Soil Organic Carbon (SOC) is a determining factor of soil health and agricultural crop productivity. The SOC level has generally declined since European settlement in Australia, primarily due to the clearing of native vegetation for agricultural purposes. To enhance soil health and crop yield, it is necessary to determine the attainable SOC potential and site-specific soil constraints that limit SOC build-up. To achieve this, we applied a Boundary Line Analysis (BLA) model to a dataset consisting of 1,782 soil sample sites, collected from 72 farms across the grain-growing regions of Australia. Laboratory measured soil properties, including clay content, electrical conductivity (EC), cation exchange capacity (CEC), pH, exchangeable sodium percentage (ESP), and silt+clay from both the topsoil (0–30 cm) and subsoil (30–60 cm) were used as predictors in two separate BLA model to assess attainable SOC levels in the topsoil (0–30 cm), representing topsoil-driven and subsoil-driven constraints, respectively. Upper-envelope functions between SOC and each predictor variable were derived using a locally estimated scatterplot smoothing (LOESS) model in the BLA framework. A separate BLA was performed for different rainfall regions across Australia to reflect differences in attainable SOC. Digital soil maps for each soil property for a case study farm in the Wimmera, Victoria, were used to illustrate how the BLA can be used to identify limiting factors at the within-field level. Next, we applied the developed BLA models to that case study farm to determine the attainable SOC at the farm scale across six key soil properties. The results of the entire regional BLA model revealed that the attainable SOC in the topsoil varied from 0.98 % (mean minimum) to 1.39 % (mean maximum), in contrast to a mean actual (measured) SOC of 0.68 % (SD = 0.37; IQR = 0.45–0.82) across the region. This signifies a sequestration potential of 0.3–0.71 % in the uppermost soil layer, depending on rainfall zones and management practices. In addition, digital maps of the case study farm generated by applying BLA-derived models to spatial layers of soil predictors determine the attainable SOC, show its spatial pattern, and identify the most limiting factors across soil depths. The resulting map also illustrates the potential SOC gain after ameliorating the constraints. This BLA model is reproducible and can be readily applied across the 72 individual farms in Australia (if they have Digital Soil Maps) to quantify attainable SOC and identify site-specific soil constraints. Thus, providing a rapid and effective decision-support tool for farmers and farm managers to implement site-specific soil and agronomic management, thereby improving soil carbon levels.

The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.

Soil Organic Carbon (SOC) is a determining factor of soil health and agricultural crop productivity. The SOC level has generally declined since European settlement in Australia, primarily due to the clearing of native vegetation for agricultural purposes. To enhance soil health and crop yield, it is necessary to determine the attainable SOC potential and site-specific soil constraints that limit SOC build-up. To achieve this, we applied a Boundary Line Analysis (BLA) model to a dataset consisting of 1,782 soil sample sites, collected from 72 farms across the grain-growing regions of Australia. Laboratory measured soil properties, including clay content, electrical conductivity (EC), cation exchange capacity (CEC), pH, exchangeable sodium percentage (ESP), and silt+clay from both the topsoil (0–30 cm) and subsoil (30–60 cm) were used as predictors in two separate BLA model to assess attainable SOC levels in the topsoil (0–30 cm), representing topsoil-driven and subsoil-driven constraints, respectively. Upper-envelope functions between SOC and each predictor variable were derived using a locally estimated scatterplot smoothing (LOESS) model in the BLA framework. A separate BLA was performed for different rainfall regions across Australia to reflect differences in attainable SOC. Digital soil maps for each soil property for a case study farm in the Wimmera, Victoria, were used to illustrate how the BLA can be used to identify limiting factors at the within-field level. Next, we applied the developed BLA models to that case study farm to determine the attainable SOC at the farm scale across six key soil properties. The results of the entire regional BLA model revealed that the attainable SOC in the topsoil varied from 0.98 % (mean minimum) to 1.39 % (mean maximum), in contrast to a mean actual (measured) SOC of 0.68 % (SD = 0.37; IQR = 0.45–0.82) across the region. This signifies a sequestration potential of 0.3–0.71 % in the uppermost soil layer, depending on rainfall zones and management practices. In addition, digital maps of the case study farm generated by applying BLA-derived models to spatial layers of soil predictors determine the attainable SOC, show its spatial pattern, and identify the most limiting factors across soil depths. The resulting map also illustrates the potential SOC gain after ameliorating the constraints. This BLA model is reproducible and can be readily applied across the 72 individual farms in Australia (if they have Digital Soil Maps) to quantify attainable SOC and identify site-specific soil constraints. Thus, providing a rapid and effective decision-support tool for farmers and farm managers to implement site-specific soil and agronomic management, thereby improving soil carbon levels.