DOI: 10.5061/dryad.8pk0p2p0h
title: Pesticide use in California agriculture with information on pest control advisors

description: Pesticides support crop production, enhancing global food security, but
 are associated with serious environmental and health risks. Factors that
 promote overuse of pesticides are therefore of great concern. Pest control
 advisors (PCAs) are agricultural professionals who scout fields for pests
 and may recommend pesticide applications. We test two hypotheses regarding
 the influence of PCAs on pesticide use by California farmers, contrasting
 four groups: independent PCAs, sales PCAs, farm-staff PCAs, and farmer
 PCAs. The long-discussed conflict of interest hypothesis posits that sales
 commissions earned by PCAs who work for agricultural chemical retailers
 (“sales PCAs”) incentivize pesticide use; it predicts elevated use of all
 pesticides by farmers advised by sales PCAs. The risk aversion hypothesis
 posits that the risk of damaging pest outbreaks incentivizes pesticide
 use; it predicts elevated pesticide use when targeting pests that can
 exhibit outbreaks (arthropods and plant pathogens) but not when targeting
 non-outbreak pests (weeds). We assembled a dataset of pesticide use on
 nearly 600,000 crop-years grown in California from 2012 to 2021 by farmers
 advised by different types of PCAs. Our analysis provides little to no
 support for the conflict of interest hypothesis; farmers advised by sales
 PCAs used slightly fewer pesticides than farmers advised by independent
 PCAs (who receive no sales commission). Instead, our analysis reveals
 pesticide use consistent with the risk aversion hypothesis, with elevated
 use of pesticides by one group of PCAs (farm-staff) when managing
 arthropods and pathogens, but not when managing weeds. Risk aversion,
 rather than sales commissions, may be shaping pesticide use in California.
description: # Pesticide use in California agriculture, with information on pest
 control advisors Dataset DOI:
 [10.5061/dryad.8pk0p2p0h](10.5061/dryad.8pk0p2p0h) ## Description of the
 data and file structure This dataset, which is taken entirely from
 publicly available data, describes pesticide use on approximately 1.4
 million crop-years grown in California from 2012 to 2021 by farmers
 advised by different types of pest control advisors (PCAs).  Records are
 for pesticide use in outdoor, commercial agriculture.  The dataset was
 assembled to test whether conflicts of interest among sales PCAs, who work
 for agricultural chemical distributors and who earn commissions on
 pesticide sales, are associated with elevated use of pesticides by their
 client farmers. The values from permit_number and site_code fields were
 removed to avoid the risk of re-identification. ### Files and variables
 #### File: PCAstudy2_metadata-for_anonymized_data.csv
 **Description:** This file defines each of the variables (columns) in the
 associated data files #### File: PCAstudyPart1-anonymized.csv
 **Description:** The overall dataset is too large (ca. 1.4 million rows)
 to be handled readily by Excel. So, I split the dataset into two parts:
 part1 contains dataframe rows 1-500,000; part2 contains the remaining rows
 (500,001 - 1,412,419). ##### Variables: (please see attached metadata file
 for all variable definitions) * log_all_targets: log base 10 of the number
 of pesticide products applied against all pest targets * log_weeds: log
 base 10 of the number of pesticide products applied against weed targets *
 log_arthropods: log base 10 of the number of pesticide products applied
 against arthropod targets * log_pathogens: log base 10 of the number of
 pesticide products applied against plant pathogen targets * log_adjuvants:
 log base 10 of the total number of adjuvants applied *
 log_adjuvants_per_event: log base 10 of the total number of adjuvants
 applied per spray event (discrete date and time when a pesticide
 application was made) * prop_single_reg: proportion of all pesticide
 products applied that were marketed by a single registrant (these are
 nearly always patent-protected materials) * all_target_psyc_n_appl: total
 number of pesticide products applied against all pest targets (not log
 transformed) * psyc_n_appl_plant_growth_regulator: total number of plant
 growth regulators applied (not log transformed) * farm_org_staff:
 proportion of pest control advisor team associated with a given farmer
 (permit holder) that was farm-staff PCAs * all_indep: proportion of pest
 control advisor team associated with a given farmer (permit holder) that
 was independent PCAs * sales: proportion of pest control advisor team
 associated with a given farmer (permit holder) that was sales PCAs *
 farmer: proportion of pest control advisor team associated with a given
 farmer (permit holder) that was farmer PCAs * coname: county name * Year:
 year * area_planted: size of the field (hectares) * farm_size: size of the
 farm (hectares) * is_organic: the field is being managed conventionally
 (is_organic = 0) or organically (is_organic = 1) * log_weeds_kg: kg of
 formulated pesticide product applied that target weeds *
 log_arthropods_kg: kg of formulated pesticide product applied that target
 arthropods * log_pathogens_kg: kg of formulated pesticide product applied
 that target plant pathogens * log_all_targets_kg: kg of formulated
 pesticide product applied that target any pest group * log_weeds_ai: kg of
 active ingredients (AI) of pesticide product applied that target weeds *
 log_arthropods_ai: kg of active ingredients (AI) of pesticide product
 applied that target arthropods * log_pathogens_ai: kg of active
 ingredients (AI) of pesticide product applied that target plant pathogens
 * log_all_targets_ai: kg of active ingredients (AI) of pesticide product
 applied that target any pest group * log_adjuvants_kg: log base 10 kg of
 formulated adjuvants applied * total_kg_ha_adj: kg of formulated adjuvants
 applied * log_adjuvants_kg_per_event: log base 10 kg of formulated
 adjuvants applied per spray event (discrete date and time when a pesticide
 application was made) * total_kg_ha_pgr: kg of formulated plant growth
 regulators applied * total_kg_ha_ai_pgr: kg of active ingredients (AI) of
 plant growth regulators applied #### File: PCAstudyPart2-anonymized.csv
 **Description:** The overall dataset is too large (ca. 1.4 million rows)
 to be handled readily by Excel. So, I split the dataset into two parts:
 part1 contains dataframe rows 1-500,000; part2 contains the remaining rows
 (500,001 - 1,412,419). Variables and definitions are the same as for Part
 1. ##### Variables * log_all_targets: log base 10 of the number of
 pesticide products applied against all pest targets * log_weeds: log base
 10 of the number of pesticide products applied against weed targets *
 log_arthropods: log base 10 of the number of pesticide products applied
 against arthropod targets * log_pathogens: log base 10 of the number of
 pesticide products applied against plant pathogen targets * log_adjuvants:
 log base 10 of the total number of adjuvants applied *
 log_adjuvants_per_event: log base 10 of the total number of adjuvants
 applied per spray event (discrete date and time when a pesticide
 application was made) * prop_single_reg: proportion of all pesticide
 products applied that were marketed by a single registrant (these are
 nearly always patent-protected materials) * all_target_psyc_n_appl: total
 number of pesticide products applied against all pest targets (not log
 transformed) * psyc_n_appl_plant_growth_regulator: total number of plant
 growth regulators applied (not log transformed) * farm_org_staff:
 proportion of pest control advisor team associated with a given farmer
 (permit holder) that was farm-staff PCAs * all_indep: proportion of pest
 control advisor team associated with a given farmer (permit holder) that
 was independent PCAs * sales: proportion of pest control advisor team
 associated with a given farmer (permit holder) that was sales PCAs *
 farmer: proportion of pest control advisor team associated with a given
 farmer (permit holder) that was farmer PCAs * coname: county name * Year:
 year * area_planted: size of the field (hectares) * farm_size: size of the
 farm (hectares) * is_organic: the field is being managed conventionally
 (is_organic = 0) or organically (is_organic = 1) * log_weeds_kg: kg of
 formulated pesticide product applied that target weeds *
 log_arthropods_kg: kg of formulated pesticide product applied that target
 arthropods * log_pathogens_kg: kg of formulated pesticide product applied
 that target plant pathogens * log_all_targets_kg: kg of formulated
 pesticide product applied that target any pest group * log_weeds_ai: kg of
 active ingredients (AI) of pesticide product applied that target weeds *
 log_arthropods_ai: kg of active ingredients (AI) of pesticide product
 applied that target arthropods * log_pathogens_ai: kg of active
 ingredients (AI) of pesticide product applied that target plant pathogens
 * log_all_targets_ai: kg of active ingredients (AI) of pesticide product
 applied that target any pest group * log_adjuvants_kg: log base 10 kg of
 formulated adjuvants applied * total_kg_ha_adj: kg of formulated adjuvants
 applied * log_adjuvants_kg_per_event: log base 10 kg of formulated
 adjuvants applied per spray event (discrete date and time when a pesticide
 application was made) * total_kg_ha_pgr: kg of formulated plant growth
 regulators applied * total_kg_ha_ai_pgr: kg of active ingredients (AI) of
 plant growth regulators applied ## Code/software This R script
 (PesticideModels.R) describes the analyses performed in the paper. The
 script systematically evaluates how PCA team composition and farm
 characteristics influence pesticide use patterns across different crops,
 pesticide types, and metrics (number of applications, kg product, kg AI,
 adjuvants, PGRs). ## Access information Other publicly accessible
 locations of the data: * California Department of Pesticide Regulation,
 California Association of County Agricultural Commissioners and Sealers
 Association,and  diverse public websites