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 stuff 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 master thesis topic, check out our current topics here

Avatar Storm

Dr. Hugo Storm

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

+49 228 73-60828


We are part of the Cluster of Excellence PhenoRob. PhenoRob aims to enhance the sustainability of crop production by optimizing breeding and farm management using new technologies. Our focus in in PhenoRob is on studying the economic and environmental aspects of farm level agricultural technology. Here, we aim to understand under which conditions farmers adopt novel technologies, how they are used in practice and what economic and environmental consequences result from this. With this we aim to contribute to answering the simple, but challenging, question of “how to produce more with less”.

Phenorob Project

Research Projects

We are involved in multiple DFG and EU Horizon funded research projects.

© Maximilian Meyer


Interested in econometric or machine learning? Check out our lecture videos and materials.

© Uni Bonn


 Learn more about our research activities by listening to our research talks.

Vacancies and Thesis Topics

Currently no open position. 

Currently no open position. 



PhD Students


Shang, L., Wang, J., Schäfer, D., Heckelei, T., Gall, J., Appel, F. et al. (2023) Surrogate modelling of a detailed farm-level model using deep learning. Journal of Agricultural Economics, 00, 1– 26.

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 Agric.

Massfeller, Anna, Manuela Meraner, Silke Hüttel, and Reinhard Uehleke. 2022. Data on Farmers’ Acceptance of Results-Based Agri-Environmental Schemes. Data in Brief 45 (December): 108642.

Massfeller, Anna, Manuela Meraner, Silke Hüttel, and Reinhard Uehleke. 2022. Farmers’ Acceptance of Results-Based Agri-Environmental Schemes: A German Perspective. Land Use Policy 120 (September): 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.

Marton, Tibor A., and Hugo Storm. 2021. “The Case of Organic Dairy Conversion in Norway: Assessment of Multivariate Neighbourhood Effects.” Q Open 1 (1).

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.

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.

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.

Storm, Hugo, Kathy Baylis, and Thomas Heckelei. 2019. “Machine Learning in Agricultural and Applied Economics.” European Review of Agricultural Economics 47 (3): 849–92.

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.

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