Soil Mapping Units Research Articles

Updating the national soil map of Nepal through digital soil mapping

While most legacy soil maps are available at coarse spatial detail with composite mapping units, high resolution and detailed soil maps are desired for various land resource applications. In time and resource constrained circumstances, the application of disaggregation methods and modelling approaches that capitalise on existing, less detailed soil maps is an important alternative method for a more rapid generation of soil maps at finer resolutions. A legacy soil map of 1:1,000,000 scale for Nepal was disaggregated using “Disaggregation and Harmonisation of Soil Map units through Resampled Classification Trees” (DSMART) tool with the C5.0 classification tree algorithm and an area proportional virtual sampling technique. Environmental covariates sourced from remote sensing, digital elevation model, climatic databases, and national databases were used for predictive mapping of soils. The predicted map was found to show more detailed soil information in comparison to the original soil map. Accuracy assessment with independent datasets showed that the overall accuracy of prediction was 40.4% (51.2% on 3x3 window) while considering the level of Reference Soil Groups only, and 22.1% (32.6% on 3x3 window) for the soil groups with 1st principal qualifier. Geology was the most important covariate, followed by the minimum temperature of the coldest month, elevation, valley depth and land cover. Amidst the scarcity of spatially explicit detailed soil information, this disaggregated soil map can be a useful resource as a more detailed version of the legacy soil map of Nepal for individuals concerned with research, planning and management of land resources. Environmental covariates used in this study may be useful when disaggregating soil maps in similar environmental settings.

Properties and carbon stocks of tropical tidal peat soils deposited on coral limestone in Numfor and Supiori Islands, Papua Province

Tidal peatlands in Numfor and Supiori Island, Papua are unique because it accumulates above coral limestone on the tidal environment. The substratum layer is solid coral limestone (lithic contact). The objective of this study was to determine the characteristics and carbon stocks in these areas. The soil samples were collected from each horizon for chemical and physical analysis. Carbon stocks estimation was calculated by multiplying peat thickness, area, bulk density, and organic C content in each soil map unit. The results showed that tidal peat soil reaction was acid to neutral (pH H2O 4.9 to 7.3), while soil salinity was very high (8.32 to 22.3 dS m−1). The cation content is very high in the order of dominance of Na> Ca> Mg> K. Peat thickness varied from shallow to deep (50 to <300 cm). Soil organic C content ranged from 23.03 to 46.99% and bulk density ranged from 0.12 to 0.36 g cm−3. The average of carbon stock in each peat soil map unit was 1.151 to 1.314 t ha−1. Peat soils deposited on coral limestone in this region should be preserved as conservation areas due to its functions as a carbon sink and part of a tidal hydrological unit.

Soil Quality Assessment of the Lower Niger River Plain Alluvial Soils in Bayelsa State, Nigeria, for Sustained Productivity

Floodplain soils worldwide are very useful for agricultural production and food security. But, the floodplains of Bayelsa State are under-utilized due to lack of technical information and knowledge on the nutrient status and other soil characteristics. This study therefore, assessed the soil quality of lower Niger River floodplain soils in Bayelsa State, Nigeria for sustained productivity and food security. Alluvial soils from different river plains in Bayelsa State, Southern Nigeria were characterized and fertility assessed. Pedogenic soil samples from the levee crest, middle slope, lower slope and recent alluvial soils from the channels of present active rivers were collected from identified genetic horizons of soil profiles and analyzed for physical and chemical properties using standard methods and the soils fertility status evaluated using Soil Fertility Index (SFI) and Soil Evaluation Factor (SEF). The soils were dominantly silt loam followed by silty clay loam and loam except Elemebiri 3 (ELM3) and Trofani 3 (TFN3), dominated by loamy sand and sandy loam. pH (5.31-7.00) was moderately acid to neutral and organic matter content, generally low to moderate, ranging from 0.19-3.88%. Total N values was also low while available P was low to moderate. The exchange complex was dominated by Ca²⁺ and ECEC values were low. The SFI values were higher than the SEF values in al the Soil Mapping Units (SMUs), contributed by pH, organic matter and phosphorus. Both SFI and SEF values decreased with increase in depth for all the soil mapping units except Trofani 1 (TFN1) where the bottom layers recorded higher SFI values and Elemebiri 3 (ELM3) for SEF. Though the soils nutrient concentration was generally low to moderate, both SFI and SEF evaluated the soils as fertile contributed mainly by organic matter, available P and pH. Since biomass accumulation contributed greatly to improvement of soil fertility and physical structure, soil organic matter maintenance should be given top priority in these soils for sustained productivity and food security.

Irrigation suitability evaluation of Zamra irrigation scheme, Abergelle District of Wag-Himra Zone, Amhara Region Ethiopia

BackgroundAssessment of suitable lands for irrigation is indispensable to plan and implement different programs in a cost-effective approach for sustainable land use planning and efficient utilization of the land. Hence selecting proper land use based on land suitability evaluation will enhance the productivity per unit area and ensures the sustainable use of land resources. Therefore, the research was designed to evaluate the suitability of the soil properties of Zamra irrigation scheme for irrigation purposes.MethodFor this study about 195.16 ha of land were evaluated for irrigation suitablity. Four soil profile pits were opened in the representative site of the scheme based on slope, texture, soil color and soil depth. Soil samples were collected from the soil mapping units for analysis of texture, electrical conductivity, and calcium carbonate content, whereas slope, drainage and soil depth were directly measured at the field. The evaluation was carried out using a parametric evaluation method.ResultsThe results of the study revealed that four types of soils were identified namely, Rehodic Nitisol(eutric), Haplic Regosol (eutric) Eutric Regosol, Leptic Regosol (eutric, loamic), and Vertic Cambisol (Hypereutric). The soil texture consists of sandy loam and sandy clay loam at the surface and subsurface soil and the soil depth of study area varied from shallow to very deep. The soil of the scheme was free from salinity and the status of calcium carbonate was at a low level. The slope of the study area was ranged from flat (0–3%) to slopping (8–15%). Generally, 80.64 ha (41.32%) of the scheme was moderately suitable whereas 71.68 ha (36.73%) of the land was marginally suitable for irrigation. Due to major limiting factors of soil depth and slope about 42.84 ha of the land (21.95%) were not currently suitable for irrigation.ConclusionIn the study area, the major limitations were slope and soil depth. Therefore, to increase production and productivity of the scheme, land improvement operations are suggested.

Harmonization of a large-scale national soil database with the World Reference Base for Soil Resources 2014

Legacy soil data currently represents an irreplaceable source of data for many national and international applications. Their full use at the international level largely depends on adequate harmonization. In this study, we adopted different methods of taxonomic distance and reclassification of soil properties to convert a large-scale database originating from an extensive national mapping campaign of agricultural soils in Czechoslovakia (1961–1971), according to the criteria of the World Reference Base for Soil Resources (WRB), version 2014. The harmonization was effectuated at two levels of detail represented by two different types of soil mapping units [soil type and general soil representative (GSR)] and resulted in the conversion of original soil units into their counterparts in the WRB (Reference Soil Groups). The results showed a good potential of the adopted methods for soil data harmonization. The conceptual differences of the two harmonised classifications decreased the applicability of the taxonomic distance in the case of broadly defined and less developed soil classes lacking specific diagnostic features. The replacement of a simple conversion by soil association was a relevant approach, especially at the soil type level, where the simple one-to-one conversion showed only poor accuracy (39.5%). The accuracy at the GSR level was satisfactory (reaching ca 60%) and increased with soil association (72%). The conversion accuracy of different soil classes significantly differed from very high (e.g. 92% in Luvisols) to completely mismatched (Technosols and Histosols). The latter can be attributed to a questionable determination of soil classes in legacy maps. The study showed the potential of taxonomic distance methods in soil data harmonization and the limits of historical databases resulting from the way data were collected and recorded.

The use of continuous soil diagnostic layers as criteria for differentiation of soil map units

This article presents a method to produce accurate soil maps according to the numerical correlation of experimental data obtained from field description and soil laboratory analyses. At first, the diagnostic horizons and characteristics of all the 56 studied profiles were identified based on the standard definitions of the US soil classification system. In the next step, six thematic maps were prepared by interpolating the thickness of diagnostic horizons and/or the depth of the upper/lower boundaries of the soil layers with diagnostic characteristics. Afterward, the thematic maps were intersected to obtain the final soil map in this approach. The produced map with the present approach was compared with the conventional geopedologic soil map. The results showed that the geopedological approach could delineate 12 soil map units as consociation and 8 units as association while using the diagnostic layers as thematic maps were able to delineate all of the 20 soil map units as consociation. This approach introduced how the soil properties could be applied as a covariate in the SCORPAN model. Using interpolated soil, diagnostic layers could increase the precision and quality of soil maps.

Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations

AbstractAccurately identifying the soil map unit component at a specific point‐location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone‐based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non‐soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling‐location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth‐wise variability (i.e., depth‐support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert‐Plus) representing different levels of detail and three types of depth‐support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert‐Plus dataset (53–60%). This study also showed minimal effect from the type of depth‐support used to represent depth‐wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

Map of Anthropogenic Soil Erosion of Russia

The last map of anthropogenic soil erosion in Russia was compiled more than 20 years ago on a scale of 1 : 5 000 000. In recent years, additional data on soil erosion have been accumulated. New mapping approaches have appeared that enable us to present more detailed digital data at the country level. The geometric part of the Unified State Register of Soil Resources of Russia (on the initial scale of 1 : 2 500 000) (USRSRR) was used as the basis for map updating. Information on soil erosion was read from the old erosion map for each delineated soil area of the USRSRR. Then it was expertly analyzed and corrected, using the data on soil properties in each soil mapping unit. After that, the information on anthropogenic soil erosion was corrected based on the soil plowing map of Russia and the map of pastures. At the last stage, additional data obtained over the past twenty-year period were entered into the database. The updated map is presented in a GIS format (shapefile). The map contains information on the distribution of soils disturbed as a result of water and anthropogenic erosion.

Disaggregation of the 1:100,000 Reconnaissance soil map of the Busia Area, Kenya using a soil landscape rule-based approach

A 1:100,000 soil map of the Busia area in western Kenya published in 1991 is the main source of soil information for the area, but the map is outdated and unsuitable for new emerging demands. We disaggregated the Reconnaissance Soil Map of the Busia Area (quarter sheet No. 101) into individual soil classes to produce a soil class map that may better meet current needs. A soil landscape rule-based model was used to disaggregate the soil map units by exploiting information in the map legend and the map unit descriptions. These descriptions were used to generate rules that were applied to a fuzzy soil class map generated from a parent material map and a K-means cluster map generated from six terrain attributes, namely, multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The result was a soil class map with a spatial resolution of 30 m with an overall accuracy of 58% and a Kappa coefficient of 0.54. The soil landscape rule-based approach provides an opportunity to disaggregate traditional soil maps in cases where there is no adequate dataset. The drawback with this approach, however, is that it relies heavily on the resolution of the DEM and the type of information contained within the soil survey report describing the soil classes within the soil map units.

Characterization and classification of soils of Valapadi block of Salem district of Tamil Nadu

A detailed soil survey was carried out in Valapadi block of Salem district consists of 39 villages spread over 18551 hectares. Based on variation in physiography and landform, ten soil series and 57 soil mapping units were identified. Ten typifying pedons representing the soil series were analysed for its physico - chemical properties and characterized for mapping. Soils are deep to very deep, well drained to somewhat poorly drained, light to dark coloured, light to heavy textured. pH of soils ranged from 6.3 to 8.9 with mean value of 7.8, EC ranged between 0.1 to 0.5 dSm-1, OC varied from 0.11 to 0.68% with mean value of 0.33%, CEC ranged from 8.5 to 27.4 cmol (p+) kg-1 with mean value of 17.87 cmol (p+) kg-1. The land capability classification in the study area indicates that moderately good cultivated soils covered 18.6 percent area was affected with moderate limitations of erosion, where as the extent of moderate limitations of erosion and calcareousness was 65.9 percent. The land irrigability groupings indicate that about 18.6 percent area of the block may be used safely under irrigation. The erosion and calcareousness land covering 65.9 percent of the area were moderately suitable for sustained use under irrigation, provided proper soil and water conservation measures to be adopted.

Identifikasi Status Hara N Lahan Sawah pada Daerah Irigasi Kedunglimus Arca Kiri, Di Kecamatan Kembaran Kabupaten Banyumas

Paddy soil is land that is important in Indonesia as a major resource for growing rice in rice production. The main problems in rice cultivation in paddy fields is the transfer function, especially fertile and irrigated paddy fields into industrial areas and urban sprawl. The study aims to determine the amount of the nutrient content of N and determine the availability of nutrients N correlation with the production of rice in paddy fields in the area of the left arca of weir irrigation in Sub-District Kembaran Banyumas. The experiment was conducted in paddy fields in the area of the left arca of weir irrigation in Sub-District Kembaran Banyumas and followed with the analysis of the soil at the Laboratory Institute for Agricultural Technology in Yogyakarta and in the Laboratory of Soil Science, Faculty of Agriculture Unsoed. The experiment was conducted in March and May 2015. The study was conducted by survey method, in-depth review the level of accuracy (semi detail) with scale in map 1: 50,000. The determination of sample points is based on Homogeneous Soil Map Unit (SLH), which is made by overlay map on the map of soil types, slope maps and land use maps of research areas. The number of samples is 8, which is located on the 5 villages. The variables were observed and measured in the study include the main variables, namely N-total and N-available and include support variable-organic C content, soil pH, pH of water irrigation, CEC and interviews with farmers. The results showed that N-total content at all SLH is low, i.e. 0.166% in SLH A1f and 0.173% in SLH A2f . The content of N-available (N-NH4) in all SLH are 157 ppm in SLH A1f and 163 ppm in SLH A2f.

Mapping and Classification of upland soils Formed from Peninsular Gneiss in Rasipuram Block, Namakkal District of Tamil Nadu

A detailed soil survey was carried out in Rasipuram block of Namakkal district, Tamil Nadu, India consists of 24 villages spread over 10614 hectares. Based on variation in physiography and landform, eight soil series and 30 soil mapping units were identified. Eight typifying pedons were analysed for physical and physico-chemical properties and thus used for soil mapping at 1:10000 scale. Soils are shallow to very deep, well drained to somewhat poorly drained, light to dark reddish colour at moist condition, light (sandy loam) to heavy (clay) texture. The pH, EC, organic carbon and cation exchange capacity ranged from 7.4 to 9.4, 0.05 to 0.60 dS m−1, 0.2 to 0.7%, 0.11 to 0.60% and 10 to 45 c mol (p+) kg−1, respectively. The detailed classification of the soils from study area was Inceptisols and Alfisols soil order. The land capability classification grouping study in the study area indicates that marginally cultivable soils (IVs) covered 7.05% area was affected with severe limitations of erosion, whereas extent of moderate limitations of slight erosion and calcareousness (IIIc) was 77.34%. The erosion and calcareousness land covering 65.6% (IIIs) of the area were moderately suitable for sustained use under irrigation, provided proper soil and water conservation measures to be adopted.

Integrated Land Use Planning in Wadi El-Amal, Aswan Governorate, Egypt

The study shows how to use cutting-edge geospatial technology to better assess desert resources and provides a systematic approach that can be used elsewhere under the same conditions. A total studied area of 19320 Faddan in Wadi El-Amal located in Aswan governorate, Egypt. This study area is a desert; however, haphazard planning, management practices, and inadequate investments result in land and water resource degradation. The pedo-morphologial, physical, chemical, fertility characteristics of the selected soils were studied based on in-situ, remotely sensed and other data sources. Data on water geochemistry of water samples, collected from three dug wells were also utilized for this study. Five soil mapping units (SMUs) were prepared based on soil depth, rock fragments intensity and size, soil texture, and land topography. The SMUs are: (i) deep moderately fine textured soils with almost flat topography (4760 Faddan; 24.64%), (ii) deep gravelly medium textured soils with gently undulating topography (4740 Faddan; 24.53%), (iii) moderately deep gravelly coarse textured soils with gently undulating topography (3350 Faddan; 17.34%), (iv) shallow cobbly coarse textured soils with undulating topography (2900 Faddan; 15.01%), and (v) very shallow stony coarse textured soils with undulating topography (3570 Faddan; 18.48%). Different evaluation methods were utilized to characterize, evaluate, and plan the studied lands based on the collected data related to soil, water, environmental, political, and socioeconomic criteria. Three different evaluation methods namely qualitative desert land potentiality evaluation (QLDLPE), the American land capability classification (USDA LCC), and qualitative desert land aptness for crops (QLDLAC) were used to implement assess the potentiality and capability of the studied lands to produce various suitable crops. QLDLPE model has grouped the study area into four potentiality classes (high, moderate, slight, and low) while the USDA LCC system classified the study area into three capability classes (class-III, class-IV, and class-V). The characteristics of each soil unit were compared with the requirements of major crops of the study area and suitable crops for each unit were identified. Six land utilization types (field crops, vegetables, citrus-fruits, oil crops, forages, aromatic plants) were suggested as value-added crops for cultivation following the precision farming technique. The results of the evaluation analyses were integrated to propose a sustainable plan through integrated desert land use planning (IDLUP) methodology. Accordingly, about of 66.51% of the total study area (12850 Faddan) was determined for agricultural development, while the rest area (33.49%) was excluded from the agrarian expansion and may be used for the housing and constructions. Value-added crops were prioritized as follows: Field crops and vegetables (4760 Faddan) > citrus-fruits and oil crops (4740 Faddan) > medicinal, aromatic plants, and forages (3350 Faddan). The findings suggest strategies for coping with sustainable agricultural practices for the present study area and provide an integrated methodology for future assessments elsewhere, especially in the desert areas

Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.

A GEOPEDOLOGICAL APPROACH TO SOIL CLASSIFICATION TO CHARACTERIZE SOILS OF UPPER KABETE CAMPUS FIELD, UNIVERSITY OF NAIROBI, KENYA

&lt;p&gt;&lt;strong&gt;Background. &lt;/strong&gt;The study area exhibits a first class catena having homogenous parent material and forming a spatial continuum. Functionally and taxonomically distinct&lt;em&gt; &lt;/em&gt;soils result from differences in drainage and lateral movement of materials in the soil. &lt;strong&gt;Objective.&lt;/strong&gt; To classify the soils using a geopedological approach which involves a strong relationship between pedology and geomorphology. &lt;strong&gt;Methodology.&lt;/strong&gt; The area was delineated into Soil Mapping Units (SMUs) through augering into soils defined by different macro-relief. Mapping units were demarcated according to slope categories namely 0 to 5%, 5 to 8%, 8 to 16%, 16 to 30% and &amp;gt;30% connoted as flat to gently undulating (AB), undulating (C), rolling (D), moderately steep (E) and steep (F) respectively. Profile pits were dug in the five identified mapping units using Stratified Random Sampling technique. Identified SMUs include UmIr/F, UmIr/E, UxIr/D, UxIr/C and UxIr/AB in the order of decreasing slope gradient. The first entry represents the physiographic unit (Uplands, U), followed by physiographic position (lower middle uplands, m or uplands, undifferentiated levels, x), geology (I), color (r) and slope class respectively. A soil map with a legend describing the mapping units was produced using a scale of 1:10000. Topographic influence on soil properties was presented by Pearson’s correlation coefficient (r) with p-value included where the influence was significant. Statistical analysis was done using IBM SPSS 25&lt;sup&gt;th&lt;/sup&gt; edition and MS Excel. &lt;strong&gt;Results.&lt;/strong&gt; All the mapping units are well drained and deep to very deep (&amp;gt;80 cm). The color of the upper B horizon is predominantly dark reddish brown. The texture of top horizon is clay in UmIr/F and UmIr/E and is clay loam to clay, sandy clay loam to clay and loam to clay loam in UxIr/D, UxIr/C and UxIr/AB respectively, lucidly exposing the influence of topography on the depth of clay illuviation (clay: r = 0.724; p ≤ 0.01). The structure is predominantly subangular blocky throughout the profiles with the top horizon of cultivated areas having predominantly granular structure. Saturated hydraulic conductivity (K&lt;sub&gt;sat&lt;/sub&gt;) generally decreases with increasing clay content down the profiles and the bulk density ranges from 0.9 to 1.2gcm&lt;sup&gt;-3&lt;/sup&gt;. Means of soil reaction of top horizons generally slightly decrease with decreasing gradient (r = 0.231) having lower values in cultivated areas. Percent organic carbon regularly decreases down the profiles with higher values in uncultivated, steeper areas (r = 0.521; p ≤ 0.05). In the top horizon: Total nitrogen is predominantly medium across the study area ranging from 0.2 to 0.56% (r = 0.185) and follows the organic carbon trend; Available phosphorus is deficient (&amp;lt;20 ppm) in the study area. Bases are sufficiently to richly supplied while micronutrients are richly supplied. All soils are non-saline and non-sodic; the cation exchange capacity (CEC) soil is predominantly medium across the profiles ranging from 15 to 27.6 cmol(+)/kg with values increasing slightly with increasing slope (r = 0.320). Based on data collected from description of the profiles and physicochemical data of the soils and according to IUSS Working Group WRB (2014) soil classification legend, the soils were classified as Mollic Nitisols. &lt;strong&gt;Implications.&lt;/strong&gt; The soils are generally fertile for crop production but organic manure is recommended to buffer the acidic soil reaction, improve nitrogen and phosphorus sources. &lt;strong&gt;Conclusion.&lt;/strong&gt; Soil characterization, land evaluation and precise input application are encouraged.&lt;/p&gt;

Input map and feature selection for soil legacy data

Techniques that disaggregate complex soil-terrain polygons from legacy maps are becoming more relevant, as cost effective highly detailed soil information is required to advise agriculture, hydrology, ecology, engineering, and a variety of other disciplines. Disaggregation involves the spatial placement of individual soil classes from soil legacy polygons which have multiple soil classes, while specifying the approximate proportion of each soil class and verbally or diagrammatically explaining their distribution in the landscape. One of the most common disaggregation approaches is known as DSMART (“Disaggregation and Harmonisation of Soil Map Units through Resampled Classification Trees”). However, DSMART is computationally intensive and has many parameters that must be optimised. This study aimed to address these drawbacks including input map selection, feature selection, and resample size optimisation. The research site was selected in the upper reaches of the Mvoti river catchment covering 317 km2 in KwaZulu Natal province, South Africa. The catchment consists of 20 soil-terrain polygons drawn at a 1:250,000 scale from the South African Land Type Survey (LTS). First, the optimal input map derived from landform elements (geomorphons) was selected through a spatially resampled Cramer’s V test to determine the association between the legacy polygons (proportion of terrain) and the geomorphon units. This was done for five different aggregated geomorphons with different parameters. Second, three feature selection algorithms (FSAs) were embedded into DSMART to determine if the algorithms could improve accuracy and computational efficiency. Third, the FSAs were compared using 25, 50, 100, and 200 resamples per polygon. The results indicate that the Cramer’s V test is a rapid method to determine the optimal input map. All FSAs achieved a significantly greater accuracy then when disaggregating the original legacy polygons and were more computationally efficient than when using all 52 covariates. This study has implications when disaggregating large and small datasets by improving computational efficiency while maintaining an acceptable accuracy.

Assessment of inland valley soils for sugarcane (Saccharum officinarum L.) production in some floodplains in central Nigeria

Abstract Sugarcane is an energy crop with great economic potentials. Information on soil evaluation for sugarcane in central Nigeria is very scanty. This study was carried out to evaluate the suitability of some soils of the floodplains in central Nigeria for sugarcane cultivation. A semi-detailed soil survey was carried out on 18,500 hectares of land straddling the floodplains of Rivers Niger and Benue in Korton-Karfe Local Government Area of Kogi State. Land resource survey was carried out using a 500 m×500 m grid pattern. Identified soil types were further examined with standard soil profiles. Samples were collected from the soil genetic horizons as well as surface (0–30 cm) soils at selected sampling points for fertility analysis. Parametric and non-parametric suitability evaluation methods were used to relate land qualities with land use requirements for commercial sugarcane cultivation. Relationships between evaluation methods were assessed using Spearman’s rank correlation coefficients. Nine soil mapping units were identified in the study areas which were mainly Inceptisols (55.4%) and Alfisols (22.5%) which correlates with Fluvisols (40.4%), Cambisols (15.1%) and Lixisols (22.5%) in the World Reference Base (WRB) classification system. The evaluation methods used revealed that 0%, 4.95%, 49.48%, 23.55% of the soils were highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N) for sugarcane cultivation, respectively. Potentially, 0%, 9.52%, 44.91% and 23.55% of the soils were found to be highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N) for sugarcane cultivation, respectively. The soils were strongly limited by low soil nutrient availability, soil acidity and flood hazard. Soil management practices such as application of organic manures, fertiliser and liming could be adopted to ameliorate the soil acidity and supply deficient nutrients while land development strategies such as drainage, flood control and possibly sub-soiling would mitigate other major limitations to sugarcane cultivation.

Arctic and tundra soils on the new digital soil map of Russia, 1 : 2.5 M scale

V.V. Dokuchaev Soil Science Institute has initiated a project on compilation of a new Digital Soil Map of Russia on the basis of the Soil Map of the Russian Federation (SMRF) 1 : 2.5 M scale (1988) revised and interpreted in ideology and nomenclature of the new substantive-genetic Classification System of Russian Soils (CSRS). The first stage implies the conversion of soil mapping units on the original map into the CSRS with a corresponding renaming of soils in the attribute database to the digitized version of the map for each soil polygon. During the second stage, a new digital model of the soil cover is developed with the use of digital soil mapping technologies, basic soil map, and new materials, including satellite images and digital elevation models. The legend section “Tundra Soils” contains 16 soil units forming their own areas or found in various combinations (soil complexes). As a result of the reclassification and careful analysis of each soil polygon, the soils of Arctic and Subarctic tundra have obtained a more detailed and differential representation on the new map, and their diagnostics based on the morphology of the profiles and major soil properties have been specified. The most significant changes in the initial content of the map concern the soils referred to as gley soils on the SMRF. A separate group of cryozemic soils has been specified. Weakly developed soils (petrozems, psammozems, and pelozems) and lithozems have been int r oduced on the map for the first time. Differential decisions are suggested for the soils of “spotty tundra” with sorted and nonsorted circles and for the soils of cryogenic fissures and cracks. The results of the study have made it possible to refine the diagnostics and nomenclature of soils in the CSRS.

Determination of Hydrologic Soil Group from Soil Map Unit of Indonesia in Pataruman Water Catchment Area

Hydrologic Soil Group (HSG) for each region is different depending on soil conditions. It is one of the key elements in the NRCS-CN determination to account for the rain-runoff relationship. But unfortunately in Indonesia there is no map of HSG. Adidarma (2013) has conducted the research about HSG determination by using hydrogeology map. However, the map has a long process of pedotransfer and HSG parameter approach is still generally and qualitative. Furthermore, Ramadan (2017) has done research of HSG determination with HWSD map; the map is a global map which depth of information type of soil is less profound. Related to those problems, the researcher will conduct the determination of HSG in accordance with the condition of land in Indonesia by using Land Map Unit (SPT) Indonesia. HSG will be overlay with the transparent of land in Pataruman Water Catchment Area (WCA) until obtained the value of NRCS-CN and then the discharge plan will be calculated and will be calibrated with debit recording in Bendung Pataruman.