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
| dc.contributor.advisor | Rozanov, Andrei Borisovich | en_ZA |
| dc.contributor.advisor | De Clercq, W. P. | en_ZA |
| dc.contributor.advisor | Seifert, Thomas | en_ZA |
| dc.contributor.author | Wiese, Liesl | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science. | en_ZA |
| dc.date.accessioned | 2019-02-19T17:53:44Z | |
| dc.date.accessioned | 2019-04-17T08:04:20Z | |
| dc.date.available | 2020-02-19T03:00:08Z | |
| dc.date.issued | 2019-03 | |
| dc.description | Thesis (PhDAgric)--Stellenbosch University, 2019. | en_ZA |
| dc.description.abstract | 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. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: Die landbou- en omgewingsbelang van die handhawing en toename van grondorganiese koolstof (GOK) word wêreldwyd toenemend erken. Tot ‘n groot mate kan hierdie erkenning toegeskryf word aan grond wat uit die grootste aardse koolstofpoel bestaan, sowel as die grond se responsiwiteit op grondgebruik en bestuur. Grondgebruik en grondgebruikverandering is belangrike faktore wat GOK-vlakke beïnvloed, met byvoorbeeld veranderinge van natuurlike plantegroei (woude, grasveld en vleilande) na gewaslande wat beduidende GOK-verliese tot gevolg het. Die bogrond (0-30 cm diepte) is veral sensitief vir veranderinge in grondgebruik en bestuur en die hoogste variasie in GOK-vlakke word in hierdie sone waargeneem. In hierdie studie is GOK-inhoud in die eerste meter grond gekwantifiseer en gekarteer onder verskillende grondgebruike en bestuurstelsels deur gebruik te maak van 'n vertikale GOK-verspreidingsmodel, die toepassing van naby-infrarooi (NIR) spektroskopie vir GOKanalise en die bepaling van die onsekerheid van die kaarte wat geskep is deur verskillende benaderings. Die studiegebied is gekies as 'n kwaternêre opvanggebied van 317 km-2 suid en suidoos van Greytown in die KwaZulu-Natalse Middellande, Suid-Afrika. Die opvanggebied vertoon komplekse topografie en oorheersende skalie- en dolerietmateriaal. Grond in die gebied het 'n hoë organiese koolstofinhoud van 0,08 tot 22,85 % (gemiddeld = 3,48 %), met kleiinhoud wat wissel van 3 tot 49 % (gemiddeld = 14.7 % klei) en pH (H20) tussen 3,3 en 6,7 (gemiddelde pH(H20) = 4.5). Vertikale GOK-verspreidingsfunksies is ontwikkel vir 69 grondprofiele wat in verskillende grondgebruike (hoofsaaklik bosbouplantasies, grasveld en gewaslande) in en om die opvanggebied gemonster is. Bulk digtheid monsters is geneem op 2,5, 7,5, 12,5, 17,5, 30, 40, 50, 75 en 100 cm dieptes. Die doel was om die aantal grondwaarnemings wat nodig is vir GOKrekeningkunde tot een punt naby die grondoppervlak te verminder deur negatiewe eksponensiële vertikale diepte funksies van GOK verspreiding toe te pas. Om dit te bereik is die eksponensiële funksies genormaliseer met die volumetriese GOK-inhoud wat naby aan die oppervlak waargeneem word en gegroepeer as 'n funksie van grondgebruik en grondtipes. Normalisering het die aantal modelparameters verminder en moontlik gemaak om die die eksponensiële afname kurwe eienskappe met die GOK inhoud op die oppervlak te vermenidgvuldig ten einde 'n voldoende verteenwoordigende waarde van grondkoolverspreiding tot 1 m by daardie waarnemingspunt te bepaal. Die integraal van die eksponensiële funksie is gebruik om die grondkoolstofopberging tot 1 m te bereken. Die vertikale GOK-verspreidingsfunksies is verfyn vir grond onder mielieproduksiestelsels wat verminderde bewerking en konvensionele bewerking toepas. In hierdie gronde word die vertikale GOK-verdelings deur stuksgewyse, maar steeds deurlopende funksies beskryf. Die GOK-verspreiding binne die bewerkingslaag (0-30 cm) toon 'n lineêre afname onder verminderde bewerking en konstante waarde onder konvensionele bewerking, gevolg deur 'n eksponensiële afname tot 1 m (30-100 cm). Die waarde van die voorspelling van GOK konsentrasies in grondmonsters deur gebruik te maak van nat oksidasie (Walkley-Black metode) en droë naby-infrarooi (NIR) spektrometrie, is beoordeel deur dit met die droëverbrandingsmetode te vergelyk. NIR-spektrometrie word beskou as 'n besonder belowende metode, aangesien dit in beide proksimale en afstandswaarneming toepassings gebruik kan word. Daarbenewens is die effek van die gebruik van gepaarde monsters met enkele GOK-bepaling versus gepaarde monsters met herhaalde (drie keer) analise met alle (verwysings- en toets) metodes getoets. Daar is getoon dat die gebruik van gepaarde toetse sonder replikasie die presisie van GOK-voorspellings van alle metodes dramaties verminder, moontlik as gevolg van die hoë veranderlikheid van GOK - inhoud in verwysingswaardes wat deur droë verbranding ontleed word. Terwyl redelike merietesyfers vir al die metodes behaal is, het die ontleding van nie-gerepliseerde gepaarde monsters getoon dat die relatiewe RMSE vir die GOK NIR-metode slegs onder 10 % val vir waardes bo ~8 % GOK. Vir die gekorrigeerde SOC Walkley Black-metode val die relatiewe RMSE feitlik nooit onder 10% nie, wat hierdie metode as semi-kwantitatief oor die reeks lewer. Daar is tot die gevolgtrekking gekom dat, vir die vergelyking van grondanalisemetodes, dit noodsaaklik is dat die verwysingsmonster analise vir alle metodes (verwysings- en toetsmetodes) herhaal word (ten minste drie keer) om die "ware" waarde van analiet te bepaal as die gemiddelde waarde wat met behulp van die verwysingsmetode geanaliseer is. Ten slotte is die bogenoemde elemente van vertikale GOK verspreidingsmodelle, te wete as 'n funksie van grondgebruik en grondtipe, wat SOC-voorrade vir 1 m voorspel met slegs 'n oppervlakmonster (0-5 cm) en die gebruik van NIR-spektroskopie as GOK-analise metode, gekombineer ten einde die veranderinge in GOK-voorspellingsfoute deur kartering te evalueer. Resultate dui op 'n dramatiese verbetering in die akkuraatheid van GOKvoorspellings met toenemende detail in die insetparameters deur vertikale GOKverspreidingsfunksies te gebruik wat gedifferensieer word as ‘n funksie van grondgebruik en grondgroepering. Tog het die relatiewe fout meestal 20% oorskry, wat as onaanvaarbaar hoog vir koolstofrekeningkunde, handels- en belastingdoeleindes beskou kan word, en die GOK-voorraad akkuraatheid het verminder in terme van kaart R2 en RMSE. Die resultate was oor die algemeen positief in terme van die progressiewe toename in kompleksiteit wat in verband met GOK-voorspellings en toon die behoefte aan 'n aansienlike toename in monsternemingsdigtheid om die akkuraatheid van kaarte te behou of te verhoog. Dit sal 'n toename in oppervlakmonsters insluit vir die voorspelling van GOK-voorrade deur die vertikale GOK-verspreidingsmodelle te gebruik, asook 'n toename in die monsterneming van profiele om meer grondsoorte in te sluit en die profieldigtheid per landgebruik te verhoog ten einde die vertikale GOK voorspellingsmodelle te verbeter. | af_ZA |
| dc.description.version | Doctoral | en_ZA |
| dc.embargo.terms | 2020-02-19 | |
| dc.format.extent | xx, 119 leaves : illustrations (some color), maps (some color) | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/105603 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Digital soil mapping | en_ZA |
| dc.subject | Soils -- Carbon content | en_ZA |
| dc.subject | Agricultural ecology | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Soil mapping -- South Africa -- Kwazulu-Natal | en_ZA |
| dc.subject | Humus | en_ZA |
| dc.subject | Near infrared reflectance spectroscopy | en_ZA |
| dc.subject | Soil organic matter | en_ZA |
| dc.title | 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 | en_ZA |
| dc.type | Thesis | en_ZA |