discriminant analysis is used to identify those combinations of factors that are best able to statistically discriminate between a given country’s cost groups (intra-country comparisons) and between country cost pairs (inter-country comparisons).

canonical Dlscrlminant Analysis The Saskatchewan-U.S. northern plains data are somewhat unique in that they are relatively disaggregated farm-level data. However, disaggregated data can lead to statistical problems in that many production inputs can be strong complements or strong substitutes, making many commonly used parametric techniques inappropriate. Dis- criminant analysis is a statistical technique that allows the researcher to study the difference between two or more groups with respect to several variables simultaneously. Mathe- matically , canonical discriminant analysis weighs and linearly combines variables so that groups are as statistically distinct as possible (Klecka 1980; Anderson 1984; Goldstein and Dillon 1978; Morrison 1967). The canonical discriminant function is:

n

fh = Uo + ui C xikm (3) i=l

where fh = the value of the canonical dis-

criminant function, i = variable number, k = group,

m = case of group k,

ui = estimated coefficient and X* lh = discriminating variable.

For the series of k discrim&nt functions, the estimated coefficients, Ui, maximize differences in group means while maintaining independence between discriminant functions.

Wilk’s lambda, A, is used to measure the statistical ability of tire discriminant function to discriminate between groups. Near zero values lbr A indicate high discrimmating abiity.


The number of discriminating functions is not known a priori. In general, successive discriminating functions are iteratively added and individually assessed until the incremental discriminating power falls below a predeter- mined level.

When variables have differing units such as percentages, indices or dollars per farm, their interpretation is less straightforward; accordingly, standardized canonical coefficients are reported. These coefficients represent the relative variable contribution to the corresponding discriminating function. The structure coefficients are the product- moment correlation between a single variable and the discriminant function. By indicating the similarity between an individual variable and the discriminant, the structure coefficients are useful in labeling a particular discriminant function. In addition, the adjusted canonical R2, which indicates the degree of association between the groups and discriminant function, is reported.

RESULTS

The results are divided into two sections: l intra-country and l inter-country cost group comparisons.

Because it is important to understand intra- country cost variability and its determinants before examining inter-country cost comparisons, Saskatchewan and U. S . northern plains intra-country variability are examined first, followed by inter-country comparisons of cost pairs.

Intra-Country Cost Group Comparisons In order to examine the role of yield in deter- mining cost group, per-acre costs are shown in parentheses. A total of 23 variables out of the 33 variables examined (Table 4) are chosen for further study, based on theoretical soundness and overall contribution to model classification ability. Results are reported in Table 5.

Saskatchewan Cost Groups Yields clearly differ between the three Saskatchewan cost groups: yields are 32.2,

25.2 and 18.1 bushels per acre, respectively, for low, middle and high-cost-per-bushel groups. A total of eight variables are significant at the 5 % level or less. These include five direct determinants - fertilizer (FERZ), chemicals (CHEM), seed costs (SEED), fuel and electricity (POWER), repairs (REPAIR) - and three indirect determinants - farm overhead (GFOH), interest cost (INPD) and property taxes (TAXES).

Interestingly, if the same per-bushel groups are maintained, low-variable-cost-per- bushel producers are also low-cost-per-acre producers, but not by very much - only by 5-10% over their neighboring cost group. Total variable costs per bushel were $2.39 ($43.14 per acre) for the high-cost group, compared with $1.16 ($37.27 per acre) for the low-cost group. Hence, while yields are important, low-cost producers must have achieved lower costs through more judicious input use or through lower unit costs. This is the case in terms of chemical, repair and fuel and general overhead costs; low-cost producers also generate lower per-acre costs, and middle and high-cost producers generate higher per-acre charges. This pattern is most strongly displayed by repairs and fuel where there is a per-bushel difference of 229% between the highcost and the low-cost group.

Fertilizer costs exhibit a very different pattern; low-cost-per-bushel producers spend more on fertilizer per acre than either the middle-cost or high-cost producers. Average fertilizer costs for low, medium and high-cost producers are $0.36 ($11.60), SO.39 ($9.82) and $0.59 ($10.65) per bushel (acre), respectively. Presumably, higher fertilizer costs are associated with greater yields or higher cropping intensity.

Two discriminating functions are estimated: CDFl and CDF2 and are both significant at the 0.01% level as measured by Wilk’s lambda. The adjusted squared canonical correlation is 0.736 and 0.115 for CDFl and CDF2, respectively. CDFl can be character&d by the variables with highest total canonical structure. Hence, general farm overhead (GFOH) and power (POWER) best describe CDFl with total canonical structure


Table 4. Variable definitions

Label units Unit basis Description

Direct determinants: AVC

CUSTOM dollars FERT dollars CHEM dollars SEED dollars INTPD dollars PAID dollars POWER dollars REPAIR dollars UNPAID dollars

Indirect determinants: AFC

GFOH LAND

DEPR NLCX’ ROC TAXES

Financial C-ASST NC-ASST CR DAR

c-LUB NC-LIAB Pm-REV NET WORTH

Size ACRES

Tenure PCT-CSH Pa--oW7v PCT-SHR

Type FALLOW PCT-EQV PCT-LIVE PCT-OC PCT-TG PCT-WHT

dollars bushel dollars bushel dollars bushel dollars bushel dollars bushel dollars bushel

dollars dollars

total farm total farm total farm

dollars dollars % dollars

acres

total farm total farm total farm total farm

total farm

total farm total farm total farm

total farm total farm total farm total farm total farm total farm

bushel bushel bushel bushel bushel bushel bushel bushel bushel

Custom operations, excluding technical services Fertilizer costs Chemical costs seed cost Interest paid Paid labor Total fuel and electricity Repair costs Total unpaid and operator labor

General farm overhead Net land rent Machine replacement cost Return to nonland capital Return to operating capital Taxes and insurance

Total current assets Total noncurrent assets Current ratio Debt asset ratio Current liabilities Noncurrent liabilities Real estate value/total assets Total assets - total liabihies

Total wheat acres, planted

Percentage cash leased land Percentage owned land Percentage share crop land

Percentage fallow Equipment value/total assets Total livestock income/total income Total income from other crops/total income Total gross wheat income/total income Wheat acres/total farm acres

w

Table 5. Intra-country canonical discriminant analysis results of cost crops, by country a 2

Variable label R2

Saskatchewan cost groups

Canonical coefficients

Standardized Total structure

CDFl CDF2 CDFl CDl?Z R2

U.S. northern plains cost groups


Standardized Total structure

CDFl CDFZ CDFl CDF2

CUSTOM FERT CHEiU SEED

PAID POWER REPAIR GFOH TAXES NC-ASST CR DAR NC-L&L3 PCT-REV PC7-EQV ACRES PCT-ow?v FALCOW PCTUVE PCT-oc PCT-TG PCT-WHT

0.00 0.05** 0.18*** 0.15*** 0.19*** 0.00 0 29*** . 0.21*** 0.40*** 0.09*** 0.00 0.03 0.05* 0.03 0.02 0.03 0.01 0.02 0.01 0.05* 0.01 0.00 0.04*

-0.075 0.168 0.020 0.060 0.03 5.138 0.454 0.140 0.244 -0.104 0.21*** 8.509 0.642 0.309 0.481 0.086 0.20*** 4.524 0.415 -0.042 0.431 -0.096 0.26*** 4.879 0.492 -0.723 0.492 -0.073 0.29*** -0.090

-0.068 -0.200 0.021 -0.096 0.27*** -0.483 0.143 0.041 0.609 -0.088 0.45*** - 18.383 0.457 -0.062 0.522 0.056 0.19*** 3.943 1.044 0.055 0.715 0.000 0.37*** 1.093 0.109 -0.193 0.340 0.031 0.33*** 2.531 0.403 -0.593 0.019 -0.043 0.02 0.246

-0.024 0.045 -0.184 -0.137 0.06** -0.1% 0.045 0.512 0.159 0.335 0.05* 0.123 0.339 0.327 0.146 0.217 0.05** 0.829 0.032 0.254 0.067 0.279 0.01 -0.182 0.167 -0.382 0.157 -0.235 0.01 0.180

-0.703 -0.052 -0.020 0.193 0.05** -0.918 0.017 0.491 0.035 0.249 0.01 -3.255 0.186 0.560 0.097 -0.004 0.02 0.316

-0.197 -0.704 -0.204 -0.235 0.02 -2.438 -0.3 17 -0.511 0.028 -0.197 0.01 -3.002

0.115 -1.121 0.021 0.137 0.06** 10.343 0.029 0.942 -0.021 0.425 0.33*** 0.404

-1.816 -0.038 0.352 9 -2.907 0.513 0.053 z -1.861 0.4% -0.03 1 - 1.033 0.567

F 0.113 z

0.139 0.603 -0.050 2 0.892 0.574 -0.124 g 7.614 0.746 0.074 F

-1.821 0.487 0.066 % 0.048 0.677 -0.058 $

-0.052 0.635 -0.128 # -0.457 0.160 -0.076 s

0.482 -0.156 0.397 2 0.521 0.222

0.244 0.177 g

-0.179 -0.345 -0.087

0.096 R 0.121 p

-0.123 0.091 0.079 @ 1.125 0.149

-0.609 0.077 0.365 9 0.158

0.172 0.164 0.108 -0.393 0.141 -0.183 -0.488 -0.082 -0.090 -4.166 0.261 -0.102 -2.295 0.630 -0.235


coefficient values of 0.715 and 0.609, respectively. Since the fuel and repairs associated with pickup trucks are a major component of GFOH, CDFl can be labelled as a farm fuel and repair discriminator. Per- centage of total income from wheat (XT- WM) best describes the second discriminant function, CDF2, although only a moderate relationship is indicated.

Northern Plains Cost Groups Yield and cost patterns similar to Saskatch- ewan cost groups are displayed by the various U.S. northern plains cost groups (Table 2). Yields are 34.6, 23.9 and 17.1 bushels per acre for the low, middle and high-cost groups, respectively. Average variable costs are $1.22, ($42.22), $2.10 ($50.13) and $3.58 ($61.29) per bushel (acre) respectively, for the low, middle and high-cost groups.

In comparison with Saskatchewan, four more variables differ signiiicantly between the various cost groups. Based on a simple uni- variate F-test, a total of 12 variables are significant at the 5 % level or less (Table 5). Sig- nificant variables include fertilizer (FEW), chemicals (CHEW), current ratio (CR), seed costs (SEED), farm overhead (GFOH), intered cost (INTO), paid labor (PAID), machine replacement cost or depreciation (DEPR), return to nonland capital (NLCAP), return to operating capital (OP-CAP), fuel and electricity (POWER), and repairs (REPAIR).

Like Saskatchewan, the pattern of both lower per-bushel and per-acre costs is exhibited by a number of variables but unlike Saskatchewan this also included fertilizer costs. Low per-bushel groups had a lower fertilizer cost per-acre relative to the other groups: fertilizer per-bushel costs were $0.26, $0.51 and $0.91, and per acre costs were $9.00, $12.17 and $15.58, respectively, for the low, middle and high per-bushel cost groups. The cumulative effect of these cost differences is to generate 72 (19) and 193 (45) percent higher average variable costs per bushel (acre) for the middle and high-cost group over the low-cost group.

As in Saskatchewan, two discriminating functions, CDFl and CDF2, are estimated.


Both CDFl and CDF2 are significant at the 0.01% level as measured by Wilk’s lambda and all observations are correctly classified. The adjusted squared canonical correlation of CDF 1 is 0.766. CDF 1 is best described by return to operating capital (OP-CAP), general farm overhead (GFOH) and total repairs (REPAIR) with values of 0.746, 0.677 and 0.635, respectively. This is very similar to the estimated Saskatchewan CDFl , except for the influence of operating capital. CDF2 has a relatively low adjusted squared canonical correlations value of 0.114, no closely related variables and only two minimally related variables: current ratio (CR) and harvested wheat acreage (ACRES).

Inter-Country Comparisons Saskatchewan and U.S. northern plains farms are compared two different ways:

l a 2 x 1 Saskatchewan-U. S. northern plains comparison of all cost groups and

l three pair-wise country comparisons. In the following analysis, only one canonical discriminant function is estimated for each comparison. Out of a total of 33 variables, 15 variables are chosen for further study based on:

l a priori appropriateness, l additional information contributed and l cost classification ability.

Inter-Country - All Groups Mean per-bushel cash costs (Table 2) are $1.67 and $2.25, respectively, for Saskatchewan and the U.S. northern plains states, or a difference of $058/bu. When fixed opportunity costs are included, per-bushel costs are $5.96 and $6.45, and the country difference decreases to $0.49/bu. In a 2 x 1 inter- country comparison (not shown), eight variables are statistically significant at the 5 % level: seed (SEED), fertilizer (FERT), paid labor (PAID), interest paid (ZmD), percentage of total income from wheat (PCT-TG), capital replacement (DEPR), return on nonland capital (NLCAP), and interest on operating capital (OP-CAP). The discriminating equation is significant at the 0.01% level and is most closely associated with DEPR

(machine replacement cost) and NL-CAP (return to nonland capital). DEPR is a direct measure of machine depreciation and an indirect measure of machine investment, while NL-CAP is a function of interest rates and machine investment. These variables may be good discriminators because of differing machine depreciation rates between the countries or because of differing interest rates. A total of five farms are misclassified.

In a 3 x 2 comparison of cost sub- groups, 15 variables, including six direct cost determinants, are significantly different at the 5% level and all observations are correctly classified. Hence, cost groups are clearly important in assessing inter-country difference. In order to further examine this issue, inter-country costs are compared among the three cost pairs.

Low-cost Pair The Saskatchewan and U.S. northern plains low-cost groups are similar in many respects. Total variable cash expenses are $l.l6/bu and $1.22/bu, respectively, for Saskatchewan and the U.S. northern plains low-cost farms. Saskatchewan yields are 32.2 bu/ac or 7.5 % lower than the 34.6 bu/ac yields of the U . S. northern plains farms. The only statistically important direct cost variable is seed cost (SEED), which can account for all of the $.06/bu difference in average variable cash costs. The higher U.S. northern plains seed cost is a potentially transitory phenomenon caused by widespread reseeding in the spring of 1989.

As other fixed cash expense items are included, the differences broaden. Total cash requirements are $1.78/bu and $2.05/bu, respectively, or about a 13 % difference. While many of the individual fixed cash cost components vary, Saskatchewan and the U.S. northern plains low-cost total economic costs are nearly identical - $3.95/bu and !&LOS/bu, respectively, or a 3% difference.

Even though these producers have nearly identical A&, there are five statistically significant indirect determinants: total current assets (C-ASS’), fallow (FALLOW), percentage of total gross farm income from wheat


(PCT-TG), return to nonland capital (NLCAP) and total unpaid labor (UNPAID). However, only percentage fallow (23.6% and 15.6%, respectively, for Saskatchewan and the U.S. northern plains) and wheat income as a percentage of gross income (37.0% and 54.2%, respectively, for Saskatchewan and the U.S. northern plains farms) have much potential meaning, as they indicate different cropping intensity and farm organization. Nevertheless, these variables are only weakly associated with intra-country cost differences and apparently their differences are offset by other factors. In the case of the other indirect determinants, differences may be due to differing interest rates (NLCAP) or differences in methodologies (UNPAID).

Mid-cost Pair Mid-cost pairs differ considerably more than low-cost pairs: total variable cash expenses are $l.S6/bu and $2.10/bu, respectively, for Saskatchewan and the U.S. northern plains mid-cost farms - a 25.7% difference. As other fixed cash expense items are included, the differences remain about the same. Total cash requirements are $2.85/bu and $3.69/bu, respectively, or about a 22.8% difference. This difference narrows as fixed cost components are included - mid-cost farm total economic costs are $5.37/bu and $6.10/bu, respectively, for Saskatchewan and the U.S. northern plains, or a 12.0% difference. Costs narrow as more fixed costs are included because U.S. northern plains producers employ more hired labor and custom operations than do their Saskatch- ewan counterparts. However, this does not mean that U.S. northern plains producers employ more machine services. Nonland capital and depreciation mostly consists of the opportunity costs of machinery. The sum of these two are $1.3 l/bu and $l.O5/bu, respectively, for Saskatchewan and the U.S. northern plains. This more than offsets the difference in custom services as the combined machinery-related ownership plus custom operation costs are $1.32/bu and $1.24/bu, re spectively , for Saskatchewan and the U.S. northern plains.

As might be expected because of the larger cost difference, more variables are significantly different - nine variables are statistically different at the 5 % level or less. Significant variables include four variable cash costs: seed costs, paid labor, fuel, oil and lube, and repairs. Another direct determinant variable is only slightly less significantly different - fertilizer costs. Together, the four variables with positive canonical structure coefficients can account for the entire cost difference.

Statistically significant indirect determinants include an interest-related variable and two farm-type variables: percentage livestock income and percentage of total gross farm income from wheat. The adjusted squared canonical correlation is 0.77 and is best characterized by paid labor, unpaid labor and seed costs. The cause underlying labor costs may be procedural or it could be related to differences in machine performance that generate direct labor requirements.

High-cost Pair The discriminant function for the high-cost pair has an adjusted squared canonical correlation of 0.729 and no misclassifications (Table 6). The average squared canonical correlation of the direct determinants is 0.304, indicating a low level of relationship. Total variable cash expenses are $2.39/bu and $3.58/bu, respectively, for Saskatchewan and the U.S. northern plains high-cost farms or a 33.2 % difference.

Only two variable cash costs - seed costs (SEED) and fertilizer (FERT) - are significantly different at the 5 % level or less. Paid labor (PAID) is significant at the 6% level. Collectively, these three variables account for $1.10ofthe$1.19/budifferenceinmeantotal variable cash cost. Significant indirect determinant variables include two fixed-cost variables - return to nonland capital (NLCAP) and return on operating capital (OP-CAP) - one financial variable - current ratio (CR) - and two farm-type variables - percentage livestock income (PCT-LWZT) and percentage of total gross farm income from wheat (PCT-TG).

Table 6. Inter-country Saskatchewan-U.S. northern plains cost group canonical discriminant analysis results g

Variable name R*

Low-cost producers


Standardized Structure

Mid-cost producers High-cost producers

Canonical coefficients Canonical coefficients

R2 Standardized Structure R2 Standardized Structure

CUSTOM FERT CHEM SEED

PAID POWER REPAIR UNPAID LAND NL-CAP C-ASST NC-ASST CR DAR C-LIAB NC-LlAB Pa--REV NET WORTH ACRES PCT-OWN PCT-SHR FALLOW PCT-EQV PCT-LIVE PCT-oc

0.01 0.268 0.350 0.09** 0.626 0.323 0.04 0.562 -0.219 0.03* 0.470 0.197 0.01 0.151 -0.084 0.02 0.063 -0.164 0.07** 0.412 0.311 0.22*** 0.721 0.521 0.01 -0.666 -0.121 0.04** -0.300 0.231 0.05 0.168 0.249 0.17*** 0.731 0.450 0.00 -0.747 0.034 0.04* 0.413 0.222 0.01 -0.272 -0.128 0.04** -0.100 -0.233 0.13*** 0.525 0.413 0.29*** 0.770 0.594 0.00 -0.336 -0.056 0.00 -0.287 0.031 0.16*** 0.708 0.468 0.12*** 0.191 0.377 0.10*** - 1.382 -0.371 0.01 0.237 0.085 0.00 1.733 0.026 0.01 0.599 0.081 0.04 0.418 -0.224 0.01 0.023 0.128 0.01 -0.299 -0.105 0.01 0.455 -0.118 0.01 0.095 -0.129 0.00 -0.130 0.024 0.01 -0.135 -0.092 0.00 -0.154 0.004 0.03 0.131 0.204 0.00 0.242 0.053 0.00 0.000 -0.036 0.00 0.000 0.049 0.02 -0.414 -0.161 0.01 -0.092 0.110 0.04 -1.204 -0.229 0.00 -0.388 -0.014 0.00 -0.169 -0.060 0.05** 0.594 0.240 0.07** -0.183 -0.316 0.02 -0.609 -0.152 0.00 0.137 -0.022 0.00 0.193 0.053 0.00 -0.03 1 -0.076 0.01 0.653 0.104 0.03 0.166 -0.183 0.02 0.733 -0.172

0.05 0.139 0.09** 0.398 0.00 0.321 0.15*** 0.731 0.11*** -0.661 0.07* 0.588 0.05 0.808 0.00 -0.243 0.18*** -0.006 0.04 -0.656 0.08** 0.263 0.02 -0.431 0.02 4.445 0.10*** -0.474 0.00 0.592 0.00 -0.443 0.02 - 1.824 0.01 -0.153 0.01 0.000 0.02 -0.101 0.03 -0.896 0.00 -0.154 0.02 -2.119 0.01 -0.050 0.12*** 0.112 0.06* -0.523

0.244 0.332

-0.028 2 0.434 2

0.369 i 0.285 3 0.242

-0.032 ii 0.473 F -0.215 g

0.305 & E -0.148 2 0.170

-0.353 5

0.012 p r

0.023 R 0.158 8 0.080 P 0.098 ij m 0.168

-0.199 0.048

-0.142 0.115 0.379

-0.281


gg dd

I

::

38 dd

Nr\l

s?:B dd

I

As other cash expense items are included in costs, country differences narrow slightly. Total cash requirements are $5.02/bu and $7.07/bu, respectively, for Saskatchewan and the U.S. northern plains, or about a 29.0% difference. While many of the individual fixed-cost components vary, the high-cost Saskatchewan and the U.S. northern plains COP, are nearly identical - $9.12/bu and $9SO/bu, respectively, or a 4.0% difference. Hence, many of the individual cash costs advantages are offset by higher fixed costs.

The adjusted squared canonical correlation is 0.73. While the estimated function can effectively discriminate between the producers of the two countries, there are no single variables strongly associated with the canonical discriminant function. Apparently, differences are the result of many factors.

SUMMARY AND CONCLUSION

In brief summary, the U.S. northern plains data are filtered so that both data sets represent producers with similar size and type characteristics and produce the same type of wheat. These producers have similar yields and for the most part, probably employ similar production techniques, as inputs can flow freely in both directions because there are no import duties on agricultural inputs. Cost differences are assessed in a two-fold fashion by:

l identifying significantly different costs between the groups in question, and

l identifying those factors most associated with the estimated discriminating functions.

The results indicate that, in general, there are more differences between cost groups within Saskatchewan or the U.S. northern plains than between the two countries. Relative to producers in the same country, low-per-bushel- cost producers in both countries have higher yields, most have lower variable inputs on both a per-acre and a per-bushel basis, and most have lower percentage debt loads.

In terms of inter-country comparisons, low-cost producers in Saskatchewan and the U.S. northern plains have nearly identical


AVC,, with only one significant variable cash cost difference, seed costs, which may be a spurious result. The only significant A VC, difference is displayed by the mid and high- cost groups. For these groups, Saskatchewan producers had significantly lower average variable cash costs because of lower seed, fertilizer, fuel, custom operations and hired labor costs. Saskatchewan’s lower variable cash advantages are almost entirely offset by higher fixed costs associated with higher machine- related ownership costs and higher amounts of fallow. Higher amounts of fallow may be associated with a more arid climate. Of more concern is higher fixed machine costs. Higher machinery ownership charges may be caused by lower combine and truck utilization brought about by shorter and more variable harvesting seasons, but they may also indicate a reluctance to take advantage of custom operations.

Implications for Canadian Agricultural Policy While this study is unable to garner enough detail to establish indirect linkages of govem- mental actions to the variable cash costs, some points can be made. First, low-cost producers in both countries seem to have achieved better than average yields without incurring higher per-acre costs. It is important that farmers be encouraged to find ways to more judiciously use inputs. Secondly, while all Saskatchewan cost groups had lower COP, than their U.S. northern plains counterparts, they also had higher fixed machinery costs due to higher investments. Hence, it is critical to avoid programs that lead to overinvestment such as sub- sidized interest and investment tax credits.

Limitations These results must be viewed with caution. Despite efforts to eliminate data problems, the data sets still may not represent the same profile of producers. Although the estimation methodology was standardized as much as possible, some differences related to labor used and fixed capital investments may not have been completely eliminated; indeed, our experience would suggest that the only way to

guarantee uniformity is to use the same survey instruments and procedures. In addition, there are many factors that influence farmer deci- sions such as government programs and policies, taxes, weather, insects, disease, input prices and output prices, which may have differed between countries. Finally, these data represent a “snapshot” of Saskatchewan and U.S. northern plains costs of producing wheat, and thus they cannot address questions as to long-run cost efficiency relationships.

NOTES

‘For more information about the composition of the FCRS, refer to Morehart, Shapouri and Dis- mukes (1992). *Additional differences are that the Top Manage- ment participants are younger and better educated than the general population (Chikwana 1989) and have higher debt levels (Koroluk and Culver 1993). 3Note that Saskatchewan data incorporate farmer- supplied unit prices. The U.S. northern plains data are based on a combination of an independent survey of state-level prices and farmer-supplied unit

$ rices. Saskatchewan uses direct and indirect cost allo-

cation procedures. Direct allocation procedures are based on those items whose use can be linked directly to a specific enterprise, and total use is based on the enumeration of all machines and machine systems used on the farm. Since all use is included, the direct use share for a given enterprise is easily calculated. However, it is sometimes difficult to directly link the use of equipment or building to a specific enterprise (for example: pickup trucks; trucks used for hauling chemicals and water; farmyard use of tractors, snow blowers, mowers; and shop items). These are placed in a second category and are allocated according to their share of direct cash operating costs. The U.S. col- lects single enterprise data, and the farmer must therefore allocate costs. ‘While liability classifications initially differed, (short, intermediate and long-term versus short and long-term liabilities), this could easily be corrected. ?n Canada, the corresponding 1989 FCC rate varied from 12% to 13.25%. 71n its COP calculations, the USDA defines capital replacement cost as the difference between beginning and ending real asset values and is that amount which would preserve the real value of the investment. Hence, it is a measure of real “economic” depreciation. Initially, the Saskatchewan


data were based on nominal values. They were subsequently converted to real values. In addition, the two data sets differed in terms of machine valuation dates. Because the Saskatchewan data are based on a calendar fiscal year for financial pur- poses, machines are valued at 1 January instead of the preceding fall harvesting date. This means that the Saskatchewan data have approximately a three-month lag in valuation. Finally, the Saskatch- ewan data originally used nominal interest rates and depreciation rates instead of real rates. This was corrected to match the U.S. data. ?‘he 5% charge is based on the average cash rent divided by the fair market value of farmland.

REFERENCES

Ahearn, M., D. Culver and R. Schoney. 1990. Usefulness and limitations of cop estimates for evaluating international competitiveness: A comparison of Canadian and U.S. wheat. American Journal of Agricultural Economics 72: 1283-91. Ahearn, M. C. and U. Vasavada. 1992. Costs and Returns for Agricultural Commodities: Advances in Concepts and Measurements. Boulder, Colo. : Westview Press. Anderson, T. W. 1984. An Introduction to Multi- variate Statistical Analysis. New York: John Wiley and Sons. Brown, W. J. 1992. lIre Cost of Producing Wheat in Five Wheat Growing Regions. Staff Paper 9241. Saskatoon: University of Saskatchewan, Depart- ment of Agricultural Economics. Chikwana, R. 1989. An economic analysis of crop selection under risk in the brown and dark brown soil zones of Saskatchewan. PhD thesis. Saskatoon: University of Saskatchewan. Goldstein, M. and W. R. Dillon. 1978. Discrete Discriminant Analysis. New York: John Wiley and Sons. Gustafson, C. R., E. G. Nielsen and M. J. Morehart. 1989. Financial Characteristics of Record-keeping and Average Farms in North Dakota. Agricultural Economics Report 245. Fargo: North Dakota State University, Department of Agricultural Economics. KIecka, W. R. 1980. Discriminant Analysis. Beverly Hills: Sage Publications. Koenigstein, K. W. and D. A. Lii. 1989. Com- parison of Illinois Farms Using 1987Data: USDA Versus FBFM. Series E, Agriculture of Economics Paper 89 E-434. Urbana: University of Illinois, Department of Agricultural Economics.

Koroluk, R. and D. Culver. 1993. Ottawa, Agriculture Canada, personal communication, 23 November. Le Stum, H. and D. Camaret. 1990. European Community experience in costs of producing wheat. American Journal of Agricultural Eco- nomics 72: 1298- 1303. Morehart, M. J., H. Shapouri and R. Dismukes. 1992. Major Statistical Series of the U.S Depart- ment of Agriculture, Volume 12: Cost of Production. Agricultural Handbook 67 1, vol. 12. Washington, D.C.: USDA, ERS, Agriculture and Rural Economy Division. Morrison, D. F. 1967. Multivariate Statistical Methods. New York: McGraw-Hill. Mueller, A. G. 1954. Comparison of farm management service farms and a random sample of farms in western Illinois. Journal of Farm Economics 36: 258-92. Ontario. Ministry of Agriculture and Food. 1989. Comparison of Input Prices Between Ontario and Michigan. Vol. 21, no. 741. Toronto: OMAF. Ortman, G. F., V. Stulp and N. Rask. 1986. Comparative Costs of Agricultural Commodity Among Major Exporting Gxntries. Working Paper 1325. Columbus: Ohio State University, Depart- ment of Agricultural Economics and Rural Sociology. Parks, L. L., K. A. Rockeman and 0. L. Walker. 1980. Enterprise Budgets: A Multiple Use Data Base for Agricultural Banks in Developing Countries. International Development Series 80- 1. Stillwater: Oklahoma State University, Department of Agricultural Economics. Perry, G. M., C. J. Nixon and K. J. Bunnage. 1992. Taxes, farm programs, and competitive advantage for U.S. and Canadian farmers: A case study. American Journal of Agricultural Economics 74: 299-309. Seecharan, R. 1988 production Costs for Wheat: Comparison between Major Wheat Producing Regions in Canada and the United States. Paper 2237a. Ottawa: Agriculture Canada, Policy Branch. Sharples, J. A. 1990. Cost of production and productivity in analyzing trade and competitiveness. American Journal of Agricultural Economics 72: 1278-82. Stanton, B. F. 1986. Production Costs for Cereals in the European Community: Comparisons with the United States, 1977-1984. Research Bulletin 86-2. Ithaca: Cornell University, Department of Agricultural Economics.

comparison of costs of wheat production in saskatchewan and the u.s. northern plains

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