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., and Rühlmann, J.: Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data, Precis. Agric, 22, 493–523, https://doi.org/10.1007/s11119-020-09766-8, 2021.
Bogena, H. R., Huisman, J. A., Meier, H., and Weuthen, A.: Hybrid wireless underground sensor networks: Quantification of signal attenuation in soil, Vadose Zone J., 8, 755–761, https://doi.org/10.2136/vzj2008.0138, 2009.
Bogena, H. R., Weuthen, A., and Huisman, J. H.: Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management, Sensors, 22, 9792, https://doi.org/10.3390/s22249792, 2022.
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Spatial-temporal variability of soil moisture and its estimation across scales, Water Resour. Res., 46, W02516, https://doi.org/10.1029/2009WR008016, 2010.
Brown, M., Heinse, R., Johnson-Maynard, J., and Huggins, D.: Time-lapse mapping of crop and tillage interactions with soil water using electromagnetic induction, Vadose Zone J., 20, https://doi.org/10.1002/vzj2.20097, 2021.
Cardell-Oliver, R., Hübner, C., Leopold, M., and Beringer, J.: Dataset: LoRa Underground Farm Sensor Network, in: Proceedings of the 2nd Workshop on Data Acquisition To Analysis – DATA'19, New York, NY, USA, 10 November 2019, 26–28, https://doi.org/10.1145/3359427.3361912, 2019.
Choi, M., Jacobs, J. M., and Cosh, M. H.: Scaled spatial variability of soil moisture fields, Geophys. Res. Lett., 34, L01401, https://doi.org/10.1029/2006GL028247, 2007.
Deumlich, D., Ellerbrock, R. H., and Frielinghaus, Mo.: Estimating carbon stocks in young moraine soils affected by erosion, CATENA, 162, 51–60, https://doi.org/10.1016/j.catena.2017.11.016, 2018.
DIN ISO 11277: Soil quality – Determination of particle size distribution in mineral soil material – Method by sieving and sedimentation (ISO 11277:1998 + ISO 11277:1998 Corrigendum 1:2002), Beuth-Verlag, Berlin, https://doi.org/10.31030/9283499, 2002.
Donat, M., Geistert, J., Grahmann, K., Bloch, R., and Bellingrath-Kimura, S. D.: Patch cropping – a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes, Comput. Electron. Agr., 197, 106894, https://doi.org/10.1016/j.compag.2022.106894, 2022.
Deutscher Wetterdienst (DWD) Climate Data Center (CDC): Monatssumme der Stationsmessungen der Niederschlagshöhe in mm für Deutschland, Version v21.3, Deutscher Wetterdienst,
https://cdc.dwd.de/sdi/pid/OBS_DEU_P1M_RR/BESCHREIBUNG_OBS_DEU_P1M_RR_de.pdf (last access: 30 May 2024), 2021.
ESRI: ArcGIS Release 10.7.0, Redlands, CA,
https://desktop.arcgis.com/de/quick-start-guides/10.7/arcgis-desktop-quick-start-guide.htm, (last access: 30 May 2024), 2011.
Fischer, C., Roscher, C., Jensen, B., Eisenhauer, N., Baade, J., Attinger, S., Scheu, S., Weisser, W. W., Schumacher, J., and Hildebrandt, A.: How Do Earthworms, Soil Texture and Plant Composition Affect Infiltration along an Experimental Plant Diversity Gradient in Grassland?, PLOS ONE, 9, 6, https://doi.org/10.1371/journal.pone.0098987, 2014.
Fischer, G. F., Nachtergaele, S., Prieler, S., van Velthuizen, H. T., Verelst, L., and Wisberg, D.: Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome, 2008.
Graf, A., Bogena, H. R., Drüe, C., Herdelauf, H., Pütz, T., Heinemann, G., and Vereecken, H.: Spatiotemporal relations between water budget components and soil water content in a forested tributary catchment, Water Resour. Res., 50, 4837–4857, https://doi.org/10.1002/2013WR014516, 2014.
Grahmann, K., Reckling, M., Hernandez-Ochoa, I., Bellingrath-Kimura, S., and Ewert, F.: Co-designing a landscape experiment to investigate diversified cropping systems, Agr. Syst., 217, 103950, https://doi.org/10.1016/j.agsy.2024.103950, 2024.
Hohenbrink, T. L. and Lischeid, G.: Does textural heterogeneity matter? Quantifying transformation of hydrological signals in soils, J. Hydrol., 523, 725–738, https://doi.org/10.1016/j.jhydrol.2015.02.009, 2015.
Hohenbrink, T. L., Lischeid, G., Schindler, U., and Hufnagel, J.: Disentangling the Effects of Land Management and Soil Heterogeneity on Soil Moisture Dynamics, Vadose Zone J., 15, 1–12, https://doi.org/10.2136/vzj2015.07.0107, 2016.
Hupet, F. and Vanclooster, M.: Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field, J. Hydrol., 261, 86–101, 2002.
IUSS Working Group WRB: World Reference Base for Soil Resources 2014, Update 2015, International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, World Soil Resources Reports No. 106, FAO, Rome,
https://openknowledge.fao.org/server/api/core/bitstreams/bcdecec7-f45f-4dc5-beb1-97022d29fab4/content, (last access: 30 May 2024), 2015.
Jolliffe, I. T.: Principal component analysis, Springer Series in Statistics, Springer, New York, ISBN 978-1-4757-1906-2, 2002.
Joshi, C. and Mohanty, B. P.: Physical controls of near-surface soil moisture across varying spatial scales in an agricultural landscape during SMEX02: Physical controls of soil moisture, Water Resour. Res., 46, W12503, https://doi.org/10.1029/2010WR009152, 2010.
Kaiser, H. F.: The Application of Electronic Computers to Factor Analysis, Educ. Psychol. Meas., 20, 141–151, https://doi.org/10.1177/001316446002000116, 1960.
Karlen, D. L., Hurley, E. G., Andrews, S. S., Cambardella, C. A., Meek, D. W., Duffy, M. D., and Mallarino, A. P.: Crop Rotation Effects on Soil Quality at Three North ern Corn/Soybean Belt Locations, Agron. J., 98, 484–495, https://doi.org/10.2134/agronj2005.0098, 2006.
Khan, H., Farooque, A. A., Acharya, B., Abbas, F., Esau, T. J., and Zaman, Q. U.: Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields, Agronomy, 10, 1854, https://doi.org/10.3390/agronomy10121854, 2020.
Korres, W., Koyama, C. N., Fiener, P., and Schneider, K.: Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions, Hydrol. Earth Syst. Sci., 14, 751–764, https://doi.org/10.5194/hess-14-751-2010, 2010.
Korres, W., Reichenau, T. G., Fiener, P., Koyama, C. N., Bogena, H. R., Cornelissen, T., Baatz, R., Herbst, M., Diekkrüger, B., Vereecken, H., and Schneider, K.: Spatio-temporal soil moisture patterns – A meta-analysis using plot to catchment scale data, J. Hydrol., 520, 326–341, https://doi.org/10.1016/j.jhydrol.2014.11.042, 2015.
Koudahe, K., Allen, S. C., and Djaman, K.: Critical review of the impact of cover crops on soil properties, International Soil and Water Conservation Research, 10, 343–354, https://doi.org/10.1016/j.iswcr.2022.03.003, 2022.
Krauss, L., Hauck, C., and Kottmeier, C.: Spatio-temporal soil moisture variability in Southwest Germany observed with a new monitoring network within the COPS domain, Meteorol. Z., 19, 523–537, https://doi.org/10.1127/0941-2948/2010/0486, 2010.
Lange, B., Germann, P. F., and Lüscher, P.: Greater abundance of
Fagus sylvatica in coniferous flood protection forests due to climate change: impact of modified root densities on infiltration, Eur. J. Forest Res., 132, 151–163, https://doi.org/10.1007/s10342-012-0664-z, 2013.
Lehr, C. and Lischeid, G.: Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors, Hydrol. Earth Syst. Sci., 24, 501–513, https://doi.org/10.5194/hess-24-501-2020, 2020.
Lischeid, G., Frei, S., Huwe, B., Bogner, C., Lüers, J., Babel, W., and Foken, T.: Catchment Evapotranspiration and Runoff, in: Energy and Matter Fluxes of a Spruce Forest Ecosystem, vol. 229, Springer, Cham, Cham, 355–375, https://doi.org/10.1007/978-3-319-49389-3_15, 2017.
Lischeid, G., Dannowski, R., Kaiser, K., Nützmann, G., Steidl, J., and Stüve, P.: Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers, J. Hydrol., 596, 126096, https://doi.org/10.1016/j.jhydrol.2021.126096, 2021.
Lloret, J., Sendra, S., Garcia, L., and Jimenez, J. M.: A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture, Sensors, 21, 7243, https://doi.org/10.3390/s21217243, 2021.
Lueck, E. and Ruehlmann, J.: Resistivity mapping with Geophilus Electricus - Information about lateral and vertical soil heterogeneity, Geoderma, 199, 2–11, https://doi.org/10.1016/j.geoderma.2012.11.009, 2013.
Mahmood, R., Littell, A., Hubbard, K. G., and You, J.: Observed data-based assessment of relationships among soil moisture at various depths, precipitation, and temperature, Appl. Geogr., 34, 255–264, https://doi.org/10.1016/j.apgeog.2011.11.009, 2012.
Manns, H. R., Berg, A. A., Bullock, P. R., and McNairn, H.: Impact of soil surface characteristics on soil water content variability in agricultural fields, Hydrol. Process., 28, 4340–4351, https://doi.org/10.1002/hyp.10216, 2014.
Martini, E., Wollschläger, U., Musolff, A., Werban, U., and Zacharias, S.: Principal Component Analysis of the Spatiotemporal Pattern of Soil Moisture and Apparent Electrical Conductivity, Vadose Zone J., 16, vzj2016.12.0129, https://doi.org/10.2136/vzj2016.12.0129, 2017.
Nied, M., Hundecha, Y., and Merz, B.: Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin, Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, 2013.
Nunes, M. R., van Es, H. M., Schindelbeck, R., Ristow, A. J., and Ryan, M.: No-till and cropping system diversification improve soil health and crop yield, Geoderma, 328, 30–43, https://doi.org/10.1016/j.geoderma.2018.04.031, 2018.
Pan, F. and Peters-Lidard, C. D.: On the Relationship Between Mean and Variance of Soil Moisture Fields, J. Am. Water Resour. As., 44, 235–242, https://doi.org/10.1111/j.1752-1688.2007.00150.x, 2008.
Paroda, Raj. S., Suleimenov, M., Yusupov, H., Kireyev, A., Medeubayev, R., Martynova, L., and Yusupov, K.: Crop Diversification for Dryland Agriculture in Central Asia, in: CSSA Special Publications, edited by: Rao, S. C. and Ryan, J., Crop Science Society of America and American Society of Agronomy, Madison, WI, USA, 139–150, https://doi.org/10.2135/cssaspecpub32.c9, 2015.
Placidi, P., Morbidelli, R., Fortunati, D., Papini, N., Gobbi, F., and Scorzoni, A.: Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors, Sensors, 21, 5110, https://doi.org/10.3390/s21155110, 2021.
Prakosa, S. W., Faisal, M., Adhitya, Y., Leu, J.-S., Köppen, M., and Avian, C.: Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area, Electronics, 10, 77, https://doi.org/10.3390/electronics10010077, 2021.
R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing (Version 4.1.0,
http://www.R-project.org (last access: 30 May 2024)), Vienna, 2021.
Rodriguez, C., Mårtensson, L.-M. D., Jensen, E. S., and Carlsson, G.: Combining crop diversification practices can benefit cereal production in temperate climates, Agron. Sustain. Dev., 41, 48, https://doi.org/10.1007/s13593-021-00703-1, 2021.
Rossini, P. R., Ciampitti, I. A., Hefley, T., and Patrignani, A.: A soil moisture-based framework for guiding the number and location of soil moisture sensors in agricultural fields, Vadose Zone J., 20, e20159, https://doi.org/10.1002/vzj2.20159, 2021.
Salam, A.: Internet of Things for Sustainable Community Development: Wireless Communications, Sensing, and Systems, Springer International Publishing, Cham, Switzerland, https://doi.org/10.1007/978-3-030-35291-2, 2020.
Salam, A. and Raza, U.: Signals in the Soil: Developments in Internet of Underground Things, Springer International Publishing, Cham, Switzerland, https://doi.org/10.1007/978-3-030-50861-6, 2020.
Scholl, P., Leitner, D., Kammerer, G., Loiskandl, W., Kaul, H.-P., and Bodner, G.: Root induced changes of effective 1D hydraulic properties in a soil column, Plant Soil, 381, 193–213, https://doi.org/10.1007/s11104-014-2121-x, 2014.
Scholz, H. and Grahmann, K.: Dataset of TDR soil moisture data from a LoRaWAN based soil sensing network of a selection of sensors at patchCROP for December 2020 to August 2021 for a principal component analysis, BonaRes Repository [data set], https://doi.org/10.4228/zalf-3rsc-6c30, 2022.
Selzer, T. and Schubert, S.: Water dynamics of cover crops: No evidence for relevant water input through occult precipitation, J. Agron. Crop Sci., 209, 422–437, https://doi.org/10.1111/jac.12631, 2023.
Si, B. C.: Spatial Scaling Analyses of Soil Physical Properties: A Review of Spectral and Wavelet Methods, Vadose Zone J., 7, 547–562, https://doi.org/10.2136/vzj2007.0040, 2008.
Sponagel, H., Grottenthaler, W., Hartmann, K. J., Hartwich, R., Janetzko, P., Joisten, H., Kühn, D., Sabel, K. J., and Traidl, R. (Eds.): Bodenkundliche Kartieranleitung (German Manual of Soil Mapping, KA5), 5th edition, Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, ISBN 978-3-510-95920-4, 2005.
Strebelle, S., Payrazyan, K., and Caers, J.: Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics, SPE J., 8, 227–235, https://doi.org/10.2118/85962-PA, 2003.
Tamburini, G., Bommarco, R., Wanger, T. C., Kremen, C., van der Heijden, M. G. A., Liebman, M., and Hallin, S.: Agricultural diversification promotes multiple ecosystem services without compromising yield, Sci. Adv., 6, eaba1715, https://doi.org/10.1126/sciadv.aba1715, 2020.
Taylor, J. and Whelan, B.: A General Introduction to Precision Agriculture, Australian Center for Precision Agriculture,
https://www.agriprecisione.it/wp-content/uploads/2010/11/general_introduction_to_precision_agriculture.pdf (last access: 30 May 2024), 2010.
Thomas, B., Lischeid, G., Steidl, J., and Dannowski, R.: Regional catchment classification with respect to low flow risk in a Pleistocene landscape, J. Hydrol., 475, 392–402, https://doi.org/10.1016/j.jhydrol.2012.10.020, 2012.
Trnka, M., Rötter, R. P., Ruiz-Ramos, M., Kersebaum, K. C., Olesen, J. E., Žalud, Z., and Semenov, M. A.: Adverse weather conditions for European wheat production will become more frequent with climate change, Nat. Clim. Change, 4, 637–643, https://doi.org/10.1038/nclimate2242, 2014.
Vachaud, G., Passerat De Silans, A., Balabanis, P., and Vauclin, M.: Temporal Stability of Spatially Measured Soil Water Probability Density Function, Soil Sci. Soc. Am. J., 49, 822–828, https://doi.org/10.2136/sssaj1985.03615995004900040006x, 1985.
Vanderlinden, K., Vereecken, H., Hardelauf, H., Herbst, M., Martínez, G., Cosh, M. H., and Pachepsky, Y. A.: Temporal Stability of Soil Water Contents: A Review of Data and Analyses, Vadose Zone J., https://doi.org/10.2136/vzj2011.0178, 2012.
Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., and Vanderborght, J.: On the spatio-temporal dynamics of soil moisture at the field scale, J. Hydrol., 516, 76–96, https://doi.org/10.1016/j.jhydrol.2013.11.061, 2014.
Zhao, X., Li, F., Ai, Z., Li, J., and Gu, C.: Stable isotope evidences for identifying crop water uptake in a typical winter wheat–summer maize rotation field in the North China Plain, Sci. Total Environ., 618, 121–131, https://doi.org/10.1016/j.scitotenv.2017.10.315, 2018.
Zhao, Y., Peth, S., Wang, X. Y., Lin, H., and Horn, R.: Controls of surface soil moisture spatial patterns and their temporal stability in a semi-arid steppe, Hydrol. Process., 24, 2507–2519, https://doi.org/10.1002/hyp.7665, 2010.
