Data science in agricultural economics

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

DSC_6655_web.jpg
© HugoStorm
Avatar Storm

Dr. Hugo Storm

Junior Research Group Leader, Data science in agricultural economics, PhenoRob

Room 1.007

Meckenheimer Allee 174

53115 Bonn

About

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

Research Projects

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

More information

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

Course Gitlab Repository

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

Github repository, 2024 course

Videos form 2020 course

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.

Vacancies and Thesis Topics

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! 

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! 

Currently no open position.

Currently no open position. 

Staff

GroupPicture
© HugoStorm

Senior Researchers

PhD Students

Avatar Massfeller

Anna Massfeller M.Sc.

External PhD Students

Avatar Yang

Chen Yang

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


Publications

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

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