Ecologically and economically efficient and sustainable use of agricultural landscapes

Landscapeand Urban Planning, 27 (1993) 237-248 Elsevier Science Publishers B.V., Amsterdam

237

Ecologically and economically efficient and sustainable use of agricultural landscapes

Rolf Werner Department ofAgricultural Economics, Hohenheim University, D-70593 Stuttgart, Germany

Abstract

Agricultural landscape use serves many purposes and many different people. For this reason, many public and private institutions are involved in the problem of optimal landscape use. In farms and rural family households basic decisions are taken on the intensity of agricultural landscape use. The decision-makers of these private institutions try to adapt optimally to the socio-economic framework. Public institutions play a key role in creating the socio-economic framework by policy-making. Thus, their members determine at least the use of agricultural landscapes and whether the use is ecologically and economically efficient and sustainable or not. To guide landscape use in an optimal direction, however, is a hard task for public decision-makers because nobody knows exactly what an ecologically and economically efficient and sustainable use of agricultural landscapes is. This problem results from unknown values, unknown interrelations between technology and nature, and unknown evolutionary processes in nature and in societies. Thus, there is a broad area for speculation about optimal landscape use. Such speculation, however, can be minimized by analysing options for action systematically. This is demonstrated in the case of an agricultural landscape in Southern Germany. In this case study, eight parts of a naturally homogeneous region are analysed by using multi-criteria valuation, linear programming and trade-off analyses. By combining these methods, the ecological and economical status quo of the uses of an agricultural landscape and possibilities for its improvement are evaluated to find out how the landscape can be used ecologically and economically in the most efficient way. Trade-offs representing a production frontier between the production of marketable and nonmarketable landscape goods are identified. With these findings, efficient agricultural policy options are revealed for the simultaneous achievement of ecological and economic efficiency and sustainability.

Introduction

To determine an optimal use of agricultural landscapes is a hard task. Many private and social interests have to be considered. Every landscape user puts his or her own values on the outcomes of landscape use and on the ef- forts which have to be undertaken to draw benefits from the landscape resources. As these values are subjectively based on individual attitudes and experiences they are difficult to capture by revealed preferences and are also difficult to express in an aggregated degree of benefit. This problem is known in economics as the debate of individualism versus collectiv- ism (see, e.g. Kiilp, 198 1 (p. 471); Randall, 1985). The evaluation problem, however, is

only one face of the Janus-headed problem which has to be overcome; the probiem of physical measurement also has to be solved. However, many interrelations between technology and nature are unknown despite much scientific progress in natural sciences as a whole and in ecology as a special subject.

The problem is outlined in Fig. 1. The black box represents the ecosystem where natural sciences face the problem of finding out which physical inputs will lead to which physical outputs. The surroundings of the black box represent the much bigger black box of the socio- economic system, within which social sciences and especially economics operate by using the method of apriorism (see, e.g. Blaug, 1980). Some economists use that approach as long as

0 1993 Elsevier Science Publishers B.V. All rights reserved 0169-2046/93/$06.00

238 R. M~erner / Landscape and Crban Plannmg 27 (1993) 237-248

Goals of Landscape Use

- Food and Raw Materials by /qriculture - Raw Materials by Forestry - Mining of Drinking Water - Renewal of Air Quality - Settlement and Recreation - Amenity - Biodiversity - Sustainability -

Outputs and Performance (AGRICULTURAL) Landscape

Inputs and Activities

Evaluation

Goods as Achievements of Goals

Resource + USC?

Income and Income Distribution

Welfare in Units of Utility 7

Fig. 1. The problem of measurement and evaluation in landscape use research.

individual attitudes toward values are not operated on scales of utility. Meanwhile, the neo- classical economists measure values on scales of cost-benefit analysis which express income values and changes of income distribution. This study follows the ‘beliefs’ of cost-benefit analysis only halfway, and combines several evaluation methods to maximize the transpar- ency of ecological and economic interrelations which change with the variation of observable landscape use and with policy-induced measures to increase the sustainability of landscape

use. On the basis of these revealed interrelations, conclusions will be drawn on the ecologically and economically efficient and sustainable use of agricultural landscapes and on how to achieve this by taking good decisions on environmental policies.

Methodological foundation of the landscape evaluation model

The landscape evaluation model of this study is founded on the methodological possibilities

R. Werner /Landscape and Urban Planning 27 (1993) 23 7-248 239

of multi-criteria evaluation, trade-off analyses and linear programming. Within the framework of linear programming, models of landscape uses are optimized to transform these uses from their present status into an optimal status. These states of landscape use are evaluated by using the methods of multi-criteria valuation and trade-off analyses. The optimization models and the valuation models inter- act over the following valuation steps of multi- criteria analyses.

Step 1

(a) First, identification of the objects to be evaluated is necessary. These objects are the landscapes, which are used in different ways by differently managed farm enterprises and by different infrastructural investments undertaken by farm enterprises, rural family households and public institutions which control, es- tablish and organize private and public property rights on land, conduct land consolidation schemes, are responsible for the public infrastructure of, for example, roads, railways and waterways, and supervise land use especially in environmentally sensitive areas such as water protection zones or wildlife habitats.

(b) Next, a goal function is set. In the goal function, the utility value of a landscape is maximized. The goal function splits into several objective functions. In the landscape models, one economic and four ecological objective functions are distinguished. In the economic objective function, farm income is maximized, whereas in the ecological objective functions biodiversity is maximized, and soil erosion, nitrogen surplus and pesticide use are minimized. The economic objective function represents the economic performance of landscape use from the farm enterprise’s point of view and, in the wider range of macroecon- omics, the provision of food and nonfood markets with agricultural raw materials. The ecological objectives represent the performance of landscape goods such as: landscape amenities;

recreational qualities; drinking, municipal, fish and other water from nonpolluted and noneu- trophic ground and surface water sources; clean air which is free of pesticide and ammoniac clouds, as well as of greenhouse and ozone-layer gases.

In the linear optimization models, the economic objective function is, considered as the goal function of the linear programming models, whereas the ecological objectives are represented by constraints where the standards are thresholds of objective achievements.

In the trade-off analyses, the achievement of the economic objective is put against the achievement of the ecological objectives. Thus, trade-off points are gained for each landscape and the trade-offs can be plotted in graphs with two dimensions. Comparing the trade-offs of the landscapes, the interrelations of objective achievement are revealed, especially if shifts from one landscape use to another are as- sumed. Under the assumption that these shifts can be conducted without transaction costs, the connection line through neighbouring trade-off points is to be interpreted as an economic transformation line. Trade-offs based on a systematic increase of the ecological constraints and standards in linear farm-income maximization models can be used to construct a transformation curve which is identical to a production frontier representing the maximal simultaneous transformation of natural landscape resources into ecological nonmarket goods and economic market goods. Finding this kind of production frontier will be halfway to a full utility-cost analysis. In addition to the production frontier, only tangential indifference curves of landscape consumers, i.e. the consumers of market and nonmarket goods, have to be found. As that seems to be impos- sible, one can try to find out whether the consumers of nonmarket goods are willing to pay enough to compensate farmers for their income losses which they have to take into account if they produce more of the nonmarketable landscape goods at the cost of the

240 R. Werner/Landscape and Urban Plannq 27 (1993) 23 7-248

marketable landscape goods. In doing this, a full cost-benefit analysis is carried out in which the production frontier is halfway.

Step 2

This consists of identification of the evaluation criteria, i.e. properties of the objects which can be empirically operationalized. The evaluation criterion for measuring the achievement of the economic objective by a certain landscape use is the farm income of land use by plant production in monetary units per hectare of agricultural land. To measure the achievement of the biodiversity objective, the density of species is identified as an opera- tional criterion. The density of species is ex- pressed by the number of wild plant species di- vided by the square root of that area in which these species are found. The area is identical to the size of the landscapes analysed and evaluated. Taking the square root of the area is in accordance with area species models which prove that the number of species is a degres- sive function of habitat size. Thus, this model assumption allows us to compare landscapes as habitats with different sizes. The erosion objective is operated by using either the criterion of the C-factor from the universal soil loss equation (USLE) model or by directly calculating soil erosion in tons per hectare of agricultural land on the basis of that model. The nitrogen surplus objective is simply measured by nitrogen balances on the landscape level where nitrogen surplus in kilograms per hectare of agricultural land is calculated. Under the assumption of a constant nitrogen stock in the topsoil of this land, the surplus of the farmer- induced variable nitrogen inputs over the farmer-induced variable nitrogen outputs with agricultural yields harvested is a measure of long-term nitrogen emissions from agricultural landscape use into the ecosphere. To measure the achievement of the pesticide use objective, the monetary value of pesticide inputs per hectare of agricultural area is identi-

fied as the best of the criteria which can be operationalized most effectively.

Step 3

This consists of measurement of the evaluation criteria. This is done simply by field study work in landscapes, which helps to specify the evaluation criteria and the models underlying, these criteria. The landscapes analysed have to be homogeneous in terms of the natural conditions of soils, climate and weather, and het- erogeneous in terms of agricultural use.

Step 4

In this step, the scales of measurement are transformed into scales of values. The scales of measurement cannot be compared directly with each other because they are based on different denominators. Transforming them into relative scales of index values makes them comparable. To obtain an index scale one has to express low objective values of measurement relative to high ones. This is done first by positioning the criteria specified for the different landscapes analysed on the objective dimension of their measurement scale, second by giving the lowest objective achievement the index value of zero and the highest objective achievement the index value of 100,and third by expressing all other objective achievements relative to the minimum and maximum objective achievements.

Step 5

This consists of aggregating the evaluated criteria to obtain one economic and one ecological index. As only one economic objective is considered, the index gained in Step 4 is directly the economic index. For the four ecological objectives, the indices gained in Step 4 are simply aggregated by calculating average index

R. Werner / Lmdxape and Urban Planning 2 7 (1993) 23 7-248 241

values from the four index values gained for each landscape. Instead of simple averages weighted simply against the number of obser- vations, other weights for aggregation can subjectively be determined. However, according to the Laplace criterion, it is not reasonable to calculate any other than a simply weighted average value.

Step 6

In this step, the weighted average values are transformed to aggregate them. This transformation is a mathematical one, following the same procedure as in Step 4. The transformation is not necessary for the aggregated economic values because in Step 5 they remain part of an index between zero and 100, but it is necessary to bring the weighted average values of the aggregated ecological indices into the standard and aggregate scale of relative index values between zero and 100. The results of this evaluation step are used in the graphical models of trade-off analyses. However, it is also possible to put each ecological index gained in Step 4 separately against the economic index. The problem of the models of trade-off analyses is that they are comprehensible only if the value dimensions of the objectives are put into graphs as pairs against each other. Therefore, it is worth while to construct the aggregated index of the ecological indices. Another advan- tage of the aggregated ecological value index is that its reliability and validity seems to be higher than that of each single ecological index because the average of systematic but unknown positive and negative measurement er- rors on the four ecological dimensions comes closer to the unknown exact real values of measurement and because there are good reasons for multicollinearity between the ecological dimensions so that the average index scale increases the validity of the ecological value dimension.

Step 7

Finally, the indices are aggregated to a value of utility. This evaluation step of multi-criteria analysis makes it necessary to set up values as weights for aggregation. Much less than in Step 5, where the evaluated ecological criteria are aggregated, no good reasons can be found to set up these weights. However, using trade-off analyses, aggregation is not necessary because these weights are represented by the shape of indifference curves which express the indifference of landscape consumers between more nonmarketable landscape goods and the compensation of farmers for their reduced farm income per hectare because of a reduced production of marketable landscape goods such as food and nonfood raw materials.

Empirical foundation of the landscape evaluation model

The empirical foundation of the model is located within Kraichgau, which is a relatively homogeneous region with a rolling landscape and a base of limestone under a thick layer of loamy soils. Kraichgau is located between the two crossing axes of Stuttgart-Mannheim and Karlsruhe-Heilbronn; it is east of the Rhine Valley, southwest of the Neckar Valley and north of the Black Forest in the southwest of Germany. Within Kraichgau, eight smaller landscapes have been selected to be analysed in detail. The agricultural use of these landscapes is described in Table 1. Landscapes K1 to KrII are located close together and near the villages of Gondelsheim and Obergrombach; Landscapes Kiv-Kv, are close together and near the villages of owisheim and Miinzesh- eim; Landscapes KvII and KvII, are also close together, and are near the villages of Adelsho- fen and Ittlingen.

The figures in Table 1 characterize the quality of agricultural land and the agricultural use of the landscapes. The agricultural land is to- tally arable and within and in the close sur-

242

Table 1 Landscapes analysed

R. U?erner /Landscape and &ban Plannmg 2 7 (1993) 237-248

Landscape

KI KI, Kill KI” K” K “I K “II K “Ill

Size (ha) 159.03 46.05 17.87 38.51 32.56 17.04 274.99 51.91 Land fertility rate (%) 66 67 68 68 65 70 69 75 Field size (ha) 5.39 1.48 0.99 1.28 0.50 0.52 2.90 0.96 Farm size (ha) 28.30 23.09 4.40 12.70 7.20 7.20 27.30 13.60 Livestock density (MU” ha-l ) 0.78 0.31 0.06 0.18 0.07 0.06 0.80 0.82

Croppingpattern Winter cereals (o/o) 51.78 35.24 29.36 Summer cereals (O/o) 2.34 17.78 14.24 Sugar beet (O/o) 28.12 1.52 1.40 Maize (O/o) 14.28 26.05 3.10 Others (o/o) 3.48 19.41 51.90

“MU, manure unit (one cow represents 1 MU; 1 MU = 80 kg N).

36.23 50.78 50.54 51.78 55.60 18.52 32.44 40.73 7.88 4.69 2.08 1.78 _ 25.33 3.08

23.77 5.64 1.46 9.00 27.73 19.40 9.36 7.27 6.01 8.90

roundings of these landscapes no land is used for forestry. The average land fertility rate var- ies over the landscapes from 65 to 75% so that the natural yield conditions are not signifi- cantly different. However, the structure of lots and plots, represented by the average field sizes, as well as the structure and the management of the farms which crop these lots and plots, represented by the average farm size, livestock density and cropping pattern, indi- cate substantive differences over the landscapes. Homogeneity in natural conditions and heterogeneity in land use and farm management fulfil the preconditions for the specifica- tion of the landscape evaluation model.

Empirically, the model is founded on very sophisticated surveys of farms, farm management activities on lots and plots, and of biodiversity and wildlife habitats within the landscapes during the years 1987, 1988 and 1989. Beyond this, surveys have been conducted to adjust and specify the universal soil loss equation of Wishmeier and Smith ( 1978 ) to the special natural and land use situations within the landscapes analysed (Zwischenbericht, 1989,1992).

Results of the landscape evaluation model

The results of the evaluation criteria quantified by measurement (Step 3 of the multi-criteria analyses) are shown in Table 2. Compar- ing the quantified criteria of the landscapes with the figures in Table 1 which characterize landscape use by agriculture, some correlations can easily be discovered. For example, it seems that the bigger the farms, the higher is land productivity in economic terms and the lower in ecological terms. However, this finding can be interpreted only from an under- standing of the driving forces of structural change in the agricultural sector.

There are good reasons to explain that areas which were restructured by land consolidation schemes show a tendency to the intensification of agricultural landscape use as well as changes in the farm size structure (see, e.g. in Table 1, average field size in combination with average farm size), and more generally that farms managed by modern, well-trained, dynamic and entrepreneurial farmers prospered economically at the cost of nature whereas farms managed by more traditional, romantic, and more indigenous farmers flourished ecologi-

R. Werner /Landscape and Urban Planmng 27 (1993) 237-248

Table 2 Evaluation criteria quantified by measurement

243

Landscape Evaluation criterion

Density of species (no. (ha)-1)

C-Factor of the USLE

Nitrogen surplus (kg N ha-’ LFa)

Pesticide use (DM ha-’ LFa)

Farm income of land use by plant production (DM ha-’ LFLF’)

KI 14.7 0.15295 +71.75 330.10 2475.0 K,, 30.2 0.16829 +20.01 156.87 1357.4 Km 45.8 0.13006 + 11.23 154.21 717.5 K*, 20.4 0.15746 +28.92 141.48 1312.2 K” 42.0 0.10766 +21.07 122.32 773.5 KW 57.0 0.09099 +29.31 170.39 978.4 K “II 11.9 0.14381 + 74.35 285.47 2419.8 K “III 16.2 0.16102 +96.91 237.14 1572.1

“LF, ‘landwirtschaftlich genutite Flbche’, i.e. agricultural land.

tally but not economically. As long as these relatively static farms survive economically, they seem to have a high production of nonmarketable landscape goods but a low production of marketable and income-generating goods. However, once these farms are given up for the sake of economically prospering farms, the structure of marketable and nonmarketable landscape goods is shifted to that of the more modern, larger and viable farms. So there are good arguments to suggest that structural change comes from economic reasons, through the selection of farms in response to the good economic behaviour of the farmers according to the economic incentives created on markets and by politics but not according to landscape goods, which are ignored by markets and policy-makers.

However, despite such experiences and economic hypotheses, farm size alone in terms of area of cultivated land is not a good indicator of the structure of economic and ecological productivity rates because small farms in terms of land can be big farms in terms of income capacity, which depends, besides land, on livestock density, cropping patterns (especially if vegetable crops or such like are grown), mar- keting systems, production systems (conven-

tional or organic) as well as on full-time or part- time farm management. Thus, it is clear that any farm size figure alone, whatever criterion of size is used, cannot be a good indicator of the structure of economical and ecological productivity rates. Thus, only the empirically gained bundle of evaluation criteria shown in Table 2 is a good measure of the economic and ecological productivity of agricultural landscape use.

The quantified evaluation criteria are transformed according to Step 4 of the multi-criteria analyses into scales of values. Table 3 shows the results of this operation. Comparing the relative values achieved in the landscapes, some more evidence is gained in respect of the correlations between the achievements of the economic objective and those of the ecological objectives. In general, the economic productivity of agricultural landscape use is nega- tively correlated to the ecological productivity of agricultural landscape use. The higher the farm income of land use by plant production, the lower the achievement of the ecological objectives. Thus, the economic rationality of the farm selection argument worked very well according to the economic incentives of markets and politics in the past.

244

Table 3 Evaluation criteria transformed into scales of values

R. Werner /Landscape and Urban Plannmg 2 7 (1993) 23 7-248

Landscape Evaluation criterion

Density of C-Factor of Nitrogen Pesticide use Farm income of species the USLE surplus (DM ha-’ LF) land use by (no. (ha)-i) (kgNha-‘LF) plant production

(DM ha-’ LF)

KI 6.21 19.86 29.36 0.00 100.00 Kn 40.57 0.00 89.75 83.38 36.41 Kill 75.17 49.47 100.00 84.65 0.00 KW 18.84 14.02 79.34 90.78 33.84 K” 66.74 78.45 88.52 100.00 3.19 KW 100.00 100.00 78.91 76.87 14.84 K “II 0.00 31.68 26.33 21.48 96.86 KW, 9.53 9.41 0.00 44.74 48.63

This correlation becomes most evident if the values of the ecological objectives are aggregated and brought into index values which are directly comparable with the index values of farm income. The results of these operations according to Steps 5 and 6 of the multi-criteria analyses can be seen in Fig. 2, where the trade- offs of the existing landscape uses are shown in comparison with landscape uses optimized within the framework of linear programming models.

The trade-offs of the existing landscape uses are easily connected to the inner transformation line. Two optimized models of landscapes also belong to that transformation line. These model landscapes were gained by optimizing landscape use within the framework of a linear optimization model where the existing landscape uses were taken as activities which could be combined according to the maximization of farm income and to the setting of standards for some ecological objectives in the constraints. In one model landscape, a maximum of 23 kg of farmer-induced variable nitrogen surplus per hectare of agricultural land use was set up as a standard, and in the other model landscape a standard of 30 species per square root of hectare was set up in addition to that standard.

The results of these optimizing model calculations make it clear that on the basis of the

experiences gained with the analyses of the existing landscape uses, no significant improve- ments of trade-off positions are possible. Therefore, incentive and command-and-order policies were analysed on the basis of many results gained with special linear farm optimization models on the impacts of best policies on farm income and ecological performance. The results of these analyses were used as landscape-specific activities in a second landscape use optimization model with which the trade- off positions close to the outer transformation line were gained. That transformation line is more or less identical to the production frontier of marketable and nonmarketable landscape goods by making best use of the natural landscape resources.

The production frontier forms the following trade-off positions for the various landscapes studied:

Landscape Ki: This landscape had the highest farm income but lowest ecological performance.

Landscape Ki + : Landscape Ki plus soil conservation measures to avoid excessive soil erosion. These measures are minimum tillage systems in combination with intermediate cropping to minimize periods of open soil. Ac- cording to the findings of Schach ( 1986; cited by Werner 1989, p. 190) these measures can

R. Werner/Landscape and Urban Plannmg 27 (1993) 237-248 245

Index of the Ecological Objectives

K .I.1 **.. “I Ku K;“’

I II I /I K ..** “II + .*** K K;;;;

IV \

K o .’ K

80

60

0 20 40 60 80 100 120

index of the Economical Objective

718

522 Farm income 2475 DM/ha

Fig. 2. Trade-offs of the existing and the optimized landscape uses.

be designed with no impact on farm income. They have to be enforced on farms by the implementation of persuasive policies such as ex- tension programmes and the establishment of special advisory boards combined with some gentle and sensitive command-and-order policies such as the prescription that leaving soils open on erosive sites for a long period requires the permission of a soil conservation board.

Landscape Kr+ + : Landscape Kr+ plus nitrogen policies to avoid excessive nitrogen sur- pluses. These policies are designed to create the most efficient reduction of nitrogen surplus relative to farm income loss-a levy quota sys-

tem for purchased mineral nitrogen fertilizers is established, the use of organic manure from livestock keeping is taken into account in the quota system, organic manuring is restricted to maximum levels, and the greening of crop ro- tation is prescribed (see Krayl ( 1988 ), cited by Werner ( 1989, p. 202), and the latest findings on the optimal shaping of such policies, by Krayl ( 1993) ). The levy quota system consists of a very high tax on purchased mineral nitrogen fertilizers and the right of the farmers to obtain a tax reimbursement for a farm-specific quota of purchased mineral nitrogen fertilizers. The nitrogen tax should be taken di-

246 R. Werner /Landscape and I’rhan Plannmg 2 7 (1993) 237-248

rectly from the synthesizers and importers of nitrogen fertilizers. The farm-specific nitrogen quota is to be determined according to land resources and the numbers of livestock on the farms. The quota system is to be designed in such a way that the replacement of mineral nitrogen by organic nitrogen from livestock is fa- voured. This gives, on the one hand, an incentive to farms with low livestock density to obtain organic manure from farms with a high livestock density although they meanwhile lose quota rights for tax reimbursement. On the other hand, farms with a high livestock density have the chance to get rid of excess manure.

Landscape KvII + + -t : This is the landscape with highest farm income if, in addition to soil conservation measures and nitrogen policies, a network of wildlife habitats is also main- tained. These networks are optimally designed by landscape planning: the natural potential of agricultural landscapes is maximally used through biodiversity, considering also the physical preservation of a landscape by measures which serve water and erosion management; field size structure allows low-cost production of food and nonfood raw materials by agriculture; the land losses of the farms in fa- vour of habitat implementation are minimized. The implementation of such networks is only possible by command-and-order and compensation policies. The amount of compensation payments is minimized if the acceptance of such network policies by the farmers is maximized by persuasive policies which in- form the farmers about real income losses which farmers obtain if they optimally adopt their farm organizations to the changes necessary. These persuasive policies have to con- vince the farmers to love not only their crop- land but also the rest of the landscapes in their homeland. The real income losses by such networks were estimated specifically for each landscape analysed according to the findings of farm optimization models summarized by Werner (1989, p. 223).

Landscape Kv,, + + + + : Landscape Kvrr

+ + + plus pesticide policies which restrict the use of environmentally harmful pesticides. As it is very hard to declare one pesticide to be more harmful than another, it is simply as- sumed that the pesticide policy costs a net farm income of DM 100 in Landscape Kvir to reach the same achievement level for the pesticide- use objective as in the best landscape, which is K,. For the remaining landscapes, the income effects are differentiated in relation to the monetary pesticide uses in Landscape Kvu and in Landscape Kv, and according to the levels of pesticide use in these remaining landscapes. However, before these calculations were conducted, it was considered that the nitrogen policies reduce pesticide use. Thus, the landscape- specific consideration of pesticide policies is conceded without the need for a too-detailed scientific study of the good and bad effects of pesticides. That remains the task of politi- cians, who have to decide, with the help of specialized scientists, which pesticides should be taken out of use and which not. Taxing pesticides does not seem to be an effective pesticide-use reduction policy because taxes cannot be differentiated efficiently according to tox- icity, and because the prohibition of the harmful pesticides will induce the development of new but less harmful pesticides, which must be paid for by the still saleable pesticides so that the prices of these pesticides at least increase.

Landscape Ki + + + + : remaining landscapes with soil conservation measures, nitrogen policies, a network of wildlife habitats, and pesticide policies.

Summary and conclusions

The preservation of agricultural landscapes costs farm income. Income losses are, however, rather low in relation to environmental gains if landscape preservation policies are designed optimally by setting up the socio-economic framework in such a way that the decision-makers in farms and rural family households make their decisions not only to

R. Werner /Landscape and Urban Planning 2 7 (1993) 237-248 247

maximize income by the production of marketable food and nonfood landscape goods but also in terms of the production and consump- tion of nonmarketable landscape goods such as biodiversity, soil conservation, clean air and clean water, as well as landscape amenities gained as a result of these goods. As the analysis of optimally designed policy measures has shown, policies involving persuasions, incentives, commands-and-orders and compensation have to be combined in a differentiated way to achieve the required ecological objectives precisely enough while income losses remain compensatory and policy measures manageable.

Compensation policies have not yet been ex- tensively discussed. One very efficient approach seems to be active farm-gate price policies, as Dubgaard argued ( 1992, p. 68) and as Krayl calculated ( 1992, p. 150). These policies do not need as much bureaucracy as direct income compensation payments and, in combination with input taxes, they give the farmers the right incentives to earn their income from the market while reducing the intensity of input use. Later, the consumers pay for landscape preservation not mainly as taxpay- ers but as the consumers of the marketable product of landscape use.

Surveys of environmental economists indi- cate that in industrialized countries consumers are willing to pay more for the environmental gains of nonmarketable landscape goods than farmers lose in income by the reduced production of marketable landscape goods (see, e.g. Drake, 1989, p. 5; Pearce and Markandya, 1989; Willis, 1990, p. 13; Navrud, 1991). Therefore, the production frontier investi- gated is to be tangential to indifference curves for high levels of ecological performance. To achieve such trade-off positions the necessary policies which have been analysed and discussed should be implemented. However, so far, agricultural and environmental policies have not taken these lindings into account on a broad perspective. The reason for this igno-

rance, besides the lack of acceptance on the farmers’ side, is the political economists’ san- guine belief in the benefits of free trade. Low- cost producers who do not consider the disas- trous outcomes of market production on nature and local landscape consumers are, however, much more competitive in food and nonfood markets than environmentally ideal- istic farmers, regional governments, nations and trade blocks. Therefore, at least for the agricultural products of landscape use, the implementation of new trade and market rules is urgently needed so that an efficient shaping of market and land use policies on the regional level of nations, or at least of trade blocks, can be designed to induce an efficient combined production of marketable food and nonfood agricultural raw materials as well as of nonmarketable landscape goods.

Acknowledgements

This paper draws heavily on research work conducted together with Dipl.-Ing agr. Wolf- gang Assfalg. A comprehensive documenta- tion of that work has been given in Assfalg and Werner ( 1992). In this paper some further literature can be found. A helpful source of un- derstanding and of some more literature is Werner (1989). All this work has been conducted within and in the context of the inter- disciplinary research project of the Sonderfor- schungsbereich 183 (SFB 183 ) which is heavily funded by the German Research Foun- dation (DFG), and within which many scientists are working together on the general topic of environmentally sound landscape use by agriculture. In particular, the results presented here would not have been brought to a success- ful end if much data collection and other basic research work had not been done by many workers. Thus, not only do I have to thank Wolfgang Assfalg for his help but also many unnamed colleagues as well as the German Re- search Foundation.

248 R. Werner/Landscape and Urban Planning 27 (1993) 237-248

References

Assfalg, W. and Werner, R., 1992. Die optimale Nutzung von Agrarlandschaften. Ber. Landwirtsch., 70(3): 358-386.

Blaug, M., 1980. The Methodology of Economics or How Economists Explain New York, Cambridge University Press, pp. xxxviii, 286.

Drake, L., 1989. Swedish agriculture at a turning point. In: Smaskriftsserien nr. 17, Alternativa produktionsformer i jordbruket-ekonomi. Institutionen fdr ekonomi, p. 17.

Dubgaard, A., 1992. Moglichkeiten und Grenzen von Gko- ko-Steuern in der Landwirtschaft. In: oko-ko-Steuem als Ausweg aus der Agrarkrise, Ergebnisse der intemationa- len Tagung vom 15. bis 17. Juni 1992 in Stuttgart-Hoh- enheim. Schriftenreihe fnr landliche Sozialfragen, Heft S115. Agrarsozialen Gesellschaft e.V., Gottingen, pp. 66- 80.

Krayl, E., 1988. Modellierung des Stickstoffkreislaufs zur Er- mittlung optimaler Grundwasserschutzstrategien in land- wirtschaftlichen Betrieben. Diplomarbeit, Stuttgart- Hohenheim.

Krayl, E., 1992. Einkommenswirkungen stickstoffbegrenzen- der MaBnahmen. In: Gko-ko-Steuern als Ausweg aus der Agrarkrise, Ergebnisse der internationalen Tagung vom 15. bis 17. Juni 1992 in Stuttgart-Hohenheim. Schriftenreihe fiir llndliche Sozialfragen, Heft S 115, Agrarsozialen Ge- sellschaft e.V., Gottingen, pp. 150- 160.

Krayl, E., 1993. Strategien zur Verminderung der Stick- stoffverluste aus der Landwirtschaft, In: Landwirtschaft und Umwelt, Schriften zur Umweltbkonomik, Band 8, Vank Kiel, pp. X, 247.

Kiilp, B., 1981. Wohlfahrtsokonomik I: Grundlagen. In: W. Albers et al. (Editors), Handworterbuch der Wirtschafts-

wissenschaft, Band 9, Vandenhoeck & Ruprecht, Gottin- gen, pp. 469-486.

Navrud, S., 1991. The use of benefit estimates in environmental decision-making in Norway. In: J.-P. Barde and D.W. Pearce (Editors), Valuing the Environment: Six Case Studies. Earthscan, London.

Pearce, D.W. and Markandya, A., 1989. Environmental Pol- icy Benefits: Monetary Valuation. OECD, Paris.

Randall, A., 1985. Methodology, Ideology and the Econom- ics of Policy: Why Resource Economists Disagree. Am. J. Agric. Econ., 67(5): 1022-1029.

Schach, P., 1986. Auswirkungen von Erosionsschutz- mal)nahmen auf Organisation und Einkommen landwirt- schaftlicher Betriebe. Diplomarbeit, Stuttgart- Hohenheim.

Werner, R., 1989. Methoden und Modelle zur Optimierung der Intensitlt der Landschaftsnutzung durch Landwirt- schaft und erste Ergebnisse. In: Landwirtschaft und Um- welt, Schriften zur Umweltokonomik, Band 4, Vank Kiel, p. xiv.

Willis, K.G., 1990. Valuing non-market wildlife commodi- ties: an evaluation and comparison of benefits and costs. Appl. Econ., 22( 1): 13-30.

Wishmeier, W.H. and Smith, D.D., 1978. Predicting rainfall erosion losses. US Dep. Agric. Agric. Handb. 537.

Zwischenbericht, 1989. Arbeits- und Ergebnisbericht zum Sonderforschungsbereich 183 “Umweltgerechte Nutzung von Agrarlandschaften” an der Universitat Hohenheim, Zwischenbericht 1987- 1989, Stuttgart-Hohenheim, 669 PP.

Zwischenbericht, 1992. Arbeits- und Ergebnisbericht zum Sonderforschungsbereich 183 “Umweltgerechte Nutzung von Agrarlandschaften” an der Universitlt Hohenheim, Zwischenbericht 1990-1992, Stuttgart-Hohenheim, 733 PP.

Documents

Ecologically and economically efficient and sustainable use of agricultural landscapes