Soil Mapping Units Research Articles

Development of Food Agriculture Based on Superiority Cultivation Area in Tanjung Api-Api, Banyuasin District

Leading commodity development area is a developed area for one or a combination of superior commodities that comply with the land use characteristics that provide the highest farm household's income, communities, and the region. This research was aimed to determine and describe the factors which have a significant effect on the superiority of Tanjung Api-Api (TAA) area, measure and analyze of the superiority of TAA cultivation area for food crops,and formulate a grand strategy matrix for food crops development based on its regional. The descriptive methods, Policy Analysis Matrix (PAM), and Grand Strategy Analysis. The factors which affect the superiority of TAA cultivation area include ecological, socio-cultural, and economic factors. The result shows, rice and corn plants included in the SI category or suitable for cultivation in the TAA region with Soil Mapping Unit (SPT) value of 20.70%. Socio-cultural aspects that affect the development of food agriculture such as the labors, Village Unit Cooperative (KUD), and Farmers Group that support agricultural development. Rice and corn crops have 0.69 and 0.97 on the competitive advantage (PCR) value respectively, therefore the rice and corn plants are concluded to have a competitive advantage. The comparative advantages (DRCR) value shows 0.68 and 0.65 for rice and corn plants respectively, which have comparative advantages and have the potential to develop in TAA area. Food crops in the TAA area are in a good competitive position. The strategy that must be applied under these conditions is to support an aggressive growth policy (Growth-oriented strategy).

Classification, physicochemical, soil fertility, and relationship to Coffee robusta yield in soil map unit selected

The research was aimed (1) to classify, characterize the physicochemical properties, determine the fertility of the soil, and (2) to obtain the relationship of soil fertility on the character yield for Coffee robusta in the 10 units of the soil map (SMUs) selected in Silima Pungga-Pungga Sub-district, Dairi District. This research was conducted in Silima Pungga-Pungga sub-District, Dairi District, North Sumatra Province, Indonesia with coordinates 2080’-2088’NL and 98004’-98017'EL from July 2014 until June 2017. This research was conducted by overlay the maps, classifying soil profiles, characterizing soil, soil fertility assessing, and regression analysis of soil fertility with the yield for Coffee robusta using IBM SPSS Statistics v.20 software. The result showed the ten from 18 SMUs selected for Coffee robusta in Silima Pungga-Pungga sub-District, Dairi District, and has the highest land area sequentially, such as SMU 11, 14, and 1. Based on the ten SMUs selected, found in two representative soil profiles, include the profile 1 (SMU 1, 2, 8, 9, 11, 13, 14, 16, 18) covering an area of ​​1,703.30 ha with the inceptisol and profile 10 (SMU 10) covering an area of 176.81 ha with the entisol. Inceptisol order has greater the soil physicochemical properties compared to entisol from ten SMUs selected for Coffee robusta. The effect of cation exchange capacity, base saturation, P-total, K-total, and C-organic have significantly increased the productivity Coffee robusta by 89.30%, however the effect was not significant to the 100 grains of dry weight. Key words: Entisol; Coffee robusta; Inceptisol; Overlay; Soil fertility.

Evaluation of the Soil Vulnerability Index for artificially drained cropland across eight Conservation Effects Assessment Project watersheds

The USDA Natural Resources Conservation Service (NRCS) has proposed the Soil Vulnerability Index (SVI) as a standard tool to classify inherent soil vulnerability of cropland to loss of sediment and nutrients by runoff and leaching. The tool uses soil properties and topography, and does not consider crop management, except for the presence of artificial surface or subsurface drainage. For artificially drained cropland, SVI vulnerability to runoff remains unchanged but vulnerability to leaching is raised by two classes out of four to reflect the increased risk of nitrate (NO3) transport. The SVI was reviewed within different contexts, but there is a need for SVI evaluation when artificial drainage is present. Thus, the objectives of this evaluation were to (1) evaluate SVI vulnerability to runoff and leaching for artificially drained cropland, and (2) propose changes to the SVI ruleset based on the findings of Objective 1. The SVI was evaluated for eight sites with artificial drainage located in regions ranging from Idaho to Maryland. Seven sites were watersheds ranging in size from 600 to 113,600 ha, with 44% to 84% cropland consisting of row crops or small grains. The eighth site consisted of six fields ranging from 7 to 30 ha in size. Consistency between SVI vulnerability, hydrologic processes that take place on the landscape, and outcomes such as crops grown were examined, using the accumulated experience and knowledge of the coauthors of this paper. Overall, SVI vulnerability to runoff and leaching was consistent with earlier research for sites with artificial subsurface drainage unless rainfall intensities were greater than they are in the Upper Mississippi and Ohio-Tennessee River basins. SVI vulnerability to leaching was greater than expected in case of surface drainage. In addition, complex soil map units can cause incorrect vulnerability classification at field scale. At the watershed or regional scale, the leaching component should be considered both with and without artificial drainage so that the causes of the vulnerability (permeable soils or artificial drainage) can be distinguished.

GEOMORPHOLOGY, CHARACTERISTICS AND MINERALOGY OF SOME SOILS IN EL-FARAFRA OASIS, NEW VALLEY GOVERNORATE, EGYPT

The studied area belongs to El-Farafra Oasis, New Valley Governorate. It is representing an area of about 372062 feddans. The aim of this work was to study the geomorphology, characteristics and mineralogy of some soils in El-Farafra Oasis, New Valley governorate, Egypt. Visual interpretation of 2 sentinel images and digital elevation model (DEM) were used to produce the geomorphic units and soil maps of the studied area. Two geomorphic units were identified namely plateau and depression floor. Eighty-two minipits and eighteen soil profiles were chosen to represent the main soil mapping units in the studied area. Soil samples were collected from the representative soil profiles for physical, chemical and mineralogical analysis. The results could be summarized as follows: X-ray diffraction analysis indicated that kaolinite is alternatively dominated in the studied soils (about 80 %) followed by smectite. Illite, interstratified minerals and chlorite are found as the lowest abundant clay minerals in these soils. The mineralogical composition of the sand fraction indicated that, the light fraction is composed almost entirely of quartz which constitutes about 95%. Feldspar minerals (orthoclase plagioclase and microcline) are detected in trace amounts. The presence of feldspars indicates that, the soils are young from the pedological point of views. Heavy minerals are generally dominated by opaque’s (about 55 %). Non-opaques are mainly dominated by pyroboles (pyroxenes + amphipoles) followed by ultra-stable minerals (zircon, rutile and tourmaline), Para metamorphic minerals (garnet, kyanite, staurolite, silimanite) and epidote. While the other minerals (biotite andalusite, glaucohite and apatite) are detected in less pronounced amounts. Distribution of the resistant minerals (biotite and amphipoles) weathering ratio, values indicate that, the soils are generally heterogeneous either due to their multi-origin or due to a subsequent variation along the course formation.

Pedogeomorphological Categorization of Selected Soils in Mbogo - Komtonga Irrigation Scheme, Mvomero District, Morogoro Region, Tanzania

Pedogeomorphological categorization of selected soil profiles developed on alluvial deposits in Mbogo - Komtonga traditional irrigation scheme, Mvomero District in Morogoro Region, was carried out during February 2017. Using standard grids, pedogeomorphic approach and standard manuals, detailed soil survey was conducted which enabled delineation of soil mapping units from which the representative profiles were identified, described and sampled. Eighteen samples were collected at a depth of 0–30 cm and from each horizon of the selected soil Master pits and analyzed for physico – chemical characterization. Based on FAO soil survey system of classification, the representative profiles were classified as Eutric Fluvisols and/or Eutric Cambisols. The pedon was deep to moderately deep, well to moderately well drained, with brownish black clay top soils or dull yellowish brown soil colors with sub soils stratified with fS, C, CL and SCL. Top soil pH was strongly to medium acid to medium or slightly acid sub soils. OC showed no decline in soil quality. N was very low to low, P and K levels were medium to low or very low; CEC was high to very high in all the profiles. Ca2+ and Mg2+ in the top soils were high to very high and very low or low to medium in the sub soils. Na+ was rated as low to medium in the top soils of all profiles and low to very low in the sub soils. Base saturation was > 50% and was rated as high. Topsoil Bd and total porosity were ideal to medium. AWC was medium and water storage capacity (AWSC) was good and sufficient for paddy production and other upland crops. These results suggest that where the soil parameters were low to very low as for N, P and SOM should be included in the overall soil fertility management program. Soil reaction may be regulated during irrigation development by provision of sufficient drainage, discharge and flood control structures and minimum application of lime if required.

Disaggregation of conventional soil map by generating multi realizations of soil class distribution (case study: Saadat Shahr plain, Iran).

Conventional soil maps generally depict information about soil spatial distribution in the framework of crisp boundaries of tessellated soil polygons. Such maps in standard soil survey procedures determine map unit composition on the basis of relative acreage occupied by individual major and minor soil components within soil map unit without addressing the specific location of each component in the polygon boundary. These limitations in addition to the sharp-edge boundaries of conventional soil maps are considered obstacles for modern land resource management. To increase detail in the polygon of conventional soil maps, we have produced a spatially disaggregated soil class map of a relatively flat agricultural plain called Saadat Shahr in South-Central Iran, using DSMART algorithm. DSMART is a known DSM-based disaggregation and harmonization algorithm that works through resampled classification trees to estimate the probability of the existence of each possible soil classes and also to prepare the maps of the most probable soil class, second most probable, and so on in raster format. The conventional soil map and 124 georeferenced profiles, as well as a set of numerical and categorical auxiliary data in 10-m resolutions in the extent of the study area utilized as the SCORPAN variables, were used as the inputs of the DSMART algorithm. A set of 78 independent sampling points generated by Latin hypercube sampling scheme were investigated and then used for validation of the DSMART raster outputs. The results indicated an improvement in disaggregated maps in the case of allocating soil components within the map units. In the generated DSMART, overall accuracy for seven soil subgroups was 68%. The best prediction obtained for Typic Xerorthents and Typic Calcixerepts, meanwhile a few classes were poorly predicted. For second most probable and third most probable maps, 17% and 0.5% of predicted soils match that observed respectively. This study revealed that DSMART as a disaggregation method can be used for enhancing existence soil map with poor descriptive data in the case of allocating soil classes in a more detailed way compared to the relevant original map.

Study of Morphological, Physical and Chemical Characteristics of Salt Affected Soils Using Remote Sensing Technologies at Basrah Province

This study was carried out to determine some morphological, physical and chemical properties of sedimentary salt affected soils in Basrah region east of Shatt al-Arab, and from the madina north to Abi Al-Khaseeb in the south of an area of 2819.06km2. Soil mapping units were separated using ERDAS software calculated from B4, B3, B2 and GIS spectral belts. The results of the morphological description of all the pedons showed that they represent the state of the newly formed undeveloped soils mainly composed of river sediments with the absence of diagnostic subsurface horizons. According to the prevalence of the prevailing wavelength 7.5R at drought was more than 60% of total horizons with almost equal proportions of Wavelengths 7.5YR, 5YR and 10R. Where the dominance between the wavelengths 2.5Y, 10R, 5YR and the colours ranged from Light yellow to reddish-brown heavy. Since the salinity of the soil is very important for the purposes of classification of soil and the preparation of maps and reclamation of these soils, this study aimed to study the characteristics of soils affected by salinity has separated into maps units using remote sensing technologies and geographic information systems.

Soil Fertility Evaluation for Strategic Direction Jati Plus Perhutani (JPP)

Soil fertility evaluation conducted to assess the characteristics of the soil and determine the significant constraint factor and alternative supervision. This research aims to determine the limiting factors of soil fertility in Jati Plus Perhutani (JPP) clonal forestry and routine plantation at the research sites in a few years of planting and determining the strategic directives of supervision. Total land of 12 soil map units evaluated based on a representative profile in the semi-detail level on the research sites of FMU Ngawi and Pemalang. Data collection of soil fertility was based on Soil Research Institute and the National Soil Survey Center guidelines. Classification of soil fertility shows that the soil was dominated by silty texture and has a flat to a moderately steep slope. Soils at the research sites have low infiltration rates, high ability to restrain water, difficult cultivation, and a high potential for a run-off on sloping land. Assessment of soil fertility status at the research sites shown of CEC and base saturation high to very high. C organic, P2O5, and K2O are rated very low to low, strategic directives for land supervision and cultivation in the research sites are the application of organic substances, intensive soil management and implementation of soil conservation.

Preprocessing procedures and supervised classification applied to a database of systematic soil survey

ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, “Dois Corregos” (“Brotas” 1:100,000-scale sheet), “Sao Pedro” and “Laras” (“Piracicaba” 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of Sao Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of Sao Paulo. The target variable was a soil map unit of four types: local “soil unit” name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.67 were obtained after preprocessing procedures with Random Forest, the algorithm that performed best. Information from conventional map units of semi-detailed (4th order) 1:100,000 soil survey generated models with values for accuracy, precision, sensitivity, specificity and Kappa indexes that support their use in programs for systematic soil surveying.

Land evaluation for crop production in Upper Kabete Campus field, University of Nairobi, Kenya

BackgroundThe study area exhibits a first class catena; variation in soils is a result of differences in drainage and lateral movement of materials. The site was delineated into Soil Mapping Units (SMUs) through augering, into soils differing in macro relief. Slope categories included 0 to 5%, 5 to 8%, 8 to 16%, 16 to 30% and > 30%. Profile pits were opened in the five mapping units using stratified random sampling technique. Identified SMUs include UmIr/F, UmIr/E, UxIr/D, UxIr/C and UxIr/AB in order of decreasing slope gradient. Land suitability classification was based on the top horizons and the FAO 1976 criterion for land evaluation was used. The study aimed at identifying the most suitable areas for envisaging production of common crops grown in the area and limitations to production increase. A table of relevant land qualities and their diagnostic factors was constructed, rated and matched with suitability class-defining criteria to determine current suitability. Potential suitability was determined by considering the possibility of resolving the current limitations.ResultsMapping unit UmIr/F and UmIr/E qualified as class S2 for the selected crops majorly due to limitations of workability and susceptibility to erosion based on their slope gradient. Nutrient availability and possibility of use of implements also rated as S2. Unit UxIr/D qualified in S2 category due to the same limitations. Mapping unit UxIr/C and UxIr/AB qualified in the S2 category due to limitations of nutrient availability. After improvements, all the mapping units qualified in class S1.ConclusionsThese observations indicate that workability and erosion susceptibility due to steep topography coupled with lack of enough nutrients are the greatest production limitations in the study area. Increasing slope gradient also led to decreasing possibility of use of farm implements. Terracing can reduce the deleterious influence of slope on soil quality, shifting suitability to a higher class, should the steep area be used for cultivation. There is a need for sufficient inputs and cover cropping to maintain high soil quality. Land evaluation should be part of good agricultural practices in any production system.

Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).

Comparing Field Sampling and Soil Survey Database for Spatial Heterogeneity in Surface Soil Granulometry: Implications for Ecosystem Services Assessment

Lithospheric-derived resources such as soil texture and coarse fragments are key soil physical properties that contribute to ecosystem services (ES), which can be valued based on “soil” or “mineral” stocks. Soil survey data provides an inexpensive alternative to detailed field measurements which are often labor-intensive, time-consuming, and costly to obtain. However, both field and soil survey data contain heterogeneous information with a certain level of variability and uncertainty in data. This study compares the potential of using field measurements and information from the Soil Survey Geographic database (SSURGO) for coarse fragments (CF), sand (S), silt (Si), clay (C) and texture class (TC) in the surface soil (Ap horizon) for the 147-hectare Cornell University Willsboro Research Farm, NY. Maps were created based on following methods: a) utilizing data from the SSURGO database for individual soil map unit (SMU) at the field site and using representative or reported values across individual SMU; b) averaging the field data within a specific SMU boundary and using the averaged value across the SMU; and c) interpolating field data within the farm boundaries based on the individual soil cores. This study demonstrates the important distinction between mapping using the “crisp” boundary soil SSURGO databases compared to the actual spatial heterogeneity of field interpolated data. Maps of CF, S, Si, C, and TC values derived from interpolated field core samples were dissimilar to maps derived by using averaged core results or SSURGO values over the SMUs. Dissimilarities in the maps of CF, S, Si, C, and TC can be attributed to several factors (e.g., official soil series data being collected from “type locations” outside of the study areas). Correlation plot of clay estimates for each SMU showed statistically significant correlations between SSURGO and field-averaged (r = 0.823, p = 0.003) and field-interpolated clay (r = 0.584, p = 0.028) estimates, but no correlation was found for CF, S, and Si.

Validation of digital maps derived from spatial disaggregation of legacy soil maps

Spatial disaggregation of soil map units involves downscaling existing information to produce new information at a finer scale than that of the original source. Currently, it is becoming a powerful tool to address the spatial distribution of soil information over large areas, where legacy soil polygon maps are the only source of soil information. Because of the high expense of additional resampling, few studies have sought to validate disaggregated soil maps using independent sampling. This study implemented spatial disaggregation approach to measure the quality of soil property predictions derived from disaggregated soil maps, using stratified simple random sampling of a study area of 6848 km2 (11 strata and 135 soil profiles). In a previous study, the existing legacy soil polygon map of Brittany (France) at 1:250,000 scale was spatially disaggregated at 50 m resolution using an algorithm called Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART), which uses soil-landscape expert rules of soil distribution in space. By fitting equal-area spline functions, soil properties were then estimated at six depth intervals according to GlobalSoilMap specifications. To validate disaggregated soil maps, two approaches were developed according to the soil attribute nature (continuous or categorical). For categorical soil properties (soil parent material, soil drainage class, soil type and soil depth class), the overall strict purity (the degree to which all classification criterion are respected) by the most probable STU (Soil Typological Unit) map was estimated at 34%, while the overall average purity reached 70%. The overall partial soil-type purity reached 60%, the overall partial parent material purity reached 78% and the overall partial soil drainage class as well as soil depth class purities reached 65% and 78%, respectively. Continuous soil properties (clay content, fine silt content, coarse silt content, total silt content, fine sand content, coarse sand content, coarse fragments, Cation Exchange Capacity (CEC) and pH) were validated at two soil depth intervals (5–15 and 30–60 cm) using 260 soil samples. In general, soil property predictions were unbiased except for coarse fragments and CEC in the 5–15 cm layer. Validation statistics (R2, RMSE, RRMSE and ME) were better for the 30–60 cm layer except for soil particle-size distribution. Thus, differences in prediction accuracies among strata (the validation support) denote areas where more soil data or better soil prediction models are needed to improve the disaggregation process.

Studying Rain Water Catchment Potentialities in the Northwest Coast of Egypt Using Remote Sensing and Geographic Information System

Sustainable agricultural development of the desert areas of Egypt under the scarcity of irrigation water is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used in agriculture irrigation to increase crop production. The present study includes data related to agricultural production in the form of geospatial layers including climate, soil, land covers unite and rain water catchment areas. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following based on the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil mapping units of the study area identified from Land Master Plan maps it was(Very shallow gravelly and rocky soils and barren rock). Climatic data collected from the Central Laboratory for Climate. The results showed that the study area receives a significant amount of precipitation almost every three years, however, water harvesting methods are inappropriate to store water to be used in agricultural during drought seasons. The amount of precipitation(81.9 mm), surface water runoff(4.46 mm), potential evapotranspiration(70.5 mm), and actual evapotranspiration(7.10 mm) for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level while the lowest one was -3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes.

English

Successful crop production requires the knowledge of soil variation and the evaluation of the agricultural potentials of the land. Detailed land resource survey of approximately 100 ha land in Abeokuta North Local Government Area of Ogun State was carried out. Identified soil mapping units were examined in modal profiles. The soils were classified using the USDA Soil Taxonomy, FAO/IUSS World Reference Base (WRB) system and local soil series methods. Five soil mapping units were identified and were classified as Ilaro Series (Kanhaplic Haplustalf), Ibeshe (Typic Kandiustalf), Kulfo (Udic Kandiustalf), Molo (Rhodic Kandiustalf) and Otteyi Series (Lithic Haplustept). The soils were classified as Alfisols (71.56%) and Inceptisols (28.44%). Land Capability Classification (LCC) revealed that Ilaro, Ibeshe and Molo series belong to Capability Class II while Kulfo series was Class III but Otteyi series is of non-arable Class VI. Otteyyi Series had severe limitations of very shallow soil depth, high slope gradient and high gravel content. The area has good potential for crop production if soil fertility can be managed with appropriate fertilizer application and seasonal water deficit ameliorated by supplemental irrigation. Otteyi Series that is non-arable could be used for farm buildings and other physical infrastructures or animal grazing. Key words: Land evaluation, fertility capability class, soil series, limitations, arable cropping. &nbsp

Derivation of human trophoblast stem cells from human pluripotent stem cells

Conventional soil maps contain valuable knowledge on soil–environment relationships. Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data. Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship. Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit. This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps. The proposed method consists of three major steps. Firstly, the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates. Secondly, for each environmental covariate, these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area. And lastly, the extracted soil–environment relationships are applied to updating the conventional soil map with new, improved environmental data by adopting a soil land inference model (SoLIM) framework. This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County, Wisconsin, United States. The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole. Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.

Changes in deep soil organic carbon and soil properties beneath tree windbreak plantings in the U.S. Great Plains

Agroforestry systems such as tree windbreaks became a common practice in the U.S. Great Plains following a large tree planting program during the Dust Bowl of the 1930s. Tree windbreaks combine the potential to increase biomass and soil carbon (C) storage while maintaining agricultural production. However, our understanding of the effect of trees on soil organic carbon (SOC) is largely limited to the upper 30 cm of the soil. This study was conducted in the Great Plains to examine the impact of tree plantings ranging in age from 15 to ~ 115-years on SOC storage and relevant soil properties. We quantified SOC stocks to 1.25 m depth within eight tree plantings and in the adjacent farmed fields within the same soil map unit. Soil samples were also analyzed for inorganic carbon, total nitrogen, pH (in water and KCl), bulk density, and water stable aggregates. Averaged across sites, SOC stocks in the 1.25 m were 16% higher beneath trees than the adjacent farmed fields. Differences ranged from + 10.54 to a – 5.05 kg m−2 depending on the site, climate, and tree species and age. The subsurface soils (30-125 cm) beneath trees stored 7% more SOC stocks than the surface 30 cm (9.54 vs. 8.84 kg m−2), respectively. This finding demonstrates the importance of quantifying C stored at deeper depths under tree-based systems when tree SOC sequestration is being assessed. Overall, our results indicate the potential of trees to store C in soils and at deeper depths.

Protocol for the use of legacy data and magnetic signature on soil mapping of São Paulo Central West, Brazil.

The demand for information on the soil resource to support the establishment of public policies for land use and management has grown exponentially in the last years. However, there are still difficulties to the proper use of already existing information for soil mapping. Here we aimed to establish a protocol for soil mapping using legacy data, magnetic signature and soil attributes evaluation. A total of 493 soil samples were collected at 0-0.20 m in the geological domain of Western Plateau of São Paulo State. This work has three parts: First, we performed a classification analysis using soil mapping units (SMU) extracted from conventional soil map and Support Vector Machines algorithm (SVM). As covariates, we used categorical information, such as geology, dissection and landform maps. Second, we used soil attributes to perform a cluster analysis using k-means as partitioning method. To choose the optimal number of clusters, the same number of SMU showed in the conventional soil map (e.g. 34 clusters) were used. The last step was to compare soil and clusters maps predicted by SVM with the conventional soil map. Results showed good performance of SVM for both classifications (clusters and SMU), with overall accuracy of 0.60 and 0.90 respectively. In addition, the distribution of soil attributes within each cluster was more homogeneous and well distributed than within SMU, showing that is very possible to use numerical classification for soil mapping. Future soil surveys could use cluster analysis as a preliminary evaluation for better understanding of tropical soil variations.

Comparision of fertility capability and taxonomic classification systems to classify the soil map units in some parts of Chaharmahal-va-Bakhtiari province

Although fertility capability classification (FCC) has high performance in land evaluation and soil maps interpretation, so far it has been less attended in land evaluation studies. Therefore, qualitative (FCC method) and quantitative (Riquer index) land fertility capability evaluation for Wheat and Rice cultivation and comparison of Soil Taxonomy and WRB classification with FCC were chosen as this study objective. After field and laboratory studies, the soil map was prepared with the scale of 1: 50,000. Then, FCC and Riquer methods were used to determine the land fertility capability for Wheat and Rice cultivation for each of the soil map unites. Investigations showed that the FCC and Riquer methods have presented similar results and well interpreted and classified soils. The results of Riquer method indicated that the fertility capability of these lands is good except in gravelly and shallow parts, thus Wheat and Rice can be cultivated in these soils. A comparison of the current soil classification systems and the FCC results showed that the problems and inherent characteristics of studied soils were pointed well by the FCC system, whereas they have been expressed differently in Soil Taxonomy and WRB classifications. Gleyic condition, dryness and subsoil gravel are the most limitative factors to Wheat and Rice cultivation in this area.

Land Suitability Assessment using GIS for Davana (Artemisia pallens) Cultivation in Karnataka, India

Davana is one of the commercially important aromatic crops grown in Karnataka state, India. Agro-ecological conditions of davana growing areas of Karnataka state was characterized and evaluated. Soil parameters were correlated with herbage yield, oil content and its quality to establish soil-site suitability criteria. To identify the suitable zones for davana cultivation elsewhere in Karnataka, a GIS based agro-ecological evaluation was carried out by using soil resource information (121 soil mapping units on 1:250 K scale) of Karnataka state. Each mapping unit was evaluated by matching soil-site suitability criteria with climate and soil parameters to identify the suitable areas for davana cultivation. Around 45.2 lakh ha area is highly suitable, 65.3 lakh ha is moderately suitable, and 54.2 lakh ha is marginally suitable for davana cultivation. Highly and moderately suitable areas need to be focused for expansion of davana as a component of major cropping or farming systems and also to establish forward and backward linkages.