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Cross-achievements between policies for drinking water protection
Journal of Environmental Management (2002) 64, 77–83 doi:10.1006/jema.2001.0517, available online at http://www.idealibrary.com on
Cross-achievements between policies for drinking water protection Hild Rygnestad†* , Jørgen Dejgaard Jensen† , Tommy Dalgaard‡ and Jesper S. Schou§ †
Danish Institute of Agricultural and Fisheries Economics, Agricultural Policy Research Division Danish Institute of Agricultural Sciences, Department of Agricultural Systems § National Environmental Research Institute, Department of Policy Analysis ‡
Received 12 February 2001; accepted 26 September 2001
Environmental dynamics have important spatial dimensions, which calls for a spatial approach in policy analyses. Further to this, assessing agri-environmental policies involves analyses of individual measures as well as their combined effects on farmer behaviour and the environment. The integration of an economic behavioural model in a spatial framework has enabled analyses of a geographically targeted subsidy scheme for drinking water protection in combination with a uniform tax on commercial nitrogen fertilizer. Results show that policy measures for reducing nitrogen use can have combined effects (cross-achievements), thereby affecting each other’s cost-effectiveness. Cross-achievements between a nitrogen fertilizer tax and a subsidy scheme based on elicitation are shown not to be additive, making partial analyses of policy measures more uncertain. 2002 Academic Press
Keywords: Economic modelling, Geographical Information System, nitrogen fertilizer, land conversion, fertilizer tax.
Introduction Agricultural production impacts on the surrounding environment through its use of soil, water and air both as input factors and through its production output. Output factors range from positive amenities such as desired landscape features to negative externalities such as pollution of water bodies. Thus, farmer behaviour is one of the major components affecting the agri-environment, as acknowledged in the numerous policy instruments governing the agricultural sector in many developed countries. Because of the wide array of rules and regulations, cross-achievements between policy instruments are increasingly recognised (Spash and Falconer, 1997). However, this issue is seldom Ł Corresponding author. Present address: 2250 North Triphammer Road, Townhouse 7C Ithaca, NY 14850, USA. Email: [email protected]
0301–4797/02/010077C07 $35.00/0
analysed quantitatively (see Vatn et al., 1997, for an exception). The aim of this paper is to contribute a quantitative analysis of crossachievements between two specific instruments. In particular, the focus is on the cost-effectiveness of reducing nitrogen use in a region of Denmark using a fertilizer tax and/or a subsidy scheme for land conversion. Rules and regulations should be assessed both based on their effects on agricultural production and on the environment. Economic models are typically used to analyse current production conditions and predict farmer behaviour, but the applicability of these models is limited if they do not include geographical aspects. Geographical aspects feature strongly in the dynamics of environmental issues, with an increasing number of policy measures aimed at agriculture in selected geographical areas. However, in the absence of an appropriate analytical basis, agri-environmental analyses in Denmark have only exceptionally had a geographical focus (see Skop and Schou, 1999; Paaby et al., 1996). In 2002 Academic Press
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other countries recent studies have applied a range of methodological approaches with a geographical focus. For example, Bouman et al. (1999), Stoorvogel et al. (1995) and Vatn et al. (1997) have applied apply normative behavioural models to analyse spatial effects of agricultural production. Guipponi et al. (1999) and O’Callahan (1995) have used simulation models combined with spatial information. The present paper joins the line of studies applying descriptive econometric models to analyse farmer behaviour (e.g. Moxey and White, 1994; Bateman et al., 1999; Bouman et al., 1999). It focuses on integrating an econometric behavioural model with geographical data detailing agricultural structures and soil types. Building an existing work, the model simulates behaviour of different farm types, and behavioural changes are linked directly to each of the farms in the study area. Thus, the spatial aspect is maintained throughout the analysis. Bjerringbro and Hvorslev municipalities in the midwest of Denmark form the study area. A tax on fertilizers is chosen as an example of a general policy measure implemented on a national scale. In contrast to the tax, the subsidy-based land conversion scheme represents a targeted policy measure that can be applied at a local or regional scale. Cross-achievements between the fertilizer tax and the subsidy scheme are analysed through their cost-effectiveness, where the socioeconomic costs are measured as loss of valueadded (farm profits) less the tax revenue. The most cost-effective policy for reducing nitrogen fertilizer use is that which leads to the lowest costs per unit of fertilizer. Costs are related to agricultural production and exclude transaction and administration costs, and do not therefore constitute an efficiency or welfare analysis. Effects are related to factors that can be regulated directly rather than to derived environmental effects. Thus, estimates are provided according to reduced nitrogen use or converted farm area, which provides a clear focus on quantifying crossachievements. In line with this, the aim is not to quantify environmental effects, whereby different indicators should be used (see for example Vatn et al., 1997, on nitrate leaching or Guipponi et al., 1999, on environmental vulnerability indices). Note also that, because the tax and subsidy scheme are not designed according to existing policies in Denmark, the current analysis is not an attempt at forecasting. We aim to combine and compare different policy designs in a spatial context. Results show the applicability of the integrating framework, particularly for analysing geographically targeted policy measures in combination
with uniform measures. Future research areas are identified including the need for improved methods to use existing data in a geographical framework.
Materials and methods The overall approach of the paper is to analyse cross-achievements between two different policy measures for drinking water protection. The methodology underlying the analyses combines an economic behavioural model for agriculture with data on agricultural structures and production and environmental characteristics. Farmers’ response to policy changes is simulated using an econometric behavioural model of the Danish agricultural sector (ESMERALDA). In line with economic theory, agricultural sector behaviour in terms of production, input use, land allocation, livestock density, costs, agricultural income etc. is described as a function of price relations and quantitative restrictions at the farm level, based on the assumption that farmers exhibit cost minimising behaviour (the dual approach, see e.g. Chambers, 1988). This econometric-descriptive approach can be viewed as an alternative to the normative approach reflected in mathematical programming-based agricultural sector models (e.g. Stoorvogel et al., 1995; Vatn et al., 1997; Bouman et al., 1999) and relies on parameters derived from empirically observed behaviour. ESMERALDA consists of a large number of ‘model farms’, with data requirements corresponding to the contents of FADN data. These farms are divided into eight groups: part-time farms and full-time crop, cattle and pig farms on loamy and sandy soil, respectively. This division reflects the assumption that behavioural parameters may vary between these farm categories, e.g. due to general differences in overall cropping patterns, relatively large production of manure on livestock farms etc. For each of these eight farm categories, sets of behavioural parameters have been estimated econometrically, using panel data techniques on FADN data from 1973 to 1996. Estimated parameters include price responses to price as well as responses to exogenous changes in structural variables (e.g. land allocation or herd size) at the farm level. See Jensen et al. (2001) for a more detailed description of the ESMERALDA model. In order to address the specific agricultural structure and conditions in the study area, we apply a set of structural data, comprising land use and livestock numbers on each of the 878 farms in the study
Cross-achievements between policies for drinking water protection
area in 1998, provided by the GLR/CHR database (National Danish Digital Agricultural Registers) primarily for administration of EU area and livestock payments (Danish Ministry of Food, Agriculture and Fisheries, 1999). These are geo-referenced and combined with data on soil types and fields situated within designated drinking water areas (OSD) (Dalgaard et al., 2002). The official EU classification methods are applied to the farm data such that farms are identified according to whether they are full-time or part-time producers, and according to their production characteristics: cropping, cattle or pigs/poultry/furred animals (European Commission, 1985; Rygnestad et al., 2000). As opposed to these structural data, there are no economic data directly available for the study area. Accounting data from the approximately 2000 FADN-farms included in ESMERALDA have been extracted from a nation-wide sample; only a few farms are situated in the study area. Approximate economic data (production values, costs, income figures) for farms in the study area have been estimated, combining data from the FADNand GLR/CHR databases. Each economic variable for a specific farm in the study area is approximated by a weighted average of the corresponding economic variable from the 10 farms in the accounts database most similar to the farm in the study area. Similarity is sought on key structural variables: farm area, land allocation (cereals, oilseeds/pulses, root crops and roughage) and numbers of dairy cows and pigs. The approximation is evaluated on the sum of squared deviations of the structural variables, and the weights are determined on the basis of these sums of squared deviations (Rygnestad et al., 2000). The results provide estimates for each farm’s likely economic variables (given their behaviour is comparable to other farms with similar production structure) and form the basis for the behavioural simulations in ESMERALDA. In the analysis below, we focus on two farm level indicators: (1) average gross margin, used as an estimate of the opportunity cost of converting agricultural land to permanent grass or fallow, and (2) nitrogen fertilizer use, applied as a simple environmental indicator. In this paper the costs of any form of adjustment are estimated according to the farm’s average gross margin (i.e. loss of added value from changes in production). As such the estimated gross margin is also taken to indicate compensation needed to induce participation in the land conversion scheme. Note that the estimated gross margins only include agricultural income and costs, thereby excluding off-farm
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income (such data cannot be approximated satisfactorily using FADN data). Due to this, as well as yearly variation in income and costs, some estimated margins are negative. In such cases it is not possible to predict participation in the subsidy scheme, and these farms are excluded from the results (approximately 15% of the study area). To illustrate cross-achievements between policy measures, a full scenario is constructed involving two components: (1) introduction of a 50% tax on commercial nitrogen fertilizer, and (2) a targeted subsidy scheme for land conversion, in order to convert 80% of the designated OSD areas to either permanent grass (for farms with grazing cattle) or fallow (for other farms). A tax on commercial fertilizer has the following effects on most farms: (1) Input substitution, where fertilizers are substituted by other inputs which become relatively cheaper at the margin. (2) Lower cropping intensity in individual crops. (3) Changes in land use towards crops which are relatively more profitable when fertilizer prices are higher. These three elements of adjustment have separate effects on input use, production, costs, gross margin, etc., and the total effect of the tax constitutes the sum of these elements. ESMERALDA enables calculation of these effects separately and in combination. Note that changes in trade in manure between farms are not accounted for. In contrast to the tax, the second scenario component only affects farms in designated areas. The subsidy scheme is based on elicitation, whereby participating farmers receive the equivalent of their approximated average gross margin to convert their land use. Eligible farms can only participate with those parts of the farm that are in the designated areas, and existing permanent grass and fallow fields are used to meet the goal of 80% conversion. Furthermore, it is assumed that farms that lie completely inside the designated area convert all their land to fallow. Farms with no cattle also choose fallow, whereas permanent grass areas can contribute to fodder production and are favoured by farms with cattle production. In the latter case, remaining agricultural activities are reduced proportionally to the area of land converted. In contrast to the price change, these exogenous changes in land use have only impacts on production, gross margin etc. through their land use effects.
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Results While the two scenario components have different effects with regard to drinking water protection, a common denominator is evaluated through nitrogen fertilizer use. Results in Table 1 indicate that the nitrogen tax has the largest impact on farms’ nitrogen use. In total, the full scenario leads to a 21% reduction in total nitrogen use (including nitrogen in animal manure), with the tax and the land conversion scheme giving 11 and 10% reduction, respectively. In this particular case, the sum of reductions in the two partial regulations only slightly exceeds that of the full scenario. The loss of added value in the land conversion scheme is significantly higher than under the tax scheme. In the latter, farmers’ loss of gross margin is DKK 10Ð3 million (of which DKK 5 million is tax revenue), whereas in the former, the loss is approximately DKK 35 million (assumed compensated by corresponding subsidies). In the full combined scenario, total loss of farmers’ gross margin (net of compensation) amounts to DKK 39Ð1 million of which DKK 4Ð6 million is revenue from the tax. This is considerably lower than the sum of the costs in the two partial schemes. A policy’s cost-effectiveness can be assessed as a cost, in this case in terms of loss of added value. It is defined as loss of farm profits less revenue from nitrogen taxation, divided by the reduction in nitrogen use. Policy effects as presented in Table 1 show that the cost-effectiveness of the full scenario at reducing nitrogen use per hectare in the total study area is DKK 39 per kg reduced nitrogen. The tax is the most cost-effective instrument at DKK 11 per kg reduced nitrogen, because it is linked closely to nitrogen use. Nitrogen use is linked to land use in the land conversion scheme; however, the costeffectiveness ratio for the whole area is lower than for the tax (DKK 82 per kg reduced N) because only farms in designated areas are targeted, rather Table 1.
than those with the lowest adjustment costs. Note that these are only indicative results as the costeffectiveness of the two measures is not directly comparable because both costs and effects on nitrogen use are different. Nevertheless, the results indicate that if the aim is a general reduction in fertilizer use, the tax seems to be clearly more cost effective than the land conversion scheme. This conclusion is supported strongly by the fact that the tax leads to a higher reduction in fertilizer use than the land conversion scheme as well as significantly lower costs per unit of reduced nitrogen fertilizer. Another observation is that cross-achievements between two policy components are identifiable. First, the reduction in nitrogen use is 4 000 kg less in the full scenario than the sum of each component (Table 1). This is because, before areas are converted to permanent grass and fallow, total nitrogen use has already been reduced due to the tax. Second, the costs of running the subsidy scheme fall from DKK/ha 9 086 to DKK/ha 8 979 if the tax is already in place (Table 2). These costsavings exist even if, as shown in Table 1, the tax revenue is lowered with more land under extensive cropping (i.e. lower fertilizer use). Results from partial analyses of the two policy measures can therefore not be added to find an estimated cost per kg reduced nitrogen use. This stresses the importance of integrated, geographical analyses of cross-achieving policy measures, especially as the cross-achievement issues most likely will get more complex if focus is changed from nitrogen use to the environmental effects. It is imperative to consider the geographical effects of nitrogen reduction in the full scenario. While the nitrogen tax component leads to a general reduction across the whole study area, the subsidy scheme targets the reduction on designated areas with low fertilizer rates on permanent grass and fallow. Provided that these areas are correctly identified as more important for nitrogen reduction than others, the environmental benefits
Economics and nitrogen use with fertilizer tax and land conversion subsidy (partial and combined)a
Total nitrogen use, 1000 kg N Loss of added value, DKKc 1000 tax revenue, DKKc 1000 Average costs, DKK/kg N
Basis
After 50% tax
4 216
459 5 222 5 046 11Ð4
After conversion of 80% of OSDb 428 35 052 81Ð8
After 50% tax and conversion of 80% of OSDb 884 34 637 4 456 39Ð2
Notes a Excludes farms with estimated negative gross margins in the basis. b OSDDareas designated for drinking water protection. c Loss of added valueDchanges in total gross margins for Agenda 2000 and tax scenarios net of fertilizer tax payments.
Cross-achievements between policies for drinking water protection Table 2. Area and subsidy costs of converting designated drinking water area to permanent grass or fallowa 80% of OSD converted from basis scenario Converted area, ha Total subsidy costs, DKK 1000 Average costs per ha, DKK/ha
Full scenario: 80% of OSD converted after 50% taxb,c
3 858
3 858
35 052
34 637
9 086
8 979
Notes a Excludes farms with estimated negative gross margins in the basis. b Includes a new round of elicitation after scenario for the 50% nitrogen tax. c OSDDareas designated for drinking water protection.
are likely to be higher with targeted subsidy scheme. Conversely, only a small part of the reduction in nitrogen use on the total 32 000 ha, as caused by the uniform measures, can be expected to have an effect on the 3 858 ha in OSD areas. Thus, uniform measures are not likely to be cost-effective for targeting specific geographical areas.
Discussion and conclusions Issues of policy design Analysing cross-achievements between uniform and targeted measures is only possible if spatial data are incorporated with the economic model. Results show that policy measures can have combined effects (cross-achievements), thereby affecting each other’s cost-effectiveness (see Vatn et al., 1997, for similar analyses). Firstly, as a positive cross-achievement, the necessary subsidy payments for land conversion are lower with an existing fertilizer tax thereby increasing the costeffectiveness of the subsidy scheme. Secondly, and as a negative cross-achievement, total costeffectiveness of nitrogen reduction after land conversion is lower when a tax is already in place. Thus, cross-achievements between a tax and a subsidy scheme based on elicitation are shown not to be additive, making partial analyses of policy measures more uncertain. Uniform measures, such as the 50% tax, require all farmers that use commercial nitrogen to adjust regardless of their geographical placement. Further analyses not included in this paper indicate that even though uniform measures lead to
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different adjustments between farm types, there appears to be no geographical pattern in their effects because there is little clustering of farm types in Bjerringbro and Hvorslev municipalities (see Rygnestad et al., 2000). In relation to this, it is expected that if the analysis had included a greater or different area, one might have found that a geographical approach contributed more information. For example, Schou et al. (2000) found significant differences in the marginal abatement costs of reducing nitrogen leaching in a case study of Vejle County. One reason for this is the clustering of different farm types in different areas of the county. Together with the results derived here, this indicates that even though the location of farm matters with respect to designing cost-effective policies, a more aggregated approach where policies are differentiated according to an environmentally based zoning may lead to a significant improvement. Similarly, modelling of different types of targeted policy measures, e.g. graduated subsidy schemes, and other indicators of their effects seem to be important in practical policy making. While this analysis is focused on a subsidy scheme based on elicitation, current schemes in Denmark, and most other countries, are based on fixed payments per ha. Such payments inadvertently lead to overcompensation of all participating farmers whose loss in gross margin is smaller than the subsidy. Thus, elicitation may attract a greater number of participants than a fixed payment system – given the same budget. This conclusion does not account for differences in administration and transaction costs, which may be higher under an elicitation scheme. See Badger (1999), Latacz-Lohman and Van der Hamsvoort (1998) and Falconer and Whitby (1999) for further discussion of these issues. Note also that the scheme requires a well functioning bidding process. Adequately managed cross-achievements between policy measures can also lead to overachievement of policy goals. For example, if the combined reduction in nitrogen use in Table 1 lies above the policy goal, one or more of the policy components should be redesigned. This is typically the situation if several parallel measures are linked closely to the same goal. While one linkage is illustrated for farms’ nitrogen use it could be useful to include indicators that deal explicitly with both negative and positive environmental effects from agriculture (e.g. negative environmental effects from agricultural production support; increased use of some plant protection chemicals due to restrictions on nitrogen use). Further to this, indicators could be aimed at incorporating
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geographical issues such as distance source, transport and accumulation of pollutants.
Issues of modelling framework The developed analytical framework combines an econometric agricultural sector model with geographical data on agricultural structures, soil types and area designations, and hence provides an opportunity for integrated economicenvironmental analysis related to the agricultural sector, taking into account various spatial aspects at a fairly disaggregated level. The framework is based on databases conforming to EU standards (FADN and GLR/CHR). The quality of the analysis relies on data availability and quality. As suggested by Caspersen and Kristensen (1999) the applied GLR/CHR data on land use and livestock numbers is the best available option particularly with regard to coverage as well as it being comparable to other EU countries. Other data limitations, as discussed in Rygnestad (2000), relate to how accurate the geo-referencing process is and whether it provides a complete picture of the area in question. The discussion also raises the importance of linking different databases consistently, particularly when data collection is infrequent and information is updated only occasionally. One methodological improvement could be to enhance the use of existing data at a spatial level. Beyond obtaining more detailed data than those which exist in the GLR/CHR and FADN databases, the actual geographical placement of fields can add further analytical powers, particularly in the case of targeted policy measures. A stochastic model to predict the placement of crops could also add knowledge to the spatial effect of policy instruments on environmental issues such as distance to source, transport and accumulation of pollutants. This would also be a logical progression if the focus changed to incorporate environmental effects and indicators such as nitrate loss to air, water and soil. Because the farms’ approximated gross margins are used to determine participation in the land conversion scheme, their levels are important to determine effects of each measure or scenario. While the approximation procedure minimises the sum of squared deviations of structure variables, there are discrepancies particularly in cases where a certain farm’s structure is an outlier compared to most farms in the database (see Rygnestad et al., 2000). The estimated gross margins are further dependent
on yearly changes in agricultural income and cost levels. In spite of these uncertainties, the results can indicate the type of farms that are likely to participate in the subsidy scheme. It is assumed in the analyses that only those parts of the farm situated in designated OSD areas are converted. Thus, their adjustment costs should be related to their marginal gross margin rather than the farm’s average. If the farm has heterogeneous land with low productivity in designated areas, the above cost estimates are too large. The method still provides an indication of which farms are most likely to participate in a subsidy scheme. Table 2 includes a maximum estimate for the costs of land conversion. Because fallow areas often are included in the farms’ rotation plans, the possible support obtained during elicitation (and thus costs incurred) is likely to vary from year to year. Calculating maximum costs includes support for all areas that make up the policy goal regardless of the initial situation. If minimum costs are estimated it must be assumed that existing permanent grass and fallow areas are situated in the designated areas or that no support is offered to relocate them. Whether a maximum or minimum approach is used will change the cost levels of the analysed policy while maintaining the original conclusions on participation and policy effects. In sum, by integrating an economic model in a spatial framework, it is possible to evaluate geographically targeted and uniform policy measures. More to the point, the combined effects can be assessed as has been illustrated with quantified cross-achievements. While this paper has focussed on drinking water protection, other agrienvironmental issues can be incorporated given appropriate modelling and indicators.
Acknowledgements The Danish Environmental Research Programme 1997– 2000 has funded the main parts of this study. Co-funding has been obtained from the research programme: ‘Agriculture and Rural Districts – Economy and Development’ funded by the Danish Ministry of Food, Agriculture and Fisheries.
References Badger, R. (1999). The cost effectiveness of agrienvironmental policy instruments. Agricultural Economics Society Annual Conference, Belfast, 26–29 March.
Cross-achievements between policies for drinking water protection Bateman, I. J., Ennew, C., Lovett, A. A. and Rayner, A. J. (1999). Modelling and mapping agricultural output values using farm specific details and environmental databases. Journal of Agricultural Economics 50(3), 488–511. Bouman, B. A. M., Jansen, H. G. P., Schipper, R. A., Nieuwenhuyse, A., Hengsdijk, H. and Bouma, J. (1999). A framework for integrated biophysical and economic land use analysis at different scales. Agriculture, Ecosystems and Environment 75(1–2), 55–73. Caspersen, O. H. and Kristensen, S. P. (1999). GIS i landbrugsforskningen (GIS in agricultural research). In GIS i Denmark 2 (GIS in Denmark 2) (T. Balstrøm et al., ed.). Copenhagen: Teknisk Forlag A/S. Chambers, R. G. (1988). Applied production analysis – a dual approach. Cambridge: Cambridge University Press. Dalgaard, T., Rygnestad, H., Jensen, J. D. and Larsen, P. E. (2002). New methods to map and simulate agricultural activity at the landscape scale. Danish Journal of Geography, Special Issue 3 (in press). Danish Ministry of Food, Agriculture and Fisheries (1999). Directive for the Digital Danish Agricultural Registers (Forskrift for GLR/CHR Generelt LandbrugsRegister/Centralt HusdyrbrugsRegister). Danish Ministry of Food, Agriculture and Fisheries, Copenhagen. European Commission (1985). Decision establishing a community typology for agricultural holdings. Official Journal of the European Communities, No 85/377, L 220/1–32, 17. Falconer, K. and Whitby, M. (1999). The invisible costs of scheme implementation and administration. In Countryside Stewardship: Farmers, policies and markets (G. van Huylenbroeck and M. Whitby, eds), pp. 67–88. London: Elsevier. Guipponi, C., Eiselt, B. and Ghetti, P. F. (1999). A multicriteria approach for mapping risks of agricultural pollution for water resources: the Venice lagoon watershed case study. Journal of Environmental Management 56(4), 259–269, doi:10.1006/jema.1999.0283. Jensen, J. D., Andersen, M. and Kristensen, K. (2001). A regional econometric sector model for Danish agriculture: Documentation of the regionalized ESMERALDA model. Statens Jordbrugs- og Fiskeriøkonomiske Institut (Danish Institute of Agricultural and Fisheries Economics) Report 129. (www.sjfi.dk) Latacz-Lohman, U. and Van der Hamsvoort, C. P. C. M. (1998). Auctions as a means of creating a market for public goods from agriculture. Journal of Agricultural Economics 49(3), 334–345.
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Moxey, A. and White, B. (1994). Efficient compliance with agricultural nitrate pollution standards. Journal of Agricultural Economics 45, 27–37. O’Callaghan, J. R. (1995). NELUP: an introduction. Journal of Environmental Planning and Management 48(1), 5–20. Paaby, H., Juhl Jensen, J., Kristensen, P., Møller, F. and Skop, E. (1996). Reducing nutrient loadings of marine waters: a cost effectiveness analysis. In Modelling the economy and the environment (B. Madsen, C. Jensen-Butler, J. Birk Mortensen and A. M. Bruun Christensen, eds), pp. 285–307. Berlin: Springer. Rygnestad, H. (2000). Integrating environmental economics and policy analyses in a geographical information system. Danish Institute of Agricultural and Fisheries Economics, Working Paper 4/2000 (www.sjfi.dk). Rygnestad, H., Jensen, J. D. and Dalgaard, T. (2000). ˚ Malrettede eller generelle politiske virkemidler? Økonomiske analyser i geografisk perspektiv. (Targeted or uniform policy measures? Economic analyses in a geographical perspective). Danish Institute of Agricultural and Fisheries Economics, Working Paper 17/2000 (www.sjfi.dk). SJFI (1998). Landbrugsregnskabsstatistik 1997–1998 (Agricultural account statistics 1997–1998). Danish Institute of Agricultural and Fisheries Economics, Report 82 A (www.sjfi.dk). Schou, J. S., Skop, E. and Jensen, J. D. (2000). Integrated agri-environmental modelling: analysis of two nitrogen tax instruments in the Vejle Fjord watershed, Denmark. Journal of Environmental Management 58, 199–212. Skop, E. and Schou, J. S. (1999). Modeling the effects of agricultural production. an integrated economic and environmental analysis using farm account statistics and GIS. Ecological Economics 29, 427–442. Spash, C. L. and Falconer, K. (1997). Agri-environmental policies: cross-achievements and the role for crosscompliance. In The implementation of nitrate policies in Europe: Processes of change in environmental policy and agriculture (F. M. Brouwer and W. Kleinhanss, eds), pp. 23–42. Wissenshaftsverlag Vauk Kiel KG. Stoorvogel, J. J., Schipper, R. A. and Jansen, D. M. (1995). USTED: a methodology for a quantitative analysis of land use scenario. Netherlands Journal of Agricultural Science 43, 5–18. Vatn, A., Bakken, L. R., Botterweg, R., Lundeby, H., Romstad, E., Rørstad, P. K. and Vold, A. (1997). Regulating nonpoint-source pollution from agriculture – an integrated modelling analysis. European Review of Agricultural Economics 26(2), 207–229.
Cross-achievements between policies for drinking water protection Hild Rygnestad†* , Jørgen Dejgaard Jensen† , Tommy Dalgaard‡ and Jesper S. Schou§ †
Danish Institute of Agricultural and Fisheries Economics, Agricultural Policy Research Division Danish Institute of Agricultural Sciences, Department of Agricultural Systems § National Environmental Research Institute, Department of Policy Analysis ‡
Received 12 February 2001; accepted 26 September 2001
Environmental dynamics have important spatial dimensions, which calls for a spatial approach in policy analyses. Further to this, assessing agri-environmental policies involves analyses of individual measures as well as their combined effects on farmer behaviour and the environment. The integration of an economic behavioural model in a spatial framework has enabled analyses of a geographically targeted subsidy scheme for drinking water protection in combination with a uniform tax on commercial nitrogen fertilizer. Results show that policy measures for reducing nitrogen use can have combined effects (cross-achievements), thereby affecting each other’s cost-effectiveness. Cross-achievements between a nitrogen fertilizer tax and a subsidy scheme based on elicitation are shown not to be additive, making partial analyses of policy measures more uncertain. 2002 Academic Press
Keywords: Economic modelling, Geographical Information System, nitrogen fertilizer, land conversion, fertilizer tax.
Introduction Agricultural production impacts on the surrounding environment through its use of soil, water and air both as input factors and through its production output. Output factors range from positive amenities such as desired landscape features to negative externalities such as pollution of water bodies. Thus, farmer behaviour is one of the major components affecting the agri-environment, as acknowledged in the numerous policy instruments governing the agricultural sector in many developed countries. Because of the wide array of rules and regulations, cross-achievements between policy instruments are increasingly recognised (Spash and Falconer, 1997). However, this issue is seldom Ł Corresponding author. Present address: 2250 North Triphammer Road, Townhouse 7C Ithaca, NY 14850, USA. Email: [email protected]
0301–4797/02/010077C07 $35.00/0
analysed quantitatively (see Vatn et al., 1997, for an exception). The aim of this paper is to contribute a quantitative analysis of crossachievements between two specific instruments. In particular, the focus is on the cost-effectiveness of reducing nitrogen use in a region of Denmark using a fertilizer tax and/or a subsidy scheme for land conversion. Rules and regulations should be assessed both based on their effects on agricultural production and on the environment. Economic models are typically used to analyse current production conditions and predict farmer behaviour, but the applicability of these models is limited if they do not include geographical aspects. Geographical aspects feature strongly in the dynamics of environmental issues, with an increasing number of policy measures aimed at agriculture in selected geographical areas. However, in the absence of an appropriate analytical basis, agri-environmental analyses in Denmark have only exceptionally had a geographical focus (see Skop and Schou, 1999; Paaby et al., 1996). In 2002 Academic Press
78
H. Rygnestad et al.
other countries recent studies have applied a range of methodological approaches with a geographical focus. For example, Bouman et al. (1999), Stoorvogel et al. (1995) and Vatn et al. (1997) have applied apply normative behavioural models to analyse spatial effects of agricultural production. Guipponi et al. (1999) and O’Callahan (1995) have used simulation models combined with spatial information. The present paper joins the line of studies applying descriptive econometric models to analyse farmer behaviour (e.g. Moxey and White, 1994; Bateman et al., 1999; Bouman et al., 1999). It focuses on integrating an econometric behavioural model with geographical data detailing agricultural structures and soil types. Building an existing work, the model simulates behaviour of different farm types, and behavioural changes are linked directly to each of the farms in the study area. Thus, the spatial aspect is maintained throughout the analysis. Bjerringbro and Hvorslev municipalities in the midwest of Denmark form the study area. A tax on fertilizers is chosen as an example of a general policy measure implemented on a national scale. In contrast to the tax, the subsidy-based land conversion scheme represents a targeted policy measure that can be applied at a local or regional scale. Cross-achievements between the fertilizer tax and the subsidy scheme are analysed through their cost-effectiveness, where the socioeconomic costs are measured as loss of valueadded (farm profits) less the tax revenue. The most cost-effective policy for reducing nitrogen fertilizer use is that which leads to the lowest costs per unit of fertilizer. Costs are related to agricultural production and exclude transaction and administration costs, and do not therefore constitute an efficiency or welfare analysis. Effects are related to factors that can be regulated directly rather than to derived environmental effects. Thus, estimates are provided according to reduced nitrogen use or converted farm area, which provides a clear focus on quantifying crossachievements. In line with this, the aim is not to quantify environmental effects, whereby different indicators should be used (see for example Vatn et al., 1997, on nitrate leaching or Guipponi et al., 1999, on environmental vulnerability indices). Note also that, because the tax and subsidy scheme are not designed according to existing policies in Denmark, the current analysis is not an attempt at forecasting. We aim to combine and compare different policy designs in a spatial context. Results show the applicability of the integrating framework, particularly for analysing geographically targeted policy measures in combination
with uniform measures. Future research areas are identified including the need for improved methods to use existing data in a geographical framework.
Materials and methods The overall approach of the paper is to analyse cross-achievements between two different policy measures for drinking water protection. The methodology underlying the analyses combines an economic behavioural model for agriculture with data on agricultural structures and production and environmental characteristics. Farmers’ response to policy changes is simulated using an econometric behavioural model of the Danish agricultural sector (ESMERALDA). In line with economic theory, agricultural sector behaviour in terms of production, input use, land allocation, livestock density, costs, agricultural income etc. is described as a function of price relations and quantitative restrictions at the farm level, based on the assumption that farmers exhibit cost minimising behaviour (the dual approach, see e.g. Chambers, 1988). This econometric-descriptive approach can be viewed as an alternative to the normative approach reflected in mathematical programming-based agricultural sector models (e.g. Stoorvogel et al., 1995; Vatn et al., 1997; Bouman et al., 1999) and relies on parameters derived from empirically observed behaviour. ESMERALDA consists of a large number of ‘model farms’, with data requirements corresponding to the contents of FADN data. These farms are divided into eight groups: part-time farms and full-time crop, cattle and pig farms on loamy and sandy soil, respectively. This division reflects the assumption that behavioural parameters may vary between these farm categories, e.g. due to general differences in overall cropping patterns, relatively large production of manure on livestock farms etc. For each of these eight farm categories, sets of behavioural parameters have been estimated econometrically, using panel data techniques on FADN data from 1973 to 1996. Estimated parameters include price responses to price as well as responses to exogenous changes in structural variables (e.g. land allocation or herd size) at the farm level. See Jensen et al. (2001) for a more detailed description of the ESMERALDA model. In order to address the specific agricultural structure and conditions in the study area, we apply a set of structural data, comprising land use and livestock numbers on each of the 878 farms in the study
Cross-achievements between policies for drinking water protection
area in 1998, provided by the GLR/CHR database (National Danish Digital Agricultural Registers) primarily for administration of EU area and livestock payments (Danish Ministry of Food, Agriculture and Fisheries, 1999). These are geo-referenced and combined with data on soil types and fields situated within designated drinking water areas (OSD) (Dalgaard et al., 2002). The official EU classification methods are applied to the farm data such that farms are identified according to whether they are full-time or part-time producers, and according to their production characteristics: cropping, cattle or pigs/poultry/furred animals (European Commission, 1985; Rygnestad et al., 2000). As opposed to these structural data, there are no economic data directly available for the study area. Accounting data from the approximately 2000 FADN-farms included in ESMERALDA have been extracted from a nation-wide sample; only a few farms are situated in the study area. Approximate economic data (production values, costs, income figures) for farms in the study area have been estimated, combining data from the FADNand GLR/CHR databases. Each economic variable for a specific farm in the study area is approximated by a weighted average of the corresponding economic variable from the 10 farms in the accounts database most similar to the farm in the study area. Similarity is sought on key structural variables: farm area, land allocation (cereals, oilseeds/pulses, root crops and roughage) and numbers of dairy cows and pigs. The approximation is evaluated on the sum of squared deviations of the structural variables, and the weights are determined on the basis of these sums of squared deviations (Rygnestad et al., 2000). The results provide estimates for each farm’s likely economic variables (given their behaviour is comparable to other farms with similar production structure) and form the basis for the behavioural simulations in ESMERALDA. In the analysis below, we focus on two farm level indicators: (1) average gross margin, used as an estimate of the opportunity cost of converting agricultural land to permanent grass or fallow, and (2) nitrogen fertilizer use, applied as a simple environmental indicator. In this paper the costs of any form of adjustment are estimated according to the farm’s average gross margin (i.e. loss of added value from changes in production). As such the estimated gross margin is also taken to indicate compensation needed to induce participation in the land conversion scheme. Note that the estimated gross margins only include agricultural income and costs, thereby excluding off-farm
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income (such data cannot be approximated satisfactorily using FADN data). Due to this, as well as yearly variation in income and costs, some estimated margins are negative. In such cases it is not possible to predict participation in the subsidy scheme, and these farms are excluded from the results (approximately 15% of the study area). To illustrate cross-achievements between policy measures, a full scenario is constructed involving two components: (1) introduction of a 50% tax on commercial nitrogen fertilizer, and (2) a targeted subsidy scheme for land conversion, in order to convert 80% of the designated OSD areas to either permanent grass (for farms with grazing cattle) or fallow (for other farms). A tax on commercial fertilizer has the following effects on most farms: (1) Input substitution, where fertilizers are substituted by other inputs which become relatively cheaper at the margin. (2) Lower cropping intensity in individual crops. (3) Changes in land use towards crops which are relatively more profitable when fertilizer prices are higher. These three elements of adjustment have separate effects on input use, production, costs, gross margin, etc., and the total effect of the tax constitutes the sum of these elements. ESMERALDA enables calculation of these effects separately and in combination. Note that changes in trade in manure between farms are not accounted for. In contrast to the tax, the second scenario component only affects farms in designated areas. The subsidy scheme is based on elicitation, whereby participating farmers receive the equivalent of their approximated average gross margin to convert their land use. Eligible farms can only participate with those parts of the farm that are in the designated areas, and existing permanent grass and fallow fields are used to meet the goal of 80% conversion. Furthermore, it is assumed that farms that lie completely inside the designated area convert all their land to fallow. Farms with no cattle also choose fallow, whereas permanent grass areas can contribute to fodder production and are favoured by farms with cattle production. In the latter case, remaining agricultural activities are reduced proportionally to the area of land converted. In contrast to the price change, these exogenous changes in land use have only impacts on production, gross margin etc. through their land use effects.
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Results While the two scenario components have different effects with regard to drinking water protection, a common denominator is evaluated through nitrogen fertilizer use. Results in Table 1 indicate that the nitrogen tax has the largest impact on farms’ nitrogen use. In total, the full scenario leads to a 21% reduction in total nitrogen use (including nitrogen in animal manure), with the tax and the land conversion scheme giving 11 and 10% reduction, respectively. In this particular case, the sum of reductions in the two partial regulations only slightly exceeds that of the full scenario. The loss of added value in the land conversion scheme is significantly higher than under the tax scheme. In the latter, farmers’ loss of gross margin is DKK 10Ð3 million (of which DKK 5 million is tax revenue), whereas in the former, the loss is approximately DKK 35 million (assumed compensated by corresponding subsidies). In the full combined scenario, total loss of farmers’ gross margin (net of compensation) amounts to DKK 39Ð1 million of which DKK 4Ð6 million is revenue from the tax. This is considerably lower than the sum of the costs in the two partial schemes. A policy’s cost-effectiveness can be assessed as a cost, in this case in terms of loss of added value. It is defined as loss of farm profits less revenue from nitrogen taxation, divided by the reduction in nitrogen use. Policy effects as presented in Table 1 show that the cost-effectiveness of the full scenario at reducing nitrogen use per hectare in the total study area is DKK 39 per kg reduced nitrogen. The tax is the most cost-effective instrument at DKK 11 per kg reduced nitrogen, because it is linked closely to nitrogen use. Nitrogen use is linked to land use in the land conversion scheme; however, the costeffectiveness ratio for the whole area is lower than for the tax (DKK 82 per kg reduced N) because only farms in designated areas are targeted, rather Table 1.
than those with the lowest adjustment costs. Note that these are only indicative results as the costeffectiveness of the two measures is not directly comparable because both costs and effects on nitrogen use are different. Nevertheless, the results indicate that if the aim is a general reduction in fertilizer use, the tax seems to be clearly more cost effective than the land conversion scheme. This conclusion is supported strongly by the fact that the tax leads to a higher reduction in fertilizer use than the land conversion scheme as well as significantly lower costs per unit of reduced nitrogen fertilizer. Another observation is that cross-achievements between two policy components are identifiable. First, the reduction in nitrogen use is 4 000 kg less in the full scenario than the sum of each component (Table 1). This is because, before areas are converted to permanent grass and fallow, total nitrogen use has already been reduced due to the tax. Second, the costs of running the subsidy scheme fall from DKK/ha 9 086 to DKK/ha 8 979 if the tax is already in place (Table 2). These costsavings exist even if, as shown in Table 1, the tax revenue is lowered with more land under extensive cropping (i.e. lower fertilizer use). Results from partial analyses of the two policy measures can therefore not be added to find an estimated cost per kg reduced nitrogen use. This stresses the importance of integrated, geographical analyses of cross-achieving policy measures, especially as the cross-achievement issues most likely will get more complex if focus is changed from nitrogen use to the environmental effects. It is imperative to consider the geographical effects of nitrogen reduction in the full scenario. While the nitrogen tax component leads to a general reduction across the whole study area, the subsidy scheme targets the reduction on designated areas with low fertilizer rates on permanent grass and fallow. Provided that these areas are correctly identified as more important for nitrogen reduction than others, the environmental benefits
Economics and nitrogen use with fertilizer tax and land conversion subsidy (partial and combined)a
Total nitrogen use, 1000 kg N Loss of added value, DKKc 1000 tax revenue, DKKc 1000 Average costs, DKK/kg N
Basis
After 50% tax
4 216
459 5 222 5 046 11Ð4
After conversion of 80% of OSDb 428 35 052 81Ð8
After 50% tax and conversion of 80% of OSDb 884 34 637 4 456 39Ð2
Notes a Excludes farms with estimated negative gross margins in the basis. b OSDDareas designated for drinking water protection. c Loss of added valueDchanges in total gross margins for Agenda 2000 and tax scenarios net of fertilizer tax payments.
Cross-achievements between policies for drinking water protection Table 2. Area and subsidy costs of converting designated drinking water area to permanent grass or fallowa 80% of OSD converted from basis scenario Converted area, ha Total subsidy costs, DKK 1000 Average costs per ha, DKK/ha
Full scenario: 80% of OSD converted after 50% taxb,c
3 858
3 858
35 052
34 637
9 086
8 979
Notes a Excludes farms with estimated negative gross margins in the basis. b Includes a new round of elicitation after scenario for the 50% nitrogen tax. c OSDDareas designated for drinking water protection.
are likely to be higher with targeted subsidy scheme. Conversely, only a small part of the reduction in nitrogen use on the total 32 000 ha, as caused by the uniform measures, can be expected to have an effect on the 3 858 ha in OSD areas. Thus, uniform measures are not likely to be cost-effective for targeting specific geographical areas.
Discussion and conclusions Issues of policy design Analysing cross-achievements between uniform and targeted measures is only possible if spatial data are incorporated with the economic model. Results show that policy measures can have combined effects (cross-achievements), thereby affecting each other’s cost-effectiveness (see Vatn et al., 1997, for similar analyses). Firstly, as a positive cross-achievement, the necessary subsidy payments for land conversion are lower with an existing fertilizer tax thereby increasing the costeffectiveness of the subsidy scheme. Secondly, and as a negative cross-achievement, total costeffectiveness of nitrogen reduction after land conversion is lower when a tax is already in place. Thus, cross-achievements between a tax and a subsidy scheme based on elicitation are shown not to be additive, making partial analyses of policy measures more uncertain. Uniform measures, such as the 50% tax, require all farmers that use commercial nitrogen to adjust regardless of their geographical placement. Further analyses not included in this paper indicate that even though uniform measures lead to
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different adjustments between farm types, there appears to be no geographical pattern in their effects because there is little clustering of farm types in Bjerringbro and Hvorslev municipalities (see Rygnestad et al., 2000). In relation to this, it is expected that if the analysis had included a greater or different area, one might have found that a geographical approach contributed more information. For example, Schou et al. (2000) found significant differences in the marginal abatement costs of reducing nitrogen leaching in a case study of Vejle County. One reason for this is the clustering of different farm types in different areas of the county. Together with the results derived here, this indicates that even though the location of farm matters with respect to designing cost-effective policies, a more aggregated approach where policies are differentiated according to an environmentally based zoning may lead to a significant improvement. Similarly, modelling of different types of targeted policy measures, e.g. graduated subsidy schemes, and other indicators of their effects seem to be important in practical policy making. While this analysis is focused on a subsidy scheme based on elicitation, current schemes in Denmark, and most other countries, are based on fixed payments per ha. Such payments inadvertently lead to overcompensation of all participating farmers whose loss in gross margin is smaller than the subsidy. Thus, elicitation may attract a greater number of participants than a fixed payment system – given the same budget. This conclusion does not account for differences in administration and transaction costs, which may be higher under an elicitation scheme. See Badger (1999), Latacz-Lohman and Van der Hamsvoort (1998) and Falconer and Whitby (1999) for further discussion of these issues. Note also that the scheme requires a well functioning bidding process. Adequately managed cross-achievements between policy measures can also lead to overachievement of policy goals. For example, if the combined reduction in nitrogen use in Table 1 lies above the policy goal, one or more of the policy components should be redesigned. This is typically the situation if several parallel measures are linked closely to the same goal. While one linkage is illustrated for farms’ nitrogen use it could be useful to include indicators that deal explicitly with both negative and positive environmental effects from agriculture (e.g. negative environmental effects from agricultural production support; increased use of some plant protection chemicals due to restrictions on nitrogen use). Further to this, indicators could be aimed at incorporating
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geographical issues such as distance source, transport and accumulation of pollutants.
Issues of modelling framework The developed analytical framework combines an econometric agricultural sector model with geographical data on agricultural structures, soil types and area designations, and hence provides an opportunity for integrated economicenvironmental analysis related to the agricultural sector, taking into account various spatial aspects at a fairly disaggregated level. The framework is based on databases conforming to EU standards (FADN and GLR/CHR). The quality of the analysis relies on data availability and quality. As suggested by Caspersen and Kristensen (1999) the applied GLR/CHR data on land use and livestock numbers is the best available option particularly with regard to coverage as well as it being comparable to other EU countries. Other data limitations, as discussed in Rygnestad (2000), relate to how accurate the geo-referencing process is and whether it provides a complete picture of the area in question. The discussion also raises the importance of linking different databases consistently, particularly when data collection is infrequent and information is updated only occasionally. One methodological improvement could be to enhance the use of existing data at a spatial level. Beyond obtaining more detailed data than those which exist in the GLR/CHR and FADN databases, the actual geographical placement of fields can add further analytical powers, particularly in the case of targeted policy measures. A stochastic model to predict the placement of crops could also add knowledge to the spatial effect of policy instruments on environmental issues such as distance to source, transport and accumulation of pollutants. This would also be a logical progression if the focus changed to incorporate environmental effects and indicators such as nitrate loss to air, water and soil. Because the farms’ approximated gross margins are used to determine participation in the land conversion scheme, their levels are important to determine effects of each measure or scenario. While the approximation procedure minimises the sum of squared deviations of structure variables, there are discrepancies particularly in cases where a certain farm’s structure is an outlier compared to most farms in the database (see Rygnestad et al., 2000). The estimated gross margins are further dependent
on yearly changes in agricultural income and cost levels. In spite of these uncertainties, the results can indicate the type of farms that are likely to participate in the subsidy scheme. It is assumed in the analyses that only those parts of the farm situated in designated OSD areas are converted. Thus, their adjustment costs should be related to their marginal gross margin rather than the farm’s average. If the farm has heterogeneous land with low productivity in designated areas, the above cost estimates are too large. The method still provides an indication of which farms are most likely to participate in a subsidy scheme. Table 2 includes a maximum estimate for the costs of land conversion. Because fallow areas often are included in the farms’ rotation plans, the possible support obtained during elicitation (and thus costs incurred) is likely to vary from year to year. Calculating maximum costs includes support for all areas that make up the policy goal regardless of the initial situation. If minimum costs are estimated it must be assumed that existing permanent grass and fallow areas are situated in the designated areas or that no support is offered to relocate them. Whether a maximum or minimum approach is used will change the cost levels of the analysed policy while maintaining the original conclusions on participation and policy effects. In sum, by integrating an economic model in a spatial framework, it is possible to evaluate geographically targeted and uniform policy measures. More to the point, the combined effects can be assessed as has been illustrated with quantified cross-achievements. While this paper has focussed on drinking water protection, other agrienvironmental issues can be incorporated given appropriate modelling and indicators.
Acknowledgements The Danish Environmental Research Programme 1997– 2000 has funded the main parts of this study. Co-funding has been obtained from the research programme: ‘Agriculture and Rural Districts – Economy and Development’ funded by the Danish Ministry of Food, Agriculture and Fisheries.
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