The Surface Mining Control and Reclamation Act (SMCRA) of 1977 requires that mine operators reclaim mined land to achieve productivity levels equal to or greater than premined conditions. Presently, the standard for evaluating reforestation success is based solely on tree-seedling survival. This method is an estimator of stand density and not an indication of site productivity. There exists a need to evaluate mine soils based on their capability of growing merchantable timber. This model would aid the reclamation process by providing a means for assessing mine soils based on their quality and productivity. / Master of Science

The goal of this diploma thesis is to apply the calculation methods of productivity in the level of national economics and company economics. Subsequently we found out the results of particular productivity and evaluate their development in both levels. Afterwards we compared individual results of the company with industry.

1. Seventy-five samples of soils were taken in Washington County, Virginia. These samples were taken while the soil survey was in progress. In this group thirty-one different soil types were included. The samples were taken from all parts of the county. Notes were made of the soil type, slope of the land, drainage conditions, erosion, and land utilization. When samples were taken from cultivated fields, the yield of the crop was estimated. When samples were taken from pasture, notes were made of the type and condition of the vegetation. 2. The following properties were determined and studied in relation to soil type and plant growth: pH, available phosphate, available potash, percent organic matter, percent colloids, base exchange capacity, exchangeable hydrogen and present base saturation. The results of these determinations were used in conjunction with the physical properties, which had been determined by the soil survey, in studying the various relationships. 3. The laboratory methods of determination were devised by soil investigators. The reliability of these methods was discussed by means of reference to literature. 4. Soils of the same textural class varied widely in percentage colloids. 5. Soils that are relatively high in available potash varied widely in base exchange capacity, pH, percent base saturation, and available phosphate. 6. Soils at like pH values showed no relationship of percent organic matter to percent base saturation. 7. Soils above pH 6.0 contained 125 pounds per acre or more available potash; they were above 65 percent base saturation and were widely variable in available phosphate and organic matter. Soils at similar pH values showed wide variations in available phosphate. 8. Within the same textural class, and under similar conditions of land utilization, soils varied widely in percent organic matter. 9. There was some variation in pH values of residual soils of limestone origin. However, the average values of five samples of each were very close together. The average for Dunmore was 5.4, Hagerstown 5.5, and Clarkesville 5.6. 10. Available phosphate and potash, as well as pH, were closely related to yield of corn and tobacco. 11. Available phosphate and closely related to quality of meadow. In soils where available phosphate was above 50 pounds per acre quality of meadow was good. Potash was present in sufficient quantity not to be a limiting factor. 12. A close relationship was found between some of the soil properties studied. The coefficient of correlation between base exchange capacity and percent organic matter was .7191 ± .0376; between pH and exchangeable hydrogen -.6500 ± .0450; between pH and percent base saturation .9008 ± .0147. This high degree of correlation of pH and percent base saturation, it appears, would hold only for a large number of soils. Within this group, soils at like pH values vary considerably in percent base saturation. There was little relationship between percent colloids and base exchange capacity. 13. In all cases, in pasture soils, where available phosphate was below nine pounds per acre, pastures were of poor quality. There was a gradual decline in the minimum range of available phosphate and potash in the soils from the good, medium and poor quality pastures. There is also a gradual decline in the average pH values as quality of pasture declines. 14. Evidence is shown that, within a soil type, the properties of the soil, under varying conditions of soil management vary widely. The soil type name gives no indication of the fertility of the soil at the present time. However, associated with the type name are certain physical properties which determine, largely, the possibilities or limitations of that soil type, or the degree of productivity that the type may be built up to under ideal conditions of soil management. / Master of Science

Land degradation and soil nutrient depletion have become serious threats to agricultural productivity in sub-Saharan Africa. Soil fertility depletion in smallholder areas has been cited as the fundamental biophysical cause of declining per-capita food production in Africa. Manure application is a well established and known practice, but not effectively used among South African smallholders. This study investigated the practice, constraints and perceptions of improving soil quality through manure application through a case study of three smallholder farmer groups. Three groups from rural areas of KwaZulu-Natal (Mkhambatini, Mooi River and Richmond) were selected to participate in the study. Participatory methodologies were used to identify and clarify the study problem. Three participatory focus group discussions, one per area, were conducted with farmers at the study sites to discuss farming methods, experience and perceptions of manure use, manure management practices and constraints farmers experience with manure use. Force Field Analysis was used for each group to explore for forces against and in support for manure use. Random soil and manure samples were collected for laboratory analysis to determine fertility levels. Some farmers indicated that soil fertility was low. However, half the sample perceived the land to be productive to some extent. The study showed that 40 per cent of farmers reported improved soil fertility following the application of manure. Due to the limited availability of livestock manure, farmers prefer to use both livestock manure and commercial fertilisers. Furthermore, the study found that except for young farmers (20 per cent of the sample), farmers had not received formal training and very limited extension advice on composting and manure use and management. The study participants were aware of the consequences of declining soil fertility and were attempting to improve soil quality. However, low livestock numbers and poor management led to inadequate amounts of manure, and, limited access to information on manure and compost use. Unless better knowledge of optimal soil nutrient management practice is acquired by the farmers, soil fertility levels will continue to decline, further reducing production potential and rural household food security. Government needs to revisit extension support to meet the needs of smallholders and offer training on sound soil management, sustainable production methods, composting and livestock management. A handbook with graphic detail should be accompanied to provide smallholders with information and advice on how to manage soil fertility. / Thesis (M.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.

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)

The link between aboveground net primary productivity (ANPP) and resource gradients generated by complex terrain (solar radiation, nutrients, and moisture) has been established in the literature. Belowground ecosystem stocks and functions, such as soil organic carbon (SOC), dissolved organic carbon (DOC), and belowground productivity have also been related to the same topography and resource distributions, and therefore it is expected that they share spatial and temporal patterns with ANPP. However, stand structure on complex terrain is a function of multiple trajectories of forest development that interact with existing resource gradients, creating feedbacks that complicate the relationships between resource availability and ANPP. On a 96 ha forested watershed in the H.J. Andrews Experimental Forest in the Western Cascades range of Oregon, spatiotemporal heterogeneity in the secondary succession of a replanted Pseudotsuga menziesii stand following harvest results from the interaction of stand composition and abiotic drivers and may create unique "hot spots" and "hot moments" that complicate gradient relationships. In this dissertation, I tested the hypotheses that (chapter 3) multiple successional trajectories exist and can be predicted from a general linear model using specific topographic, historical, and biological parameters and that an estimated "maximum ANPP" may better represent stand characteristics than ANPP measured at a particular moment in time. I also test that (chapter 4) the distribution of light fraction carbon (LFC; C with a density of less than 1.85 g/cm��) is spatially variable, elevated on hardwood-initiated sites (hardwood biomass > 50% of biomass), and positively correlated with litter fall and ANPP. Chapter 4 also tests that heavy fraction carbon (HFC; C with a density of greater than 1.85 g/cm��) is a function of both soil mineralogy, stand composition, and ANPP, such that edges observed spatially in site mineralogy (changes in soil type) are reflected in sharp changes in the composition of the forest community and the magnitude of HFC stores. Finally, I hypothesized (chapter 5) that in complex terrain, dissolved organic carbon (DOC) export can be predicted from landform characteristics, relates to ANPP, and may be measured by several methods which are well-correlated with one another. In chapter 6, I discuss how litter fall measurements can be extrapolated to a watershed extent, and use litter fall as an example of the error that can occur in scaling up measurements taken at a small scale, within a heterogeneous stand on complex terrain, to a landscape scale extent. / Graduation date: 2013

The physical and chemical properties of soils can greatly influence the vegetation patterns in a landscape. This is especially so through the effect that particular characteristics of soils have on the water balance and nutrient cycling in savanna ecosystems. Areas in the savanna environment found in Hluhluwe iMfolozi Park have experienced a number of changes in the vegetation patterns observed. This study, therefore, looks at the effect that soil characteristics may have on the vegetation growth in this area and on the changes that have taken place over time. Fixed-point photographs, taken every four years, were used to choose fourteen sites in the Park, which showed either a ‘change’ or ‘no-change’ in vegetation from 1974 to 1997. The sites consisted of four which had ‘no-change’ in vegetation, two sites with a slight increase (5- 20%) in tree density, three sites with a greater increase in tree density (>20%), two sites with a slight decrease in tree density (5-20%), and three sites with a greater decrease in tree density (>20%). Transects were then carried out at each site, in which the soil was classified to the form and family level. Each horizon was then sampled and the field texture, structure, Munsell colour and depth of each horizon and profile recorded. The data recorded in the field were statistically analysed through a principal component analysis (PCA). The type of horizon, horizon boundary, structure type, colour group and depth for the top and subsoil were included in the models and were analysed with the number given to each site for each of the three sections of the Park, namely Hluhluwe, the Corridor and iMfolozi. The most prominent textures at all sites were sandy loam, loam, clay loam and silt loam for both the top and subsoil for all site categories. The texture classes were also compared across the Hluhluwe, Corridor and iMfolozi sections. The dominant textures in the Hluhluwe and Corridor sections are loam, clay loam and silt loam for both top and subsoils. Sites sampled in the iMfolozi section appear to have textures mainly associated with the clay loam and sandy loam classes. The structure classes of the soil including sub-angular blocky, granular and crumb which are associated with a moderate structure appear to be the most dominant type in all categories for the topsoil; single-grain and sub-angular blocky classes the main types for the subsoil. Generally the colour of the soil at all the sites sampled was yellower than 2.5YR and the values and chromas mostly fell within the range of 3-5 and 2-6, respectively. This is also shown in the PCA results obtained, which associate particular soil characteristics with the various sites sampled for the different vegetation change categories investigated. The samples collected were also analysed in the laboratory after being air-dried. The laboratory analysis included measurements of pH, exchangeable acidity, organic carbon, extractable phosphorus, particle size distribution and cation exchange capacity (CEC). The data recorded in the laboratory were also analysed by PCA. This was used to determine which soil properties are associated with the particular sites investigated. The pH of the soil, in all areas, fell within a wide range. The pH is influenced by the rainfall in the area and thus sites sampled in the Hluhluwe section are more acidic than those sampled in the Corridor and iMfolozi sections. The topsoils had a higher pH for all the samples and were in the range between 5 and 7. The exchangeable acidity measurements were low, although they were higher in the subsoil as opposed to the topsoil. The nutrient contents did not appear to vary greatly between the different sites in the Park. Generally extractable phosphorus, CEC and organic carbon were low across the Park. The particle size analysis showed that the clay percentage increases between the top and subsoil for all the sites sampled. The silt and various fractions of sand percentages vary across all sites and are lower than the clay percentage at all sites except the A horizon of the ‘slight increase’ sites. The ‘no-change’, and ‘increase’ sites have a higher percentage of clay as compared to the silt and sand fraction for both the A and B horizon. The ‘slight increase’ sites have a higher percentage of sand in the A and B horizon, the ‘slight decrease’ sites have a more equal percentage between the sand, silt and clay fractions in the A horizon and a greater percentage of clay in the B horizon. The ‘decrease’ sites have a greater percentage of clay and silt in the A and B horizon. While certain soil properties have a definite effect on the plant growth, no relationship between specific soil properties and vegetation changes was shown. However, it is likely that the soil structure and texture affect the vegetation patterns, through their influences on the water and nutrient holding capacity. With an increase in the clay percentage and more strongly structured soils, plants can access more water and nutrients and this will increase the tree density in an area. However, the recent changes in the vegetation patterns observed in the Park appear to be more associated with other environmental factors. The soil properties analysed would have generally been more constant at the sites sampled, particularly over the relatively short period of time in this study. Therefore, the changes which were recorded in the fixed-point photographs would have been enhanced by other factors experienced in the Park, including fire and the effect that grazers and browsers have on the vegetation. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

This thesis studies the role accounting plays in the monitoring and reporting of soil quality in one sector of the agricultural industry, broadacre farming. A survey was conducted with broadacre farmers in the Loddon Catchment, Victoria, Australia. The primary aim was to determine the effectiveness accounting plays in providing information to decision makers relative to the productive capacity in soil quality and not just on profits. The capital asset in this study was defined as soil quality. Soils and soil quality in particular, are major elements in determining land value. The concern is decisions are being made by potential buyers and other decision makers, particularly policy makers, with regards to soil quality on the basis of incomplete and often misleading information. It is proposed that a major reason is due to the fact that different participants in the agricultural and accounting industries require and use different information. The accounting systems used by farmers are those that have been developed for the manufacturing sector which may not be appropriate for managing long-term, complex resources such as soil. The farmers themselves did not find formal accounting reports useful for decision making because these reports are based on uniform standards and market prices. The topic of soil quality and land degradation is viewed from two perspectives. In one perspective, the proprietary view; the accounting emphasis is on the ownership of assets and the change, both in income and capital, in these assets over time. In this case the accounting equation is seen as assets - liabilities = equities. The proprietor takes all the risk. A more recent perspective in accounting, the entity view, emphasises the assets whether financed from equity or debt and where the accounting equation is seen as assets = equities. The emphasis changes to the income flow from these assets and more interest is shown in current market prices as a reflection of the future value of these assets Profit is not necessarily a good indicator of what farmers are doing for their capital asset. There needs to be greater emphasis on costs undertaken for the conservation of soil. Those costs should be considered an investment and put into the balance sheet and not the profit and loss statement. The major finding of study demonstrates that decision making groups have different