scholarly journals Determining the effects of the forest stand age on the soil quality index in afforested areas: A case study in the Palandöken Mountains

2021 ◽  
Author(s):  
Emre Çomaklı ◽  
Bülent Turgut
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Aggregate Stability ◽  
Principal Component ◽  
Erosion Control ◽  
Soil Samples ◽  
Total Carbon ◽  
Stand Age ◽  
Soil Quality Index ◽  
Score Functions

Afforestation is an essential strategy for erosion control. The objective of this study was to determine the soil quality index (SQI) in established afforested areas of different ages for erosion control in Erzurum, Turkey. Three afforested areas were selected as plots considering their establishment periods: + 40 years old (AA<sub>&gt;40</sub>), 10–40 years old (AA<sub>10–40</sub>), and less than 10 years old (AA<sub>&lt;10</sub>). Forty soil samples were taken in each plot area over the 0–15 and 15–30 cm depths. The soil samples were analysed for the texture, mean weight diameter, aggregate stability, pH, electrical conductivity, total nitrogen, total carbon, and total sulfur contents. These properties were used as the soil quality indicators, whereby the analytic hierarchy process (AHP) and principal component analysis (PCA) were used to establish their relative importance for describing the soil quality. The indicators were scored using the linear score functions of “more is better” and “optimum value”. For determining the SQI, the additive method (SQI<sub>A</sub>), the weighted method with AHP (SQI<sub>AHP</sub>), and the weighted method with PCA (SQI<sub>PCA</sub>) were used. The SQI scores of the plots showed statistically significant differences. In all three methods, the highest SQI value was obtained from the AA<sub>&gt;40</sub> plots.

scholarly journals Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

2021 ◽  
Vol 13 (4) ◽  
pp. 1824
Author(s):  
Mohamed K. Abdel-Fattah ◽  
Elsayed Said Mohamed ◽  
Enas M. Wagdi ◽  
Sahar A. Shahin ◽  
Ali A. Aldosari ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Western Desert ◽  
Geographical Information ◽  
Soil Quality Index

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

DETERMINING THE SOIL QUALITY INDEX IN THE BATUMI DELTA, GEORGIA

Agrociencia ◽  
2021 ◽  
Vol 55 (1) ◽  
pp. 1-18
Author(s):  
Bülent Turgut ◽  
Merve Ateş ◽  
Halil Akıncı Akıncı
Keyword(s):  
Factor Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Organic Matter Content ◽  
Aggregate Stability ◽  
Clay Content ◽  
Matter Content ◽  
Soil Quality Index ◽  
Analytic Hierarchy ◽  
Score Functions

The soil quality index is a quantitative assessment concept and it is used in the evaluation of ecosystem components. Because of the high potential for agriculture and biodiversity, deltas are the most valuable parts of the ecosystem. This study aimed to determine the soil quality index (SQI) in the Batumi Delta, Georgia. For this purpose, the study area was divided into five plots due to their morphological positions (L1, L2, L3, L4, and L5). A total of 125 soil samples were taken for analysis including clay content (CC), silt content (SC), sand content (SaC), mean weight diameter (MWD), aggregate stability (AS), amount of water retained under -33 kPa (FC) and -1500 kPa (WP) pressures and organic matter content (OM). These properties were used as the main criteria, and the Analytic Hierarchy Process (AHP) and Factor Analysis were used for weighting them. Sub-criteria were scored using expert opinion and the linear score functions, such as “more is better” and “optimum value”. For determining SQI, the additive method (SQIA), the weighted method with AHP (SQIAHP), and the weighted method with factor analysis (SQIFA) were used. The resulting SQI scores of the three methods were ordered as SQIAHP>SQIA>SQIFA, but these differences were not significant. However, the SQI scores of the plots (p≤0.01) showed statistically significant differences and were ordered as L5>L4>L3>L2>L1.

scholarly journals The Assessment of Soil Quality Index for Paddy Fields with Indicator Biology in Jatipurno Districts, Wonogirl

2019 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Supriyadi Supriyadi ◽  
Widyatmani Sih Dewi ◽  
Desmiasari Nugrahani ◽  
Adila Azza Rahmah ◽  
Haryuni Haryuni ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Management Practices ◽  
Principal Component ◽  
Biological Indicators ◽  
Paddy Fields ◽  
Soil Quality Index ◽  
Data Set ◽  
Organic Rice ◽  
Minimum Data

Increased rice needs in an extensive use of paddy fields in the Jatipurno, Wonogiri. Managing rice fields can reduce soil quality. Proper management can improve soil quality, Jatipurno has management such as organic, semi-organic and inorganic paddy field management which have a real effect on soil quality. Assessment of soil quality is measured by physical, chemical and biological indicators, where each factor has a different effect. The chemical indicators are often used as the main indicators for determining soil quality, whereas every parameter has the opportunity to be the main indicator. So, biological indicators can play indicators. The main indicators are obtained from the correlation test (p-values &le; 0,05 - &lt; 0,01) and Principal Component Analysis with high value, eigenvalues &gt; 1 have the potential to be used as Minimum Data Sets. The result is biological can be able to use as the Minimum Data Set such as microbial carbon biomass, respiration, and total bacterial colonies. The Soil Quality Index (SQI) of various paddy management practices shows very low to low soil quality values. The management of organic rice systems shows better Soil Quality Index with a score of 0,20 compared to other management. The practice of organic rice management shows that it can improve soil quality.

scholarly journals Using Soil Quality Index Plus to assess soil conditions and limiting factors for dryland farming

2020 ◽  
Vol 17 (2) ◽  
pp. 100
Author(s):  
Latief Mahir Rachman
Keyword(s):  
Plant Growth ◽  
Soil Quality ◽  
Quality Index ◽  
Assessment System ◽  
Organic Fertilizers ◽  
Total Carbon ◽  
Soil Conditions ◽  
Soil Quality Index ◽  
Dryland Farming ◽  
Soil Information

Agriculture 3.0 and Agriculture 4.0 requires appropriate agricultural practices, including soil data that are practical, accurate, and easy to understand. Using soil type maps and land suitability class maps for soil information not only challenges users but also does not provide soil quality information such as production potential and plant growth and production inhibitors. Other techniques that can provide more appropriate soil information for agricultural purposes are thus needed. This research suggests the soil assessment system Soil Quality Index Plus, which provides accessible information regarding soil conditions and plant growth and production inhibitors in the context of dryland farming. Field trials were conducted in 36 locations across five regencies in West Java, Indonesia. Soil Quality Index Plus accurately assessed soil quality by using 11 key parameters as a dataset: effective depth, texture class, bulk density, drainage, pH, cation exchange capacity, total organic nitrogen, available phosphate, exchangeable potassium, aluminum saturation, and total carbon organic. The majority of the soils studied were classified as medium soil quality, with low organic carbon being the most common limiting factor. Improved fertilizer management, especially the use of organic fertilizers, phosphate- and nitrogen-based fertilizers, and agricultural lime should be implemented in particular areas.

scholarly journals Environmental soil quality index and indicators for a coal mining soil

2015 ◽  
Vol 7 (1) ◽  
pp. 617-638 ◽  
Author(s):  
R. E. Masto ◽  
S. Sheik ◽  
G. Nehru ◽  
V. A. Selvi ◽  
J. George ◽  
...  
Keyword(s):  
Coal Mining ◽  
Soil Quality ◽  
Quality Index ◽  
Underground Mining ◽  
Principal Component ◽  
Soil Parameters ◽  
Soil Quality Index ◽  
Mining Sites ◽  
Opencast Mine

Abstract. Assessment of soil quality is one of the key parameters for evaluation of environmental contamination in the mining ecosystem. To investigate the effect of coal mining on soil quality, opencast and underground mining sites were selected in the Raniganj Coafield area, India. The physical, chemical, biological parameters, heavy metals, and PAHs contents of the soils were evaluated. Soil dehydrogenase (+79%) and fluorescein (+32%) activities were significantly higher in underground mine (UGM) soil, whereas peroxidase activity (+57%) was higher in opencast mine (OCM) soil. Content of As, Be, Co, Cr, Cu, Mn, Ni, and Pb was significantly higher in OCM soil, whereas, Cd was higher in UGM. In general, the PAHs contents were higher in UGM soils probably due to the natural coal burning in these sites. The observed values for the above properties were converted into a unit less score (0–1.00) and the scores were integrated into environmental soil quality index (ESQI). In the unscreened index (ESQI-1) all the soil parameters were included and the results showed that the quality of the soil was better for UGM (0.539) than the OCM (0.511) soils. Principal component analysis was employed to derive ESQI-2 and accordingly, total PAHs, loss on ignition, bulk density, Be, Co, Cr, Ni, Pb, and microbial quotient (respiration: microbial biomass ratio) were found to be the most critical properties. The ESQI-2 was also higher for soils near UGM (+10.1%). The proposed ESQI may be employed to monitor soil quality changes due to anthropogenic interventions.

scholarly journals Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

2021 ◽  
Vol 6 (2) ◽  
pp. 173
Author(s):  
Putri Tunjung Sari ◽  
Indarto Indarto ◽  
Marga Mandala ◽  
Bowo Eko Cahyono
Keyword(s):  
Principal Component Analysis ◽  
Soil Quality ◽  
Paddy Field ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Paddy Fields ◽  
Organic Fertilizers ◽  
Soil Quality Index ◽  
Sentinel 2

The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia. Input data for this study consist of land cover (interpreted from the Sentinel-2 image), soil type, and slope maps. Furthermore, the procedure to calculate soil quality index (SQI) include (1) spatial analysis to create the land unit, (2) preparation of soil sampling, (3) soil chemical analysis, (4) principal component analysis (PCA), and (5) reclassifying soil quality index (SQI).  The PCA results showed that three variables i.e., % sand, total- P, and % silt were strongly correlated to SQI, while three classes namely very low, low, and medium of SQI were sufficiently used to describe the spatial variability of the paddy field. Furthermore, approximately 41.14% of the paddy field area were classed as very low while 52.23%, and 6.63% were categorized as low and medium SQI respectively. Based on the results, about 93.37% of paddy fields in Jember Regency still require improvement in soil quality via the addition of ameliorants such as organic fertilizers to increase quality and productivity. This application needs to focus on areas with very low-low quality hence, the quality increased to the medium category. Keywords : Mapping; Soil Quality Index (SQI); PCA; Paddy field Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals Development of soil biological quality index for soils of semi-arid tropics

2019 ◽  
Author(s):  
Selvaraj Aravindh ◽  
Chinnappan Chinnadurai ◽  
Danajeyan Balachandar
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Tamil Nadu ◽  
Microbial Biomass Carbon ◽  
Principal Component ◽  
Soil Types ◽  
Consensus Protocol ◽  
Soil Quality Index ◽  
Biological Quality ◽  
Biological Health

Abstract. The Agricultural intensification, an inevitable process to feed the ever-increasing population, affects the soil quality due to management-induced changes. To measure the soil quality in terms of the soil functioning, several attempts were made to develop the soil quality index (SQI) based on a set of soil attributes. However, there is no universal consensus protocol available for SQI and the role of soil biological indicators in SQI is meagre. Therefore, the objective of the present work is to develop a unitless soil biological quality index (SBQI) scaled between 0 and 10, which would be a major component of SQI in future. The long-term organic manure amended (OM), integrated nutrient management enforced (INM), synthetic fertilizer applied (IC) and unfertilized control (Control) soils from three different predominant soil types with three different cropping patterns of the location (Tamil Nadu state, India) were chosen for this. The soil organic carbon, microbial biomass carbon, labile carbon, protein index, dehydrogenase activity and substrate-induced respiration were used to estimate the SBQI. Five different SBQI methods viz., simple additive (SBQI-1 and SBQI-2), scoring function (SBQI-3), principal component analysis-based statistical modeling (SBQI-4) and quadrant-plot based method (SBQI-5) were developed to estimate the biological quality as unitless scale. All the five methods have same resolution to discriminate the soils and INM ≈ OM > IC > Control is the relative trend being followed in all the soil types based on the SBQIs. All the five methods were further validated for their efficiency in 25 farmers' soils of the location and proved that these methods can be effectively used to scale the biological health of the soil. Among the five SBQIs, we recommend SBQI-5, which relates the variables to each other to scale the biological health of the soil.

scholarly journals Development of a Soil Quality Index for Soils under Different Agricultural Management Conditions in the Central Lowlands of Mexico: Physicochemical, Biological and Ecophysiological Indicators

2020 ◽  
Vol 12 (22) ◽  
pp. 9754
Author(s):  
Héctor Iván Bedolla-Rivera ◽  
María de la Luz Xochilt Negrete-Rodríguez ◽  
Miriam del Rocío Medina-Herrera ◽  
Francisco Paúl Gámez-Vázquez ◽  
Dioselina Álvarez-Bernal ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Agricultural Soils ◽  
Agricultural Practices ◽  
Principal Component ◽  
Agricultural Management ◽  
Soil Quality Index ◽  
Agricultural Industry ◽  
Data Variability

The Bajío—Mexico’s central lowlands—is a region of economic importance because of its agricultural industry. Over time, agricultural practices have led to soil deterioration, loss of fertility, and abandonment. In this study, six agricultural soils were analyzed: AGQ, CTH, CTJ, JRM, CRC, and CYI, and used to develop a soil quality index (SQI) that includes the use of physicochemical, biological, and ecophysiological indicators to differentiate soil quality. Principal component analysis (PCA) was used, reducing the indicators from 46 to 4, which represents 80.4% of data variability. It was implemented the equation of additive weights using the variance of the principal components as a weight factor for the SQI. The developed SQI was according to the indicators WHC, SLT, N-NO3−, and qCO2, differentiating the quality of soils from the agricultural management in low quality (JRM < CYI < AGQ) and moderate quality (CTJ < CRC < CTH). The use of biological and ecophysiological indicators added to the PCA and the equation of additive weights allowed establishing an SQI with a minimum of indicators, sensitive to agricultural management, facilitating its interpretation and implementation for the Mexican Bajío region and soils in similar conditions around the world.

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