Management and Landscape Influences on Soil Organic Carbon in the Southern Piedmont and Coastal Plain
Type of DegreeDissertation
DepartmentAgronomy and Soils
MetadataShow full item record
Southern Piedmont and Coastal Plain soils have undergone severe degradation, which is reflected in low soil organic carbon (SOC) contents. Restoration of SOC would improve soil quality and C sequestration, leading to a more sustainable agriculture and potentially mitigating the greenhouse effect. This dissertation examined the effects of soil management, climate, and landscape attributes on total SOC and related fractions within upland, well drained, Southeastern Ultisols. In Chapter 1, SOC fractions under croplands and pastures were determined on 87 fields distributed within the Piedmont and Coastal Plain. Total SOC (TOC) (0-20 cm) followed the order: pasture (38.9 Mg ha-1) > conservation tillage (27.9 Mg ha-1) > conventional tillage (22.2 Mg ha-1). Management affected TOC primarily at the soil surface (0-5 cm). Variation in TOC was explained by management (41.6%), clay content (5.2%), mean annual temperature (1.0%), and mean annual precipitation (0.1%). Higher soil clay content and precipitation, and slightly cooler temperatures contributed to higher TOC. All SOC fractions were strongly correlated across a diversity of soils and management systems (r = 0.85 to 0.96). In Chapter 2, TOC within two Piedmont pastures (Alabama and Georgia) was spatially evaluated and related to easily obtainable secondary data; i.e., terrain attributes, remote sensing data (aerial photographs), and field-scale electrical conductivity. Ordinary kriging, multiple linear regression, and artificial neural networks were used to produce spatially distributed TOC maps. Elevation and remote sensing data explained 67% of TOC variability at the Alabama site, while elevation, slope, compound topographic index and electrical conductivity explained 35% of TOC variability at the Georgia site. At both sites, the most accurate TOC maps were produced with the artificial neural network approach (Prediction efficiency = 62% and 49% for Alabama and Georgia, respectively). In Chapter 3, the Environmental Policy Integrated Climate (EPIC) model was used to predict cotton (Gossypium hirsutum L.) and corn (Zea mays L.) yield and SOC dynamics on different landscape positions of a Coastal Plain soil. Simulated and measured yield were closely related (r2 = 0.88). Greatest disagreement occurred on the sideslope position, while the best agreement was found in the drainageway. Simulated TOC was moderately related to measured TOC (r2 = 0.41); highest agreement occurred on the sideslope. The following conclusions can be made: a) on-farm measurement of TOC stocks validated research station data and provided much-needed quantitative information of SOC stocks under pastures; b) terrain attributes and remote sensing data explained TOC variation within pastures; and c) with correct parameterization, EPIC would be an effective tool for evaluating field-scale SOC dynamics affected by short-term management decisions.