Soil state variables in space and time: first steps towards linking proximal soil sensing and process modelling
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The benefits of process-oriented modelling for management recommendations at the field scale are constrained by high spatial variability of soil properties and lack of dense information on soil types, variability and patterns. Geo-electrical mapping provides dense information about the soil, but sensor output is influenced by several factors. The agro-ecosystem model HERMES simulates nitrogen and water flow in the soil–plant–atmosphere system and was applied to 60 soil sampling points from a well-documented field with a wide range of soil texture in North Rhine-Westphalia, Germany. Validation of HERMES resulted in satisfactory root mean square errors for yield (0.62 t ha−1), water (36.08 mm) and nitrogen in the soil (74.93 kg ha−1) over the whole simulation period of 3 years. For the same field, values of electrical conductivity (ECa) ranged from 20 to 90 mS m−1. Clay and sand contents of three soil layers were highly correlated with the measured ECa. Derived regression models showed R2 values between 0.69 and 0.85, and cross-validation of the statistical equations resulted in a very good reflection of observed soil texture for two of the three soil layers. The subsequent calculation of soil texture at the mapping points of ECa produced an improved resolution of this key attribute to initialize model simulation. The temporal and spatial patterns derived from this work linking the ECa sensor to a process model allow the designation of sub-areas within the field according to the soil nitrogen and water supply. However, results could be improved by considering more than one up-scaled soil property for process-modelling.
KeywordsSoil variability Soil sensing Process models Site-specific management
This work was conducted within the Project I4S (http://www.bonares.de/en/portfolio/i4s/) which is part of the BonaRes programme funded by the German Federal Ministry of Education and Research (BMBF), Grant Number 031A564 J.
- Ad hoc AG Boden. (2005). Bodenkundliche Kartieranleitung (5th ed.) (German Soil Mapping Guide). Stuttgart, Germany: Schweitzerbart.Google Scholar
- Bivand, R., & Lewin-Koh, N. (2016). maptools: Tools for reading and handling spatial objects. R package version 0.8-39. Accessed October 1, 2018, from http://CRAN.R-project.org/package=maptools.
- Bivand, R., & Rundel, C. (2016). rgeos: interface to geometry engine—Open Source (GEOS). R package version 0.3-19. Accessed October 1, 2018, from http://CRAN.R-project.org/package=rgeos.
- Fowler, D., Coyle, M., Skiba, U., Sutton, M. A., Cape, J. N., Reis, S., et al. (2013). The global nitrogen cycle in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Science, 368(20130164), 1–13.Google Scholar
- Gebbers, R., Dworak, V., Mahns, B., Weltzien, C., Büchele, D., Gornushkin, I., et al. (2016). Integrated approach to site-specific soil fertility management. In Proceedings of the 13th international conference on precision agriculture. https://www.ispag.org/proceedings/?action=abstract&id=2084&search=authors.
- Kersebaum, K. C. (2011). Special features of the HERMES model and additional procedures for parameterization, calibration, validation, and applications. In L. R. Ahuja & L. Ma (Eds.), Methods of introducing system models into agricultural research (pp. 65–94). Madison, WI, USA: ASA, CSSA, SSSA.Google Scholar
- Kersebaum, K. C., Lorenz, K., Reuter, H. I., Schwarz, J., Wegehenkel, M., & Wendroth, O. (2005). Operational use of agro-meteorological data and GIS to derive site specific nitrogen fertilizer recommendations based on the simulation of soil and crop growth processes. Physics and Chemistry of the Earth, 30, 59–67.CrossRefGoogle Scholar
- Kersebaum, K. C., Lorenz, K., Reuter, H. I., & Wendroth, O. (2002). Modelling crop growth and nitrogen dynamics for advisory purposes regarding spatial variability. In L. R. Ahuja & L. Ma (Eds.), Agricultural system models in field research and technology transfer (pp. 229–252). Boca Raton, FL, USA: CRC Press LLC.Google Scholar
- Korsaeth, A. (2003). Relations between electrical conductivity, soil texture and chemical properties on a clay soil in Southern Norway. In Apelsvoll Research Centre (Eds.), DIAS Report Plant Production No. 100 (pp. 139–142). Kapp, Norway: The Norwegian Crop Research Institute.Google Scholar
- R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed October 1, 2018, from http://www.R-project.org/.
- Schmidhalter, U., Maidel, F. X., Heuwinkel, H., Demmel, M., Auernhammer, H., Noack, P. O., et al. (2008). Precision farming—adaptation of land use management to small scale heterogeneity. In P. Schröder, J. Pfadenhauer, & J. C. Munch (Eds.), Perspectives for agroecosystem management (pp. 121–200). Amsterdam, The Netherlands: Elsevier.CrossRefGoogle Scholar
- Torri, D., Poesen, J., Monaci, F., & Busconi, E. (1994). Rock fragment content and fine soil bulk density. CATENA, 23, 64–71.Google Scholar