Publikationen DAKIS


Bazzo, COG Kamali, B. Hütt, C. Bareth, G. Gaiser, T. (2023): A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV. Remote Sensing, 15(3), 639;

Donat, M., Geistert, J., Grahmann, K. et al. (2023): Field path optimization to reduce headland and turning maneuvers at regional scales: automated detection of cultivation direction in the state of Brandenburg, Germany. Precision Agric .

Enders, A; Gaiser, T; Srivastava, A.K. et al., (2023): SIMPLACE – A versatile modelling and simulation framework for sustainable crops and agroecosystems. In Silico Plants.

Faye, B., Webber, H., Gaiser, T., Müller, C., Zhang, Y., Stella, T., Latka, C., Reckling, M., Heckelei, T., Helming, K., and Ewert, F. (2023): Climate change impacts on European arable crop yields: Sensitivity to assumptions about rotations and residue management. European Journal of Agronomy 142, 126670.   

Gao, J; Zeng, W; Ren, Z; Ao, C; Lei, G; Gaiser, T; Srivastava, A.K. (2023): A fertilization decision model for maize, rice, and soybean based on machine learning and swarm intelligent search algorithms. Agronomy.


Kimball, B; Gaiser, T ; Srivastava, A.K. et al., (2023): Simulation of evapotranspiration and Yield of Maize: An Inter-comparison among 41 maize models. Agricultural and Forest Meteorology. .

Melzer, M., Bellingrath-Kimura, S., Gandorfer, M.(2023). Commercial farm management information systems - A demand-oriented analysis of functions in practical use. Smart Agricultural Technology, 4, 100203.

Mohr, J., Tewes, A., Ahrends, H., and Gaiser, T. (2023): Assessing the Within-Field Heterogeneity Using Rapid-Eye NDVI Time Series Data. Agriculture 13, 1029.  

Montzka, C., Donat, M., Raj, R., Welter, P., Bates, J.S. (2023): Sensitivity of LiDAR parameters to aboveground biomass in winter spelt. Drones 7(2), 121.

Mouratiadou et al. (2023): The Digital Agricultural Knowledge and Information 1 System (DAKIS): employing digitalization to encourage 2 diversified and multifunctional agricultural systems, Environmental Science and Ecotechnology, Volume 16, 100274,

Rahman, M. H. u., Ahrends, H. E., Raza, A., and Gaiser, T. (2023): Current approaches for modeling ecosystem services and biodiversity in agroforestry systems: Challenges and ways forward. Frontiers in Forests and Global Change 5.

Shaaban, M., Voglhuber-Slavinsky, A., Dönitz, E., Macpherson, J., Paul, C., Mouratiadou, I., Helming, K., & Piorr, A. (2023): Understanding the future and evolution of agri-food systems: A combination of qualitative scenarios with agent-based modelling. Futures, 149, 103141.

Shaaban, M. (2023): Viability of the social–ecological agroecosystem (ViSA). SoftwareX, 22, 101360.

Sosulski, T; Srivastava, A.K. et al., (2023): Carbon storage potential and carbon dioxide emission from mineral fertilized and manured soil. Applied Sciences.

Voglhuber-Slavinsky, A.; Lemke, N.; MacPherson, J.; Dönitz, E.; Olbrisch, M.; Schöbel, p.; Moller, B.; Bahrs, E.; Helming, K. (2023): Valorization for Biodiversity and Ecosystem Services in the Agri-Food Value Chain. Environmental Management. .


Donat, M., Geistert, J., Grahmann, K., Bloch, R., & Bellingrath-Kimura, S. D. (2022). Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes. Computers and Electronics in Agriculture, 197, 106894.

Feld-Magazin. (seit 2017). Abgerufen 16. September 2022, von

Golicz, K., Bellingrath-Kimura, S., Breuer, L., & Wartenberg, A. C. (2022). Carbon accounting in European agroforestry systems – Key research gaps and data needs. Current Research in Environmental Sustainability4, 100134.

Grimpe, J.; Lucke, S.; Lorenz, T.; Bloch, R.; Cremer, T. (2022). Agroforst auf trockenen Böden. In Lumbrico (11), 41-45

Guarin, J.R., Gaiser, T; Srivastava, A.K., et al., (2022): Evidence for increasing global wheat yield potential. Environmental Research Letters. 17-12. doi.10.1088/1748-9326/aca77c

Habib-ur-Rahman, M., Raza, A., Ahrends, H. E., Hüging, H., & Gaiser, T. (2022). Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions. Precision Agriculture23(3), 912–938.

Jacobs, S. R., Webber, H., Niether, W., Grahmann, K., Lüttschwager, D., Schwartz, C., Breuer, L., & Bellingrath-Kimura, S. D. (2022).: Modification of the microclimate and water balance through the integration of trees into temperate cropping systems. Agricultural and Forest Meteorology, 323, 109065. ​​​​​​​  

Lorenz, T., Gerster, L., Elias Wodzinowski, D., Wartenberg, A., Martetschläger, L., Molitor, H., Cremer, T., & Bloch, R. (2022). Innovative Teaching and Learning Formats for the Implementation of Agroforestry Systems—An Impact Analysis after Five Years of Experience with the Real-World Laboratory “Ackerbaum”. Forests13(7), 1064.

MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development42(4), 70.

Olbrisch, M. (2022). Agricultural Digital Policy. In M. Nachtmann & J. Dörr (Hrsg.), Handbook Digital Farming (S. 34–39).

Schwartz, C., Klebl, F., Ungaro, F., Bellingrath-Kimura, S.-D., & Piorr, A. (2022). Comparing participatory mapping and a spatial biophysical assessment of ecosystem service cold spots in agricultural landscapes. Ecological Indicators, 145, 109700. ​​​​​​​ 

Shaaban, M. (2022). The Viability of the Social-Ecological Agroecosystem (ViSA) Spatial Agent-based Model (1.0.0). CoMSES Net.

Shaaban, M., Mouratiadou, I., & Piorr, A. (2022). Cooperative versus non-cooperative behaviour: Using agent-based modelling to identify spatial supply-demand mismatches of ecosystem services and to coordinate conflicting actors’ demands.


Brose, A. H.; Gundlach, B.; Schuberth, J.; Gerster, L.; Martetschläger, L.; Bloch, R.; Cremer, T. (2021): Innovative Lehr- und Lernformate für neue Ackerbausysteme – dreijährige Erfahrungen aus dem Reallabor „Ackerbaum“. In: Kuratorium für Technik und Bauwesen in der Landwirtschaft e. V. (KTBL) Darmstadt (Hg.): Boden gut machen – neue Ackerbausysteme. Darmstadt, 16.-17.03.2021 (KTBL-Tage), 54-55.

Frohberg, M., Weidling, S., & Langendoerfer, P. (2021). Challenges in Developing a Wireless Sensor Network for an Agricultural Monitoring and Decision System. In B. Ghita & S. Shiaeles (Hrsg.), Selected Papers from the 12th International Networking Conference (Bd. 180, S. 224–240). Springer International Publishing.

Härtel, I. (2021). Agrar-Digitalrecht für Agrarsysteme der Zukunft. At - Automatisierungstechnik69(4), 278–280.

Montzka, C., Bayat, B., Tewes, A., Mengen, D., & Vereecken, H. (2021). Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany). Frontiers in Forests and Global Change4, 667151.

Mouratiadou, I., Lemke, N., Zander, P., Shaaban, M., Macpherson, J., Gaiser, T., Melzer, M., Hosseini-Yekani, S.-A., Niemann, N., Lingemann, K., Piorr, A., Helming, K., & Bellingrath-Kimura, S. D. (2021, September): Digital Agricultural Knowledge and Information System: The DAKIS decision support platform for management design and ecosystem services provision. Landscape 2021, Berlin-Online.

Raza, A., Ahrends, H., Habib-Ur-Rahman, M., & Gaiser, T. (2021). Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops. Land10(4), 422.

Schöbel, P. (2021). Die behördliche Kontrolle durch Blockchain-Technologie in der Agrar- und Ernährungswirtschaft – Datenschutz- rechtliche Anforderungen. Frankfurt (Oder).

Schwartz, C., Shaaban, M., Bellingrath-Kimura, S. D., & Piorr, A. (2021). Participatory Mapping of Demand for Ecosystem Services in Agricultural Landscapes. Agriculture11(12), 1193.

Shaaban, M., Schwartz, C., Macpherson, J., & Piorr, A. (2021). A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes. Land10(2), 131.

Ungaro, F., Schwartz, C., & Piorr, A. (2021). Ecosystem services indicators dataset for the utilized agricultural area of the Märkisch-Oderland District-Brandenburg, Germany. Data in Brief34, 106645.


Bellingrath-Kimura, S. D., & Bloch, R. (2020). Smart Farming – Eine Chance für nachhaltige Agrarsysteme? In M. Göpel, H. Leitschuh, A. Brunnengräber, P. L. Ibisch, R. Loske, M. Müller, J. Sommer, & E. U. von Weizsäcker (Hrsg.), Die Ökologie der digitalen Gesellschaft. S. Hirzel Verlag.

Dönitz, E., Voglhuber-Slavinsk, A., Moller, B., & Fraunhofer Institute for Systems and Innovation Research ISI. (2020). Agribusiness in 2035 – Farmers of the Future.

Härtel, I. (2020). Künstliche Intelligenz in der nachhaltigen Landwirtschaft – Datenrechte und Haftungsregime. Natur und Recht42(7), 439–453.

MacPherson, J., Paul, C., & Helming, K. (2020). Linking Ecosystem Services and the SDGs to Farm-Level Assessment Tools and Models. Sustainability12(16), 6617.

Tewes, A., Hoffmann, H., Krauss, G., Schäfer, F., Kerkhoff, C., & Gaiser, T. (2020). New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations. Agronomy10(3), 446.

Tewes, A., Hoffmann, H., Nolte, M., Krauss, G., Schäfer, F., Kerkhoff, C., & Gaiser, T. (2020). How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level? Remote Sensing12(6), 925.

Tewes, A., Montzka, C., Nolte, M., Krauss, G., Hoffmann, H., & Gaiser, T. (2020). Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat. Agronomy10(11), 1813.


Härtel, I. (2019). Agrar-Digitalrecht für eine nachhaltige Landwirtschaft 4.0. Natur und Recht41(9), 577–586.