Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

Broeg, Tom; Blaschek, Michael; Seitz, Steffen; Taghizadeh‐Mehrjardi, Ruhollah; Zepp, Simone; Scholten, Thomas

doi:10.3390/rs15040876

Cited by 21 publications

(14 citation statements)

References 63 publications

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“…4 . Currently, statewide or regional Digital Soil Mapping products are also being generated for Germany (e.g., Broeg et al, 2023 ; Gebauer et al, 2022 ; Möller et al, 2022 ; Sakhaee et al, 2022 ; Zepp et al, 2021 ) that may act as nationwide data bases in the future.…”

Section: Discussionmentioning

confidence: 99%

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Säurich,

Möller,

Gerighausen

2024

Environ Monit Assess

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Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day$$^{-1}$$ - 1 by 2030 and to eliminate it by 2050. About 55 ha day$$^{-1}$$ - 1 of soil are damaged (average 2018–2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day$$^{-1}$$ - 1 and 0.8 ha day$$^{-1}$$ - 1 respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany’s sustainable development strategy.

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Section: Discussionmentioning

confidence: 99%

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Säurich,

Möller,

Gerighausen

2024

Environ Monit Assess

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“…A similar approach was taken by Broeg et al. (2023) when comparing model effectiveness by training their soil property models with a dataset from one German state, validating the model with a dataset from another German state, and then training the model with a mixed‐dataset and applying the model to both states. The variance inflation factor (VIF) was then determined using the “vif” function (Fox & Weisberg, 2019) to quantify the extent of correlation between one predictor and the others in a MLR model.…”

Section: Methodsmentioning

confidence: 99%

“…MLR models of soil properties (Al + ½ Fe extracted by AO, bulk density, and phosphate retention) and raster covariate data (see Figure4) were determined for the 16 sampled pedons and for a combined set of the 16 sampled pedons and the 18 NRCS pedons using the Best Subsets function called "regsubsets" (Lumley based on Fortran code by Alan Miller, 2020). A similar approach was taken byBroeg et al (2023) when comparing model effectiveness by training their soil property models with a dataset from one German state, validating the model with a dataset from another German state, and then training the model with a mixed-dataset and applying the model to both states. The variance inflation factor (VIF) was then determined using the "vif" function(Fox & Weisberg, 2019) to quantify the extent of correlation between one predictor and the others in a MLR model.…”

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confidence: 99%

Predicting the extent of andic soils across western Haleakalā, Maui

Hodges

Boettinger

2023

Soil Science Soc of Amer J

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Maui is one of five Hawaiian Islands affected by orographic climate effect, exhibiting a massive precipitation gradient across western Haleakalā. However, high variability of volcanic ash deposits as a parent material across the study area complicates the ability to isolate the influence of climate on soil formation. Little is documented about the spatial extent of ash deposition, frequency and intensity of volcanic ejecta events, and composition of ash. Therefore, andic soils, which contain short range order (SRO) aluminosilicates and iron oxides that result in unique soil chemical and physical properties, are challenging to map. Using environmental and andic property data from 16 pedons sampled in the study area—bulk density, phosphate retention, and aluminum plus ½ iron extracted by ammonium oxalate—we applied multiple linear regression to create spatial prediction models of these three soil properties. Predicted soil properties were then used to classify andic soils. The mean prediction for an independent set of pedons showed a soil classification accuracy of 50% in the study area for Andisols (data to 60 cm), andic intergrades (data to 75 cm), and non‐andic soils. Soil property predictions using depth‐weighted average data to 1 m increased soil classification user accuracy of Andisols to 87.5%, andic‐intergrades to 100%, and non‐andic soils to 83.3%. Whether a soil exhibits andic soil properties within 60 or 75 cm is irrelevant when considering prior or current presence of ash in a soil. Accounting for all available pedon data with depth proves most important when attempting to predict andic properties and classes.

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“…Soil surfaces on agricultural land are subject to greater human interference than other soil surfaces [15] and are thus an important source of dust emissions from wind erosion. The loss of organic carbon from agricultural soils is a huge challenge for coping with the greenhouse effect and represents the loss of basic land materials [29].…”

Section: Introductionmentioning

confidence: 99%

Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

Liu

Zhao

et al. 2023

Agriculture

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Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 109 g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation.

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Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

Cited by 21 publications

References 63 publications

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Predicting the extent of andic soils across western Haleakalā, Maui

Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

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