I4S - Integrated System for Site-Specific Soil Fertility Management

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Publikationen

2024

  • Schmidinger, J.; Schröter, I.; Bönecke, E.; Gebbers, R.; Rühlmann, J.; Kramer, E.; Mulder, V.; Heuvelink, G.; Vogel, S. (2024): Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming. Precision Agriculture: p. 1-27. Online:  https://doi.org/10.1007/s11119-024-10122-3

  • Vogel, S.; Emmerich, K.; Schröter, I.; Bönecke, E.; Schwanghart, W.; Rühlmann, J.; Kramer, E.; Gebbers, R. (2024): The effect of soil moisture content and soil texture on fast in situ pH measurements with two types of robust ion-selective electrodes. Soil. (1): p. 321-333. Online: https://doi.org/10.5194/soil-10-321-2024
  • Schmidinger, J.; Barkov, V.; Tavakoli, H.; Correa Reyes, J.; Ostermann, M.; Atzmüller, M.; Gebbers, R.; Vogel, S. (2024): Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors. Geoderma. (October): p. 117017. Online: https://doi.org/10.1016/j.geoderma.2024.117017
  • Tavakoli, H.; Correa Reyes, J.; Vogel, S.; Oertel, M.; Zimne, M.; Heisig, M.; Harder, A.; Wruck, R.; Pätzold, S.; Leenen, M.; Gebbers, R. (2024): The RapidMapper: State-of-the-art in mobile proximal soil sensing based on a novel multi-sensor platform. Computers and Electronics in Agriculture. (November): p. 109443. 
    Online: https://www.sciencedirect.com/science/article/pii/S0168169924008342

2023

  • Dueri, S., Léonard, J., Chlebowski, F., Rosso, P., Berg-Mohnicke, M., Nendel, C., Ehrhardt, F., Martre P. (2023): Sources of uncertainty of N2O emission forecast under contrasting global environmental conditions. Agricultural and Forest Meteorology 340: 109619
  • Erler, A., Riebe, D., Beitz, T., Löhmannsröben, H.-G., Leenen, M., Pätzold, S., Ostermann, M., Wójcik, M. (2023). Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study. Sensors 2023, 23, 7178. https://doi.org/10.3390/s23167178
  • Horf, M.; Bönecke, E.; Gebbers, R.; Kling, C.; Kramer, E.; Rühlmann, J.; Schwanghart, W.; Vogel, S. (2023): Utility of Visible and Near-Infrared Spectroscopy to Predict Base Neutralizing Capacity and Lime Requirement of Quaternary Soils. Precision Agriculture. : p. 288-309. Online: https://doi.org/10.1007/s11119-022-09945-9
  • Kersebaum, K.C., Wallor. E. (2023) Process based modelling of soil-crop interactions for site-specific decision support in crop management. In: Cammarano, D., van Evert, F.K., Kempenaar, C. (Eds.) Precision Agriculture: Modelling. Book series Progress in Precision Agriculture. Springer Nature, Cam, Switzerland. https://doi.org/10.1007/978-3-031-15258-0_2
  • Maiwald, M., Sowoidnich, K., Sumpf, B. (2023). Pilot investigations on solids, liquids and gases using a portable shifted excitation Raman difference spectroscopy sensor system. Proc. SPIE 12396, 1239602, https://api.semanticscholar.org/CorpusID:257619370
  • Nendel, C., Reckling, M., Debaeke, P., Schulz, S., Berg‐Mohnicke, M., Constantin, J., Fronzek, S., Hoffmann, M., Jakšić, S., Kersebaum, K.C. and Klimek‐Kopyra, A., (2023). Future area expansion outweighs increasing drought risk for soybean in Europe. Global Change Biology, 29(5), pp.1340-1358. https://doi.org/10.1111/gcb.16562
  • Pätzold, S., Ostermann, M., Heggemann, T., Wehrle, R. (2023): Impact of potassium fertilization on mobile proximal gamma-ray spectrometry: case study on a long-term field trail. Precision Agriculture (2023). https://doi.org/10.1007/s11119-023-10071-3
  • Sowoidnich, K., Maiwald, M., Ostermann, M., Sumpf, B. (2023). Shifted excitation Raman difference spectroscopy for soil component identification and soil carbonate determination in the presence of strong fluorescence interference. J. Raman Spectrosc., https://doi.org/10.1002/jrs.6500
  • Tavakoli, H., Correa Reyes, J.E., Sabetizadeh, M., Vogel, S. (2023): Predicting key soil properties from Vis-NIR spectra by applying novel pre-treatment and machine learning approaches. Soil and Tillage Research 229, 105684, Online: https://doi.org/10.1016/j.still.2023.105684

2022

  • Sowoidnich, K., Vogel, S., Maiwald, M., Sumpf, B. (2022): Determination of Soil Constituents Using Shifted Excitation Raman Difference Spectroscopy. Applied Spectroscopy 76(6), 712-722. https://doi.org/10.1177/00037028211064907
  • Maiwald, M., Sowoidnich, K., Sumpf, B. (2022): On-site shifted excitation Raman difference spectroscopy for soil investigations, Proceedings of SPIE, 11978, 1197804-1-1197804-7. https://doi.org/10.1002/jrs.6400
  • Nkebiwe, P.M., Sowoidnich, K., Maiwald, M., Sumpf, B., Hartmann, T.E., Wanke, D., Müller, T. (2022): Detection of calcium phosphate species in soil by confocal µ-Raman spectroscopy, Journal of Plant Nutrition and Soil Science, 185(2), 221-231. https://doi.org/10.1002/jpln.202100233
  • Maiwald, M., Sowoidnich, K., Sumpf, B. (2022): Portable shifted excitation Raman difference spectroscopy for on-site soil analysis. Journal of Raman Spectroscopy, vol. 53, no. 9, pp. 1560-1570, https://doi.org/10.1002/jrs.6400
  • Leenen M, Pätzold S, Tóth G, Welp G. 2022. A LUCAS-based mid-infrared soil spectral library: Its usefulness for soil survey and precision agriculture. J. Plant Nutr. Soil Sci 185, 370-383, https://doi.org/10.1002/jpln.202100031
  • Heggemann T, Wehrle R, Pätzold S. 2022: On-the-go gamma spectrometry and its evaluation via support vector machines: really a valuable tool for site-independent soil texture prediction? Proceedings of the 15th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL
  • Rosso, P.; Wallor, E.; Richter, L.; Wehrhan, M. (2022) Comparison of plant proximal sensing approaches for nitrogen supply detection in crops. Agronomy Journal. https://doi.org/10.1002/agj2.21189
  • Vogel, S.; Bönecke, E.; Kling, C.; Kramer, E.; Lück, K.; Philipp, G.; Rühlmann, J.; Schröter, I.; Gebbers, R. (2022): Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping. Precision Agriculture. p. 127-149. Online: https://doi.org/10.1007/s11119-021-09830-x
  • Tavakoli, H.; Correa, J.; Vogel, S.; Gebbers, R. (2022): RapidMapper – a mobile multi-sensor platform for the assessment of soil fertility in precision agriculture. VDI-Berichte Nr. 2406, 2022, Proceedings of the International Conference on Agricultural Engineering (AgEng-Land.Technik 2022), 22 – 23 November 2022, Berlin, p. 351-357.
  • Dueri, S., Brown, H., Asseng, S., Ewert, F.,, Webber, H., George, M., Craigie, R., Guarin, J.R., Pequeno, D.N.L., Stella, T., Ahmed, M., Alderman P.D., Basso, B., Berger, A.G., Bracho Mujica, G., Cammarano, D., Chen, Y., Dumont, B., Rezaei, E.E., Fereres, E., Ferrise, R., Gaiser, G., Gao, Y., Garcia-Vila, M., Gayler, S., Hochman, Z., Hoogenboom, G., Kersebaum, K.C., Nendel, C., Olesen, J.E., Padovan, G., Palosuo, T., Priesack, E., Pullens, J.W.M., Rodríguez, A., Rötter, R.P., Ruiz Ramos, M., Semenov, M.A., Senapati, N., Siebert, S., Srivastava, A.K., Stöckle, C., Supit, I., Tao, F., Thorburn, P., Wang, E., Weber, T.K.D., Xiao, L., Zhao, C., Zhao, J., Zhao, Z., Zhu, Y., Martre, P. (2022): Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment. J. Exp. Botany, erac221. DOI: 10.1093/jxb/erac221
  • Groh, J., Diamantopoulos, E., Duan, X., Ewert, F., Heinlein, F., Herbst, M., Holbak, M., Kamali, B., Kersebaum, K.-C., Kuhnert, M., Nendel, C., Priesack, E., Steidl, J., Sommer, M., Pütz, T., Vanderborght, J., Vereecken, H., Wallor, E., Weber, T.K.D., Wegehenkel, M., Weihermüller, L., Gerke, H.H. (2022) Same soil, different climate: Crop model intercomparison on translocated lysimeters. Vadose Zone J. 21, e20202. DOI: 10.1002/vzj2.20202
  • Kersebaum, K.C. (2022) Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change. In: Kondrup, C. et al. (Eds.) Climate Adaptation Modelling. Springer Climate, Cham. Switzerland, 87-93. DOI: 10.1007/978-3-030-86211-4_11
  • Dueri, S., Léonard, J., Chlebowski, F., Rosso, P., Berg-Mohnicke, M., Nendel, C., Ehrhardt, F., Martre P. (2022) Sources of uncertainty of N2O emission forecast under contrasting global environmental conditions. Global Change Biology. Submitted to Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2023.109619

2021

  • Vogel, S., Bönecke, E., Kling, C., Kramer, E., Lück, K., Philipp, G., Rühlmann, J., Schröter, I., Gebbers, R. (2021): Direct prediction of site‑specific lime requirement of arable fields using the base neutralizing capacity and a multi‑sensor platform for on‑the‑go soil mapping. Precision Agriculture, https://doi.org/10.1007/s11119-021-09830-x
  • Wallach, D., Palosuo, T., Thorburn, P., Hochman, Z., Andrianasolo, F., Asseng, S., Basso, B., Buis, S., Crout, N., Dumont, B., Kersebaum, K.C., … Wallor, E, …, Seidel, S.J. (2021) Multi-model evaluation of phenology prediction for wheat in Australia. Agric. Forest Meteor. 298–299, 108289, http://dx.doi.org/10.1101/2020.06.06.133504
  • Kostková, M., Hlavinka, P., Pohanková, E., Kersebaum, K C., … Trnka, M. (2021) Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe. The Journal of Agric. Sc. (online first) https://doi.org/10.1017/S0021859621000216
  • Theurer, L.S., Maiwald, M., Sumpf, B. (2021): Shifted excitation Raman difference spectroscopy: A promising tool for the investigation of soil, European Journal of Soil Science 72(1), pp. 120-124. https://doi.org/10.1111/ejss.12928
  • Schulte-Ostermann, S., Wagner, P. (2021): Reduktion negativer Umwelteffekte mit Hilfe einer teilflächenspezifischen Phosphordüngung? GI-Edition Lecture Notes in Informatics. Reduktion negativer Umwelteffekte mit Hilfe einer teilflächenspezifischen Phosphordüngung
  • Schulte-Ostermann, S., Wagner, P. (2021): Reduktion negativer Umwelteffekte mit Hilfe einer teilflächenspezifischen Phosphordüngung? In: Meyer-Aurich, A., Gandorfer, M., Hoffmann, C., Weltzien, C., Bellingrath-Kimura, S. & Floto, H. (Hrsg.), Gesellschaft für Informatik e.V., S. 277-282. Reduktion negativer Umwelteffekte mit Hilfe einer teilflächenspezifischen Phosphordüngung
  • Aumüller-Gruber, C (2021): Bodenfruchtbarkeit digital erfassen, Landwirtschaftliche Zeitschrift Rheinland 37, p.41.
  • Heil, K.; Schmidhalter, U. An Evaluation of Different NIR Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil. Sensors 2021, 21, 1423. https://doi.org/10.3390/s21041423
  • Heil, K.; Gerl, S.; Schmidhalter, U. Sensitivity of Winter Barley Yield to Climate Variability in a Pleistocene Loess Area. Climate 2021, 9, 112, 
    https://doi.org/10.3390/cli9070112
  • Zimmermann, B., Schlepphorst, R., Junghans, V., Kersebaum, K.C., Ruehlmann, J., Haubold-Rosar, M. (2021) Water consumption and yield effects of variable-rate site-specific and deficit irrigation strategies in highly structured moraine landscapes, Germany. Agric. Wat. Mangem.
  • Strobbia, P., Cupil-Garcia, V., Odion, R., Crawford, B.M., Wang, H.-N., Liu, Y.,  Maiwald, M., Sumpf, B., Vo-Dinh, T. (2021). Translation of SERS Sensing to Real-World Settings through the Combination with Shifted-Excitation Raman Difference Spectroscopy (SERDS), Proc. SPIE 11661, 1166103.
  • Theurer, L.S., Sumpf, B., Maiwald, M., Fricke, J., Ginolas, A., Tränkle, G. (2021): Ten emitter dual-wavelength Y-branch DBR laser diode array emitting 1 W at 785 nm with a spectral emission width below 60 pm, Journal of Physics Communications, 5(10), 105017. https://ui.adsabs.harvard.edu/link_gateway/2021JPhCo...5j5017T/doi:10.1088/2399-6528/ac2e55

2020

  • Erler, A.; Riebe, D.; Beitz, T.; Löhmannsröben, H.-G.; Gebbers, R. Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR). Sensors 2020, 20 (2), 418. http://dx.doi.org/10.3390/s20020418
  • Vogel, S., Bönecke, E., Kling, C., Kramer, E., Lück, K., Nagel, A., et al. (2020). Base neutralizing capacity of agricultural soils in a quaternary landscape of North-East Germany and its relationship to best management practices in lime requirement determination. Agronomy, 10, 877. http://dx.doi.org/10.3390/agronomy10060877
  • Bönecke, E., Meyer, S., Vogel, S., Schröter, I., Gebbers, R., Kling, C., et al. (2020). Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data. Precision Agriculture, 22(2), 493–523. https://doi.org/10.1007/s11119-020-09766-8
  • Leenen, M. (2020): Implementation of soil information in precision agriculture via diffuse reflectance infra-red spectroscopy, Bonner Bodenkundliche Abhandlungen
  • Vrábel, J., Képeš, E., Duponchel, L., Motto-Ros, V., Fabre, C., Connemann, S., Schreckenberg, F., Prasse, P., Riebe, D., Junjuri, R., Gundawar, M. K., Tan, X., Pořízka, P., Kaiser, J. (2020): Classification of challenging Laser-Induced Breakdown Spectroscopy soil sample data - EMSLIBS contest. Spectrochim. Acta, Part B 2020, 169, 105872. http://dx.doi.org/10.1016/j.sab.2020.105872
  • Falconnier, G., Corbeels, M., Boote, K.J., Nendel, C., Ibrahim, E.S., Kamali, B., Kersebaum, K.C. et al. (2020). Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa. Global Change Biology 26 (10), 5942-5964. https://doi.org/10.1111/gcb.15261
  • Groh, J., Diamantopoulos, E., Duan, X., Ewert, F., Herbst, M., Holbak, M., Kamali, B., Kersebaum, K-C., Kuhnert, M., Lischeid, G., Nendel, C., Priesack, E., Steidl, J., Sommer, M., Puetz, T., Vereecken, H., Wallor, E., Weber, T. K. D., Wegehenkel, M., ... Gerke, H. H. (2020). Crop growth and soil water fluxes at erosion-affected arable sites: Using weighing lysimeter data for model intercomparison. Vadose zone journal, 19(1), [20058]. https://doi.org/10.1002/vzj2.20058
  • Heil, K.; Lehner, A.; Schmidhalter, U. (2020). Influence of Climate Conditions on the Temporal Development of Wheat Yields in a Long-Term Experiment in an Area with Pleistocene Loess. Climate MDPI, 8(9) DOI: http://dx.doi.org/10.3390/cli8090100
  • Pätzold, S., Leenen, M., Frizen, P., Heggemann, T., Wagner, P., Rodionov, A. (2020): Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming. Precision Agriculture 21, 4, 737-761. DOI: https://link.springer.com/article/10.1007/s11119-019-09693-3
  • Pätzold, S., Leenen, M., Heggemann, T. (2020). Proximal mobile gamma spectrometry as tool for precision farming and field experimentation. Soil Systems 4 (2), 31. https://doi.org/10.3390/soilsystems4020031
  • Strobbia, P., Odion, R.A., Maiwald, M., Sumpf, B., Vo-Dinh T. (2020), Direct SERDS sensing of molecular biomarkers in plants under field conditions, Anal. Bioanal. Chem., vol. 412, no. 14, pp. 3457-3466. https://doi.org/10.1007/s00216-020-02544-5
  • Techen, A.; Helming, K.; Brüggemann, N.; Veldkamp, E.; Reinhold-Hurek, B.; Lorenz, M.; Bartke, S.; Heinrich, U.; Amelung, W.; Augustin, K.; Boy, J.; Corre, M.; Duttmann, R.; Gebbers, R.; Gentsch, N.; Grosch, R.; Guggenberger, G.; Kern, J.; Kiese, R.; Kuhwald, M.; Leinweber, P.; Schloter, M.; Wiesmeier, M.; Winkelmann, T.; Vogel, H. (2020): Soil research challenges in response to emerging agricultural soil management practices. Advances in Agronomy, 161, 179-240 https://doi.org/10.1016/bs.agron.2020.01.002
  • Vogel, S.; Bönecke, E.; Kling, C.; Kramer, E.; Lück, K.; Philipp, G.; Rühlmann, J.; Gebbers, R. (2020): Base Neutralizing Capacity of Agricultural Soils in a Quaternary Landscape of North-East Germany and its Relationship to Best Management Practices in Lime Requirement Determination. Agronomy, 877, 1-19. https://doi.org/10.3390/agronomy10060877
  • Yin, X., Kersebaum, K.C., et al. (2020): Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models. Field Crops Research 255, 107863. http://dx.doi.org/10.1016/j.fcr.2020.107863
  • Vrábel, J., Képeš, E., Duponchel, L., Motto-Ros, V., Fabre, C., Connemann, S., Schreckenberg, F., Prasse, P., Riebe, D., Junjuri, R., Gundawar, M. K., Tan, X., Pořízka, P., Kaiser, J. (2020). Classification of challenging Laser-Induced Breakdown Spectroscopy soil sample data - EMSLIBS contest. Spectrochim. Acta, Part B 2020, 169, 105872 http://dx.doi.org/10.1016/j.sab.2020.105872
  • Sumpf, B. (2020), High-brightness wavelength stabilized diode lasers for sensor systems and non-linear frequency conversion, Conf. on Lasers and Electro-Optics (CLEO 2020), OSA Technical Digest, Electronic pp. AF3I.1.
  • Maiwald, M., Sumpf, B. (2020), Improving Raman spectroscopy using diode lasers at 785 nm for shifted excitation Raman difference spectroscopy, Proc. SPIE 11257, Photonics West, San Francisco, USA, Feb 1-6, 1125708. https://api.semanticscholar.org/CorpusID:215769502
  • Bönecke, E.; Meyer, S.; Vogel, S.; Schröter, I.; Gebbers, R.; Kling, C.; Kramer, E.; Lück, K.; Nagel, A.; Philipp, G.; Gerlach, F.; Palme, S.; Scheibe, D.; Zieger, K.; Rühlmann, J. (2020): Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data. Precision Agriculture.: p. 1-31. Online: https://doi.org/10.1007/s11119-020-09766-8
  • Vogel, S., Bönecke, E., Kling, C., Kramer, E., Lück, K., Nagel, A., Philipp, G., Rühlmann, J., Schröter, I., Gebbers, R. (2020): Base neutralizing capacity from agricultural fields in the quaternary landscape of North-East Germany. Dataset, BonaRes Repository, BonaRes Data Centre (Leibniz Centre for Agricultural Landscape Research (ZALF)). http://dx.doi.org/10.3390/agronomy10060877
  • Schulte-Ostermann, S., Wagner, P. (2020): Teilflächenspezifische Phosphordüngung: Beitrag zur Verbesserung der Phosphoreffizienz? In: Gandorfer, M., Meyer-Aurich, A., Bernhardt, H., Maidl, F. X., Fröhlich, G. & Floto, H. (Hrsg.), Gesellschaft für Informatik e.V., S. 289-294.
  • Pietzner, D., Wagner, P. (2020): Quantifizierung der maximalen Anpassungsgüte von Sensormesswerten. In: Gandorfer, M. et al. (Hrsg.): Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Digitalisierung für Mensch, Umwelt und Tier. GI-Edition, Lecture Notes in Informatics (LNI) - Proceedings, Vol. P-299, Bonn, S.229-234. ISBN: 978-3-88579-693-0.
  • Juli 2020 Artikel in der „deutschebaumschule.de“

2019

  • Büchele, D., Chao, M., Ostermann, M., Bald, I. (2019). Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF, Scientific Reports, 9, 17588. https://doi.org/10.1038/s41598-019-53426-5
  • Heil, K.; Schmidhalter U. (2019). Theory and Guidelines for the Application of the Geophysical Sensor EM38. Sensors MDPI. 19(19) https://doi.org/10.3390/s19194293
  • Leenen, M., Welp, G., Gebbers, R., Pätzold, S. (2019). Rapid determination of lime requirement by mid-infrared spectroscopy: A promising approach for precision agriculture. Journal of Plant Nutrition and Soil Science, 182, 953-963. https://doi.org/10.1002/jpln.201800670
  • Riebe, D.; Brinkmann, P.; Beitz, T. Löhmannsröben, H.-G.; Gebbers, R. (2019): Application of Laser-induced breakdown spectroscopy for proximal soil sensing in precision agriculture. Sensors 19(23), 5244. https://doi.org/10.1111/ejss.12889
  • Stella, T., Mouratiadou, I., Gaiser, T., Berg-Mohnicke, M., Wallor, E., Ewert, F., Nendel, C. (2019). Estimating the contribution of crop residue management to soil organic carbon conservation. Environmental Research Letters 14, 094008. http://dx.doi.org/10.1088/1748-9326/ab395c
  • Wallor, E., Kersebaum, K.C., Gebbers, R. (2019). Soil state variables in space and time: First steps towards linking proximal soil sensing and process modelling. Precision Agriculture 20, 313-334. https://doi.org/10.1007/s11119-018-9617-y
  • Wallor, E., Kersebaum, K.-C., Lorenz, K., Gebbers, R. (2019) Soil state variables in space and time: first steps towards linking proximal soil sensing and process modelling. Precision Agriculture 20, 2, 313-334. https://link.springer.com/article/10.1007/s11119-018-9617-y
  • Sumpf, B., Fricke, J., Ginolas, A., Maaßdorf, A., Maiwald, M., Müller, A., Tawfieq, M., Theurer, L.S., Vu, T.N., Wenzel, H. (2019), Tunable Y-branch dual-wavelength diode lasers in the VIS and NIR range for sensor applications, Proc. SPIE 10939, Photonics West, San Francisco, USA, Feb 1-6, 1093913.
  • Sumpf, B., Müller, A., Maiwald, M. (2019), Tailored diode lasers - enabling Raman spectroscopy in the presence of disturbing fluorescence and background light, Proc. SPIE 10894, Photonics West, San Francisco, USA, Feb 1-6, 1089411.
  • Wallor, E., Bourouah, M., Kersebaum, K.C., Gebbers, R. (2019). Assessing sampling strategies and soil sensors performance in the detection of field scale variability of plant-available nitrogen. Conference Proceedings Precision Agriculture`19, 571-578, Stafford, John V. (Ed.).
  • Vogel, S.; Gebbers, R.; Oertel, M.; Kramer, E. (2019): Evaluating soil-borne causes of biomass variability in grassland by remote and proximal sensing. Sensors. (20): p. 4593. Online: https://doi.org/10.3390/s19204593
  • Maiwald, M., Sumpf, B. (2019). Rapid and Adjustable Shifted Excitation Raman Difference Spectroscopy at 785 nm. Proc. SPIE 10894, 108940W. https://doi.org/10.1002/jrs.5456

2018

  • Adamchuk, V.A., Ji, W., Viscarra Rossel, R., Gebbers, R., Trembley, N. (2018). Proximal soil and plant sensing. In: Shannon, K., Sudduth, K., Clay, D.(eds.): Precision farming basics. American Society of Agronomy, Madison, WI USA  https://doi.org/10.2134/precisionagbasics.2016.0093
  • Boenecke, E.; Lück, E., Rühlmann, J.; Gruendling R., Franko U. (2018). Determining the within-field yield variability from seasonally changing soil conditions. Precision Agriculture 19 (4): 750 – 769. https://doi.org/10.1007/s11119-017-9556-z
  • Gebbers, R. (2018): Proximal soil sensing and monitoring techniques. In: Stafford, J. (ed.): Precision agriculture for sustainability. Burleigh Dodds Scientific Publishing, Cambrige, UK. pp. 29-78.
  • Kersebaum, K.C., Wallor, E., Lorenz, K., Beaudoin, N., Constantin, J., Wendroth, O. (2018). Modelling cropping systems with HERMES–model capability, deficits and data requirements. In: Bridging Among Disciplines by Synthesizing Soil and Plant Processes. ASA-CSSA-SSSA Book.
  • Heil, K.; Heinemann, P.; Schmidhalter, U. (2018). Modeling the Effects of Soil Variability, Topography, and Management on the Yield of Barley. Frontiers in Environmental Science 6. https://doi.org/10.3389/fenvs.2018.00146
  • Wallor, E., Kersebaum, K.C. et al. (2018). The response of process-based agro-ecosystem models to within-field variability in site conditions. Field Crops Research 228, 1-19. https://doi.org/10.1016/j.fcr.2018.08.021
  • Wallor, E., Kersebaum, K.-C., Lorenz, K., Gebbers, R. (2018). A comprehensive data set demonstrating the spatial variability of soil properties and crop growth conditions at field scale. Open Data Journal for Agricultural Research 5, 1-10. https://doi.org/10.18174/odjar.v5i0.15888
  • Schulte-Ostermann, S., Wagner P. (2018): Bedarfsgerechte Grunddüngung – Düngekosten und Ertragswirkungen. In: Ruckelshausen, A. et.al. (Hrsg.) (2018): Informatik in der Land-, Forst- und Ernährungswirtschaft 38/2018, Kiel, S. 227 – 230.
  • Büchele, D., Rühlmann, M., Ostermann, M., Schmid, T. (2018). Entwicklung einer online-RFA Analyse zur Bestimmung von Bodennährstoffen, Analytic Journal, Januar 2018.
  • Rühlmann, M., Büchele, D., Ostermann, M., Bald, I., Schmid, T. (2018). Challenges in the quantification of nutrients in soils using laser-induced breakdown spectroscopy - a case study with calcium, Spectrochimica Acta Part B, 146, 115-121. https://doi.org/10.1016/j.sab.2018.05.003

2017

  • Gebbers et al. (2017). The I4S approach to site-specific soil fertility management based on proximal soil sensing. Abstract Book Pedometrics 2017, Wageningen
  • Heggemann et al. (2017). Soil texture estimation via mobile gamma-spectrometry: advanced evaluation using support vector machines. Abstract Book Pedometrics 2017, Wageningen
  • Heggemann T, Welp G, Amelung W, Angst G, Franz SO, Koszinski S, Schmidt K, Pätzold S. 2017. Proximal gamma-ray spectrometry for site-independent in situ prediction of soil texture on ten heterogeneous fields in Germany using support vector machines. Soil Till. Res. 168, 99-109. https://doi.org/10.1016/j.still.2016.10.008
  • Heil, K.; Schmidhalter, U. (2017). Improved evaluation of field experiments by accounting for inherent soil variability. European Journal of Agronomy. 89. https://doi.org/10.1016/j.eja.2017.05.004
  • Käthner, J.; Ben-Gal, A.; Gebbers, R.; Peeters, A.; Herppich, W.; Zude-Sasse, M. (2017): Evaluating spatially resolved influence of soil and tree water status on quality of European plum grown in semi-humid climate. Frontiers in Plant Science. 8 (1053), 1-10. https://doi.org/10.3389/fpls.2017.01053
  • Koszinski, S., Heggemann, T., Ibs-Von Seht, M., Pätzold, S., Petersen, H., Steuer, A., Welp, G., Sommer, M. (2017). Gamma radiometric mapping of soil texture at field and regional scale. Abstract Book Pedometrics 2017, Wageningen, 124.
  • Leenen et al. (2017): Building a national (German) mid infrared database for soils. Abstract Book Pedometrics 2017, Wageningen
  • Maiwald, M., Müller, A. Sumpf, B. (2017). In-situ shifted excitation Raman difference spectroscopy: development and demonstration of a portable sensor system at 785 nm. SPIE BiOS. SPIE Digital Library. https://doi.org/10.1117/12.2249059
  • Mizgirev, A., Chudy, T., Marz, M., Wagner, P., Rühlmann, J. (2017): Sensor Fusion - Evaluierung der Eignung von geoelektrischer und Gammasensorik für die indirekte Bestimmung von Phosphor im Boden. In: Ruckelshausen, A. et.al. (Hrsg.)(2017): Informatik in der Land-, Forst- und Ernährungswirtschaft 37/2017, Osnabrück, S. 105-108.
  • Ostermann, M., Schmid, T., Büchele, D., Rühlmann, M. (2016). In Echtzeit quer durchs Perioden­system - Per Online-RFA und -LIBS Elementgehalte in Böden bestimmen“, Laborpraxis 40, 12, 22-24
  • Riebe et al. (2017): Laser-induced breakdown spectroscopy (LIBS) for efficient quantitative determination of elemental plant nutrients in soils: A contribution to precision agriculture. Abstract Book Pedometrics 2017, Wageningen
  • Riebe, D. et al. (2017): Calibration-free laser induced breakdown spectroscopy for the quantitative determination of soil nutrients. EMSLIBS 2017, Pisa https://doi.org/10.1016/j.sab.2009.11.006
  • Rühlmann, M. et al. (2017): Design of an online-analysis technique for the determination of major nutrients in soils using double-pulsed laser-induced breakdown spectroscopy. EMSLIBS 2017, Pisa. https://api.semanticscholar.org/CorpusID:115328127
  • Wagner, P., Marz, M. (2017): Precision Farming - Direkte und indirekte Erhebung von Makronährstoffen. In: Ruckelshausen, A. et.al. (Hrsg.) (2017): Informatik in der Land-, Forst- und Ernährungswirtschaft 375/20175, Gesellschaft für Informatik e.V. (GI), Bonn, S. 153-156.
  • Wallor, E., Kersebaum, K.C., Lorenz, K., Gebbers, R. (2017). Connecting crop models with highly resolved sensor observations to improve site-specific fertilisation. Advances in Animal Biosciences: Precision Agriculture (ECPA), 8:2, pp 689–693. https://doi.org/10.1017/S2040470017000358
  • Wallor, E., Kersebaum, K.C., Lorenz, K., Gebbers, R. (2017). A comprehensive data set demonstrating the spatial variability of soil properties and crop growth conditions at field scale. Open Data Journal for Agricultural Research. http://dx.doi.org/10.18174/odjar.v5i0.15888
  • Dworak, V., Mahn, B., Selbeck, J., Gebbers, R., Weltzien, C. (2017). Terahertz spectroscopy for proximal soil sensing: Particle size analysis. Sensors, 17(10), 1-23. https://doi.org/10.3390/s17102387
  • Heil, K., Schmidhalter, U. (2017). The Application of EM38: Determination of soil parameters, selection of soil sampling points, use in agriculture and archaeology, MDPI Sensors. https://doi.org/10.3390/s17112540
  • Kersebaum, K.C., Wallor, E., Lorenz, K., Beaudoin, N., Constantin, J., Wendroth, O. (2017). Modelling cropping systems with HERMES–model capability, deficits and data requirements. In: Bridging Among Disciplines by Synthesizing Soil and Plant Processes. ASA-CSSA-SSSA Book. https://doi.org/10.2134/advagricsystmodel8.2017.0005
  • Wagner, P., Marz, M. (2017): Precision Farming - Langzeitversuche mit Grunddüngungsstrategie. In: Ruckelshausen, A. et.al. (Hrsg.) (2017): Informatik in der Land-, Forst- und Ernährungswirtschaft 3537/20152017, Gesellschaft für Informatik e.V. (GI), Bonn, S. 157-160.
  • Mizgirev, A., Chudy, T., Marz, M., Wagner, P., Rühlmann, J. (2017): Sensor Fusion - Evaluierung der Eignung von geoelektrischer und Gammasensorik für die indirekte Bestimmung von Phosphor im Boden. In: Ruckelshausen, A. et.al. (Hrsg.)(2017): Informatik in der Land-, Forst- und Ernährungswirtschaft 37/2017, Osnabrück, S. 105-108.

2016

  • Marz, M., Wagner, P., Arslanova, L. (2016): Mobile Röntgenfluoreszenzanalytik als Baustein für Sensorfusion-Ansätze für die Bestimmung von Makronährstoffen im Boden? - ein Werkstattbericht. In: Ruckelshausen, A. et.al. (Hrsg.)(2016): Informatik in der Land-, Forst- und Ernährungswirtschaft 36/2016, Osnabrück, S. 117-120.

  • Wagner, P., Marz, M. (2016): Ist das Raster zu groß. In Bauernzeitung, Ratgeber Pflanzenbau und Technik, Sonderheft Oktober 2016, Berlin.

2015

  • Heil, K., Schmidhalter, U. (2015). Comparison of the EM38 and EM38-MK2 electromagnetic induction-based sensors for spatial soil analysis at field scale. Computers and Electronics in Agriculture 10, 267-280. http://dx.doi.org/10.1016/j.compag.2014.11.014