@article{DonatGeistertGrahmannetal.2022, author = {Donat, Marco and Geistert, Jonas and Grahmann, Kathrin and Bloch, Ralf and Bellingrath-Kimura, Sonoko D.}, title = {Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes}, series = {Computers and Electronics in Agriculture}, journal = {Computers and Electronics in Agriculture}, number = {197}, publisher = {Elsevier}, issn = {0168-1699}, doi = {10.1016/j.compag.2022.106894}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4365}, pages = {1 -- 15}, year = {2022}, abstract = {Agricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called 'patches' grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relative variance. Independent cross validation showed an increased performance of the classification algorithm with increasing number of GS used for clustering. The applied cluster analysis resulted in robust field segregation according to different yield potential zones and provides an innovative method for a novel cropping system.}, language = {en} } @article{MouratiadouLemkeChenetal.2023, author = {Mouratiadou, Ioanna and Lemke, Nahleen and Chen, Cheng and Wartenberg, Ariani and Bloch, Ralf and Donat, Marco and Gaiser, Thomas and Hanike Basavegowda, Deepak and Helming, Katharina and Ali Hosseini Yekani, Seyed and Krull, Marcos and Lingemann, Kai and Macpherson, Joseph and Melzer, Marvin and Nendel, Claas and Piorr, Annette and Shaaban, Mostafa and Zander, Peter and Weltzien, Cornelia and Bellingrath-Kimura, Sonoko Dorothea}, title = {The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems}, series = {Environmental Science and Ecotechnology}, volume = {16}, journal = {Environmental Science and Ecotechnology}, publisher = {Elsevier}, issn = {2666-4984}, doi = {10.1016/j.ese.2023.100274}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-8012}, pages = {13}, year = {2023}, abstract = {Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity, biodiversity, and the provision of ecosystem services. The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems. We present the Digital Agricultural Knowledge and Information System (DAKIS) to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture. To develop the DAKIS, we specified, together with stakeholders, requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools. The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity, the capacity to foster communication and cooperation between farmers and other actors, and the ability to link multiple spatiotemporal scales and sustainability levels. To overcome these challenges, the DAKIS provides a digital platform to support farmers' decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources. The approach integrates remote and in situ sensors, artificial intelligence, modelling, stakeholder-stated demand for biodiversity and ecosystem services, and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. Ultimately, the DAKIS embeds the consideration of ecosystem services, biodiversity, and sustainability into farmers' decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers' objectives and societal demands.}, language = {en} } @article{DogarBrogiO'Learyetal., author = {Dogar, Salar Saeed and Brogi, Cosimo and O'Leary, Dave and Hern{\´a}ndez-Ochoa, Ixchel M. and Donat, Marco and Vereecken, Harry and Huisman, Johan Alexander}, title = {Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production}, series = {SOIL}, volume = {11}, journal = {SOIL}, number = {2}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen, Germany}, issn = {2199-398X}, doi = {10.5194/soil-11-655-2025}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-10152}, pages = {655 -- 679}, abstract = {Abstract Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures aboveground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI-NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and aboveground variability. The final EMI-NDVI-derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modeling and soil surveying.}, subject = {-}, language = {en} }