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Connecting crop models with highly resolved sensor observations to improve site-specific fertilisation

Published online by Cambridge University Press:  01 June 2017

E. Wallor*
Affiliation:
Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Landscape System Analysis, Müncheberg, Germany
K.C. Kersebaum
Affiliation:
Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Landscape System Analysis, Müncheberg, Germany
K. Lorenz
Affiliation:
Regional Farmers Association Brandenburg, Teltow, Germany
R. Gebbers
Affiliation:
Leibniz Institute for Agricultural Engineering and Bioeconomy, Department of Engineering for Crop Production, Potsdam, Germany
*
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Abstract

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

Type
Spatial Crop Models
Copyright
© The Animal Consortium 2017 

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References

Adamchuk, VI, Hummel, JW, Morgan, MT and Upadhyaya, SK 2004. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture 44 (1), 7191.CrossRefGoogle Scholar
Bivand, RS, Pebesma, EJ and Gomez-Rubio, V 2013. Applied spatial data analysis with R, Second edition Springer, NY, USA.CrossRefGoogle Scholar
Bivand, R and Lewin-Koh, N 2016. maptools: Tools for Reading and Handling Spatial Objects. R package version 0.8-39. http://CRAN.R-project.org/package=maptools.Google Scholar
Brevik, EC 2012. Analysis of the representation of soil map units using a common apparent electrical conductivity sampling design for the mapping of soil properties. Soil Horizons 53 (2), 3237.CrossRefGoogle Scholar
Core Team R 2014. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Austria, Vienna, URL http://www.R-project.org/.Google Scholar
Fowler, D, Coyle, M, Skiba, U, Sutton, MA, Cape, JN, Reis, S et al. 2013. The global nitrogen cycle in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Science 368 (1621), 20130164.CrossRefGoogle ScholarPubMed
Galloway, JN, Aber, JD, Erisman, JW, Seitzinger, SP, Howarth, RW, Cowling, EB and Cosby, BJ 2003. The nitrogen cascade. BioScience 53 (4), 341356.CrossRefGoogle Scholar
Gebbers, R, Dworak, V, Mahns, B, Weltzien, C, Büchele, D, Gornushkin, I et al. 2016. Integrated approach to site-specific soil fertility management. Proceedings of the 13th International Conference on Precision Agriculture, St. Louis, Missouri, USA, July 31-August 4.Google Scholar
Geesing, D, Diacono, M and Schmidhalter, U 2014. Site-specific effects of variable water supply and nitrogen fertilization on winter wheat. Journal of Plant Nutrition and Soil Science 177, 509523.CrossRefGoogle Scholar
Johnson, CK, Eskridge, KM and Corwin, DL 2005. Apparent soil electrical conductivity: applications for designing and evaluating field-scale experiments. Computers and Electronics in Agriculture 46 (1), 181202.CrossRefGoogle Scholar
Kersebaum, KC and Richter, J 1991. Modelling nitrogen dynamics in a soil-plant system with a simple model for advisory purpose. Fertilizer Research 27, 273281.CrossRefGoogle Scholar
Kersebaum, KC 1995. Application of a simple management model to simulate water and nitrogen dynamics. Ecological Modelling 81, 145156.CrossRefGoogle Scholar
Kersebaum, KC, Lorenz, K, Reuter, HI, Schwarz, J, Wegehenkel, M and 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, 5967.CrossRefGoogle Scholar
Kersebaum, KC 2007. Modelling nitrogen dynamics in soil–crop systems with HERMES. Nutrient Cycling in Agroecosystems 77 (1), 3952.CrossRefGoogle Scholar
Kravchenko, AN, Harrigan, TM and Bailey, BB 2005. Soil electrical conductivity as a covariate to improve the efficiency of field experiments. Transactions of the ASAE 48 (4), 13531357.CrossRefGoogle Scholar
Lawes, RA and Bramley, RGV 2012. A simple method for the analysis of on-farm strip trials. Agronomy Journal 104 (2), 371377.CrossRefGoogle Scholar
Liu, J, You, L, Amini, M, Obersteiner, M, Herrero, M, Zehnder, AJB and Yang, H 2010. A high-resolution assessment on global nitrogen flows in cropland. Proceedings of the National Academy of Science USA 107 (17), 80358040.CrossRefGoogle ScholarPubMed
Martre, P, Wallach, D, Asseng, S, Ewert, F, Jones, JW, Rötter, RP et al. 2015. Multimodel ensembles of wheat growth: many models are better than one. Global Change Biology 21, 911925.CrossRefGoogle ScholarPubMed
Mertens, FM, Pätzold, S and Welp, G 2008. Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity. Journal of Plant Nutrition and Soil Science 171, 146154.CrossRefGoogle Scholar
Pätzold, S, Mertens, FM, Bornemann, L, Koleczek, B, Franke, J, Feilhauer, H and Welp, G 2008. Soil heterogeneity at the field scale: a challange for precision crop rotation. Precision Agriculture 9, 367390.CrossRefGoogle Scholar
Pebesma, EJ 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30, 683691.CrossRefGoogle Scholar
Pebesma, EJ and Bivand, RS 2005. Classes and methods for spatial data in R. R News 5 (2) http://cran.r-project.org/doc/Rnews/.Google Scholar
Rudolph, S, van der Kruk, J, von Hebel, C, Ali, M, Herbst, M, Montzka, C, Pätzold, S, Robinson, DA, Vereecken, H and Weihermüller, L 2015. Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements. Geoderma 241–242, 262271.CrossRefGoogle Scholar
Schmidhalter, U, Maidel, FX, Heuwinkel, H, Demmel, M, Auernhammer, H, Noack, PO and Rothmund, M 2008. Precision Farming – Adaptation of Land Use Management to Small Scale Heterogeneity. In: Perspectives for Agroecosystem Management Schröder P, Pfadenhauer J and Munch JC (Eds.) Elsevier BV, Amsterdam, pp. 121200.CrossRefGoogle Scholar
Tarr, AB, Moore, KJ, Bullock, DG, Dixon, PM and Burras, CL 2005. Improving map accuracy of soil variables using soil electrical conductivity as a covariate. Precision Agriculture 6 (3), 255270.CrossRefGoogle Scholar
Venables, WN and Ripley, BD 2002. Modern Applied Statistics with S, Fourth Edition Springer, New York. USA.CrossRefGoogle Scholar