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

Iván Daniel Filip, Pablo Luis Peri, Natalia Banegas, José Nasca, Mónica Sacido, Claudia Faverin and Ronaldo Vibart ()
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Iván Daniel Filip: Centro de Investigaciones y Transferencia (CIT), Formosa, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ruta 11 km 1164, Formosa 3600, Argentina
Pablo Luis Peri: Instituto Nacional de Tecnología Agropecuaria (INTA), Ciudad de Buenos Aires C1033AAE, Argentina
Natalia Banegas: Instituto de Investigación Animal del Chaco Semiárido (IIACS-CIAP-INTA), Chañar Pozo s/n, San Miguel de Tucumán 4113, Argentina
José Nasca: Independent Researcher, Terratio, San Miguel de Tucumán 4000, Argentina
Mónica Sacido: Independent Researcher, Pujato 3764, Roldan 2134, Argentina
Claudia Faverin: Instituto Nacional de Tecnología Agropecuaria (INTA), Ruta 226 km 73,5, cc 276, Balcarce 7620, Argentina
Ronaldo Vibart: Grasslands Research Centre, AgResearch Ltd., Tennent Drive, Private Bag 11008, Palmerston North 4442, New Zealand

Sustainability, 2025, vol. 17, issue 11, 1-18

Abstract: 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.

Keywords: simulation models; systems dynamics; carbon inputs; Rhodes grass; Mollisols; RothC (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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