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Towards identifying the economically optimum sampling density for variable-rate soil constraint management.

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Title: Towards identifying the economically optimum sampling density for variable-rate soil constraint management.
Authors: Roberton, S. D.1,2 (AUTHOR) stirling.roberton@csiro.au, Bennett, J. McL.2,3,4 (AUTHOR), Lobsey, C. R.2 (AUTHOR), Bishop, T. F. A.5 (AUTHOR)
Source: Precision Agriculture. Aug2025, Vol. 26 Issue 4, p1-29. 29p.
Abstract: The investment in soil sampling is often disproportionate to the level of investment for soil amelioration. While advanced geostatistical Digital Soil Mapping (DSM) methods exist to map spatially variable soils, they are rarely used in precision agriculture, due to the increased soil sampling requirement, the cost of which is perceived as prohibitive. Consequently, soil constraints are regularly managed on blanket-rate basis, where a single rate is applied across spatial variable soils. What sampling density is required to deploy these methods in practice, and can an increased investment in soil sampling enable more economically optimised soil amelioration? This paper presents a method to answer this question in Australian broadacre agriculture. The method, which seeks to identify the economically optimum sampling density, is tested on a 100 ha broadacre grain field in Central NSW, Australia. Sampling densities ranging from 0.1 to 3 samples per ha are tested, through observing the accuracy of gypsum recommendations as a practice to treat sodic soils. A status quo Blanket-rate (BR) approach is tested against DSM via the deployment of ordinary kriging to create continuous variable-rate (VR) recommendations. Application errors are assessed both in terms of cost of over-applied product, as well as lost yield potentialy due to under-application. While for this test site, an economically optimum sampling density of 1 core per 5 ha and 1 core per 2 ha for 0–20 cm topsoil management and 0–60 cm profile management was identified, we propose the development Nopt function. Development of this function would allow for optimum sampling investments to be estimated at new sites, in consideration to the inherent site variability and the economic magnitude of soil amelioration. Through development of this function and it’s applied example in this paper, we seek to develop the value proposition for increased soil data collection in Australian Agriculture. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The investment in soil sampling is often disproportionate to the level of investment for soil amelioration. While advanced geostatistical Digital Soil Mapping (DSM) methods exist to map spatially variable soils, they are rarely used in precision agriculture, due to the increased soil sampling requirement, the cost of which is perceived as prohibitive. Consequently, soil constraints are regularly managed on blanket-rate basis, where a single rate is applied across spatial variable soils. What sampling density is required to deploy these methods in practice, and can an increased investment in soil sampling enable more economically optimised soil amelioration? This paper presents a method to answer this question in Australian broadacre agriculture. The method, which seeks to identify the economically optimum sampling density, is tested on a 100 ha broadacre grain field in Central NSW, Australia. Sampling densities ranging from 0.1 to 3 samples per ha are tested, through observing the accuracy of gypsum recommendations as a practice to treat sodic soils. A status quo Blanket-rate (BR) approach is tested against DSM via the deployment of ordinary kriging to create continuous variable-rate (VR) recommendations. Application errors are assessed both in terms of cost of over-applied product, as well as lost yield potentialy due to under-application. While for this test site, an economically optimum sampling density of 1 core per 5 ha and 1 core per 2 ha for 0–20 cm topsoil management and 0–60 cm profile management was identified, we propose the development Nopt function. Development of this function would allow for optimum sampling investments to be estimated at new sites, in consideration to the inherent site variability and the economic magnitude of soil amelioration. Through development of this function and it’s applied example in this paper, we seek to develop the value proposition for increased soil data collection in Australian Agriculture. [ABSTRACT FROM AUTHOR]
ISSN:13852256
DOI:10.1007/s11119-025-10238-0