Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy

doi:10.1016/S1002-0160(12)60010-1

Pedosphere

Volume 22, Issue 2, April 2012, Pages 230-242

https://doi.org/10.1016/S1002-0160(12)60010-1 Get rights and content

Abstract

Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility. As timing, human and monetary resources often limit the amount of available data, geostatistical techniques provide a valid scientific approach to cope with spatial variability, to interpolate existing data and to predict values at unsampled locations for accurate SOM status survey. Using geostatistical and geographic information system (GIS) approaches, the spatial variability of some physical and chemical soil parameters was investigated under Mediterranean climatic condition in the Abruzzo region of central Italy, where soil erosion processes accelerated by human induced factors are the main causes of soil degradation associated with low SOM content. Experimental semivariograms were established to determine the spatial dependence of the soil variables under investigation. The results of 250 soil sampling point data were interpolated by means of ordinary kriging coupled with a GIS to produce contour maps distribution of soil texture, SOM content related to texture, and C/N ratio. The resulting spatial interpolation of the dataset highlighted a low content of SOM in relation with soil texture in most of the surveyed area (87%) and an optimal C/N ratio for only half of the investigated surface area. Spatial location of degraded area and the assessment of its magnitude can provide decision makers with an accurate support to design appropriate soil conservation strategies and then facilitate a regional planning of agri-environmental measures in the framework of the European Common Agricultural Policy.

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Citation Excerpt :
Machine learning algorithms have been successfully applied to predict SOC using divergent algorithms such as quantile regression forest (Nguemezi et al., 2021), random forests (Hengl et al., 2015; Hounkpatin et al., 2018; Gomes et al., 2019; Tian et al., 2022), Cubist (Adhikari et al., 2014; Gray et al., 2015a, 2015b; Rossel et al., 2016), generalized linear model boosting (Gomes et al., 2019), support vector regression (Ließ et al., 2016; Ottoy et al., 2017; Rudiyanto et al., 2018; Wang et al., 2018; Biney et al., 2022), and boosted regression tree (Yang et al., 2016; Wang et al., 2020; Ottoy et al., 2022). Geostatistical approaches (GeoSA) have been widely used to estimate SOC content and stocks (Oliver and Webster, 1991; Kanevski et al., 2004; Duffera et al., 2007; Marchetti et al., 2012) because they have a high ability to distinguish the continuous nature of soil properties, such as SOC, and detect random variations during modeling, which depends on the spatial correlation of soil properties within the landscape. Prediction using sampling points is carried out by the spatial behavior and spatial distribution of parameters to minimize the error (Heuvelink and Webster, 2001).
Soil organic carbon (SOC) stocks account for nearly 75% of the active carbon pool in terrestrial ecosystems. SOC is also one of the most important soil quality components and has a vital role in agricultural productivity. The main objective of this study was to test the ability of regression kriging (RK) to predict the spatial distribution of SOC stocks in the Vertisols of Gezira State, Sudan using environmental covariates (ECOVs) and legacy soil data. The variables involved 2098 legacy soil datasets (LSDs). Seven ECOVs were used: precipitation (PR), temperature (T), normalized difference vegetation index (NDVI), land use/cover (LULC), digital elevation model (DEM), terrain slope (TS), and valley depth (VD), all downloaded from the repository of the United States Geology Survey (USGS) except PR and T. The RK model was validated using root mean squared error (RMSE), normalized root mean squared error (nRMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). The NDVI and topography were the major ECOVs affecting spatial SOC distribution and its stocks in the area. The results showed that low elevation areas had higher SOC content compared to higher regions and the SOC stocks were 4.14 Kg C ha⁻¹, 0.86 Kg C ha⁻¹, 0.11 Kg C ha⁻¹, 0.02 Kg C ha⁻¹, and 0.004 Kg C ha⁻¹ at gradient levels of <5°, 5–10°, 10–15°, 15–20°, and < 20°, respectively. Additionally, the spatial prediction revealed that agricultural land use (cropland) had the greatest SOC stocks (4.49 Kg C ha⁻¹), whereas grasslands and forest lands were the lowest (0.01 Kg C ha⁻¹). The RK model performed fairly well (RMSE = 1.83 g kg⁻¹; nRMSE = 36%; MAE = 1.7%; MSE = 3.63%; R² = 0.98) and gave satisfactory results to predict SOC stocks in the top 30 cm of the Vertisols. The findings of this study highlight that Vertisols could play a vital role in climate change mitigation via high SOC stocks and ability for C sequestration. It was recommended that the RK model should be extended to include much denser LSDs and test it with additional ECOVs combined with most specific soil properties that would have significant influence on SOC stocks in Vertisols, particularly soil pH, clay content, and clay mineralogy to achieve higher accuracy.

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: Supported by the Italian Ministry of Agricultural, Food and Forestry Policies (No.DM 19366).

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Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy

Abstract

Geoderma.

Geoderma.

Soil Till. Res.

Soil Till. Res.

Geoderma.

Environ. Int.

Soil Till. Res.

Agr. Ecosyst. Environ.

J. Environ. Radioact.

C.R. Acad. Sci.-Series IIA-Earth Planet. Sci.

J. Environ. Radioact.

Geoderma.

Geoderma.

Compu. Electron. Agr.

Soil Biol. Biochem.

Potential soil carbon sequestration in a semiarid Mediterranean agroecosystem under climate change: Quantifying management and climate effects

Plant Soil.

Basic Linear Geostatistics

The Importance of Soil Organic Matter: Key to Drought-Resistant Soil and Sustained Food Production

Nitrogen-Total

Optimal interpolation and isarithmic mapping of soil properties: I. The semi-variogram and punctual kriging

J. Soil Sci.

Soils and Landscapes of Abruzzo-Soil Map of Abruzzo Region (in Italian). Agenzia Regionale per I Servizi di Sviluppo Agricolo, Regione Abruzzo

Proposal for a Directive of the European Parliament and of the Council Establishing a Framework for the Protection of Soil and Amending Directive 2004/35/EC

Thematic Strategy for Soil Protection. Communication from the Commission to the Council, the European Parliament, the European Economic and Social Committee, and the Committees of the Region-COM (2006)231 Final

Soil Regions of Italy

Soil Systems in North Carolina

Temperature sensitivity of soil carbon decomposition and feedbacks to climate change

Nature.

Down to Earth: Soil Degradation and Sustainable Development in Europe: A Challenge for the 21st Century. Environmental Issue Report No. 16