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Accueil du site → Doctorat → Italie → MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

Università degli Studi di Milano (2018)

MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

SCHILLACI, CALOGERO

Titre : MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

Auteur : SCHILLACI, CALOGERO

Université de soutenance : Università degli Studi di Milano

Grade : Doctoral Thesis 2018

Résumé partiel
Soil organic carbon (SOC) represents the biggest carbon pool of the biosphere, bigger than the living plant pool. In agriculture, SOC is of pivotal importance for sustainable soil management and is a main soil fertility indicator. As soils are responsible for food production and the provision of various ecosystem services, there is a sturdy interest in understanding how land use and management affect natural plant and crop growth, and ecosystem resilience and functioning. These processes require time and soil sustainability is to be evaluated in a long-term economic perspective by policy makers with the aim of maintaining adequate, and likely improved, conditions of the soil and the whole farm for the future. Thus, long-term actions for crop sustainability could also admit little short-time yield reduction if yield potential, stability and environmental health are maintained at the long-time. Food production and ecosystem services provision depend on the maintenance, or increase, of SOC in agricultural soil, since SOC act as a short-term nutrient reservoir, increase water holding capacity and soil infiltration rate, reduce soil compaction, and favour soil resilience against pollutants. These effects should be taken into account at both a narrow and broad geographical breadth. When aiming to manage SOC at broad geographical extent, a detailed knowledge of SOC distribution and likely change in time is required. However, such a knowledge relies on correct sampling method and modelling procedures that in turn depend on the environmental variability of the area under study. Mediterranean areas are frequently variable as an harbour, the area has been subjected to a high share of soil and above-ground biodiversity and experienced long cultivation history and intensification since the last century, which increased their fragility. In this environment, the acquisition of reliable information on SOC can require a highly dense sampling, which can also negatively affect some relict environment. In addition, sampling can imply a high cost for field work and laboratory analyses. The aim of my Ph.D. work was thus to investigate the main factors related to SOC spatial distribution in agricultural land under various pedoclimatic conditions in semiarid Mediterranean areas, using a legacy soil database (1968-2008) of SOC and soil bulk density. The dissertation is structured in six chapters : the first one is a general introduction where the rationale of the dissertation is explained, and the research questions are stated. The second chapter is a novel approach to systematically collecting literature from international peer-review issues, namely systematic map. The third one is an analysis of the legacy soil database, which intends to make the database ready to be used for the SOC assessment and for the digital soil mapping. The fourth chapter touches an issue dealing with SOC stock mapping with the boosted regression tree and a set of covariates to produce local SOC benchmarks to be compared with European and Global SOC maps. The fifth chapter fits in the same modelling frame and it is addressed at the SOC dynamics using the most widespread legacy sampling campaign. A high number of available spatial data were collected and computed and used to calibrate the SOC models. At this stage, due to the ungridded structure of the data, a machine learning based model has been used (Boosted Regression Trees). The last chapter is a comparison of models (geostatistical, machine learning and linear), and shows useful information about the way that the error is reported by each algorithm. Soil maps are not just produced for the sake of creating attractive geographical visualizations : they have a very precise task to fulfil, i.e. provide accurate and reliable information on soil properties that decision makers can use to plan interventions of any kind. The use of the Regression Kriging and Boosted Regression Trees models, which resulted in the best prediction performance in terms of R2 and RMSE, highlighted the SOC dependence on environmental factors, and the prediction of the agricultural land covers.

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Page publiée le 29 avril 2020