Managing and Analyzing Pesticide Use Data for Pest Management, Environmental Monitoring, Public Health, and Public Policy Downloaded from pubs.acs.org by SWINBURNE UNIV OF TECHNOLOGY on 11/26/18. For personal use only.
Chapter 20
Economic and Pest Management Analysis of Proposed Pesticide Regulations John Steggall,1,2,* Steve Blecker,1 Rachael Goodhue,2 Karen Klonsky,2 Kevi Mace,1 and Robert Van Steenwyk3 1California
Department of Food and Agriculture, Sacramento, California 95814, United States 2University of California, Davis, California 95616-8628, United States 3University of California, Berkeley, California 94720, United States *California Department of Food and Agriculture, 1220 N Street Sacramento, California 95814, United States; E-mail:
[email protected] This chapter covers elements of using California’s pesticide use report data for economic and pest management analyses of proposed pesticide regulation. Federal and state agencies considering pesticide regulatory changes often need to consider potential economic and pest management impacts. Detailed pesticide use data greatly facilitate analyses of pesticide regulatory proposals. California has required full use reporting for agricultural pesticides since 1990. Data collected include product applied, crop, area treated, amount applied, location, date and time. Combined with knowledge of pest management, these data provide a detailed view of past practices. Because of the wide array of crops grown in California, it is rarely possible to analyze every crop which might be affected by a regulation. The importance of a particular active ingredient to a crop is generally related to percentage of acres treated and the availability of effective alternatives. Other variables which may be considered include pricing and relative efficacy of products as well as active ingredient mode of action in regard to resistance management. More complex analyses may involve pest management models, weather or GIS data, including soils and the proximity of sensitive habitats or urban dwellings.
© 2018 American Chemical Society
Introduction Though any government regulatory agency charged with regulating pesticides would benefit from precise analysis of potential economic and pest management effects of proposed regulations, only California has detailed usage data to support such analyses. This chapter is meant to provide insight into how comprehensive pesticide use data are being used to evaluate pesticide regulatory proposals. The components and rationale for the analyses are presented along with a case study and a worked example to demonstrate methodology. This information should enable others to conduct economic and pest management analyses using California’s data and also demonstrate the usefulness of detailed and comprehensive use reporting. California has two agencies that primarily impact the state’s agricultural industry: the California Environmental Protection Agency (Cal-EPA) and the Department of Food and Agriculture (CDFA). Within Cal-EPA, several departments affect agriculture, including the state and regional Water Resources Control Boards and the Department of Pesticide Regulation (CDPR). CDPR has primary responsibility for pesticide registration and regulation. CDFA’s role in pesticide regulation is to provide CDPR unbiased analyses on potential economic and pest management impacts of pesticide regulations CDPR is considering. Within CDFA, the Office of Pesticide Consultation and Analysis (OPCA) is responsible for analyzing CDPR’s proposed regulations. OPCA’s consultative arrangement was conceived primarily as a means of ensuring that economic impacts on California’s agricultural sector would be properly considered prior to adoption of new pesticide regulations. OPCA tracks a continuous stream of regulations concerning various pesticide active ingredients (AIs). The office monitors developments related to management of pesticides near agricultural-urban interfaces, volatile organic compound (VOC) emissions and toxic air contaminants, surface water and groundwater topics, worker safety issues, and a variety of other environmental matters. In consultation with pest management and agricultural economics experts from the University of California (UC), OPCA provides comments to CDPR on regulatory issues, including restricting use or delisting of registered pesticides. OPCA was conceived in 1991 as part of the Governor’s Reorganization Plan No. 1, which removed California’s pesticide regulatory program from CDFA and relocated it within the newly-created CDPR inside Cal-EPA. Formal requirements for OPCA’s relationship with CDPR are delineated in several sections of the California Food and Agriculture Code (FAC). FAC section 11454.2 explains OPCA’s consultative role with CDPR (1). FAC Section 12841.1 specifies that up to three-fourths mill ($0.00075) per dollar of sales of agricultural pesticides will be collected by CDPR and transferred to OPCA for pesticide consultation activities (2). PUR Data The PUR data include the main PUR table and several auxiliary tables which can be linked to perform various types of queries or statistics. The main PUR 464
table and the auxiliary tables are often referred to as the PUR database. In this chapter, database variables (columns) will be italicized. Each row of the main PUR table consists of information regarding a single pesticide application. Table columns include year (year), application date (applic_dt), time (applic_time), location (county, township, range, section), crop (site_code), product (prodno), amount used (lbs_prd_used), area treated (acre_treated), etc. PUR data are available directly from CDPR in various forms. Basic data can be obtained using CDPR’s CalPIP web application (3) but more complicated analysis generally requires working with raw data. Raw data files can be downloaded from CDPR’s file transfer protocol (FTP) site (3). Each year of PUR data are packaged in a single compressed (zip) archive. The uncompressed archive includes a set of PUR data files and associated product and chemical properties files which can be linked to the PUR data via unique identifier data columns. These files can be loaded into many statistical packages or relational database software for analysis.
Economic and Pest Management Analysis CDPR’s unique pesticide use report (PUR) database facilitates detailed economic and pest management analyses in California. Proposed regulatory analyses vary greatly, from targeting specific AIs, AI categories, application techniques, and/or location relative to human habitation or environmental factors. OPCA’s analyses have considered all these factors. If an analysis focuses on a particular pesticide AI or AIs, it is often necessary to ascertain the critical uses for that AI. Such “critical use analyses” simply determine the crops and uses which are most important for an AI. Since critical use analysis is a part of most AI-focused regulatory analyses and is somewhat standardized, its components are discussed below. If specific risks of an AI are well understood, CDPR may consider potential mitigation plans. In this case, the analysis determines the importance of that AI in the context of various mitigation scenarios. Some regulations do not target specific AIs but address how and when pesticides may be applied. An example of such an analysis is presented in the Case Study, which examines a regulation that would limit pesticide spray methods and allowable spray times within a certain buffer distance to school sites. The type and accuracy of any analysis is limited by the types and quality of available data. The PUR database provides analysts with relatively precise information on when and where pesticides are being used within California. Without reliable data on pesticide use on a commodity, regulatory agencies may assume all planted acreage is treated with a pesticide, a conservative though not realistic scenario (4). Though the PUR is the most comprehensive and detailed database of pesticide use in the world, it still has limitations in the types of analysis it supports. Some of these limitations will be covered here and also in chapters 3 and 24 in this volume. 465
Determining Critical Uses for an AI A regulatory analysis which focuses on an AI or AIs will need to assess how and where that AI is used and the relative importance of its uses. These “critical use analyses” are meant to provide insight on the agronomic and economic importance of an AI for pest management of particular crops, especially in situations where alternative AIs are not economical or efficacious. The analyses are essentially an exercise in how pest management would be done if a particular AI was taken off the market. As described in detail below, critical use analyses compare two basic scenarios—pest management with and without the regulated AI. The scenarios are constructed with recent PUR data and are identical except one scenario excludes the regulated AI, with alternative AIs taking the place of that AI in proportion to their relative percentage use. The analysis should assess whether there will be short or long-term changes in production cost, crop quality or other aspects, such as resistance management. Crops are chosen on the basis of their economic importance, their use of the AI relative to other crops, and the degree to which they rely on the AI, i.e., the availability of effective alternative AIs. Critical use analyses are usually reviewed by affected commodities, pesticide registrants, and academic researchers. After review, an analysis may be used by CDPR to determine possible impacts of regulations, where efforts should be focused to mitigate exposure, or potentially allow for critical uses when viable alternatives are limited. If the analysis reveals exposure mitigation strategies, CDPR might ask for additional analysis for those measures. The following sections outline the key elements of a standard critical use analysis. Crop Selection Crop selection for critical use analysis needs to balance several factors, including the value of the crops, the total area treated, and the relative importance of the AI for pest management. Though it is important to note all the crops on which an AI is used, it is usually impractical to analyze every commodity. Crops’ total value of production for the state is often considered as a proxy for economic importance. Ideally, the crop list should include the majority proportion of the total area treated with the AI, though in some cases, this may produce an unmanageably long crop list. It is also important to account for low acreage commodities that may have a small number of, or even no, registered alternative AIs. Treated acreage and percent acres treated data from the PUR are the key metrics. Treated acreage is the sum of PUR acre_treated data over a period of time for some area, e.g., state, county, region. Percent acres treated is calculated by dividing acre_treated by amount of acreage planted. Unfortunately, PUR acre_planted data are notoriously unreliable (see Figure 1) and most analysts use harvested acreage data from California county agricultural commissioner crop reports compiled by USDA’s National Agricultural Statistics Service (NASS) (5). These reports will be called “county crop reports” henceforth. Unfortunately, the use of county crop report harvested acreage data is an imperfect solution–the data 466
are only available by county on an annual basis and the data are not integrated with the PUR database.
Figure 1. PUR planted acre data problem. For some crops, regional differences in pests and/or conditions may influence pest populations and AIs necessary for an effective pest management program. After identifying regional differences in pest management programs for each crop, data on alternative AIs can be evaluated for each region. Pest management scientists and agricultural economists with UC and UC Cooperative Extension (UCCE) are consulted to ensure that the final crop list balances the relative economic importance of the crops, as well as the crops’ relative reliance on that AI (Figure 2). 467
Figure 2. Working with pest management experts. Target Pests To understand the importance of an AI for a crop, analysts and regulators need to understand how the AI is used to control pests and the alternatives growers may have if that AI is restricted or not available. Unfortunately, identifying target pest(s) for an AI is not possible with PUR data, as growers are not required to report target pest when they submit pesticide use reports. Comprehensive lists of pests for many crops and treatment recommendations are maintained by UC IPM (10). Pests on most crops have long lists of management options, so the UC IPM guidelines may provide limited help in narrowing down how an AI might be used. Many crops have a suite of pests which may be controlled by an AI to varying degrees. Though there may be multiple options for addressing a limited or moderate infestation, the number of alternatives to a given AI for a severe infestation may be smaller, increasing the value of that AI under certain conditions. A useful way to present pest complexes and their potential for control with an AI is the critical use matrix (Figure 3).
Figure 3. Critical use matrix. 468
PUR data for agriculture do include date and time of application which allow comparison of temporal use trends to life history data for pests. A plot of pests’ active phases on a one-year calendar provides a useful overview of their seasonality (Figure 4, cf. Goodell et al. chlorpyrifos report to CDPR (11)), which relates the economic importance of a pest to the availability of alternative control methods. This can be refined for a particular year by overlaying pest phenology data calculated with degree-day models (12). Degree-day models are available for a variety of insects, weeds and plant disease pathogens (13). By plotting AI usage on a weekly or monthly basis and overlaying pest phenology data, a reasonable operating assumption can be made regarding which pests are being targeted by which AIs. In addition, experts such as UCCE Farm Advisors and Specialists can provide valuable insight into pest-pesticide combinations. This information is then used to assess alternative products and strategies for controlling pests targeted by the AI subject to regulation (henceforth referred to as the “regulated AI”).
Figure 4. Seasonality of important pests. Regulated AI and Alternative AI Use Trends on Crops Examining long term (~10 years) trends in use data for the regulated AI and its alternatives provides a good overview of chemical pest management history. In particular, it is important to note how usage changes when new AIs are introduced or when an AI is removed from the market. Annual usage statistics for PUR amount (pounds of AI) applied and area (acres) treated for the state are often sufficient, though number of applications may also be informative, as they can reflect general pest pressure in a given year. Data aggregated on a regional level should be examined if there are significant geographical differences in pest management. If trends are relatively stable for a crop, the dataset can be pared down to the three most recent years. If recent years include anomalous weather, it may be useful to examine more than three years of data or consider a separate analysis for, say, wet and dry years. Including multiple years of data should allow a more robust analysis by reducing the chances of choosing an anomalous year. California’s multi-year droughts and occasional very wet years underscore the importance of using multiple years of data. Trends in usage should be evaluated in the context of economic, biological, regulatory, or other factors. For instance, it should be noted if an AI’s market share is declining owing to competition with new, cheaper, or more effective AIs. Many AIs are formulated into a variety of products and marketed for various crops and uses. Product pricing can influence shifts in product or even AI use. It may 469
be necessary to consult with field experts to determine transient factors which may also be regional in effect. Weather and pest outbreaks can strongly influence pesticide usage though they tend to have a smaller effect on AI or product choice. Pest resistance to particular AIs can be a factor though unlikely to cause dramatic short-term changes. Finally, regulatory changes affecting use, such as preharvest interval (PHI), reentry interval (REI), restrictions on application techniques, use restrictions to manage the development of resistance, or buffer zone changes, can influence changes in use over time. For the regulated and alternative AIs, specific product usage should be examined. Again, three years of data for PUR amount (pounds AI) applied and acres-treated are generally sufficient. Dramatic trends in product usage should be noted. A representative product—generally the most popular product—should be selected. Pricing and application rate for the representative product will be used to standardize economic calculations (see below). Product pricing should be obtained from several local and online distributors. To aid future interactions with distributors, they are generally kept anonymous in any reports. Complete product names, EPA registration numbers, and formulations will facilitate obtaining prices. For the list of AIs being considered, a standard set of product data is needed for pest management and economic analysis. For a representative product for each AI, the following can be organized in several tables: • • • • • • • • • • • • • • • •
Representative product for AI Chemical classification IRAC insecticide mode of action (14, 15) WSSA herbicide site of action (16) FRAC fungicide mode of action (17, 18) Price/unit Standard application rate Application method, e.g., ground/air/chemigation Material cost per acre Application cost per acre Percent of product that is the AI Seasonal usage if applicable to discussion Effects on natural enemies and bees Residual (short/long, i.e., half-life in field) Application/handling difficulty REI, PHI, and/or other label restrictions, if applicable to discussion
Tables The following list of tables are often useful for critical use analysis: 1. 2.
Top crops (~20): for each year, amount (pounds) AI applied and acrestreated Percent of crop acreage treated with AI and its alternatives 470
3. 4.
Harvested acres (county crop reports), pounds of regulated AI applied, and percent of total use represented by regulated AI For each crop, percent product usage per AI, price per (standard quantity), using most recent PUR year ○
5.
List up to the top five products per AI
For the commodity, summary of increases or decreases in cost for treatments when the regulated AI is unavailable (assume constant acreage)
Comparative Efficacy The efficacy of the AI subject to regulation should be compared relative to the alternative AIs, preferably from pesticide trial literature. Ideally, the cited efficacy trials were done in the same region and on the same crops as those in the critical use analysis. Expert opinion on efficacy issues, especially from scientists who have conducted field trials (Figure 2), is also invaluable. The main topics of concern include issues such as: • • • • •
Substantial differences in AI efficacy Potential yield loss or quality problem with alternative AIs Notable differences for products with the same AI Issues related to timing within season or growing region Phytotoxicity, if any
Resistance Insect pests, weeds and plant diseases have evolved resistance to pesticides designed to control them (19–21). The management of pesticide resistance should be a consideration for agencies assessing the regulation of an AI. Over time, pest species evolve resistance to pesticides as the least susceptible individuals survive treatment. Resistance is often specific to a particular chemical class of pesticide that has the same mode of action. Management of resistance typically involves pesticide rotation, i.e., switching between chemical classes with different modes of action to slow the evolution of pest resistance. When pesticides are incorporated into transgenic plants, susceptible cultivars are often interplanted to reduce selection pressure and delay the evolution of resistance (22). The loss of an AI with a particular mode of action can create resistance problems as growers will have fewer modes of action to deploy. The remaining AIs will be potentially less efficacious, and growers may have to apply them at higher rates to control pests. The discussion of pesticide resistance should consider short- and long-term effects and regional issues. Though resistance is relatively difficult to document, literature and expert opinion can provide valuable insights. Many AIs have been categorized according to chemical class and mode of action by the Insecticide Resistance Action Committee (IRAC) (14, 15), the Weed 471
Science Society of America (WSSA) (16), and the Fungicide Resistance Action Committee (FRAC) (17, 18). Efficient ways to present AI attributes, including mode of action is detailed in Figure 4 (with pest attributes) or Figure 5 (with AI and product details).
Figure 4. Mode of action, i.e., modes of action classification developed by the Insecticide Resistance Action Committee (IRAC, irac-online.org). Viable alternative practices are a count of the number of effective modes of action, biocontrol, or cultural control.
Figure 5. Alternative active ingredients. Special Considerations for Herbicides Insecticides and fungicides often target single pest species, but herbicides usually must control a suite of weeds, each with different susceptibilities. Herbicides thus present a particular challenge in alternative AI analyses as pest-pesticide combinations can be quite broad. Even herbicides formulated to control a narrow range of annual grasses vs. perennial weeds, for example, still 472
affect a wide range of weeds. As some herbicides provide better control over certain types of weeds than others, they are often mixed in the same tank with herbicides of other modes of action. Tank mixing can be detected in PUR data by careful querying based on the specific agricultural field identifier and the date and time of application. In addition, depending on the AI, growers may need to consider controlling weeds post-emergence rather than pre-emergence. Post-emergence control may take the form of mechanical control options such as mowing, tillage or hand weeding, in combination with chemical control, which further complicates analysis. Thus, a simple replacement of the regulated herbicide AI with an alternative AI may not be feasible. The analysis has to account for these complexities and may require a different set of calculations than those for most insecticides or fungicides. Non-Chemical Alternatives Integrated pest management (IPM) is a system level approach for pest control in production agriculture where the goal is to reduce pest damage to a level which minimizes economic loss to the grower (23). Multiple techniques are employed, including biological control, resistant cultivars, and cultural controls such as habitat modification. Pesticides are used only after monitoring indicates that pests may cause economic damage, and treatments are made with the goal of minimizing harm to non-target organisms. Though it may seem expedient to analyze only chemical alternatives to the regulated AI, it is advisable to consider how chemical control fits into the whole pest management system for a crop. Critical use analysis should consider the biological context for pest management of each crop for which the regulated AI is important. For instance, invasive pests often drive increased use of pesticides until researchers and growers reconfigure pest management programs to account for the new pest, e.g., Farnsworth et al. (24). Changes in pest management may include new biological control agents brought in to control the invasive pest, e.g., DiTomaso et al. (25). Such a system is likely to see dramatic changes in short- and long-term pest management and policy makers would do well to understand how regulation may affect outcomes. Economics in Critical Use Analyses Policy makers want to know the short- and long-term consequences of regulating an AI. Ideally, assessing the economic importance of an AI should take into account all the variables discussed elsewhere in this chapter. For instance, an assessment would account for how pesticide registrants might reposition products and pricing and how growers would change pest management practices. In reality, it is difficult to precisely predict the interrelated and cascading economic effects of regulating an AI. Hence, it is only practical to calculate short term effects, though factors and scenarios which may affect long-term changes should be discussed. For basic critical use analysis, several assumptions are made to simplify calculations of short-term economic effects. Possible outcomes are evaluated using a single comparison: the baseline or status quo based on recent three years 473
of PUR data, and a scenario where the AI subject to regulation is unavailable for use. It is also assumed that growers will continue using alternative AIs in the same proportions as found in recent PUR data and that the pricing of products will remain the same. Again, given the complexity of possible outcomes, defining a realistic post-regulatory scenario is not practical, hence these assumptions furnish a pragmatic framework for the analysis. Registrants may incorporate an AI into a variety of products, each with differing amounts of AI and pricing per unit of product. To further simplify calculations and reduce the effort required to collect pricing data, economic analyses can be performed using pricing from a single product for the affected AI and each of its alternatives for each crop examined. For the economic analysis of regulating an AI, status quo (baseline) and regulated scenarios are calculated and compared. The following sections outline the steps in an economic analysis of a regulated AI. A detailed example is shown in the Worked Example.
Variables calculated or given AA = amount (pounds) AI applied AC = application cost per acre (labor, gas, equipment) AIPD = lbs of AI/lbs of product (percent of product that the AI constitutes) AT = area (acres) treated PAT = percent area treated CPA = cost per area (acre) treated PPP = price per pound TAT = total acres treated with all AIs TC = total cost of product + application cost UR = use rate (pounds product per acre) Subscript R = regulated AI variable. As multiple products contain the regulated AI, these are denoted with a number or x following the R subscript. Subscript A = alternative AI. There are often multiple alternative AIs, and these are denoted as an i or number after the A. Subscript t = year Subscript SQS = Status quo scenario Subscript RS = Regulated scenario
Status quo or baseline scenario
Use rate for AIs (lbs of product/ac) 1. Calculate average use rate for the AI subject to regulation (AIR) and each alternative (AIi). For each AI and across all three years, sum the pounds of AI use of all products and divide by the sum of acres treated. This is the use rate in lbs of AI per acre. To get the use rate in lbs of product per acre, multiply by 1 over 474
the percent of the product that is AI (for example, if a product is 70% AI per lb of product, multiply the lbs of AI per acre by 1/0.7):
Cost per acre treated for the AI subject to regulation (AIR) and each alternative (AIAi) ($/ac) 2. Calculate total cost per acre for the AI subject to regulation (AIR) and each alternative (AIAi) by multiplying the cost per unit AI by the average use rate per acre, measured in (pounds AI)/acre, from step 1. Pricing is obtained from distributors as described in the text. Prices should be converted in to $/lb for all AIs using information on the label. Add application cost per acre, e.g., standard labor, equipment costs (AC).
Total cost AI subject to regulation (AIR) and each alternative (AIAi) per year ($) 3. Total cost of treating with each AI is the cost per acre of the representative product (step 2) times the total number of acres-treated.
Total cost of status quo pest management program per year ($) 4. Steps 1-3 are calculated for each AI and each year. Total cost of status quo program in each year is the sum of costs for the AI subject to regulation (AIR) and each alternative (AIAi).
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Regulated scenario, i.e., AI subject to regulation (AIR) is not available for use per year ($) Since it is difficult to predict how pesticide vendors might reposition their products in the absence of the regulated AI, it is assumed that growers would continue using alternative AIs in the same proportions as found in the most recent PUR data, and that prices would not change. In essence, the acreage treated with the regulated AI is reallocated to its alternative AIs, according to their relative percentage of acres treated in the most recent PUR data.
Percent acres-treated for alternative AIAi per year 5. Calculate percent of total use for each alternative AI, assuming regulated AI was unavailable, by dividing the acres-treated for each alternative by the total acres-treated with all alternative AIs.
Total acres treated with alternative AIAi (regulated scenario) per year (ac) 6. To calculate the amount of acreage reallocated from the regulated AI to each alternative AI, multiply the percentage found in step 5 by the total acres-treated for the regulated AIS, then add the actual acres treated for that alternative AI:
Cost of treating with alternative AIAi (regulated scenario) per year ($) 7. The cost of treatment under the regulated scenario (without the regulated AI) is determined by multiplying the cost of the representative product for each alterative AI by the number of modified acres treated for each alternative found in step 6.
Total cost of alternatives pest management program per year ($) 8. The total cost of the regulated pest management program (regulated AI is unavailable) is the summation of the individual alternative AI costs in the regulated scenario.
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Comparing regulated vs baseline scenarios per year 9. The increase or decrease in cost from eliminating the regulated AI is calculated by subtracting the total cost of the regulated program from the total cost of the status quo program. The percent increase or decrease in cost is the percentage change in cost if the regulated AI were not available.
A detailed example of the process is presented in the Worked Example below. This provides an estimate of the economic value of the AI subject to regulation, which is a foundation for discussing its general importance in pest management for each crop. At a basic level, regulation may affect short- and long-term marketing plans for pesticide registrants. In certain cases, it may be possible to predict how companies will reposition products and how the relative effects of pricing will affect product popularity. Though it may be reasonable to speculate on such outcomes, basing a quantitative analysis on such uncertainties is inadvisable. Additional Analyses In addition to the simple economic analysis just described, more sophisticated analyses are possible, depending on the availability of appropriate data. Farm cost studies provide a detailed accounting for a hypothetical farm of the costs of production and returns given a range of yields and commodity prices. If farm cost studies, e.g., those produced by UC Davis (26), are available, it may be useful to compare the increase or decrease in pest management costs to total pest management costs. If sufficient information is available on a potential decline in overall yield or in marketable yield, it may be possible to estimate changes in gross returns. A more sophisticated per acre cost analysis holds prices constant and if yield change information is available, a change in per acre net revenue analysis is possible. As a general rule though, growers adapt to regulatory changes and rarely allow significant yield losses or changes in quality. With cost study data, a partial equilibrium market analysis is possible. For instance, if regulation cause increases in production costs resulting in crop acreage decline, returns per acre may increase on remaining acreage, depending on the own-price elasticity of demand and the importance of California production in the market, competing producers in other regions, states or countries not subject to the same regulation, etc.
Worked Example: Economic Calculations for Critical Use Study of Carbaryl on Olive This section presents a slightly modified version of a relatively simple critical use analysis for the active ingredient carbaryl. Olive was considered for a critical use analysis of the insecticide carbaryl, where it is used for controlling olive scale (Parlatoria oleae) and black scale (Saissetia oleae). Though olive scale rarely causes economic damage, black scale can become a serious problem if not properly 477
managed. Black scales excrete a sticky honeydew on leaves of infested trees which provides a habitat for the sooty mold fungus. Infestations reduce tree vigor and fruit production the following year. Biological control, pruning, and ant control are key to managing black scale (ants can disrupt biological control). Though these pest control methods generally provide sufficient control in northern and coastal orchards, in warmer regions, horticultural oils and/or insecticides may be necessary if serious scale infestations develop. Apart from horticultural oils, carbaryl is one of only three insecticides available for scale control. Although carbaryl use is discouraged because of its negative effects on beneficial insects, there may be occasions when its use may be necessary. There are six products containing carbaryl registered in California (formula variable identifiers in parentheses): Drexel Carbaryl 4L (AIR1), Loveland Carbaryl 4L (AIR2), Sevin Brand 4F (AIR3), Sevin 80S (AIR4), Sevin XLR (AIR5), and Sevin SL (AIR6). There are two alternative AIs for scale on olives: buprofenzin (AIA1) and pyriproxyfen (AIA2). In olives in California, there is only one product for each of those, which simplifies this example. The products are Applaud IGR and Esteem 0.86EC, respectively. A baseline application cost of $40/ac is assumed for all applications. Variable definitions and subscripts are repeated here for convenience. AA = amount (pounds) AI applied AC = application cost per acre (labor, gas, equipment) AIPD = lbs of AI/lbs of product (percent of product that the AI constitutes) AT = area (acres) treated PA = percent of product that is AI PAT = percent area treated CPA = cost per area (acre) treated GP = gallons of formulated product required for one pound of AI PPP = price per pound TAT = total acres treated with all AIs TC = total cost of AI product + application cost UR = use rate (pounds AI per acre) Subscript R = regulated AI variable. As multiple products contain the regulated AI, these are denoted with a number or x following the R subscript. Subscript A = alternative AI. There are often multiple alternative AIs, and these are denoted as an i or number after the A. As multiple products contain the alternative AI, these are denoted with a number or x following the i subscript. Subscript t = year Subscript SQS = Status quo scenario Subscript RS = Regulated scenario The goal of this exercise is to fill in Table 1, given the PUR and price data presented in Tables 2, 3, 4, and 5. Using the steps outlined in the text above, the cells in Table 1 are calculated in the following steps, and presented sequentially in Tables 6 and 7, with Table 8 displaying the final results.
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Table 1. Estimated insecticide cost in olive from 2011 to 2013. 2011
2012
2013
Cost with carbaryl: Cost without carbaryl: Increase/decrease in cost: Percent increase/decrease in cost:
Table 2. Pounds of AI used and acres treated with each product containing carbaryl in 2011, 2012, and 2013 for olive. Lbs of AI used
Acres treated
Trade name
2011
2012
2013
2011
2012
2013
D. Carbaryl 4L
459
1,177
2,572
139
303
630
L. Carbaryl 4L
--
66
239
--
44
40
Sevin 4F
2,159
1,639
1,631
596
646
484
Sevin 80S
550
14
--
90
2
--
Sevin XLR
6,464
9,582
4,364
1,650
2,796
1,275
--
--
40
--
--
7
9,632
12,479
8,846
2,475
3,791
2,436
Sevin SL Total
Table 3. Pounds of AI used and acres treated with alternative AIs in 2011, 2012, and 2013 for olive. Lbs of AI used
Acres treated
AI
2011
2012
2013
2011
2012
2013
Buprofenzin
603
579
2,417
347
354
1497
Pyriproxyfen
181
74
74
1050
697
727
784
653
2,491
1,397
1,051
2,224
Total
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Table 4. Pounds of AI in pounds of representative products. Representative product
Lbs AI/lbs product
Sevin XLR (carbaryl)
0.44
Applaud IGR (buprofenzin)
0.7
Esteem 0.86 EC (pyriproxyfen)
0.11
1. Use rate for AIs (lbs product/ac)
2. Cost per acre treated ($/ac) for the AI subject to regulation (AIR) and each alternative (AIAi) Table 5. Price per pound of representative products. Representative product
Price/lb 5.26
Sevin XLR (carbaryl)
25
Applaud IGR (buprofenzin)
80.85
Esteem 0.86EC (pyriproxyfen)
480
3. Total cost of AI subject to regulation (AIR) and each alternative (AIAi) per year
2011
2012
2013
4. Total cost of status quo pest management per year
481
2011
2012
2013
Table 6. Estimated insecticide cost in olive from 2011 to 2013.
Cost with carbaryl:
2011
2012
2013
$380,174
$441,864
$446,618
Cost without carbaryl: Increase/decrease in cost: Percent increase/decrease in cost:
5. Percent acres-treated for alternative AIs (AIAi) per year
2011
2012
482
2013
6. Total acres treated with alternatives AIAI (regulated scenario) per year
2011
2012
2013
7. Cost of treating with alternative AIAi (regulated scenario) per year
483
2011
2012
2013
Total cost of alternative pest management program per year
2011
2012
484
2013
Table 7. Completed estimated insecticide cost in olive from 2011 to 2013. 2011
2012
2013
Cost with carbaryl:
$380,174
$441,864
$446,618
Cost without carbaryl:
$487,431
$593,927
$514,457
Increase/decrease in cost: Percent increase/decrease in cost:
8. Comparing regulated vs baseline scenarios per year 2011
2012
2013
485
Table 8. Estimated insecticide cost in olive from 2011 to 2013. 2011
2012
2013
Cost with carbaryl:
$380,174
$441,864
$446,618
Cost without carbaryl:
$487,431
$593,927
$514,457
Increase/decrease in cost:
$107,257
$152,062
$67,839
28%
34%
15%
Percent increase/decrease in cost:
Case Study: CDPR Schools and Daycare Regulation In late 2015, CDPR provided OPCA a draft of a regulation which proposed to restrict pesticide applications near California schoolsites. The regulation was put into effect on 1 Jan 2018. UC Davis and OPCA’s analysis of the regulation, Goodhue et al. (8), was completed in July 2016. This report (8) is an excellent example of a regulatory analysis which makes extensive use of PUR data, coupled with several other data sources, plus pest management models. Detailed methodology is provided, including SQL computer code. A brief summary will be presented here. CDPR’s “schools regulation” was developed to address concerns regarding school children’s exposure to pesticides. Though CDPR determined that health risk to children is low when pesticides are used according to regulations and label requirements, this does not account for exceptional circumstances or violations. For this regulation, schoolsites include public K-12 schools and licensed child daycare facilities. The regulation’s goals were to reduce the likelihood of pesticides drifting onto school children and provide more information to schools on agricultural pesticide use near their facilities. The schools regulation prohibited certain types of pesticide applications within a ¼ mile of public K-12 schools and child daycare facilities from 6 am and 6 pm, Monday through Friday. Prohibited application methods include those made by aircraft, sprinklers, air-blast sprayers, and fumigations. Most dust and powder pesticide applications, such as sulfur, are also prohibited during these times.
GIS Data For an accurate analysis, it was necessary to know how many schoolsites were located next to agricultural fields, including precise data on the agricultural operations near the school facilities. The analysis thus required high quality geographic information system (GIS) data for agricultural fields and schoolsites. CDPR supplied agricultural field boundary GIS data for thirteen counties. These county field boundary data are collected via the CalAgPermits system (27) but unfortunately these data are only available to the public by contacting each county 486
separately. The thirteen counties were chosen because they were able to supply GIS data and also because they are important agricultural counties, collectively accounting for two-thirds of California’s total value of crop production. Location data for daycares and K-12 schools were obtained from the California Department of Social Services and the California Department of Education. Since the school and daycare datasets only included postal addresses or latitude and longitude data, these had to be joined with parcel map GIS data (US Census TIGER data) (28) to develop facility boundary information. The GIS data for agricultural fields were joined with PUR data to provide a detailed understanding of the crops grown and pesticides used near schools and daycares. Other data included soil hydrologic group GIS data from the Natural Resources Conservation Service (NRCS) (29) and ten years (1996-2005) of weather data from the California Irrigation Management Information System (CIMIS) (30).
Pest Management Any regulatory analysis must take into account all current regulations to determine the net effect of a new regulation. The PUR database provides an effective snapshot, or baseline scenario, of pesticide use prior to a regulation. The challenge is to determine how usage will change under the new regulatory regime. The schools regulation prohibits several types of application techniques during specific time periods and locations. PUR data include several columns which specify application techniques, application times, and location (31). Base level PUR data only provide location to the “section” level, i.e., one square mile or 640 acres (32), but GIS field boundary data from counties allow more precise location data. Application method is defined by a number of data columns, including an aerial vs. ground indicator variable (aer_grnd_ind), a fumigation indicator variable and fumigation method (fumigant_sw, fume_method), as well as the formulation code variable (formula_dsc) which allows determination of dust or powder formulated products. Application date and time (applic_dt, applic_time) provide the day, hour and minute the application was completed. Full documentation of PUR data fields is available at CDPR (31). Goodhue et al. (8) examined existing state and county regulations and determined that several parts of the schools regulation would have no appreciable effect on pest management operations. For instance, fumigant applications are restricted by the schools regulation, but these were already prohibited near schools for the distance and time intervals specified. See Figure 6 for more information regarding PUR fumigant analysis. Sprinkler chemigation was also banned during school hours (weekdays, 6 am – 6 pm) but there would be little economic effect since these applications can be done at night. After eliminating these possible effects, the analysis could focus on determining the regulation’s effects on limiting aerial and air-blast to night (6 pm – 6 am) and weekend times. California growers routinely apply pesticides at night, but a certain percentage of daytime applications may be difficult to shift to nights or weekends—the regulation’s main effect would likely come from this conflict. 487
6. Fumigant issues. The only ground-applied methods limited by the regulation are air-blast or air-assisted. As mentioned, aerial applications can be distinguished from ground applications in PUR data, but it is not possible to differentiate between air-blast and other ground application methods. In order to identify such applications as precisely as possible given this data limitation, UC Cooperative Extension scientists were consulted and several “rules of thumb” were developed. First, herbicides are not applied with air-blast equipment. Second, air-blast methods are primarily used in orchard and vine crops to apply insecticides and fungicides in order to get proper coverage in thick canopies and trees. Air blast sprayer applications can be identified using the PUR crop name (site_code, site_name) variables and the active ingredient specific type variable (typepest_cat) contained in the PUR data. See Figure 7 for further discussion on site_code and site_name issues.
7. PUR crop (site) naming issues. 488
Using the application times reported in the PUR database, all applications using aerial or air blast methods were divided into time periods of 6 am – 6 pm, nights, and weekends. These data allowed Goodhue et al. (8) to ascertain the kinds of crops and acreage affected and the potential number of aerial and air-blast applications which would need to be shifted to nights or weekends. The PUR data, joined with agricultural field boundary data, provided precise estimates of crops and acreage within ¼ mile of school and daycare facilities. In total, 58 percent of aerial and air-blast applications would have been prohibited under the regulation. Almond and grape (wine, table and raisin) were most affected, a total of 48 percent of all prohibited applications. For field crops, alfalfa, corn, cotton, and processing tomato were most affected. The analysis confirmed that evening and weekend applications were already being done, suggesting that growers may have flexibility to make applications outside the prohibited time period. Under ideal conditions, it is possible that the remainder of weekday applications could be shifted to nights or weekends, but weather and field conditions are not always suitable for applying pesticides. Fields can be too muddy for ground rig applied air-blast machinery and applications would not be effective immediately before, during, or right after rain events. The next task was to determine the probability of weather affecting critical pesticide applications across the many crops grown near schoolsites. Goodhue et al. (8) were able to narrow the analysis to disease control applications after Cooperative Extension scientists noted that the timing for insect and weed control sprays is flexible enough to be shifted to nights or weekends. Yet, the timing of fungicide application to control diseases is quite sensitive and typically must be done in a narrow window before or after rain events. Fungicide applications can be selected from PUR data using the typepest_cat variable. Given the large number of crops and the complexity of how each pest management program would interact with weather, the decision was made to limit the analysis to grape and almond crops (8). The PUR analysis found these two crops would be most affected by the schools regulation, as measured by acreage in the thirteen counties within ¼ mile of a schoolsite: grape (5,319 acres) and almond (7,245 acres). For the thirteen counties examined, these crops are very important, and furthermore, fungicide applications during the rainy winter and spring are critical in their pest management programs. To determine the likelihood that growers might miss critical fungicides sprays, it was necessary to examine how weather events could affect the ability of growers to get their ground rigs into grape and almond orchards. Of course, soil type strongly affects drainage and how soon a field might be accessible. To conclude the analysis, the critical winter and spring periods were analyzed over a ten-year period (1996-2005), a period for which bloom data were available for almond in the Central Valley. Soil hydrologic data from NRCS (29) were overlaid on the GIS agricultural field boundary layer to precisely determine soil types for the affected orchards. Rules, based on soil type and hourly amounts of rain, wind speed, and temperature, were developed to determine the potential for spraying after rain events. IPM disease models, based on temperature and rain, were developed to determine the conditions required for fungicide applications for almond and grape. Probabilities that one or more fungicide sprays could not 489
be completed were calculated by applying these rules to the historical weather dataset (1996-2005). The final report (8), information about the regulation, and a statewide extrapolation of the economic analysis (34) are available for further examination.
Acknowledgments We thank Daniel Tregeagle and Tor Tolhurst for their constructive comments which helped improve the manuscript. We also thank the anonymous reviewer for helpful insights in how to improve the scientific quality of the manuscript.
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