The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data
Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms—it helps the soil’s biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is bot...
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2361702 |
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| author | Tessnika Sewpersad Sifiso Xulu Michael Gebreslasie |
| author_facet | Tessnika Sewpersad Sifiso Xulu Michael Gebreslasie |
| author_sort | Tessnika Sewpersad |
| collection | DOAJ |
| description | Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms—it helps the soil’s biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and time-efficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 °C), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area’s average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400–2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale. |
| format | Article |
| id | doaj-art-260a72978c7544c89674a772ea09f17c |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
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| series | Geocarto International |
| spelling | doaj-art-260a72978c7544c89674a772ea09f17c2024-12-10T08:23:09ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2361702The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy dataTessnika Sewpersad0Sifiso Xulu1Michael Gebreslasie2School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South AfricaSchool of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South AfricaSchool of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South AfricaSoil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms—it helps the soil’s biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and time-efficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 °C), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area’s average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400–2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale.https://www.tandfonline.com/doi/10.1080/10106049.2024.2361702SOMspectroscopyPLSR modelraw spectrapre-processed spectra |
| spellingShingle | Tessnika Sewpersad Sifiso Xulu Michael Gebreslasie The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data Geocarto International SOM spectroscopy PLSR model raw spectra pre-processed spectra |
| title | The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data |
| title_full | The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data |
| title_fullStr | The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data |
| title_full_unstemmed | The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data |
| title_short | The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data |
| title_sort | assessment of soil organic matter in the kwazulu natal province of south africa and its relationship to spectroscopy data |
| topic | SOM spectroscopy PLSR model raw spectra pre-processed spectra |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2361702 |
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