An approach for mapping Net Ecosystem Productivity (NEP) as a pragmatic indicator of soil ecosystem service greenhouse gas (GHG) regulation including carbon sequestration in EU Member States
Trabajo presentado en Annual Science Days - EJPSoil- European Joint Programme on Soil, celebrado en Vilnius (Lituania) del 10 al 14 de julio de 2024.Modelling the spatio-temporal distribution of Soil Ecosystem Services (SESs) can provide insights to identify their drivers (e.g., land use, agricultural management), improving our understanding of SESs and their relationships, and the implementation of environmental policies. The SES regulation of greenhouse gas (GHG) fluxes from agricultural soils in EU, would especially benefit from such spatio temporal modelling. Within SERENA project funded by EJP SOIL EU programme, to fill this gap, we are developing an approach to be included in a cookbook for the estimation of the net ecosystem productivity (NEP Gross primary production, GPP and Ecosystem respiration, Reco) as pragmatic indicator of the GHG regulation selected in the first stage of the project. The selection was based on the ranking of different types of GHG indicators from a literature review. Based on different criteria (scientific soundness, data availability, and ability to convey information), we were not able to select an ¿ideal¿ indicator which provided complete information (such as the sum of all GHG fluxes) for this SES, but instead selected NEP as a ¿pragmatic¿ GHG indicator. At the next stage, we realized that methods to estimate NEP based on the analysis of light-use efficiency models were impractical to be implemented by project partners. It was also suggested not to use mechanistic models for assessing NEP since methods should be easily applicable, even without scripting knowledge. Thus, we focused on a newly developed empirical model that could relate NEP to spatially exhaustive environmental covariates and be applicable with open GIS software. This was done by relating the well-known Fluxnet database of eddy covariance measures to spatially exhaustive covariates for agricultural areas (3600 8-day estimates of CO2 fluxes). The approach for mapping NEP in EU member states includes three main stages:
1) GPP estimation from Fluxnet stations that grow/have grown wheat in the EU (and one US station) were related to the MODIS 8-days GPP values, monthly average temperature (WorldClim), and a recent high temporal resolution database of daily soil volumetric moisture.
2) Reco estimates from the selected Fluxnet stations were fitted with a thermal performance model to monthly average temperature (WorldClim).
3) The NEP estimate is calculated as GPP-Reco, and after the calculation, there is an additional last step where its finer spatial distribution is made explicit with the EU-2018 crop layer at 10-m resolution, published by JRC, for locations recorded as wheat.
Whereas the fitting quality for each independent component of NEP was relatively good, the overall fitting of the NEP indicator was not. Improvement could be obtained by applying other model fitting techniques (e.g. Gaussian Process Regression), using high-resolution environmental variables (with a weekly step), and trying to incorporate soil properties that have a much lower temporal variability (scales of several years) than the temporal scale of the main CO2 flux data (weekly, seasonal and yearly). However, such improvements most certainly would come with a cost in terms of cookbook applicability
