Modelling pasture management practices for soil organic carbon gain in livestock systems

• Published in: Grass and forage science Vol. 78; no. 1; pp. 37 - 49
• Main Authors: Schimpf, Karen Gisele, Errecart, Pedro Manuel, Canziani, Graciela Ana
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01-03-2023
• Subjects: Calibration; Carbon; Climate change; Climate change mitigation; Dry matter; elongated wheatgrass; Emissions; Fertilization; Grazing; Livestock; Modelling; Net Primary Productivity; Organic carbon; Organic soils; Pasture; pasture growth; Pasture management; pasture management practices; Sensitivity analysis; SGS pasture model; Soil analysis; Soil dynamics; Soil management; soil organic carbon; Soils
• ISSN: 0142-5242; 1365-2494
• DOI: 10.1111/gfs.12580

Our ability to develop strategies to mitigate climate change includes an understanding of, and our capacity to predict soil organic carbon (SOC) dynamics in livestock systems. Here we assess the capability of the Sustainable Grazing System (SGS) Pasture Model for predicting pasture growth (elongated wheatgrass, Thinopyrum ponticum) and SOC accumulation in different environments and under a range of pasture management practices in hydrohalomorphic soils located in South‐eastern Buenos Aires Province, Argentina. After Model calibration, aerial net primary productivity (ANPP) and TSOC content under two grazing intensities (7.5 and 11 cm post‐grazing target heights) and two N fertilization levels (0 and 100 kg N ha−1 yr−1) were simulated over a 10 year‐period. The SGS Pasture Model predicted 87% of the observed ANPP, with observed and predicted ANPPs averaging 1.46 and 1.42 Mg ha−1 yr−1, respectively. There were differences in simulated ANPP between fertilized and unfertilized treatments both at high and low grazing intensities for the last year of the period. Total SOC contents from the modelling showed differences between high (83.7 to 84.2 Mg ha−1) and low (86.8 to 87.5 Mg ha−1) grazing intensities, with treatments receiving N also showing higher carbon stocks. The positive effect of reduced grazing intensity on soil carbon was explained by an increased input of aerial and subterranean dry matter into the soil. Sensitivity analysis showed that SGS is a robust model, capable of performing effectively under a variety of conditions. Hence, it can be used for exploring management practices to mitigate the impact of livestock systems on emissions and SOC stocks.