BIOCHAR COMO ESTRATEGIA DE EMISION NEGATIVA EN SUELOS CALCAREOS
PID2021-128896OB-I00
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Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia
Subprograma Subprograma Estatal de Generación de Conocimiento
Convocatoria Proyectos de I+D+i (Generación de Conocimiento y Retos Investigación)
Año convocatoria 2021
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023
Centro beneficiario AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS (CSIC)
Identificador persistente http://dx.doi.org/10.13039/501100011033
Resultados relacionados
Resultados totales (Incluyendo duplicados): 1Encontrada(s) 1 página(s)
Set de datos (Dataset). 2026
Data and code for "Challenges in Soil Carbon Modelling and Measurement: A Decade of Experimental Data vs. RothC Simulations in an Organic Olive Grove"
DIGITAL.CSIC. Repositorio Institucional del CSIC
- Contreras, Francisco
- Cayuela, María Luz
- Sánchez García, María
- Ronchin, Erika
- Mondini, Claudio
- Sánchez-Monedero, Miguel Ángel
This dataset contains the input data, R scripts, and model outputs required to reproduce the RothC-based soil organic carbon modelling workflow developed for an organic olive grove field experiment in Jumilla, southeastern Spain. The dataset includes data and code for: (i) historical RothC spin-up and SOC pool initialization, (ii) forward simulations for Control, Compost, Biochar, and Compost–Biochar Mixture treatments, and (iii) kinetic parameter inversion from laboratory incubation data for exogenous organic matter decomposition.
The repository includes raw and processed SOC measurements, monthly climate and irrigation data, amendment application schedules, treatment-specific plant C inputs derived from aboveground biomass estimates, incubation CO₂-C release data, and fitted kinetic parameters. The R scripts are organized as a reproducible workflow and generate the SOC trajectories, treatment-level model outputs, incubation model fits, and diagnostic files used to support the manuscript., Modelling the persistence of soil organic carbon (SOC) is currently recognised as a key approach to enhance our understanding of its potential contribution to climate change mitigation. Despite its value, SOC modelling is challenged by soil heterogeneity and the limited availability of reliable data for model calibration and validation, often resulting in discrepancies between simulated and measured SOC dynamics. This study employs a modified version of the RothC model, adapted for amended soils, to simulate soil C dynamics under an 11-year experiment in an organic olive grove. The experiment evaluated four treatments of soil amendment: Compost, Biochar, a Mixture of both, and a control soil without amendment. By comparing the SOC data simulated by the RothC model with experimental field-sampling data, we assessed the model’s accuracy in estimating SOC accumulation and stability in the soil. Both field measurements and RothC simulations consistently identified biochar as the most effective amendment for soil carbon accumulation over the 11-year period, followed by the Mixture and Compost treatments. Estimated soil carbon sequestration rates ranged from 1.67 to 2.66 Mg C ha⁻¹ yr⁻¹ based on field measurements and from 2.98 to 5.34 Mg C ha⁻¹ yr⁻¹ according to model simulations. However, treatment-dependent discrepancies were observed between modelled and field-based SOC stocks. While Compost and Mixture showed close agreement, Biochar exhibited the largest mismatch, likely due to its intrinsic properties that complicate field quantification and are not fully represented in current SOC models, posing challenges for monitoring and verification within carbon accounting frameworks., Peer reviewed
The repository includes raw and processed SOC measurements, monthly climate and irrigation data, amendment application schedules, treatment-specific plant C inputs derived from aboveground biomass estimates, incubation CO₂-C release data, and fitted kinetic parameters. The R scripts are organized as a reproducible workflow and generate the SOC trajectories, treatment-level model outputs, incubation model fits, and diagnostic files used to support the manuscript., Modelling the persistence of soil organic carbon (SOC) is currently recognised as a key approach to enhance our understanding of its potential contribution to climate change mitigation. Despite its value, SOC modelling is challenged by soil heterogeneity and the limited availability of reliable data for model calibration and validation, often resulting in discrepancies between simulated and measured SOC dynamics. This study employs a modified version of the RothC model, adapted for amended soils, to simulate soil C dynamics under an 11-year experiment in an organic olive grove. The experiment evaluated four treatments of soil amendment: Compost, Biochar, a Mixture of both, and a control soil without amendment. By comparing the SOC data simulated by the RothC model with experimental field-sampling data, we assessed the model’s accuracy in estimating SOC accumulation and stability in the soil. Both field measurements and RothC simulations consistently identified biochar as the most effective amendment for soil carbon accumulation over the 11-year period, followed by the Mixture and Compost treatments. Estimated soil carbon sequestration rates ranged from 1.67 to 2.66 Mg C ha⁻¹ yr⁻¹ based on field measurements and from 2.98 to 5.34 Mg C ha⁻¹ yr⁻¹ according to model simulations. However, treatment-dependent discrepancies were observed between modelled and field-based SOC stocks. While Compost and Mixture showed close agreement, Biochar exhibited the largest mismatch, likely due to its intrinsic properties that complicate field quantification and are not fully represented in current SOC models, posing challenges for monitoring and verification within carbon accounting frameworks., Peer reviewed