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Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina

Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region,...

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Main Authors: Filip, Iván Daniel, Peri, Pablo Luis, Banegas, Natalia Romina, Nasca, Jose Andres, Sacido, Mónica, Faverin, Claudia, Vibart, Ronaldo
Format: info:ar-repo/semantics/artículo
Language:Inglés
Published: MDPI 2025
Subjects:
Carbono Orgánico del Suelo
Bosques
Bosque Primario
Praderas
Estimación de las Existencias de Carbono
Soil Organic Carbon
Forests
Primary Forests
Grasslands
Carbon Stock Assessments
Bosque Nativo
Región Chaqueña, Argentina
Chaco Seco
Online Access:http://hdl.handle.net/20.500.12123/22478
https://www.mdpi.com/2071-1050/17/11/5012
https://doi.org/10.3390/su17115012
Summary:Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region, the world’s largest dry tropical forest, were used to test the predictive performance of the Rothamsted Carbon Model (RothC) after its implementation in an object-oriented graphical programming language. RothC provided promising predictions (i.e., precise and accurate) of the SOC stocks under two representative land covers in the region, native forest and Rhodes grass [relative prediction error (RPE) < 10%, concordance correlation coefficient (CCC) > 0.9, modelling efficiency (MEF) > 0.7]. Comparatively, model predictions of the SOC stocks under degraded Rhodes grass swards were suboptimal. The predictions were sensitive to C inputs; under native forests and Rhodes grass, a high C input improved the predictive performance of the model by reducing the mean bias and increasing the MEF values, compared with mean and low C inputs. Larger datasets and revisiting some of the underlying assumptions in the SOC modelling will be required to improve the model’s performance, particularly under the degraded Rhodes grass land cover.

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