scholarly journals Nutrient index, microbial activity and soil quality depleted under sugarcane mono-sequencing in Indian subtropics

2021 ◽  
Author(s):  
S. R. Singh ◽  
S. Singh ◽  
Tapendra Srivastava ◽  
M. Tripathi ◽  
R. Dohare ◽  
...  
Keyword(s):  
Microbial Activity ◽  
Soil Quality ◽  
Management Practices ◽  
Central Zone ◽  
Uttar Pradesh ◽  
Soil Samples ◽  
Sensitivity Index ◽  
Soil Quality Index ◽  
Available Nitrogen ◽  
Nutrient Index

Sugarcane mono-sequencing is one of the prominent systems of Uttar Pradesh but wide variations in cane yields are the main cause of concern across the cane producing zones. Hence, 131, 198 and 122 composite soil samples (0-20 cm depth) were taken from cane producing Eastern zone (EZ), Central zone (CZ) and Western zone (WZ) of Uttar Pradesh, respectively to assess the causes of yield variations and impact of sugarcane mono-sequencing on nutrient index (NI), microbial activity and soil quality. Sugarcane mono-sequencing depleted soil organic carbon (SOC), available nitrogen (Na), potassium (Ka), sulphur (Sa) and Zn as 48.9, 98.2, 39.0, 43.3 and 26.4% soil samples falls in low categories, respectively. However, soil degradation aggregated more in EZ soils due to acute deficiency of macro-nutrients as well as high content of Cu, Fe and Mn in low-lying areas. Total microbial counts, microbial biomass of C and N, basal soil respiration, acid and alkaline phosphatase were highest in WZ, but Na, Pa, Ka, Sa, FDA, β-glucosidase and urease activity was greater in CZ soils. As a result, NI was greater in CZ (2.19) followed EZ (1.98) and WZ (1.97), but soil quality index (SQI) was found in the order of CZ (0.795) >WZ (0.785) > EZ (0.708). The sensitivity index (S) was lowest in CZ soils (1.420) indicated that WZ (1.810) and EZ (1.915) soils are more preferable and sensitive to perturbations and management practices. Na was the most vital indicator for sugarcane production followed by SOC, AZO and Sa.

scholarly journals Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health

SOIL ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 173-185 ◽  
Author(s):  
R. Zornoza ◽  
J. A. Acosta ◽  
F. Bastida ◽  
S. G. Domínguez ◽  
D. M. Toledo ◽  
...  
Keyword(s):  
Land Use ◽  
Human Health ◽  
Soil Quality ◽  
Management Practices ◽  
Land Use Changes ◽  
Aggregate Stability ◽  
Health Indicators ◽  
Nutrient Content ◽  
Sustainable Land Use ◽  
Soil Quality Index

Abstract. Soil quality (SQ) assessment has long been a challenging issue, since soils present high variability in properties and functions. This paper aims to increase the understanding of SQ through the review of SQ assessments in different scenarios providing evidence about the interrelationship between SQ, land use and human health. There is a general consensus that there is a need to develop methods to assess and monitor SQ for assuring sustainable land use with no prejudicial effects on human health. This review points out the importance of adopting indicators of different nature (physical, chemical and biological) to achieve a holistic image of SQ. Most authors use single indicators to assess SQ and its relationship with land uses – soil organic carbon and pH being the most used indicators. The use of nitrogen and nutrient content has resulted sensitive for agricultural and forest systems, together with physical properties such as texture, bulk density, available water and aggregate stability. These physical indicators have also been widely used to assess SQ after land use changes. The use of biological indicators is less generalized, with microbial biomass and enzyme activities being the most selected indicators. Although most authors assess SQ using independent indicators, it is preferable to combine some of them into models to create a soil quality index (SQI), since it provides integrated information about soil processes and functioning. The majority of revised articles used the same methodology to establish an SQI, based on scoring and weighting of different soil indicators, selected by means of multivariate analyses. The use of multiple linear regressions has been successfully used for forest land use. Urban soil quality has been poorly assessed, with a lack of adoption of SQIs. In addition, SQ assessments where human health indicators or exposure pathways are incorporated are practically inexistent. Thus, further efforts should be carried out to establish new methodologies to assess soil quality not only in terms of sustainability, productivity and ecosystem quality but also human health. Additionally, new challenges arise with the use and integration of stable isotopic, genomic, proteomic and spectroscopic data into SQIs.

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.

Assessment of tillage effects on soil quality of pastures in South Africa with indexing methods

Soil Research ◽  
2015 ◽  
Vol 53 (3) ◽  
pp. 274 ◽  
Author(s):  
P. A. Swanepoel ◽  
C. C. du Preez ◽  
P. R. Botha ◽  
H. A. Snyman ◽  
J. Habig
Keyword(s):  
South Africa ◽  
Soil Quality ◽  
Management Practices ◽  
Soil Disturbance ◽  
Warning Signs ◽  
Chemical Degradation ◽  
Soil Quality Index ◽  
Physical Quality ◽  
Soil Physical Quality

Soil quality of pastures changes through time because of management practices. Excessive soil disturbance usually leads to the decline in soil quality, and this has resulted in concerns about kikuyu (Pennisetum clandestinum)–ryegrass (Lolium spp.) pasture systems in the southern Cape region of South Africa. This study aimed to understand the effects of tillage on soil quality. The soil management assessment framework (SMAF) and the locally developed soil quality index for pastures (SQIP) were used to assess five tillage systems and were evaluated at a scale inclusive of variation in topography, pedogenic characteristics and local anthropogenic influences. Along with assessment of overall soil quality, the quality of the physical, chemical and biological components of soil were considered individually. Soil physical quality was largely a function of inherent pedogenic characteristics but tillage affected physical quality adversely. Elevated levels of certain nutrients may be warning signs to soil chemical degradation; however, tillage practice did not affect soil chemical quality. Soil disturbance and the use of herbicides to establish annual pastures has lowered soil biological quality. The SQIP was a more suitable tool than SMAF for assessing soil quality of high-input, dairy-pasture systems. SQIP could facilitate adaptive management by land managers, environmentalists, extension officers and policy makers to assess soil quality and enhance understanding of processes affecting soil quality.

scholarly journals Available nutrients status in black soils of Varanasi district of eastern part of Uttar Pradesh

2018 ◽  
Vol 10 (4) ◽  
pp. 1238-1242
Author(s):  
Ashish Rai ◽  
Surendra Singh
Keyword(s):  
Uttar Pradesh ◽  
Mean Value ◽  
Soil Samples ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
High Status ◽  
Available Nitrogen ◽  
Available N ◽  
Mean Values ◽  
Fertility Status

Soil fertility status of the intensively vegetables growing in black soils of Varanasi (UP), India is not available. Therefore, present study was under-taken to assess the fertility status by collecting 100 surface (0-15 cm depth) soil samples using geographical positioning system (GPS). Soil samples were analysed following standard methods. The pH of the surface soils ranged from 7.3 to 8.4 with a mean of 7.9 indicating alkaline nature of soil. The EC of the soils were normal and ranged from 0.17 to 0.48 dS m-1 with mean value of 0.30 dS m-1. The organic carbon content of the soils ranged from 3 to 9 g kg-1 with a mean of 5.8 g kg-1. The range (mean) values of available N, P and K were 106 to 291 (184), 8 to 65 (27) and 145 to 358 (229) kg ha-1, respectively. The soils were found 99% low in available N content and nutrient index rating of available P was found high. The range (mean) values of B, Zn, Cu, Fe and Mn were, 0.11 to 0.53 (0.40), 0.38 to 3.04 (1.08), 0.22 to 1.96 (0.79), 2.9 to 16.40 (8.42) and 0.68 to 12.64 (3.24) mg kg-1 soil, respectively. The available sulphur of the soils ranged from 7 to 33 mg S kg-1 with mean of 15 mg S kg-1 soil. Soils were categorised in to low, medium and high status, subsequently 99 % soils were found low in available nitrogen whereas 63% soil samples were found medium in available phosphorus, and 88% were medium in available potash. 26, 48 and 26% soils were low, medium and high in S, respectively and the corresponding Figs. for B were 90, 10 and 0%. In case of cationic micronutrients, 7, 58 and 35% soils were low, medium and high in Zn and the corresponding Figs. for Available Mn were 30, 44 and 26%. Fe was found 6, 59 and 35% in low, medium and high category respectively. There was no copper deficiency found in black soils of Varanasi.

scholarly journals Soil quality index as affected by temporal long term tea cultivation in Jorhat District of Assam

2020 ◽  
Vol 41 (6) ◽  
pp. 1685-1695
Author(s):  
N. Baruah ◽  
◽  
B.K. Medhi ◽  
Sanjay Swami ◽  
R.K. Thakuria ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Age Groups ◽  
Minimum Data Set ◽  
Soil Quality Index ◽  
Available Nitrogen ◽  
Systems Methodology ◽  
Data Set ◽  
Minimum Data ◽  
Tea Plantations

Aim: The investigation was undertaken to identify the Minimum Data Set (MDS) for Soil Quality Index (SQI) assessment in continuouslong-term tea cultivation systems. Methodology: In the study under very deep, fine loamy, well-drained soil, five age groups of tea plantations viz. less than 15 years, 15-30 years, 30-45 years, 45-60 years and more than 60 years were selected and identified minimum data set and soil quality index. Results: In very deep, fine loamy, well-drained soil under continuous tea cultivation, SQI, 14.74 was obtained for less than 15 years, 14.06 for 15-30 years, 11.12 for 30-45 years, 12.94 for 45-60 years and 11.37 for more than 60 years of plantation, respectively. Interpretation: The most sensitive soil quality indicators identified in very deep, fine loamy, well-drained soil were pH for less than 15 years, total nitrogen for 15-30 years, available nitrogen for 30-45 years, organic carbon for 45-60 years and exchangeable aluminium for more than 60 years of continuous tea cultivation.

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.

Soil quality index to assess soil management practices in the semi-arid of Morocco

2021 ◽  
Author(s):  
Hiba Et-Tayeb ◽  
Khalid Ibno Namr ◽  
El Houssine El Mzouri ◽  
Bouchra El Bourhrami
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Management Practices ◽  
Soil Management ◽  
Soil Quality Index ◽  
Semi Arid

scholarly journals Degradation of soil quality by the waste leachate in a Mediterranean semi-arid ecosystem

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sh. Yeilagi ◽  
Salar Rezapour ◽  
F. Asadzadeh
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Crop Yields ◽  
Wheat Yield ◽  
Sensitivity Index ◽  
Soil Quality Index ◽  
Data Set ◽  
Total N ◽  
Quality Degradation ◽  
Waste Leachate

AbstractThe assessment of soil quality indices in waste leachate-affected soils is vital to understand the threats of land quality degradation and how to control it. In this respect, a study was conducted on the effects of uncontrolled landfill leachate on soil quality index (SQI) in calcareous agricultural lands using 28 soil variables. Using the total data set (TDS) and minimum data set (MDS) approaches, the SQI was compared between leachate-affected soils (LAS) and control soils by the integrated quality index (IQI) and nemoro quality index (NQI) methods. The results revealed that LAS were significantly enriched by soil salinity-sodicity indices including electrical conductivity (EC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP), fertility indices including total N, available P and K, organic carbon, and cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, and Na), the available and total fractions of heavy metals (Zn, Cu, Cd, Pb, Ni). After the leachate got its way into the soil, the values of IQI and NQI were dropped ranging 5–16% and 6.5–13% for the TDS approach and 5–15.2% and 7.5–12.2 for the MDS approach, respectively. Clearly, the data showed that soil quality degradation was encouraged and stimulated by the leachate. Among the different models of SQI applied in the present study, IQI determined by MDS was the optimal model to estimate soil quality and predict crop yields given the analysis of the correlations among the SQI models, the correlations between the SQI models and wheat yield, and sensitivity index values.

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.

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