Browsing by Author "Wiese, Liesl"

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    Mapping soil organic carbon stocks by combining NIR spectroscopy and stochastic vertical distribution models : a case study in the Mvoti River Catchment, KZN, South Africa
    (Stellenbosch : Stellenbosch University, 2019-03) Wiese, Liesl; Rozanov, Andrei Borisovich; De Clercq, W. P.; Seifert, Thomas; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
    ENGLISH ABSTRACT: The agricultural and environmental importance of maintaining and increasing soil organic carbon (SOC) has been increasingly recognized globally. To a large extent, this recognition can be attributed to soil being the largest terrestrial carbon pool, as well as to soil’s responsiveness to land use and management. Land use and land use change are major factors affecting SOC levels with changes from natural vegetation (forests, grasslands and wetlands) to croplands, for example, causing significant SOC losses. The topsoil (0-30 cm depth) is especially sensitive to changes in land use and management and the highest variation in SOC levels is observed in this zone. In this study SOC stocks in the first meter of soil were quantified and mapped under different land uses and management systems using a vertical SOC distribution model, applying near-infrared (NIR) spectroscopy for SOC analysis and estimating the uncertainty of the maps created using different approaches. The study area was chosen as a quaternary catchment of 317 km-2 south and southeast of Greytown in the Midlands area of KwaZulu-Natal, South Africa. The catchment exhibits complex topography and predominantly shale and dolerite parent material. Soils in the area have high organic carbon content ranging from 0.08 to 22.85 % (mean = 3.48 %), with clay content ranging from 3 to 49 % (mean = 14.7 % clay) and pH(H20) between 3.3 and 6.7 (mean pH(H20) = 4.5). Vertical SOC distribution functions were developed for 69 soil profiles sampled from different land uses (mainly forestry plantations, grasslands and croplands) in and around the study catchment. Bulk density samples were taken at 2.5, 7.5, 12.5, 17.5, 30, 40, 50, 75 and 100 cm depths. The aim was to reduce the number of soil observations required for SOC accounting to one point close to the soil surface by applying negative exponential vertical depth functions of SOC distribution. To achieve this, the exponential functions were normalized using the volumetric SOC content observed close to the surface and grouped as a function of land use and soil types. Normalization reduced the number of model parameters and enabled the multiplication of the exponential decline curve characteristics with the SOC content value observed at the surface to present an adequately represented value of soil carbon distribution to 1 m at that observation point. The integral of the exponential function was used to calculate the soil carbon storage to 1 m. The vertical SOC distribution functions were refined for soils under maize production systems using reduced tillage and conventional tillage. In these soils, the vertical SOC distributions are described by piecewise, but still continuous functions where the distribution within the cultivated layer (0-30 cm) is a linear decline under reduced tillage or a constant value under conventional tillage, followed by an exponential decline to 1 m (30-100 cm). The value of predicting SOC concentrations in soil samples using wet oxidation (WalkleyBlack method) and dry near-infrared (NIR) spectrometry was assessed by comparing them to the dry combustion method. NIR spectrometry is considered to be an especially promising method, since it may be used in both proximal and remote sensing applications. In addition, the effect of using paired samples with single SOC determination versus paired samples with replicated (three times) analysis by all (reference and test) methods was tested. It was shown that the use of paired tests without replication dramatically decreases the precision of SOC predictions of all methods, possibly due to high variability of SOC content in reference values analysed by dry combustion. While reasonable figures of merit were obtained for all the methods, the analysis of non-replicated paired samples has shown that the relative RMSE for the SOC NIR method only falls below 10 % for values above ~8 % SOC. For the corrected SOC Walkley Black method the relative RMSE practically never falls below 10 %, rendering this method as semi-quantitative across the range. It was concluded that for method comparison of soil analysis, it is essential that reference sample analysis be replicated for all methods (reference and test methods) to determine the “true” value of analyte as the mean value analysed using the reference method. Finally, the above elements of vertical SOC distribution models as a function of land use and soil type, predicting SOC stocks to 1 m using only a surface (0-5 cm) sample, and the use of NIR spectroscopy as SOC analysis method were combined to assess the changes in SOC stock prediction errors through mapping. Results indicated a dramatic improvement in precision of SOC stock predictions with increasing detail in the input parameters using vertical SOC distribution functions differentiated by land use and soil grouping. Still, the relative error mostly exceeded 20 % which may be seen as unacceptably high for carbon accounting, trade and tax purposes, and the SOC stock accuracy decreased in terms of map R 2 and RMSE. The results were generally positive in terms of the progressive increase in complexity associated with SOC stock predictions and showed the need for a substantial increase in sampling density to maintain or increase map accuracy while increasing precision. This would include an increase both in surface samples for the prediction of SOC stocks using the vertical SOC distribution models, as well as an increase in the sampling of profiles to include more soil types and increase the profile density per land use to improve the vertical SOC prediction models.
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    Spatial soil information in South Africa : situational analysis, limitations and challenges
    (Academy of Science of South Africa, 2015) Paterson, Garry; Turner, Dave; Wiese, Liesl; Van Zijl, George; Clarke, Cathy; Van Tol, Johan
    Soil information is vital for a range of purposes; however, soils vary greatly over short distances, making accurate soil data difficult to obtain. Soil surveys were first carried out in the 1920s, and the first national soil map was produced in 1940. Several regional studies were done in the 1960s, with the national Land Type Survey completed in 2002. Subsequently, the transfer of soil data to digital format has allowed a wide range of interpretations, but many data are still not freely available as they are held by a number of different bodies. The need for soil data is rapidly expanding to a range of fields, including health, food security, hydrological modelling and climate change. Fortunately, advances have been made in fields such as digital soil mapping, which enables the soil surveyors to address the need. The South African Soil Science fraternity will have to adapt to the changing environment in order to comply with the growing demands for data. At a recent Soil Information Workshop, soil scientists from government, academia and industry met to concentrate efforts in meeting the current and future soil data needs. The priorities identified included: interdisciplinary collaboration; expansion of the current national soil database with advanced data acquisition, manipulation, interpretation and countrywide dissemination facilities; and policy and human capital development in newly emerging soil science and environmental fields. It is hoped that soil information can play a critical role in the establishment of a national Natural Agricultural Information System.