Comparison of a digital soil map and conventional soil map for management of topsoil exchangeable sodium percentage. (5th November 2020)

• Record Type: Journal Article
• Authors: Li, Nan; Zhao, Dongxue; Arshad, Maryem; Sefton, Michael; Triantafilis, John
• Is Part Of: Soil use and management. Volume 38:Number 1(2022)
• Page Start: 121
• Page End: 134
• Publication Date: 2020-11-05
• Subjects: digital soil mapping -- Fuzzy k‐means clustering -- regression kriging -- sodicity -- soil management; Soil management -- Periodicals; 631.4
• DOI: 10.1111/sum.12666
• Languages: English
• ISSNs: 0266-0032

Abstract

Abstract: The soil in the sugarcane growing area of far north Queensland is often sodic (exchangeable sodium percentage—ESP > 6%). Gypsum therefore needs to be applied to reduce potential for land degradation. To accurately map ESP, a digital soil map (DSM) approach can be used. In this paper, we compare and contrast various aspects of DSM for mapping topsoil (0–0.3 m) ESP, including a suitable model (i.e. linear mixed model (LMM), Cubist and regression kriging (Cubist‐RK)), usefulness of digital data (in combination or alone) and how many calibration data (i.e. n = 20, 30, …120) are required. We compare these with ordinary kriging (OK) of soil data and using prediction agreement (i.e. Lin's concordance—Lin's) and accuracy (root mean squared error—RMSE). We compare all of these results with a DSM derived from numerical clustering (fuzzy K‐mean—FKM) of digital data to identify management zones ( k = 2, 3, 4 and 5) and a conventional Soil Order map ( k = 5 Orders). We do this by calculating mean squared prediction error (MSPE). Prediction of topsoil ESP by OK using 120 samples gave moderate agreement (Lin's = 0.72) with accuracy satisfactory given RMSE (3.69) was less than half standard deviation of measured ESP (½ SD = 3.75). Moreover, a minimum number of 100 samples would be required for OK. However, when digital data were used to value add to soil data in models, the results were equivocal, given Cubist (Lin's = 0.74) and Cubist‐RK (0.79) outperformed OK, while LMM (0.65) was inferior to OK. In addition, a smaller sample size (i.e. 70 and 60, respectively) was enough for Cubist and Cubist‐RK to permit the development of accurate predictions of ESP given the RMSE was less than ½ SD of measured ESP. Prediction of ESP (considering 120 samples) using only the γ‐ray data (Lin's = 0.77) was superior to ECa (0.72), however, using both in combination was best (0.79). The MSPE ( n = 120) indicated creating DSM from clustering of digital data was best for k = 4 zones (MSPE = 27.60); however, Cubist‐RK (13.40), Cubist (14.75), OK (15.56) and LMM (15.76) were able to provide better prediction of ESP. Nevertheless, all DSM generated smaller MSPE than a conventional Soil Order map (32.33). We recommend using Cubist‐RK and both digital data, is the optimal approach to develop a DSM for application of gypsum to enable implementation of Six‐Easy‐Steps soil management guidelines for Proserpine.