Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia)

Chartin, Caroline; Stevens, Antoine; Goidts, Esther; Krüger, Inken; Carnol, Monique; Wesemael, Bas van

doi:10.1016/j.geodrs.2016.12.006

Cited by 28 publications

(24 citation statements)

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“…The data was collected in Wallonia, which is the southern part of Belgium and covers an area of c. 16,800 km². This region has been selected for a number of studies on the spatial distribution of SOC in agricultural soils, such as the evolution of SOC stocks over the last fifty years (Goidts and van Wesemael, 2007) and the mapping of SOC stocks (Chartin et al, 2017). Detailed information on the study area can be found in these papers.…”

Section: Study Areamentioning

confidence: 99%

“…Firstly, 434 profiles were sampled in two campaigns (August 2004-August 2005and October 2006-May 2007) from 15 units with homogeneous land use, belonging to the same agricultural region and with similar soil texture, drainage and stoniness (referred to as LSU, or landscape unit; Goidts et al, 2009). The sampling was extended from March 2014 to June 2014, covering another 30 LSU's with 158 sampling sites (Chartin et al, 2017). These 592 sites were marked with an electromagnetic ball marker (3 M) buried at 1 m below surface in order to allow a precise relocation.…”

Section: Soil Monitoring Networkmentioning

confidence: 99%

“…In April and May 2015, we re-sampled 30 sites in cropland and 30 sites in grassland selected among the 434 profiles originally sampled by Goidts et al (2009). The sites were selected from ten LSU's using a latin hypercube for site selection (Minasny and McBratney, 2006), constrained by geographical coordinates and concentration of fine silt and clay from the maps produced by Chartin et al (2017). Furthermore, we used 37 sites selected among the 158 additional sites mentioned above in order to increase the variability in environmental conditions.…”

Section: Soil Monitoring Networkmentioning

confidence: 99%

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An indicator for organic matter dynamics in temperate agricultural soils

Wesemael

Chartin

Wiesmeier

et al. 2019

Agriculture, Ecosystems & Environment

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The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 μm and analysing the SOC concentration in the soil as well as in the fine fraction (< 20 μm). Eight biological indicators measured in an earlier study across the same monitoring network for the 0-10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers' fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r² = 0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5-10 years).

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Section: Study Areamentioning

confidence: 99%

Section: Soil Monitoring Networkmentioning

confidence: 99%

Section: Soil Monitoring Networkmentioning

confidence: 99%

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An indicator for organic matter dynamics in temperate agricultural soils

Wesemael

Chartin

Wiesmeier

et al. 2019

Agriculture, Ecosystems & Environment

Self Cite

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“…()) would serve in‐depth comparison between assessments. Several methods exist to estimate uncertainty, for example through stochastic Monte‐Carlo simulations (Chartin et al., ) or decision trees (Stumpf et al., ). Such measures can be used to guide and optimize soil sampling (Stumpf et al., ).…”

Section: Discussionmentioning

confidence: 99%

The devil is in the detail: Discrepancy between soil organic carbon stocks estimated from regional and local data sources in Flanders, Belgium

Ottoy

Vanierschot

Dondeyne

et al. 2019

Soil Use and Management

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Storage of soil organic carbon (SOC) is an essential function of ecosystems underpinning the delivery of multiple services to society. Regional SOC stock estimates often rely on data collected during land‐use‐specific inventory schemes with varying sampling depth and density. Using such data requires techniques that can deal with the associated heterogeneity. As the resulting SOC assessments are not calibrated for the local scale, they could suffer from oversimplification of landscape processes and heterogeneity. This might especially be the case for sandy regions where typical historical land use practices and soil development processes determine SOC storage. The aims of this study were (a) to combine four land‐use‐specific SOC stock assessments to estimate the total stock in Flanders, Belgium, and (b) to evaluate the applicability of this regional‐scale estimate at the local scale. We estimated the SOC stock in the upper 100 cm of the unsealed area in Flanders (887,745 ha) to be 111.67 Mt OC, or 12.6 ± 5.65 kg OC m−2 on average. In general, soils under (semi‐) natural land‐use types, for example forests, store on average more organic carbon than under agriculture. However, overall agricultural soils store the largest amounts of SOC due to their vast spatial extent. Zooming in on a sandy location study (13.55 km2) revealed the poor performance of the regional estimates, especially where Histosols occurred. Our findings show that a greater spatial sampling density is required when SOC stock estimates are needed to inform carbon‐aware land management rather than to provide for regional reporting.

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“…Estudios enfocados en la dinámica de nutrientes de los suelos, en donde el COS es uno de los parámetros más relevantes, obtuvieron una cobertura territorial del COS usando kriging (Arbelo et al, 2008). De este modo, varios países a nivel mundial han empezado a realizar estimaciones de COS en su intento de encontrar metodologías más precisas, como por ejemplo el uso de simulaciones estocásticas, un plan de muestreo y modelos matemáticos (Chartin et al, 2017).…”

Section: Antecedentesunclassified

Mapeo digital de carbono orgánico del suelo mediante imágenes satelitales y algoritmos de autoaprendizaje en el ecosistema Herbazal del Páramo, provincia de Chimborazo, Ecuador

Izurieta¹,

Elizabeth²

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Lugar y fecha de presentación oral y pública: La Plata, 02 de julio del 2019 I RESUMEN Se presenta una metodología para predecir de manera cuantitativa el almacenaje del Carbono Orgánico del Suelo (COS) en zonas alto andinas, en donde, la complejidad paisajística evidenciada por la topografía irregular, dificultades de acceso, clima frío -frío intenso, alta nubosidad, así como su geología, hacen de dicha tarea un gran desafío. La zona de estudio corresponde al ecosistema Herbazal del Páramo (HP) de la provincia de Chimborazo, Ecuador. La metodología emplea variables espectrales derivadas de imágenes satelitales Landsat 8 (sensores OLI y TIRS), Modelo Digital de Elevación (MDE), datos de secuestro de COS in situ en el perfil 0 -30 cm y variables meteorológicas. Las variables son obtenidas mediante el procesamiento de imágenes satelitales y Sistemas de Información Geográfica (SIG), éstas variables son usadas en la calibración de un algoritmo de autoaprendizaje con Random Forest Regression (RFR) para la predicción de COS. Se logró encontrar 9 variables que controlan la dinámica de distribución de COS en el área de estudio, y a través de ellas generar el modelo final de predicción de COS con una precisión del 82 %, validada mediante muestreo aleatorio simple (MAS). Las variables halladas son: Formación Geológica, Taxonomía, Precipitación, Altura, Orientación, Factor LS, Índice BI, Temperatura Ambiente, Temperatura Superficial -TOA Brightnees Temperature. Mediante SIG se generó el mapa de COS en la zona de estudio y se cuantificó la extensión de las mayores concentraciones de COS, en donde, el mayor porcentaje del área de estudio tiene altas reservas de COS, entre los 150-205 Mg/ha, equivalente al 57 % del área total. A través del presente estudio y sus resultados se presenta una nueva alternativa de cuantificación de COS. El modelo calibrado puede ser extendido sin necesidad de muestrear in situ, muy útil en zonas complejas como el ecosistema páramo. El mapeo digital de COS permitió revelar las elevadas reservas de COS existentes en el ecosistema de estudio, representando un gran aporte en la caracterización de los suelos del territorio nacional. Finalmente, los resultados obtenidos pueden ser utilizados para crear normativas provinciales y nacionales, para evitar la degradación del suelo en este tipo de ecosistemas, considerando la importancia del HP en la estabilización de las estructuras del suelo, regulación hídrica y su aporte en la mitigación del cambio climático. Palabras clave: Landsat 8, Random Forest, índices multiespectrales, SIG, árboles de decisión análisis espacial. II ABSTRACT The methodology presented is used to predict the storage of Organic Soil Carbon (SOC) in quantitative form, in high Andean areas, where a landscape matter evidenced by irregular topography, access difficulties, cold weather -intense cold weather, high cloudiness and its geology, make this task a great challenge. The study area corresponds to the Herbazal del Páramo (HP) ecosystem of the province of Chimborazo, Ecuador. The method...

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Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia)

Cited by 28 publications

References 44 publications

An indicator for organic matter dynamics in temperate agricultural soils

An indicator for organic matter dynamics in temperate agricultural soils

The devil is in the detail: Discrepancy between soil organic carbon stocks estimated from regional and local data sources in Flanders, Belgium

Mapeo digital de carbono orgánico del suelo mediante imágenes satelitales y algoritmos de autoaprendizaje en el ecosistema Herbazal del Páramo, provincia de Chimborazo, Ecuador

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