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 biological quality index for soils of semi-arid tropics

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 483-497
Author(s):  
Selvaraj Aravindh ◽  
Chinnappan Chinnadurai ◽  
Dananjeyan 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. Agricultural intensification, an inevitable process to feed the ever-increasing population, affects soil quality due to management-induced changes. To measure the soil quality in terms of soil functioning, several attempts were made to develop a 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 present work aims to develop a unitless soil biological quality index (SBQI) scaled between 0 and 10, which would be a major component of SQI in the 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 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 (SBQI1 and SBQI2), scoring function (SBQI3), principal component analysis-based statistical modelling (SBQI4), and quadrant-plot-based method (SBQI5), were developed to estimate the biological quality as a unitless scale. All five methods have the 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 five methods were further validated for their efficiency in 25 farmers' soils of the location and proved that these methods can scale the biological health of the soil. Among the five SBQIs, we recommend SBQI5, which relates the variables to each other to scale the biological health of the soil.

scholarly journals Performance Analysis and Soil Quality Indexing for Dalbergia sissoo Roxb. Grown in Marginal and Degraded Land of Eastern Uttar Pradesh, India

Land ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 63 ◽  
Author(s):  
Sheikh Adil Edrisi ◽  
Vishal Tripathi ◽  
Purushothaman Chirakkuzhyil Abhilash
Keyword(s):  
Soil Quality ◽  
Microbial Biomass Carbon ◽  
Uttar Pradesh ◽  
Principal Component ◽  
Climatic Conditions ◽  
Dalbergia Sissoo ◽  
Land Restoration ◽  
Degraded Land ◽  
Soil Quality Index ◽  
Degraded Lands

The successful utilization of marginal and degraded lands for biomass and bioenergy production depends upon various factors such as climatic conditions, the adaptive traits of the tree species and their growth rate and respective belowground responses. The present study was undertaken to evaluate the growth performance of a bioenergy tree (Dalbergia sissoo Roxb.) grown in marginal and degraded land of the Mirzapur district of Uttar Pradesh, India and to analyze the effect of D. sissoo plantations on soil quality improvement over the study years. For this, a soil quality index (SQI) was developed based on principal component analysis (PCA) to understand the effect of D. sissoo plantations on belowground responses. PCA results showed that among the studied soil variables, bulk density (BD), moisture content (MC), microbial biomass carbon (MBC) and soil urease activity (SUA) are the key variables critically influencing the growth of D. sissoo. The SQI was found in an increasing order with the growth period of D. sissoo. (i.e., from 0.419 during the first year to 0.579 in the fourth year). A strong correlation was also observed between the growth attributes (diameter at breast height, R2 = 0.870; and plant height, R2 = 0.861) and the soil quality (p < 0.01). Therefore, the developed SQI can be used as key indicator for monitoring the restoration potential of D. sissoo growing in marginal and degraded lands and also for adopting suitable interventions to further improve soil quality for multipurpose land restoration programs, thereby attaining land degradation neutrality and United Nations Sustainable Development Goals.

scholarly journals Assessing Soil Quality for Sustainable Cropland Management Based on Factor Analysis and Fuzzy Sets: A Case Study in the Lhasa River Valley, Tibetan Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 3477 ◽  
Author(s):  
Fuqiang Dai ◽  
Zhiqiang Lv ◽  
Gangcai Liu
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Soil Quality ◽  
Quality Index ◽  
Soil Types ◽  
The Tibetan Plateau ◽  
Soil Quality Index ◽  
Total N ◽  
Organic C ◽  
Land Use Types

Ecologically fragile cropland soils and intensive agricultural production are characteristic of the valley area of the Tibetan Plateau. A systematic assessment of soil quality is necessary and important for improving sustainable cropland management in this area. This study aims to establish a minimum data set (MDS) for soil quality assessment and generate an integrated soil quality index for sustainable cropland management in the Tibetan Plateau. Soil samples were collected from the 0–20 cm depths of agricultural land in the middle and lower reaches of the Lhasa River. These samples were analyzed by routine laboratory methods. Significant differences were identified via statistical test between different soil types and land use types for each soil property. Principal component analysis was used to define a MDS of indicators that determine soil quality. Consequently, effective porosity, pH, total organic C, total N, available P, and catalase were identified as the final MDS. The soil quality index was obtained by the fuzzy-set membership function and the linear weighted additive method. The soil quality index differed significantly between the soil types and land use types. The soil quality can be ranked based on their indices in the following order: 1. Grain land with meadow soils, 2. Grain land with steppe soils, 3. Greenhouse vegetable land with fluvo-aquic soils, 4. Grain land with fluvo-aquic soils. The soils with higher soil quality indices exhibited better soil structure, higher nutrient contents, and superior resistance to water and nutrient loss. While the intensive tillage practices associated with vegetable production could reduce the values for effective porosity, pH and catalase, the application of appropriate fertilizers increased the values for total organic C, total N and available P. Therefore, the MDS method is an effective and useful tool to identify the key soil properties for assessing soil quality, and provides guidance on adaptive cropland management to a variety of soil types and land use types.

scholarly journals Soil attributes and quality under treated domestic sewage irrigation in sugarcane

2018 ◽  
Vol 22 (2) ◽  
pp. 137-142 ◽  
Author(s):  
Eduardo A. A. Barbosa ◽  
Edson E. Matsura ◽  
Leonardo N. S. dos Santos ◽  
Aline A. Nazário ◽  
Ivo Z. Gonçalves ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Microbial Biomass Carbon ◽  
Chemical Properties ◽  
Domestic Sewage ◽  
Soil Quality Index ◽  
Reservoir Water ◽  
Soil Attributes ◽  
Subsurface Drip ◽  
Surface Reservoir

ABSTRACT Using domestic sewage to irrigate and supply nutrients to plants is a sustainable practice; however, due to the physical and chemical properties of the domestic sewage, soil attributes and quality may be changed with its application. The aim of this study was to evaluate soil quality after two cycles of sugarcane irrigated with treated domestic sewage and surface reservoir water via subsurface drip irrigation, with and without nutritional supplementation by fertigation, and a non-irrigated control with top-dressing fertilization. Soil quality was established by applying the methodology proposed by Karlen & Stott. Physical, chemical and microbiological indicators were selected to compose the basic soil functions used to determine the quality index. Application of treated domestic sewage with fertigation increased soil electrical conductivity, Na+ content and exchangeable sodium percentage. Reservoir water applications with fertigation increased microbial biomass carbon and reduced the metabolic quotient, besides promoting significant effects on soil acidification indicators in comparison to reservoir water irrigation without fertigation. Despite the alteration of some soil attributes, no significant changes in the soil quality index were observed among the treatments.

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 Changes in soil quality after converting <i>Pinus</i> to <i>Eucalyptus</i> plantations in southern China

Solid Earth ◽  
2015 ◽  
Vol 6 (1) ◽  
pp. 115-123 ◽  
Author(s):  
K. Zhang ◽  
H. Zheng ◽  
F. L. Chen ◽  
Z. Y. Ouyang ◽  
Y. Wang ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Soil Quality ◽  
Quality Index ◽  
Microbial Biomass Carbon ◽  
Southern China ◽  
Soil Quality Index ◽  
Microbial Biomass Nitrogen ◽  
The Third ◽  
Eucalyptus Plantations ◽  
Time Substitution

Abstract. Vegetation plays a key role in maintaining soil quality, but long-term changes in soil quality due to plant species change and successive planting are rarely reported. Using the space-for-time substitution method, adjacent plantations of Pinus and first, second, third and fourth generations of Eucalyptus in Guangxi, China were used to study changes in soil quality caused by converting Pinus to Eucalyptus and successive Eucalyptus planting. Soil chemical and biological properties were measured and a soil quality index was calculated using principal component analysis. Soil organic carbon, total nitrogen, alkaline hydrolytic nitrogen, microbial biomass carbon, microbial biomass nitrogen, cellobiosidase, phenol oxidase, peroxidase and acid phosphatase activities were significantly lower in the first and second generations of Eucalyptus plantations compared with Pinus plantation, but they were significantly higher in the third and fourth generations than in the first and second generations and significantly lower than in Pinus plantation. Soil total and available potassium were significantly lower in Eucalyptus plantations (1.8–2.5 g kg−1 and 26–66 mg kg−1) compared to the Pinus plantation (14.3 g kg−1 and 92 mg kg−1), but total phosphorus was significantly higher in Eucalyptus plantations (0.9–1.1 g kg−1) compared to the Pinus plantation (0.4 g kg−1). As an integrated indicator, soil quality index was highest in the Pinus plantation (0.92) and lowest in the first and second generations of Eucalyptus plantations (0.24 and 0.13). Soil quality index in the third and fourth generations (0.36 and 0.38) was between that in Pinus plantation and in first and second generations of Eucalyptus plantations. Changing tree species, reclamation and fertilization may have contributed to the change observed in soil quality during conversion of Pinus to Eucalyptus and successive Eucalyptus planting. Litter retention, keeping understorey coverage, and reducing soil disturbance during logging and subsequent establishment of the next rotation should be considered to help improving soil quality.

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.

scholarly journals Mapping Soil Quality in Various Land Uses as a Basis for Soil Management in Wonogiri, Indonesia

2020 ◽  
Vol 11 (2) ◽  
pp. 127-135
Author(s):  
Mujiyo MUJIYO ◽  
Suntoro SUNTORO ◽  
Restu Prasetyaning TYAS ◽  
Aktavia HERAWATI ◽  
Hery WIDIJANTO
Keyword(s):  
Land Use ◽  
Soil Quality ◽  
Microbial Biomass Carbon ◽  
Principal Component ◽  
Soil Management ◽  
Agroforestry System ◽  
Land Uses ◽  
Soil Quality Index ◽  
Data Set ◽  
Quality Status

Soil quality is closely related to environment because soil is not only viewed as a growing media for plants but also encompasses various environmental and health functions. It is important to know the quality of soil in order to keep it healthy, productive, and optimally functioning. This research aims to evaluate soil quality status in various land uses and to learn the land factors that are related to soil quality. Soil quality index (SQI) represents the soil quality status. SQI will then be used as the basis for soil management. A descriptive explorative research study was carried out in the Giritontro Sub-district, Wonogiri District, Indonesia. SQI indicators were obtained from 12 existing Land Mapping Units (LMU). SQI was obtained by determining the Minimum Data Set (MDS) with a Principal Component Analysis (PCA) test. Then SQI was mapped and statistically analyzed to determine the influence of land use and the determinant factors of SQI. Results showed that SQI in all area is class 3 or moderate. SQI was significantly influenced by land use. SQI in paddy field is 9.09% higher than crop fields and 2.27% higher than of plantations. Indicators which are significantly related to SQI are bulk density, porosity, cation exchange capacity, available P, available K and microbial biomass carbon (MBC). The type of soil management that can be implemented to improve soil quality includes addition of organic or inorganic fertilizer and adoption of an agroforestry system.

scholarly journals The effect of land use on soil quality in Giriwoyo Sub-district, Wonogiri Regency

2020 ◽  
Vol 8 (2) ◽  
pp. 2559-2568
Author(s):  
M Mujiyo ◽  
Yosua Yoga Setyawan ◽  
Aktavia Herawati ◽  
Hery Widijanto
Keyword(s):  
Land Use ◽  
Soil Quality ◽  
Quality Index ◽  
Microbial Biomass Carbon ◽  
Available P ◽  
Soil Quality Index ◽  
Data Set ◽  
Total N ◽  
Organic C

Determination of soil quality in Giriwoyo Sub-district, Wonogiri Regency, will generate a Soil Quality Index which can be used as a reference for soil cultivation for optimal productivity. This research was a descriptive exploratory with a survey approach. The survey area consisted of 12 land mapping units (LMU) with 3 replications for each LMU. Determination of LMU based on soil type, land use, slope and rainfall. The parameters used were BD (bulk density), porosity, organic C, pH, CEC (cation exchange capacity), BS (base saturation), available P, available K, total N, and MBC (microbial biomass carbon) that represented the physical, chemical and biological properties of the soil. Principal Component Analysis (PCA) analysis was performed to obtain the Minimum Data Set (MDS). The Soil Quality Index (SQI) at each LMU was calculated by multiplying the PCA result score (Wi) with the score for each selected indicator (Si). The result showed that the Soil Quality Index at each LMU was low. The highest Soil Quality Index was found in fields land use with an SQI of 0.34. The soil indicator that limited the soil quality was available P.

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