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PhenoRob – Robotics and Phenotyping for Sustainable Crop Production

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Achieving sustainable crop production with limited resources is a task of immense proportions. In order to achieve this, the University of Bonn together with Forschungszentrum Jülich conducts research in the Cluster of Excellence “PhenoRob – Robotics and Phenotyping for Sustainable Crop Production” to develop methods and new technologies that observe, analyze, better understand and specifically treat plants. The cluster research focuses on improving the fundamental understanding of relevant processes, determinants and outcomes. PhenoRob, the only Cluster of Excellence in agriculture in Germany, aims at optimizing breeding and farming management using new technologies.
(For more information on the cluster see www.phenorob.de).

Within PhenoRob, our group co-leads and contributes to

Core Project 6: Technology Adoption and Impact

Core Project 6 (CP6) focuses on technology adoption and its sustainability impacts at farm, factor market, and landscape scale. This project studies the adoption potential and related impacts of robotics and phenotyping (PhenoRob) technologies on agricultural development, welfare, and the environment at farm and landscape scales. It involves collaboration between agricultural economics and informatics and engages with projects across the whole cluster to understand the conditions under which PhenoRob technologies may contribute to sustainable agricultural transformation.

Objectives

  1. Understand determinants and spatial dynamics of adoption and diffusion of phenotyping and robotics-based production technologies
  2. Explore pathways of socio-economic and environmental impacts at farm and landscape scale
  3. Develop a simulation model for PhenoRob technology diffusion and impacts
  4. Capture dynamic feedbacks between crop management, ecosystem functions and factor as well as service markets

Contribution

Feasibility assessment of envisioned impact pathways (input reduction, resource use efficiency, climate resilience, integrated management). As a key long-term output, we develop an agent-based model (ABM) of technology diffusion. The ABM serves as a virtual laboratory that allows us to explore the relevance of alternative economic theories and related empirical phenomena for the diffusion of PhenoRob-type technologies in crop production.

The project employs a mix of conceptual, empirical and modelling approaches: (1) We develop an economically motivated conceptual framework of pathways through which key attributes of PhenoRob technologies may affect economic and environmental outcomes in Germany and beyond. Building on a systematic literature review, we (2) design choice experiments to understand and quantify the role of selected technology attributes for adoption at farm level and (3) develop empirical impact assessment tools based on expert knowledge, case studies, and secondary data. (4) ABM model development proceeds in two steps: First, stylized models are developed that allow quick modifications for general testing purposes to identify relevant agent types, their behavioral representation, processes, and agent interaction through various institutions. We assess relevance with respect to technology diffusion rates and related environmental and economic impacts, for example on labor markets, at farm and regional scale. Second, the stylized ABMs will be integrated, expanded and parameterized based on the above-mentioned experimental and empirical analyses. Beyond studying farm and agricultural service agents, the ABM will allow us to assess the potential impact of new phenotyping opportunities on the plant breeding system.

There are two PhD projects and one Post-doc position as part of Core Project 6 on "Technology Adaption and Impact" of the Cluster of Excellence PhenoRob: Robotics and Phenotyping for Sustainable Crop Production" financed by the German Research Foundation.

 

Our group contributes to core project 6 by:

Agent-based modelling

    • Development of different stylized ABMs to simulate alternative adoption and diffusion mechanisms at farm and landscape scale informed by theory and empirical cases
    • Case studies of different actors in the technology service sector
    • Theoretical and empirical analysis of potential labor market impacts

Surrogate Modelling

    • Data generated with existing modelling system FarmDyn to train neural networks (likely Convolutional and Recurrent NNs)
    • Development of specific techniques to predict farmers‘ decisions capturing corner solutions and kinks using domain knowledge
    • Identification of optimal NN approaches regarding training data (targeted sampling) given required dimension/accuracy of output vector

Survey and structured interviews as well as theoretical analyses supporting the

    • assessment of current state phenotyping precursor technologies within breeding sector overall
    • benchmarking study / survey evaluating technology use within German breeding sector
    • development of an analytical framework of social-ecological-technical system for breeding

Related publications

Storm, H., Baylis, K., & Heckelei, T. (2019). Machine learning in agricultural and applied economics. European Review of Agricultural Economics. URl: https://doi.org/10.1093/erae/jbz033

Staff working at ILR on the project

Maria Gerullis, Researcher

Linmei Shang, Researcher

Sebastian Rasch, Researcher

Thomas Heckelei, Project lead

 

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This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070 - 390732324.



Last updated: Tuesday, March 24, 2020