2------------------------------------------------------------------------------------------------------------- This file was generated on 2025-10-27 by Niklas Möhring A GENERAL INFORMATION 1. Title of the dataset: Code of "Expected Effects of a Global Transformation of Agricultural Pest Management" 2. Brief description of the research project and its aims: The here presented dataset provides code to replicate the analysis of Möhring et al. (2025) on expected effects of a global transformation of agricultural pest management based on an online survey conducted in 2022 with 517 senior scientific experts from key agricultural regions and disciplines. The assessment framework covers 24 indicators in the economic, human health, food security, social, and environmental domains. It is anonymized. It further contains information on respondent characteristics and co-variates for the socio-economic and environmental state of the assessed regions from literature. The data was collected in 2022 with an online survey in Limesurvey. The data was collected to assess expected effects of a global transformation to pest management with zero or minimal pesticide use. This is a pressing challenge in global agriculture and relates to national and global policy targets on pesticide reduction. The code in R can be used to replicate results of the analysis. 3. Author Information A. Investigator Contact Information Niklas Möhring1, Malick N. Ba2, Anna Braga3, Sabrina Gaba4, Vesna Gagic5, Per Kudsk6, Ashley Larsen7, Robin Mesnage8, Urs Niggli9, Matin Qaim10, Pepijn Schreinemachers11, Christian Stamm12, Wim de Vries13, Robert Finger14 1 Production Economics Group, University of Bonn; Bonn, Germany. 2 World Vegetable Center; Cotonou, Benin. 3 Department of Chemical Engineering, Universidade Federal de São Paulo; São Paulo, Brazil. 4 Centre d'Etudes Biologiques de Chizé, UMR7372, CNRS & Université de La Rochelle, Villiers-en-Bois, France. USC 1339 Agripop, Centre d'Etudes Biologiques de Chizé, INRAE, Villiers-en-Bois, France. 5 Department of Agriculture and Fisheries Queensland; Brisbane, Australia. 6 Department of Agroecology, Aarhus University; Aarhus, Denmark. 7 Bren School of Environmental Science & Management, UC Santa Barbara; Santa Barbara, United States. 8 Department of Medical & Molecular Genetics , Kings’s College London; London, United Kingdom. 9 Institute of Agroecology; Aarau, Switzerland 10 Center for Development Research (ZEF), University of Bonn; Bonn, Germany. 11 World Vegetable Center; Bangkok, Thailand. 12 Department of Environmental Chemistry, Eawag, Swiss Federal Institute of Aquatic Science and Technology; Dübendorf, Switzerland. 13 Environmental Systems Analysis Group, Wageningen University and Research; Wageningen, the Netherlands. 14 Agricultural Economics and Policy Group, ETH Zurich; Zurich, Switzerland. B. Project Supervisor (Principal Investigator) Contact Information Name: Niklas Möhring Institution: University of Bonn Address: Meckenheimer Allee 174, 53115 Bonn, Germany Email: mohring@uni-bonn.de C. In case of questions related to this dataset, please contact: Name: Niklas Möhring Institution: University of Bonn Address: Meckenheimer Allee 174, 53115 Bonn, Germany Email: mohring@uni-bonn.de 4. Date of data collection: Start Date: 2022-03-20 ; End Date: 2022-10-20 5. Information about funding sources that supported the collection of the data: Swiss National Science Foundation: IZSEZ0 209440 INRAE: Metaprogram SuMCrop Fresh and Secure Trade Alliance: AM22000 DFG: Excellence Strategy Grant EXC-2070-390732324-PhenoRob 6. Language of the dataset: English 7. Geographic location of data collection: Global B DATA & FILE OVERVIEW 1. File List: "Replication_Mohring et al_Code_R.txt" --> Code for replication in R "cords_local_experts.csv" --> Auxiliary dataset for replication 1 - coordinates of regions of expertise "spam2020V1r0_global_H_TA.csv" --> Auxiliary dataset for replication 2 - geospatial distribution of global agricultural production (from IFPRI) "Codebook_cords_local_experts.csv" --> Codebook for the dataset 1 "Codebook_spam2020V1r0_global_H_TA.csv" --> Codebook for the dataset 2 2. Are there multiple versions of the dataset? No 3. Relationship between files: Auxiliary dataset for replication 1 and auxiliary dataset for replication 1 are required to fully replicate the code in R. 4. Additional related data collected that was not included in the current data package: The main dataset (survey data) for the replication is available here: Möhring, Niklas; Ba, Malick; Braga, Anna; Gaba, Sabrina; Gagic, Vesna; Kudsk, Per; Larsen, Ashley; Mesnage, Robin; Niggli, Urs; Qaim, Matin; Schreinemachers, Pepijn; Stamm, Christian; de Vries, Wim; Finger, Robert, 2025, "Dataset for „Expected Effects of a Global Transformation of Agricultural Pest Management“", https://doi.org/10.60507/FK2/XWSS9W, bonndata C SHARING/ACCESS INFORMATION 1. Was data derived from another source?: Auxiliary dataset for replication 1 is based on Möhring, Niklas; Ba, Malick; Braga, Anna; Gaba, Sabrina; Gagic, Vesna; Kudsk, Per; Larsen, Ashley; Mesnage, Robin; Niggli, Urs; Qaim, Matin; Schreinemachers, Pepijn; Stamm, Christian; de Vries, Wim; Finger, Robert, 2025, "Dataset for „Expected Effects of a Global Transformation of Agricultural Pest Management“", https://doi.org/10.60507/FK2/XWSS9W, bonndata Auxiliary dataset for replication 2 is from International Food Policy Research Institute (IFPRI), 2024, "Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 1.0.0", https://doi.org/10.7910/DVN/SWPENT, Harvard Dataverse, V1. 2. Licenses/restrictions placed on the data: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ 3. Links to publications that cite or use the data: Möhring, Niklas; Ba, Malick; Braga, Anna; Gaba, Sabrina; Gagic, Vesna; Kudsk, Per; Larsen, Ashley; Mesnage, Robin; Niggli, Urs; Qaim, Matin; Schreinemachers, Pepijn; Stamm, Christian; de Vries, Wim; Finger, Robert, 2025, "Dataset for „Expected Effects of a Global Transformation of Agricultural Pest Management“", https://doi.org/10.60507/FK2/XWSS9W, bonndata 4. Links to other publicly accessible locations of the data: NA 5. Links/relationships to ancillary datasets: NA D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The data was collected based on an original indicator framework with a global online survey of 517 experts form disciplines ranging from pest management sciences, to socio-economics, ecology and toxicology in LimeSurvey in 2022. The assessment framework covers 24 indicators in the economic, human health, food security, social, and environmental domains. The data is anonymized. It further contains information on respondent characteristics relating heir expertise on the domains, and their scope and region of expertise. The survey data was matched with co-variates for key socio-economic and environmental characteristics of the assessed regions from literature. The here provided code can be used to replicate all figures and analyses on the dataset in Möhring et al. (2025). For detailed information see: Möhring, N., Ba, M. N., Braga, A., Gaba, S., Gagic, V., Kudsk, P., Larsen, A., Mesnage, R., Niggli, U., Qaim, M., Schreinemachers, P., Stamm, C., de Vries, W., Finger, R. (2025). Expected Effects of a Global Transformation of Agricultural Pest Management. Nature Communications (In Press). 2. Methods for processing the data: The data was not processed, it was only anonymized (ids, comments, e-mail adresses etc. were deleted). 3. Instrument- and/or software-specific information needed to interpret the data: To run the code, R is needed. The code was created in R version 4.2.1. 4. People involved in sample collection, processing, analysis and/or submission: Niklas Möhring1, Malick N. Ba2, Anna Braga3, Sabrina Gaba4, Vesna Gagic5, Per Kudsk6, Ashley Larsen7, Robin Mesnage8, Urs Niggli9, Matin Qaim10, Pepijn Schreinemachers11, Christian Stamm12, Wim de Vries13, Robert Finger14 1 Production Economics Group, University of Bonn; Bonn, Germany. 2 World Vegetable Center; Cotonou, Benin. 3 Department of Chemical Engineering, Universidade Federal de São Paulo; São Paulo, Brazil. 4 Centre d'Etudes Biologiques de Chizé, UMR7372, CNRS & Université de La Rochelle, Villiers-en-Bois, France. USC 1339 Agripop, Centre d'Etudes Biologiques de Chizé, INRAE, Villiers-en-Bois, France. 5 Department of Agriculture and Fisheries Queensland; Brisbane, Australia. 6 Department of Agroecology, Aarhus University; Aarhus, Denmark. 7 Bren School of Environmental Science & Management, UC Santa Barbara; Santa Barbara, United States. 8 Department of Medical & Molecular Genetics , Kings’s College London; London, United Kingdom. 9 Institute of Agroecology; Aarau, Switzerland 10 Center for Development Research (ZEF), University of Bonn; Bonn, Germany. 11 World Vegetable Center; Bangkok, Thailand. 12 Department of Environmental Chemistry, Eawag, Swiss Federal Institute of Aquatic Science and Technology; Dübendorf, Switzerland. 13 Environmental Systems Analysis Group, Wageningen University and Research; Wageningen, the Netherlands. 14 Agricultural Economics and Policy Group, ETH Zurich; Zurich, Switzerland. 5. Describe any quality-assurance procedures performed on the data: Data was checked to align with hypotheses from literature. 6. Standards and calibration information: NA 7. Environmental/experimental conditions: NA E DATA-SPECIFIC INFORMATION FOR: cords_local_experts.csv 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: See "Codebook_cords_local_experts.csv" 2. Units of measurement used: See "Codebook_cords_local_experts.csv" 3. Missing data codes/symbols: See "Codebook_cords_local_experts.csv" 4. Specialized formats or other abbreviations used: See "Codebook_cords_local_experts.csv" E DATA-SPECIFIC INFORMATION FOR: spam2020V1r0_global_H_TA.csv 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: See "Codebook_spam2020V1r0_global_H_TA.csv" 2. Units of measurement used: See "Codebook_spam2020V1r0_global_H_TA.csv" 3. Missing data codes/symbols: See "Codebook_spam2020V1r0_global_H_TA.csv" 4. Specialized formats or other abbreviations used: See "Codebook_spam2020V1r0_global_H_TA.csv"