Assessment of six soil ecosystem services by coupling simulation modelling and field measurement of soil properties

Highlights

• Direct measurements and dynamic modelling allowed assessing soil ecosystem service.

• Pedogenetic variability is the main driver of soil ecosystem service provision.

• Inter-annual variability strongly affects water related ecosystem services.

• Significant correlation characterizes soil ecosystem services relationships.

Abstract

Soil is a reservoir of natural capital that provides several ecosystem services, ensuring human well-being and sustainable socioeconomic development. Many researchers nevertheless argue that there is no consensus on practical indicators to assess soil ecosystem services (SES). As many policy decisions rely on metrics and indicators to communicate concise and relevant information, an assessment of ecosystem service indicators can help identifying gaps hindering policymakers from more fully adopting ecosystem service approaches. The aim of this study was to develop a method to quantitatively evaluate six SES using a set of indicators derived from dynamic soil and crop modelling using the STICS model developed by INRA. In a 6 775 km² study area in north-western France (Brittany), 64 soil sampling points, located in agricultural areas, were selected following a stratified random sampling design based on soil parent material stratification. STICS inputs required climate data, soil property data and soil and landscape management practices. Over a baseline period from 1988 to 2018, similar crop management practices were simulated that reflected the dominant one performed by conventional farmers across the study site: applying organic and mineral fertilizers to a maize-wheat-catch crop rotation. Also, STICS outputs were used to derive six biophysical indicators characterizing six SES. Both mean and annual SES indicators were calculated and analyzed. Interrelations among SES indicators and soil properties were investigated using a Pearson correlation matrix, multivariate variance analysis and regression-based methods. The main results revealed that pedological and inter-annual variability were the main drivers of SES provision, particularly for ground water recharge, plant biomass provision, plant water provision and carbon sequestration. All SES were strongly correlated, except carbon sequestration and plant nitrogen provision, which showed weak correlations with the rest of SES. Moreover, analyzing interrelations between SES indicators and soil properties such as soil depth, soil texture and its related variables such as available water capacity played a predominant role in ensuring high levels of water-related SES indicators. Meanwhile, physicochemical soil properties were strongly correlated with carbon sequestration but less so with plant nitrogen provision. Overall, these findings characterize the effect of soil variability on SES provision using modelling and field measures of several soil properties, which can be useful for policy makers.