Thesis (M.Sc. (Soil Science)) -- University of Limpopo, 2022 / In South Africa, land use planning has received limited attention in areas perceived as suitable for agricultural production. In the lack of reliable soil type and fertility status information, crop yields remain lower than the land’s potential, with subsequent land degradation. Despite this, studies that focused on land capability and soil suitability to date have not considered the spatial variability of the soil nutrients and factors influencing their variability. However, this information is key for site-specific soil management. Therefore, it is vital to link land capability and soi suitability with the spatial variability of soil nutrients as it opens opportunities for more rational management of the soil resources since soil nutrients directly affect crop growth and consequently yield. To address this issue, a study was conducted on a 12 ha banana plantation portion of the Makuleke farm. The main objectives of this study were to (1) survey, classify and characterise soils in order to derive and map land capability classes of Makuleke farm, (2) quantify the physical and chemical properties of the soils in order to derive and map the soil suitability of Makuleke farm for banana production, (3) assess the spatial variability and structure of soil nutrients across the Makuleke farm and (4) Identify the factors of control of the spatial variability of the soil nutrients across the Makuleke farm. To begin with, a field soil survey was conducted using transect walks complemented by auger observations to sub-divide the 12 ha banana plantation portion of the farm into varied soil mapping units. Thereafter, soil classification was done to group soils based on their morphological properties and pedological processes. During soil classification, a total of 12 representative profile pits (1.5 m × 1.5 m long × 2 m deep/limiting layer) were excavated, studied, described, and sampled. At each profile pit, three replicates samples were collected at 0 – 30 cm depth intervals giving rise to 36 bulk soil samples. From the gathered soil profile information, four soil units were thus delineated and identified across the 12 ha banana plantation. For soil fertility assessment, a grid sampling strategy at 50 × 50 m was adopted to collect the samples across the 12 ha banana plantation. A total of 27 composite samples were collected at the nodes of the grid, and thereafter bagged, labelled, and transported to the laboratory. In the laboratory, all collected samples were air-dried and sieved using a 2 mm sieve in preparation for soil physical and chemical properties analysis. The land capability assessment of Makuleke farm was done using the concepts and principles of the FAO framework for Land Evaluation (FAO, 1976), but adapted to South African conditions by Smith (2006). Soil suitability assessment was done using the FAO framework for Land Evaluation (FAO, 1976) coupled with the guidelines for rainfed agriculture (FAO, 1983) and the criteria proposed by Sys et al. (1993) and Naidu et al. (2006). To assess the spatial variability and structure of the soil nutrients across the farm, classical and geostatistical techniques were employed respectively. A correlation matrix was employed to identify key factors influencing the spatial variability of soil nutrients across the farm. For interpolation, ordinary kriging was used to generate soil nutrient spatial distribution maps. In this study, four soil forms were identified and classified as Hutton, Westleigh, Glenrosa, and Valsrivier, which are broadly distinguished as Lixisols, Plinthosols, Leptosols, and Cambisols. Land capability results revealed that 17% of the 12 ha portion of the farm has very high arable potential (I), 60% of the farm has medium arable potential (III), 6% has low arable potential (IV) and 17 % is non-arable (VI), which might explain the varied banana yields in the farm. Soil suitability analysis revealed that 12% of the 12 ha farm is highly suitable (S1), 34% is moderately suitable (S2), 38% is marginally suitable (S3) and 16% is permanently not suitable (N2) for banana production. The low arable and marginally suitable portion of the farm was under Valsrivier soils which were limited by its shallow depth, shallow rooting depth, acidic soil pH, low organic carbon (OC), and the fact that it was located on a steeper slope gradient. The non-arable and not suitable portion of the farm for banana production was under Glenrosa and it was limited by its location on a steep slope gradient and was characterised by shallow effective rooting depth, low OC, low clay content, and acidic soil pH. Classical statistical techniques revealed that phosphorus (P), potassium (K), calcium (Ca), zinc (Zn), manganese (Mn), and copper (Cu) content varied highly across the banana plantation, while magnesium (Mg) and total nitrogen (TN) varied moderately. In addition, the geostatistical analysis revealed that spatial dependency was weak (Ca, Cu, and TN), moderate (Mg and Zn), and strong (P, K, and Mn) for the different soil nutrients across the 12 ha banana plantation. Soil nutrients with strong spatial dependency have a good spatial structure and are easily manageable (in terms of fertilisation, liming, and irrigation) across the farm compared to the ones with weak spatial dependency which have a poor structure. This study also found that land attributes, which are soil type and topographic position were the main factors driving the spatial variability of the soil nutrients across the farm. In terms of soil type, soils such as Valsrivier and Glenrosa with 2:1 clay-type smectite were the ones that had nutrient content compared to soils with 1:1 clay-type kaolinite (e.g., Westleigh and Hutton). Higher nutrient contents were also observed in the footslope position compared to the middleslope of the farmland. Correlation analysis revealed that Mn was the key polyvalent cation influencing the spatial variability of P, K, and Zn. Soil pH and effective cation exchanges capacity (ECEC) were the key soil factors driving the spatial variability of Ca, while ECEC was the key factor affecting the spatial variability of Mg. Moreover, the spatial variability of soil Mn and Cu was driven by soil Cu and clay content, respectively. The kriged maps showed that P, Mg, Zn, and Mn were high in the northeast part and low in the northwest part of the farm. Similarly, K and Ca were low in the northwest part, but they were high in the south to the southwest part of the study area. Total nitrogen was high in the west part and low in the east-northeast part, while Cu was evenly distributed across the plantation. This study highlights the importance of prior land use planning (i.e., land capability and soil suitability) and fertility assessment for agricultural production. The research results obtained provide the actual reference state of the capability of the land for arable farming and soil suitability for banana production at Makuleke farm. Moreover, the research results provide the spatial variability and structure of the soil nutrients which have a greater impact on the growth and yield of bananas. The results obtained in this study will be useful for site-specific management of soil nutrients and other soil management practices (e.g., irrigation, fertilisation, liming, etc.), developing appropriate land use plans, and quantifying anthropogenic impacts on the soil system and thus improving land productivity. / National Research Foundation (NRF)