Welcome to our group page!
The research of the group focuses on developing and applying novel data science tools in the area of agricultural and environmental economics.
Check out our project and staff pages to learn what we are working on, and do not hesitate to contact us if you want to work with us.
If you are looking for a bachelor or master thesis topic, check out our current topics here.
Dr. Hugo Storm
Room 1.007
Meckenheimer Allee 174
53115 Bonn
Our group strives to improve empirical methods applied in agricultural economics. Using these methods, we aim to deliver impactful data-driven insights that contribute to enhancing the social, economic, and environmental sustainability of agriculture.
We are specifically interested in applying methods at the intersection of econometrics, machine learning, and process-based simulation models. We use cutting-edge probabilistic machine learning and causal machine learning methods. A specific focus is on Bayesian Probabilistic Programming. We apply these methods in our Research but also cover them our Teaching activities.
We are part of the Cluster of Excellence PhenoRob, as well as the Collaborative Research Centre DETECT.
We are involved in multiple DFG and EU Horizon funded research projects.
Robotics and Phenotyping for Sustainable Crop Production
DFG Germany’s Excellence Strategy - EXC 2070 – 390732324
Group Contribution
Hugo Storm (Junior Research Group Leader)
Elin Martinsson (PhD Student)
Anna Massfeller (PhD Student)
More information: www.phenorob.de/
Regional Climate Change: Disentangling the Role of Land Use and Water Management
DFG Collaborative Research Centre 1502
Group Contribution
Hugo Storm (PI Project B04), together with Thomas Heckelei
Josef Baumert (PhD Student)
Elif Dönmez Altındal (PhD Students, Co-Supervised)
More information: sfb1502.de
Designing a Roadmap for Effective and Sustainable Strategies for Assessing and Addressing the Challenges of EU Agriculture to Navigate within a Safe and Just Operating Space
Horizon Europe Grant Agreement No 101060075
Group Contribution
Hugo Storm (PI)
Till Kuhn
Damilola Tominsin Aladesuru
Lucie Adenäuer
More information: brightspace-project.eu/
Land Management for Sustainability
Horizon Europe Grant Agreement No101060423
Group Contribution
Hugo Storm (PI)
Till Kuhn
Bisrat Haile Gebrekidan
More information: www.lamasus.eu/
Tracking the use and adoption of agricultural technologies through satellite remote sensing and self-supervised deep learning
DFG Individual Grant (2023-2025)
Group Contribution
Hugo Storm (PI), together with Ribana Roscher
Alexa Leyens
TransformDairyNet: Working together to upscale Cow-Calf-Contact dariy production and beyond
Horizon Lump Sum Grant No 101133326
Group Contribution
Lucie Adenäuer (Economic Advisor)
More information: transformdairynet.eu/
Finished Projects
Mind-Step, Modelling Individual Decisions to Support the European Policies Related to Agriculture
Horizon 2020 No 817566
Group Contribution
Hugo Storm Work Package leader (WP4), Task Leader T4.5
More information: mind-step.eu/partners/ubo
Teaching
Master Level
Probabilistic Programming for Applied Agricultural Economics
University of Bonn, MSc Study Program on Agricultural and Food Economics (AFECO), APO-230.
Instructor
Hugo Storm
PhD Level
Machine Learning in Applied Economic Analysis
Doctoral Certificate Program in Agricultural Economics, Module 6800
Instructors
Kathy Baylis - University of California, Santa Barbara, USA
Thomas Heckelei - University of Bonn, Germany
Hugo Storm - University of Bonn, Germany
Interested in econometric or machine learning? Check out our lecture videos and materials.
Talks
Learn more about our research activities by listening to our research talks.
The thesis topics listed below should rather serve as an inspiration, adaptations to your personal interests and skills are possible. Own suggestions of topics are also welcome!
- Impact of extreme weather events on crop production in West Africa (contact: Josef Baumert)
- Understanding spatial determinants of organic farming in Germany (English or German, contact: Josef Baumert or Anna Massfeller)
The thesis topics listed below should rather serve as an inspiration, adaptations to your personal interests and skills are possible. Own suggestions of topics are also welcome!
- Impact of flooding and strong precipitation during winter months on farmers' cropping decisions in Germany (contact: Josef Baumert)
- Impact of extreme weather events on crop production in West Africa (contact: Josef Baumert)
- Understanding spatial determinants of organic farming in Germany (contact: Josef Baumert or Anna Massfeller)
- Erfassung landwirtschaftlicher Praktiken in Deutschland - Analyse von
Bodenbearbeitung und Glyphosateinsatz im deutschen Ackerbau (Supervisor: Alexa Leyens) - [External Master Thesis position] Comparative econometric analysis of conventional, integrated and organic production systems in Switzerland, joint supervision with FiBL, Switzerland (For question contact: Hugo Storm)
- Assessing the impact of the 2014-2020 CAP greening obligations on permanent grassland extent across European countries (contact: Josef Baumert)
Currently no open position.
Currently no open position.
Senior Researchers
Dr. Till Kuhn
PhD Students
Damilola Tominsin Aladesuru MSc.
Josef Baumert
External PhD Students
Former Staff
Dr. Bisrat Haile Gebrekidan, Senior Researcher in the group
Now: International Maize and Wheat Improvement Center (CIMMYT)
Elin Martinsson, PhD Student in the group
Now: Thünen Institute, Coordination Unit Climate, Soil, Biodiversity, Link
Do, H., Whitney, C., Storm, H., Nguyen, H. T., La, N., Luedeling, E. (2025): Markets and incentives strongly drive agroforestry adoption: insights from ethnic minority smallholders in Son La, Vietnam. Agroforestry Systems, 99(5), 1-20. https://doi.org/10.1007/s10457-025-01199-4
Martinsson , E., Storm, H. (2025): Conceptualization of How Adopting Novel Technology Induces Structural and Behavioural Changes on Farms. Journal of Agricultural and Resource Economics, preprint. 10.22004/ag.econ.356163
Pahmeyer, C., Kuhn, T., Storm, H. (2025): A crop sequence dataset of the German federal state of North Rhine-Westphalia from 2019-2024. Data in Brief, 60, 111617. https://doi.org/10.1016/j.dib.2025.111617
Baumert, J., Heckelei, T., Storm, H. (2025): A dataset of yearly probabilistic crop type maps for the EU from 1990 to 2018. Data in Brief, 60, 111472. https://doi.org/10.1016/j.dib.2025.111472
Massfeller, A., Zingsheim, M., Ahmadi, A., Martinsson, E., Storm, H., (2025): Action- or results-based payments for ecosystem services in the era of smart weeding robots? Biological Conservation, 302, 110998. https://doi.org/10.1016/j.biocon.2025.110998
Aladesuru, D. T., Cechura, L., Neuenfeldt, S., Kuhn, T., Kristkova, Z. S., Kroupová, Z. Ž., Ratinger, T., Gocht, A., Müller, M., Storm, H. (2024): Impacts of agricultural production decisions on the safe and just operating space: A systematic literature review. Q Open, https://doi.org/10.1093/qopen/qoae027
Baumert, J., Heckelei, T., Storm, H., (2024): Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation. Ecological Informatics, 83 (2024), 102836. https://doi.org/10.1016/j.ecoinf.2024.102836
Khanna, M., Atallah, S. S., Heckelei, T., Wu, L., Storm H. (2024): Economics of Adoption of Artificial Intelligence-Based Digital Technologies in Agriculture. Annual Review of Resource Economics, 16:41-61. https://doi.org/10.1146/annurev-resource-101623-092515.
Massfeller, A., Storm, H., (2024): Field observation and verbal exchange as different peer effects in farmers’ technology adoption decisions. Agricultural Economics, 55(5):739-757. https://doi.org/10.1111/agec.12847
Storm, H., Heckelei, T., Baylis, K. (2024): Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis. European Review of Agricultural Economics, 51(3):589-616. https://doi.org/10.1093/erae/jbae016.
Storm, H., Seidel, S.J., Klingbeil, L., Ewert, F., Vereecken, H., Amelung, W., Behnke, S., Bennewitz, M., Börner, J., Döring, T., Gall, J., Mahlein, A., McCool, C., Rascher, U., Wrobel, W., Schnepf, A., Stachniss, C., Kuhlmann. H. (2024): Research Priorities to Leverage Smart Digital Technologies for Sustainable Crop Production. European Journal of Agronomy, 156, 127178. https://doi.org/10.1016/j.eja.2024.127178
Mittenzwei, K., Berglann, H., Hoveid, Ø., Matthews, A., Storm, H. (2024): Decomposing household income differences between farmers and non-farmers: Empirical evidence from Norway. Journal of Agricultural Economics, 75(2):672-687. https://doi.org/10.1111/1477-9552.12579
Do, H., Whitney, C., La, N., Storm, H., Luedeling, E. (2024): Adapting agroforestry to upland farming systems: narratives from smallholder farmers in Northwest Vietnam. Agronomy for Sustainable Development, 44(17). https://doi.org/10.1007/s13593-024-00954-8
Martinsson, E., Hansson, H., Mittenzwei, K., Storm, H. (2024): Evaluating Environmental Effects of Adopting Automatic Milking Systems on Norwegian Dairy Farms. European Review of Agricultural Economics, 51(1):128–156. https://doi.org/10.1093/erae/jbad041
Shang, L., Wang, J., Schäfer, D., Heckelei, T., Gall, J., Appel, F., Storm, H. (2024): Surrogate modelling of a detailed farm-level model using deep learning. Journal of Agricultural Economics, 75:235–260. https://doi.org/10.1111/1477-9552.12543
Storm, H., Heckelei, T., Baylis, K., Mittenzwei, K. (2023): Identifying Farmers’ Response to Changes in Marginal and Average Subsidies Using Deep Learning. American Journal of Agricultural Economics, 106(4):1544-1567. https://doi.org/10.1111/ajae.12442
Shang, L., Pahmeyer, C., Heckelei, T. , Rasch, S., Storm, H. (2023): How much can farmers pay for weeding robots? A Monte Carlo simulation study. Precision Agriculture, 24:1712-1737. https://doi.org/10.1007/s11119-023-10015-x
Massfeller, A., Meraner, M., Hüttel, S., Uehleke, R. (2022): Data on Farmers’ Acceptance of Results-Based Agri-Environmental Schemes. Data in Brief, 45(December): 108642. https://doi.org/10.1016/j.dib.2022.108642
Massfeller, A., Meraner, M., Hüttel, S., Uehleke, R. (2022): Farmers’ Acceptance of Results-Based Agri-Environmental Schemes: A German Perspective. Land Use Policy, 120(September): 106281. https://doi.org/10.1016/j.landusepol.2022.106281
Baylis, Kathy, Thomas Heckelei, and Hugo Storm 2021. “Chapter 83 - Machine Learning in Agricultural Economics.” In Handbook of Agricultural Economics, edited by Christopher B. Barrett and David R. Just, 5:4551–4612. Elsevier. https://doi.org/10.1016/bs.hesagr.2021.10.007
Marton, Tibor A., and Hugo Storm. 2021. “The Case of Organic Dairy Conversion in Norway: Assessment of Multivariate Neighbourhood Effects.” Q Open 1 (1). https://doi.org/10.1093/qopen/qoab009
Martinsson, Elin, and Helena Hansson. 2021. “Adjusting Eco-Efficiency to Greenhouse Gas Emissions Targets at Farm Level - The Case of Swedish Dairy Farms.” Journal of Environmental Management 287 (112313): 112313. https://doi.org/10.1016/j.jenvman.2021.112313
Cardona Santos, Elsa, Hugo Storm, and Sebastian Rasch. 2021. “The Cost-Effectiveness of Conservation Auctions in the Presence of Asset Specificity: An Agent-Based Model.” Land Use Policy 102: 104907. https://doi.org/10.1016/j.landusepol.2020.104907
Rasch, Sebastian, Tobias Wünscher, Francisco Casasola, Muhammad Ibrahim, and Hugo Storm. 2021. “Permanence of PES and the Role of Social Context in the Regional Integrated Silvo-Pastoral Ecosystem Management Project in Costa Rica.” Ecological Economics: The Journal of the International Society for Ecological Economics 185: 107027. https://doi.org/10.1016/j.ecolecon.2021.107027
Storm, Hugo, Kathy Baylis, and Thomas Heckelei. 2019. “Machine Learning in Agricultural and Applied Economics.” European Review of Agricultural Economics 47 (3): 849–92. https://doi.org/10.1093/erae/jbz033
Vroege, Willemijn, Manuela Meraner, Nico Polman, Hugo Storm, Wim Heijman, and Robert Finger. 2020. “Beyond the Single Farm – A Spatial Econometric Analysis of Spill-Overs in Farm Diversification in the Netherlands.” Land Use Policy 99: 105019. https://doi.org/10.1016/j.landusepol.2020.105019