K. Grahmann: The smaller the better? Why a reduction in field size combined with field robotics can get us closer towards pesticide free agriculture


Bio Information

Kathrin Grahmann is PostDoc in the working group Resource Efficient Cropping Systems at the Leibniz Institute of Agricultural Landscape Research (ZALF). She is the scientific coordinator of the recently implemented patchCROP experiment near Müncheberg. She collected previous research experience at CIMMYT in Mexico and the National Agricultural Research Institute (INIA) in Uruguay. Her current research activities focus on short- and long- term soil quality and soil nutrient dynamics in heterogeneous agricultural landscapes.


Presentation Abstract

Smart use of agricultural landscapes through digitalization should account for spatial heterogeneities of soils and temporal crop diversification in order to achieve high resource use efficiency, decreasing external input supply and stable yields while at the same time, optimizing the provision of ecosystem services and mitigating ecosystem damages. There are many possibilities and diverse, worldwide efforts to strengthen digitalized agriculture.

A multidisciplinary research project on “Sustainable cropping systems of the future through spatio-temporal diversification” (patchCROP) was initiated by the Leibniz Centre of Agricultural Landscape Research (ZALF) in 2019 in order to implement a long-term on-farm experiment in the agricultural landscape context. The landscape experiment was designed from a multidisciplinary perspective to address multiple-level problems including soil heterogeneity, climate change adaption strategies, cropping system resilience, and dependency on external inputs, especially chemical synthetic pesticides and fertilizers. The continuation of patchCROP will establish a research platform (landscape laboratory) for innovative and smart agricultural technologies like field robotics for field operations, proximal and remote sensing for field monitoring and artificial intelligence and machine learning for data processing.

The possibilities and impacts of visionary technologies like multidimensional sensing systems, internet of (underground) things and robotics for precision agriculture can be evaluated in patchCROP from three perspectives: crop physiological, ecological (including soil and biodiversity) and technological. In this presentation we will focus on the concepts and execution of pesticide reduction strategies within patchCROP and which digital tools are tested to support sustainable agricultural practices.

Acknowledgements

The design and implementation process was supported and driven by ZALF researchers (Frank Ewert, Moritz Reckling, Michael Glemnitz, Ralf Bloch, Sonoko Bellingrath-Kimura, Munir Paul Hoffmann, Johann Bachinger, Marco Donat, Peter Zander, Ruth Ellerbrock, Dietmar Barkusky); by researchers from University of Bonn (Ixchel Hernandez-Ochoa, Thomas Döring, Thomas Gaiser), Julius Kühn Institute (Silke Dachbrodt-Saaydeh, Jürgen Schwarz, Bettina Klocke, Lukas Schütz) and other institutes (Hans-Peter Piepho, University of Hohenheim). We acknowledge the funding of the trial by PhenoRob, DAKIS and ZALF and appreciate the maintenance of the undergoing research activities by the technicians Gerlinde Stange, Lars Richter, Sigrid Ehlert and Maria Schnaitmann and the M.Sc. student David Caracciolo.


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