Evaluating the spatial and vertical distribution of agriculturally important nutrients — nitrogen, phosphorous and boron — in North West Iran

Shahbazi, Farzin; Hughes, P. A.; McBratney, Alex B.; Minasny, Budiman; Malone, Brendan

doi:10.1016/j.catena.2018.10.005

Cited by 38 publications

(19 citation statements)

References 33 publications

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“…As one of soil formation factors, topography plays an important role in TN modeling and can determine the hydrothermal conditions and distribution of soil-forming substances [10]. Soil TN increased significantly as the elevation increased, possibly reflecting less human disturbance and better moisture-temperature conditions at high altitude [17,28,29]. Precipitation and temperature acted as climate proxies and the most robust predictors of TN in our study as shown in Figures 3 and 9.…”

Section: Spatial Distribution Of Soil Tnmentioning

confidence: 61%

“…The contents of soil TN were recorded by soil genetic horizons, so we derived in the uppermost 20 cm soil TN using the equal-area smoothing spline function, which proposed by Bishop et al [37], debuted in soil depth functions by Malone et al [38], and widely employed in several studies [17,19].…”

Section: Soil Datamentioning

confidence: 99%

“…The current literature is replete with the applications of DSM approaches in modeling and mapping soil total nitrogen (TN) of China. The multiple DSM approaches range from multiple linear regression model [11][12][13], geostatistical models [14,15], support vector machine (SVM) [16] to tree-based models such as boosted regression trees (BRTs) and random forest (RF) model [17][18][19]. It is worth noting that single models are good at handling certain types of data, when data is complex, the uncertainty of modeling and predicting is large [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

et al. 2019

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Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land.

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Section: Spatial Distribution Of Soil Tnmentioning

confidence: 61%

Section: Soil Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

et al. 2019

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“…The CCC evaluates both accuracy and precision and is often referred to as the goodness‐of‐fit along a 45° line (e.g. Shahbazi, Hughes, McBratney, Minasny, and Malone, ). The R 2 and MEC also measure the goodness‐of‐fit between the predicted values and observed values.…”

Section: Methodsmentioning

confidence: 99%

Digital soil mapping based on empirical mode decomposition components of environmental covariates

Sun

Wang

Wang³

et al. 2019

European J Soil Science

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In digital soil mapping (DSM) there is an increasing interest in incorporating multiscale variations of soil and environmental covariates to improve mapping accuracy. Empirical mode decomposition (EMD) is a frequently used method for multiscale analysis but has been rarely applied to DSM. This study examines if DSM with EMD covariates results in a more accurate map of a test area than DSM with original covariates. First, EMD was used to decompose terrain attributes of an area. These EMD components were then used to map soil organic carbon (SOC) concentration, pH, clay content and cation exchange capacity (CEC) by two DSM methods: multiple linear regression (MLR) and regression kriging (RK). The same two DSM methods were used with original covariates; in addition, ordinary kriging (OK) maps were produced without covariates. The resulting maps were compared by various accuracy measures. The use of EMD components for DSM improved correlations between soil properties and terrain attributes, and improved the goodness‐of‐fit of MLR models significantly and prediction accuracies by 2.5–18% of root mean square error (RMSE) for SOC, clay and CEC. By contrast, the use of EMD components for pH increased the goodness‐of‐fit of the MLR model slightly and decreased the prediction accuracy by 30% of RMSE. This study concluded that the use of EMD components for DSM improved mapping accuracies if the goodness‐of‐fit of the MLR models was significantly improved by EMD components. It is possible that sampling based on EMD components may provide a sample set that would improve mapping accuracy further. Highlights Empirical mode decomposition (EMD) components of terrain attributes were applied to soil mapping. EMD components improved correlations between soil properties and terrain attributes. EMD components improved the goodness‐of‐fit of multiple linear regression (MLR). Significant improved goodness‐of‐fit of MLR resulted in improved mapping accuracies.

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“…Cubist, RF, and BaRT are decision tree algorithms that differ based on their ways of dealing with variance reduction. RF and Cubist are widely used for DSM purposes and are well-described in the literature Shahbazi et al, 2019a). BaRT is an algorithm that reduces the variation of a mathematical learning method as a general procedure (Keskin et al, 2019).…”

Section: Machine Learning Methods and Geostatistical Approachmentioning

confidence: 99%

Geotechnologies applied in digital soil mapping

Mendes¹

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Geotechnologies applied in digital soil mappingThe civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 -20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world.

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Evaluating the spatial and vertical distribution of agriculturally important nutrients — nitrogen, phosphorous and boron — in North West Iran

Cited by 38 publications

References 33 publications

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

Digital soil mapping based on empirical mode decomposition components of environmental covariates

Geotechnologies applied in digital soil mapping

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