Preliminary datasets from a meta-analysis on soil organic carbon under regenerative agricultural systems in Brazilian family farming

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Authors: Carlos Eduardo Pacheco Lima, Mariana Rodrigues Fontenelle
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Abstract

Preliminary datasets from a meta-analysis on soil organic carbon under regenerative agricultural systems in Brazilian family farming Overview This repository contains preliminary findings and forest plots from a meta-analysis assessing Soil Organic Carbon (SOC) responses to regenerative agricultural systems in Brazil. The study focuses on family farming contexts, comparing regenerative practices against conventional management across three main systems: SPDH: No-tillage vegetable systems (Sistema de Plantio Direto de Hortaliças). SAF: Agroforestry systems (Sistemas Agroflorestais). ORG/AGRO: Organic and agroecological management. Key Findings & Figures 1. Organic and Agroecological Systems (ORG/AGRO) The random effects model revealed the highest overall mean SOC increase. Mean Difference: 10.52 [2.71; 18.33] Heterogeneity ($I^2$): 100% 2. No-tillage Vegetable Systems (SPDH) SPDH consistently outperformed conventional systems in increasing surface SOC. Mean Difference: 5.27 [3.06; 7.48] Heterogeneity ($I^2$): 100% 3. Agroforestry Systems (SAF) Agroforestry systems showed a moderate but statistically significant SOC increase. Mean Difference: 3.38 [0.62; 6.14] Heterogeneity ($I^2$): 95.9% Methodology Notes Study Scope: Field-scale experimental evidence in Brazilian family farming. Statistical Model: Random effects model (due to high heterogeneity). Note: Preliminary text written by Generative Artificial Intelligence with human results interpretation and text elaboration support. Authors Carlos Eduardo Pacheco Lima (Embrapa Hortaliças) Mariana Rodrigues Fontenelle (Embrapa Hortaliças) License Creative Commons Attribution 4.0 International (CC BY 4.0)

Fields of Study & Subjects

Soil Carbon and Nitrogen Dynamics Plant and soil sciences Soil Management and Crop Yield

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
12 January 2026
Document type
Preprint