On the efficient use of a catchment's land and water resources: dryland salinization in Australia

Ecological Economics 38 (2001) 441– 458 www.elsevier.com/locate/ecolecon

ANALYSIS

On the efficient use of a catchment’s land and water resources: dryland salinization in Australia Romy Greiner a,*, Oscar Cacho b a

CSIRO Sustainable Ecosystems, Da6ies Laboratory, PMB Aitken6ale, Towns6ille, Qld. 4814, Australia b School of Economics, Uni6ersity of New England, Armidale, N.S.W. 2351, Australia Received 6 November 2000; received in revised form 27 March 2001; accepted 28 March 2001

Abstract This paper presents an investigation into the questions of long-term Pareto-optimal use of a catchment’s land and water resources in the face of encroaching dryland salinization. An optimal control approach is adopted for problem analysis and translated into a dynamic catchment optimization model of salinity management. Model results suggest that, for the catchment under consideration, it is economically efficient to restrict soil salinization to a fraction of the area at risk. In the model, large-scale land-use changes, as required for maximization of the present value of the catchment, are implemented within a decade. Optimal control activities focus on the areas potentially affected by salinity rather than the ‘recharge areas’ of the catchment, suggesting that upstream recharge is largely Pareto irrelevant. Pigovian and Coasean policy approaches are discussed under specific consideration of: (1) salinization as a stock externality; (2) the non-point source pollution character of recharge; and (3) the isolation paradox surrounding salinity management. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Catchment management; Optimal control; Dryland salinity; Stock externality

1. Introduction Farms are embedded in large-scale biophysical and ecological processes as small portions of landscapes, catchments and regions; hence, farming activities are commonly associated with externali* Corresponding author. Tel.: + 61-7-47538630; fax: +617-47538650. E-mail addresses: [email protected] (R. Greiner), [email protected] (O. Cacho).

ties (Vatn and Bromley, 1997). Dryland salinization is an example of such an externality, whereby farm activities in upstream parts of catchments have hydrological effects on the well-being of farmers and communities downstream. Salinization is emerging as a major resource degradation problem in dryland catchments around the world (Szabolcs, 1989; Ghassemi et al., 1995). Dryland salinization is a land-degradation process associated with high and rising saline groundwater tables in the low-lying (discharge) areas of a

0921-8009/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 1 - 8 0 0 9 ( 0 1 ) 0 0 1 9 2 - 6

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catchment. The underlying cause is replacement of perennial native vegetation with farming and grazing systems that enable a larger proportion of rain to recharge the groundwater system. This additional recharge can be caused in the uphill (recharge) areas of the catchment but also in the discharge areas themselves. Soil salinity causes soil productivity, farm incomes and land values to decline. A modeling analysis for a catchment in Australia found the majority of costs of dryland salinization to be external to the affected farms (Greiner, 1996a). In Australia, the estimated area of land affected by dryland salinity may rise from the current 1.2 million hectares to 15 million hectares (Walker et al., 1999). Up to 5 million hectares may eventually become saline in the State of New South Wales alone (Bradd and Gates, 1995). Bennett et al. (1997) estimate the costs associated with dryland salinity in Australia to be on the order of $270 million per year, comprising agricultural, infrastructure and environmental costs of $130 million, $100 million and $40 million, respectively. Salinity affects not only farmers, but also urban and rural households, businesses, public utilities, government agencies and local councils (Martin and Metcalfe, 1998). Farm-level analyses (for example, Greiner, 1996a; Mueller et al., 1999) have aided in designing mitigation strategies for salinity-affected farms. They also provide an indication of whether on-farm salinity control is feasible and economically viable. In practice, farmers may not be able to halt the salinization process when mitigating land-use options require high levels of investment with little or delayed return, as is the case with tree plantations. In isolation, farm-level analyses suggest that groundwater table rise should be managed at its source, by reducing recharge in uphill areas to a level that does not cause groundwater table rise and consequent dryland salinization with all its associated costs. From the standpoint of society, the long-term efficient use of land and water resources is of concern. With respect to soil salinization, tradeoff decisions arise at various levels and three of them are considered in this study: (i) should salin-

ity be controlled?; (ii) who should engage in salinity control activities and who should bear the costs?; and (iii) what management activities, from a list of options, should be implemented, to what extent and when? This paper contributes to knowledge through the analysis of dryland salinization and the debate of policies for its management in three ways. First, it presents a dynamic partial–spatial equilibrium approach to the study of soil salinization and its management at the catchment scale, which is the appropriate spatial unit. Second, the paper contributes to the policy debate by interpreting Pigovian and Coasean approaches to externality problems. Third, the model used in this analysis contains an unprecedented array of land-use options, thus enhancing the relevance of model results for long-term catchment management. The catchment is defined by the hydrological boundaries of the area (watershed) and includes hydrological linkages between recharge areas and discharge areas. To analyze economic relations between these areas, the catchment under investigation is divided into regions of distinct hydrological characteristics for catchment management. A long-term dynamic approach is used to deal with the transitional nature of land-use change and associated transaction costs, and to represent the cumulative characteristics of stock externalities. The model explicitly considers: (1) salinization as a stock externality; (2) the non-point source pollution character of recharge; and (3) the isolation paradox surrounding recharge control for salinity management. The isolation paradox refers to a case where everyone in a group would be better off with coordinated action than with no action at all (Randall, 1999), but action is not taken because of lack of coordination and the small effect of a single individual’s action.

2. Salinity as an externality problem From an economic perspective, the two classical approaches to dealing with externality problems are the Pigovian and Coasean models. The standard Pigovian approach is to make emitters liable for the economic damage that the receiver

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incurs (Pigou, 1946). In contrast, the Coasean approach is not concerned with physical causation or morality, but argues that efficient elimination of externalities must start by determining which party could change behavior most cheaply (Coase, 1960). As the mechanism of choice for internalizing external costs to a Pareto-optimal level, Pigou recommends taxing the emission. In the case of dryland salinization, this would equate to a tax per unit of recharge to the groundwater system. The emitter would be liable for damages caused and a tax would create the conditions necessary for restoring optimality. The Pareto-optimal level of tax on the emitter (uphill farmer) would be equivalent to the marginal damage per unit of emission (recharge) and equal its marginal abatement cost. Vatn and Bromley (1997, p. 141) argue that a Pigovian tax may not be an efficient measure for correcting externalities; while it may be morally right to make the emitter liable, it may not always be efficient to tax this side of the conflict. The establishment of a market and associated bargaining is the preferred means of achieving Pareto-optimality under the Coasean approach. Applied to dryland salinization, the Coasean approach could translate into a market for recharge entitlements. These would be issued to both uphill and downhill farmers. Through trade within the catchment, a price for recharge entitlements would evolve, reflecting the optimal level of recharge. In the absence of transaction costs, the price per recharge entitlement would be equivalent to the marginal benefit from recharge-generating land-use practices uphill and the marginal costs of associated groundwater table rise to farmers in the discharge zone. Hodge (1982) pursues the Coasean line of thinking in developing a policy approach to ensure a socially optimal level of clearing of native vegetation in salinity-prone catchments. He advocates a scheme involving the use of transferable rights to cleared land. However, he concedes that this approach only applies to catchments that still possess a cover of native vegetation above that required for salinity control. In principle, the Pigovian and Coasean approaches arrive at the same solution (Pareto-optimum) where the marginal social damage equals the marginal social abatement cost. However, the in-

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come distribution effects may be quite different. With the Pigovian approach the emitter pays a tax that is directly related to the amount of emission, while with tradable permits the distributional effects depend largely on the initial allocation of permits and the relative efficiency of different producers. Vatn and Bromley (1997, p. 142) interpret the Coasean position as one where, given an initial allocation of entitlements, the only relevant argument in considering responsibility for action is the level and incidence of transaction costs. Transaction costs may be so high it is ‘efficient’ to maintain the status quo, or they may be low enough, at least for one side, to permit a bargained transaction. Transaction costs are also associated with implementing a Pigovian tax system. In the face of transaction costs, the presence of externalities can be interpreted as a rational outcome. Randall (1983, p. 137) states that ‘‘the non-existence of certain markets is a rational market response to transaction costs in excess of potential gains from trade’’. Another important point is that, when the benefits to society from the economic activities of emitters and receivers are considered, it may not be Pareto-optimal to cut emissions to zero. Scheele (1996), for example, in a study on the conflict between agricultural production and groundwater quality, demonstrates that minimizing groundwater pollution, and therefore external costs, from agriculture may lead to socially sub-optimal resource allocation. Fig. 1 provides an economic interpretation of the hydrological interdependencies between parts of a catchment. The catchment is spatially segregated into an upstream (recharge) area and a downstream (discharge) area where salinization occurs. As shown in Fig. 1, for a given time period, increasing upstream recharge (R) is associated with increasing benefits from agriculture upstream (Bu). Recharge causes salinization in the discharge zone of the catchment and hence reduced profit from agriculture downstream (Bd). The difference between potential profit from downstream agriculture (in the absence of recharge) and actual profit is the opportunity cost (OC) to society of upstream recharge, and is the external cost that

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downstream farmers experience through soil salinization. The privately optimal level of uphill recharge (Ru in Fig. 1) is associated with opportunity cost OCu and total benefit for the catchment Buc . From a catchment perspective, the private solution Ru is sub-optimal, as there is scope for welfare gains by decreasing the level of recharge to R*, the socially optimal level. At this point, the marginal opportunity cost (MC) associated with upstream recharge equates the marginal benefit (MB) to upstream farmers (Fig. 1). Under the private (upstream) optimum, the cost of the externality is indicated by point a, while under the social optimum the externality occurs at point b. Only a portion of total externalities is Pareto relevant (E*); if avoided, these externalities increase catchment well-being (Buchanan and

Stubblebine, 1962). The salient conclusion is that, at the catchment-optimal level of recharge, the opportunity cost is not reduced to zero, but to OC*, which corresponds to externality Eo. This level of externality is Pareto irrelevant; only the proportion of externalities labeled E* is Pareto relevant. This simple model shows that it may not be efficient to reduce uphill recharge to a level where it does not cause salinization downstream. Due to its static perspective, however, it fails to describe the transition from the present to the optimal situation, which must also address the issue of transaction costs associated with land-use changes. The salinity problem requires a dynamic representation, as the management question is one of finding a transitional path from current use of the catchment’s land and water resources to the best achievable sustainable and efficient use. The requirement for a dynamic expression of the problem is further supported by the stock-externality character of salinization. Unlike flow externalities, which dissipate when the flow of emissions ceases, stock externalities accumulate and persist into the future. Examples of stock externalities include the greenhouse effect caused by accumulation of carbon dioxide in the atmosphere, depletion of the ozone layer due to accumulation of chlorofluorocarbons, topsoil depletion and groundwater contamination (Nordhaus, 1982; Brito and Intriligator, 1987; Ko et al., 1992).

3. Economic model

Fig. 1. Optimal level of upstream recharge (adapted from Scheele, 1996): R, upstream recharge (m3); B, net benefits ($), u , upstream; d, downstream; c, entire catchment; OC, opportunity cost of recharge ($); E, external costs ($); o, Pareto irrelevant; *, Pareto relevant; MC, marginal opportunity cost of recharge ($); MB, marginal benefit of recharge ($).

Consider a catchment with two areas, a recharge (uphill) area, and a discharge (downhill) area. The recharge area contains I farms, denoted by the subscript i= 1, …, I, each of area Ki. The discharge area contains J farms, denoted by the subscripts j= 1, …, J, each of area Kj. Land quality in the discharge area may be affected by salinity. Three possible salinity states are considered (s=1, 2, 3): s= 1 indicates no salinity, s=2 indicates slight salinity, and s= 3 indicates severe salinity. Depending on recharge, land can change

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from state 1 to state 2, or from state 2 to state 3. Slight salinization can be reversed, hence state transitions from s= 2 to s =1 are possible. In contrast, severe salinization is not reversible and, hence, the area of land in state 3 cannot decrease overtime. On any given farm in the discharge area, the total area of land equals the sum of land of each salinity state:

T

Max:NPV = %



445

I

% Bi {Rit }

t=1 i=1 J

n

3

+ % % Bjs {Rjst,Kjst } ·l − t j − 1s = 1 J

3

− % % Vjs {Kjs(T + 1)}·l − (T + 1) subject to: Kj 1(t + 1) − Kj 1t = − F1t

3

Kj·t = % Kjst

(1)

j= 1, …, J; t= 1, …, T

Water recharge originates in both the recharge and the discharge area, but affects only the discharge area. The relationship between the amount of recharge received by the discharge area and the area of land affected by salinity depends on hydrological connections. At any time period t (t =1, …, T), land-state transitions depend on recharge (R). The transitions to slight salinization and severe salinization are, respectively:

Kj 2(t + 1) − Kj 2t = F1t − F2t

s=1

I

J

3

D F2t = % f R 12{Rit }+ % % f 12{Rjst } i=1

(2a)

j = 1s = 1

I

J

3

D F3t = % f R 23{Rit }+ % % f 23{Rjst } i=1

(4)

j = 1s = 1

(2b)

j = 1s = 1

D Functions f R 12 and f 12 account for emergence of slight salinization caused by recharge originated in the recharge and discharge areas, respectively. D Similarly, functions f R 23 and f 23 account for the emergence of severe salinization. These functions transform recharge (mm) to area affected by salt (hectares). The state transition equations can therefore be defined as:

Kj 1(t + 1) =Kj 1t −F2t Kj 2(t + 1) =Kj 2t +F2t −F3t

(3a) (3b)

Kj 3(t + 1) =Kj 3t +F3t (3c) Given a planning horizon of T years, and assuming that the objective is to maximize the present value of the stream of net benefits obtained from agriculture by the catchment as a whole, the problem is:

(5a)

j= 1, …, J; t= 1, …, T

(5b)

Kj 3(t + 1) − Kj 3t = F2t j= 1, …, J; t= 1, …, T Kjs1 = K( js

(5c)

j= 1, …, J; s= 1, 2, 3

(6)

where Bi and Bjs represent the net benefit obtained in recharge and discharge farms, respectively, Vjs is the loss of land value caused by salinity state s on farm j, and l is the discount factor (1+ r) for the discount rate r. Rit and Rjst are the control variables and Kjst are the state variables. The final-value function (Vjs ) accounts for the effect of final salinity state on the future productivity of the land, but it may also include other costs, such as in scenario C3 described later. This constrained problem is solved by defining the Lagrangean function: T

L= %



I

J

3

n

% Bi {Rit }+ % % Bjs {Rjst,Kjst } ·l − t

t=1 i=1

j − 1s = 1

3

J

− % % Vjs {Kjs(T + 1)}·l − (T + 1) j = 1s = 1 T+1

+ % %uj 1t (Kj 1(t + 1) − Kj 1t + F1t ) t=2 j

+ uj 2t (Kj 2(t + 1) − Kj 2t − F1t + F2t ) +uj 3t (Kj 3(t + 1) − Kj 3t + F2t ) +% %ujs1(Kjs1 − K( js ) j

(7)

s

The first-order conditions for maximization of Eq. (7), after rearrangement, yield:





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446

(Bi − t (f (f R (f R l = − uj 1t −uj 2t − 12 + 23 (Rit (Rit (Rit (Rit R 12

(f R + uj 3t 23 (Rit i = 1, …, I; t= 1, …, T



(Bjs − t (f D (f D (f D 12 12 23 l = − uj 1t −uj 2t − + (Rjst (Rjst (Rjst (Rjst



(8)

(f D +uj 3t 23 (Rjst j = 1, …, J; s = 1, 2, 3; t = 1, …, T ujst =

(9)

(BjS − t l + ujs(t + 1) (Kjst

j = 1, …, J; s = 1, 2, 3; t = 1, …, T

(10)

Rit = Rit {Xit }

i= 1, …, I; t=1, …, T

and

(VjS ujs(T + 1) = l − (T + 1) (Kjs(T + 1) j = 1, …, J; s = 1, 2, 3

planning period are given by the marginal reduction in capital value of the land resource caused by salinization. This model provides the theoretical basis of the work reported here. However, the actual optimization problem contains an additional level of complexity. Recharge cannot be directly controlled (i.e. as if adjusting a water tap) because it depends on rainfall and on the amount of water captured by plants and returned to the atmosphere through evapotranspiration. Hence, recharge is indirectly controlled through adjustments in land use and is subject to random fluctuations in rainfall. So we redefine recharge as:

Rjst = Rjst {Xjst } (11)

The conditions of Eqs. (8) and (9) equate marginal benefits to marginal user costs of recharge originating in the recharge and discharge zones, respectively. The conditions of Eqs. (10) and (11) define the shadow prices (ujst) of recharge to the groundwater system. In an optimal control framework, this problem is solved backwards, starting from time T+1 and solving Eq. (11) to obtain the terminal u values. By substituting these results into Eq. (10) and solving recursively for Eqs. (8) –(10), we obtain the optimal trajectories of the state (K) and control (R) variables as well as the shadow prices. The u values will be negative because salinity is a ‘bad’ rather than a ‘good’. Salinization is specified as a hydrologically based function of recharge generated by upstream and downstream land uses, as defined in Eqs. (2a) and (2b). The non-decreasing nature of the function for severely salt-affected land (s =3) models the stock characteristics of the externality in the form of a strictly convex salinization function. This implies that f23 ]0. The optimal solution, and therefore the optimal level of land salinization, occurs where the marginal cost of recharge equals the present value of net benefits generated by the land causing that recharge. The shadow prices at the end of the

j= 1, …, J; s= 1, 2, 3; t=1, …, T where each X is a vector of land uses adopted in a farm and for a land state, expressed as fractions of land area occupied by the given plant or livestock system.

4. A numerical model for the Liverpool Plains The Liverpool Plains catchment covers an area of 1.2 million hectares in the North West Slopes and Plains of New South Wales, Australia. It is a sub-catchment of the Murray Darling Basin. Approximately 1200 farms operate in the catchment. The catchment is known for its black-soil plains, which support highly productive cropping enterprises. In a small proportion of the plains, groundwater is pumped for irrigation of crops. An area of 195 000 hectares of the plains has groundwater tables within 5 m from the soil surface (Fig. 2). This area may become saline within three decades if current trends of rising groundwater tables continue (Broughton, 1994a,b). To explore the roles of recharge and discharge areas within the catchment, the catchment has been spatially divided into four areas (Greiner, 1997). These ‘aggregated unique mapping areas’ are characterized by maximum internal homo-

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447

Fig. 2. Areas at risk from salinization in the Liverpool Plains catchment. The grey shaded area in the map of Australia indicates the Murray-Darling Basin; the black dot shows the location of the Liverpool Plains within the Basin. (Map kindly provided by AGSO.)

geneity in their hydrological, topographical and productivity characteristics, with maximum heterogeneity between them. One may therefore conclude that they play quite different roles in the management of soil salinization. Fig. 3 shows the unique mapping areas. There are two upstream or recharge areas in the catchment, the Liverpool Range and the Sedimentary Hills, which represent 20 and 40% of the catchment, respectively. The Liverpool Range is the major source of recharge to the regional groundwater system, being the area of highest rainfall in the catchment. The Sedimentary Hills comprises a variety of mainly recharging areas

within the catchment. The third area shown in Fig. 3 comprises the black-soil plains. A comparison with Fig. 2 shows that salinity is most likely to emerge here. The black-soil plains comprise two aggregated unique mapping areas: the Dryland Plains and the Irrigated Plains, representing 30 and 10% of the catchment area, respectively. They differ in that aquifers in the Irrigated Plains allow for irrigation from groundwater pumping. The four areas are connected through surface and groundwater flows. Fig. 4 illustrates these flows. The model used in this study is ‘Spatial Optimization Model for Analyzing Catchment Management’ (SMAC) (Greiner, 1996b). The model

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R. Greiner, O. Cacho / Ecological Economics 38 (2001) 441–458

accounts for different hydrological, land-use and productivity characteristics in four catchment areas, as well as for distinct farm organization and land-use systems. Model farms with specific land, labor and capital resources are assigned to each catchment. The model farms are based on farm survey data (ABARE, 1996). Model farms are described in Table 1. The land-use options of each model farm, as well as yields, recharge and runoff, are defined by the catchment area it represents (Table 2). In the model, spatial separation is realized within an optimal-control approach, basically forming a multi-period computable partial spatial equilibrium framework (Lambert, 1985; Baumol, 1977).

Spatial-equilibrium models have been employed to determine the optimal level of production, consumption, commodity prices and inter-regional flows of commodities. In analogy to a market model, SMAC estimates the costs and benefits of recharge to the groundwater system, and determines the socially optimal level of recharge and soil salinization. Of particular interest are the trade-offs between the costs of preventing salinization and the costs resulting from salinization (Salerian, 1991). There are also trade-offs between the different options for recharge control, and between control activities in the discharge and in the recharge areas of the catchment. Dynamic spatial optimization creates a ‘future vision’ for the catchment, providing an under

Fig. 3. Aggregated unique mapping areas (UMA) for the Liverpool Plains catchment. For simplicity, the Dryland and Irrigated Plains are shown as a unit. (Map kindly provided by AGSO.)

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449

Fig. 4. Conceptual model of water flows between recharge and discharge areas in the Liverpool Plains catchment. The Liverpool Range and Hills are the recharge areas of the catchment. The Dryland and Irrigated Plains are the discharge area where the water tables rise and cause salinity. (Figure kindly provided by AGSO.)

standing of the socially optimal spatial and temporal land-use patterns across the catchment. This knowledge is critical for informing the process of developing a catchment management strategy and guiding policy to support the necessary transition. Model results show what land-use changes ought to be made, in what part of the catchment, at what point in time and at what rate. Results also include the optimal trajectory of salinization under the assumed economic conditions. The objective function of SMAC maximizes total discounted net present value of after-tax income generated by four types of model farms, weighted by the proportion of catchment area represented by each (Table 1). Conceptually, each farm type represents a different landscape type within the catchment. SMAC deals with climatic variability through a discrete stochastic-programming approach, which is detailed in Greiner (1997). A description of the operational relationships between the farm and catchment model, and the procedures for their parameterization is given in Greiner (1999). The hydrological formulation in Eqs. (2a) and (2b) is the core specification in SMAC. It summarizes point and catchment hydrology and links land use in various parts of the catchment

through recharge and hydrological connections to salinization in the catchment’s discharge area. Table 2 contains a sample of the point hydrological data on mean recharge. Table 3 quantifies the hydrological connections between the parts of the catchment areas that were schematically outlined in Fig. 4. Research is underway to improve hydrological understanding at the catchment and point scale, and may lead to an update of these parameters for future model runs (Paydar et al., 1999; Ringrose-Voase et al., 1999). The hydrological connections are quantified as the proportions of total recharge and runoff from the uphill areas that join the groundwater pool under the Plains. The Liverpool Range is of more significance as a recharge area than the Sedimentary Hills due to hydrogeological and geographical characteristics. Within its current scope, SMAC delivers a high-level abstraction of agricultural production and soil salinization in a large catchment. This approach takes the perspective of a catchment manager and provides valuable insights into the economic dimension of an important commonproperty management problem. The model has been calibrated to replicate the expectations of hydrologists that the total area at

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risk would become salt-affected over three decades if current land-use practices were to continue (Broughton, 1994a,b). In the absence of fully specified hydrological data, SMAC could not be fully validated. However, all model aspects and data were verified and the consistency of the mathematical framework was tested. The confidence in the SMAC model has been boosted by hydrological modeling experiments in the catchment that substantiate hydrological and spatial aspects of SMAC results (Dawes et al., 1997).

5. Scenarios As a basis for comparison, a business-as-usual scenario was simulated to establish how salinization would progress in a situation where present land-use patterns were maintained into the future irrespective of soil salinization. This scenario is then compared with three optimization runs. The three optimization scenarios incorporate various costs attributed to soil salinization. Scenario C1 costs soil salinization on the basis of loss of soil productivity only. Scenario C2 includes productivity losses but also accounts for the loss of capital value of salt-affected land based on current (unimproved) land value. This scenario is equivalent to the optimal-control framework al-

ready described. In scenario C3, the loss in land value assumed in scenario C2 is doubled as a proxy for additional costs of salinization. These costs could be associated with, for example, deterioration of farm infrastructure on salt-affected land, downstream costs of salinization beyond the catchment boundary, or loss of biodiversity due to salinity-induced vegetation die-back. Alternatively, these costs may represent the scarcity rent of fully productive land as an increasing proportion of land becomes salt-affected (Carlson et al., 1993; van Kooten, 1993). For non-renewable resources, scarcity rent as a measure of resource scarcity is commonly equated to the shadow price of the resource and reflects the opportunity cost of current resource extraction (Hotelling, 1931; Ruth, 1993).

6. Results Under the business-as-usual scenario, the model simulates a linear development of salinization and estimates that 191 000 hectares will be salt-affected at the end of the planning period (Fig. 5). This area is the sum of the areas under salinity classes s= 2 (slight salinity) and s= 3 (severe salinity). This simulation replicates the concern of hydrologists that, if landholders were to ignore

Table 1 Major characteristics of the model farmsa

Area of holding (ha) Cropping area (ha) Irrigated area (ha) Number of cattle Debt ($103) Land value ($106) Farms representedb Proportion of catchment area representedb a

Dryland Plains

Irrigated Plains

Liverpool Range

Sedimentary Hills

Mean

RSE

Mean

RSE

Mean

Mean

RSE

1000 800 0 130 100 1.0 368 29.6

17 * n/a 23 39 18

960 800 270 100 285 2.0 128 9.9

12

1600 320 0 340 280 1.8 158 20.4

820 330 0 150 170 0.7 606 40.1

10 * n/a 18 63 28

* 37 25 28 16

RSE b

* n/a 20 54 29

The model farms were designed based on means of actual properties surveyed by ABARE (1996); these values were used to calibrate the model (rounded means of ABARE (1996) farm-survey properties located in the four catchment areas). RSE, Relative standard error, the same as the coefficient of variation. No RSE can be calculated for cropping area (*) as this is not a survey estimate but derived from other data sources. b Not provided by ABARE (1996).

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Table 2 Area-specific parameters used in SMAC (excerpt from Greiner 1997)

Spatially discrete parameters Mean crop yields (t/ha) ( for land not salt-affected unless specified) Wheat, after long fallow Wheat, after short fallow Wheat, irrigated Sorghum, after long fallow Sorghum, after short fallow Lucerne, for hay Carrying capacity (DSE/ha) a Native pasture Improved pasture Lucerne Oats Saltbush, on fully productive land Saltbush, on slightly salt-affected land Saltbush, on severely salt-affected land

Dryland Plains

Irrigated Plains

Liverpool Range

3.930 2.840 n/a 4.350 3.380 8.550

3.930 2.840 5.000 4.350 3.380 8.550

1.540 1.470 n/a 1.880 1.650 n/a

1.470 1.400 n/a 1.790 1.570 n/a

5.3 7.4 9.0 9.5 n/a n/a n/a

5.0 7.0 8.6 9.0 n/a n/a n/a

84.0 84.0 47.2 n/a 102.0 135.5 77.6 84.3 78.0 n/a 29.5 n/a

54.5 54.5 23.4 n/a 60.0 82.5 43.6 48.0 52.5 n/a 14.0 n/a

7.0 10.0 n/a n/a 10.0 7.0 3.0

Mean recharge of crops (mm/m 2) Wheat, after long fallow Wheat, after short fallow Wheat, after summer crop Wheat, irrigated Winter fallow, after crop Winter fallow, after fallow Lucerne Improved pasture Oats Saltbush Dense trees Salinity encroachment factorb

18.1 15.4 5.4 n/a 13.0 36.0 4.2 4.2 n/a −148.0 −163.0 0.01

Spatially independent parameters Product prices Wheat ($/t) in ‘average season’ Sorghum ($/t) in ‘average season’ Lucerne ($/t) in ‘average season’ Livestock ($ per kg liveweight)

120 110 100 1.60

Production costs Wheat ($/ha) Sorghum ($/ha) Lucerne ($/ha) Livestock Other parameters Discount rate (%) Interest rate on business loan (%) Cost-price squeeze (p.a., %)c a

7.0 10.0 n/a n/a 10.0 7.0 3.0 18.1 15.4 5.4 33.0 13.0 36.0 4.2 4.2 n/a −148.0 −163.0 0.01

Sedimentary Hills

140 100 100 Depending on grazing system and in-season rainfall 7.0 12.0 0.5

DSE, Dry sheep equivalent, the amount of feed required to maintain a 48 kg sheep for 1 year. If the groundwater table is at or above the critical level, a 1 mm water table rise leads to salinization of 0.01% of the area. c Relative costs constant, relative prices declining by 0.5% per year. b

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Table 3 Hydrological connections between recharge and discharge areas defined as the proportion of recharge and runoff in upstream (recharge) areas that contributes to groundwater pool under Plains (discharge) areas (Greiner, 1997) Water connection to discharge areas (Plains)

Recharge and lateral shallow groundwater flow (%) Runoff infiltration (%) depending on in-season rainfall Very dry season Average season Very wet season

Recharge areas

Liverpool Range

Sedimentary Hills

60

33

80 50 30

40 27 13

the salinity hazard, the entire area at risk could be salt affected within just over three decades. This trajectory can be interpreted as mining of a finite soil resource, the finite resource being the area at risk of soil salinization, until exhaustion of the resource. The other three trajectories shown in Fig. 5 result

Fig. 5. Salinization trajectories in the business-as-usual scenario (B) and under optimality conditions with different costof-salinity assumptions (C1, C2 and C3). The total farm area in the Dryland Plains is 368 000 ha; hence, the 200 000 ha of salt-affected area eventually reached under scenario B represent 54% of the farm area in the discharge zone.

from optimization runs. Salt-affected areas in all optimization trajectories are well below the business-as-usual line; this indicates that, under business-as-usual, the land resource is being mined far above the socially optimal rate, even when salinity does not affect land value (scenario C1). The rate of salinity emergence in scenario C1 is only about one-half of that under business as usual. As expected, the higher the costs associated with soil salinization, the lower the area of salt-affected land in the optimal solution at the end of the planning period. In scenario C2, salinity reaches a steady state halfway through the planning period when the salt-affected area stabilizes at approximately 38 000 hectares. This is little more than the area estimated to show some signs of salinization already (Broughton, 1995). The results of scenario C3, which takes into account additional costs associated with salinization, indicate that a total control of salinization would be efficient. The initial advance of salinization is caused by the lag between land-management changes and hydrological responses. The majority of saline land under this scenario is reversibly affected and reverts to fully productive land within the 30-year period; only a small proportion of that land remains saline as it has crossed the threshold into irreversibly affected. For the given assumptions, results suggest that land-use change should focus on two catchment areas: the Dryland Plains, where salinity occurs; and the Liverpool Range, the major recharge area. Land use in the Irrigated Plains and the Sedimentary Hills remains largely unchanged under optimization compared with the present-day situation. These two areas are not discussed henceforth. Fig. 6 shows the optimal land-use trajectories for the Liverpool Range and Dryland Plains in scenario C2. In the Liverpool Range, cropping on the foot slopes is replaced with permanent pasture. In the Dryland Plains, a combination of responses to salinity and groundwater-table control measures are required. These land-use changes are implemented quickly, over a period of less than 10 years. In the Dryland Plains it is optimal to introduce deep-rooted and salt-tolerant native species into the landscape. Saltbush pasture is adopted on slightly salt-affected land and replaces conventional pas-

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Fig. 6. Optimal land-use changes in the Dryland Plains and Liverpool Range under scenario C2.

ture. Lucerne for hay production becomes an integral part of crop rotations. A proportion of the current cropping and pasture area is planted to salt-tolerant trees for groundwater-table management. The recharge and runoff reductions associated with the suggested land-use changes are substantial (Table 4). The change from cropping to pasture in the Liverpool Range reduces recharge to the regional groundwater system by 98 000 megaliters (Ml) per year (from 389 000 to 291 000 Ml), and reduces runoff by 44 000 Ml per year (from 310 000 to 266 000 Ml). This reduces the effective additions to the groundwater system under the Plains areas from 394 000 to 314 000 Ml per year, a 20% reduction. The land-use changes in the Dryland Plains achieve a discharging water balance, represented by negative numbers in the recharge column in

Table 4. This is the result of the introduction of deep-rooted vegetation that transpires more water than the area receives from rain, thereby drawing on groundwater for transpiration. While the discharge (i.e. negative recharge) balance of 65 000 Table 4 Annual recharge and runoff in Dryland Plains and Liverpool Range after suggested change to land-use systems as calculated by SMACa

Business usual Scenario Scenario Scenario a

as C1 C2 C3

Dryland Plains

Liverpool Range

Recharge

Runoff

Recharge

Runoff

75

61

389

310

−65 −131 −219

45 40 31

291 291 291

266 266 266

Results presented as thousands of megaliters.

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Fig. 7. Marginal cost of recharge for scenarios C1 and C3. The vertical axis represents the impact of an additional megaliter of recharge, originating either in the Dryland Plains (solid line) or the Liverpool Range (dotted line), on the present value of the catchment over the remainder of the planning horizon.

Ml in scenario C1 does not stop the encroachment of salinization, a further increase to 131 000 Ml in scenario C2 sees salinization controlled at a low level. It takes a substantial increase in the transpiration capacity of the vegetation in scenario C3 to increase the discharge balance to a level where groundwater tables in the Dryland Plains decline and the initial symptoms of salinization are reversed. These results indicate that a large proportion of the recharge and runoff associated with uphill land use in the Liverpool Plains catchment is Pareto irrelevant. Hence, from a catchment perspective, it is efficient that the majority of costs associated with dryland salinization and its control are borne by the Dryland Plains. This result becomes clear when the dual solution of the mathematical programming problem is inspected. Fig. 7 compares the marginal cost of recharge for the Dryland Plains and the Liverpool Range. At any given time, the marginal cost, or dual value, measures the impact of an additional megaliter of recharge on the total net present value of agricultural production in the catchment over the remainder of the planning period. Four aspects of Fig. 7 warrant explanation. First, the marginal cost of recharge generated in the Dryland Plains is substantially larger than the marginal cost of recharge generated in the Liverpool Range. This can be attributed to the immedi-

ate and comprehensive impact that any recharge generated in the discharge area has in comparison with the delayed and diminished impact of the same amount of recharge generated upstream. Second, there is a sharp decline of the dual cost after the first year. This is because, in all scenario runs, model farms were forced to initially adopt the current land-use systems. The sharp decline in dual costs across all scenarios is caused by changes in land-use systems from year 2. Third, dual costs decline over the optimization period. Two factors contribute to this decline: future values are discounted and, because salinity is a ‘stock’ or cumulative process, recharge at the beginning of the optimization period has a longer lasting and potentially compounding effect on productivity than recharge that occurs later in the optimization period. Fourth, compared with scenario C1, the marginal costs of recharge are higher in scenario C3 where a more comprehensive costing of soil salinization is adopted. The marginal costs remain on a plateau towards the end of the optimization period because of the salvage value in the final year. Interestingly, the salvage value assumed for land in scenario C3 was found to be virtually identical to the optimization-derived shadow price for fully productive land in the final year of optimization for scenario C1.

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7. Policy implications Clearly, the effectiveness of a Pigovian approach to salinity management by taxing recharge as the ‘emission’ generated by (upstream) farmers has to be questioned for several reasons. First, there is no clear distinction between emitters and recipients of recharge as recipients can also be emitters. Second, the marginal cost of recharge differs between different parts of the catchments. A marginal cost-based emission tax approach would therefore require a spatially discrete tax system on the basis of hydrogeological definition across entire landscapes. Due to the strictly convex damage function associated with stock externalities, a first-best Pigovian tax would also require a continually changing tax rate (Fig. 7; Ko et al., 1992). Another serious limitation of a recharge-based tax is that it cannot encourage a discharging water balance in those parts of the catchment where this is required under Pareto-optimal conditions. The benefit of a Coasean approach of tradable recharge entitlements would be that recharge control and associated land-use change would occur in those areas that could do so most efficiently. This approach would result in potential income transfer from upstream into discharge areas of a catchment. The conclusion that a Coasean approach is superior to a Pigovian approach in this study hinges on two factors particular to our problem: (1) the recipients of the externality are also emitters; and (2) the marginal cost of the externality varies both in space and time, requiring an extremely complex Pigovian tax. Despite its theoretical appeal, a Coasean approach to recharge control would meet difficulties. To provide and sustain an efficient solution, the sufficient condition is that property rights are fully specified, enforced and transferable; the diffuse and sporadic character of recharge, however, would make this condition difficult to attain. Associated issues of uncertainty and asymmetric information (see, for example, Shortle and Abler, 1992; Malik et al., 1994) render recharge-based policies operationally impractical. Randall (1999, p. 30) concludes that ‘‘the

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difficulty of monitoring non-point sources has precluded the public from enjoying the benefits of adequate controls and farmers from profiting from gainful permit trades’’. If indirect measures or indicators of recharge could be established in the form of observable proxies, this could open the way for policy intervention. Examples where policies for individual non-point source pollutants have been based on observable proxies include taxes on chemical inputs such as nitrogen and subsidies for specified land-management practices. Hodge (1982) suggested a scheme involving the use of ‘transferable rights to cleared land’ to achieve salinity control based on the notion that recharge correlates reasonably well with vegetation cover. He suggests ‘cleared land’ as a measurable proxy for recharge generated. Given that most catchments have been cleared far beyond the social optimum and revegetation would be required to create an efficient land-use pattern, ‘area under tree cover’ might be a more appropriate proxy. It also has the advantage that it can be monitored from remotely sensed data. ‘Area under tree cover’ would also fulfil the exclusiveness-of-property-rights condition for an efficient Coasean solution. If met in combination with the conditions already outlined, Randall (1999, p. 29) argues that privatization can provide a better way to control an externality than government regulation. The endpoint of his line of thinking is that government intervention to rectify market failure is not merely unnecessary but undesirable. Translated to our case-study catchment, there would be no need for government intervention to control salinity, given that the benefits and costs of recharge control would fall mostly on discharge areas. Why, then, do farmers not readily adopt landuse change for recharge control and salinity management? Uncertainty and lack of information provide no comprehensive explanation. An important additional reason is that, for recharge control efforts to lead to effective salinity management, sufficient scale and rate of land-use change are critical. Individual action fails to make a difference unless everybody else joins the action. Salinity management therefore suffers from the

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‘isolation paradox’ in which everyone would be better off with coordinated action than with no action at all (Randall, 1999, p. 30), but lack of coordination prevents action. Coordinated action would not necessarily require government intervention but could be initiated by a variety of institutions including private enterprises and voluntary associations. Catchment management bodies, if endowed with regulatory powers and supported by state-of-the-art technology, could serve this function given that their statute is consistent with the dimensions and scale of the problem itself. Political realities, however, may constitute a major impediment to the rapid implementation of new institutions. One such reality is landholders’ suspicion of innovative and cooperative solutions, and expressed preference for traditional government-based policies including tax incentives, regulations and traditional costsharing arrangements to facilitate implementation (LPLMC, 1996). In the face of the urgency for implementing land-use change for salinity control (Walker et al., 1999), there might be a case for government intervention in the short term. For example, by providing incentives for voluntary revegetation and by facilitating the establishment and administration of catchment-scale markets for ‘area under tree cover’. In the longer term, education, engagement and participatory processes may break the isolation paradox surrounding salinity management and enable all legitimate stakeholders to develop and pursue a shared vision for their catchment.

8. Concluding remarks This paper reports on the results of a dynamic catchment-optimization model for dryland salinity control. The model results suggest that only a proportion of the total upstream recharge represents Pareto-relevant externalities and that farmers in the discharge (salinization) area of the catchment would benefit from land-use change in their area. Application of the dynamic catchment-optimization model is shown to be capable of guiding catchment management by: (1) establishing that

current land-use patterns and associated rates of recharge and salinity encroachment are Pareto inferior; (2) showing that it is possible and efficient to control the area of salinization towards a steady state that is only a fraction of the land area at risk; (3) illustrating the required type, scale and rate of land-use change; and (4) identifying the areas within the catchment where such changes would be most efficiently implemented. In the face of the ‘isolation paradox’ confronting downstream farmers and the lack of imperative for upstream farmers to change their land use, implementation of recommended land-use changes in both recharge and discharge areas could be encouraged by Coasean-style policy intervention, which would be preferably based on a visible proxy for recharge control, e.g. in the form of ‘area under tree cover’. It was shown that a Pigovian tax approach would be both operationally infeasible and unable to encourage the efficient degree of downstream land-use change. In the longer term, new catchment-scale institutional approaches that are built on the commonproperty character of many natural resources, including hydrological systems, may be able to break the isolation paradox surrounding recharge control by providing stakeholder-negotiated and implemented solutions.

Acknowledgements The authors thank John Dillon, Phil Simmons, Nick Abel and Greg Hertzler for their valuable comments on different stages of the manuscript. The research was funded by the Land and Water Resources Research and Development Corporation (LWRRDC) and the Deutsche Forschungsgemeinschaft (DFG).

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