Background Paper Prepared for the
Organisation of Economic Cooperation and Development (OECD)
Workshop on Pesticide Risk Indicators
21-23 April, 1997
Copenhagen, Denmark
(revised May 19, 1997)

Department of Fruit and Vegetable Science
Cornell University
Ithaca, New York, USA

E-mail: LCL3@cornell.edu

Support for this research has been provided by NRICGP/USDA Grant No. 95-37313-1940 and by the Department of Fruit and Vegetable Science, Cornell University, Ithaca, NY USA.

Many groups of individuals and types of institutions--including farmers and other land managers, consumers and consumer groups, food retailers and agribusinesses, regulatory agencies and regulatory "watchdogs"--have a stake in better understanding the non-target impacts of pesticides used in agriculture, landscaping, materials preservation, and elsewhere in modern society. In the past, much of the attention on pesticides focused narrowly on monitoring costs to producers and efficacy in controlling target pests. When non-target impacts were considered, the quantity of pesticides applied was generally used as the only indicator of risk. However, especially as new classes of more potent chemicals have been developed which require far lower dosages than older types of pesticides, it has become increasingly apparent that pesticide weight is not a sufficient proxy for risk. Thus a diverse research community is working to develop methods for more accurately estimating the impacts of pest control products and methods on one or more environmental indicators.

This paper is an overview of pesticide impact assessment systems which compare the characteristics and effects of different pest controls and generate an index or ranking of pest control options, or which compare pesticide risks over time or in different places. These type of assessment tools are sometimes called "pesticide risk indicators." The paper begins by defining and critically examining this EIA methodology. A typology is suggested for distinguishing among three categories of assessment systems: (1) decision aids for farmers/growers and other land managers, (2) research and policy tools for use by governments, industry or academia, and (3)"ecolabeling" systems designed to influence consumer opinion and market behavior. Systems are differentiated by objective, arena of activity, target audience, and by how an economic component figures into the assessment. Methods for constructing and calculating the rankings are described in the fourth section. The fifth section illustrates the procedures and specific uses for a number of farmer decision aids and policy tools. The paper concludes by looking to the future, at trends in the development and demand for pesticide ranking and indexing systems.

• simulation of environmental effects--either by computer modeling or physically in microcosms or mesocosms;
• sampling, monitoring and tracking changes in biophysical indicators (indicators such as species diversity, soil respiration rate, and chemical levels in the environment);
• surveys and qualitative research methods, which include observation, interviewing, and participatory action, with tabulated or descriptive presentation of results; and
• indexing or ranking the extent and severity of pesticide (both chemical and non-chemical pest controls) impacts on one or more environmental indicators.

The last of these approaches--indexing systems--draw on, synthesize and compare information collected by other EIA methodologies and thus become tools for decision-making and policy formation. Many types of environmental variables can be evaluated with the indexing methodology, not only those which can be sampled, monitored or mathematically modeled. Indexing systems can thus make the leap from assessing test endpoints to also assessing decision endpoints. To illustrate the difference using an example from pesticide impacts on honey bees: Measures of pesticide toxicity to bees can provide information about pesticide lethality (LD_{x HB}), or the effective dose at which certain behaviors (such as nectar-collecting activity) change (ED_{NECTAR-COLLECTING}). However, a beekeeper is less interested in these test endpoints than in knowing how a pesticide application will affect hive survival or, perhaps, crop pollination. Therefore, the decision points of interest to beekeepers are how the impact on honey bee colonies might be reduced by using a different pest control, a lower dosage or a different time of application.

Pesticide indexing and ranking systems can help answer such questions by comparing alternatives. These methods can also contribute to policy analysis by monitoring trends in pesticide risk and evaluating the success of risk reduction initiatives. Especially when there are significant economic, safety and environmental ramifications from an assessment, it is vitally important that it be based on meaningful environmental criteria and indicators. Natural questions to then ask are: Which environmental parameters should be included in an assessment as descriptors of the environment? What indicators should be used to register effects on the selected environmental variables? and How should these impacts be measured, scored and interpreted?

The images which come to mind upon hearing the term "environmental impact" are different for each of us, depending upon our vantage and how we value the components of the environment. In some contexts the environment is considered distinct from public health and other social impacts, but in this paper all non-target effects are included under the rubric of environmental impacts. A complete set of enviro-social parameters for assessing consequences of pest control activities includes:

• health of farmworkers, consumers and the general public--including impacts on particularly vulnerable sub-populations;
• lethal and sub-lethal impacts on other non-target biota;
• direct and indirect impacts on natural- and agroecosystems, including effects on habitat and food sources;
• consumption and degradation of natural resources;
• pollution of soil, air and water--which can have consequences for human and non-human biota; and
• costs to society for remediation of environmental problems.

The choice of assessment parameters from this vast array of potential indicators is not trivial, as illustrated by the radically different results from several pesticide ranking systems, shown in Figure 1. The rank order of pesticides depends in part upon the components of the analysis--the pesticides considered, the variables assessed, the choice of specific measurable endpoints as the indicators of impacts on these variables; the mathematical structure of the model, including relative weighting of variables and scoring of results; the method for filling data gaps; and whether usage data are factored into the equation (i.e., a ranking by hazard or by hazard potential/risk). A similar comparison by Pease et al. (1996) found only one insecticide--permethrin--among the ten most hazardous pesticides, as rated by both the Environmental Impact Quotient (EIQ) and by a US National Oceanic and Atmospheric Administration model (Kovach et al. 1992). Results differed because of the factors just listed. More specifically, the NOAA focus is on estuarine species while the EIQ has a terrestrial emphasis. Thus dimethoate and parathion get high hazard ratings from the EIQ because both are acutely toxic to birds, but a relatively low NOAA ranking because that model does not consider impacts to avian species. Also, a number of the fruit and vegetable pesticides surveyed by the EIQ were not ranked by NOAA because they are little used in California, where that model was applied. Conversely, the wood preservative pentachlorophenol and persistent insecticide toxaphene are not included in the EIQ dataset, but receive high hazard ratings from the NOAA model because of pentachlorophenol's high bioconcentration factor (an environmental parameter not included in the EIQ) and toxaphene's toxicity to fish and invertebrates.

Note: These rankings are not weighted by pesticide release, usage, environmental concentration, exposure or typical dosage and thus should not be interpreted as presenting the greatest danger or risk.

The question of whether and how to weight and integrate impacts on different organisms and environmental components is also not trivial. The answer can determine how policy-makers and the public come to view different products. The lists of "most dangerous" compounds--which are the succinct results of pesticide impact assessments--are remembered long after the basis for drawing the conclusions is forgotten. Despite these issues regarding the application and interpretation of ranking systems, many constituencies are demanding some type of handle for comparing the environmental impacts of different pest control products and methods. Thus researchers from a range of disciplines are attempting to construct meaningful and reliable evaluative tools.

In part the difference in terminology simply depends on what one is accustomed to using, but it may also reflect a difference in disciplinary orientation and methodological approach. The products generated may also be somewhat different, and put to different uses. Systems ecologists and other systems analysts think in terms of describing and quantifying energy and resource flows in complex systems, with a primary objective of identifying causes and effects of change in systems, and a penchant for understanding interactive effects. Pesticide impact assessments drawing from this disciplinary heritage--particularly those used as farmer decision tools--often retain a systems perspective and resultant complexity, as well as the related terminology. On the other hand, policy makers looking for a more terse analysis may have a predilection for "indicators" of trend highlights, which do not pretend or attempt to holistically model systems.

In this paper the term "indicator" generally refers to the component parts of assessment systems rather than to the totality of the assessment. These indicators are the measurable endpoints that provide information about an effect or impact on the environment. For example, the half life of a pesticide in the soil is an indicator of how long the pesticide persists in the environment. An indicator of pesticide risk to birds may be a function of the pesticide’s acute toxicity to birds, its foliar persistence and its impact on food sources for birds (e.g.: on insects, worms, seeds). I.e.: Risk Indicator_BIRDS = f(Acute Toxicity, Foliar Persistence, Food Source). Risk to birds may be calculated from the LD_{50 BIRDS}, Half Life _LEAVES and LD_{50 WORMS} which are, respectively, the measureable endpoints that indicate the impact on these toxicity and exposure variables. Yet another example of an indicator is the Groundwater Ubiquity Score, calculated as GUS = log 10(t1/2) x [4-log10 (K_OC)] (Gustafson 1989). This algebraic model is used by itself as an indicator of pesticide leaching potential and is also used in combination with other indicators of different environmental impacts to create multiattribute impact assessments. Composite or integrated assessment systems are generally composed of a set of such risk or impact indicators.

Another semantic distinction is that in this paper the term impacts is generally used, rather than either hazards or risk, to describe consequences of pest control activities. The rationale is that the word hazard is supposed to convey a type of harm, e.g.: "lethal hazard to amphibians," while risk is supposed to convey a probability of harm. By incorporating pesticide usage and exposure indicators, indexing systems typically convey more information than merely a listing of hazards, but they are generally constrained by their structure and available input data to be less than a probablistic assessment of risk. The use of the term impacts is intended to capture this approximation of "hazard potential" and risk characterization.

One of the primary objectives for developing methods for assessing environmental impacts of agriculture is to be able to respond to questions such as this from farmers and other land managers. Decision makers in the field need information and methods for choosing pest control practices which have the least negative impacts on the environment, and on human health and safety. Policy makers then need to make broad brush appraisals of the impacts of such choices.

More broadly, the impetus to develop and use pesticide impact ranking systems is to facilitate the shift to more benign pest control products and practices, and thus reduce pesticide load on the environment. It is an uphill battle: Total worldwide pesticide use is at a record high--4.7 billion pounds in 1995--and continues to grow. In the US, pesticide use reached an all time high of more than 1.2 billion pounds in 1995. Despite the fact that many newer pesticides are used at very low volume per unit area, this is more than double the amount applied 35 years ago when Rachel Carson wrote Silent Spring (1962). The biggest users remain in North America (29% of sales in 1996) and Western Europe (26% of sales), followed by all of Asia with 25% (PANUPS 1997). However, the largest increase in sales is in Latin America, accounting for one third of global sales growth and more than 10% of use. International cooperation in pesticide impacts assessment is therefore essential, because some of the most hazardous chemicals that have been banned from some countries continue to be used elsewhere, often in tropical climates where risks may be greater due to social as well as biophysical factors such as higher temperatures and greater skin hydration (PANUPS 1995).

Simply projecting risk from pesticide usage data is not sufficient, and is even less meaningful now that highly potent chemicals are used at very low volumes. Risk-weighted indexes of usage are a more powerful measure and avoid the shell game of shifting risk from less potent chemicals used at higher volumes to more potent chemicals used at lower volumes. However, the analytic task of pesticide impacts assessment has become more complex as the number of chemical classes of pesticides has quintupled from approximately 25 in the 1970s to about 130 today, and the modes of pesticide activity affecting the environment have also diversified (Wauchope et al. 1994).

About 1% of the 8 million chemicals listed in the Chemical Abstract Services Registry are commercially produced. Although fewer than 1% of these are used for pest control (Davis 1994), the compilation and interpretation of hazard data for 500-1000 pesticidal chemicals (the higher number reflects newly identified microbial and biochemical pesticides that have been added to the synthetic chemical arsenal of pesticides) is a daunting task. It is, however, a necessary prerequisite for development of reliable and meaningful comparative and summary assessments of pesticide impacts on the environment. Data limitations and complexities are just one of the challenges in developing pesticide environmental impact ranking systems. Other issues are the identification and integration of suitable environmental indicators; estimation of situation-specific variabilities; the lack of a common currency for comparing disparate types of impacts; and the bias against considering future impacts.

Choosing Environmental Indicators and Deciding How to Integrate Them

No one species or group of biota reacts most sensitively to all pesticides, and thus is useful as a surrogate for all others in toxicity testing. With other environmental perturbations as well, we cannot rely on a single indicator species or abiotic effect to tell all we need to know about impacts of management decisions. Thus there is a tension in the development of assessment systems between the advantages of ranking pesticides based upon impacts in one environmental dimension versus advantages of composite assessments of impacts in multiple environmental dimensions. The drawback to the former is that no single parameter can fully describe an environmental impact, and thus conclusions can be misleading unless system objectives and limitations are made explicit. On the other hand, the challenge of multi-parameter optimizations are in specifying test endpoints of significance and then integrating results--either into a composite picture of environmental impacts or by prioritizing the most critical impacts in a given situation. To grasp the conceptual challenge this poses, think about how you would weigh impacts on human beings in relation to impacts on other biota, especially if they were dissimilar in magnitude and type.

There is also no one set of social or environmental indicators that is most appropriate to use in assessing impacts under all sets of circumstances. Even if a system developer were to decide upon a protocol for weighting, valuing and integrating impacts in the context of one assessment scenario, these issues would re-emerge in a different configuration when the question again arises on a different scale, for a different target audience, or with different situation-specific conditions. To illustrate: the types of data required for a farmer to choose a least impact but efficacious pest control may not be the same as the data required for a national assessment of agriculture practices. IPM farmers want to avoid pesticides that harm parasites and predators of the pests in their fields, but these producers might be misled by a decision model based on national data for pesticide impacts on beneficial organisms. The only such data included in the US EPA Ecological Effects dataset, for example, are acute toxicity of pesticides to worker honey bees (Atkins et al.; US EPA 1996). Even were toxicity dose responses comparable for honey bees and other beneficials, the significance of effects on these groups of organisms is likely to be quite different. When honey bees are repelled from a field by pyrethroid pesticides, for example, they survive and move on to another nectar source. However, if beneficial parasites and predators are repelled from a field, they are then not available to work in that field as biological control agents. As shown by this example, the honey bee acute toxicity data may not be sufficent or appropriate to use in a micro scale assessment for advising farmers about pesticide effects in their agroecosystems, but may be valuable as a proxy for other ecological effects in a macro scale analysis of pesticide risk trends. The design of an assessment system must, therefore, be appropriate to the objectives of the audience served.

Some assessment systems include variables that do not really assess environmental impact. These include the variable "availability of alternatives" which could bias comparative assessments of pesticides toward dangerous pesticides with no conventionally-known alternatives. Production cost factors are also sometimes included in assessment equations alongside environmental factors.

Dealing with Situation-Specific Variability

Environmental impacts result from interactions among inherent pesticide properties and situation-specific factors, including particulars of the site, weather conditions, the diurnal and seasonal timing of application, and the application method (packaging, equipment, location, and degree of care). Interactive effects are highly variable, so that it is difficult to make reliable, application-specific predictions of ecological effects. The challenges of incorporating this variability have been addressed in various ways, depending in part on the decision-making context and designated user group. The complexity of indexing systems is generally a function of methods used to estimate exposure: the simplest systems generally consider only toxicity (and to one group of organisms) or use toxic release or production figures as a surrogate for exposure. More complex systems also evaluate one or more exposure pathways (Davis 1994).

Finding a Common Currency

Data Limitations

Data are required at all stages of environmental assessment of agriculture. Because many environmental impacts are generated on different temporal and spatial scales than they are experienced, the complete set of data for assessing these impacts cannot be collected on-farm. This important factor distinguishes environmental assessments from farm production cost assessments.

However, toxicological and ecological effects datasets of pesticides are incomplete. Of the 150 commonly used pesticides evaluated by Pease et al. 1996, data for invertebrates were available for 44 pesticides and persistence (field half-life), for 106 pesticides. In addition, some of the existing ecological effects data are inappropriate to use for assessing relative impacts because standardized testing protocols were not used and so the data are not comparable (Levitan et al. 1995). Moreover there are very limited data and no standardized datasets on impacts of new biopesticides, such as microbial and fungal pesticides. These gaps and inconsistencies in data have been less of an impediment for pesticide screening systems and case-by-case evaluations (as is done for pesticide registration), but complete and reliable datasets are critical for comparing relative impacts or creating a rank order of pesticides.

Most available data on pesticide environmental impacts result from single-species toxicity tests mandated for pesticide registration. Ecotoxicologists have questioned the predictive value of such tests, noting that interactive effects of pesticide inputs at the community and ecosystem levels can be different than inferred from single-species effects, and that the higher level impacts can be of greater long-term environmental significance (Cairns 1986, 1991; Karr 1992). Unfortunately, no standardized datasets exist for pesticide impacts at these higher levels of ecological organization---and data availability strongly influences which parameters and types of impacts can be included in a comparative assessment system.

In addition to limitations associated with testing single species of organisms, most ecotoxicological data are also limited because the test pesticides are generally applied in single doses of individual active ingredients, whereas biota in the environment are constantly exposed to chemical mixtures that change spatially and over time (Yang 1994). Acute impacts may result from exposure to pesticide mixtures, but long-term impacts may also result from mixtures of pesticides with other chemicals in the environment. Also datasets for impacts at standard pesticide dosages may not suffice for assessing risk from aberrantly high doses, such as those following a spill or toxic release, or at the low dosages that are only recently becoming sufficiently suspect to cause concern. Existing datasets also may not be sensitive to impacts on particularly susceptible populations--the unborn, the very young and rapidly growing, pregnant females, people on medications, and biota (human and other) whose immune systems are compromised. Evidence is accumulating which show that mixes of certain chemicals at individually-low doses can have impacts that may be magnitudes of order greater than simple additive individual low-dosage effects (McLachlan and Arnold 1996). Cumulative impacts from repeated or extended exposures can also be different than impacts from single, larger exposures. Bioconcentration of fat soluble compounds has long been known to be a cause of cumulative impacts. However, cumulative effects also result from weakened immune systems and from allergic and other auto-immune responses triggered by chemical sensitivity. These cumulative effects involve short-lived and water soluble compounds as well as persistent culprits. Little is known about such cumulative and interactive effects, especially at low or fluctuating levels and in mixtures, and particularly in terrestrial systems. Yang (1994) concludes that:

• the toxicology of long-term, low level exposures to chemical mixtures produces subtle effects, unlike acute toxic responses to higher doses;
• such toxic interactions are possible at environmentally-realistic levels;
• the toxic responses may be from unconventional endpoints that are not usually tested;
• there is a possibility that residual effects may become interactive with later exposures; and
• these exposures may pose a safety risk to the public.

While these comments are intended to apply to human subjects, these principles and concerns can probably be extrapolated to non-human biota, some populations of which may be more vulnerable to such risks because of limited mobility and physiological factors. We should have to assume (and indeed anecdotal evidence is being compiled which supports this point) that not only do we learn about possible impacts on human beings from mammalian studies, but also that the sublethal disorders noted in human populations may well have parallel effects on other non-target organisms.

Many ecologists have suggested that environmental assessments consider indicators of impacts on organisms in all trophic positions and for all key roles in basic ecological processes. This would include the role of invertebrates and microorganisms in decomposition and humus formation, and the role of herbivores in interactions with vegetation (De Snoo et al. 1994). The problem with this idealized vision is of course that there is no dataset covering this array of ecological indicators. Moreover, for many of these indicators there are no protocols for collecting data and/or no accepted correlations between the direction and magnitude of presumed cause and effect. This results in an interesting tension between the knowledge among biophysical scientists of what would ideally constitute an array of test endpoints for a holistic assessment of environmental impacts, and the paucity of data to make such evaluations possible.

Bias Against Future as Compared to Present Impacts

Long-term and cumulative impacts are more difficult to comprehend and quantify than short-term impacts. As a result, less data are available and less weight tends to be given to these impacts in environmental assessments. We also tend not to consider impacts associated with future events, such as the future leaking of improperly stored pesticides, changes in soil pH leading to release of previously bound chemicals, narrowing of the gene pool, or increased pest resistance to pesticides (Riha et al. 1997). In general, there is a bias against variables for which data are difficult to collect or for which results are uncertain.

The remainder of this paper takes a pragmatic look at how assessments of pesticide impact can be structured to meet specific objectives, within the limits of current knowledge and technical information.

• decision aids used by farmers, farm advisors (e.g. Extension Agents) and other land managers in choosing among pest control options and evaluating the impacts of their choices
• research and policy tools generated and used in government, industry and academia
• "ecolabeling" systems (also called "green labels") designed to influence consumer opinion and market behavior.

Not only are there multiple societal values involved in estimating potential hazards of pesticide use, but also methods, scale of analysis, and results differ depending upon target audiences and intended uses of the indicator. Some assessment tools wear more than one hat, but most are not equally suited for multiple roles. This section and the one which follows lay out some of the principal and idealized characteristics and distinctions among the types of assessments on the basis of the following factors:

• objectives
• decision maker
• arena of activity
• unit of analysis
• economic dimension
• factors considered
• format of results
• method of calculation

Another handle or typology for discriminating among assessment tools is by whether systems are based on impact or behavioral criteria and indicators. Indicators of impacts include many pesticide test endpoints, such as single species toxicity test results (e.g.: the LD₅₀), estimates of exposure, measures of residues on food and in the environment, sublethal effects (e.g.: impacts on behavior, reproduction, genetic diversity), secondary impacts on habitat and food sources, and impacts at higher levels of ecological organization (e.g.: impacts on species richness, biomass, ecosystem productivity). As were detailed earlier, assessments based upon impact criteria have been constrained by data limitations and inconsistencies.

In contrast to indicators of impacts, examples of behavioral indicators include: Was X crop produced by organic methods? Does this farm practice Integrated Pest Management (IPM)? Which/ How many/ What types of IPM techniques? Were biochemical or synthetic chemical pesticides used to control target pests? Assessments based on this type of indicator are not similarly constrained by gaps in the data because data points are observational. There are really two types of indicators of behavior: those based on current behaviors and those based on future or promised behaviors. Examples of the latter include the promise that fewer pesticides will be used by producers in coming years, or promises of better health safeguards for farmworkers. Assessments based on promised behaviors perform some very useful functions: They are used as ecolabeling criteria where the objective of the system is to use incentives to develop compacts between producers and accreditors in order to shift behaviors. They may also be useful in developing policy tools which project future risk trends.

Systems based on behavioral indicators are a creative means for circumventing some of the difficulties of multiattribute optimization, and the problem of gaps in impact data. They do so by assuming a positive relationship between certain sets of behaviors and impacts. More specifically, behavioral assessments are generally predicated on the assumption that IPM and organic methods are more benign in their environmental impacts than post World War II "conventional" chemical pest control strategies. This is why people "buy organic" and why the IPM research community is interested in developing IPM accreditation standards and IPM ecolabels. However, assessments based on impacts data are essential for undergirding, supporting and challenging these implicit assumptions about the impacts of certain behaviors.

In the same way that the term "environmental impacts" is used in this paper as a shorthand for the broad array of impacts that are sometimes separately listed as public health, ecological, economic or social, the phrase "farmer decision tools" is used here as a shorthand to refer to all site-based, field-scale decision aids intended to be used by property managers. This group does include farmers, but also includes homeowners and others (e.g.: lawn maintenance companies, golf course managers, foresters, etc.) who must deal with pests of buildings and grounds. In other words this typology and these assessment tools, are not limited to agricultural applications.

The objective of this type of assessment tool is to inform people who make pest management choices about potential environmental consequences of their decisions. Farmer decision models are generally structured as a comparison of pest control options, based upon some subset of key enviro-social indicators. As decision aids, the models are most reliable when tailored to meet situation-specific conditions for a given field or farm. However, the manipulation of situation-specific data (e.g.: soil type, local climate, application timing and equipment) can be cumbersome and overwhelming, so that succinct and user-friendly measurement tools may find more widespread use. Some of the seemingly more simple models make use of variables which impute situation-specific characteristics, rather than actually measuring and utilizing situation-specific data.

Because of the complexity of determining which pest control management practice is "best" under a particular set of situation-specific conditions, the development of reliable decision aids for farmers presents a formidable challenge. The results (recommendations) of most such assessments should be linked with the decision making process because the ranking of pest control options may differ depending upon the situation-specific conditions. Thus farm-scale decision tools may best be presented in a workbook format or as a computerized expert system. These formats, unlike standalone rankings of pesticides by risk, permit "if-then-else" routines that can be responsive to situation-specific variability.

The typical unit of analysis in producer decision tools is either a discrete pest control application or practice, or else the compendium of decisions made during one or more production seasons. Together these decisions constitute a pest control strategy. Results are often expressed as a listing of unitless impact points for an array of pest control products. Results may also be expressed as a similar impact index of pest controls, but specifically as these are used to combat a given target pest (see figures accompanying the PestDecide© farmer decision tool for an example). The reason for this level of specificity is that pesticides are applied at different rates and times, with different expected efficacy, to combat different target pests. Non-target impacts may differ accordingly.

Farmer decision tools typically identify pest controls by trade product names--rather than only by the common name of the pesticide active ingredient--because efficacy and non-target impacts also differ with formulation. Unfortunately, input data are rarely specific to formulated products and, therefore, the impacts of formulations are at best adjusted by the concentration of active ingredient and the type of product (e.g.: granular, liquid, vapor). However, impact data do not generally reflect the actual characteristics of the trade product, as affected by the mixture of adjuvants and other inert ingredients in the formulation. The full season time horizon that is considered by a number of farm-scale assessments provides a broader and more realistic picture of environmental impacts than a snapshot evaluation of a single pest control application. Some examples: The application of a lower impact (and thus seemingly preferable) pest control at one point in the season may necessitate additional subsequent applications of various pest controls later in the season, causing a larger cumulative impact. Repeated applications of certain lower impact pesticides may lead to more rapid build-up of pest resistance, and thus ultimately to a renewed reliance on more hazardous pesticides. Or the buildup of weed seeds following a period without herbicide use could necessitate more herbicide use later in the season or in subsequent seasons. These interactive and cumulative factors would be missed by an assessment limited to the impacts from a single pest control application. Some of the full season assessments are crafted to do double duty as ecolabels, with accreditation offered to the yields of production scenarios that meet threshold criteria (or which receive a certain number of impact points).

The value of assessment tools for farm managers depends on their ability to provide valid information pertinent to specific pest management decisions. The problematic of these decision aids is to integrate the many factors contributing to each management decision--a farmer’s personal concerns for applicator safety and the agroenvironment, altruistic or regulated interests in protecting human health and the broader environment, business interests in production costs and in securing a market niche. An assessment model would be hard pressed to deal with all of these factors, but it is not unusual for farmer decision tools to include production cost estimates for each pest management choice or scenario. In contrast, assessments designed as policy tools may instead weigh economic costs to society at large, whereas ecolabels tend to leave the economic assessment to the consumer (or intermediaries) in the marketplace.

In this typology a wide range of assessment systems used for many distinct purposes fall under the aegis of policy tools. The types and objectives of policy tools include:

• screening systems to enable regulatory agencies and industry to quickly assess preliminary data for danger signals about new or potential pest controls
• analyses by government agencies and environmental watchdog groups, either to monitor trends in pesticide use and risks over time, or to compare risks of using different pesticides and pest management regimes
• evaluations of potential risks from individual pest control products and practices, to use as basis for regulations, restrictions and warnings. (For examples, see the WHO and US EPA human health toxicity warning systems designed for pesticide labels.)
• criteria for programs to tax pesticide use
• evaluations of the success and/ or costs and benefits of programs and policies. Also, evaluations of which risk reduction policies and research trajectories have the greatest impact or provide greatest value for their cost. (e.g.: Is it more effective to tax pesticide use or to regulate spray buffers around fragile areas or to invest in better application technology?)
• assess adoption of IPM on farms and set IPM research and extension priorities. Evaluate the success of IPM in meeting the objectives of controlling pests in a cost effective and more environmentally benign manner. Determine if IPM makes a difference. To whom? At what cost?

The types of decision-makers targeted by policy tools are as different as this list of objectives. They include IPM research and extension teams and their funders working in academia, extension, and budget offices of government and foundations, as well as policy-makers at all levels of government and in the environmental research and activist communities. The arena of activity for policy tools could be said to be "at the roundtable," in contrast to the farmer’s field or the marketplace. The unit of analysis for assessing the success of IPM programs may be a single production unit (i.e.: a farm) or the set of farms in a region which produce similar commodities. For other policy tools, the unit of analysis may be the total quantities of a pest control product used in a state, a nation, or internationally. Thus the situation-specific variability and details of importance in making pest management decisions at the field scale often wash out at this larger scale. Because results of the analysis will not differ, for the most part, on the basis of situation-specific responses from producers, risk indexes used for policy purposes may be published separately from the decision-making process, as tables in a scientific paper or as a pesticide label.

Another distinction is that most national datasets lump all forms of a pesticide active ingredient and all the formulated products in which it is found, whereas the ideal farm-scale decision tool will be sensitive to the different toxicities of formulated products (due to adjuvants and different formulation types) and to factors which mitigate exposure to non-target organisms (including packaging and application equipment). A major difference between the objectives of farm-scale decision tools and most assessments for policy purposes is that the former are generally constructed as comparisons among available options for control of specific pests--since a choice must be made from among the finite group of alternatives. Risk assessments for policy purposes are rarely constrained in the same way. Thus in a ranked list of hazardous pesticides all of the options for a specific task may be among the most or least hazardous.

Although a number of assessment tools have been created for the policy and research arenas, systems which holistically measure and express global (or other large scale) pesticide risk and trends in risk reduction are still at basic stages of development. Procedurally, most such ranking systems weight toxicity factors by pesticide usage figures to create a set of most hazardous, highly used pesticides. For the most part, risk to just one or a very few enviro-social indicators is assessed (most usually these are acute and/or chronic lethality to human beings, and water pollution). Data which would enable a more comprehensive assessment of non-target impacts are uneven and unavailable for many other important test endpoints, and for many pest control chemicals and alternative products. Were data available, methods for integrating and weighting multiple criteria are still imperfectly developed for these purposes, as they are also for farm-scale decision aids. Beyond these challenges, additional questions remain regarding development of pesticide risk reduction yardsticks at a national or international scale:

• Can/should broad scale measurement tools be constructed as extrapolations from (in essence a summation of) farm-scale decision aids? Is it possible to sum from different situation-specific realities?
• How should geographically-different factors be accounted--including both regional climate and soil differences, and behavioral and institutional differences in such things as protections afforded pesticide applicators?
• How should pesticide risk policy tools deal with variability in non-target exposure? At what point in the assessment should exposure factors (including both fate and usage rate variables) be considered?
• Is it adequate or even preferable for policy purposes to develop pesticide risk measures based solely on (relatively) intrinsic physico-chemical and toxic properties?
• Is there a mandate to produce a composite hazard index (grouped by category or ranked by score) that can be used separately from the process of generating the results? Can such a product be a useful policy guide or is too much information lost and too many assumptions made for it to be reliable? Is a "checklist" or an algebraic equation the preferred approach for a risk yardstick of this type?
• Should comparisons of impact and risk consider only the set of direct impacts from pest controls (i.e. toxicity of pesticides) or should assessments also include a broader array of environmental consequences resulting from other methods of pest control (i.e. the consumption and degradation of fossil fuels, water and other resources)?
• How should an economic dimension be incorporated into pesticide policy tools?

To address this last point, there is an opportunity--if not an obligation--to consider economic cost in a broader social context in policy tools than they are typically considered in farm-scale decision tools. For the latter, system developers have an obligation of sorts to consider production costs borne by the farmer, as well as the environmental and public health effects on society. However, economic costs also manifest as medical expenses, diminution of the quality of life, remediation and restoration costs, etc. Thus even when these costs are not explicitly considered by an assessment model, analysts can tacitly recognize these multiple dimensions of economic cost by labeling "production costs" as such, rather than giving them the title "economic costs" which allows the narrower meaning to "own" the more encompassing term.

It is also important to consider how economic costs are incorporated into environmental impact assessments. Several of the most widely used farm decision tools include production costs among the variables determining the pesticide ranking. If the ranking is interpreted as an environmental assessment, however, then it incorrectly implies that the magnitude and severity of environmental impacts varies with the producer’s cost for pesticide use. Since this is not the case, it is important to assess such costs separately from environmental impacts. One such method is shown in Figure 2. When the economic costs of environmental protection are high (i.e., if farm costs for producing without pesticides are burdensome), there should be a policy debate about whether and how to shift that economic burden from the farmer (or the consumer) to the broader society. However, these costs should not be allowed to unduly influence the assessment of environmental impact and potential risk.

Reduced pesticide risk is only one of a number of environmentally-sensitive criteria reflected by ecolabels. The "organic" or "bio" ecolabel is probably the most common label based upon pest control criteria, but many other labels which reflect environmentally-sensitive criteria--such as recycling, local production, no rainforest destruction or cruelty to animals--have become familiar in the marketplace. Many of the emerging ecolabeling programs are built on incentives based on promises of behavioral change in the future: For some specified duration of time, products or companies may be granted a third party ecolabel on the basis of having agreed to change and improve on past environmental performance. Promises may include some percentage reduction in pesticide use, an effort to use less of potentially dangerous pesticides, or a willingness to better protect worker safety.

An idealized objective of a number of programs is that their ecolabels reflect a product "life cycle assessment" of all enviro-social impacts from "cradle to grave"--or from the extraction of basic inputs to the eventual disposal of waste goods. More realistically, however, most ecolabels reflect a subset of enviro-social criteria of importance to those affixing the label. For example, some of the criteria for ecolabels on manufactured goods are that recycled materials or energy-conserving processes are used in production; that the product is less caustic than alternatives; that inputs derived from animals are not used in production; that products are not tested on animals; or that old-growth forest and rainforest resources are not destroyed in the production process. Criteria underlying ecolabels on agricultural products may include:

• "organic" or "IPM" standards that exclude or limit use of hazardous pesticides, or which shift acceptable pest control towards preventative practices;
• soil management to reduce erosion and the runoff and leaching of agrochemicals;
• local origin of farm produce--either as a measure of support of local rural communities and open space, and/or as an indicator of less fossil fuel use in transport;
• guarantee of safe pesticide disposal methods or farmworker safety standards.

Some products carry the stamp or label of more than one certification program, each with criteria in a different enviro-social domain. Some ecolabels incorporate social criteria and human rights standards, such as fair trade practices and adequate recompense for labor. The emergence of ecolabeling as a factor in the market is causing concern in some circles about its potential as a barrier to free trade (Dawkins).

Common characteristics of most ecolabeling programs are that:

• the unit of analysis is the consumer product as found in the marketplace, rather than particular production inputs, or processes. In practice, however, entire production facilities (e.g.: farms), product lines (e.g.: name brands), or commodities are given the ecolabel accreditation.
• the arena of activity is the marketplace, rather than the farm field or the regulator’s roundtable.
• the targeted decision-maker is the consumer, rather than the farmer or regulator.

Although the consumer is the decision maker who decides whether to buy the labeled product, the defining characteristic of ecolabeling is that label standards are set before the product reaches the marketplace by an accrediting body that decides whether to confer the "green" designation. Consumers are able to weigh the information conveyed by the ecolabel logo as they also balance their need for the product against its cost. However, the consumer has neither the onus nor the opportunity to independently assess the factors going into the designation. They cannot modify the decision about whether or not a product is labeled. Consumer generally remain unaware of the decision criteria behind the visible logo. The results of the assessment are thus conveyed via a product logo or label that is always viewed separately from the assessment process.

The accrediting body may be a government or international agency; a third party non-governmental organization (NGO) or private firm; or a self-regulating group of producers and/or product distributors. Increasingly, coalitions of different interest groups are developing labeling criteria cooperatively. This broader base of support imbues projects with internal "checks and balances" and thus increases their exposure and legitimacy in the public eye. Some labels rest on the transformation of government regulations (or international treaty regulations) into a marketing tool. The US "organic" designation will eventually be backed by USDA National Organic Standards, for example; and the "dolphin-safe" label on tuna fish reflects the standards of the Inter-American Tropical Tuna Commission (Lefferts and Heinicke 1996). Government agencies and NGOs support ecolabeling because it is seen as voluntary, rather than regulatory, and thus a less heavy-handed mechanism for inducing a shift in behavior.

A mix of personal benefits and altruism motivate consumers to buy labeled products. While some ecolabels are similar to health and safety labels in flagging products that may cause personal harm or directly improve consumer well-being, most ecolabels only promise benefits for the environment and the greater good. For example, consumers do not benefit directly by using recycled paper products in the same way that they believe they derive health benefits by heeding warnings from the Surgeon General or by eating food without pesticide residues. In fact, retail- and industry-sponsored food-labeling programs are often adamant in claiming that advantages of the ecolabel accrue only to the environment, and that ecolabeled food is not safer for consumers or superior to the other food products they sell.

Ancillary to the issue of price premiums is the question of who should pay for them. While it may be fair for consumers to pay a premium if their product choice is motivated by the chance of gaining an extra margin of personal safety, is it also fair for environmentally-conscious and conscientious consumers (and risk-taking altruistic producers) to shoulder the additional cost when their buying habits are motivated by broader enviro-social concerns? Conversely, is it equitable for the global society to be burdened by the costs of environmental remediation necessitated by more hazardous production practices? Ecolabeling focuses attention on the potential of market mechanisms to shift these costs to appropriately targeted producers and consumers. It also calls attention to the interface of market mechanisms with policy initiatives--such as the application of surcharges for purchasing environmentally dangerous or degrading products--and raises questions about which approach is more efficient, effective and tenable. Also at issue with ecolabel programs (albeit perhaps more of a concern with manufactured goods than most farm products) is that there may be significant costs of contracting with the accrediting group. If these costs are borne by producers, then the benefits of the program may be biased towards larger firms which can afford the accreditation procedure. Government and not-for-profit labeling schemes can perhaps circumvent this bias by waiving costs for small-scale producers.

• a criteria "checklist"
• an algebraic equation

Other measurement systems have potential application in policy as well as ecolabeling arenas. Methods for assessing adoption of IPM, for example, can be adapted to the farm scale for accrediting IPM producers or to the national scale for estimating trends in IPM adoption (Vandeman et al. 1994; Benbrook et al. 1996). Likewise the Environmental Working Group’s study of fruits and vegetables most likely to have pesticide residues has been published in the popular media as a type of "commodity ecolabel," but is also having reverberations in regulatory and policy-setting arenas (Wiles et al. 1995).

This is the "how to" section. Methods are described for calculating results for the various types of pesticide impact assessments discussed in the previous section. "Results" may be in the format of continuous, numerical scores or may be categorical groupings (such as high, moderate, low or no impact) which describe the extent of impact, hazard or risk. In some assessments, the categories are translated into the colors at a stop light: "red" indicating a high impact or risk; "yellow," a moderate impact and the need for caution; and "green" for "go ahead"--indicating there is little or no impact from the practice. Some systems assign scores to these categories, and the scores serve as the "common currency" that is weighted and summed to create a composite assessment rating for a pest control practice. Numerical scores can thus be derived from categories of impact, may be derived directly from toxicity tests (such as an LD₅₀ value), or may be a ratio of environmental concentration to an effective concentration that causes a measurable impact. Whatever the format of the results--whether numerical or categorical--they are generally arrived at via one of three methods:

• a logical chain of decision rules, i.e.: a decision tree
• an algebraic equation
• a "checklist"

Because indexing is still an imperfectly-developed methodology for fully describing the enviro-social impacts of pesticides, and also because the demand for tools to measure pesticide impacts is coming fast and furiously from numerous constituencies, the field is dynamic--new techniques are emerging, being refined and synthesized. Thus this "how to" primer does not cover all possible permutations and combinations of methods. Its broad brush is intended to help readers detect and dissect the inner workings of assessment tools that use an indexing approach. Hopefully this will enable readers to better evaluate which enviro-social factors are considered--and how--by any particular assessment system. By reading this more abstract discussion of methods in conjunction with the descriptions of specific assessment tools presented later, both may be more comprehensible. This section also continues in the trajectory of the previous section by highlighting some of the technical challenges and limitations of this methodology, particularly with regard to scoring, weighting and integrating multiattribute criteria. An awareness of these "sticky points" is key to improving upon hazard indexing tools.

One method for deriving a composite environmental impact index rating for pesticides is to use a set of decision rules. This approach is somewhat akin to following a flow chart or using a dichotomous identification key. The logical chain of decision rules creates a knowledge-based system that can solve complex problems. It is a series of algorithms that can be written on paper as a decision tree, or is amenable to conversion to an interactive, computer based "expert" system. In either case, knowledge is represented in a series of if-then-else rules (Plant and Stone 1991). Answers to a first tier of questions direct the decision-maker to a next tier of questions, which will not be the same for all respondents. Or the response to an algorithm generates a score, while a different response generates a different score (Figure 3).

Advantages to this approach are that it can utilize qualitative as well as quantitative information from different sources. Unlike algebraic models, decision tree models do not necessarily depend upon a complete array of comparable data for each pest control evaluated. With decision rules, detailed input data may be required only when deemed necessary by responses to previous questions. Decision trees can therefore utilize descriptive and anecdotal accounts of impacts. For example, there is mushrooming interest, but limited information, about potential endocrine-disrupting impacts of pest controls. Certainly no rigorously compiled datasets exist which contain results from controlled tests which all follow the same protocols for all pest controls. Because of the many possible modes of action of endocrine disruption, and the likelihood that effects are synergistic at low levels of long term exposure, such protocols may be difficult to develop and such tests may be impossible to conduct. Therefore, it is not facile to include this important environmental variable into an algebraic model, but neither is it intellectually or perhaps morally honest to ignore these sublethal impacts in deriving new assessments of pesticide environmental impacts. Existing information could be incorporated into a decision tree assessment model via a decision rule of the format: "If evidence exists which suggests that x pest control may have endocrine disrupting potential, then this pest control is to be classified as a potential endocrine disrupter and treated as..." Pest controls in the suspect category could then be assigned a certain negative hazard score. The amount and type of supporting evidence required could be specified, but need not all be from the same source or in the same form (the O’Bryan and Ross 1988 screening model includes criteria of this type).

Decision trees, used alone or in conjunction with algebraic equations, can also flexibly treat situation-specific conditions, such as those which affect exposure potential. A person knowledgeable about field conditions (e.g.: the farmer, the local farm advisor or agricultural researcher) can input situation-specific data, so that the system uses appropriate decision rules for the situation. This type of interactive assessment system can generate better estimates for exposure and other situation-specific variables than can a non-interactive system. Again drawing on honey bees for an example: The impact of a pest control on honey bees is a function of toxicity of the pesticide to the bee and the likelihood of exposure. Honey bees fly only in daylight, however, so that exposure to pesticides is minimal if application is at night. An additional complicating factor is that some very toxic pesticides repel bees before killing them, so the bees are not effectively exposed. Conversely, some less acutely toxic pesticides do not kill the adult worker bees and nor are they repellent, so the chemicals are carried from the field back to the hives where they are stored and fed to the brood. These pesticides can, therefore, affect hive survival and the honey bee population more profoundly than would be expected from a rank order of pesticides determined solely from acute dermal toxicity to adult bees. It is well nigh impossible to capture the complexity of these ecotoxicological effects within the constraints of an algebraic model.

A logical chain of decision rules can segment these interactive effects into a set of ‘if-then-else’ algorithms which assign appropriate value to the x_{HONEY BEE EXPOSURE} variable. These decision rule-generated values could be linked to an algebraic model by a workbook exercise or simple computerized routine. The flexibility of the decision tree approach makes it well-suited for farm-scale decision tools and other impact assessments which should be sensitive to situation-specific conditions. However, this format creates an unbreakable link between the process of decision-making and the results of the assessment, making it somewhat cumbersome and perhaps unsuitable for the policy arena, where a terse summary of impacts may be preferred.

A number of pesticide ranking systems are based on simple algebraic equations, using a format similar to plant breeding selection indexes (Cotterill and Dean 1990) and multiattribute indicators used in the social sciences (Putnam 1993). A generalized form of these equations is:

Environmental Impact Index Value_COMPOSITE = f(b₁x₁ + b₂x₂ + b₃x₃... + b_ix_i).

Each x_i can represent a single test measure, or can be a function of several variables. For example, an x_i estimate of potential pesticide risk to birds could be based solely upon acute lethality (LD₅₀) to adults of one species, or could be a composite of acute lethality (LD₅₀) scores for several species (t, for toxicity), the pesticide’s effects on bird reproduction (r), its persistence in the environment (p), and drift potential (d). In this latter multiattribute assessment of impacts on birds, the first two factors are both indicators of toxicity to birds and the last two are indicators of potential exposure to birds. The model could be structured such that f(x_i) is an additive function equal to (t_j + r_j + p_j + d_j); a multiplicative function, equal to (t_j x r_j x p_j x d_j); or the toxicity scores could be summed and multiplied by the sum of scores estimating bird exposure: (t_j + r_j) x (p_j x d_j). These are several of the simpler functional forms used to estimate impacts on an environmental parameter, but the conceptual possibilities are almost limitless.

Ratios are another commonly used algebraic format:

Generally with these models, the numerator (y_i) is the concentration of the chemical that is measured or predicted to be found in the environment, while the denominator (x_j) is the measurable endpoint for a toxic concentration (e.g., the EC₅₀ or the dosage at which 50% of the organisms of a species cease to function effectively). Exposure potential is therefore treated as an integral part of the Index Value for each variable.

The validity of most algebraic models depends upon complete sets of quantitative input data for all pest controls evaluated by the system. Data gaps have been a limiting factor in the number and types of environmental variables that have been included in this type of assessment model. Algebraic models may be preferred over the logical chain of decision rules when the objective of an assessment system is to generate a tabular ranking of pest controls that can be viewed and utilized separately from the evaluative process. These models are more suitable when the parameters considered are measurable indicators of (relatively) inherent properties, such as LD₅₀ toxicity values. They are less suitable for assessments which are intended to reflect situation-specific conditions and to be sensitive to such variability.

The complexities inherent to deriving a single composite index value, as well as other problems with the structure and input data, limit the utility and reliability of both of these pioneering algebraic models for assessing pesticide impact. Later in this section, improved methods for scoring and structuring algebraic models will be shown, but first an alternative approach, suitable for meeting some assessment objectives, is suggested.

Some multiattribute assessment systems avoid the complications inherent to deriving a single composite index value. These assessments do not attempt to integrate the scores, rank positions or hazard categories for all of the environmental variables considered by the system. Rather the evaluation of each variable stands alone, to be checked off on a list of criteria taken into account by the assessment. Thus no overall rank order or "best choice" among pesticide options is produced. The "checklist" can be used when a composite assessment is not needed. It has been the method of choice for many ecolabeling and other accreditation systems, especially when disparate criteria are used to judge products and/or processes. It has also been used for chemical screening tools where the objective is to prioritize research and regulatory action for chemicals that are flagged as potentially hazardous in any parameter (see, for example, O’Bryan and Ross 1988). There is less need in such systems to generate a composite rank order of pesticides. A generic notation for this type of system is:

|Index Value₁ = f(b₁x₁)|Index Value₂ = f(b₂x₂) |Index Value₃ = f(b₃x₃)|...|Index Value_i = f(b_ix_i)|.

Index values for the set of variables can be presented in a table or spreadsheet array (Figure 4), or ranked by one parameter with "warning flags" to indicate potential hazards in other parameters. For example the Planetor system (1995) is organized primarily as an economic analysis of alternative farm management scenarios. Scenarios with potentially hazardous ramifications in any of several environmental parameters--such as erosion potential or the use of a highly toxic pesticide--are flagged. The checklist method is one solution to the difficult conceptual problem of ranking pest controls and products in more than one dimension--such as public health hazard as well as consumption of non-renewable resources, or environmental impacts and also costs of production.

Weighting of Variables

Weighting is an algebraic way of expressing the relative importance of the variables considered in an assessment. Weights can reflect either the greater/lesser importance of certain variables to the ecosystem (e.g.: impacts to aquatic organisms may be weighted heavily when farm fields are near bodies of surface water) or to the evaluation of the system (e.g.: human life may be valued more than impacts to aquatic organisms).

The most powerful means of assigning weight to a variable in an impact assessment model, however, is to include it in the algebraic equation. No matter what the apparent claims or uses to which an assessment is put, the output of an indexing system reflects no more than the sum of its parts--which are the environmental variables or indicators included in the model. Beyond this truism, a second important point to recognize about the weighting of variables is that all multiattribute assessments which generate a single index score have, effectively, assigned weights of relative importance to each environmental parameter. When no b coefficient is explicitly expressed, implicitly the value of b_i= 1. Thus in models without any b coefficients, all x variables have equal weight. Although the "checklist" method (described above) should obviate the problem of assigning relative importance weights to disparate enviro-social parameters, practitioners need guard against the temptation to mentally create a composite index value while scanning across a spreadsheet of scores for independent indicators.

The EIQ (Kovach et al. 1992) and PestDecide© (Penrose et al. 1995b) models both use weighted b coefficients. However, variables are sometimes also inadvertently weighted by other means--such as using different units or scales of measurement for different variables, or applying inconsistent standards in setting criteria for classification into hazard categories or for assigning scores to the hazard categories. Examples are: using acute mortality (LD₅₀) as the test endpoint for one group of organisms while using effective concentration (EC₅₀) for a temporary disorder as the input data for another group of biota. Or comparing toxicity data from field tests measuring mortality at recommended rates of application--and thus implicitly including a field exposure factor--with toxicity input data from standardized laboratory dosage, unmodified by potential field exposure. Unequal weighting occurs if both sets of toxicity data are multiplied by an indicator of exposure, so that the exposure factor is doubly weighted for the first group.

Weighting factors can be assigned by the system developers, by a regulating or accrediting body, or by stakeholders in a production system (e.g.: producers and/or consumers). Some systems--particularly those which use a workbook procedure for calculating impact ratings--may enable end-users (i.e.: farmers, farm advisors, accrediting groups) to adjust or modify weighting factors to better reflect local conditions or personal considerations. For example, potential impacts from pesticide drift would logically be weighted quite differently when assessing greenhouse production than open field conditions.

Weighting is sometimes criticized because it involves "value judgments." It should not be inferred, however, that these judgments are necessarily prejudicial or illogical. To illustrate, the choice and weighting of factors in a ecolabeling system designed to affect consumer purchasing may logically be quite different than in a system intended as a decision aid for farmers. The former might emphasize a set of global enviro-social concerns, whereas field-scale assessments may instead focus on the local agro-ecosystem, including environmental impacts from pesticides on soil microorganisms and the beneficial arthropods at work in a particular production scenario. As in most matters, there is no one objective reality in environmental impact assessment. System developers have a responsibility first, to acknowledge this and secondly, to insure that the value judgments implicit in their systems reflect the expert judgments of scientists and other stakeholders.

Rigorous decision-making procedures have been developed which formalize and justify parameter selection and weighting of multiple criteria in assessment models. Theories underlying these procedures draw from optimization and risk analysis, game theory, social judgment theory, and multiattribute utility theory (see for example Saaty, 1977; Watson and Buede, 1987; Spires, 1991; Keeney and Raiffa, 1993). These methods have not been stringently applied to many of the pesticide impact assessment indexes--nor are they necessarily to be recommended--because consensus on the relative importance of different variables or modules may not be the primary objective of the weighting system.

Dealing with Data Gaps

Any assessment of pesticide environmental impacts will undoubtedly confront the problem of data gaps and also, in many cases, the opposite situation of more than one datum for an indicator. The protocols chosen to deal with these situations can influence both the scores for individual pest controls and the rank order of pesticides by risk potential. Missing data for some variables may be available from published Quantitative Structure Activity Relationship (QSAR) values. QSARs are multiple regression equations, similar to economic production functions, which estimate environmental effects of chemicals on the basis of known effects from chemicals with similar chemical structures and activity. There is a large literature debating their reliability and proffering alternative QSAR equations for different chemical groups and effects (for a sampling, see various issues of the journals Chemosphere, Environmental Science and Technology and The Science of the Total Environment). In certain situations data gaps can be filled by a procedure that is less formal than using QSARs, but similar in concept: Where gaps result from exemptions from testing requirements because certain pest controls are presumed to have no negative impact, then scores reflecting this assumption can be used to fill these data gaps. Alternatively, some assessment models fill data gaps with the mean scores for similar class of chemicals (this has been as broad a group as "herbicides" but can also be a more narrowly defined group of chemicals, with similar mode of action or similar molecular structure). Caution should be exercised in imputing values to fill data gaps because biological activity of a chemical may be due to small structural details, rather than the primary structure of the chemical--on which the QSAR is generally based.

Several methods are available for choosing from among, or utilizing, multiple data points. If data quality are thought to differ, then the most reliable data from the most appropriate tests should be chosen. If multiple data of similar quality are available, then the most conservative data should be used. This is often called the "most-sensitive species" approach (see discussion of the Ipest in Section 5). Alternatively, multiple data points can be averaged by taking the geometric mean (Watkin and Stelljes 1993). These authors also give EPA extrapolation factors to use when data are available for related species, or related test endpoints. The uncertainty factor increases with increased taxonomic difference. A factor of 5 is used between species in a genus; 10 within families; 20 within an order, with adjustments for greater knowledge on a case-by-case basis. To adjust for acute to chronic exposure a factor of 10 is used; also to adjust for a most sensitive or endangered species. Note that these extrapolation factors provide a buffer for uncertainty, but are not appropriate to use in filling data gaps.

Assigning Scores to x_i

In deriving an index value for potential pesticide risk, environmental endpoint data are first compiled in the units of the measurable parameter (e.g.: LD₅₀s are in units of mg kg^-1; pesticide soil half-life in units of hours, days or weeks; and solubility in ppb). These raw data do not convey meaningful information about potential hazard or impact, however, unless presented in a context that provides some interpretation of significance (e.g.: At what solubility is there a high risk of pesticide leaching to groundwater? How does the lethal dose correspond to the dosage applied?). Moreover, the disparate units of measure cannot be combined into a composite index value, if that is required. Thus, for indexing purposes, environmental and public health data are generally transformed either to numerical scores or to hazard categories (which may also be scored). The following discussion highlights points to consider when developing, reviewing or applying scoring systems. Much of it is relevant both to scoring each x_i, as well as to deriving a composite index value.

US EPA criteria for classifying pesticides into Toxicity Categories to meet pesticide labeling requirements (40 CFR 156.10h) are shown in Column A. Categories are numbered (Col. B) and associated with a warning label (Col. C). The decision rule for assigning test values to hazard categories should be read as follows: If acute oral LD₅₀ is less than or equal to 50 mg kg^-1, then use the EPA Human Hazard Signal Word "Danger" on the pesticide label, and classify the pesticide in Toxicity Category I. Columns D-H are fabricated to illustrate how several scoring functions would assign scores (numerical or descriptive) to test values.

As shown in Figure 5, criteria are set (Column A) to evaluate the raw impact data so that the array of pesticides can be grouped into hazard categories and described (Columns C and D), and/or transformed to numerical scores (as in Columns E-H). Column A shows the doses of pesticide (mg kg^-1) that are considered to have high, moderate, low or no lethal effect on 50% of test organisms. These are the criteria or decision rules for assigning pesticides to hazard categories and/or assigning scores. Column B gives the "numerical name of the category." Several assessment models have made the mistake of confounding these numerical names or category labels with quantitative scores which are manipulated mathematically. The EPA use of Roman numerals diminishes this temptation (Column B), but the effect is shown by the sequential scores in Column E. As a result of using this arithmetic progression, the range of scores in Column E is limited to a 4-fold difference between the most hazardous chemicals and the most benign methods of pest control. This is far less than the >100-fold range of raw scores (Column A).

Moreover, the numerical intervals between categories do not correlate with measured differences in toxic effect between benign and dangerous pest controls. The result is a compressed index, which does not reflect real differences among pesticides. A second ramification of inadvertently using category labels as scores is that benign impacts receive a positive score greater than zero. The scores for benign effects thus contribute to the hazard index value even though the impact does not add to hazard potential. The distortions caused by rating neutral effects as 1 rather than 0 are compounded in systems where exposure and toxicity terms are multiplied to derive an index value. At issue is the anomaly that a high dose of a relatively harmless input, which would not be hazardous at any reasonable exposure, can receive a rating comparable to a highly toxic input used at a lower dose, at which it might still be problematic (i.e.: 1 x 4 = 4 x 1).

Column C gives the US EPA human hazard signal word and Column D is simply another way to verbally describe these categories, using words that are more generally applicable to hazard indicators. Columns E-H are possible ways to score these hazard categories. The four sets of scores in Figure 5 are assigned to the same set of impact categories, yet mathematically they carry different weight. The problems with the scores in Column E are discussed above, and the scores in Column F also rate neutral effects with a positive number. However these scores have an 8:1 ratio between maximum and minimum effects. Is this a sufficient ratio to reflect the range of impacts? It is far less than the range of raw scores (Column A), but may be preferable to the 100:1 range of scores in Column H, which is closer to the range of raw test values. While this range may permit greater sensitivity to differences among chemicals, the 100:1 range of scores would produce composite index values of an unwieldy magnitude. The composite index value could also easily be swayed by a single high scoring variable.

Ideally, the range of scores should parallel the range in magnitude of impacts. An important factor to consider is that the range of impacts may not have a linear correlation with the range of test results. The magnitude of impacts may instead have a threshold dose-response relationship. Unfortunately, relationships between indicator values and meaningful breakpoints in biological, ecological or other impacts are rarely known or are ignored by assessment procedures, and arbitrary scoring criteria are common. Note, for example, that even the criteria for establishing hazard categories (Column A) are based upon magnitudes of order, not on biophysical or ecological response to threshold doses.

A "criteria and indicator matrix" can provide a useful framework for compiling and organizing relevant information about each environmental indicator proposed for inclusion in the assessment system. Each row represents an environmental variable. Columns contain information about the variable, including the criteria for each hazard category, and the score for that category, if it is to be scored. Additional columns contain information about sources of data for the variable, and citations for the sources of expert judgment used in setting scoring and category thresholds (Levitan and Merwin, in progress).

Scoring functions

It is sobering to note that even when the same environmental variable is considered (e.g.: pesticide persistence), and the same indicator is used to measure impact (e.g.: soil half life), different scoring methods can generate radically different results which may affect the ordinal hazard ranking of pesticides. This is illustrated by the Environmental Health Policy Program (University of California at Berkeley) comparison of three methods for scoring their proposed pesticide impact assessment model (Pease et al. 1996 is described in more detail in Section 5). The rank order of pesticides generated by categorical, non-continuous scoring functions is compared with rank orders derived from linear and non-linear continuous scoring functions. The five most hazardous pesticides, as calculated by each scoring method, are listed in Figure 6. The disparity in results is unnerving! Which rank order is correct?

Categorical or "step" functions are used to score the UC Berkeley Environmental Health Policy Program Ranking System. Adjacent columns show the rank order for the same pesticides, using the same assessment system, scored by linear and non-linear functions (Pease et al. 1996).

Raw test results (or measures) are plotted on the x axis. Test values can be (a) assigned to a hazard category by a categorical scoring function, or (b, c) assigned a numerical score by a continuous scoring function.

A different approach to scoring treats exposure potential as an integral part of the index value:

With this method, the index value (the score or category assigned to an impact) is a function of the ratio between the predicted environmental concentration (PEC) and the effective concentration (EC), or dosage at which a pesticide causes a measurable impact. When the PEC is greater than the impact threshold (PEC ÷ EC > 1), then risk is high. The drawback to this analytically astute procedure is that the resultant index value is applicable only under the defined environmental conditions (the approach is more fully explained in Calow 1993, 1994; Klein 1993; Urban and Cook 1986).

The two final steps in deriving a rank order for comparing pesticides are (1) to calculate and (2) then to rank the index values for the impacts of each pest control product or method. To calculate index values, the array of pest controls is assessed by an algebraic or decision tree model, as have been described. Then the set of index values (scores or categories) is ranked or grouped. Composite index values can be expressed as raw scores (e.g.: the EIQ produces index values between 6.7 and 176.7) or transformed to normalized scores (e.g.: on a 0 to 1, or 0 to 10 or 0 to 100 scale), either of which can be ranked for purposes of comparison. As was mentioned in the context of scoring functions, index values can also be transformed to hazard, impact or behavioral categories such as:

Ranking systems that are intended to communicate a summary judgment for the purpose of advising policy-makers or affecting public opinion have generally transformed data to these types of hazard or impact categories. Classification into categories has been based upon threshold criteria (as has been described earlier in this section), or categories can be formed from percentile or numerical groupings of raw scores. Each of the following examples describes membership in a "high impact" category, based on a numerical or percentile grouping:

• the 10% most dangerous pesticides
• the 25% of orchards using most environmentally damaging pesticides
• the 25 most toxic pesticides

This approach focuses attention on the highest risks or on places where the greatest change may be affected (see summaries of the UC Berkeley Environmental Health Policy Program Ranking System and PestDecide© in Section 5).

Creating categories based upon numerical or percentile groupings can be contrasted with Metcalf’s method of grouping insecticides on the basis of threshold values of composite index scores (Metcalf 1982). The Metcalf Pest Management Rating equation can generate index values from 3 to 15. Pesticides with scores greater than 13 were categorized as too hazardous to use in IPM program; those with scores less than 7 were deemed suitable for IPM, while intermediate scores signaled the need for cautious use (Figure 8). By this method, all pesticides with similar risk potential are grouped together, regardless of how many others fall into the same group. Ideally, threshold criteria reflect significant breakpoints in the extent or severity of impacts, but in practice arbitrary cutoffs between categories are common.

This topic was addressed in the previous section vis-à-vis the typology of pesticide assessment systems, and is recapped here in the context of calculation methods.

Most information about pesticide impacts is compiled for pesticide active ingredients (a.i.) as they behave in relatively pure concentrations of "technical products." However, both potency and non-target impacts of the a.i. may differ in different formulated trade products. The formulations are generally a mix of a small percentage of the pesticide a.i. and a large percentage of what are called "inert ingredients" or adjuvants. Adjuvants can affect both toxicity and exposure by changing such properties as solubility, mobility, biological activity, dispersal, adherence and rainfastness. They can, therefore, affect efficacy, either directly or by changing the properties of the technical product in the formulation (Foy 1992; Kudsk and Streibig 1993). Thus information about formulations would be preferable for some assessment models, particularly farm-scale decision tools. However, information from tests with formulated products and information about the inert ingredients in different products have been considered proprietary, and generally withheld from the public. A variety of methods have been used for approximating the impacts of formulations from the limited information known about them (see WHO 1996 and Mayer and Ellersieck 1986, for examples).

When the primary objective of the assessment is to communicate with the public or with policy makers about relative risks of different pesticidal products, a standard quantity of pesticide a.i. is more likely to be used as the unit of analysis. In contrast to these various units of analysis (pesticide a.i. or formulated product, pesticide product as used to control a specified target pest, and the summary of pesticide products used over the course of a production season), the typical unit of analysis in ecolabeling schemes is the agricultural product, rather than the inputs used to create the product.

The following summarizes key points about scoring index values:

• A score of 0 should be used for benign or null effects.
• A maximum impact scoring threshold should be set as a means of minimizing the effects of outlier values and data clusters on indicator scores. In other words, the range of test values above a threshold cutoff are all assigned the same maximum score.
• The ratio of possible scores for an indicator variable should correlate with the magnitude, extent or severity of impacts in that environmental dimension; this may or may not also correlate with the range of raw test values.
• If categorical output is desired--or necessitated by non-continuous input data--ranges of raw data values or ranges of numerical scores can be grouped into hazard categories using expert judgments about relationships between test values and impacts.
• Sensitivity analysis should be applied to ensure that the index value retains the desired balance among factors. To illustrate this issue: risk-weighted impact measures are being developed because it is widely held that pesticide usage is not sufficient as an indicator of risk; however scores generated by a number of currently used assessment models are in fact driven by usage data. The range of possible scores for each variable, and the functional relationship among variables may need to be adjusted to create the desired balance between potency and exposure endpoints.

The following summarizes key points about approaches to deriving index values:

• "Checklist" models are particularly suitable for screening systems and ecolabels where a single numeric index or indicator value is not required. They are particularly useful for assessment tasks which encompass disparate criteria, where it would be difficult to use a "common currency" for scoring and evaluation. The checklist format can be adapted to models where results are viewed separately from the evaluative process (e.g. ecolabels) or models where end users evaluate the checklist criteria themselves.
• Algebraic models may be preferred when it is necessary to generate tabular indexes or comparisons of pesticides that can be viewed and used separately from the evaluative process. Results in this format are typically used for most policy applications and for ecolabels.
• Algebraic models may be most suitable when the parameter(s) considered are primarily measurable indicators of (relatively) inherent properties, such as dermal LD₅₀ toxicity values. Most algebraic models require complete quantitative input datasets for all pest controls that are evaluated. This has been a limiting factor in the types of environmental parameters that have been included in algebraic models, and also constrains the situation-specific flexibility of such models and limits the realism of their results.
• Decision tree models (logical chains of decision rules) permit sensitivity to situation-specific conditions. They are, therefore, the most suitable approach for farm decision tools. Since results of multi-step models are dependent on "if-then-else" interactive answers, the results of such models cannot be separated from the evaluative process, unless homogeneous environmental conditions are assumed for certain applications of the model. Their application to large scale policy tools may, therefore, be limited.
• Algebraic equations and decision tree models both require attention to the conceptual and mathematical implications of the scoring function, the ratio of high and low scores, scoring criteria, and the weighting of variables.

• Purpose of the system: What/whose perceived need led to development of the system?
• Who are intended to use and make decisions based on the system: Producers (of specific crops?), farm advisors, land use planners, food purchasing agents, retailers, researcher scientists, regulators, consumers, the press, environmental and other public interest groups?
• What is the scale of analysis: A farm field, a farm, a bioregion, a political jurisdiction (e.g.: a county, state, country), the planet?
• What is the time frame of the analysis: An individual pest control application, the production season, a multi-year time horizon?
• What is the unit of analysis: The pest control product, a pest control method, a pest control as applied to a specific target pest, a farm system, a production scenario, a product?
• Which environmental effects/variables are taken into account: Inherent properties of pest control products and methods, or site and situation-specific parameters as well? Impacts on human health, non-human biota, ecosystem level effects?
• Does the system assess impacts or behaviors: Does it measure/estimate potential hazards from pest control practices or does it measure and mark the adoption of certain practices, such as organic farming or IPM?
• What are the principles behind the calculation(s), classifications and decision rules?
• What is the format of the output: Can results (e.g.: a rank order of pesticides, a list of accredited products or producers) be viewed and used separately from the process of the analysis, or does each user go through the assessment process to produce customized results (i.e.: does a farmer use a workbook or computerized expert system to determine which is the least risk option under situation-specific farm conditions)? Are results displayed on a can of "ecolabeled" food in the supermarket? on a computer screen as part of an interactive expert system? in a spreadsheet or on a list that is printed on a piece of paper? in a document?...
• Stage of development of the system: Is it still evolving? Is it published? What would be involved in adapting the system for other user groups?

"Efficacy" is included as a variable in the Responsible Choice equation as an indirect measure of dosage and the number of pesticide applications. Thus the "efficacy" factor has environmental impacts as well economic ramifications for the grower. The "efficacy" rating is based on a subjective comparison of available options. Since pesticides are differently effective against different pests, the Stemilt ratings are therefore specific to target pest organisms, rather than to pesticide active ingredients. For example, the Point Summary for Captan is 9.0 when used on Apple Scab (Venturia inaequalis), but 12.0 when used to fight Bull's Eye Rot (Pezicula malcortis). Thus pesticide ratings can be compared only when pesticides are used to combat the same pest.

Toxicity to farmworkers is assessed from laboratory data on acute lethality to rats (LD₅₀). Consumer exposure is indirectly assessed from the "preharvest interval"--the amount of time legally required between the last field application and the harvest. This time period is established as part of the pesticide registration process, taking pesticide persistence, toxicity and average food consumption factors into account. Indicators of other impacts on human beings and non-human biota are derived from exposure estimates based on several pesticide physico-chemical properties, including "leaching potential," "soil sorption" and "soil half-life." A key objective of the Responsible Choice program is to encourage IPM practices and reduce dependence on chemical pesticides. The Responsible Choice equation thus includes two indicators of impacts on arthropods that prevent or combat orchard pests: "effects on beneficials" is a measure of acute toxicity to insects in the field at the time of pesticide application, and "biological disruption" is a measure of long-term impacts on beneficials as a result of the pesticide application.

The Stemilt research team has derived point summaries for each pest their contract growers are likely to encounter, based on the factors and weights listed in Figure 12. Point summaries range from 4.5 for Bacillus thuringiensis used on cherry leafrollers to 56.6 for Methidathion used on apple scale. The points assigned assume that pesticides are applied at the label rate; if less is used, a proportional number of points are assigned. Growers are supplied with a handbook of pest control recommendations for IPM growers that includes a table listing fruit pests, pesticide options and the point summaries (Figure 13) (Stemilt Handbook). The intent is that growers look to this table to guide their choice of pesticides to use in combating a specific pest. Growers are also encouraged to keep spray records which enable a tally of cumulative points used in producing a crop (Figure 14). These records enable Stemilt scientists to analyze management systems and update the Responsible Choice program. They have found seasonal point ranges from 20-420, which indicates that there is tremendous potential value in being able to compare environmental impacts and productivity of different production systems. The Stemilt scientists set annual target point budgets, based on results from previous seasons and availability of chemicals with lower point ratings. Since the program began in 1989, the frequency distribution of point totals reported in the grower spray records have declined significantly.

The Stemilt system is an evolving decision-making tool for growers that addresses impacts of fruit production in the Pacific Northwest. Data underlying the ratings for efficacy and impacts on beneficials are particular to growing conditions in that environment. Other data are drawn primarily from material collected in the process of federal pesticide registration. The Responsible Choice Point Summaries are providing a mechanism for tracking pesticide use and IPM practice, and encouraging environmental accountability. As data gaps are filled or additional factors deemed necessary for inclusion, the equation can be modified. This approach can be adapted to other crop scenarios and growing conditions by recalculating the rating scheme and fitting it to a relevant set of crop pests and pathogens.

Note: The Responsible Choice equation does not include toxic effects on non-human biota other than certain beneficial arthropods, nor other toxic impacts to human beings than acute toxicity.

1993 Stemilt Growers PEAR Spray Record Return Form by Nov. 1, 1993

Grower:______________ Grower Number:_____________ Page____of____

White Copy: Grower Yellow Copy: Stemilt

The target audience and procedures for using the PestDecide© decision system are similar to the Stemilt Responsible Choice program. Both pesticide rating systems are intended to guide fruit growers in their choice of pesticides and to provide them with a measure of their compliance with IPM. Both systems are also intended to form the basis for IPM fruit accreditation programs and to serve as "ecolabels" to influence consumer decisions in the marketplace. The PestDecide© decision system has been developed and piloted by an agriculture research group in New South Wales, Australia. The authors expect that widespread use of the system will depend upon its acceptance by the national fruit growers association, which cooperated in the research, and will be driven by the need to certify export fruit and meet the demands of the domestic market for residue-free produce (Penrose personal communication 1996, Penrose et al., 1994 a and b, 1995 a and b).

The PestDecide© index is the sum of ratings assigned to ten variables, each modified by a weighting factor (Figure 15). Ratings for all variables range from 1-5 and weights range from 1-4. Thus the lowest score a pesticide can receive is 10 and the highest is 200. The first set of four variables comprise a "Potential for Residues Index" (PRI)--similar to the consumer impacts component of the EIQ. The PRI is essentially a consumer protection rating that ranks the likelihood of finding pesticide residues on food, taking into account both intrinsic pesticide properties and situation-specific factors. The PRI score for pesticide "activity" increases with the highest concentration of chemical recommended for field spray, on the premise that more active compounds require lower dosages and thus are less likely to leave residue (Figure 16). As shown in Figure 16, materials that are generally considered benign but applied at high rates (e.g.: copper, sulfur, mineral oils, lime) are classified as "green" and rated separately. The "site of application" is rated on the basis of the likelihood that pesticide residues will remain on tree fruit crops: Score = 1 when pesticide application is to the ground; 2, dormant, non-bearing or post harvest trees; 3, blossoms; 4, fruit on trees (petal fall to harvest); and 5, post-harvest fruit. The "timing" score increases as harvest approaches: score = 1 during dormancy or after harvest; 2, first half of growing season; 3, second half of growing season; 4, < 7 days before harvest; and 5, post-harvest fruit dip. In calculating scores, the last time of application (excluding post harvest dip) is considered. The "persistence" score is an indirect measure based on the statutory period between last spray and harvest: score = 1 when there is no legally-mandated waiting period; 2, 1-3 days; 3, 4-14 days; 4, 15-42 days; and 5, >42 days. Note that the "persistence" variable equals Stemilt’s "preharvest interval."

The second set of six variables comprises a "Values Index" (VI) that is intended to rate impacts of growers’ management decisions. The VI includes production cost factors as well as environmental and public health indicators. The "efficacy" score is based on a subjective judgment of pesticide efficacy in comparison with general expectations for modern pesticides, across all products and targets. More effective pesticides are assigned lower scores; where information is lacking a score of 3 (moderate efficacy) is used. This variable is similar to Stemilt’s "efficacy" variable and, like it, has implications both for environmental impacts and economic cost to producers.

In earlier iterations of the PI, "cost" was also rated subjectively, in comparison with typical prices paid for alternative pesticides. Absolute rating criteria have been developed for use in Australia, based on the cost of 100L of dilute high volume spray purchased in the largest volume typically used in a commercial orchard, and assuming the highest rate of recommended use: Score = 1 when cost is less than $1.00/100L; 2, $1.01-$2.00; 3, $2.01-$3.00; 4, $3.01-$4.00; 5, >$4.00.

The "environmental effects" rating for each pesticide is based on the Environmental Impact Quotients (EIQ) developed by Kovach et al. (1992): Score = 1 when EIQ is 0-25; 2, 26-35; 3, 36-45; 4, 46-60; and 5, >60. Recall from description of EIQ, however, that nearly half of the EIQ score is based on human toxicity factors, and that impacts on beneficial arthropods comprise two-thirds of the ecological effects score (Figure 9). "Mammalian toxicity" is used as an indicator of hazard to pesticide applicators; ratings are based on the same test endpoint (LD₅₀) as Stemilt’s "dermal LD₅₀" factor. Score = 1 when LD₅₀ >1000 mg kg^-1; 2, LD₅₀ = 501-1000; 3, LD₅₀ = 51-500; 4, LD₅₀ = 5-50; and 5, LD₅₀ < 5 mg kg^-1.

"Compatibility with IPM" is the degree of disruption to biological control of other pests caused by the application of a pesticide. This variable is rated on a subjective scale for a particular situation, with lower scores for least disruption. This variable is similar to the sum of Stemilt’s "effect on beneficials" and "biological disruption" variables, but is broader in scope in that it considers other biological controls (e.g.: pathogens) in addition to beneficial arthropods. "Availability of alternative pesticides" assigns lower scores when fewer viable alternatives exist, and increases to 5 when more than four choices of active ingredient are available. Weighting coefficients are expected to be assigned to variables by accreditation groups and, therefore, will reflect a consensus of expert judgments. The following weights have been assigned for the prototype trial use of the PestDecide© system. Note that this weighting scheme puts greatest emphasis on consumer risk from pesticide residues:

Pesticide Index (PI) scores are calculated by an assessment team. Scores are provided to growers in the PestDecide© manual, in a table similar to the Stemilt Responsible Choice Summary (Figure 13, Figure 17). As with the Stemilt system, PI scores can differ for different target pests due to differences in efficacy, relative cost and availability of alternative controls, timing and site of application. PI calculations are also specific to pesticide formulations, rather than active ingredients. Despite a possible range of 10-200, all scores calculated by the PestDecide© development team for pesticides used on apples fell between 45-100. PI values assume pesticide application at label rates; proportional adjustments can be made if application is ≥ 20% less than the minimum, or more than the maximum recommended.

The pesticide index (PI) forms the basis for the PestDecide© decision system. To use this system, growers complete a worksheet (Form A) supplied in the PestDecide© manual to plan their spray schedule. They then complete another worksheet to record their actual spray program (Form B) (Figure 18). These worksheets enable growers to calculate their annual point summaries based upon the scores assigned to pest control methods by the PestDecide© Index. Growers attempt to keep their annual point total below the threshold for IPM accreditation that is set annually by a research team, grower group or accreditation body. Threshold scores are determined by monitoring the annual cumulative scores recorded by growers in a region, and then setting the target for subsequent years at the score achieved by 75% of orchards in the same season. This presumes that the remaining 25% of orchards use excess pesticide, a conclusion based on research showing some 200% variability in pesticide use by apple growers within a district in the same season (Penrose et al. 1994a). Flexibility in setting target scores is recommended to allow for growing conditions in different regions, wet seasons when scores may increase by nearly 25%, and invasion of sporadic pests.

Name:.......................................... District:........................................

Form A Spray Program Plans

The following sheets are to help you plan you pest and disease control program, and check whether you are likely to meet your target.

Orchard Date Beginning

Variety

Machine Used End

The following sheets are to enable you to record each spray as it is applied.

At the end of the season you can see if you actually achieved your PestDecide goal.

Name:............................................... District:........................................

Orchard

Variety

Spray Machine

*Adjusted proportionally for a rate differing from that recommended, where approved.

1. Assessment of the presence (Presence) of a pesticide in the environment is based solely upon the pesticide application rate (log₁₀ g ha^-1).
2. Assessment of risk of groundwater contamination (Rgro) is a function of (a) pesticide leaching potential, (b) where the pesticide is applied (i.e.: on a crop, on the soil, in the soil), (c) risk of leaching from soil, and (d) chronic toxicity to humans. Pesticide leaching potential is quantified by the "groundwater ubiquity score" (GUS) which is a function of pesticide half-life in the soil and pesticide mobility in the soil as estimated from the organic carbon sorption coefficient (K_OC): [GUS= (log₁₀ DT₅₀) x (4 - log₁₀ K_OC)] (Gustafson 1989). Pesticides applied to a crop are assumed to pose less of a risk to groundwater than pesticides applied to soil. The percent crop cover is used an indicator of risk to groundwater, based on the rationale that with greater crop cover less pesticide reaches the soil and also that transpiration is greater, thus reducing drainage. Soil leaching potential can be estimated from models specific to soil characteristics, or based directly on soil organic matter content, which is considered the most important soil characteristic affecting leaching. Chronic toxicity to humans is estimated from the log₁₀ of the Acceptable Daily Intake (ADI) which is the dosage considered safe for daily intake over an average lifetime.
3. Assessment of risk of surface water contamination (Rsur) is based upon (a) runoff potential of the field site, (b) drift potential, (c) where the pesticide is applied (see above), (d) field half life and (e) toxicity to aquatic organisms (EC₅₀ or LC₅₀ to the most sensitive of three organisms in the aquatic food chain: algae, daphnia or fish). Runoff risk can be transformed to a 0 to 1 scale from a site specific model which considers factors such as slope gradient, slope length, soil texture, soil surface conditions, crop cover, and distance to surface water. Alternatively, the user can estimate the score by making a similar assessment. Percentage drift is a function of application technique and distance to surface water, and is estimated by the user. Pesticide application within the soil is considered a means to avoid runoff risk; again, application to crops presents less of a risk as crop cover increases. Aapplication to the soil surface is considered to create maximum risk. Runoff risk also increases with pesticide persistence, measured in terms of soil half life (DT₅₀ days).
4. Assessment of risk of air contamination (Rair) is based upon the (a) pesticide volatility, (b) place of application, (c) field half life and (d) chronic toxicity to human beings. Henry’s Law Constant, a non-dimensional ratio of vapor pressure and water solubility, is used an an indicator of pesticide volatility (log₁₀ K_H). Application of the pesticide by incorporation into the soil is considered to present less risk of air contamination than application to the soil surface or crop. Variables for field half life and ADI, as an indicator of chronic toxicity to human beings, have been discussed previously.

The Ipest system relies on "fuzzy logic" to determine the extent to which threshold criteria for favorable conditions (i.e., conditions which are safe or which have little environmental impact) are met for each environmental variable. Fuzzy logic is an extension of conventional Boolean logic that can handle partial truth (Information Sciences 1985); it was first described in 1965 in the now-classic paper "Fuzzy Sets" by Lofti Zadeh (1965). The Ipest system defines membership criteria for two "fuzzy subsets" for each input variable: F (favorable, no potential environmental impact) and U (unfavorable, maximum potential for negative impact). Membership criteria are based upon decision rules drawn from the literature or the judgment of the authors, or on input from end-users of the system (Figure 20). When a test or observational endpoint value is within the defined range of safety, it is a member of the subset F. When an endpoint value is within the range defined for maximum potential for negative impact, it is a member of the subset U. When endpoints fall within a transitional range, they have partial membership in both subsets. Full membership in subset F has a value = 0 and full membership in subset U has a value = 1. Ipest uses sinusoidal-shaped membership functions to assign membership values between 0 and 1 to endpoints within the transitional range for each variable (Figure 19). Input data for the pesticides evaluated by this system are drawn from several sources, primarily the INRA database AGRITOX and the toxicity database compiled at the Dutch National Institute of Public Health and Environmental Protection (RIVM) (Emans et al. 1992; Linders et al. 1994).

Scores for the input variables (0 - 1) are aggregated into scores (also 0 - 1) for the four intermediate indicator modules (i.e.: Presence, Rsur, Rgro and Rair) according to a set of decision rules based on Sugeno’s (1985) inference method and Boolean operators to combine assessments and values for dissimilar factors. E.g., if A is Favorable and B is Favorable, then the risk of groundwater contamination is considered nil and the score = 0, but if A is Unfavorable or B is Unfavorable, then the risk is considered moderate and the score is >0 and <1. If both conditions are Unfavorable, the score = 1. According to Sugeno’s inference method, the truth value of a decision rule is the smallest of the truth values of a set of premises linked by and. Examples of sets of decision rules for deriving scores for the indicator modules are shown in Figure 21. The summary Ipest score is derived from an aggregation of scores for the four indicator modules, using a set of 16 decision rules reflecting the "expert judgment" of the authors.

The decision rule methodology for ranking pesticide impacts enables a certain flexibility in the choice of input parameters to be considered under situation-specific conditions (see, for example,Figure 22). A similar flow chart approach was also used by Hornsby (1992) and Goss and Wauchope (1990) in their assessments of groundwater risk based on pesticide behavior in soils. Microsoft Access databasing software for Windows 95, version 7.0 has been used to implement the Ipest expert system with a dataset of relevant pesticide properties. The Ipest indicator was developed as part of a broader set of agro-ecological indicators for assessing impacts of agricultural practices on the environment (e.g.: nutrient management, crop sequence, etc.) (Bockstaller et al. 1997).

Figure 21 Summary of decision rules describing the effect of the input variables Rate and Field half-life on the hypothetical module Environmental Impact. F = favorable, U = unfavorable . (fromVan der Werf and Zimmer, in progress)

• to provide a mechanism for making the environmental impact of pesticide use tangible for farmers
• to encourage farmers to make environmentally-sensitive choices of pest controls, and
• to evaluate progress made in reducing environmental impacts of pesticides.

The Yardstick was introduced to farmers in the Netherlands in 1994 following several years of limited use of a prototype. It has since been used widely by extension advisors working with farmers in training courses and study groups, and also by individual farmers. A motivation for using the Yardstick is that it has become linked with an ecolabeling program that confers a price premium on accredited produce, and with a system of incentive payments paid by water companies to farmers who use the Yardstick to document reduced pesticide leaching (Joosten 1995). It has also been adapted as a policy tool for evaluating pesticide risk reduction in the Netherlands (Reus and Pak 1993; Reus et al. 1995; Reus and Faasen 1995; Reus 1996; Reus 1997).

The Yardstick calculates Environmental Impact Points (EIP) for three environmental parameters: water organisms, soil organisms and groundwater. The calculation is made by following a set of procedures (i.e.: a decision tree) in a workbook. Results (answers) from each tier of questions are captured in a set of tables (see Figure 23). Rather than deriving one "best pesticide option" based on a composite tally of EIP for the three parameters, decision-makers are directed to consider which of the three is most critical under their situation-specific conditions. Thus farmers in the Groundwater Protection Zone, where water supplies are being polluted by agricultural chemicals, use the groundwater EIP Yardstick; farmers whose fields are near surface water use the Yardstick calculation of EIP for aquatic organisms; and the Yardstick for soil organisms is used where soil biology is the more important consideration.

Calculation of risk to water organisms is based on a comparison between the acute toxic concentration (LC₅₀) and the concentration of the pesticide predicted to be found in field drainage ditches as a result of dispersion by drift. The predicted environmental concentration (PEC) is based on dosage per ha, method of application, and the depth of the drainage ditch (standard ditch depth is assumed to be 25 cm). Drift percentage depends on factors such as distance to the ditch, type of spraying nozzle, spraying pressure, wind speed and other application variables. Drift percentage is assumed to range from 0% for pesticides applied as seed treatments or as granules; to 0.5% for pesticides sprayed on rows; 1% for full field spraying of arable crops; 10% for full field spraying of fruits; and 100% for aerial spraying. A multiplication factor of 0.1 converts units from kg ha^-1 to mg l^-1.

Calculation of risk to soil organisms compares the predicted concentration of a pesticide in the top 2.5 cm of soil immediately after application with its acute toxicity to earthworms (LC₅₀). Long-term risk to soil life is based on a comparison of pesticide concentration in the top 20 cm of soil two years after application with its no observable effects concentration (NOEC) for soil organisms. EIP_{SOIL ORG} is given as the larger number from these two calculations. The predicted concentration is based on pesticide soil degradation rate, its mobility within the soil compartment (using soil organic matter content as an indicator of mobility) and application rate per ha. The PESTLA simulation model is used to calculate leaching potential and persistence under Dutch soil conditions (Boesten and Van der Linden 1991).

EIP calculations for leaching into groundwater uses PESTLA to calculate groundwater concentration at a depth of 1 to 2 meters based on pesticide degradation rate in soil and pesticide mobility within the soil compartment. Soil organic matter, dosage per ha, and the season of application are also considered.

1. from the workbook, choose the table of information appropriate to given site-specific conditions. For calculating risk to groundwater, for example, the site-specific conditions are percent soil organic matter and season of the year when pesticide application is made;
1. from the table, select the score(s) for the pesticide(s) in use or those being compared;
1. multiply score by the recommended (or actual) application rate of active ingredient.

Figure 23 Environmental yardstick readings, showing the impact on groundwater of springtime use of 3 herbicides in green maize at the rate of 1 kg a.i. ha^-1 in different seasons with different organic matter concentrations (Joosten 1995).

Numbers in bold face reflect the conditions chosen for this example: spring application of pesticides on soils with 3-6% organic matter.

After choosing the appropriate set of numbers, the EIP is calculated by multiplying the application rate (kg ha^-1) by the base EIP score, as given for 1 kg per ha. EIP Scores < 100 pose acceptable level of risk. The "best choice" in terms of impact on groundwater is highlighted in this example--EIP points < 100 only for Forlene 60WP.

Since 1995 the Yardstick has also been used to evaluate the success of the Dutch pesticide reduction policy. EIP kg^-1 for each pesticide are multiplied by the quantity of pesticide sold nationally. Scores are tallied for all pest control products. For this scale of application, situation-specific differences in method of application (affecting drift), dosage, soil type (affecting mobility), etc. are not accounted. Instead 1% drift, 3-6% soil organic matter content, and application in the springtime are assumed. These assumptions do not affect the analysis of trends unless in fact there are differences in season and method of application over the course of the time series. CLM concluded that risk to groundwater and soil organisms did in fact decline in proportion with reductions in use (quantity of use decreased 41% in the decade preceding 1993), but that risk to water organisms did not. Moreover, they conclude that ten pesticides which account for 5% of sales are responsible for 88% of groundwater pollution. By multiplying the EIP for specific pesticides by national usage data, rough estimates can be made about the degree to which pollution can be reduced by product substitution.

Average EIP per ha of cultivated land can be calculated by dividing total national EIP (for all pesticides or for categories of pesticide) by agricultural area (excluding grassland where pesticides are little used). A comparison between these EIP scores and the national acceptable standard of 100 EIP ha^-1 gives a rough indication of the difference between the Dutch standard for acceptable levels of pesticide pollution in each environmental compartment and the pollution levels calculated by the CLM Yardstick. This effectively sets a target for the amount by which pollution must be reduced to meet national standards.

Data are from rodent acute LD₅₀ values compiled by the World Health Organization (WHO 1994) or, when unavailable, from those listed in Farm Chemicals Handbook (1996). Chronic toxicity is assessed as a composite of the EPA-established Reference Dose (RfD), cancer potency factor (Q*), and cancer classification, plus an estimate of endocrine-disrupting capacity. For non-oncogenic, non-endocrine-disrupting pesticides, the chronic toxicity indicator value is = 0.1 ÷ RfD. When oncogenicity or endocrine disruption are factors, the RfD component of the formula accounts for about two-thirds of the indicator value. (Note that risk calculations for the non-oncogenic, non-endocrine disrupters are not advantaged by this procedure.) The authors found that the functional relationship among these indicators of chronic toxicity did not significantly affect rank order.

Six indexes of risk were derived by separately ranking the herbicides, insecticides and fungicides currently used in US agriculture in two listings: (1) in order of relative acute toxicity and (2) in order of relative chronic toxicity to human beings. No composite assessment of environmental or human health impact is implied by these rankings. Use-weighted toxicity averages were calculated for each pesticide class, for the years 1971, 1982 and 1992. The use-weighted acute toxicity score for herbicides, for example, is calculated by multiplying pounds used of each herbicide by its LD₅₀ value, then dividing by the total number of pounds of herbicide applied. The most recent usage data is from the National Center for Food and Agricultural Policy (Gianessi and Anderson 1995) and earlier data, from the USDA ERS.

Use-weighted toxicity values were also derived for the median and most toxic and least toxic decile of each class of pesticide. The "most toxic" decile of herbicides, for example, is the group of most toxic active ingredients which together account for 10% of the pounds applied. These data were used to calculate a toxicity differential between the most and least toxic pesticide options. To illustrate: the differential for acute toxicity is calculated by dividing the average LD₅₀ of the least toxic decile by the average LD₅₀ of the most toxic decile. Results from this calculation can help prioritize research and regulatory activity. For example, the most toxic decile of fungicides is only 10 times more toxic than the least toxic decile, suggesting to the Consumer Union group that there may be little to be gained by advocating restriction of just the most toxic fungicides. In comparison, the most toxic decile of insecticides is nearly 3,000 times more toxic than the least toxic decile--suggesting that restriction of highly toxic insecticides and adoption of more benign alternatives could significantly reduce pesticide risk to human beings.

Charles Barnard, an economist with the Environmental Indicators Branch of USDA Economic Research Service (ERS), has developed two indicators of pesticide risk to human beings that will be publicly unveiled in the "Pesticide Use" module of the forthcoming USDA ERS Agricultural Handbook entitled Agricultural Resources and Environmental Indicators (1997). Historically ERS has relied on pounds of pesticides applied as the basis of pesticide usage data. Pesticide weight was therefore also being used as the de facto proxy for pesticide risk. Currently several initiatives are underway at ERS to develop other risk indicators. Among these are Barnard’s potential risk measures for acute and chronic toxicity based upon "toxicity/persistence units" (TPUs). The objective is to improve time series analysis of national pesticide risk by using these as toxicity-weighted measures of pesticide use. The method also enables identification of geographic regions at greater risk from pesticide use, and identification of pesticide classes and uses posing greatest risk.

With the Chronic Risk Indicator, one TPU = the presence of one Reference Dose (RfD) of the pesticide in the environment for one day. Calculations of TPUs for each pesticide active ingredient are made by dividing the quantity (lb.) of pesticide applied in a given region (e.g.: a nation) by the RfD (converted to lb.) and multiplying by soil half life data from USDA Agricultural Research Service (ARS). The ARS dataset represents the mid-point of values reported in the literature for the first soil half life, which is the number of days for the first half of applied active ingredient to degrade. These data are a composite of test results from a variety of soil conditions, moisture levels and temperatures. In other words, they are drawn from aggregated data that is suitable for a large scale analysis, such as this, but perhaps not valid to use as the basis for farm-scale decision-making.

The Acute Risk Indicator is calculated in the same manner, but acute oral LD₅₀ data for rats (or related mammals) are used in place of the RfD. The LD₅₀ is, in the words of the authors, a "severe threshold"--the lethal dose for half of the tested population. Thus this risk indicator is intended to provide only a relative index of potential risk to human beings from acute exposure, not an absolute risk potential.

The objective of the SYNOPS model that is being developed at the Institute for Technology Assessment in Plant Protection, Kleinmachnow, Germany, is to assess pesticide environmental risk trends since the German Plant Protection Act was amended in 1986 (Gutsche and Rossberg, In progress). SYNOPS is a more ambitious ecological model than the other national pesticide risk assessments presented in this review in that it attempts to incorporate exposure parameters and other site-specific input data. It does so by making a set of assumptions about typical soil and water conditions in the nation, and essentially treating all arable farmland in Germany homogeneously as a single field site.

The prototype application of the model is a comparison of risk in 1987 and 1994 from the insecticides most commonly used on German farmland. Results show a decline in risk, primarily due to a reduction in quantities applied and to a lesser extent due to a shift to less hazardous products. Steps in making this assessment, described below in more detail, are:

1. Determine the "application pattern" of the pesticide
1. Assess environmental exposure
1. Assess related biological risk
1. Aggregate the calculated indices
1. Visualize (communicate) the risk potential.

Determine the "Application Pattern" of the Pesticide. Input data regarding the amount of each pesticide applied and to which specific crops, are from pesticide registration applications. Pesticide quantities are apportioned to environmental compartments (soil, water, air) at different rates depending upon plant growth stage and features of the particular crop. It is assumed that most of the applied chemical reaches the soil during early growth stages when plants are small, and that the proportion reaching the plants, rather than deposited on the soil, increases during the growing season. A small percentage (1-4%) is apportioned to the air, via drift, depending upon application method to particular crops.

Environmental Exposure. Exposure to each environmental compartment (soil, water, air) is calculated for short and long term predicted environmental concentration (sPEC and lPEC, respectively). The long term environmental concentration (lPEC) is a function of degradation rate and pesticide adsorption to soil or sediment particles. Data for organic content, depth to water table, soil movement, temperature, etc. are based on the aforementioned assumptions about typical conditions. The change in environmental concentration of the a.i. as a function of time is calculated with input parameters for DT₅₀ and DT₉₀ for soil, water photolysis and hydrolysis.

Biological Risk is calculated as an acute (abr) and chronic (cbr) risk to earthworms, aquatic invertebrates, algae and fish, using results of the previous calculations. Risk to earthworms draws on calculations for PEC in soil, while risk to daphnia (representative of aquatic organisms), algae and fish draw on PEC for water. The notation for calculations of acute risk are:

Similar relationships between the short term environmental concentration and lethal concentration to 50% of organisms are derived for algae and fish. Chronic biological risk (cbr) is calculated as a ratio between long term environmental concentration and the "no observable effect concentration" (NOEC) for the same organisms. NOEC is multiplied by the time duration of the NOEC experiments (t) so that the calculations lose their time dimension. The notation is:

Aggregation of Indices. All told, 238 values are calculated for all applicable conditions for risk indicators in steps 1-3. SYNOPS offers two methods for aggregating these data:

1. Calculation of a weighted mean value for each index, with weights relative to proportional crop area for the application.
1. Adjust the weighted mean value by the probability that a given pesticide is applied to a given crop (i.e.: adjust for pest pressure by multiplying the first value by the application probability).

Visualization of Results. "Risk graphs" are developed to communicate results visually (Figure 25). They are circular graphs divided into sectors, and further divided into segments corresponding to the individual aggregated index. The index value determines the arc and radius of the segment. Visually, therefore, large risk graphs suggest a high risk potential for a chemical.

The impact scores draw from two types of input data:

• laboratory-derived toxicity tests and measures of pesticide physico-chemical properties
• observational data collected by California EPA, Department of Pesticide Regulation.

The comprehensive California regulatory program monitors pesticide usage, incidence of pesticides in groundwater, and cases of acute illness resulting from pesticide exposure (CalEPA, DPR 1995). As diagrammed in Figure 26, the four indicators of impacts on human health are: acute lethality (LD₅₀), from laboratory tests performed on rodent surrogates; chronic toxicity, using EPA’s reference doses (RfD) of safe threshold levels for lifetime exposure; a cancer index derived from EPA’s cancer classifications and estimates of carcinogenic potency; and observed acute illness rate in California.

The EPA cancer classification categorizes pesticides by whether available evidence indicates that the pesticide is a known carcinogen (group A), probable carcinogen (B), possible carcinogen (C), or unlikely carcinogen (D). The California Environmental Health ranking system assigned scores to these groupings and used the scores as weighting coefficients for this variable: groups A and B = 1; C = 0.8; D = 0. Coefficients are multiplied by EPA estimates of carcinogenic potency for a unit amount of each active ingredient.

Data on farmworker illnesses are from 1984-1990 records of the California Pesticide Illness Surveillance Program, as analyzed by Pease et al. (1993). Comparable assessments of acute illnesses from nonagricultural pesticide use are from Robinson et al. (1994). Ecological health scores are from avian, invertebrate and fish acute toxicity data (LD₅₀ and LC₅₀ measures), and a bioaccumulation or bioconcentration factor. Natural resource impacts are estimated from pesticide solubility in water, field half-life, soil adsorption and the observed rate of pesticide detection in California groundwater, based on analyses of 290,000 well water samples 1978-1993 (Pease et al. 1995). These same pesticide physico-chemical properties data used to estimate natural resource impacts are also used by other assessment systems, often to estimate exposure potential (e.g.: the EIQ and SPISP (Knisel et al. 1994)).

Scores for each variable are calculated by creating hazard categories based on the statistical distribution of data for each hazard measure. Pesticides in the top 10% for each test endpoint (e.g.: lowest 10% of LD₅₀ values or highest incidence of observations in groundwater) receive a score = 4; score = 3 for the 75-90 percentile grouping; score = 2 for the 50-75 percentile group; and score = 1 for the 0-50 percentile group. Greater discrimination is made at the high end of the scale because of greater interest in the most hazardous group of pesticides. A score = 0 is possible for the three hazard measures (farmworker illnesses, groundwater detections, cancer index) that may have values = 0. This is a relational approach to scoring--it is not based on biologically-relevant threshold criteria, but on a comparison with all other evaluated pesticides (not only those which are options in targeting a specific pest).

The California Environmental Health Policy Program has also experimented with an approach to scoring called "elicited scoring functions." This method, derived from Von Winterfeldt and Edwards (1986), involves first assigning hazard scores to several data points in the distribution and then selecting a model for extrapolating scores to the remaining data points. The shape of the utility function for each attribute is an exponential function that best reflects the relationship between hazard data and scores. The intent is to base the scoring function for each variable on a selection of expert judgments elicited from a representative group of decision-makers. Conceptually and mathematically these scoring functions are similar to the "fuzzy membership functions" used in the Ipest assessment system developed by van der Werf and Zimmer (1997); both of these scoring functions are means of avoiding sharply defined impact categories and the consequent assigning of the same index value to all endpoints within a given interval.

The authors suggest assigning weighting coefficients to reflect perceptions of the importance accorded human health (0.7), ecosystem (0.2) and natural resource (0.1) impacts in current policy debates. However, the current version of the hazard index (Figure 26) weights all factors equally. Within the three broad categories of impact, all variables are also weighted equally. Although they have weighted all factors equally, the authors suggest that the procedure of sequentially assigning weighting coefficients, first to larger categories and then to sub-sets of categories, is conceptually easier than simultaneously weighting all twelve variables. The summary hazard index is constructed as an additive function:

Total Hazard Index Value =x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12,

where each x_i is the value of the attribute associated with a particular pesticide. Data gaps are filled using the median score for each measure as the default value.

* These weighting coefficients are suggested by Pease et al. (1996) because they reflect the relative importance of these factors in policy debates, but the weights are not used in calculating the pesticide hazard index (Figure 27).

The World Health Organization (WHO) and US Environmental Protection Agency (EPA) have both established human health risk criteria for using specific cautionary words and symbols on pesticide product labels--words such as "danger," "warning," and "caution." The EPA system is supported by regulatory authority (US 40 CFR 156.10). By law, the cautionary word designated by EPA must appear on the front panel of pesticide containers sold in the US. Labels for pesticides most acutely dangerous to human beings must also display a skull and crossbones and the word "poison" in red letters against a contrasting background. Precautionary warnings about environmental and physico-chemical hazards are also required to appear elsewhere on the label.

The WHO classification system does not carry similar regulatory authority, but since 1975 has been recommended by the World Health Assembly for use by WHO member states, international agencies and regional bodies (WHO 1996). WHO advocates displaying the hazard warnings on pesticide labels in local languages, and using attention-getting, brightly colored, internationally-recognized symbols for poison. The target audience for both warning systems are pesticide handlers--people who transport and apply pesticides, and who generally have greatest risk of exposure. Pesticide handlers may or may not be in a position to be decision makers about pesticide use, but these hazard signals may help them take appropriate precautions for safeguarding farmworker health.

Details of the calculations for each of these classifications are described below. In addition to using some different input data and different methods of interpretation, other major differences between them are the means by which results are displayed and the product to which the results are applied: The WHO classification is published as a list of all classified active ingredients, whereas results of the EPA system are seen in descriptions of individual pesticide formulated products but not in a list format. Results of both classification systems are intended to be prominently displayed on pesticide product labels to serve as cautionary warnings for pesticide handlers.

The WHO Recommended Classification of Pesticides by Hazard (1996) is based solely on one public health parameter--acute lethality to human beings. Pesticide active ingredients are grouped into four categories depending upon acute toxicity (LD₅₀) of the active ingredient to rodents, which are typically used as surrogates for human beings. The LD₅₀ thresholds are modified by the type of pesticide formulation (solid or liquid) and by whether data are from oral or dermal tests (Figure 29). Results are given for 955 pesticides in a series of seven tables (Figure 28). The gaseous or volatile fumigants listed in Table 7 cannot be classified because the WHO classification criteria are not applicable to them. However, it is noted that fumigants are highly hazardous, and that recommended limits for occupational exposure have been set for these pesticides in many countries. Including these fumigants, twenty percent (169) of the 845 pesticide active ingredients thought to be currently in use are rated (or are assumed to be) highly or extremely hazardous.

Although oral toxicity tests are more common and typically more sensitive than dermal tests, the predominant route of exposure in pesticide handling is through the skin. Thus criteria for both oral and dermal test data are given (Figure 29). Users are instructed to use dermal test data when available, especially if they result in a higher hazard classification. LD₅₀ values are supplemented by occasional notes on other test endpoints (such as cancer effects) which, in some cases, affect the pesticide hazard classification. Irritant effects are also noted, but do not change the classification. LD₅₀ values are generally interpreted conservatively. Where confidence intervals are provided, for example, the lower limit is used; where there is a gender difference in response to a pesticide dose, data from the more sensitive gender are used.

Although the WHO manual makes a strong argument for using pesticide formulation (trade product) data as the basis for hazard rankings, because of data limitationsThe WHO Recommended Classification is based upon the toxicity of pesticide technical products (active ingredients). Procedures are outlined for estimating a formulation-based LD₅₀ from the data for technical products. These estimates do not, however, reflect actual properties of the inert ingredients in formulations or of the mixture.

Classification is determined by the greatest hazard. Thus if threshold criteria are met for any one of the five hazard indicators, the pesticide is assigned to a lower-numbered (more cautionary) category. Each category is associated with a specific cautionary word:

• Toxicity Class I -- Danger
• Toxicity Class II -- Warning
• Toxicity Class III -- Caution
• Toxicity Class IV -- Caution

EPA fully integrates sublethal irritant effects into the classification, whereas irritants are noted but do not influence the WHO ranking. The greater severity of lethality (as compared with sublethal effects) is conveyed by requiring pesticides that are in the "danger" category because of lethal effects to display the word "poison" and a skull and crossbones. Pesticides in the "danger" category because of sublethal (e.g.: irritant) effects do not display these additional cautions.

* Label must also say "Poison" and display skull and crossbones.

"Screening" refers to the rapid appraisal of new or existing chemicals. Chemicals may be flagged during the screening process either because they pose a potentially significant public health or environmental risk, because insufficient information is known about them, or because they are used in large quantity and thus may have significant environmental exposure and impact. Screening systems are important in the chemical industry as a prevention against wasting resources by developing products later found to be unsuitable. Conversely, screening may flag particularly promising pest controls. And screening systems are important in the policy arena as a means of prioritizing research and regulatory efforts. Unlike farmer decision tools and policy instruments that carry significant economic or regulatory weight, there is far less onus on screening models to be balanced or to provide the best composite advice or directive. Their objective is to be quick and probing. Davis et al.’s compendium, Comparative Evaluation of Chemical Ranking and Scoring Methodologies (1994), is a valuable survey of the gray literature on chemical hazard scoring systems (most of which were developed in the 1980s), with an emphasis on screening systems. Few of the 51 models reviewed there are directly applicable to assessments of pesticide impacts and risk (see, however, the O’Bryan and Ross [1988] model described in more detail below and also Sax and Lewis [1989]).. None of the systems reviewed are suitable for use as farm-scale decision aids.

A Chemical Scoring System developed by the US EPA's Office of Toxic Substances and the Oak Ridge National Laboratory ranks pesticides and other chemicals on the basis of eleven independently-rated parameters (O'Bryan and Ross, 1988). It is designed to help regulators prioritize their work by screening for chemicals which may pose high risks, or about which little is known. It can also be used to compare chemicals on the basis of specified criteria, but it is not designed for composite comparisons among pesticide alternatives.

The range of parameters considered by this scoring system reflects a broad conceptualization of the environment. Parameters are: (1) oncogenicity, (2) genotoxicity, and (3) developmental toxicity to human beings; (4) mammalian toxicity; (5) aquatic plant and animal toxicity; (6) a bioconcentration factor (using the chemical's octanol/water partition coefficient as a measure of hydrophobicity); (7) annual production volume (as an indicator of use); (8) environmental fate; and estimates of (9) occupational, (10) consumer and (11) environmental exposure. Most parameters are scored from 0-9 (Figure 31).

Scoring criteria reflect the extent of available evidence, its reliability and the conclusions drawn from the evidence. The message conveyed by some of the scores is ambiguous because points are used both to indicate potential risks and also to convey that more information is needed before potential risk can be determined. Scores increase when the evidence comes from limited or less reliable sources, but they grade into still higher scores when limited evidence indicates potential hazard. The highest scores are given where negative effects are clearly shown in rigorous tests. Since the objective is to screen for potentially hazardous chemicals about which additional information is urgently needed, the highest scores should perhaps be assigned where incomplete information indicates a likely risk, rather than when negative impact has already been fully ascertained.

O'Bryan and Ross creatively use a variety of data and information sources to assess each parameter. This remains one of the few systems to assess severity of impacts as well as magnitude of impacts. The "severity factor" is a means of drawing a distinction between minor and temporary sublethal effects and those which may cause irreversible damage. In deriving a score for mammalian toxicity, for example, three different toxicity measures are used (a) acute lethality, (b) non-lethal acute effects and (c) subchronic/chronic effects. The highest of the three scores is retained in the final scoring. The latter two scores are each derived by multiplying a "severity score" of 0 to 3 (no observed effects = 0, mild effects = 1, moderately serious = 2, and life-threatening effects = 3) by a potency factor (i.e. a "dose/response score") of 1 to 3. The "dose/response score" represents the concentration at which the measured effect occurs. Thus a moderately severe effect resulting from a high dose of the chemical receives a score of 2 (calculated as 2 x 1), whereas a life threatening response to a low dose earns a score of 9 (3 x 3).

Another important feature of this system is that it considers sub-lethal effects such as reproductive success and fitness. Acute lethality to mammals and aquatic toxicity scores are each straightforward hazard classifications based on standard dose/response toxicity end-point measures such as the LD50 or EC50. Categorical scores (1-9) are used for Production Volume (i.e.: low scores for thousands of pounds produced and higher scores for billions of pounds). This information would perhaps be more useful if scores were instead proportional to the actual quantity of chemicals used.

Occupational and Consumer Exposure are each calculated as the sum of three separate scores ranging from 0 to 3: (a) potential number of people exposed, (b) probability or frequency of exposure, and (c) intensity of exposure. The environmental exposure parameter has four categories which represent incrementally greater release of the chemical into the environment. Scores from these categories are intended to be modified by a persistence factor, by the potential for off-target transport, and by the proximity of human and non-human biota to discharge sites to derive a final score between 0 and 9. Finally, the environmental fate parameter is the product of separate scores for (a) transport and (b) transformation. These are multiplied to arrive at a persistence rating for each of three environmental compartments -- soil, air and water. The criterion for each score is the half life of the chemical in the given environment, ranging from less than one day (1) to greater than one year (5). The transport factor reflects the time the chemical remains in a given environmental compartment, and transformation reflects its degradation rate. Scores for each compartment are between 1 - 25; these scores are added for the total environmental fate score, which may be as high as 75.

Seven toxicological (and ecotoxicological) endpoints and four indicators of exposure potential are evaluated (see Figure 32). Each toxicity endpoint is scored 0 to 5 using a threshold-bounded scoring function (see Figure 33) of the type recommended in Section 4 of this paper (Generic Methods for Ranking Pesticides). Exposure endpoints are scored 1 to 2.5. The algorithm for the summary hazard value is:

Each of the component parts is calculated by summing scores for the relevant variables, i.e.: Human Health Effects = aHV_OR + bHV_INH + cHV_CAR + dHV_NC; Environmental Effects = eHV_MAM + fHV_FA + gHV_FC; and the Exposure Factor = hHV_BOD + iHV_HYD + jHV_BCF. Weighting coefficients can be adjusted for particular applications. Sensitivity analysis indicates, however, that the algorithm is not sensitive to small changes in weighting (≤ to a factor of 2). With all weighting coefficients = 1, the range of possible raw scores is 0 - 262.5. These scores are normalized to a 0-100 scale to generate the Hazard Index. The index is not intended to be interpreted as a quantitative measure of hazard or risk, but as a relative ranking. The authors caution that values should not be considered significantly different unless they vary by more than 20% of the total range.

Hazard Values (tHV) can be generated as shown above, or weighted by usage (for pesticides) or release (for Toxic Release Inventory chemicals). The quantities used (or released) of the 158 high volume chemicals initially evaluated (including 21 pesticides) fell within a 10-fold range--from less than 1000 pounds to greater than half billion pounds. To insure that the release-weighted tHVs reflect a balanced assessment of toxicity and usage factors, rather than be dominated by the latter, a release-weighting factor (RWF) is generated: RWF = ln [releases (lbs)] - 10. The natural log scale is used to attain a range of 10 integers over the range of actual release amounts. Thus an RWF between 1 and 10, rather than total pounds released, is multiplied by the non-release-weighted tHV to generate a release-weighted Hazard Index. By subtracting 10 from the natural log, a bottom threshold is set whereby RWF = 1 for releases < 60,000 pounds.

Experimental test data for the screening tier of CHEMS-1 are drawn from sources such as the Hazardous Substances Data Bank (***) the Registry of Toxic Effects of Chemical Substances (***), and the Agrochemicals Handbook (Kidd and James). For certain endpoints, QSARs and SARs are used rather than experimental data; this approach is also used to fill data gaps. SARs for carcinogenicity are based on whether the chemical contains one or more molecular substructures that have been related to carcinogenicity. QSARs for acute aquatic LC₅₀ are a function of the octanol-water partitioning coefficient (K_ow). Sublethal chronic effects data for aquatic organisms are generally not available; therefore the NOEL for organic chemicals with log K_OW in the range 2 to 5 is calculated as:

log BCF = 0.910 (log K_OW) - 1.975 log (6.8 x 10^-7 K_OW + 1) - 0.786.

The process for choosing from multiple available data points is handled differently for various endpoints: When acute oral LD₅₀ data are available for more than one species of rodent, data for the most sensitive species are used. Given a choice of experimental rodent inhalation LD₅₀ data, the test with a duration closest to 4 hours is used. Where multiple data points are available for aquatic LC₅₀, the order of preference is: (1) 96-h flow through tests with flathead minnow (Pimephales promelas); (2) same test with other freshwater fish (except trout); (3) static 96-h test with the flathead minnow; (4) static 96-h test with other species (excluding trout); (5) 48-h test data.