Major Soil Type, Soil Classification, and Soil Maps

2017 ◽  
pp. 69-101
Author(s):  
Aminaton Marto ◽  
Safiah Yusmah Mohd Yusoff
Keyword(s):  
Soil Type ◽  
Soil Classification ◽  
Soil Maps

Major Soil Type, Soil Classification, and Soil Maps

2017 ◽  
pp. 69-101
Author(s):  
Aminaton Marto ◽  
Safiah Yusoff
Keyword(s):  
Soil Type ◽  
Soil Classification ◽  
Soil Maps

scholarly journals Mineralogy of Gypsiferous Soil and the Effect of a dditiveLime Stone waste powder on its Physical Properties

2020 ◽  
pp. 83-91
Author(s):  
Thalar Othman Rashid ◽  
Nadhmia Najmaddin Majeed
Keyword(s):  
Grain Size ◽  
Specific Gravity ◽  
Soil Type ◽  
Soil Classification ◽  
Limestone Powder ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Atterberg Limit ◽  
Gypsum Content ◽  
Physical Testing

The presence of gypsum in soil as bonding agent alters its behavior with a large influence on itsphysical properties.Soil samples were taken from two locations of different gypsum content(S1 = 30.5% and S2= 20%) inMakhmur area. TheUnified soil classification system indicated that soil type was clay with low plasticity(CL). Basic methods of physical testing of soils, such as grain size analysis,specific gravity and atterberg limit were applied. Stabilizationof the gypsiferous soil was performed by addinglimestone waste powder takenfrom Said sadiqandPirmam areas,with different percentages(5%, 15%,25%).The results show that the addition of limestone powder to the tested soils decreases their liquid and plastic limits.

scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on DSMART algorithm

2019 ◽  
Author(s):  
Yosra Ellili ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

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 available at 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 (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at 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 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 algorithm (DSMART with extra soil profiles, DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1 : 25 000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, to spatially locate differences between disaggregated maps. The main results show that adding soil landscape relationships in the disaggregation process enhances the performance of prediction of soil type distribution. Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy measures 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 twofold higher when validated using 3 × 3 windows. They achieved 28.5 % for DSMART with soil landscape relationships, 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships as well as extra soil observations constraints 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 to obtain consistent soil maps with clear internal disaggregation of SMU across the landscape.

scholarly journals Clay-illuvial soils in the Polish and international soil classifications

2015 ◽  
Vol 66 (4) ◽  
pp. 204-213 ◽  
Author(s):  
Cezary Kabała ◽  
Elżbieta Musztyfaga
Keyword(s):  
Environmental Conditions ◽  
Soil Type ◽  
Soil Classification ◽  
The Other ◽  
Soil Types ◽  
Diagnostic Features ◽  
Hierarchical Order ◽  
History Of

AbstractSoil with a clay-illuvial subsurface horizon are the most widespread soil type in Poland and significantly differ in morphology and properties developed under variable environmental conditions. Despite the long history of investigations, the rules of classification and cartography of clay-illuvial soils have been permanently discussed and modified. The distinction of clay-illuvial soils into three soil types, introduced to the Polish soil classification in 2011, has been criticized as excessively extended, non-coherent with the other parts and rules of the classification, hard to introduce in soil cartography and poorly correlated with the international soil classifications. One type of clay-illuvial soils (“gleby płowe”) was justified and recommended to reintroduce in soil classification in Poland, as well as 10 soil subtypes listed in a hierarchical order. The subtypes may be combined if the soil has diagnostic features of more than one soil subtypes. Clear rules of soil name generalization (reduction of subtype number for one soil) were suggested for soil cartography on various scales. One of the most important among the distinguished soil sub-types are the “eroded” or “truncated” clay-illuvial soils.

An innovative approach for visualization of subsurface soil properties

2004 ◽  
Vol 84 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Z. Hu ◽  
B. Bass ◽  
C. W. Chan ◽  
G. H. Huang
Keyword(s):  
Soil Type ◽  
Concentration Distribution ◽  
Soil Layer ◽  
Soil Classification ◽  
Soil Sampling ◽  
Contaminated Site ◽  
Distribution Data ◽  
Contaminant Concentration ◽  
Type Distribution ◽  
Site Characteristics

Subsurface characterization is an important requirement in the decision-making process of selecting a remediation technique for petroleum-contaminated sites. The soil type distribution is one of the most important site characteristics, because it affects selection of the site remediation technique. The visualization of soil type distribution and also the contaminant concentration distribution in the subsurface can help the decision-maker understand the site and select the proper remediation technique. In this paper, we describe the software Soil-Visual (1.0, 1.1), which is used for visualizing the soil sampling data, the soil type distribution, and contaminant concentration distribution of a contaminated site. This software has two functions: (1) to determine the soil particle size distribution and contaminant concentration distribution of the entire site from limited soil sampling data; and (2) to visualize the multi-dimensional soil type distribution and contaminant concentration distribution data of each soil layer on a two-dimensional map. The red-green-blue (RGB) color illustration method has been used in this software to convert the multi-dimensional soil sampling data into a bitmap. Key words: RGB bitmap, soil classification, visualization

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

scholarly journals Polish Soil Classification, 6th edition – principles, classification scheme and correlations

2019 ◽  
Vol 70 (2) ◽  
pp. 71-97 ◽  
Author(s):  
Cezary Kabała ◽  
Przemysław Charzyński ◽  
Jacek Chodorowski ◽  
Marek Drewnik ◽  
Bartłomiej Glina ◽  
...  
Keyword(s):  
Soil Type ◽  
Classification Scheme ◽  
Soil Classification ◽  
Parent Material ◽  
Soil Taxonomy ◽  
Soil Science Society ◽  
Sixth Edition ◽  
Living Organisms ◽  
Diagnostic Horizons ◽  
Scientific System

Abstract The sixth edition of the Polish Soil Classification (SGP6) aims to maintain soil classification in Poland as a modern scientific system that reflects current scientific knowledge, understanding of soil functions and the practical requirements of society. SGP6 continues the tradition of previous editions elaborated upon by the Soil Science Society of Poland in consistent application of quantitatively characterized diagnostic horizons, properties and materials; however, clearly referring to soil genesis. The present need to involve and name the soils created or naturally developed under increasing human impact has led to modernization of the soil definition. Thus, in SGP6, soil is defined as the surface part of the lithosphere or the accumulation of mineral and organic materials permanently connected to the lithosphere (through buildings or permanent constructions), coming from weathering or accumulation processes, originated naturally or anthropogenically, subject to transformation under the influence of soil-forming factors, and able to supply living organisms with water and nutrients. SGP6 distinguishes three hierarchical categories: soil order (nine in total), soil type (basic classification unit; 30 in total) and soil subtype (183 units derived from 62 unique definitions; listed hierarchically, separately in each soil type), supplemented by three non-hierarchical categories: soil variety (additional pedogenic or lithogenic features), soil genus (lithology/parent material) and soil species (soil texture). Non-hierarchical units have universal definitions that allow their application in various orders/types, if all defined requirements are met. The paper explains the principles, classification scheme and rules of SGP6, including the key to soil orders and types, explaining the relationships between diagnostic horizons, materials and properties distinguished in SGP6 and in the recent edition of WRB system as well as discussing the correlation of classification units between SGP6, WRB and Soil Taxonomy.

scholarly journals Data Mining Classification Algorithms for Analyzing Soil Data

2021 ◽  
pp. 17-28
Author(s):  
Kazheen Ismael Taher ◽  
Adnan Mohsin Abdulazeez ◽  
Dilovan Asaad Zebari
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Soil Type ◽  
Soil Classification ◽  
Well Being ◽  
Experimental Result ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Climate Regulation ◽  
Learning Classifier

Rapid changes are occurring in our global ecosystem, and stresses on human well-being, such as climate regulation and food production, are increasing, soil is a critical component of agriculture. The project aims to use Data Mining (DM) classification techniques to predict soil data. Analysis DM classification strategies such as k-Nearest-Neighbors (k-NN), Random-Forest (RF), Decision-Tree (DT) and Naïve-Bayes (NB) are used to predict soil type. These classifier algorithms are used to extract information from soil data. The main purpose of using these classifiers is to find the optimal machine learning classifier in the soil classification. in this paper we are applying some algorithms of DM and machine learning on the data set that we collected by using Weka program, then we compare the experimental result with other papers that worked like our work.  According to the experimental results, the highest accuracy is k-NN has of 84 % when compared to the NB (69.23%), DT and RF (53.85 %). As a result, it outperforms the other classifiers. The findings imply that k-NN could be useful for accurate soil type classification in the agricultural domain.

SOIL CLASSIFICATION AND SOIL MAPS

Soil Science ◽  
1949 ◽  
Vol 67 (2) ◽  
pp. 169-176 ◽  
Author(s):  
J. W. MOON ◽  
W. S. LIGON ◽  
J. R. HENDERSON
Keyword(s):  
Soil Classification ◽  
Soil Maps

scholarly journals Identification and predictability of soil quality factors and indicators from conventional soil and vegetation classifications

2021 ◽  
Author(s):  
Paul Simfukwe ◽  
Paul W Hill ◽  
Davey L Jones ◽  
Bridget Emmett ◽  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Quality ◽  
Soil Type ◽  
Soil Classification ◽  
Biological Indicators ◽  
Soil Types ◽  
Quality Factors ◽  
Soil Attributes ◽  
Wide Range

Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter , Organic matter humification , Soluble nitrogen , Microbial biomass , Reduced nitrogen and Soil humification index . Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.

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