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Stellenbosch University (2015)

Stochastic modelling of soil carbon stocks under different land uses : a case study in South Africa

Ros Mesa, Ignacio

Titre : Stochastic modelling of soil carbon stocks under different land uses : a case study in South Africa

Auteur : Ros Mesa, Ignacio

Université de soutenance : Stellenbosch University

Grade : Master of Science (MS) 2015

The research was conducted in the Kwa-Zulu Natal midlands, South Africa. The vertical distribution of soil organic carbon (SOC) stocks were successfully predicted by stochastic exponential models developed for the three main land uses in the area, which are farmlands, forestry plantations and grasslands. These models, in combination with regular surface sampling, may be used for monitoring SOC dynamics in the area and mapping SOC stocks. Bulk density measurements are needed in combination with SOC content (%wt) to calculate such SOC stocks. Considering the disadvantages of bulk density sampling and measurement, an effort was made to determine if one of the commonly-used existing stochastic models could be used to successfully predict bulk densities for soils with known texture and SOC content to replace direct measurements, taking into account that different managements might affect final results. Statistica software was used to correlate the Saxton & Rawls model predictions and associated regressions with measured values for the study area. A clear distribution trend was achieved using Statistica and the correlations were fair with r2 values close to 0.5 for individual regressions and substantially higher for area averages. However, considering the depth-stratified averages and correcting for the effects of particle density changes for soils with high soil organic matter, high correlations for 2 of the 3 studied land uses were achieved (r2 values of 0.99 and 0.81 in forests and grasslands respectively). Therefore, although Saxton and Rawls (2006) predictions of bulk density may be used, it is preferable to conduct direct bulk density determinations. The proposed models to calculate the vertical distribution of SOC would substantially reduce the cost of soil carbon inventories to 1m soil depth in the study area by limiting observations to the soil surface. Triplicate 5cm-deep soil core samples would be collected at the soil surface per observation point for determination of ρb (bulk density) and Corg (organic carbon). On average, the accuracy of the normalized depth-distribution model is rather high for grasslands and forests/forest plantations (R2 = 0.98), but somewhat lower for cultivated lands (R2 = 0.96) due to mixing of the plough layer to cultivation depth. Carbon stocks to 1m depth were calculated as an integral of the normalized exponential distribution, multiplied by the value of Corg observed at the soil surface and expressed on volume basis as carbon density (Cv, kg∙m-3). The resulting stock assessment was compared to the observed values using piece-integration for sampled depth increments to give SOC stocks on an area basis (kg∙m-2). The estimated prediction error on average was 1.2 (9%) and 3.7 kg∙m-2 (21.6%) in grasslands and forests respectively, while for cultivated lands the error was 1.3 kg.m-2 (9.5%). Further improvement to reduce these errors may be achieved by introducing the soil type as variable and grouping the functions by soil type rather than land uses. The results of this work were presented at the seminar of the department of Soil Science, Stellenbosch University (Ros et al., 2014), the combined congress of the South African Soil Science, Horticulture and Agronomy societies (Rozanov et al., 2015), the First Global Soil Map conference, France (Wiese et al., 2013), the 20th International Congress of Soil Science, Korea (Wiese et al. 2014) and were submitted for publication in Geoderma special issue dedicated to digital soil mapping of soil organic carbon following the presentation at the 20th ICSS, Korea (Wiese et al., 2014).


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