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Analysing dryland salinity management on a catchment scale with an economic-ecological modelling approach
ECOLOGICAL ENGINEERING ELSEVIER
Ecological Engineering 4 (1995) 191-198
Analysing dryland salinity management on a catchment scale with an economic-ecological modelling approach R. G r e i n e r
*, K . A . P a r t o n
Department of Agricultural and Resource Economics, University of New England, Arrnidale NSW 2351, Australia
Accepted 7 November 1994
Abstract This article presents the development of a mathematical programming model aimed at investigating the economics of managing soil salinisation at a catchment level. The model integrates agronomic, hydrological and farm financial modules within a system of model farms that represent different land management units within the catchment. A range of methodological requirements are identified to capture the complexity and characteristics of the salinisation problem. Two of these requirements are specifically addressed, one being the need for synthesising detailed hydrological data. This is achieved by employing a soil-water simulation model. The second aspect deals with introducing climate-related risk into the model through discrete stochastic programming. The paper concentrates on discussing the advantages and limitations of the methods applied. Keywords: Salinity management; Catchment-level; Socio-economics; Hydrology; Linear pro-
gramming; Simulation
1. Introduction Being a regional rather than a site-specific land degradation problem and involving time lags, externalities and complex spatial relationships within a catchment, dryland salinity has b e c o m e a challenge facing analysts. The problem is multifaceted involving, for example, losses in soil productivity for agriculture once salting occurs, reductions in land values in affected areas and external costs that the process imposes on communities (Hertzler and Barton, 1992; Oram, 1987).
* Corresponding author. Elsevier Science B.V. SSDI 0925-8574(94)00058-1
192
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
Weather
I Increment ~ _ ~ year
n individual farm ~ . ~ n farm plans ~ economicmodelsI - I
+
Prices
n farm outcomes
Input and output price distributions Potential yield I distributions for each crop and pasture
Recordnatural ~ . _ asset situation IRe.cordn farm financialL, asset situations ]
Aggregatesalinity effectsto the catchmentlevel
I Farm profits
Fig. 1. A generaldescriptionof the modellingapproach. Moreover, the reclamation of salted areas remains a costly, slow, high-risk and low-return exercise (Powell, 1993). Land management practices are considered the main controllable variable within a range of determinants of a catchment's hydrological balance, with changes in the balance leading to dryland salting. Agricultural land use, however, has to be analysed within its socio-economic context, including the economic and policy environment facing primary production. The study aims at showing ways of preventing further soil salting in a catchment by capturing the essential components of the system's inherent complexity in a tractable analytical framework. A stepwise modelling approach has been established for depicting the bio-physical and socio-economic system relationships for the Goran Basin, a sub-catchment of the Murray Darling Basin in eastern New South Wales with severe salinity problems (Greiner, 1883).
2. Requirements of salinity modelling A general description of an iterative modelling approach to this complex system is shown in Fig. 1. There are individual farm economic models representing the decision-making of farmers across the catchment. Based on information about resource constraints (including the natural asset position of the farm) and expected prices of inputs and outputs, a farm plan is established for each model farm for the
193
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
agricultural census data
soil, climatic, agronomic data
CLU~STER ANALYSIS
"PER~FECT" STATISTICAL ANALYSIS
mod~ farms
bio-physic~ coefficients
I SINGLE-FARM LP-MODEL discounted ~arm incomes land management recharge, water table movement
hydrological properties of catchment
(CATCHMENT LP-MODEL external etI~ects land management recharge, water table movement Fig. 2. Model structure.
first season. These plans are put into operation and they are affected by a set of prices and whether to produce a predicted outcome for each farm. The significant dimensions of this outcome, as far as the current study is concerned, are salinity effects at the catchment level and farm profitability. The first of these is carried forward to the next year as an additional resource constraint and an influence on future potential yields of the individual farms. Farm profitability influences the level of financial assets available to the farm business and these assets are carried forward as a constraint on the farm business for the next year. The process is then repeated for the second and subsequent seasons. In this manner, the impact of farming activities on salinity is revealed and the trade-off between profitability and salinity is assessed. In Fig. 2 more specific details of our modelling approach are presented. The complex system of agricultural production in a catchment context poses the following range of requirements on the analytical approach. (1) Land-use practices are not only a function of the soil and climatic conditions of an area but are also heavily determined by socio-economic conditions of production. In order to capture these aspects of production in a regional analysis, the model-farm approach is chosen. (2) The bio-physical section consists of the relationships between rainfall and (i) crop and pasture yields, as major constituents of the financial viability of farming activities, and (ii) the recharge occurring under various land management options, as the major determinant of watertable movements and the progress in salinisation. Due to the lack of long-term field and experimental data, a simulation model is utilised to ascertain these connections. (3) Salinity management adds another dimension to resource allocation decisions in farming. The capacity of a hydrological system to tolerate recharge water without displaying adverse effects can be considered as a resource constraint.
194
(4)
(5)
(6)
(7)
(8)
(9)
R. Greiner, KM. Parton /Ecological Engineering 4 (1995) 191-198
Then, linear programming as a technique of optimising an objective value under such resource constraints is considered an adequate way to quantitatively analyse the allocation problem. The observed variability in rainfall in the study area leads to the question of how the uncertainties associated with agricultural production can be incorporated into a linear programming model. There is a need to internalise risk through stochastic production coefficients. A discrete stochastic programming approach for seasonal rainfall, which has been identified as the major biophysical external variable, is pursued. Recharge as a function of rainfall, evapotranspiration and soil water storage, is only an indirect salinisation factor, whereas the depth of the watertable is the actual determinant. Thus, a soil-specific link has to be established between the volume of deep percolation and the change in watertable depth. A hydro-geological model would be the ideal tool, but considering its data and modelling demands, the adoption of site-specific assumptions for the relationship between the components mentioned appears the more practical way. Rising watertables result in water logging and salinisation which lead to yield losses and loss of productive land. This relationship creates the need to incorporate into the linear programming model a functional connection between watertable depth and agricultural production. Within a catchment, a spatial differentiation has to be made between recharge areas and discharge areas. Once the model is simultaneously applied to a number of model farms in the region, a component for lateral groundwater movement must be estimated which aggregates the impacts of on-farm recharge in discharge areas. This enables the quantification of those external effects of land use systems which are transmitted through the catchment's groundwater system. There is no hydrological catchment model available as yet to be applied to the Goran Basin so that assumptions on spatial linkages are adopted and are subjected to sensitivity analysis. There are time lags inherent in the salinisation process. The poly-period model structure accounts for temporal effects such as time lags between upslope recharge and lateral influx into discharge areas. Salinity management options include changes in land use such as pasture establishment on cropping country. To assess these investments, long-term farm financial calculations based on after-tax farm income must be made.
3. Methodological aspects 3.1. Model farms as representatives of land use systems
When looking at the cross-section of the Goran Basin, we find distinct land-use patterns in different locations of the catchment, with black-soil cropping systems in the basin, mixed farming in the valleys and cattle and sheep grazing on the rocky outcrops. Given the relative homogeneity within these segments of the catchment,
R. Greiner, K.A. Patton ~Ecological Engineering 4 (1995) 191-198
195
a small number of representative farms is used to represent the different land use systems (Hanf and Noell, 1989). Buckwell and Hazell (1972), and Hanf (1989) suggest aggregating farm groups by relative factor endowment by using cluster analysis. Farm groups resulting from cluster analysis are charaeterised by maximum internal homogeneity and maximum external heterogeneity for the variables used in the clustering procedure (Hair et al., 1987). A hierarchical cluster analysis is applied to the farms located in the Gunnedah Shire and represented in the 1991 agricultural census. In this analysis of dryland salinity the resulting model farms not only portray differently structured agricultural enterprises, but more importantly, they also represent different land management units which have specific impacts on the hydrological balance in the catchment. The different farms are likely to have distinct needs for land management changes to achieve hydrological sustainability of farming in the catchment.
3.2. Estimating bio-physical model coefficients A significant component of the linear programming model of the agricultural production systems is the set of bio-physical coefficients which represent the relationship between (i) rainfall and crop/pasture yields and (ii) rainfall and the recharge occurring under different land management systems. Studies conducted in the Goran Basin have monitored changes in groundwater tables in different places of the catchment through movements in bore depths (Hamilton, 1992). However, they do not quantify the impact of land-use variables on the hydrological equilibrium (Peck and Hurle, 1973). This has been achieved using PERFECT, a simulation model of "Productivity, Erosion, Runoff Functions to Evaluate Conservation Techniques" (Freebairn et al., 1990). It simulates the interactions between soil, climate and land management practices over a long timeframe (Littleboy et al., 19192; Thomas et al., 1992). PERFECT was found suitable for application in the Goran Basin as a means of obtaining estimates of the elements of a soil's hydrological balance and the crop yields for a range of land types (see Table 1). The figures presented in Table 1 show significant differences in recharge for the same soil depending on whether it is under crop or fallowed, where the lack of plant evaporation leads to increased deep percolation. However, the biggest
Table 1 Average crops yields and recharge for different land management practices Yields (t/ha)
Winter wheat Winter fallow Sorghum Sunflower Summer fallow
Recharge (ram)
Black soil
Red earth
Black soil
Red earth
2.703 n.a. 2.523 0.943 n.a.
1.611 n.a. 1.975 0.632 n.a.
5 7 3 2 7
20 53 18 31 58
196
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
influence is the soil type as can be expected given difference in clay content and hence water-storage capacity (Gardner et al., 1993). These figures by themselves suggest that reduced cropping of the red earth, which involves a shift towards more perennial deep-rooting vegetation, may be critical to maintaining or lowering watertables in the catchment. 3.3. Introducing climate-related risk into the model Rainfall in the region is highly variable with a coefficient of variation of 0.27 over the last 100 years. Mean rainfall for Gunnedah is 606 mm. Above average rainfall in the past 10 years has accelerated the rise in watertables and visibly increased the salt affected area (Hamilton, 1992). For the Murrumbidgee Irrigation Area, Greiner et al. (1993) found that the impact of rainfall outweighed the influence of land-use management and irrigation practices. For a catchment in the Brigalow Belt in Queensland, Thorburn et al. (1991) found that climatic variability had a significant impact on recharge as did land use and soil type. Such results emphasise the need to account for rainfall variability in the linear programming model. Kingwell et al. (1991) have introduced climatic variability through Discrete Stochastic Programming (DSP) (Cocks, 1968) in the MUDAS model. Using the adaptation of the MUDAS method, for both summer and winter season, "average", "dry" and "wet" conditions are defined at the long-term rainfall mean and one standard deviation above and below the mean. Allocating frequencies to these season types allows random selection of weather conditions for both seasons in each year. Besides, scenarios of climatic extremes can be constructed to calculate the impact of shifted rainfall patterns on recharge, watertable movement, and agricultural production. In order to introduce the DSP approach into the linear programming model, the simulation results derived from PERFECT runs were analysed for correlations between the external variable rainfall and recharge and crop yields (see Table 2). These figures do not account for the whole range of climatic variability found under field conditions and they cannot fully explain the sporadic occurrence of recharge after high-rainfall events. However, they cover a wide range of observed rainfall patterns and estimated recharge levels and can be classified as sufficient
Table 2 Seasonal rainfall variation in the model and impact on recharge and yield on red earth Recharg e (mm) in a dry average wet season Yield ( t / h a ) in a dry average wet season
0 20 50
1.016 1.611 2.131
0 53 112
n.a. n.a. n.a.
0 18 45
1.480 1.975 2.471
0 31 75
0.468 0.632 0.795
0 58 119
n.a. n.a. n.a.
R. Greiner, KJt. Parton ~Ecological Engineering 4 (1995) 191-198
197
for the kind of analytical approach applied to salinity management questions at a catchment level.
4. Conclusions For the quantitative analysis of land management at a catchment level with respect to the development of dryland salinity, a linear programming model approach has been established. Key elements are the establishment and incorporation of coefficients which represent the linkages between rainfall, as the major bio-physical external variable of the system, and crop yields and recharge under a range of the land use options. PERFECT, a vertical soil water and crop growth simulation model, is applied to representative soils in the study area in order to overcome data shortages and gain quantitative estimates of the linkages involved in the hydrological process leading to salinisation. Rainfall variability is identified as a major force in the movement of watertables and the development of salinity. A discrete stochastic programming approach is used to account for the impact of climate-related risk. The advantages and limits of both methodological aspects are discussed. Given our observations in the development of the model, the mathematical programming approach can be expected to answer a range of important questions dealing with the ecological engineering of hydrological systems. Results will provide agronomic action plans for different land management units as reflected in the model farms. Any need for significant changes in land use will be identified. Once applied on a catchment scale, the results will show the scope for introducing (economic) feedback mechanisms from discharge to recharge locations and provide a useful tool for developing integrated catchment management strategies in an attempt to manage soil salinisation. Preliminary results suggest that, for the Goran Basin, significant land use changes will be required to combat the salinity problem. These will involve tree planting on agricultural land rather than just the more marginal changes that farmers and regional planners have in mind, like including a lucerne pasture phase in cropping rotations.
Acknowledgements The assistance of Mark Silburn, Queensland Department of Primary Industries, in customising the PERFECT code for the study purposes is gratefully acknowledged. The project is jointly funded by the Australian National Soil Conservation Program and the German Alexander von Humboldt-Foundation.
References Buckwell, A.E. and P.B.R. Hazell, 1972. Implicationsof aggregationbias for the constructionof static and dynamiclinear programmingsupplymodels. J. Agric.Econ., 23: 119-131.
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R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
Cocks, K.D., 1968. Discrete Stochastic Programming. Manage. Sci., 15 72-79. Freebairn, D.M., M. Littleboy, G.D. Smith and K.J. Coughlan, 1990. Climatic risk in crop production: models and management for the semiarid tropics and subtropics. In: R.C. Muchow and J.A. Bellamy (Eds.), Proceedings of the International Symposium on Climatic Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. CAB International, pp. 283-305. Gardner, E.A., D.M. Freebairn, J.E. Doherty, R.H. Shaw, M. Littleboy, F. Titmarsh, D.M. Silburn, R.D. Connolly and D. Bebgie, 1993. Hydrological Tools to Assist Decision Making in Integrated Catchment Management. Platform Papers, Vol. 2, Technical Papers. Australian Water & Wastewater Association, Gold Coast, QLD, pp. 378-386. Greiner, R., 1993. Assessing options for dryland salinity management in the Liverpool Plains N.S.W. Aust. J. Soil Water Conserv., 6: 49-53. Greiner, R., M.J. Bryant and S.A. Prathapar, 1993. Managing accessions to the water table under irrigated areas. Markets for Riverine Resources, Stage 2, Report to the N.S.W. Department of Water Resources, Centre for Water Policy Research, University of New England, Armidale, N.S.W.. Hair, J.F., R.E. Anderson and R.L. Tatham, 1987. Multivariate Data Analysis. Macmillan, New York. Hamilton, S., 1992. Lake Goran Catchment Groundwater Study. Department of Water Resources Technical Services Division. Hanf, C.-H., 1989. Agricultural Sector Analysis by Linear Programming Models. Approaches, Problems, Experiences. Forum, No. 20. Vauk-Verlag, Kiel. Hanf, C.-H. and C. Noell, 1989. Experiences with farm sample models in sector analysis. In: S. Bauer and W. Henrichsmeyer (Eds.), Agricultural Sector Modelling. Vauk-Verlag, Kiel, pp. 103-134. Hertzler, G. and J. Barton, 1992. Dynamic Model of Dryland Salinity Abatement, Agricultural Economics Discussion Paper 4/92, University of Western Australia, Perth. Kingwell, R., D. Pannell and S. Robinson, 1991. Climatic risk and the value of information. Paper presented at the Australian Agricultural Economics Society 35th Annual Conference, Armidale N.S.W.. Littleboy, M, D.M. Silburn, D.M. Freebairn, D.R. Woodruff, G.L. Hammer and J.K. Leslie, 1992. Impact of soil erosion on production in cropping systems. I. Development and validation of a simulation model. Aust. J. Soil Res., 30: 757-774. Oram, D.A., 1987. The Economics of Dryland Salinity and its Control in the Murray River Basin of Victoria: A Farm Level Approach. Occasional Paper No. 11, School of Agriculture, La Trobe University, Melbourne, Australia. Peck, A.J. and Hurle, D.H., 1973. Chloride balance of some farmed and forested catchments in southwestern Australia. Water Resour. Res., 9: 648-657. PoweU, J., 1993. Dryland salinity in the Murray-Darling Basin. Aust. J. Soil Water Conserv., 6: 45-48. Thomas, E.C., E.A. Gardner, M. Littleboy and P. Shields, 1992. Using Cropping Systems Models in Land Evaluation. Proceedings of the Conference on Engineering in Agriculture, Albury, NSW, pp. 85-89. Thorburn, P.J., B.A. Clowie and P.A. Lawrence, 1991. Effect of land development on groundwater recharge determined from non-steady chloride profiles. J. Hydrol., 124: 43-58.
Ecological Engineering 4 (1995) 191-198
Analysing dryland salinity management on a catchment scale with an economic-ecological modelling approach R. G r e i n e r
*, K . A . P a r t o n
Department of Agricultural and Resource Economics, University of New England, Arrnidale NSW 2351, Australia
Accepted 7 November 1994
Abstract This article presents the development of a mathematical programming model aimed at investigating the economics of managing soil salinisation at a catchment level. The model integrates agronomic, hydrological and farm financial modules within a system of model farms that represent different land management units within the catchment. A range of methodological requirements are identified to capture the complexity and characteristics of the salinisation problem. Two of these requirements are specifically addressed, one being the need for synthesising detailed hydrological data. This is achieved by employing a soil-water simulation model. The second aspect deals with introducing climate-related risk into the model through discrete stochastic programming. The paper concentrates on discussing the advantages and limitations of the methods applied. Keywords: Salinity management; Catchment-level; Socio-economics; Hydrology; Linear pro-
gramming; Simulation
1. Introduction Being a regional rather than a site-specific land degradation problem and involving time lags, externalities and complex spatial relationships within a catchment, dryland salinity has b e c o m e a challenge facing analysts. The problem is multifaceted involving, for example, losses in soil productivity for agriculture once salting occurs, reductions in land values in affected areas and external costs that the process imposes on communities (Hertzler and Barton, 1992; Oram, 1987).
* Corresponding author. Elsevier Science B.V. SSDI 0925-8574(94)00058-1
192
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
Weather
I Increment ~ _ ~ year
n individual farm ~ . ~ n farm plans ~ economicmodelsI - I
+
Prices
n farm outcomes
Input and output price distributions Potential yield I distributions for each crop and pasture
Recordnatural ~ . _ asset situation IRe.cordn farm financialL, asset situations ]
Aggregatesalinity effectsto the catchmentlevel
I Farm profits
Fig. 1. A generaldescriptionof the modellingapproach. Moreover, the reclamation of salted areas remains a costly, slow, high-risk and low-return exercise (Powell, 1993). Land management practices are considered the main controllable variable within a range of determinants of a catchment's hydrological balance, with changes in the balance leading to dryland salting. Agricultural land use, however, has to be analysed within its socio-economic context, including the economic and policy environment facing primary production. The study aims at showing ways of preventing further soil salting in a catchment by capturing the essential components of the system's inherent complexity in a tractable analytical framework. A stepwise modelling approach has been established for depicting the bio-physical and socio-economic system relationships for the Goran Basin, a sub-catchment of the Murray Darling Basin in eastern New South Wales with severe salinity problems (Greiner, 1883).
2. Requirements of salinity modelling A general description of an iterative modelling approach to this complex system is shown in Fig. 1. There are individual farm economic models representing the decision-making of farmers across the catchment. Based on information about resource constraints (including the natural asset position of the farm) and expected prices of inputs and outputs, a farm plan is established for each model farm for the
193
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
agricultural census data
soil, climatic, agronomic data
CLU~STER ANALYSIS
"PER~FECT" STATISTICAL ANALYSIS
mod~ farms
bio-physic~ coefficients
I SINGLE-FARM LP-MODEL discounted ~arm incomes land management recharge, water table movement
hydrological properties of catchment
(CATCHMENT LP-MODEL external etI~ects land management recharge, water table movement Fig. 2. Model structure.
first season. These plans are put into operation and they are affected by a set of prices and whether to produce a predicted outcome for each farm. The significant dimensions of this outcome, as far as the current study is concerned, are salinity effects at the catchment level and farm profitability. The first of these is carried forward to the next year as an additional resource constraint and an influence on future potential yields of the individual farms. Farm profitability influences the level of financial assets available to the farm business and these assets are carried forward as a constraint on the farm business for the next year. The process is then repeated for the second and subsequent seasons. In this manner, the impact of farming activities on salinity is revealed and the trade-off between profitability and salinity is assessed. In Fig. 2 more specific details of our modelling approach are presented. The complex system of agricultural production in a catchment context poses the following range of requirements on the analytical approach. (1) Land-use practices are not only a function of the soil and climatic conditions of an area but are also heavily determined by socio-economic conditions of production. In order to capture these aspects of production in a regional analysis, the model-farm approach is chosen. (2) The bio-physical section consists of the relationships between rainfall and (i) crop and pasture yields, as major constituents of the financial viability of farming activities, and (ii) the recharge occurring under various land management options, as the major determinant of watertable movements and the progress in salinisation. Due to the lack of long-term field and experimental data, a simulation model is utilised to ascertain these connections. (3) Salinity management adds another dimension to resource allocation decisions in farming. The capacity of a hydrological system to tolerate recharge water without displaying adverse effects can be considered as a resource constraint.
194
(4)
(5)
(6)
(7)
(8)
(9)
R. Greiner, KM. Parton /Ecological Engineering 4 (1995) 191-198
Then, linear programming as a technique of optimising an objective value under such resource constraints is considered an adequate way to quantitatively analyse the allocation problem. The observed variability in rainfall in the study area leads to the question of how the uncertainties associated with agricultural production can be incorporated into a linear programming model. There is a need to internalise risk through stochastic production coefficients. A discrete stochastic programming approach for seasonal rainfall, which has been identified as the major biophysical external variable, is pursued. Recharge as a function of rainfall, evapotranspiration and soil water storage, is only an indirect salinisation factor, whereas the depth of the watertable is the actual determinant. Thus, a soil-specific link has to be established between the volume of deep percolation and the change in watertable depth. A hydro-geological model would be the ideal tool, but considering its data and modelling demands, the adoption of site-specific assumptions for the relationship between the components mentioned appears the more practical way. Rising watertables result in water logging and salinisation which lead to yield losses and loss of productive land. This relationship creates the need to incorporate into the linear programming model a functional connection between watertable depth and agricultural production. Within a catchment, a spatial differentiation has to be made between recharge areas and discharge areas. Once the model is simultaneously applied to a number of model farms in the region, a component for lateral groundwater movement must be estimated which aggregates the impacts of on-farm recharge in discharge areas. This enables the quantification of those external effects of land use systems which are transmitted through the catchment's groundwater system. There is no hydrological catchment model available as yet to be applied to the Goran Basin so that assumptions on spatial linkages are adopted and are subjected to sensitivity analysis. There are time lags inherent in the salinisation process. The poly-period model structure accounts for temporal effects such as time lags between upslope recharge and lateral influx into discharge areas. Salinity management options include changes in land use such as pasture establishment on cropping country. To assess these investments, long-term farm financial calculations based on after-tax farm income must be made.
3. Methodological aspects 3.1. Model farms as representatives of land use systems
When looking at the cross-section of the Goran Basin, we find distinct land-use patterns in different locations of the catchment, with black-soil cropping systems in the basin, mixed farming in the valleys and cattle and sheep grazing on the rocky outcrops. Given the relative homogeneity within these segments of the catchment,
R. Greiner, K.A. Patton ~Ecological Engineering 4 (1995) 191-198
195
a small number of representative farms is used to represent the different land use systems (Hanf and Noell, 1989). Buckwell and Hazell (1972), and Hanf (1989) suggest aggregating farm groups by relative factor endowment by using cluster analysis. Farm groups resulting from cluster analysis are charaeterised by maximum internal homogeneity and maximum external heterogeneity for the variables used in the clustering procedure (Hair et al., 1987). A hierarchical cluster analysis is applied to the farms located in the Gunnedah Shire and represented in the 1991 agricultural census. In this analysis of dryland salinity the resulting model farms not only portray differently structured agricultural enterprises, but more importantly, they also represent different land management units which have specific impacts on the hydrological balance in the catchment. The different farms are likely to have distinct needs for land management changes to achieve hydrological sustainability of farming in the catchment.
3.2. Estimating bio-physical model coefficients A significant component of the linear programming model of the agricultural production systems is the set of bio-physical coefficients which represent the relationship between (i) rainfall and crop/pasture yields and (ii) rainfall and the recharge occurring under different land management systems. Studies conducted in the Goran Basin have monitored changes in groundwater tables in different places of the catchment through movements in bore depths (Hamilton, 1992). However, they do not quantify the impact of land-use variables on the hydrological equilibrium (Peck and Hurle, 1973). This has been achieved using PERFECT, a simulation model of "Productivity, Erosion, Runoff Functions to Evaluate Conservation Techniques" (Freebairn et al., 1990). It simulates the interactions between soil, climate and land management practices over a long timeframe (Littleboy et al., 19192; Thomas et al., 1992). PERFECT was found suitable for application in the Goran Basin as a means of obtaining estimates of the elements of a soil's hydrological balance and the crop yields for a range of land types (see Table 1). The figures presented in Table 1 show significant differences in recharge for the same soil depending on whether it is under crop or fallowed, where the lack of plant evaporation leads to increased deep percolation. However, the biggest
Table 1 Average crops yields and recharge for different land management practices Yields (t/ha)
Winter wheat Winter fallow Sorghum Sunflower Summer fallow
Recharge (ram)
Black soil
Red earth
Black soil
Red earth
2.703 n.a. 2.523 0.943 n.a.
1.611 n.a. 1.975 0.632 n.a.
5 7 3 2 7
20 53 18 31 58
196
R. Greiner, K.A. Parton / Ecological Engineering 4 (1995) 191-198
influence is the soil type as can be expected given difference in clay content and hence water-storage capacity (Gardner et al., 1993). These figures by themselves suggest that reduced cropping of the red earth, which involves a shift towards more perennial deep-rooting vegetation, may be critical to maintaining or lowering watertables in the catchment. 3.3. Introducing climate-related risk into the model Rainfall in the region is highly variable with a coefficient of variation of 0.27 over the last 100 years. Mean rainfall for Gunnedah is 606 mm. Above average rainfall in the past 10 years has accelerated the rise in watertables and visibly increased the salt affected area (Hamilton, 1992). For the Murrumbidgee Irrigation Area, Greiner et al. (1993) found that the impact of rainfall outweighed the influence of land-use management and irrigation practices. For a catchment in the Brigalow Belt in Queensland, Thorburn et al. (1991) found that climatic variability had a significant impact on recharge as did land use and soil type. Such results emphasise the need to account for rainfall variability in the linear programming model. Kingwell et al. (1991) have introduced climatic variability through Discrete Stochastic Programming (DSP) (Cocks, 1968) in the MUDAS model. Using the adaptation of the MUDAS method, for both summer and winter season, "average", "dry" and "wet" conditions are defined at the long-term rainfall mean and one standard deviation above and below the mean. Allocating frequencies to these season types allows random selection of weather conditions for both seasons in each year. Besides, scenarios of climatic extremes can be constructed to calculate the impact of shifted rainfall patterns on recharge, watertable movement, and agricultural production. In order to introduce the DSP approach into the linear programming model, the simulation results derived from PERFECT runs were analysed for correlations between the external variable rainfall and recharge and crop yields (see Table 2). These figures do not account for the whole range of climatic variability found under field conditions and they cannot fully explain the sporadic occurrence of recharge after high-rainfall events. However, they cover a wide range of observed rainfall patterns and estimated recharge levels and can be classified as sufficient
Table 2 Seasonal rainfall variation in the model and impact on recharge and yield on red earth Recharg e (mm) in a dry average wet season Yield ( t / h a ) in a dry average wet season
0 20 50
1.016 1.611 2.131
0 53 112
n.a. n.a. n.a.
0 18 45
1.480 1.975 2.471
0 31 75
0.468 0.632 0.795
0 58 119
n.a. n.a. n.a.
R. Greiner, KJt. Parton ~Ecological Engineering 4 (1995) 191-198
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for the kind of analytical approach applied to salinity management questions at a catchment level.
4. Conclusions For the quantitative analysis of land management at a catchment level with respect to the development of dryland salinity, a linear programming model approach has been established. Key elements are the establishment and incorporation of coefficients which represent the linkages between rainfall, as the major bio-physical external variable of the system, and crop yields and recharge under a range of the land use options. PERFECT, a vertical soil water and crop growth simulation model, is applied to representative soils in the study area in order to overcome data shortages and gain quantitative estimates of the linkages involved in the hydrological process leading to salinisation. Rainfall variability is identified as a major force in the movement of watertables and the development of salinity. A discrete stochastic programming approach is used to account for the impact of climate-related risk. The advantages and limits of both methodological aspects are discussed. Given our observations in the development of the model, the mathematical programming approach can be expected to answer a range of important questions dealing with the ecological engineering of hydrological systems. Results will provide agronomic action plans for different land management units as reflected in the model farms. Any need for significant changes in land use will be identified. Once applied on a catchment scale, the results will show the scope for introducing (economic) feedback mechanisms from discharge to recharge locations and provide a useful tool for developing integrated catchment management strategies in an attempt to manage soil salinisation. Preliminary results suggest that, for the Goran Basin, significant land use changes will be required to combat the salinity problem. These will involve tree planting on agricultural land rather than just the more marginal changes that farmers and regional planners have in mind, like including a lucerne pasture phase in cropping rotations.
Acknowledgements The assistance of Mark Silburn, Queensland Department of Primary Industries, in customising the PERFECT code for the study purposes is gratefully acknowledged. The project is jointly funded by the Australian National Soil Conservation Program and the German Alexander von Humboldt-Foundation.
References Buckwell, A.E. and P.B.R. Hazell, 1972. Implicationsof aggregationbias for the constructionof static and dynamiclinear programmingsupplymodels. J. Agric.Econ., 23: 119-131.
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Cocks, K.D., 1968. Discrete Stochastic Programming. Manage. Sci., 15 72-79. Freebairn, D.M., M. Littleboy, G.D. Smith and K.J. Coughlan, 1990. Climatic risk in crop production: models and management for the semiarid tropics and subtropics. In: R.C. Muchow and J.A. Bellamy (Eds.), Proceedings of the International Symposium on Climatic Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. CAB International, pp. 283-305. Gardner, E.A., D.M. Freebairn, J.E. Doherty, R.H. Shaw, M. Littleboy, F. Titmarsh, D.M. Silburn, R.D. Connolly and D. Bebgie, 1993. Hydrological Tools to Assist Decision Making in Integrated Catchment Management. Platform Papers, Vol. 2, Technical Papers. Australian Water & Wastewater Association, Gold Coast, QLD, pp. 378-386. Greiner, R., 1993. Assessing options for dryland salinity management in the Liverpool Plains N.S.W. Aust. J. Soil Water Conserv., 6: 49-53. Greiner, R., M.J. Bryant and S.A. Prathapar, 1993. Managing accessions to the water table under irrigated areas. Markets for Riverine Resources, Stage 2, Report to the N.S.W. Department of Water Resources, Centre for Water Policy Research, University of New England, Armidale, N.S.W.. Hair, J.F., R.E. Anderson and R.L. Tatham, 1987. Multivariate Data Analysis. Macmillan, New York. Hamilton, S., 1992. Lake Goran Catchment Groundwater Study. Department of Water Resources Technical Services Division. Hanf, C.-H., 1989. Agricultural Sector Analysis by Linear Programming Models. Approaches, Problems, Experiences. Forum, No. 20. Vauk-Verlag, Kiel. Hanf, C.-H. and C. Noell, 1989. Experiences with farm sample models in sector analysis. In: S. Bauer and W. Henrichsmeyer (Eds.), Agricultural Sector Modelling. Vauk-Verlag, Kiel, pp. 103-134. Hertzler, G. and J. Barton, 1992. Dynamic Model of Dryland Salinity Abatement, Agricultural Economics Discussion Paper 4/92, University of Western Australia, Perth. Kingwell, R., D. Pannell and S. Robinson, 1991. Climatic risk and the value of information. Paper presented at the Australian Agricultural Economics Society 35th Annual Conference, Armidale N.S.W.. Littleboy, M, D.M. Silburn, D.M. Freebairn, D.R. Woodruff, G.L. Hammer and J.K. Leslie, 1992. Impact of soil erosion on production in cropping systems. I. Development and validation of a simulation model. Aust. J. Soil Res., 30: 757-774. Oram, D.A., 1987. The Economics of Dryland Salinity and its Control in the Murray River Basin of Victoria: A Farm Level Approach. Occasional Paper No. 11, School of Agriculture, La Trobe University, Melbourne, Australia. Peck, A.J. and Hurle, D.H., 1973. Chloride balance of some farmed and forested catchments in southwestern Australia. Water Resour. Res., 9: 648-657. PoweU, J., 1993. Dryland salinity in the Murray-Darling Basin. Aust. J. Soil Water Conserv., 6: 45-48. Thomas, E.C., E.A. Gardner, M. Littleboy and P. Shields, 1992. Using Cropping Systems Models in Land Evaluation. Proceedings of the Conference on Engineering in Agriculture, Albury, NSW, pp. 85-89. Thorburn, P.J., B.A. Clowie and P.A. Lawrence, 1991. Effect of land development on groundwater recharge determined from non-steady chloride profiles. J. Hydrol., 124: 43-58.