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Bio-economic model on benefits of increasing information accuracy in variable rate technologies
(2021)This bio-economic modeling framework assesses the benefits of different sensor approaches to measuring environmental heterogeneity in the field, ranging from the use of satellite imagery to drones and portable N sensors, in variable rate fertilization. The model is described in detail in the following article: Späti, K., Huber, R., & Finger, R. (2021). Benefits of Increasing Information Accuracy in Variable Rate Technologies. Ecological ...Model -
Data on performance indicators for agricultural economic journals
(2021)The here presented dataset summarizes various performance indicators of ten leading journals in the field of Agricultural Economics and Policy over the period from 2000 to 2020. More specifically, we combine various data sources to derive a coherent record of indicators including acceptance rates, times between submission and first response and impact factors. The accompanying paper is: Finger, R., Droste, N., Bartkowski, B., Ang, F. ...Dataset -
Data: Pest Prevention, Risk and Risk Management: the Case of Drosophila suzukii
(2022)This dataset contains Swiss wine grape growers’ management strategies against D. suzukii, risk preference and perception, and is based on surveys by Knapp et al. (2018) in 2016, 2017, and 2018. The here presented dataset additionally contains neighborhood averages of pest management strategies. Analyses in Wang and Finger (2022) are based on the dataset, R code available on https://github.com/ivoryday/pest_prevention.git.Dataset -
Data on an economic and environmental assessment of a glyphosate ban for the example of maize production in North Rhine-Westphalia, Germany
(2020)This data is used as input for a bio-economic model that calculates optimal weed control strategies for cultivation of silage maize in North-Rhine-Westphalia and can be used to simulate agri-environmental policy scenarios such as a glyphosate ban. The model can be used to assess changes in the Energy Output and the Load of the used pesticides under different scenarios. The model is described in detail in the following article: Böcker, ...Data Collection -
Data: When my neighbors matter: spillover effects in the adoption of large-scale pesticide-free wheat production
(2023)Datasets to replicate results from "When my neighbors matter: spillover effects in the adoption of large-scale pesticide-free wheat production" by Wang, Möhring, and Finger (2023). Due to restricted information (farm coordinates) contained in raw data, compiled data for regression analysis are published. R codes published at https://github.com/ivoryday/pestifree_spillover.gitOther Research Data -
Social network data of Swiss farmers related to agricultural climate change mitigation
(2020)We present social network data of Swiss farmers, focusing on exchange and advice relations regarding agricultural climate change mitigation. The data were generated via face-to-face interviews in 2019 using the survey software Network Canvas (https://networkcanvas.com). We interviewed 50 farmers, with 25 of these participating in a regional climate protection initiative in Switzerland as well as 25 farmers located in the same region who ...Data Collection -
Adoption of pesticide-free wheat production in Switzerland (dataset)
(2020)The here presented dataset provides information on i) actual and planned participation decisions of Swiss «Extenso» wheat producers in a program for pesticide-free wheat production of the producer organization IP-SUISSE and ii) a wide range of potential determinants for program adoption. The dataset consists of survey data, as well as external data from a range of relevant data sources, matched based on farm location. The online survey ...Dataset -
PesticideLoadIndicator (R-Package)
(2021)R-package. Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.Software -
Dataset on Farmer Risk Preferences in Europe
(2020)The here presented dataset presents data obtained in a systematic review of the research on farmer risk preference measurement across Europe published in the following article: Iyer, P. Bozzola, M.*, Hirsch, S., Meraner, M. and Finger, R. (2020). Measuring Farmer Risk Preferences in Europe: A Systematic Review. Journal of Agricultural Economics 71(1): 3-26 doi: 10.1111/1477-9552.12325 Our comprehensive review of the literature ...Dataset