Determination of the optimal mathematical model, sample size, digital data and transect spacing to map CEC (Cation exchange capacity) in a sugarcane field

The cation exchange capacity (CEC) is an important property because it influences soil structural stability, nutrient availability, pH and reaction to fertilisers. To assist Australian sugarcane farmers balance sugarcane-yield and minimise fertiliser run-off, the six-easy-steps nutrient management guidelines were developed. In this research we compare and contrast various aspects of digital soil mapping (DSM) of topsoil (0–0.3 m) and subsoil (0.6–0.9 m) CEC, including: choice of model (i.e. linear mixed model – LMM, regression kriging – RK, Cubist, random forest – RF and support vector machine – SVM), digital data (i.e. gamma-ray (γ-ray) spectrometry and apparent conductivity (ECa)) in combination or independent, transect spacing (i.e. 5, 10, 20, 30, 40, 60, 80 m) and number of samples (i.e. 120, 110,…, 10) for calibration. We test these using a validation (i.e. 40) data set. The comparisons were evaluated considering the agreement between measured and predicted CEC using Lin's concordance correlation coefficient (LCCC) and accuracy using root mean square error (RMSE). The results indicate that for the DSM of topsoil CEC, the Cubist with an intermediate number of calibration samples (i.e. 80) using in combination both γ-ray and ECa was optimal in terms of agreement (LCCC = 0.79). For subsoil, a smaller number (i.e. 30) of soil samples for calibration was required to achieve good agreement (LCCC = 0.89). In terms of accuracy, the accuracy (RMSE = 5.42 cmol(+)/kg) of subsoil CEC was satisfactory, as it was less than half standard deviation (SD) (7.55 cmol(+)/kg) of measured CEC. While not the same for topsoil CEC, the accuracy (RMSE = 1.93 cmol(+)/kg) was not as satisfactory as it was over half measured topsoil CEC SD (1.68 cmol(+)/kg). The results also showed that while γ-ray alone was superior to ECa data for prediction, better results were achieved when both digital data were used in combination. In terms of a suitable transect spacing for collection of digital data to predict topsoil CEC, the small transects spacing (i.e. 5 m) was recommended. For subsoil prediction, larger transect spacing may still be appropriate (i.e. 5–60 m). The DSM approach overall enabled topsoil and subsoil prediction of CEC with good accuracy and small residuals, particularly at large calibration data sets (i.e. >80). The final DSM of topsoil and subsoil CEC therefore do allow for the implementation of the six-easy-steps nutrient management guidelines for lime. Specifically, the larger northern half (22.74 ha) requires a small (<2.5 t/ha) application rate with the southern half requiring intermediate (3–5 t/ha) to large (5 t/ha) rates

Enabling Precision Fertilisers Application Using Digital Soil Mapping in Australian Sugarcane Areas

Sugar is Australia's second largest export crop after wheat, generating a total annual revenue of almost $2 billion. It is produced from sugarcane, with approximately 95% grown in Queensland. While highly productive and contributing to the area’s economic sustainability, the soils in these areas have low fertility. The soils typically contain sand content > 60%, low organic carbon (SOC 6%). Hence, sugarcane farmers need to apply fertilisers and ameliorants to maintain soil quality and productivity. Unfortunately, the high intensity rainfall in the region results in sediments, nutrients, and ameliorants run-off from these farms, resulting in environmental degradation and threats to marine ecology in the adjacent World Heritage Listed Great Barrier Reef. To mitigate these issues, the Australian sugarcane industry introduced the Six-Easy-Step Nutrient Management Guidelines. To apply these guidelines, a labour-intensive high-density soil sampling is typically required at the field level, followed by expensive laboratory analysis, spanning the myriad of biological, physical, and chemical properties of soils that need to be determined. To assist in sampling site selection, remote (e.g., Landsat-8, Sentinel-2, and DEM-based terrain attributes) and/or proximal sensing (e.g., electromagnetic [EM] induction and gamma-ray [γ-ray] spectrometry) digital data are increasingly being used. Moreover, the soil and digital data can be modelled using geostatistical (e.g., ordinary kriging [OK]), linear (e.g., linear mixed model [LMM]), machine learning (e.g., random forest [RF], quantile regression forest [QRF], support vector machine [SVM], and Cubist) and hybrid (e.g., RFRK, SVMRK, and CubistRK) approaches to enable prediction of soil properties from the rich source of digital data. However, there are many questions that need to be answered to determine appropriate recommendations including but not limited to i) which modelling approach is optimal, ii) which source of digital data is optimal and does fusion of various sources of digital data improve prediction accuracy, iii) which methods can be used to combine these digital data, iv) what is a minimum number of samples to establish a suitable calibration, v) which soil sampling designs could be used, and vi) what approaches are available to enable prediction of soil properties at various depths simultaneously? In this thesis, Chapter 1 introduces the research questions and defines the problems facing the Australian Sugarcane Industry in terms of the applications of the Six-Easy-Steps Nutrient Management Guidelines, research aims and thesis structure. Chapter 2 is a systematic literature review on various facets of DSM, which includes digital and soil data, models and outputs, and their application across various spatial scales and properties. In Chapter 3, prediction of topsoil (0-0.3 m) SOC is examined in the context of comparing predictive models (i.e., geostatistical, linear, machine learning [ML], and hybrid) using various digital data (i.e., remote [Landsat-8] and proximal sensors [EM and γ-ray]) either individually or in combination and determining minimum number of calibration samples. Chapter 4 shows to predict top- (0-0.3 m) and subsoil (0.6-0.9 m) Ca and Mg, various sampling designs (simple random [SRS], spatial coverage [SCS], feature space coverage [FSCS], and conditioned Latin hypercube sampling [cLHS]) were assessed, with different modelling approaches (i.e., OK, LMM, QRF, SVM, and CubistRK) and calibration sample size effect evaluated, using a combination of proximal data (EM and γ-ray) and terrain (e.g., elevation, slope, and aspect, etc.) attributes. Chapter 5 shows to enable the three-dimensional mapping of CEC and pH at topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow- (0.6-0.9 m) and deep-subsoil (0.9-1.2 m), an equal-area spline depth function can be used, with remote (Sentinel-2) and proximal data (EM and γ-ray) used alone or fused together, and various fusion methods (i.e., concatenation, simple averaging [SA], Bates-Granger averaging [BGA], Granger-Ramanathan averaging [GRA], and bias-corrected eigenvector averaging [BC-EA]) investigated. Chapter 6 explored the synergistic use of proximal (EM and γ-ray), and time-series of remote data (Landsat-8 and Sentinel-2) to map top- (0-0.15 m) and subsoil (0.30-0.45 m) ESP. The results show that, across these case studies, hybrid and ML models generally achieved higher prediction accuracy. The fusion of remote and proximal data produced better predictions, compared to single source of sensors. Granger-Ramanathan averaging (GRA) and concatenation were the most effective methods to combine digital data. A minimum of less than 1 sample ha-1 would be required to calibrate a good predictive model. There were differences in prediction accuracy amongst the sampling designs. The application of depth function splines enables the simultaneous mapping of soil properties from various depths. The produced DSM of soil properties can be used to inform farmers of spatial variability of soils and enable them to precisely apply fertilisers and/or ameliorants based on the Six-Easy-Step Nutrient Management Guidelines

Digital Soil Mapping Approaches for Assisting Site-Specific Soil Management in Sugarcane Growing Areas

The Australian sugarcane industry has developed the “Six Easy Steps” nutrient and ameliorant management guidelines with the aim of optimising productivity and profitability, without adversely influencing the soil condition and causing off-farm effects. This involves knowing the spatial variation of soil properties, such as; cation exchange capacity (CEC), exchangeable calcium (Exch. Ca) and magnesium (Mg) and exchangeable sodium percentage (ESP). One way to generate soil information is to use a digital soil mapping (DSM) approach. Specifically, combine limited soil data with easier to collect ancillary data via mathematical models. This thesis focusses on developing digital soil maps (DSM) in different Australia sugarcane growing districts. Chapter 1 describes the need for DSM while Chapter 2 describes the basic components of DSM, including proximal sources of ancillary data and mathematical models. Moreover, the literature is reviewed to provide demonstrated case studies of DSM of various soil properties (e.g. CEC, Exch. Ca, Exch. Mg and ESP), with gaps identified and research chapters presented to bridge these. In Chapter 3, the application of DSM to predict CEC is explored to assist with the quantification of uncertainty due to ancillary data. In Chapter 4, the aim was to determine optimal components for DSM of topsoil Exch. Ca and Mg. In Chapter 5, the potential of wavelet analysis was explored where there was complex variation in ancillary data relative to topsoil ESP. In Chapter 6, a comparison was made of DSM to account for topsoil (0 – 0.3 m) ESP using mathematical or numerical clustering (FKM) models to create soil classes with a conventional Soil Order map (e.g. soils and land suitability of Burdekin River Irrigation Area). The results showed DSM can be applied to a wide range of soil properties and classes, especially when all the available ancillary data was used in combination. Useful guidelines on operational aspects including transect spacing (7.5 – 30 m) and soil samples for calibration (1 per hectare) were described. Future research should explore other ancillary data sources (e.g. crop yield), mathematical models (e.g. machine learning) and follow up improvement in soil condition as a function of the application of nutrient and ameliorants in accordance with the “Six Easy Steps” guidelines in the various study areas