| Titre : | Evaluation of TOC change scenarios in cropping systems with and without diversification across different scales: insights from a northern Italian case study (2025) |
| Auteurs : | C. Piccini ; S. Vanino ; C. Di Bene ; A. Marchetti ; R. Farina |
| Type de document : | Article |
| Dans : | Sustainability (vol. 17, n. 23, December 2025) |
| Article en page(s) : | p. 10823 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM SYSTEME DE PRODUCTION ; DIVERSIFICATION ; SEQUESTRATION DU CARBONE ; CARBONE ; INDICATEUR ; ITALIE |
| Résumé : | Soil organic carbon (SOC) is a key indicator used to evaluate cropping systems, as it reflects long-term productivity, sustainability, and environmental impacts like carbon sequestration. Diversifying crops within intensive farming systems is a possible strategy for enhancing the environmental sustainability of agriculture, resulting in higher rates of SOC accumulation compared to monocultures. This study seeks to evaluate the influence of diversified cropping systems on SOC content at both the field and territorial levels. In Northern Italy, two crop management approaches-one incorporating diversification and one without-were analyzed. The ECOSSE model was employed to simulate changes in SOC content over a 30-year period of diversification, compared with monocropping. The results of the model, first run in available sampling sites, were upscaled to the field to which they belong. Then, using a machine learning approach-namely Random Forest-they were interpolated at the landscape scale, extending the information to an area with similar soil, climate, and management conditions. The maps obtained with this procedure represent valuable tools to assess the long-term effects of crop diversification with legumes on soil C at different scales and can support agricultural policymakers and planners. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.3390/su172310823 |
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