Abstract
Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on criteria derived from crop simulation, apparent soil electrical conductivity (ECa), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield maps with a large information content on the spatial distribution of these constraints and to omit those with potentially misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation. Gamma-ray spectrometry helped to overcome the inability of current ECa-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate which approach is most suitable and likely to be adopted to suit an individual.
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Acknowledgements
We are grateful to the GRDC and CSIRO for co-funding this work and Mike Wong’s participation at the 6th European Conference on Precision Agriculture. We are grateful to the grower for discussing this work with us and to Mr. N. Khimashia, Ms. K. Wittwer and Mr. T. Parker for technical assistance.
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Wong, M.T.F., Asseng, S., Robertson, M.J. et al. Mapping subsoil acidity and shallow soil across a field with information from yield maps, geophysical sensing and the grower. Precision Agric 9, 3–15 (2008). https://doi.org/10.1007/s11119-008-9052-6
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DOI: https://doi.org/10.1007/s11119-008-9052-6