Mapping the impact of subsoil constraints on soil available water capacity and potential crop yield

Context: The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Information describing the impacts of soil constraints on available water capacity (AWC) and yield is important for farm management, but is rarely considered in a spatial context. Aims and methods: The depth-to three yield-limiting constraints (sodicity, salinity, and alkalinity) was mapped across ∼80 000 ha in northern New South Wales, Australia using machine learning and digital soil mapping techniques. Soil AWC was calculated using soil data and pedotransfer functions, and water use efficiency equations were used to determine potential yield loss due to the presence of soil constraints. From this, the most-limiting constraint to yield was mapped. Key results: One or more constraints were found to be present across 54% of the study area in the upper 1.2 m of the soil profile, overall reducing the AWC by ∼50 mm and potential yield by an average of 1.1 t/ha for wheat and 0.8 bales/ha for cotton. Sodicity (Exchangeable Sodium Percentage > 15%) was identified as the most-limiting constraint to yield across the study area. Implications: The simplification of multiple sources of information into a single decision-making tool could prove valuable to growers and farm managers in managing soil constraints and understanding important interactions with available water and yield

Mapping peat depth using a portable gamma-ray sensor and terrain attributes

Pristine peatlands being excellent storage for terrestrial Carbon (C) play a crucial role in regulating climate and water and provide several important ecosystem services. However, peatlands have been heavily altered (e.g., by draining the water table), increasing greenhouse gas (GHG) emissions. Restoring peatlands requires a comprehensive characterization, including knowledge of peat depth (PD; m). Traditionally, this requires the physical insertion of a push probe, which is time-consuming and labor-intensive. It has been shown that non-invasive proximal sensing techniques such as electromagnetic induction and ground penetrating radar can add value to PD data. In this research, we want to assess the potential of proximally sensed gamma-ray (γ-ray) spectrometry (i.e., potassium [K], thorium [Th], uranium [U], and the count rate [CR]) and terrain attributes data (i.e., elevation, slope, SAGAWI, and MRVBF) to map PD either alone or in combination across a small (10 ha) peatland area in ØBakker, Denmark. Here, the PD varies from 0.1 m in the south to 7.3 m in the north. We use various prediction models including ordinary kriging (OK) of PD, linear regression (LR), multiple LR (MLR), LR kriging (LRK), MLR kriging (MLRK) and empirical Bayesian kriging regression (EBKR). We also determine the minimum calibration sample size required by decreasing sample size in decrements (i.e., n = 100, 90, 80,…, 30). We compare these approaches using prediction agreement (Lin’s concordance correlation coefficient; LCCC) and accuracy (root mean square error; RMSE). The results show that OK using maximum calibration size (n = 108) had near perfect agreement (0.97) and accuracy (0.59 m), compared to LR (ln CR; 0.65 and 0.78 m, respectively) and MLR (ln K, Th, CR and elevation; 0.85 and 0.63 m). Improvements are achieved by adding residuals; LRK (0.95 and 0.71 m) and MLRK (0.96 and 0.51 m). The best results were obtained using EBKR (0.97 and 0.63 m) given all predictions were positive and no significant change in agreement and standard errors with the decrement of calibration sample size (e.g., n = 30). The results have implications towards C stocks assessment and improved land use planning to control GHG emissions and slow down global warming

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Not AvailableTo generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (ECe) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured ECe at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (ECa) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (ECe = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination (R2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m−1), small bias (mean error = −0.0628 dS m−1), large R2 = 0.82, and Lin's concordance (0.93), between measured and predicted ECe, we were well able to predict the ECe distribution at all the four depths. However, the predictions made in the topsoil (0– 0.3 m) at a few locations were poor due to limited data availability in areas where ECa changed rapidly. In this regard, improvements in prediction can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of ECa data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance

Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran

Digital soil maps can be used to depict the ability of soil to fulfill certain functions. Digital maps offer reliable information that can be used in spatial planning programs. Several broad types of data mining approaches through Digital Soil Mapping (DSM) have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses. We applied seven different techniques for the prediction of soil classes based on 194 sites located in Isfahan region. The mapping exercise aims to produce a soil class map that can be used for better understanding and management of soil resources. The models used in this study include Multinomial Logistic Regression (MnLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Bayesian Networks (BN), and Sparse Multinomial Logistic Regression (SMnLR). Two ensemble models based on majority votes (Ensemble.1) and MnLR (Ensemble.2) were implemented for integrating the optimal aspects of the individual techniques. The overall accuracy (OA), Cohen's kappa coefficient index (κ) and the area under the curve (AUC) were calculated based on 10-fold-cross validation with 100 repeats at four soil taxonomic levels. The Ensemble.2 model was able to achieve larger OA, κ coefficient and AUC compared to the best performing individual model (i.e., RF). Results of the ensemble model showed a decreasing trend in OA from Order (0.90) to Subgroup (0.53). This was also the case for the κ statistic, which was the largest for the Order (0.66) and smallest for the Subgroup (0.43). Same decrease was observed for AUC from Order (0.81) to Subgroup (0.67). The improvement in κ was substantial (43 to 60%) at all soil taxonomic levels, except the Order level. We conclude that the application of the ensemble model using the MnLR was optimal, as it provided a highly accurate prediction for all soil taxonomic levels over and above the individual models. It also used information from all models, and thus this method can be recommended for improved soil class modelling. Soil maps created by this DSM approach showed soils that are prone to degradation and need to be carefully managed and conserved to avoid further land degradation

Digital soil mapping of a coastal acid sulfate soil landscape

Coastal floodplains are commonly underlain by sulfidic sediments and coastal acid sulfate soils (CASS). Oxidation of sulfidic sediments leads to increases in acidity and mobilisation of trace metals, resulting in an increase in the concentrations of conducting ions in sediment and pore water. The distribution of these sediments on floodplains is highly heterogeneous. Accurately identifying the distribution of CASS is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) using ancillary information. Proximal sensing instruments such as an EM38 can provide data on the spatial distribution of soil salinity, which is associated with CASS, and can be complemented by digital elevation models (DEM). We used EM38 measurements of the apparent soil electrical conductivity (ECa) in the horizontal and vertical modes in combination with a high resolution DEM to delineate the spatial distribution of CASS. We used a fuzzy k-means algorithm to cluster the data. The fuzziness exponent, number of classes (k) and distance metric (i.e. Euclidean, Mahalanobis and diagonal) were varied to determine a set of parameters to identify CASS. The mean-squared prediction error variance of the class mean of various soil properties (e.g. EC1:5 and pH) was used to identify which of these metrics was suitable for further analysis (i.e. Mahalanobis) and also determine the optimal number of classes (i.e. k = 4). The final map is consistent with previously defined soil–landscape units generated using traditional soil profile description, classification and mapping. The DSM approach is amenable for evaluation on a larger scale and in order to refine CASS boundaries previously mapped using the traditional approach or to identify CASS areas that remain unmapped

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Not AvailableProblem definition: Spatial information on salinity is required at the farm level to enable suitable soil, crop and water management practices. Rationale: To facilitate this, we used an electromagnetic (EM) induction instrument for rapid measurement of apparent soil electrical conductivity (ECa—mS m–1) across the 11 ha area of the Central Soil Salinity Research Institute experimental farm in Nain, Haryana, India. Methods: The ECa was measured using an EM38 in horizontal (ECah) and vertical (ECav) modes on a grid survey. Using the ECa data, we selected 21 locations using the response surface sampling design (RSSD) module of Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. We collected soil samples at four depth increments, including two topsoil (0–0.15 and 0.15–0.30 m), a subsurface (0.3–0.6m) and a subsoil (0.6–0.9m) and measured the soil electrical conductivity (ECe—dS m–1). Results: We developed multiple linear regression to predict ECe using the ESAP software from ECah and ECav and two trend surface parameters (i.e., Easting and Northing) across the farm. The prediction accuracy and bias were compared at different depth increments, and results of the spatial distributions of ECe using ordinary kriging (OK) interpolation were described in terms of the crop and soil use and management implications. Conclusions: We conclude the overall approach allows for generations of a digital soil maps (DSMs) of ECe which serve as baseline data that will allow the monitoring of any rehabilitation effort of salt-affected soils according to their actual degree of salinity.ICAR-CSSR

Potential to map depth-specific soil organic matter content across an olive grove using quasi-2d and quasi-3d inversion of DUALEM-21 data

Owing to conducive economic conditions and a suitable climate Andalucía has become the largest olive oil producing area in the world. However, extensive rill and severe gully erosion are threatening sustainability. To conserve the soil, land management strategies have to be implemented that minimize erosion and enhance at the same time rain water conservation to safeguard crop yields. An indicator of soil quality and the success of soil management will be organic matter content (OM—%). There is therefore a need to measure, map, manage and monitor its content. Proximal sensors such as electromagnetic induction instruments may be useful in mapping this because the apparent soil electrical conductivity (ECa—mS/m) is related to clay, salinity and mineralogy, which influence organic matter content. In this research we collect data from a single frequency and multiple-coil DUALEM-21 along a transect and across an olive grove in the “La Manga” catchment in Setenil de las Bodegas in the southwest of Spain. We inverted the data using EM4Soil software and developed calibrations between estimates of true electrical conductivity (σ—mS/m) with depth against measured OM % using the quasi-2d algorithm along a single transect. We did this by using a fitted linear regression model and by varying the forward modelling algorithm (cumulative function and full solution), inversion algorithm (S1 and S2) and damping factor (λ) to determine a suitable electromagnetic conductivity image for 2-d and 3-d mapping. Our results along a detailed transect showed promise and suggest differences in OM content could be mapped down a topographic sequence. We applied this calibration to a quasi-3d model across the entire olive grove and to predict OM at various depths. The results across the olive grove were compromised in some locations and within geomorphological complex locations in the landscape, such as near the erosion gully where frequent erosion and deposition occurs. We conclude that better results may have been achieved if more detailed ECa data collection was undertaken in and around the gully and also across a larger extent.The authors acknowledge the funding for this work from the Spanish Ministry of Economy and Competitiveness and FEDER (Grants AGL2012-40128-C03-03, AGL2015-65036-C3-3-R, and AGL2015-65036-C3-1-R) and from IFAPA and FEADER (Grant AVA201601.13). Also support through PhD grant no. 8 (Res. 15/04/2010) by IFAPA is acknowledged.Peer reviewe

Digital soil assessment delivers impact across scales in Australia and the Philippines

Retrospective evaluation has consistently shown that soil information has value beyond the investment used to produce it. Digital soil mapping and assessment (DSMA) is the new paradigm for soil survey and a key source of soil and land information. It promises increased utility and flexibility for the users of soil information. Does DSMA methodology add value? What are some of the outcomes and emerging impacts? Seven examples from the burgeoning use of DSMA in and near Australia have been explored to determine the nature and extent of outcomes and impact achieved. The analysis began with a workshop of key soil scientists, involved a survey of the use of DSMA and attitudes to impact amongst practitioners of DSMA and looked at each of the seven examples in the context of the systems they seek to influence. There is evidence of progress along impact pathways in each case. In the simpler systems, the products of DSMA are being used as envisaged and change is occurring. In more complex systems, the role of soil information meshes with many other influences and impact is harder to discern. Importantly, we find that few practitioners using DSMA explicitly identify impact pathways and design projects at the outset to optimise the chance of more extensive impact. Thus, an approach to planning for impact in DSMA is proposed that could improve the chance of impact and allow for iteration as our understanding of the systems in which change is expected improves through our interaction with them