Report from the Sub-committee on Production, Economics and Employment.

4. The problem and the choice of method of analysis

4.1  Introduction
4.2  Economic analyses - experience
4.3  Method of analysis – regulation of pesticide usage in agriculture
4.3.1  Crop rotation analyses
4.3.2  Economic analyses at farm level
4.3.3  Sectoral and socioeconomic analyses
4.4  Evaluation of the concept of analysis

4.1 Introduction

The sub-committee’s mandate

According to its mandate, the sub-committee was to assess the consequences for production, economics and employment of phasing out the use of pesticides, taking into account the work on alternative methods of preventing and controlling fungal diseases, pests and weeds. Accordingly, it was to examine:

1. the consequences of the different cultivation systems for agricultural production and earnings, including the cost to the agricultural sector of restructuring production
2. the economic parameters relating to the environment, such as costs for cleaning up drinking water and soil
3. the economic consequences for upstream and downstream industries, such as dairies, abattoirs, the chemical industry and producers of alternative agents and methods
4. the economic consequences for the consumers.

In this connection, the sub-committee was to identify any areas in which a partial or total phase-out would cause particular problems and suggest how these problems could be solved, e.g. through research and development. In its assessment of the consequences for employment, the sub-committee was to include the impact on employment both in agriculture itself and in the upstream and downstream industries.

The task is centred on phasing out the use of pesticides

It is important to note that it was stated in the mandate that the analysis was to centre on phasing out the use of pesticides in agriculture. In other words, the task did not include making proposals for a societally optimal solution, which, as described in chapter 3, would require weighing environmental considerations against economic considerations.

Cleaning-up drinking water and soil

It was also stated that the costs of cleaning up drinking water and soil were to be included in the analysis. To obtain a complete picture, the sub-committee has found it important also to assess the possibilities of casting light on the economic consequences of health risks and societal benefits in the form of greater biodiversity from reduced use of pesticides.

Valuation of environmental goods required for complete analysis

It is a condition for this that losses or benefits from a better environment can be valued. As mentioned in chapter 3, there are, in principle, two approaches to such an analysis: the preference-based method, in which the value of better environment is valued directly (e.g. through interviews) or the non-preference-based method, in which the costs of repairing environmental damage are taken as a measure of the societal benefits of avoiding damage from pesticides.

Depending on whether one uses one approach or the other, the problem can be analysed on the basis of either a cost-benefit consideration (the value of avoiding damage to health and the environment) or a cost-efficiency consideration (the cost of repairing damage to health and the environment) is compared with the cost of reducing the use of pesticides in agriculture.

Using the following notation:

M_benefit = Environmental and health benefits from reducing the use of pesticides

M_cost = Cost of repairing environmental and health damage

P_cost = Cost of reducing the use of pesticides in agriculture,

the problem can be described by the following models:

Cost-benefit analysis

Model 1: M_benefit > P_cost (cost-benefit analysis)

where the benefits must be greater than the cost for the chosen reduction in pesticide usage to be socioeconomically justifiable.

Cost-efficiency analysis

Model 2: Min{M_cost; P_cost       } (cost-efficiency analysis),

which shows a comparison of the cost of repairing environmental damage and the cost of reducing pesticide usage in agriculture with a view to indicating the cheapest solution – for example, treatment of drinking water to remove pesticide residue compared with restrictions on the use of pesticides in agriculture.

Societal optimality lies outside the scope of the analysis

As described below, it is not possible to give a complete picture of the environmental and health benefits of reduced pesticide usage. The analysis is therefore concentrated on identifying the sectoral and societal costs of reducing the use of pesticides in agriculture. These can then be compared with analyses on the environmental side. As mentioned, the analyses must be based on phasing out pesticides. In other words, assessing the societally optimal solution, which corresponds to finding Max(M_benefit – P_cost), lies outside the scope of the analysis.

In the following, we first discuss existing economic analyses in the pesticide area and then, in section 4.3, the choice of methods in analyses of reduction in pesticide consumption in agriculture. This is followed in section 4.4 by an evaluation of the usefulness of the concept of analysis.

4.2 Economic analyses - experience

Research on regulation of pesticide usage – a relatively new discipline

Research on regulation of pesticide usage shows clear signs of being a relatively new discipline that has developed in the wake of the last few decades’ discussion concerning the dangers of pesticides. There are very few actual analyses in this field, probably due in part to analytical problems. Pesticides comprise several different categories of agent (herbicides, fungicides, insecticides, etc.), and within these categories there is in turn a wide range of agents, e.g. agents developed for specific crops. Furthermore, the fact that pesticides are not means of production in the normal sense of the word, but are in the nature of treatment, means that it is difficult to determine the effect of graduated treatment, which has to be known in order to determine the economically optimal use.

In a study of the literature on economic analyses of pesticide usage, Christensen & Schou (1998) write that the core of the problem lies in determining the agricultural sector’s demand for pesticides under different price assumptions, where precisely the multiplicity of agents is a problem. They also write that the selection and handling of the factors that are important for describing pesticide usage depend on the model approach and the degree of aggregation in the analysis.

In the review, we differentiate between:

a. damage threshold models
b. econometric models
c. general equilibrium models and
d. mathematical programming models

Re a

Damage threshold models

A damage threshold model is based on the fact that there is a minimum population of pests that it pays to combat with a predetermined dosing of pesticides. Below this threshold, the rational producer will refrain from taking preventive action, whereas he will take action if the number of pests exceeds the damage threshold. Damage threshold models have primarily been developed for use at field and farm level, where it is possible to define damage thresholds for different pests in different crops at a detailed level. There are, however, examples of damage threshold models being used for analysing the aggregated effects of taxes on pesticide usage.

For example, Dubgaard (1987) analysed the effect of taxes on pesticide usage at DKK 100 and DKK 200 per standard dose. At the time of the analysis, a tax of DKK 100 corresponded to an average price increase of 60 per cent, which resulted in a fall in demand of 20-25 per cent. It was assumed in the analysis that the price would lead to a technologically determined fall in pesticide usage of 15 per cent. Correspondingly, a tax of DKK 200 would result in an average price increase of 120 per cent and a 40-45 per cent fall in total pesticide demand, and it was estimated that the land rent on good land would fall by 15 per cent. The last-mentioned scenario was based on an assumption of a technologically induced fall of 25 per cent in pesticide demand.

Rude (1992) also worked with an aggregated threshold model to determine the optimal need for treatment from a farm economy point of view. With 1990 as the base year, he carried out scenario analyses for the development up to 1995 and 2004 and the use of pesticide taxes of DKK 100 and DKK 200 per standard dose. The projection is based on a linear programming model (Stryg et al., 1991), which implies adjusting the crop composition. It is assumed that technological development will result in an efficiency improvement in the use of pesticides of 1 per cent per year and a tax-induced technological reduction in pesticide consumption of 10 and 25 per cent, respectively, at the two tax levels. Depending on the time horizon, the result is a 13-19 per cent reduction in pesticide usage at a tax rate of DKK 100 and a 20-28 per cent reduction at a tax rate of DKK 200.

There is thus some variation in the results arrived at, depending on the assumptions used in the analyses.

Re b

Econometric models

In econometric models, the demand for pesticides is estimated on the basis of historical data for production and factor input. The aim here is particularly to determine the price sensitivity of the pesticide treatment with a view to assessing the effect of taxes. In the early studies, pesticides were treated as factor input in line with other means of production. This resulted in high marginal product values, which could be taken to mean that it was economically advantageous to increase the use of pesticides. However, later analyses have made it clear that pesticides must be treated as pest control that increases the realisable part of the potential production and that there will typically be an either/or situation in the treatment, cf. the above discussion of damage thresholds. Christensen & Schou (1998) describe a number of analyses based on econometric models, some of which will be discussed here.

Using an econometric model estimated on the basis of aggregated Danish data for the period 1971-85, Dubgaard (1987), found that the own price elasticities for herbicides and fungicides/insecticides were –0.8 and –0.69, which are considered high. A similar Swedish analysis based on aggregated data for the period 1948-89 shows own price elasticities of –0.93 for herbicides, -0.39 for fungicides and –0.52 for insecticides, which are also considered high (Gren, 1994).

A Dutch study of the effect of price on pesticide consumption in arable farming showed own price elasticities of –0.5 for potatoes and onions and –0.4 for cereal products (Oskam, 1992), and it is stated (Oskam & Wijftigschild, 1992) that most estimates of pesticide consumption in Dutch arable farming seem to lie in the interval –0.5 to –0.2.

On the basis of data for the years 1979-88, Oude Lansink (1997) arrives at an own price elasticity of –0.12 for pesticides in Dutch arable farming. The analysis also includes the interaction between pesticide consumption and input of land, manpower, capital and nitrogenous fertiliser. The author arrives at an intensity elasticity of 0.78 for land, compared with 0.08 for manpower and 0.14 for capital. The cross price elasticity for nitrogenous fertiliser is estimated to be –0.02, which indicates that nitrogen and pesticides are complementary. In a later analysis based on farm data (Ouda Lansink & Peerlings, 1997) an own price elasticity of -0.48 is found for pesticides and a cross price elasticity of 0.02 for nitrogenous fertiliser, which, contrary to the foregoing analysis, indicates that nitrogen and pesticides are substitutes. The results underline the importance of differentiating between analyses at farm level and analyses at crop level.

Re c

General equilibrium models

Unlike the foregoing models, which are based on partial analyses of the agricultural sector or individual farms, general equilibrium models (GE models) cover the whole economy. The basis for the models is an input-output table for the entire economy, in which agriculture can be divided into several production sectors. GE models thus enable analysis of the interaction between agriculture and other sectors in the form of adjustment of prices, while sector models typically take the prices as given. However, there are also intermediate forms in which the market adjustment is incorporated in the sector models.

The simplest models are based on an assumption that all markets are in equilibrium and that adjustments occur under completely flexible wage and price adjustment. In practice, there will often be institutional and other constraints on the adjustment, which means that the results of the models can at best be read as very long-term adjustment (e.g. 30 years). Adjustment costs are thus not in themselves included in the models’ results. Even though general equilibrium models enable numerical solutions instead of analytical ones, the results found usually represent consistent calculations and not actual prognoses.

The advantage of general equilibrium models is that the individual sectors can adjust to changes in the framework conditions (for example, in the form of a reduction in pesticide usage), so one can see the derivative effects on the entire economy of intervention in an individual sector. However, it must be regarded as a drawback that the models usually work at an aggregated level and that the results depend on, among other things, the permitted possibilities for substitution and the specific choice of substitution elasticities. In general equilibrium models, the technology is usually assumed to be given, which means that the possibility of new technology being developed is not described within the traditional framework of the models.

Christensen & Schou (1998) report some analyses in which general equilibrium models are used to elucidate the pesticide problem. On the basis of German data from 1987/88, Brockmeier et al. (1993) and Brockmeier et al. (1994) used a GE model to analyse the effect on the economy of a reduction in the consumption of chemical products (including fertiliser, pesticides, livestock medicine and other chemical inputs in the agricultural sector). The analyses show that a 95 per cent reduction in chemical inputs would reduce agricultural production by 35 per cent. The model does not include a separate description of pesticides and is also considered too rough to give a reliable estimate of the effect of the total chemical input.

Komen et al. (1997), using Dutch data for the base year 1990, have developed a GE model with a detailed description of agriculture and the agricultural industry, four sectors of which relate to pesticide usage. The model is used to describe the effect of a 100 per cent tax on pesticides with different factor mobility. With low factor mobility, a 100 per cent tax on pesticides is found to reduce pesticide consumption by 11-14 per cent in arable farming, market gardening and the service sector connected with agriculture. Production in arable farming and the chemical industry falls by just under 4 per cent, while production in market gardening and the agricultural service sector falls by 1-2 per cent. In the scenario with high factor mobility, a 100 per cent tax on pesticides results in a 25 per cent fall in pesticide consumption and a 13 per cent fall in the chemical industry’s production. Christensen & Schou (1998) state that, even if pesticides are included as a separate product category in the analysis, the degree of detailing of the input factors is considered insufficient for an adequate description of the interaction between changes in the pattern of production and pesticide consumption.

Re d

Mathematical programming models

More general mathematical programming models can be used to analyse the demand for pesticides. As an example, Sundell (1980) analysed the consequences for agriculture of separately phasing out herbicides, fungicides and insecticides and a complete phase-out of all pesticides. Rude (1992) also used a linear programming model to describe structural changes. However, the method does not seem to be very widely used for describing pesticide demand except in the case of analyses of pesticide usage in different, predetermined crop rotations. Christensen & Schou (1998) report a multicriteria analysis by Lakshminarayan et al. (1995), in which an attempt is made to optimise with respect to both economic and environmental partial goals. It is stated in this connection that there can be conflicts between economic and environmental partial goals and also between different environmental goals.

All in all, it must be concluded that the results arrived at are encumbered with considerable uncertainty and that there are only a few examples of analyses of a total phase-out of pesticides. In most of the analyses, the demand for pesticides has been analysed and used as a basis for estimating the effect of taxes on pesticide usage or the focus has been on the adjustment in agriculture with different assumptions concerning reduction of pesticide consumption. It is difficult to find examples in which the interaction between agriculture and the rest of the economy is analysed in sufficient detail to determine the consequences of phasing out pesticides for agriculture, other sectors and the economy as a whole.

4.3 Method of analysis – regulation of pesticide usage in agriculturev

The analyses are based on economic models...
... that throw light on the consequences for both the sector and society

Solution of the task must be based on economic models that can describe the behaviour of agricultural producers with different assumptions concerning the production resources available to them, applied technology, market conditions, and established political frameworks. In the last mentioned, importance is attached particularly to limiting the sector’s use of pesticides. For solution of the task, it is also important to analyse the consequences of phasing out pesticides for both primary agriculture and the upstream and downstream industries, i.e. a solution must be sought in which the economic consequences for both the agricultural sector and society are clarified. This accords with the discussion in the previous chapter, where it was stressed that the assessment of environment policy must be based on a societal assessment of the economic and environmental consequences.

However, the type of analysis used also depends on the regulatory scenarios that are to be analysed. It is not possible to design economic models that can be used for any kind of analysis. The models must be designed for the specific problems and levels that are to be addressed in the analysis.

The following scenarios have been analysed:¹⁵

Choice of scenarios

The 0-scenario
No use of pesticides.

The 0+scenario

The use of pesticides is permitted for crops that cannot otherwise meet specific purity requirements or described requirements for combating quarantine pests, cf. the Danish Plant Directorate’s executive orders.

The +scenario

The use of pesticides is permitted for crops in which there will otherwise be a big yield loss or where it is estimated that viable production cannot be maintained without pesticides. For acceptance, there must be a considerable yield loss (more than 15-20 per cent) or the production must be encumbered with such uncertainty that producers must be expected to discontinue it or be unable to fit it into the crop rotation.

The ++scenario

The basic assumption in this scenario is that producers will not suffer serious financial losses due to pests, compared with present production. It is assumed that all available damage thresholds and harrowing are used where these methods can compete with chemical prevention and control.

Free scenario

Model-calibrated present production in the farm-level analyses. This corresponds to present production but with optimised use of land and pesticides, cf. the analyses in the Farm-level Pesticide Model (FPM). In the farm-level analyses, the effect of the other scenarios is measured in relation to the free scenario.

The basis for the analyses is a set of models that have been combined into a complete concept of analysis. The general structure of the concept is shown in Figure 4.1, from which it will be seen that the analyses have three components:

Combining analyses at crop-rotation level, farm level and sectoral and socioeconomic level

1. crop rotation analyses to determine the technical and biological relationships in arable farming if pesticides are phased out,
2. farm-level economic analyses, and
3. sectoral and socioeconomic analyses.

Tying the three components together as shown in the figure ensures coherence between the analyses at the different levels, in that the crop rotation models are used as the basis for the analyses at farm level, which then form the basis for the socioeconomic analyses. It is thus possible to provide a consistent description of the consequences of phasing out pesticides at the different levels.

Concept of analysis

Figur 4.1
Outline of concept of analysis

Analyses at different levels have different foci....

As described below, the focus of the analyses at the different levels will differ. The crop rotation analyses include estimates of the agronomically feasible crop production with pesticides phased out, while the farm-level analyses focus on land use and the economic effects at farm level of economically optimal crop production with unchanged livestock production and given price conditions. The sectoral and socioeconomic analyses, on the other hand, show the result for the entire sector and for the entire economy with full adjustment of production (including livestock production), taking into account derivative economic effects on other sectors and feedback effects in the form of adjustment of product and factor prices. The different analytical approaches mean that there will be different constraints on the possibilities of adjustment and different time horizons in the analyses.

....but supplement each other in an

With the different foci of the analyses, the different models cannot be expected to produce the same result, even in those cases in which the analyses relate to the same level. It must generally be expected that the isolated loss due to regulation will be reduced when account is taken of the possibility of economic adjustment for the individual forms of production and for the sector. On the other hand, it can be difficult to conclude, a priori, whether the individual form of production will be hit more or less hard when the interaction with other industries and sales possibilities are taken into account. The value of the analyses thus lies in the fact that the results supplement each other in an integrated description of the problem.

The analyses at crop-rotation and farm level cover all the above-mentioned scenarios, whereas the sectoral and socioeconomic analyses cover only the 0- and +scenarios.

 It should be noted that the analyses cover only farming because market gardening and forestry have not been included in the concept of analysis developed.

 4.3.1 Crop rotation analyses

Crop rotation analyses…

As mentioned, the basis for the analyses is a description of the technologically and biologically feasible production methods and crop-rotation combinations if the use of pesticides is phased out in farming. The problem is that there is insufficient experience of how such phasing out of pesticides affects the structure of production and land use in farming. The existing knowledge concerning conventional farming covers mainly small changes in pesticide usage, which means that it is necessary to "extrapolate" the existing knowledge beyond the intervals for use of pesticides that are covered by the existing research and trial results.

The work was carried out by a working group under the Sub-committee on Agriculture, which had the task of elucidating (Mikkelsen, 1998, p. 5):

	the agronomic aspects of the present crop rotations, related specifically to current pesticide consumption;
	crop rotations in a scenario without pesticides, where livestock production is maintained, together with a crop pattern that includes major specialised productions;
	realistic crop losses in a production without pesticides;
	how stability of cultivation changes; and
	realistic forms of treatment in a 0-pesticide scenario and the
	practicability of the proposed alternatives for substitution of pesticides.

The sub-committee’s work also provides the agronomic basis for farm-level, sectoral and socioeconomic analyses with a phase-out of pesticides. The analyses are based on assumptions concerning production practice in the present situation and with a total phase-out of pesticides for crops on clayey and sandy soil.

12 types of farm

On the basis of farm accounts data from the National Department of Farm Accounts and Management in 1995 and 1996, which cover around 13,000 farms, the working group set up 12 types of farm and, for each of these, a crop rotation corresponding to existing production practice. The crop loss in the individual crops without use of pesticides was then assessed. For example, for cultivation of winter wheat on clayey soil without use of herbicides, the working group set the yield and agronomic assumptions concerning variety, sowing time, mechanical weed control, etc. Guidelines for production practice were similarly determined for the other scenarios, and yield levels were estimated for all crops. For a detailed description of the crop rotation analyses, readers are referred to the report from the Sub-committee on Agriculture (1999).

Data transferred to the farm model

The calculated yield losses and need for mechanical weed control were then sent to SJFI for use in the farm-level economic analyses of different scenarios. This work is outlined by the double arrow in Figure 4.1, which indicates an exchange of information, where experience from the farm-level economic analyses is confronted with the crop rotation analyses. The calculated results are thus an expression of an agronomically and partially economically optimised situation.

 4.3.2 Economic analyses at farm level

Economic analyses at farm level…

The aim of the economic analyses at farm level is primarily to calculate the economically optimal use of land with pesticides phased out. The work is based on SJFI’s farm accounts statistics, Series B, for 1995/96, broken down into farm types as shown in Table 4.1. These largely correspond to the forms of operation in the crop rotation analyses. The statistics comprise detailed data for production values and costs in the cultivation of different crops, average land use, yields, etc. When these statistics are supplemented by the above-mentioned information on crop losses, mechanical weed control, etc., it becomes possible to estimate the production economy in the different scenarios for phasing out pesticides.

Note: The data are based on SJFI’s accounts statistics for Danish agriculture. Dairy farms and pig farms follow the definition in the accounts statistics, while sugar-beet growing, seed growing and potato growing are defined as farms where at least 10 per cent of the land is used for the respective crops. Arable farms are a residual group. The breakdown between clayey and sandy soil is based on counties with mainly clayey and sandy soil. The chosen farms represent about half the total agricultural acreage.
Source: Mikkelsen et al. (1998)

The economically optimal use of land and pesticides has been calculated by means of a linear programming model (the Farm-level Pesticide Model (FPM)), which has been developed for the purpose. The criterion for optimality is highest possible gross margin II.¹⁶ For each scenario and each type of farm, the model calculates the land use that gives the biggest possible land and building rent, observing limits on the use of pesticides and various crop rotation restrictions, flexibility constraints, previous-crop effects, feed balances, labour capacity, etc. The level of livestock production is assumed to be unchanged. Livestock itself is not included in the model, but the crop rotation is composed to ensure that supplies of fodder can be maintained during the phasing-out of pesticides. Only the crops grown in present production are included in the analysis. For a more detailed description, readers are referred to Ørum (1999).

Alternatives to present production

The alternatives to present production are calculated by adjusting the gross margin for yield losses and marginal yields, changed costs for purchasing and placing pesticides, and changes in the cost of mechanical weed control. As the basis for this, use is made of machine station rates for spraying and mechanical soil treatment, together with a fixed hourly rate for manual weed control, cf. Table 4.2. As mentioned, the technical assumptions for the analyses were fixed in cooperation with the Sub-committee on Agriculture.

Source: Ørum (1999, p. 22)

Assumptions concerning set-aside

Set-aside acreage is included in the analysis in line with cultivated acreage, i.e. set-aside competes with the crops for land. It is assumed in the analyses that set-aside at farm level amounts to minimum 10 per cent and maximum 33 per cent of the acreage with reform crops including set-aside. An area at least corresponding to the set-aside acreage in 1995/96 is taken out of production as 5-year set-aside. In the analyses, this can only be reduced for farm types with cattle. It should be noted that the EU has set an upper limit of 50 per cent set-aside at farm level. In Denmark, the limit is 21.6 per cent (but can be higher in environmentally sensitive areas). The above-mentioned limit of 33 per cent has been chosen in order to allow room for a substantial increase in the set-aside acreage without going right up to the EU’s maximum. Since an increase in the set-aside acreage can make it impossible for livestock producers to get rid of the manure, it is assumed in the analyses that the set-aside percentage for animal husbandry may not exceed 10 per cent. In present production, set-aside averages 6-8 per cent of the acreage at dairy farms, compared with 8-10 per cent in other types of farm (12 per cent for arable farms on sandy soil).

Results transferred to macroeconomic model

The economic analyses at farm level provide information on the economic return and optimum land use within the different types of farm with partial optimisation of the economic return in arable farming. The analyses are also used as the basis for establishing the technological and biological assumptions in the analyses of the sectoral and socioeconomic results if pesticides are phased out. The basis for these analyses is the weighed average factor use for all farming, calculated by means of the FPM model.

4.3.3 Sectoral and socioeconomic analyses

The AAGE model – long-term economic equilibrium in society

The basis for the sectoral and socioeconomic analyses is SJFI’s AAGE model (Agricultural Applied General Equilibrium model). In principle, the model covers all Danish businesses, which are assumed to minimise production costs, and all Danish households, which are assumed to maximise utility. The model describes both the demand by businesses for semi-manufactures and primary production factors (manpower, capital and land) and the supply of goods and services, and includes a rudimentary description of the public sector. The model also treats businesses’ supply of goods for export and importation of goods and services for consumption and production. The model is characterised by all the markets being in equilibrium due to an assumption of completely flexible adjustment of prices and pay.

Model assumptions

The model is based on constant return to scale in production, i.e. the costs per produced unit are independent of the size of the production. Combined with an assumption of full competition in the markets and market-based product and factor prices, this means that there is no profit in the businesses. The database for the model is Danmarks Statistik’s input-output table for 1992, where the agricultural industry is divided into eight primary production sectors and five processing sectors.¹⁷ Primary agriculture is thus treated as an average farm with eight production sectors. In other words, the model does not offer the possibility of identifying barriers to adjustment in the industry, such as structural constraints and regional barriers to adjustment of production. The model’s output must be interpreted as the result in the long term, where such barriers are negligible.

The whole of the economy is covered

The model enables systematic description of the whole of the economy, since it captures the main interactions and feedback effects in the economic system. The model shows the adjustment of the economy in the long term, i.e. importance is attached to structural relationships in the economy. At the same time, the model makes it possible to throw light on the effect of changes in the price conditions on production and factor consumption and the derivative macroeconomic effects on consumption, employment, foreign trade, etc. This means that the model is suitable for quantifying the effects of changes in structural policy measures.

It should be noted that the model cannot handle disequilibrium aspects and the forming of expectations in the economy. It therefore tells nothing about the scope and duration of adjustments from one equilibrium to another. In relation to the present analysis, this means that the model does not say anything about the possible adjustment costs that the industry will face in the short term if pesticides are banned. It should also be noted that, like most other economic models, the model does not incorporate future technological gains and that changes in consumer preferences must basically be determined outside the model.

As described above, in the farm-level economic analyses on which the sectoral and socioeconomic analyses are based, the farms are classified according to whether they are on sandy or clayey soil. In the sectoral and socioeconomic analyses, the effect of this classification is ensured by weighing sandy-soil and clayey-soil farms together to obtain an average site land quality, which is transferred to the equilibrium model.

Set-aside expressed in extensified farming

The model does not describe set-aside separately, but the set-aside acreage is included in the analyses together with the cultivated acreage, and the set-aside payment is taken into account in the calculation of the economic return. The hectare payments do not affect the intensity of the production but are included in the land rent and thus affect land use. In the sectoral and socioeconomic analyses, set-aside will thus be expressed in extensive production on largely the same acreage.

In order to be able to use the model for analyses of the sectoral and socioeconomic consequences of phasing out pesticides, it has been necessary to adjust the model on some points (Jacobsen & Frandsen, 1999).

Firstly, in its standard form, the model describes the consumption of pesticides as a single item. The model specification has therefore been adjusted on a number of points, such that the consumption of different types of pesticides is specified for different crops. In addition, possibilities of substituting pesticides with other forms of input factors have been incorporated, which is necessary in order to model adjustment of pesticide consumption. The latter takes place in practice by inserting in the model elasticities that determine the degree of substitution in factor use. These changes have been made in consultation with the Sub-committee on Agriculture.

Secondly, it has been necessary to expand the model with a description of 0-pesticide production. This is in reality a new technology that the model’s database does not provide a basis for describing. As an innovation within general equilibrium analyses, the model has therefore been expanded by formulating for each cropping sector corresponding sectors with the same production but with a technology/factor composition that does not include pesticides (the 0-scenario) or that includes only limited use of pesticides (the +scenario).

The factor composition in the alternative sectors is determined on the basis of calculation with the Farm-level Pesticide Model (FPM), cf. Ørum (1999).

Transfer of data

The transfer of data has been effected by calculating for each cropping sector in the FPM model the percentage change in factor use when restructuring for 0-pesticide production (the calculation has been made for each of the above-mentioned farm types and weighed together to an average for all farming). The percentages thus calculated have been used as the basis for adjusting the factor input in the AAGE model. For example, Table 4.3 shows the adjustment of factor input in cereal production in the 0-scenario and the +scenario.

Source: Jacobsen & Frandsen (1999, Tables 3.4 and 3.5)

As will be seen from the table, about 28 per cent more land is needed to produce the same quantity of cereal in the 0-scenario than in present production, which corresponds to a fall of 22 per cent in the ha-yield. For the +scenario, 16 per cent more land is required, corresponding to a fall in ha-yield of 14 per cent. It will also be seen that the input of, for example, machine station, manpower and fertiliser in the production of cereals is around 18 per cent higher per produced unit in the 0-scenario than in present production, compared with 9-11 per cent in the +scenario.

In both scenarios, production of crops with the present production technology is prohibited. Technically, the scenarios are implemented by eliminating production in the traditional sectors, thereby releasing land, capital and manpower – resulting in falling land rent. In such a situation, the land is reallocated to the described alternative crops (i.e. to the types of production that do not use pesticides or that make only limited use of them). In the new equilibrium, the land is reallocated between the existing crops, so that the agricultural land rent is the same in the various types of production. Capital and manpower are reallocated to the alternative crops and to the other sectors of the Danish economy.

It is stated by Jacobsen & Frandsen (1999) that the theoretical possibilities of substitution described above are not used in the 0-scenario and the +scenario because pesticides are only used within a given, exogenous framework (quantitative regulation). The limited use of pesticides is given as a permitted quantity, depending on the crop and, for example, in a fixed ratio to the input of land (a fixed quantity per ha). This ensures that the input of pesticides in the +scenario does not exceed the limit set in the scenario.

4.4 Evaluation of the concept of analysis

The present analyses are based on a set of models that have been adapted to the needs of the analyses and that show the economic consequences of phasing out pesticides at farm level, sector level and societal level. The concept of analysis is firmly founded in economic theory, and parts of the model concept have been used for consequence analyses in connection with assessment of other political measures. A strong point is that the economic analyses are based on an agronomically well-founded concept of analysis and that the models have been adapted for the specific problem. This is an advanced analytical technique, which, together with high-quality data, provides a basis for a complete description of the problem.

The models’ assumptions affect the result

With the analyses based on model calculations, the results naturally reflect the assumptions used in the models. For example, the analyses at farm level assume full knowledge and transparency in the decision-making process, which are presumably things that only the most skilled production managers can achieve. The analyses at farm level are also focused on adjustment in the relatively short term, whereas, in the sectoral and socioeconomic analyses, the emphasis is on the long-term consequences for agriculture and the Danish economy. Caution must thus be exercised in using the results for medium-term policy planning, where there are barriers to adjustment of production. The results of the equilibrium model will underestimate the cost of adjustment in the short term. Conversely, the results of the farm-level model must be expected to overestimate the cost of adjustment in the slightly longer term, where there are greater possibilities for adjustment.

Set-aside is treated differently in the two concepts of analysis. At farm level, limits are inserted for the extent of set-aside at the individual farm, but not in the sectoral and socioeconomic analyses, where the set-aside acreage is included together with the cultivated acreage in the calculations. In both cases, account is taken of the set-aside payment, which is included in the return on the land and thus affects land use. Whereas, in the farm-level analyses, an indication is thus given of the extent of set-aside in the individual farm categories, in the sectoral and socioeconomic analyses, increased set-aside will be reflected in extensified production on a largely unchanged acreage.

Lastly, it should be noted that the models do not provide the possibility of describing technological changes. Therefore, account is not taken in the analyses of the fact that research and development will make it possible to develop crops and production methods that are better able to compete in pesticide-free farming. On the other hand, the chemical industry is constantly developing more environment-friendly products. There are thus contrary movements in the technological development that are difficult to incorporate in such a concept of analysis.

Idealised description gives direction and orders of magnitude

These factors must naturally be included in the evaluation of the results. Even though this is an idealised description of the situation with different time horizons, it is thought that the analyses, despite their limitations, give a relatively reliable indication of the direction of the changes and of the effects of the analysed scenarios.