Validation of soil quality index in soil using bioindicator plant

Soils provide a broad set of vital ecosystem services and sustains the production of food and fibers, balancing the ecosystem. Thus, from the perspective of soil quality, it is defined as an ability to balance within the ecosystem to sustain biological productivity, promoting the health of plants and animals, being evaluated by traditional indicators as physical, chemical and biological indicators, so the present work aims to estimate the soil quality index using multivariate models using soil biological attributes and validation with growth variables of the bioindicator plant. The study was developed in the agricultural area in P. Prudente, SP, the points collected were georeferenced, collections in depth of 0 -20 cm, microbiological analysis, microbial carbon and nitrogen biomass, dehydrogenase, respiration and microbial coefficient, having a bioindicator plant curly lettuce (Lucy Brown) as a validator of the soil. The results were discovered using the PCA model for the identification of autos vectors and autos values, grouping and identifying their collinearities, linear regression, r-pearson validation and cluster heuristic analysis. The microbial attributes and the bioindicator plant discriminated the agricultural areas evaluated with establishment and validation of SQI. The metabolic coefficient and N of the microbial biomass dissipation of the highest covariance values by multivariate analysis. The reforestation area with native species (SQI0.782%) and the livestock crop integration system (SQI0.765%) were evaluated as areas with better soil quality

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

Modern Applied Science ◽

10.5539/mas.v14n1p20 ◽

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 ≤ 0,05 - < 0,01) and Principal Component Analysis with high value, eigenvalues > 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.

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Soil quality index for agricultural areas under different levels of anthropopressure

International Agrophysics ◽

10.31545/intagr/113349 ◽

2019 ◽

Vol 33 (4) ◽

pp. 455-462 ◽

Cited By ~ 2

Author(s):

Agnieszka Klimkowicz-Pawlas ◽

Aleksandra Ukalska-Jaruga ◽

Bożena Smreczak

Keyword(s):

Soil Quality ◽

Quality Index ◽

Soil Quality Index ◽

Agricultural Areas ◽

Different Levels

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SOIL QUALITY INDEX ANALYSIS IN EFFORTS TO OVERCOME LAND DEGRADATION IN NGANJUK REGENCY

Jurnal Sains dan Teknologi Mitigasi Bencana ◽

10.29122/jstmb.v15i2.4564 ◽

2020 ◽

Vol 15 (2) ◽

pp. 96-106

Author(s):

Dyah Nursita

Keyword(s):

Soil Quality ◽

Land Degradation ◽

Quality Index ◽

Quality Criteria ◽

Biological Indicators ◽

Minimum Data Set ◽

Rooting Depth ◽

Soil Quality Index ◽

Data Set ◽

Minimum Data

Abstract Soil quality is ability of soil to preserve the productivity of pants, preserve maintain water supplies, and support human activities. Soil quality assessment results can be used as a recommendation in addressing land degradation. The soil quality cannot be directly measured therefore physical, chemical and biological indicators collectively are determined which influence the soil quality called minimum data set (MDS). A study and experimental analysis was conducted in August - November, 2019. The descriptive study was done in some land units in Nganjuk Regency by measuring its soil index quality using Mausbach and Seybold (1998) criteria which has been modified by Partoyo (2005). The soil quality index was analyzed using function that represented most of the soil. The soil samples were taken by purposive sampling and the texture, volume weight, porosity, C-organic, pH, P-available, K-exchangeable, N-total and rooting depth were analyzed in laboratories. Soil quality index values ranged between 0-1. The higher index value indicates better quality. The analysis result of selected soil functions (MDS) and MDS scores were than summed to determine the value of the soil quality index (SQI). The study concludes that several land units in Ngluyu, Wilangan, and Tanjunganom Districts that had low soil quality (IKT = 0.2399 - 0.3869). Meanwhile, the land units in Bagor District have very good soil quality criteria (IKT = 0.8671). Keywords: soil quality index, land degradation, minimum data set, nganjuk.

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Soil vital signs: A new Soil Quality Index (SQI) for assessing forest soil health

10.2737/rmrs-rp-65 ◽

2007 ◽

Cited By ~ 24

Author(s):

Michael C. Amacher ◽

Katherine P. O'Neil ◽

Charles H. Perry

Keyword(s):

Forest Soil ◽

Soil Quality ◽

Quality Index ◽

Soil Health ◽

Vital Signs ◽

Soil Quality Index

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A new approach to elaborate a multifunctional soil quality index

Journal of Soils and Sediments ◽

10.1007/bf02991142 ◽

2004 ◽

Vol 4 (3) ◽

pp. 201-204 ◽

Cited By ~ 1

Author(s):

Giancarlo Barbiroli ◽

Giovanni Casalicchio ◽

Andrea Raggi

Keyword(s):

Soil Quality ◽

Quality Index ◽

Soil Quality Index ◽

New Approach

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Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Agronomy ◽

10.3390/agronomy11071426 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1426

Author(s):

Ahmed S. Abuzaid ◽

Mohamed A. E. AbdelRahman ◽

Mohamed E. Fadl ◽

Antonio Scopa

Keyword(s):

Remote Sensing ◽

Soil Quality ◽

Land Degradation ◽

Quality Index ◽

Management Strategies ◽

Remote Sensing Data ◽

Western Desert ◽

Soil Quality Index ◽

Climate Conditions ◽

Sensing Data

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

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