Economic Costs of Environmental Quality Constraints

Economic Costs of Environmental Quality Constraints Calum G.Turvey and Alfons J. Weersink Department of Agricultural Economics and Business, University of Guelph, Guelph, Ontario

A persistentconcern among agriculturaland resourceeconomistsis the sustainability of agricultural systems. Agricultural economistshave been accused of having little impact on the development of public policy regarding soil conservation (Fox and Taft, 1990), and this is probably a consequence of failure in consensus of the relevant issues involved (van Kooten andFurtan, 1987).Economists have generally discarded the view that long-run productivity effects are a function for government involvementalthough studiesof this type stillpersist (Baffoe et al1987; Smith and Shaykewich, 1990).What is being recognized as justiftable intervention by some economists is the problem of off-site damage from soil erosion, sediment and phosphate loading into surface water, and groundwater nitrogen loading, although differences in philosophiesabout the nature of non-pointpollution, its extent, and methods of control are disparate (Randall 1985; Shabman 1985; van Kooten and Furtan 1987;Fox and Taft 1990).Off-siteexternalitieshave been identified in terms of recreational costs, water treatment costs, water transportationinfrastructure,and increased risk of flooding (Fox and Taft, 1990; Clark et al, 1985;Fox and Dickson, 1990). Yet despite these noticeableand (sometimes)measurableexternalities,soil erosion in Ontario is excluded from legal liability under the EnvironmentalProtection Act and the Water Resources Act (van Vuuren, 1990). While a substantial effort has been undertaken to compute the off-site costs of soil erosion,(Driveret al, Fox and Dickson), opportunity costs of conservationsuch as foregonerevenues have not adequately been dealt with. In this paper we assume the existence of environmental quality constraints on overall soil loss and soil loading into surface water for a 352 ha. watershed in Southern Ontario. We then optimize, using linear programming, watershed practices where alternative tillage practices comprise the management decisions. Shadow prices on the soil loss and soil loading constraints reflect the opportunity (marginal) costs of the environmental quality constraints.Thus we focus on the on-siteeconomic costs associated with the constraints, describethe relationshipsamong the constraints, and establish some economic criteria which may be useful in the regulation of tillage based non-pointpollution. Non-point pollution due to soil erosion in Ontario is largely attributed to intensive row cultivation of crops such as corn, soybeans, tomatoes and potatws (Stonehouse and Bohl, 1990). Remedii measures to conml both the non-point pollution and land erosion have been recommended for areas within watersheds which have a high potential to deliver pollutants (Dickenson et al, 1990). The effectiveness of alternative management practices, such as conventional or no-till tillage practices, is often measured purely in terms of sediment control and erosion containment,independentlyof their opportunity costs.Yet, in economic terms, there Can. J. Agric. Eon. 39 (1991) 677-685

677

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CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

is often little incentive to farmers for undertaking best management practices since increased variable and fixed costs, and incremental yield reductions more than offsets the benefits attributable to soil conservation (Stonehouse and Bohl, 1990; Walker, 1991). The institutionalmeasure in this study is the imposition of environmentalquality constmints on soil loss and loading for corn and soybeans production on a watershed in Southern Ontario. The method of analysis follows in 2 steps. First, a simulation model (GAMES) is used to derive gross soil loss and soil loading to surface water measures (tonneha) for various fields in the watershed, for given management practices (conventionalcorn; no-tillcorn; conventionalsoy in rotation with conventional corn; no-till soybean in rotation with no-till corn). The second stage is a two-period linear programming model which maximizes watershed profits subject to environmental quality constraints on total soil loss, and soil loading to the adjacent stream. Use of soil loss measures in optimization models have been used in previous studies (Jacobsand Casler, 1979;Wade and Heady 1977;Taylor andFrohberg 1977; McSweeny 1988; Baffoe et al 1977; Smith and Shayhewich 1990), however only one of these, Jacobs and Casler, explicitly examined the effects of restricting pollution and this was for phosphorous. This study differs from theirs in that exact measures of gross soil loss and loading are generated via the GAMES (Guelph Model for Evaluating Effects of AgriculturalManagement System on Erosion and Sedimentation; Dickinson and Rudra, 1990) Simulation Model. The GAMES Model can be used to describe and predict soil loss by fluvial erosion and the delivery of solids from field to stream in a particular watershed. The model delineates critical sediment source areas @olygons) within the watershed and provides a means of evaluating alternative soil conservation practices. The soil erosion component of GAMES uses the Universal Soil Loss Equation (USLE); (Wischmeier and Smith, 1978) and the potential field soil loss delivered to the stream is computed using a delivery ratio which takes into account fluvial sediment movement across polygons prior to entering the stream Wickenson et al1986). The GAMES model provides polygon measures of soil loading to the surface water as well as gross soil loss. Both of these measures become input to the linear programming model and both are parametricallyconstrained to obtain dual shadow prices which measure explicitly the opportunity cost of the environmentalconstraint. The linear program is a two-period model which accommodates a full corn soybean rotation and two years of continuous corn, under either conventional (moldboard plough) or no-till management practices. Each polygon in the watershed is treated as a separate field, so that any solutionis availableto it. The poIygons differ in size as well as erdbility characteristics.No constraints other than acreage restrictions and environmentalquality constraints are imposed on the model, hence it is explicitly assumed that technology, labour, financing, etc. are non-binding constraints. The model reflects an ex ante regulatory policy, assumes that soil loss and soil loading factors are known by producers, and further assumes that these outcomes are certain.

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679

RESULTS The first period of the two-period L.P. model was comprised of activities for growing conventional corn, no-till corn and, conventional and no-till soybeans grown in rotation with corn. In the second year, conventional and no-till corn, grown continuous and in rotation with period 1 soybeans, as well as conventional and no-till soybeans competing with continuous corn were the established activities. The watershed is comprised of 432 cells, or polygons, so a total of (4 x 432) 1,728 first year growing activities and (6 x 432) 2,592 second year activities were defined. Environmental quality constraints were structured to constrain total watershed soil loss and surface water soil loading. The unconstrained profit potential for the 351.9 ha watershed was $286,968 over the two-year period, with total soil loss amounting to 3,367 tonnes, and soil loading equal 121.7 tonnes. This solution grew only continuous corn and disregarded the possibility that second year yields may be lower than first year yields. This optimum was selected as the benchmark for which 77 environmentalquality constrained scenarios were compared. These were 7 soil loss classes ranging from 3,367 to 500 tonnes, and 11 soil loading classes ranging from 121.7 to 20 tonnes. The totalled 77 separate L.P.’s were run to derive total revenue, marginal cost, and cropping plans reported in Tables 1 and 2. Revenues and Costs The marginal costs of the environmental quality constraints were obtained from their shadow prices. In Table 1 the first number in each cell is the two-year profit, the second is the 2-year marginal cost of the soil loss constraintand the third is the 2-year marginal cost of the soil loading constraint. The results illustrate the general relationship between soil loss and soil loading; while holding one constraint constant and varying the other, the marginal cost of the former constraint diminishes while the other increases. For example, if soil loading was constrained to 60 tonnes and the restriction on soil loss is allowed to increase, the marginal cost of loading decreases from $209.84 to $ 0 while the marginal cost attributed to soil loss increases from $0 to $23.02/tonne.f Likewise, holding soil loss at 2,000 tonnes and allowing one soil load restriction to increase, results in amarginal cost decrease from $15.13/T. to $Om.for the former, while the latter increases from $Om.to $1401.53/T. All other things held constant, the effect of environmentalquality constraints is to increase costs at an increasingrate. This is illustrated in Figure 1which plots the marginal cost curves for the first row (soil loss) and column (soil load) in Table 1. Average costs are obtained by taking the difference between unconstrained profits and constrained profits and dividing through by the binding value of the constraint.2 Average costs are everywherenon-negative,increasing, and lying below the marginal costs curve (Figure 2). This implies that production is displaced from an optimum and efficientfarm plan with non-increasingreturns to scale to a less than profit maximizing optimum exhibiting decreasing returns to scale. This consequence of environmentalquality constraints is predicated upon the observation that the ratio of marginal cost to average cost, a measure of product flexibility, (Beattie and Taylor) is greater than 1. Decreasing returns to scale in this context implies

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CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

Table 1: N e t Revenues and Marginal Costs of S o i l Loss and Loading R e s t r i c t i o n s 8

S o i l Loss (Tonnes) S o i l Load (tonnes)

121.7

110

3000'

2500b

2000'

1500d

1000e

500'

286968 0

284045 8.58

278320 13.39

271152 15.13

263126 17.16

253541 23.02

170717 251.38

0

0

0

0

0

0

0

286531

284045 8.58

278320 13.39

271152 15.13

263126 17.16

253541 23.02

170717 251.38

3367

0

47.73 100

0

0

0

0

0

278320 13.39

271152 15.13

263126 17.16

253541 23.02

76.59

283924 8.00 30.19

0

0

0

0

0

285035 0 91.08

283538 7.74 56.85

278283 13.21 11.01

271152 15.13

263126 17.16 0

253541 23.02 0

170717 251.38 0

284036 0 117.84

282679 6.93 100.28

278102 12.96 25.27

271151 15.12 .27

263126 17.16

253541 23.02 0

170717 251.38 0

282690 0 172.65

281643 6.60 108.23

277591 10.91 80.38

271048 14.84 15.21

263126 17.16 0

253541 23.02 0

170717 251.38 0

280787

276596 9.99 110.57

270708 13.39 62.68

263097 16.85 10.00

253541 23.02

170717 251.38

209.84

280376 5.11 150.60

278134

278134

269801 12.42 112.95

262874 15.86 50.27

253541 23.02

285874 0

90

80

70

60

0 50

0

340.99

340.99

0

273833

273833 0 545.58

273202 5.23 3.38

268601 12.19 126.55

261958 14.68 126.34

253358 21.60 76.37

170717 251.38 0

267206 0 814.30

267206

266497 6.79 423.51

260617 14.03 176.43

252143 21.41 148.97

170567 250.19 84.15

256826

256605 4.14 L215.57

249884 57.50 443.52

168534 247.21 352.44

545.58 267206 0

814.30 20

0

170717 251.38 0

0

30

0

275389 9.09 157.29

0

40

0

0

170717 251.38

256826

0

256826

0

814.30 256826

0

0

0

0

1401.53

1401.53

1401.53

1401.53

a ) Max f e a s i b l e l o a d i s 109.217; b ) rnax f e a s i b l e l o a d i s 95.86; c ) max f e a s i b l e l o a d i s 81.185; d ) max f e a s i b l e l o a d i s 64.139; e ) max f e a s i b l e l o a d i s 4 8 . 9 0 ; f ) max f e a s i b l e l o a d i s 34.97. t h e f i r s t number i n each c e l l i s t h e 2 - p e r i o d n e t r e v e n u e s , t h e second i s t h e marginal c o s t of t h e s o i l l o s s c o n s t r a i n t , and t h e t h i r d i s t h e m a r g i n a l c o s t of t h e s o i l l o a d i n g c o n s t r a i n t .

overall technical and allocative inefficiency with respect to input use (i.e. land) and output (i.e. profits). The aboveresults are indicativeof the costs of environmentalquahty constraints. They show that substantial opportunity costs and scale inefficiencies result. Although, in the context of social welfare, producer surplus would decrease with respect to environmental quality constraints, a social optimum would be obtained

WORKSHOP PROCEEDINGS

681

1600 1400 h

2 1200 0

s 1000 c1

2 800

E

'

Y

s

600

* 0

; ; 400

s

200 0

0

12 22 32 42 52 62 72 82 92 102 reduction in soil loading (Tonnes )

Figure 1. marginal and average costs of soil loading constraint.

400 -0

350

0

2 300 m 5 250 b)

Y

2

3 200

2

0 150

s2 100

*

a

0

50 0

mventional 0

12 22 32 42 52 62 72 82 92 102 reduction in soil loading (Tonnes )

Figure 2. Conventional vs. no-till corn with constrained soil loading.

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CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

if the marginal cost to the farmer equals the marginal benefits to society of clearing up the externality.Although,in Ontario, there is no legislationdirected at restricting soil pollution, an important aspect of the problem for policy makers to consider is that the marginal costs of pollution abatement can be very high, and for farmers who are either debt and/or liquidity constrained, the imposition of quality constraints may cause undue hardship and exacerbate efforts to maintain solvency. Furthermore,the averagecost increasing effectof environmentalquality constraints may lead some farm groups to raise issues related to Canada's competitiveposition and question why regulator authorities would legislate controls which would only increase the cost-price squeeze observation in todays' markets. Crop Selection The above results outlined the costs of pollution abatement without regards to the means by which farmers satisfy the constraints. Table 2 reports the first year crop plans under each of the 77 soil loss-loading combinations with the first number in each cell being continuous corn acres and the second being continuous no-till corn. (Year2 farm plans were very similar, except that some solutions substituted a low number of conventional soybean hectares for continuouscorn when a rotation was permitted). The reference unconstrained profit maximizing plan grows 35 1.9ha. of conventionai continuous corn in year 1. Under all scenarios no-till corn is substituted for continuous corn. For example, if soil loading was constrained to equal 70 tonnes while soil loss was unconstrained the optimum plan is to grow 3 13.14ha. continuous corn and 38.76 ha. no-till corn. If an additional constraint on soil loss was imposed then continuous corn hectarage decreases.For example with soil loading constrained at 70 tonnes, and soil loss constrained at 2000 tonnes, conventional corn is reduced to 207.02 ha. while no-till corn increases to 144.88 ha. It is important to recognize that these hectarages were obtained by summing up activities at the polygon, and not the field, level. This approach obviously exaggerates the observed substitution effects. However, the results are in line with engineering proposals for remedial targeting of highly erosive soils (Dickenson et al). Remedial targeting involves isolating small pockets of land which are highly erosive. For example, a large degree of abatement can be obtained by managing and monitoring only a small land area. These areas may be a field, or a packet (i.e., 1 or more polygons) within a field. This is, in fact, what occurs in the optimization model; as restrictions on soil loss and loading increase the most hghly erosive polygons are the first to be converted to conservation tillage. As is evidenced by the results in Table 2, for every increment in pollution abatement the incremental substitutioneffect increases.That is, in order to satisfy the new constraints a greater area of less erosive land must be put under conservation tillage.

CONCLUSIONS The purpose of this paper was to estimate the costs to farmers of environmental quality constraints, and to provide optimum management strategies when such constraints on non-point soil loss and surface water soil loading constraints were imposed. The results of a linear programming model for a Southern Ontario

WORKSHOP PROCEEDINGS Table 2 :

683

Optimum Crop Plans Under Environmental Quality Constraints (acres)’ Soil Loss (Tonnes) 3367

3000

121.70

351.90 0

325.00 26.90

110

348.03 3.86

100

340.89

Soil Load (tonnes)

11.01

a

2500

2000

1500

1000

500

275.00 76.90

205.58 146.32

135.38 216.52

47.92 303.98

21.90 2 0 9 . LO

325.00 26.90

275.00 76.90

205.58 146.32

135.38 216.52

47.92 303.98

21.90 2 0 9 . LO

325.40 26.50

275.00 76.90

205.58 146.32

135.38 216.52

47.92 303.98

21.90 209.10

90

334.53 17.37

320.70 31.20

272.43 79.47

205.58 146.32

135.38 216.52

47.92 303.98

21.90 209.10

80

323.91 27.98

312.33 39.57

269.77 82.13

206.40 145.50

135.38 216.52

47.92 303.98

21.90 209.10

70

313.14 38.76

303.50 48.40

265.28 86.62

207.02 144.88

135.38 216.52

47.92 303.98

21.90 209.10

60

296.90 55.00

291.90 60.00

255.47 96.43

203.68 148.22

134.20 121.70

47.92 303.98

21.90 209.10

50

270.04 81.86

270.04 81.86

245.94 105.96

194.90 156.99

131.34 220.56

47.92 303.98

21.90 209.10

40

232.00 119.90

232.00 119.90

226.04 125.86

183.68 168.21

122.35 229.55

44.83 307.07

21.90 209.10

30

170.84 181.06

170.84 181.06

170.84 181.06

165.34 186.56

110.86 241.04

31.80 320.10

21.15 210.00

20

84.80 265.30

84.80 265.30

84.80 265.30

84.80 265.30

76.92 274.48

18.98 331.62

15.80 214.60

The results reported are the first year farm plans only. The first number in each cell is conventional continuous corn, and the second number is notill continuous corn.

watershed indicates that extensive marginal and average cost increases would be borne by farmers. With the focus of environmental economics being on non-point pollution control and abatement the results of the study are important.For example, if a policy objective was to reduce soil loading from the watershed into surface water by 50%, watershed revenues would fall from $286,968 to $280,787. The marginal cost of an additional tonne of abatement is $208.84 and the average cost to the watershed fanners is approximately $13.OO/tonne. As restrictions on soil loading (and gross soil loss) increases, both marginal costs and average costs increase at an increasingrate and watershed profits decrease at an increasing rate. With costs characterized as thus, regulators considering soil pollution abatement policies should attempt to establish the extent of farmer-borne costs and moderates the policy accordingly.Furthermore, regulatorscould not legislateacross the board abatement controls since every watershed is different and the associated costs of abatement will also differ. One approach may be to identify an average cost of

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CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS

abatement for all farmers and from this establish how much abatement would be required.

NOTES 'Only costs up to a soil loss constraintof lo00 will be used in discussion sincew a soil loss constraint of 500 forced some land to be held in slack.

2Not all solutions had binding constraints. The maximum loading is provided in the notes to Table 1, and the 0 gvalue cell entries represent solutions in which the right-hand side of the constraint exceeded these values.

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Taylor, C.R. and K.K. Frohberg. 1977. "The Welfare Effects of Erosion Controls, Banning Pesticides, and Limiting Fertilizer Application in the Corn Belt". Arner: J. Agric. Econ., 5925-36. Van Kooten, G.C. and W.H. Furtan. 1987. "A Review of Issues Pertaining to Soil Deterioration in Canada." cdn.J. Agric. Econ., 3533-54. Van Vuuren, W. 1990. "Causes and Consequences of Land Degradation in Ontario", in Sustainable Agriculture: Its Policy Effects on the Future of Canada and Ontario's Agrifood System. Proceedings, Dept. of Agricultural Economics and Business, University of Guelph. May. Wade, J.C. and E.O. Heady. 1977. "ControllingNon-pointSedimentSourceswith Cropland Management: A National Economic Assessment: Amer. J. Agric. Econ., 59113-24. Walker, MJ. 1991. "An Income and Risk Study of Different Tillage and Weed Control Practices in Southern Ontario: A Stochastic Dominance Approach." Unpublished M.Sc. Thesis, University of Guelph. March. Wischmeier, W.H. and D.D. Smith. 1978. "PredictingRainfallErosion Losses A Guide to Conservation Planning." USDA., Science and Education Administration Handbook,No. 537.