Spatial distribution of soil organic carbon stocks in France

Abstract

Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory.We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils.The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the Correspondence to: M. P. Martin (manuel.martin@orleans.inra.fr) previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks.

Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors

“…This is largely attributed to the humid climate and high natural vegetation (i.e., forest and wetland) cover. In this study, we have observed that forestlands have a higher SOCD than grasslands, which is different from the SOC results for China reported by Wang et al (2004) and for France reported by Martin et al (2011). We attribute these differences to the climate zones on which these studies have focused.…”

“…In addition, the estimated SOCD value of 10.19 kg m −2 for the depth range of 0-30 cm in the study area is higher than the 7.70 kg m −2 observed in the Loess Plateau of China and the value of 5.91 kg m −2 for France (Martin et al, 2011). This is largely attributed to the humid climate and high natural vegetation (i.e., forest and wetland) cover.…”

“…In the past decades, numerous studies were undertaken to investigate the storage and distribution heterogeneity of SOC in different regions, which include the North American Arctic (Ping et al, 2008), the Amazon (Batjes and Dijkshoorn, 1999), British moorland (Garnett et al, 2001), Laos (Chaplot, et al, 2010), France (Martin et al, 2011), and China (Ni, 2013;Yu et al, 2007). Globally, 32 % of SOC is stored in tropical soils, and mainly in forest soils (Eswaran et al, 1993).…”

Organic carbon stocks in Mediterranean soil types under different land uses (Southern Spain)

“…However, the reliability of these estimates depends upon the quality and resolution of the land use and soil spatial databases. Moreover, due to the large spatial variability of SOC within the map units, an elevated density of soil sampling points is required to achieve accurate estimates (Liebens and VanMolle, 2003;Martin et al, 2011).…”

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

“…carbon models (Martin et al, 2011;Hashimoto et al, 2017;Hengl et al, 2017) including applications for SOC mapping (Grimm et al, 2008;Sreenivas et al, 2016;Yang et al, 2016;Hengl et al, 2017;Delgado-Baquerizo et al, 2017;Ließ et al, 2016;Viscarra Rossel et al, 2014).Machine learning methods do not necessarily allow to extract information about the main effects of prediction factors in the response variable (e.g., SOC); consequently, a selection strategy is always useful to increase the interpretability of machine learning algorithms. With this diversity of approaches one constant question is if there is a 5 method that systematically improve the prediction capacity of the others aiming to predict SOC across large geographic areas (e.g., Latin America).…”