Assessing the Sensitivity of Site-Specific Lime and Gypsum Recommendations to Soil Sampling Techniques and Spatial Density of Data Collection in Australian Agriculture: A Pedometric Approach

Stirling D Roberton,Thomas F A Bishop,John Mcl Bennett,Craig R Lobsey

doi:10.3390/agronomy10111676

Stirling D Roberton, Thomas F A Bishop + Show 2 more

Open Access

https://doi.org/10.3390/agronomy10111676

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Abstract

There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has not been well described. The work presented here investigates the spatial errors of gypsum and lime recommendations based on industry-standard blanket-rate and zone-based variable rate application, as well as the more advanced pedometric approaches – ordinary kriging (OK) and regression kriging (RK). All methods were tested at sampling densities between 0.1–3 samples/ha for a 108 ha broadacre site in central NSW, Australia. Whilst previous work has tested the effect of sampling density on the spatial predictive performance of OK and RK, here we assess prediction accuracy as the error associated with soil management decisions based on their results (i.e., the over- and under-application error of gypsum and lime applications) in conjunction with the RMSE of prediction for soil pH and exchangeable sodium percentage (ESP). The uncertainty of each method is also tested to observe the effect of random initialisation on predictive performance. Results indicated that RK provided superior spatial predictions across all sampling densities for the application of gypsum and lime, with a blanket-rate application providing the worse results, with over- and under-application errors exceeding 200 t and 300 t respectively for 40–60 cm treatment for the entire field. Interestingly, the spatial accuracy of amendment application increased to a sampling density of 0.5 samples/ha for RK, with minimal improvement thereafter, suggesting that meaningful soil amelioration advice can be attained proximal to this density.

Highlights

Site-specific agronomic decisions are often made using limited soil information, due to the perceived cost of soil data acquisition in relation to its perceived usefulness [1,2,3]
The 1200 samples were measured for soil pH, exchangeable sodium percentage (ESP), bulk density (BD) and cation exchange capacity (CEC), along with other soil structural and chemical measurements not used in this study in accordance with Rayment et al [28]
It is evident for all sampling densities that regression kriging (RK) prevails over other methods in terms of characterising ESP at the investigation site, with ordinary kriging (OK) providing similar results as RK for pH at

Summary

Introduction

Site-specific agronomic decisions are often made using limited soil information, due to the perceived cost of soil data acquisition in relation to its perceived usefulness [1,2,3]. Agricultural practitioners will typically use surface-based “grab samples” i.e., 0–10 cm depth along a transect, which is bulked prior to analysis to derive a single representation of field condition. This subsequently results in ‘blanket-rate’ (BR) amelioration, whereby a single amendment rate is applied across the field, irrespective of spatial soil variation. There has been limited assessment of the error in agronomic recommendations at different sampling densities These errors may be highly influential on overall farm profitability, as large soil treatment investments are often made on these recommendations [4,5]. It is important to understand how both the spatial variability of soil constraints and soil sampling regimes will influence amendment application outcomes that are simultaneously profitable and socially responsible

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