Using economic incentives for pesticide usage
reductions: responsiveness to input taxation
and agricultural systems
K. Falconer
a,*, I. Hodge
baDepartment of Agricultural Economics and Food Marketing, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK
bDepartment of Land Economy, 19 Silver Street, University of Cambridge, Cambridge CB3 9EP, UK
Received 1 October 1999; received in revised form 13 December 1999; accepted 16 February 2000
Abstract
There is growing interest in using environmental taxes to address the problems of agri-cultural pesticide contamination, given the potential of economic instruments for higher e-ciency compared to regulatory approaches. However, research to date has suggested low producer responsiveness to input price changes. It is important to examine crop protection decisions and the options for adjustment in more detail. A case-study arable farm model is presented to evaluate the implications of new, currently experimental, low-input farming production systems for pesticide policy. If producers adhere to current systems, a pesticides tax at politically acceptable levels introduced as a stand-alone measure would perform poorly. Consistent with a number of studies, the model suggests that pesticide use levels could be reduced signi®cantly while actually increasing farm income levels through conversion to low-input farming. Pesticide taxation cannot be viewed in isolation as a policy tool but should be part of a package of measures, including in particular education and training to encourage and assist farming system change.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Agricultural pesticides; Price responsiveness; Low-input farming
1. Introduction: pesticide problems and eco-taxation
The post-war period has seen continued increases in both agricultural productivity and pesticide use. It has been estimated that in the absence of pesticides, global crop yields would be at least 30% lower on average (Finney, 1994). However, there are
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widespread and growing concerns of pesticide over-use, relating to a number of dimensions such as contamination of ground-water, surface water, soils and food, and the consequent impacts on wildlife and human health (Reus et al., 1994; WWF, 1995; McLaughlin and Mineau, 1996). A balance must be struck between greater environmental protection from reduced pesticide applications and the continued contribution of agriculture to production. Motivated by the `polluter pays' principle and the `precautionary principle', environmental policy objectives in Europe and elsewhere now include the achievement of overall reductions in pesticide usage and reductions targeted on more vulnerable locations; the substitution of less envir-onmentally harmful pesticides for more envirenvir-onmentally harmful ones; and improvements in crop protection eciency (CEC, 1992; Oppenheimer et al., 1996).
However, few actual goals or limits are de®ned currently, and despite the Eur-opean Environmental Action Plan, member-states are largely free to address their own priorities. Policies are based largely on codes of practice, some additional extension and research into low-input farming (Falconer, 1998). Systems of pre-commercialisation approval and registration of products exist in most OECD countries and are intended to avoid unacceptable environmental and human health risks through availability and product-speci®c conditions of use. However, approv-als-based control has many inadequacies; e.g. the practical diculties of enforcing product use conditions. Furthermore, there are no real farm-level incentives to change pesticide use or management beyond statutory requirements.
Once agricultural inputs such as pesticides are applied for crop protection, it is virtually impossible to control emissions given their diuseness, hence the focus on the reduction of inputs. For some time it has been suggested that many countries rely too heavily on regulatory instruments in their environmental policy mixes, and there is now growing interest in using more ¯exible, potentially more cost-eective approaches in policy development (OECD, 1996). Competitive ®rms are unlikely to adopt more costly practices in the absence of some type of constraint or incentive. A wide range of theoretical economic policy options exists, including, for example, taxes, subsidies, transferable permit schemes, insurance and credit instruments (Falconer, 1998; Oskam et al., 1998). The UK government has been considering the potential to apply input taxes to address some of the environmental problems of pesticides (DETR, 1997), and precedents exist, for example, in Sweden and Den-mark. A tax on any given input will increase its price relative to others, resulting in reduced use of it,ceteris paribus, and greater use of its substitutes. Environmental± economic theory predicts that higher policy cost-eectiveness is possible with taxes or transferable quotas, stemming from the compliance ¯exibility permitted for pro-ducers, rather than prescribing their abatement actions, particularly when options vary over sources (Baumol and Oates, 1971). Economic incentives target usage reductions on those producers with lowest marginal abatement costs and provide a continuous incentive for technical change.
in¯uencing actual decisions on farms and aspects such as bounded rationality. It is important to consider the practical policy implications of deviations between theo-retical predictions of response and the actual responses, and then to assess the con-ditions or pre-concon-ditions that might be required for particular instruments to be eective components of the environmental policy framework.
The objective of this paper is to assess the potential for economic incentives to encourage actual reductions in agricultural pesticide usage in arable agriculture in OECD nations, given the expected farmer responsiveness scenarios. Arable produc-tion accounts for a high percentage of pesticide use in Europe (Brouwer et al., 1994). Section 2 presents some empirical estimates of pesticide price elasticities in European member states, followed by a discussion of their policy implications. Section 3 dis-cusses the scope to increase the price responsiveness of pesticides, and presents an empirical case-study drawing on ®eld trials data for experimental, low pesticide input farming in the UK. Section 4 discusses the policy-making implications of the case-study observations, with concluding comments in Section 5.
2. Models of producer responses to pesticide taxation
2.1. Estimates of the price elasticity of demand for agricultural pesticides in EU member states
If farmers are assumed to be rational pro®t-maximisers, their production decisions are in¯uenced mainly by the relative prices of inputs and products.1The level of the
pesticide input tax required to achieve a target level of usage reduction (as a proxy for environmental quality improvement) depends on the responsiveness of input demands to price changes. Burrell (1989) reviewed methodologies to estimate price elasticities of demand for agricultural inputs such as fertiliser. One approach is to use econometric (often time-series) models in which demand is regressed on prices and shift variables. Producers are hypothesised to respond to price signals in a sys-tematic way that is stable enough to be observed from available data. Frequently, aggregate time series data are used; a critical draw-back, speci®cally in relation to demand assessments for pesticides, is the low level of detail of many econometric models. Furthermore, econometric models are typically backwards-looking: models are based on historical data and cannot include new technologies. Finally, the results of such analyses are valid only for the limited price ranges for outputs and inputs in the period of observation.
Mathematical programming provides an alternative approach to elasticity esti-mation, with the advantage of a strong relationship with technical research. Fre-quently farm-level models are built. However, there are still practical diculties when this approach is used to derive price elasticities. If models are to be manage-able, only a limited number of alternative enterprises can be included. Typically,
some output adjustment is allowed but is limited either to a single output, or con-®ned within a group of closely related outputs (which assumes limited substitution between arable crops). However, validity may be maintained because some farms are (or perceive themselves to be) constrained in this way (Burrell, 1989).
So, elasticity estimates depend on methodology and on model speci®cation. For any theoretically based research approach, there is the inevitable problem of poten-tially large dierences between actual situations and the model outputs, given the necessary simplifying assumptions. Any realistic assessment of the long-term impact of an environmental tax on potentially contaminating agricultural inputs must also make a judgement about future developments in input-using technology, whether price-induced or not. A programming model might embody new technology but only very complex models can oer insights into the dynamics of technical change or its interaction with prices (Burrell, 1989). Further empirical work is required to pro-vide a better understanding of the potential responses of actual producers, for the simple reason that until producer responses are better understood, the consequences of policy implementation cannot be speci®ed with certainty.
Recognising these limitations, various attempts have been made to measure the own-price elasticity of pesticide demand. Table 1 summarises pesticide elasticity estimates to date. Long-run elasticities would be expected to be higher, as new technologies are stimulated allowing easier, lower-cost adjustment to lower-pesti-cide-input crop practices. Technology adoption lags also vary across producers, aecting longer-run responsiveness. However, an important drawback of many models is that pesticides were aggregated into a single input, so substitution between dierent types of pesticides could not be taken into account. Given the imperfect correlation between expenditure on inputs and their physical quantities, particular interest lies with studies based on physical measures; see, for example, Oskam et al. (1992), Dubgaard (1987) and Rude (1992). There have been few attempts so far to evaluate policy instruments to achieve pesticide usage reductions taking into account technological changes, although Oskam et al. (1992) included some new techniques such as mechanical weeding in their farm optimisation modelling (see also Wossink et al., 1992).
Elasticity estimation requires an assumption that producers were producing e-ciently prior to the price change. Taxation may stimulate producers to reduce inef-®cient input usage, making empirical estimation of response elasticities rather dicult. Fleischer and Waibel (1995) also commented that responsiveness may vary with the level of regulatory action. Furthermore, given heterogeneous ®rms, the industry-level elasticity of substitution tends to exceed its ®rm-level counterpart; signi®cant industry-level elasticities could be consistent with very low farm-level substitutability, if there are compositional changes in the wake of relative price changes (Diewert, 1981; Hertel et al., 1996). Such changes are potentially important.
2.2. Implications of the elasticity estimates for pesticide policy
Table 1
Summary of pesticide demand elasticity estimates
Study Region/country Approach Estimated elasticity (averages)
Aaltink (1992) Netherlands Pro®t and cost functions ÿ0.13 toÿ0.39
Bauer et al. (1995) German regions, wheat
Non-linear programming ÿ0.02
Carpentier (1994) France, arable farms Econometric (including risk considerations)
ÿ0.3
Dubgaard (1987) Denmark Pest threshold modelling and econometric
threshold approach:ÿ0.3; econometric: herbicidesÿ0.69; insecticides and fungicidesÿ0.81
Elhorst (1990) Netherlands Econometric ÿ0.29a
Falconer (1997) UK (East Anglian arable production)
Linear programming ÿ0.1 toÿ0.3
Gren (1994) Sweden Econometric Herbicides,ÿ0.93; insecticides,ÿ0.52; fungicides,ÿ0.39
Komen et al. (1995) Netherlands Applied general equilibrium modelling
ÿ0.14 toÿ.25
Oskam et al. (1992) Netherlands Econometric ÿ0.21b
Oude Lansink (1994) Netherlands, arable farms
Econometric (panel data) ÿ0.12
Oude Lansink and Peerlings (1995)
Netherlands Aggregate economic model ÿ0.48
Papanagioutou (1995) Greece Econometric (aggregate level) ÿ0.28c,d Petterson et al. (1989) Sweden Pest threshold modelling and
inter-regional linear programming
ÿ0.2
Rude (1992) Sweden Econometric ÿ0.22 toÿ0.32
Russell et al. (1995) UK (Northwest) Regression (generalised demand model)
ÿ1.1
Schulte (1983) Three German regions
Farm-level modelling ÿ0.23 toÿ0.65e
a Combined elasticity for fertilisers and pesticides. bWith a technological trend elasticity of
ÿ0.06. c The elasticity for herbicides was
ÿ0.69 andÿ0.81 for pesticides.
dPapanagiotou (1995) also found the elasticity to be unchanged when a crop price component was added, giving rise to the conclusion that pesticide consumption is similarly inelastic to both own prices and crop prices. The average quantity of pesticide per unit land (by weight), crop prices and average pesticide prices were included.
available to them). The apparently inelastic demand for pesticides implies that high taxes will be needed to achieve signi®cant reductions. If the price elasticity of the demand for pesticides is low, the main eect of any tax will be to internalise some of the external costs, but with little change in usage levels and hence little environmental improvement. Furthermore, while there will be an adverse eect on user income levels, any pollution victims will not bene®t unless revenues are recycled into expendi-ture on amelioration measures. There may also be critical levels of taxation below which no eect will be observed (Falconer, 1997). However, sub-critical taxes and levies will still be capable of raising government revenue, which could then be recycled into the agricultural sector through expenditure on research and extension (perhaps con-tributing to greater long-run ¯exibility pesticide usage) or environmental amelioration. An important issue is whether the price elasticity of demand for pesticides is really as low as indicated from the various studies across the EU, and if so, whether it could be increased. The following section questions this. Theoretical production± economics models commonly assume pro®t-maximisation and producer rationality, so care is needed when interpreting their results: policy implications dier with the relative degree of trust placed in these elasticity estimates. Demand may be inelastic due to factors other than the underlying technology of production such as lack of knowledge of alternative production practices, hindering price-induced adjustments. There may be lower responses to taxes than anticipated on the basis of pro®t max-imisation because of behavioural factors (Falconer, 1995). For example, farmers may derive utility from professional pride in clean, weed-free ®elds, with some reluctance consequently to reduce usage.
Substantial attention has been focused to date on the physical characteristics of pesticide contamination and their implications for policy design. It is necessary now to focus on how individual producers might respond to policy implementation, and particularly factors such as the relative importance of the price mechanism com-pared to other factors and objectives such as positive conservation attitudes in motivating changes in farming systems and reductions in pesticide usage. Explicit account of farmer decision-making should be taken in environmental policy design. Crop protection actions will be a function of farmer or farm needs and objectives, perceptions of the pest problem, available resources and pest control options (tech-nology). Riskiness and risk aversion may aect the crop protection strategy (Pan-nell, 1991; Auld and Tisdell, 1987), resulting in a substitution of farmer risk with environmental risk if consequently pesticides are used at higher levels (Milon, 1986). Shortle and Dunn (1986) argued that if farmers are believed to be risk averse, agri-environmental policy should be designed to take account of uncertainty and risk preferences (see also Leathers and Quiggan, 1991). Dierent controls and approaches may be signalled as most appropriate, once decision-making factors are considered compared to the results of simple neo-classical modelling.
major limitation of many studies lies in their reductionist approach. Given the complexity of the pest control problem, simple economic optimisation models could be unrepresentative or even misleading if used as a basis for policy recommenda-tions. While such models can provide useful guidance in assessing system change, it is essential in the long-run to assess what changes in systems might be desirable, and how they might be brought about. There might exist some unexploited opportunities to improve both farm pro®ts and environmental quality; better understanding of production system eects, and the options for adjustment, is needed. The next sec-tion discusses farm adjustment opsec-tions following the implementasec-tion of a pesticides input tax.
3. Evaluating system adjustment options for a case-study farm
3.1. The empirical model
Achievement of a pesticide usage reduction objective (through policy intervention) requires assessment of the types of changes that producers could make to meet environmental goals. An important question for policy-makers is the degree to which current arable production practices can be rendered less environmentally risky by marginal crop practice change and pesticide usage adjustments (input reduction or substitution) rather than more fundamental system change (e.g. to mixed organic farming). To investigate this question, an economic optimisation model2was developed for a typical East Anglian farm business.
The farm system chosen for analysis was a specialist cereal business (combinable arable cropping, i.e. cereals, oilseeds and protein crops, excluding potatoes and sugar beet). Such farms account for around 65% of the agricultural area in East Anglia (Murphy, 1995). Cereals account for a signi®cant proportion of arable land and constitute the main output of the EU arable sector. Regional pesticide usage closely approximates the areas of arable cropping (Garthwaite et al., 1995). Two linear programming (LP) model speci®cations are presented below, to represent dierent systems based on dierent assumptions with regard to the available crop production adjustment options (see Falconer, 1997, for full details).
Farm size was set at 250 hectares, which was typical for the farm type and area (Murphy, 1995). Two farm systems were modelled, with varying degrees of adjust-ment. One production scenario (CONV) represented current commercial crop production (CCP), and was calibrated using data from Murphy (1995) for the 1993 harvest year, which was not considered to be exceptional in any way (Murphy, 1995). The other model represented an alternative, less chemical-intensive farm system (ALT), based on the same set of cropping activities and with the addition to CCP of a number of dierent low-input agro-chemical regimes. The aim was to assess the economic and environmental consequences of including low-input arable production practices in the farming system, compared with conventional `best practice' for common arable
crops. Taxes might stimulate a change in the whole mode of production so modelling dierent types of systems is likely to be useful. Understanding the input substitution possibilities is central to analysis of policy based on reduction of the usage of par-ticular contaminating inputs, and policy impacts should dier depending on the compliance options available to producers. The inclusion of as many dierent functional forms for crop production as possible is important to the degree of rea-lism of the model, as production is optimised in the face of relative price changes by substituting activities. Hence the range of options included in the model will aect the accuracy of measurement of the eects of dierent policy instruments.
A number of activities were de®ned for each crop enterprise to provide a variety of points on the production function. Attention was focused on agricultural enterprises that are potentially pro®table for a large number of farmers, especially ones that currently (or could potentially) occupy large areas of land in the catchment or region. The model included 12 crops, based on those combinable crops commonly grown in the region. Livestock activities were omitted, although in the longer-term these could oer alternative activities for incorporation in farming systems faced with pesticide usage constraints on arable production. The farm was assumed to have a degree of inertia; producers may well wish to change practices gradually, as knowl-edge and experience accumulate. Organic production was excluded because of its dierent underlying philosophy, and shortages still of relevant data for a large enough sample of farms. The model spans the short-term only, so signi®cant movement out of arable production would not be expected, although it might be a long-term response.
over 10%, then up to 50% of the CCP rate could be applied. Full-rate applications were permitted where there was already evidence that less than the full-rate would result in a crop-loss situation. Cross-combinations of pesticide regimes, nitrogen levels and crops gave a production technology set of over 100 dierent production activities in the model.3
The trial had the advantage of being reasonably comparable with commercial farms, as alternative management practices are carried out at ®eld level, alongside commercially produced crops. However, there are limitations: e.g. the observations relate to speci®c agro-ecological and meteorological conditions; they have largely hidden management costs; and they are location-speci®c. Given the range of pro-duction conditions on individual farms, the yields of the `typical' strategies are only rough approximations to the scenario for any individual farm. It was decided not to use trials data in their raw form, because of their site- and season-speci®c nature. Instead, the relative dierences between the LIF trial results and typical CCP crops (based on Murphy, 1995) were used to calculate yield and variable cost coecients relative to `conventional' production. This allowed a workable approach to incor-porating the trials data into the model. The yield coecients used are given in Table 2. The extra `crop management' costs associated with LIF were also taken into account by incorporating additional crop-walking and soil-sampling expenses. A factor in the model that cannot be controlled is management expertise, with regard to both starting levels on dierent farms and how levels might change over time, as a
Table 2
Yield coecients: LIF as percentages of CCP (full N)a
CCP LOW LOWH LOWF LOWI
WW Full N 100 97.64 100 98.4 99.21
Half N 92.15 89.66 92.41 91.49 91.88
SW Full N 100 91.95 89.60 98.66 99.83
Half N 98.83 85.91 88.09 92.28 97.82
SOSR Full N 100 55.28 69.11 75.61 79.67
Half N 47.15 33.33 33.33 25.20 41.46
WOSR Full N 100 39.29 58.04 107.14 70.54
Half N 49.11 52.56 30.36 115.18 85.71
WFBs ± 100 102.69 102.69 102.68 104.89
SFBs ± 100 83.91 71.84 102.87 100.57
Peas ± 100 110.24 ± ± ±
a ww, Winter wheat; sw, spring wheat; wosr, winter oilseed rape; sosr, spring oilseed rape; wfb, winter ®eld beans; sfb, spring ®eld beans; N, nitrogen input level.
function of the system changes implemented. Knowledge can be endogenous as well as exogenous.
Rotational constraints on the farming system were developed through discussion with ®eld experts and included in the model to ensure adherence to principles of good husbandry in the choice of crop combination. For example, the area of oil-seeds could not exceed 20% of the total rotational area, and winter oilseed rape could only follow winter barley and set-aside (due to a need to drill the former early). Field operations (stubble cultivations, drilling, harvesting, etc.) were de®ned for each crop practice, and standard estimates for their costs were included in each activity's gross margin. Operation requirements were allocated for each month and linked with labour, tractor and combine harvester availability (in hours). Timeliness is very important to the returns from agricultural systems, but its inclusion in mod-els is complicated by the stochasticity of conditions. For example, it is practically infeasible to take account of crop growth stages and chemical applications in a manageable LP model. However, ®eld operations were allocated to each month on the basis of `typical' timings. Labour availability was based on Nix (1993). Field-expert advice was taken on operations and timings. Dierent machinery and labour costs were calculated for each activity to re¯ect the dierent operations carried out. Standard cultivations costs were taken from the Central Association of Agricultural Valuers' handbook.
Financial constraints (related to the farm's cash-¯ow) were also included. The system is summarised in Fig. 1. Gross margins including all allocable costs and arable area payments are given in Table 3. Signi®cantly, the LIF trials mostly appear to dominate the CCP trials slightly in terms of gross margins.
Spray expenditure estimates are a crude approximation to usage; dierent crops use dierent chemicals and mixes of these. To improve the evaluation approach, pesticide inputs in terms of per-hectare spray units (standard or recommended doses) were incorporated into the model, and are shown in Table 4. Typical pesticide usage strategies had been developed for each crop on the basis of the available sur-vey data (Garthwaite et al., 1995). Ideally, a number of dierent chemical usage combinations would have been included, but these would have dierent cost and yield implications for which data were not available. Consideration was limited, therefore, to just one strategy for each crop.4The next section reports on the ®ndings
of the modelling exercise.
4. Case study observations
GAMS (Brooke et al., 1992) was used to solve the model. The CONV model was validated against the average (`typical') land uses for mainly cereals farms in the Eastern Counties, from published farm-level data (Murphy, 1995) and was found to be a satisfactory re¯ection of arable farming in the region. The ALT farm-plan grew
fewer hectares of cereals and more of break-crops, but was an equally plausible rotation. The net margin value for the conventional farm was higher than the esti-mate in Murphy (1995) for mainly cereal farms with combinable breaks in the Eastern Counties (£122.70/ha), possibly because of the range of crops grown in the
model. The published estimate covers a much wider range of crops (and also live-stock, forage, vegetables, etc.) and so might be expected to dier.
A comparison of the crop-land allocations and ®nancial results for the two models is shown in Tables 5 and 6. The alternative (LIF) system allows a `win±win' sce-nario, actually increasing farm management and investment income (MII) with a
Table 3
Crop activity gross margins (including cultivations costs) (£/hectare)a
CCP LOW LOWH LOWF LOWI
ww.f1 548.09 572.90 561.59 563.79 545.96
ww.f2 498.78 528.50 520.46 528.33 506.99
ww2.f1 439.45 466.82 452.95 456.88 438.17
ww2.f2 398.67 431.09 420.06 428.93 407.17
ww3.f1 355.87 385.21 369.37 374.64 355.25
ww3.f2 321.65 356.16 342.82 352.47 330.38
wb.f1 463.52 481.50 474.26 474.93 461.63
wb.f2 417.35 440.41 435.95 441.46 425.17
sw.f1 405.42 409.89 358.43 432.99 407.46
sw.f2 403.44 383.17 360.30 404.13 406.17
sb.f1 392.92 380.11 344.38 407.39 394.31
sb.f2 391.59 356.55 347.08 381.86 394.11
wosr.f1 474.22 252.74 292.41 553.56 341.14
wosr.f2 256.95 339.24 188.86 615.79 436.46
sosr.f1 400.87 279.37 296.41 344.13 325.04
sosr.f2 215.12 217.70 181.88 173.70 201.23
wfb.f1 421.33 491.46 471.21 452.42 444.30
sfb.f1 407.73 393.57 334.31 433.29 412.53
peas.f1b 406.60 488.06 ± ± ±
Setaside.f1 232.95 ± ± ± ±
a f1, CCP nitrogen; f2, half-rate nitrogen application; ww, winter wheat; sw, spring wheat; wb, winter barley; sb, spring barley; wosr, winter oilseed rape; sosr, spring oilseed rape; wfb, winter ®eld beans; sfb, spring ®eld beans.
signi®cant reduction in pesticide usage and overall pesticide hazard (especially for some ecological dimensions such as ®sh and aquatic organisms). The positive out-comes result from a switch to reduced-chemical-input practices, a dierent rotation and dierent chemical types. The MII of the ALT farm-plan exceeded that of CONV, by £42.80/ha. Other studies have also observed production approaches identi®ed as LIF to be more pro®table than CCP (e.g. Jordan and Hutcheon, 1994; Leake, 1996; Ogilvy et al., 1996).
Table 5
Optimal crop rotations for the two models as a percentage of total farmed area
Conventional (CONV) (%) Alternative (ALT) (%)
Winter wheat 50 50 (low pesticide inputs)
Winter barley 15 5 (low pesticide inputs)
Winter oilseed rape 20 20 (low fungicide inputs, low nitrogen)
Winter ®eld beans ± 10 (low pesticides inputs)
Set-aside 15 15
Table 4
Units by chemical categorya
CCP LOW LOWH LOWF LOWI
h f I h f I h f I h f i h f i
WW 3 2 1 1.5 0.5 0 1.5 2 1 3 0.5 1 3 2 0
WW2 3 2 1 1.5 0.5 0 1.5 2 1 3 0.5 1 3 2 0
WW3 3 2 1 1.5 0.5 0 1.5 2 1 3 0.5 1 3 2 0
SW 2 1 0 1 0.5 0 1 1 0 2 0.5 0 2 1 0
WB 2 2 0 1.5 1 0 1.5 2 0 2 1 0 2 2 0
SB 1 1 0 0.5 0.5 0 0.5 0 0 1 0.5 0 1 1 0
WOSR 3 1 1 1.5 0 0.5 1.5 1 1 3 0 1 3 1 0.5
SOSR 3 0 1 2.5 0 0.5 2.5 0 1 3 0 1 3 0 0.5
WFB 2 2 0 1 0 0 0 2 0 2 1 0 2 2 0
SFB 3 1 1 1.5 0.5 0 1.5 0.5 1 3 0.5 1 3 1 0
Peas 2 1 1 1 0.5 0 ± ± ± ± ± ± ± ± ±
Set-aside 1 0 0 ± ± ± ± ± ± ± ± ± ± ± ±
The degree to which CONV does in fact re¯ect current practice is highly relevant to the baseline from which reductions are to be assessed. The ®eld trials set `CCP' usage at full dosages of the sprays used, whereas anecdotal evidence suggested that a typical commercial farmer in the East Anglian region would have been unlikely to have applied chemicals at such levels (Falconer, 1995; North, personal communica-tion). There is a lack of data on the range of actual practices; in the UK, for example, physical input data are gathered separately to ®nancial farm data.
An important implication of the assertion that LIF practices represent lower pesticide input levels than CCP and that LIF is ®nancially viable, is that pesticide taxation is unnecessary. Conversion from CCP alone could achieve signi®cant usage reductions (to less than half of current levels, at the level of the individual farm). Furthermore, even if LIF is not economically viable at present, pesticide input taxation could play an important role in making it more attractive to producers, stimulating conversion. If CCPisan accurate representation of actual practice and the LIF costs are plausible in commercial as well as experimental scenarios, the question is why farmers do not adopt the more pro®table lower-input practices. One answer could be lack of knowledge (or the costs of knowledge, i.e. rational ignor-ance) of the outcomes of low-input practices; another could be risk aversion (Park et al., 1997). However, it is unknown how much of the premium over CCP practices can be accounted for by risk aversion, knowledge costs, and other factors; neither are many data yet available on the yield variance of LIF. These are clearly important areas for research.
The models used here were necessarily abstractions of reality, for example, in omitting yield riskiness and risk aversion in¯uences on decision-making. As a ®nal consideration, the farm plans generated under each policy scenario are optimal, and hence indicate the theoretically minimum farm costs in terms of lost income, for the
Table 6
Base-line CONV/ALT comparisons
CONV ALT ALT as a% of CONV
Total farm management and investment income (MII) (£)
47 001.0 57 688.0 122.7
MII per hectare (£) 188.0 230.8 122.7
Total spray expenditure (£) 23 845.0 13 855.0 58.1
Total spray units 1187.5 543.8 45.8
Total herbicide units 587.5 393.8 67.0
Total fungicide units 425.0 100.0 23.5
Total insecticide units 175.0 50.0 28.6
range of technology modelled. However, such income losses may be far greater in reality if producers are operating sub-optimally; gains may be possible from improved technical eciency. For example, it may be the case that producers do not know of the best alternative production scenario (i.e. bounded rationality), so there are gains from improved technical eciency. In addition, the model is inherently unable to take future technological changes into account, and is only partial-equili-brium, resting on the assumption that only a small proportion of the total popula-tion of farms of this type were subjected to policy implementapopula-tion. Consequently, no account could be taken of the impacts at the aggregate-level on input and factor costs, and output prices, although adjustments in these would aect the optimal farm-plan for an individual business and hence policy impacts and eectiveness.
5. Discussion
It appears that strengthened policy action is necessary if current policy goals with regard to pesticide use reduction and environmental quality improvement are to be achieved in countries such as the UK. The issue here is whether resource allocation with regard to pesticides could be improved in practice using the market mechan-isms, or whether other tools (especially education and training) might be more appropriate, in ®rst instance. Policy feasibilities depend on actual and perceived technological feasibilities, which will aect responsiveness. A greater range of tech-nical options means greater adjustability in the face of policy restraints, and increa-ses the likely appropriateness of economic incentives over regulations. However, if decision-makers perceive their compliance ¯exibility to be low (e.g. if little informa-tion on alternative strategies is available), incentive policies might not give much real advantage over regulatory options, despite producer heterogeneity. Hence, under this scenario, mechanisms such as pesticide taxation might not necessarily be the most ecient instruments through which to achieve pesticide usage reductions, especially in the short-term.
experimental observations, that a number of low-pesticide input pest management options are available and economically viable at present. However, there may be a general reluctance currently to alter current cropping systems signi®cantly, given perceived (or actual) riskiness and risk aversion (Park et al., 1997). The willingness of producers to experiment is likely to vary greatly. Behavioural and informational investigation could help to understand better the types of incentives or other policies that might be required to achieve large-scale conversion to such practices in practice. To date, pesticide input taxes have been introduced in only a few EU member states, and importantly, they have been introduced as part of apackageof reduction measures including intensi®ed extension services and the recycling of tax revenues back to the sector in the form of additional support for training, R&D and so on. Supporting technological innovation, and diusion of this to producers, is likely to be an essential link between policy design and implementation and environmental improvement (Curry, 1997). The complexity of reduced pesticide input approaches is a hurdle; better information dispersion is required, for example, through Arable Research Centres or local-level demonstration farms (e.g. established by the LEAF organisation; see Bun, 1994).
The site-speci®city of ecient management practices indicates the importance of capacity-building, in terms of developing skills and understanding, so farmers can apply the principles of low-input farming systems in their own situations. However, the level of implementation of low-pesticide techniques still appears to be low on most commercial farms (ADAS, 1996), although Cook et al. (1996) noted a move on some commercial farms away from applying full rates towards applications better-tailored to weed densities, etc. Environmental skills acquisition may be hindered by the characteristics of agricultural knowledge networks and markets, which remain predominantly production-orientated in terms of both stang and the skills oered, while trying to adjust to new environmental demands.
A policy need is to encourage greater experimentation and innovativeness with regard to crop protection. It is also important to encourage attitudinal change, for example, in terms of more experimentation and collaboration in research trials to stimulate a dynamic process of adjustment, perhaps further assisted by economic policy incentives. Environmental pricing for pesticides and LIF extension could be integrated for greater eect. For example, farmers could be exempt from pesticide tax payments if they employed a quali®ed agronomist or are suitably quali®ed themselves. Furthermore, the growing importance of advisors as decision-makers means that channelling extension results to them might be more cost-eective than disseminating knowledge to large numbers of farmers (Ward and Munton, 1992). However, the constraints on advisors' decision-making must be considered: their concerns to protect their credibility and to achieve protection ecacy (in addition to links in some cases to chemical companies) may bias their recommendations towards relatively chemically intensive controls.
communicate to farmers, who are generally keen to reduce crop protection costs but are unwilling to withhold chemical crop protection entirely. However, it is vital to identify which chemical inputs are most ¯exible and the conditions in which reduc-tions are possible without signi®cantly prejudicing ecacy. The supply by manu-facturers of dose±response curves for dierent weeds and pests, for dierent conditions, would allow more precise use and savings. However, care is needed to ensure that short-term savings do not result in higher future costs or re-treatment at high cost (Keen, 1991).
6. Conclusions
The issue is how best to reduce environmental contamination from agricultural pesticide use while maintaining farm production and incomes to as great a degree as possible. The characteristics of the contamination problem, particularly environ-mental, chemical and farmer decision factors, in¯uence the relative appropriateness of dierent policy options to a signi®cant degree. The key lies in identifying ways of encouraging farmers to change their production practices and systems, especially with regard to input levels and types. Instruments such as environmental taxation might be used to provide incentives for change, and there are precedents for their use in the context of pesticide policy elsewhere in Europe and familiarity with this type of instrument in other policy contexts. However, research suggests that price elasti-cities of demand are low, implying that high and politically problematic taxation levels would be needed to achieve signi®cant usage reductions. An important issue, therefore, is whether actual price responsiveness is as low as indicated by modelling studies, and if so, whether it could be increased. Qualitative policies such as improved training are likely to be critical components of environmental policy for pesticides, especially if taxation is introduced. No policy instrument alone will be superior or sucient.
Models are useful tools for analysing production and ecological changes under alternative policy scenarios. However, the use of the representative farm approach clearly cannot represent the diversity of farm types and the behaviour of individual farmers; the case-study results provide a broad guide only. The spatial and temporal distributions of changes, and their implications for environmental quality were out-side the scope of such analysis. Investigation of this is a priority area for further work; it is also important to examine the potential consequences of policy intervention in dierent production contexts.
in farm incomes through conversion to less pesticide-intensive farming practices. Conversion of as many farms as possible towards low-input farming could therefore go a long way towards achieving policy goals and may be more cost-eective at achieving reductions than taxes or levies although training, extension and R&D costs could be considerable. However, farmer training in reduced pesticide use practices and ways of minimising environmental impacts is likely to be crucial to the success of any policy of pesticide input taxation. Information on the constraints on adoption of low-input farming practices is not yet available, nor is information on how easily those that exist could be overcome. Provision of such information would be extremely useful for pesticide reduction policy design, and is suggested as an area for further work.
Acknowledgements
Grateful acknowledgement is made to Sarah Cook at ADAS (Boxworth) for per-mission to use data from the MAFF-funded TALISMAN ®eld trials. This work was completed as part of a PhD thesis at the Department of Land Economy, University of Cambridge, and was funded by a MAFF studentship. The authors acknowledge the comments of anonymous referees; the usualcaveatsapply.
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