Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol63.Issue3.Mar2000:
Agricultural Systems 63 (2000) 175±194
www.elsevier.com/locate/agsy
Using economic incentives for pesticide usage
reductions: responsiveness to input taxation
and agricultural systems
K. Falconer a,*, I. Hodge b
a
Department of Agricultural Economics and Food Marketing, University of Newcastle upon Tyne,
Newcastle upon Tyne, NE1 7RU, UK
b
Department 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 agricultural pesticide contamination, given the potential of economic instruments for higher eciency 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 lowinput 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
* Corresponding author.
E-mail address: k.e.falconer@ncl.ac.uk (K. Falconer).
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00007-X
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 environmentally harmful pesticides for more environmentally 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 European 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 precommercialisation 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, approvals-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 Denmark. 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 producers, 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.
The central issue with regard to the implementation of environmental taxation for
pesticides is how farmers might actually respond. The eciency of incentive-based
tools such as taxes hinges critically on supply-side knowledge levels. Policy-makers
must acknowledge the realities of producer decision-making, given the many factors
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
177
in¯uencing actual decisions on farms and aspects such as bounded rationality. It is
important to consider the practical policy implications of deviations between theoretical predictions of response and the actual responses, and then to assess the conditions or pre-conditions 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 production 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 discusses 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.1 The 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 systematic 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 estimation, with the advantage of a strong relationship with technical research. Frequently 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 manageable, only a limited number of alternative enterprises can be included. Typically,
1
Tin and Renwick (1996), for example, considered that production on the sample farms used in their
study was consistent with an assumption of variable pro®t-maximisation as a short-run objective.
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 potentially 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 provide 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-pesticide-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 eciently 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
The tax rate needed to achieve a given level of usage reduction depends on the
¯exibility of farm businesses (i.e. on the range of crop production technologies
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
179
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 to ÿ0.39
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: herbicides ÿ0.69;
insecticides and fungicides ÿ0.81
Elhorst (1990)
Netherlands
Econometric
ÿ0.29a
Falconer (1997)
UK (East Anglian
arable production)
Linear programming
ÿ0.1 to ÿ0.3
Gren (1994)
Sweden
Econometric
Herbicides, ÿ0.93;
insecticides, ÿ0.52;
fungicides, ÿ0.39
Komen et al. (1995)
Netherlands
Applied general
equilibrium modelling
ÿ0.14 to ÿ.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
Petterson et al. (1989) Sweden
Econometric (aggregate level) ÿ0.28c,d
Pest threshold modelling and ÿ0.2
inter-regional linear
programming
Rude (1992)
Sweden
Econometric
ÿ0.22 to ÿ0.32
Russell et al. (1995)
UK (Northwest)
Regression (generalised
demand model)
ÿ1.1
Schulte (1983)
Three German
regions
Farm-level modelling
ÿ0.23 to ÿ0.65e
a
Combined elasticity for fertilisers and pesticides.
With a technological trend elasticity of ÿ0.06.
c
The elasticity for herbicides was ÿ0.69 and ÿ0.81 for pesticides.
d
Papanagiotou (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.
e
Most dierences were attributed to dierences in natural conditions across the regions.
b
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 expenditure 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 contributing 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 maximisation 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 compared 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 (technology). Riskiness and risk aversion may aect the crop protection strategy (Pannell, 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, agrienvironmental 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.
An important reason why actual responses may be higher than theoretically predicted responses relates to the assumptions used in modelling, especially with regard
to the range of options available to producers. Models based only on current commercial crop production practices may suggest lower price elasticities of pesticide
demand than estimates which consider a wider range of adjustment options. A
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
181
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 recommendations. 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 section discusses farm adjustment options following the implementation 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
model 2 was 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 adjustment. 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
2
See Hazell and Norton (1986).
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 particular 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 realism 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 knowledge 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.
Experimental, ®eld-level data, for 1991±96, from the ADAS (Agricultural Development and Advisory Service) TALISMAN experiment in Cambridgeshire (see
Cook et al., 1996; Green et al., 1996) were used to generate variable cost and yield
data for each crop. The TALISMAN trials took place on well-structured soil in
Cambridgeshire. Annual rainfall is typically around 600 mm [e.g. ranging from a
relatively dry 500 mm in 1995 (Murphy, 1997) to a wet 729 mm in 1993 (Murphy,
1995)]. The trials were part of a 6-year rotation-based study of the agricultural and
ecological consequences of arable farming with reduced inputs of nitrogenous fertilisers and pesticides. A split-plot design was used, with rotations and nitrogen inputs
as the main treatments and combinations of `low-input farming' (LIF) and `current
commercial practice' (CCP) rates of herbicides, fungicides and insecticides as subtreatments. The design of the trials allowed the eect of reducing the rate of herbicides, fungicides or insecticides to be assessed, separately or in combination with
reductions in the other pesticide groups. The ®ve crop protection regime sub-treatments for each crop included CCP regimes and four LIF regimes [i.e. low inputs of
all sprays (LOW), low herbicide inputs (LOWH), low fungicide inputs (LOWF) and
low insecticide inputs (LOWI)]. Pesticides were applied at manufacturers' labelled
recommended rates for the CCP crops; this provided a recognised standard against
which comparisons could be made. Nitrogen rates were determined by the ADAS
fertiliser planning service (`Fertiplan') based upon previous cropping, soil type and
yield predictions. Reductions in pesticides on LIF crops were made primarily by omitting applications wherever possible. However, if predicted crop loss was estimated at
183
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 production 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 incorporating 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
Half N
100
92.15
97.64
89.66
100
92.41
98.4
91.49
99.21
91.88
SW
Full N
Half N
100
98.83
91.95
85.91
89.60
88.09
98.66
92.28
99.83
97.82
SOSR
Full N
Half N
100
47.15
55.28
33.33
69.11
33.33
75.61
25.20
79.67
41.46
WOSR
Full N
Half N
100
49.11
39.29
52.56
58.04
30.36
107.14
115.18
70.54
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.
3
No data were available for non-chemical practices such as mechanical weeding.
184
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 oilseeds 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 models 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). Fieldexpert 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 survey 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.4 The 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
4
It was assumed that other management factors remained constant, although in practice ®eld margins
might be managed dierently under LIF, for example, to encourage bene®cial insects.
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
185
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 estimate 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
Fig. 1. Summary of the farm-level resource allocation model.
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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.
b
Data from North, personal communication (peas were not grown in TALISMAN trials).
model. The published estimate covers a much wider range of crops (and also livestock, 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' scenario, actually increasing farm management and investment income (MII) with a
187
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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
±
±
±
±
±
±
±
±
±
±
±
±
a
h, Herbicides; f, fungicides; i, insecticides and molluscicides.
Table 5
Optimal crop rotations for the two models as a percentage of total farmed area
Winter wheat
Winter barley
Winter oilseed rape
Winter ®eld beans
Set-aside
Conventional (CONV) (%)
Alternative (ALT) (%)
50
15
20
±
15
50 (low pesticide inputs)
5 (low pesticide inputs)
20 (low fungicide inputs, low nitrogen)
10 (low pesticides inputs)
15
signi®cant reduction in pesticide usage and overall pesticide hazard (especially for
some ecological dimensions such as ®sh and aquatic organisms). The positive outcomes 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).
188
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
Table 6
Base-line CONV/ALT comparisons
CONV
ALT
ALT as a% of CONV
47 001.0
57 688.0
122.7
188.0
230.8
122.7
23 845.0
13 855.0
58.1
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
13 986.3
11 055.4
79.0
Total farm management and
investment income (MII) (£)
MII per hectare (£)
Total spray expenditure (£)
Total spray units
Total fertiliser expenditure (£)
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 communication). 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 CCP is an 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 ignorance) 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
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
189
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-equilibrium, resting on the assumption that only a small proportion of the total population of farms of this type were subjected to policy implementation. 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 mechanisms, 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 technical options means greater adjustability in the face of policy restraints, and increases the likely appropriateness of economic incentives over regulations. However, if
decision-makers perceive their compliance ¯exibility to be low (e.g. if little information 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.
Elasticity estimations are useful in assessing the consequences of implementing an
input tax at any given level. Low pesticide price elasticity implies that high tax levels
would be necessary to achieve signi®cant usage reductions, and thus environmental
eects in the short-run at least; the expectation is that longer-run elasticities would
be higher and adjustment greater. Despite an apparent low price elasticity of
demand for agricultural pesticides, there are still valid arguments for a role for pesticide taxation in environmental policy frameworks. Politically acceptable tax rates,
exemptions for some users or scheduled future tax increases may play an important
role as policy `sign-posts', encouraging improvements to be made by farmers in
anticipation of more stringent policy-making in future and starting the process of
innovation and process change. Some lead-time will be needed for incremental
adjustments to be made and as knowledge and experience accumulate. However,
two assumptions are critical: ®rst, that any reduction in usage can be acceptably
postponed, perhaps for a considerable period, and secondly, that new technologies
and long-run changes in practices will actually be stimulated. It appears, on the basis of
190
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 a package of 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 bettertailored 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.
A precursor to skills provision is a better understanding of the consequences of
dierent management practices, and in particular, the ability to demonstrate their
pro®tability. Further agronomic and economic research is urgently needed. Lowdose concepts may be relatively straight-forward to implement since they are easy to
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
191
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 reductions are possible without signi®cantly prejudicing ecacy. The supply by manufacturers 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 environmental, 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 elasticities 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 outside 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.
If farmers are producing as eciently as possible prior to policy implementation,
policies such as pesticide taxation that aim to enhance environmental quality will
necessarily do so at the expense of productivity and farm incomes. However, it
appears from the results of empirical modelling that arable farmers may not be
pro®t-maximising at present, given the comparative results for the baselines of the
CONV and ALT models. This ®nding supports many other studies of alternative,
experimental low-input agricultural systems. It appears that reductions in arable
pesticide usage could be achieved with relatively small losses and perhaps even gains
192
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 permission 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 usual caveats apply.
References
Aaltink, A.J., 1992. Economische gevolgen van de beperking van het bestrijdings middelengebruik in de
akkerbouw. Thesis, Department of Agricultural Economics, University of Wageningen.
Agricultural Development and Advisory Service (ADAS), 1996. Awareness and Use of IPM and ICM in
Agriculture and Horticulture. London, Department of the Environment.
Auld, B.A., Tisdell, C.A., 1
www.elsevier.com/locate/agsy
Using economic incentives for pesticide usage
reductions: responsiveness to input taxation
and agricultural systems
K. Falconer a,*, I. Hodge b
a
Department of Agricultural Economics and Food Marketing, University of Newcastle upon Tyne,
Newcastle upon Tyne, NE1 7RU, UK
b
Department 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 agricultural pesticide contamination, given the potential of economic instruments for higher eciency 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 lowinput 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
* Corresponding author.
E-mail address: k.e.falconer@ncl.ac.uk (K. Falconer).
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00007-X
176
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 environmentally harmful pesticides for more environmentally 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 European 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 precommercialisation 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, approvals-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 Denmark. 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 producers, 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.
The central issue with regard to the implementation of environmental taxation for
pesticides is how farmers might actually respond. The eciency of incentive-based
tools such as taxes hinges critically on supply-side knowledge levels. Policy-makers
must acknowledge the realities of producer decision-making, given the many factors
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
177
in¯uencing actual decisions on farms and aspects such as bounded rationality. It is
important to consider the practical policy implications of deviations between theoretical predictions of response and the actual responses, and then to assess the conditions or pre-conditions 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 production 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 discusses 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.1 The 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 systematic 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 estimation, with the advantage of a strong relationship with technical research. Frequently 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 manageable, only a limited number of alternative enterprises can be included. Typically,
1
Tin and Renwick (1996), for example, considered that production on the sample farms used in their
study was consistent with an assumption of variable pro®t-maximisation as a short-run objective.
178
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 potentially 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 provide 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-pesticide-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 eciently 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
The tax rate needed to achieve a given level of usage reduction depends on the
¯exibility of farm businesses (i.e. on the range of crop production technologies
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
179
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 to ÿ0.39
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: herbicides ÿ0.69;
insecticides and fungicides ÿ0.81
Elhorst (1990)
Netherlands
Econometric
ÿ0.29a
Falconer (1997)
UK (East Anglian
arable production)
Linear programming
ÿ0.1 to ÿ0.3
Gren (1994)
Sweden
Econometric
Herbicides, ÿ0.93;
insecticides, ÿ0.52;
fungicides, ÿ0.39
Komen et al. (1995)
Netherlands
Applied general
equilibrium modelling
ÿ0.14 to ÿ.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
Petterson et al. (1989) Sweden
Econometric (aggregate level) ÿ0.28c,d
Pest threshold modelling and ÿ0.2
inter-regional linear
programming
Rude (1992)
Sweden
Econometric
ÿ0.22 to ÿ0.32
Russell et al. (1995)
UK (Northwest)
Regression (generalised
demand model)
ÿ1.1
Schulte (1983)
Three German
regions
Farm-level modelling
ÿ0.23 to ÿ0.65e
a
Combined elasticity for fertilisers and pesticides.
With a technological trend elasticity of ÿ0.06.
c
The elasticity for herbicides was ÿ0.69 and ÿ0.81 for pesticides.
d
Papanagiotou (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.
e
Most dierences were attributed to dierences in natural conditions across the regions.
b
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 expenditure 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 contributing 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 maximisation 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 compared 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 (technology). Riskiness and risk aversion may aect the crop protection strategy (Pannell, 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, agrienvironmental 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.
An important reason why actual responses may be higher than theoretically predicted responses relates to the assumptions used in modelling, especially with regard
to the range of options available to producers. Models based only on current commercial crop production practices may suggest lower price elasticities of pesticide
demand than estimates which consider a wider range of adjustment options. A
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
181
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 recommendations. 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 section discusses farm adjustment options following the implementation 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
model 2 was 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 adjustment. 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
2
See Hazell and Norton (1986).
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 particular 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 realism 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 knowledge 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.
Experimental, ®eld-level data, for 1991±96, from the ADAS (Agricultural Development and Advisory Service) TALISMAN experiment in Cambridgeshire (see
Cook et al., 1996; Green et al., 1996) were used to generate variable cost and yield
data for each crop. The TALISMAN trials took place on well-structured soil in
Cambridgeshire. Annual rainfall is typically around 600 mm [e.g. ranging from a
relatively dry 500 mm in 1995 (Murphy, 1997) to a wet 729 mm in 1993 (Murphy,
1995)]. The trials were part of a 6-year rotation-based study of the agricultural and
ecological consequences of arable farming with reduced inputs of nitrogenous fertilisers and pesticides. A split-plot design was used, with rotations and nitrogen inputs
as the main treatments and combinations of `low-input farming' (LIF) and `current
commercial practice' (CCP) rates of herbicides, fungicides and insecticides as subtreatments. The design of the trials allowed the eect of reducing the rate of herbicides, fungicides or insecticides to be assessed, separately or in combination with
reductions in the other pesticide groups. The ®ve crop protection regime sub-treatments for each crop included CCP regimes and four LIF regimes [i.e. low inputs of
all sprays (LOW), low herbicide inputs (LOWH), low fungicide inputs (LOWF) and
low insecticide inputs (LOWI)]. Pesticides were applied at manufacturers' labelled
recommended rates for the CCP crops; this provided a recognised standard against
which comparisons could be made. Nitrogen rates were determined by the ADAS
fertiliser planning service (`Fertiplan') based upon previous cropping, soil type and
yield predictions. Reductions in pesticides on LIF crops were made primarily by omitting applications wherever possible. However, if predicted crop loss was estimated at
183
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 production 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 incorporating 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
Half N
100
92.15
97.64
89.66
100
92.41
98.4
91.49
99.21
91.88
SW
Full N
Half N
100
98.83
91.95
85.91
89.60
88.09
98.66
92.28
99.83
97.82
SOSR
Full N
Half N
100
47.15
55.28
33.33
69.11
33.33
75.61
25.20
79.67
41.46
WOSR
Full N
Half N
100
49.11
39.29
52.56
58.04
30.36
107.14
115.18
70.54
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.
3
No data were available for non-chemical practices such as mechanical weeding.
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 oilseeds 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 models 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). Fieldexpert 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 survey 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.4 The 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
4
It was assumed that other management factors remained constant, although in practice ®eld margins
might be managed dierently under LIF, for example, to encourage bene®cial insects.
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
185
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 estimate 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
Fig. 1. Summary of the farm-level resource allocation model.
186
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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.
b
Data from North, personal communication (peas were not grown in TALISMAN trials).
model. The published estimate covers a much wider range of crops (and also livestock, 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' scenario, actually increasing farm management and investment income (MII) with a
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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
±
±
±
±
±
±
±
±
±
±
±
±
a
h, Herbicides; f, fungicides; i, insecticides and molluscicides.
Table 5
Optimal crop rotations for the two models as a percentage of total farmed area
Winter wheat
Winter barley
Winter oilseed rape
Winter ®eld beans
Set-aside
Conventional (CONV) (%)
Alternative (ALT) (%)
50
15
20
±
15
50 (low pesticide inputs)
5 (low pesticide inputs)
20 (low fungicide inputs, low nitrogen)
10 (low pesticides inputs)
15
signi®cant reduction in pesticide usage and overall pesticide hazard (especially for
some ecological dimensions such as ®sh and aquatic organisms). The positive outcomes 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).
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
Table 6
Base-line CONV/ALT comparisons
CONV
ALT
ALT as a% of CONV
47 001.0
57 688.0
122.7
188.0
230.8
122.7
23 845.0
13 855.0
58.1
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
13 986.3
11 055.4
79.0
Total farm management and
investment income (MII) (£)
MII per hectare (£)
Total spray expenditure (£)
Total spray units
Total fertiliser expenditure (£)
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 communication). 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 CCP is an 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 ignorance) 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
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
189
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-equilibrium, resting on the assumption that only a small proportion of the total population of farms of this type were subjected to policy implementation. 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 mechanisms, 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 technical options means greater adjustability in the face of policy restraints, and increases the likely appropriateness of economic incentives over regulations. However, if
decision-makers perceive their compliance ¯exibility to be low (e.g. if little information 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.
Elasticity estimations are useful in assessing the consequences of implementing an
input tax at any given level. Low pesticide price elasticity implies that high tax levels
would be necessary to achieve signi®cant usage reductions, and thus environmental
eects in the short-run at least; the expectation is that longer-run elasticities would
be higher and adjustment greater. Despite an apparent low price elasticity of
demand for agricultural pesticides, there are still valid arguments for a role for pesticide taxation in environmental policy frameworks. Politically acceptable tax rates,
exemptions for some users or scheduled future tax increases may play an important
role as policy `sign-posts', encouraging improvements to be made by farmers in
anticipation of more stringent policy-making in future and starting the process of
innovation and process change. Some lead-time will be needed for incremental
adjustments to be made and as knowledge and experience accumulate. However,
two assumptions are critical: ®rst, that any reduction in usage can be acceptably
postponed, perhaps for a considerable period, and secondly, that new technologies
and long-run changes in practices will actually be stimulated. It appears, on the basis of
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K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 a package of 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 bettertailored 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.
A precursor to skills provision is a better understanding of the consequences of
dierent management practices, and in particular, the ability to demonstrate their
pro®tability. Further agronomic and economic research is urgently needed. Lowdose concepts may be relatively straight-forward to implement since they are easy to
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
191
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 reductions are possible without signi®cantly prejudicing ecacy. The supply by manufacturers 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 environmental, 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 elasticities 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 outside 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.
If farmers are producing as eciently as possible prior to policy implementation,
policies such as pesticide taxation that aim to enhance environmental quality will
necessarily do so at the expense of productivity and farm incomes. However, it
appears from the results of empirical modelling that arable farmers may not be
pro®t-maximising at present, given the comparative results for the baselines of the
CONV and ALT models. This ®nding supports many other studies of alternative,
experimental low-input agricultural systems. It appears that reductions in arable
pesticide usage could be achieved with relatively small losses and perhaps even gains
192
K. Falconer, I. Hodge / Agricultural Systems 63 (2000) 175±194
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 permission 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 usual caveats apply.
References
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