scholarly journals Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Jeffrey L. Smith ◽  
Jonathan J. Halvorson
Keyword(s):  
Spatial Variability ◽  
Soil Quality ◽  
Crop Production ◽  
Arable Land ◽  
Microbial Biomass C ◽  
Soil Conditions ◽  
Spatial And Temporal Variability ◽  
Biological Parameters ◽  
Field Scale ◽  
Soil Quality Index

Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK) procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO2> microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

scholarly journals Spatial Variability of Physical Soil Quality Index of an Agricultural Field

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sheikh M. Fazle Rabbi ◽  
Bina R. Roy ◽  
M. Masum Miah ◽  
M. Sadiqul Amin ◽  
Tania Khandakar
Keyword(s):  
Spatial Variability ◽  
Bulk Density ◽  
Soil Quality ◽  
Quality Indicators ◽  
Quality Index ◽  
Crop Production ◽  
Field Investigation ◽  
Agricultural Field ◽  
Soil Quality Indicators ◽  
Soil Quality Index

A field investigation was carried out to evaluate the spatial variability of physical indicators of soil quality of an agricultural field and to construct a physical soil quality index (SQIP) map. Surface soil samples were collected using10  m×10 m grid from an Inceptisol on Ganges Tidal Floodplain of Bangladesh. Five physical soil quality indicators, soil texture, bulk density, porosity, saturated hydraulic conductivity (KS), and aggregate stability (measured as mean weight diameter, MWD) were determined. The spatial structures of sand, clay, andKSwere moderate but the structure was strong for silt, bulk density, porosity, and MWD. Each of the physical soil quality indicators was transformed into 0 and 1 using threshold criteria which are required for crop production. The transformed indicators were the combined into SQIP. The kriged SQIPmap showed that the agricultural field studied could be divided into two parts having “good physical quality” and “poor physical soil quality.”

scholarly journals Soil Quality at Rice Fields with Organic, Semi-organic and Inorganic Management in Wonogiri Regency, Indonesia

2021 ◽  
Vol 36 (2) ◽  
pp. 259
Author(s):  
Supriyadi Supriyadi ◽  
Intan Lestari Prima Vera ◽  
Purwanto Purwanto
Keyword(s):  
Soil Quality ◽  
Rice Field ◽  
Public Awareness ◽  
Pearson Correlation ◽  
Principal Component ◽  
Rice Fields ◽  
Soil Conditions ◽  
Soil Quality Index ◽  
Data Set ◽  
Organic Rice

The high demand of rice is fulfilled by intensification, particularly with the use of chemical fertilizer that allegedly causes land and environmental problems in a long term. As public awareness of environmental health rises, more rice fields are managed organically and semi-organically, but there are still many that manage rice fields inorganically. Assessment of soil quality of the three types of rice field management is important to prove that organic rice fields have better soil quality than semi-organic and inorganic rice fields, as well as to evaluate soil conditions on the location. This research was conducted in Girimarto, Wonogiri, Indonesia, using a descriptive explorative method with a survey approach on three points of each management system of rice fields, which are organic, semi-organic and inorganic rice fields. Statistical analysis was performed by Pearson correlation analysis and principal component analysis (PCA) to determine the indicators affecting soil quality, which are called the minimum data set (MDS). There were selected indicators in this research, including total microbes, base saturation, cation exchangeable capacity and organic carbon. Based on the results of the study, organic rice fields have the best soil quality with a score of soil quality index (SQI) of 2.3, compared to semi-organic rice field SQI (2.2) and inorganic rice field SQI (1.7). The results indicate that organic management contributes to better soil quality and environment.

scholarly journals Spatio-temporal Soil Quality Assessment under Crop Rotation Irrigated with Treated Urban Wastewater using Fuzzy Modelling

2014 ◽  
Vol 28 (3) ◽  
pp. 291-302 ◽  
Author(s):  
Marjan Ghaemi ◽  
Ali R. Astaraei ◽  
Mehdi Nassiri Mahalati ◽  
Hojat Emami ◽  
Hossein H. Sanaeinejad
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Biological Significance ◽  
Minimum Data Set ◽  
Spatial And Temporal Variability ◽  
Urban Wastewater ◽  
Soil Quality Index ◽  
Data Set ◽  
Minimum Data ◽  
Total Data

Abstract Quantifying soil quality is important for assessing soil management practices effects on spatial and temporal variability of soil quality at the field scale. We studied the possibility of defining a simple and practical fuzzy soil quality index based on biological, chemical and physical indicators for assessing quality variations of soil irrigated with well water and treated urban wastewater during two experimental years. In this study 6 properties considered as minimum data set were selected out of 18 soil properties as total data set using the principal component analysis. Treated urban wastewater use had greater impact on biological and chemical quality. The results showed that the studied minimum data set could be a suitable representative of total data set. Significant correlation between the fuzzy soil quality index and crop yield (R2= 0.72) indicated the index had high biological significance for studied area. Fuzzy soil quality index approach (R2= 0.99) could be effectively utilized as a tool leading to better understanding soil quality changes. This is a first trial of creation of a universal index of soil quality undertaken.

Indexing soil quality: a new paradigm in soil science research

Soil Research ◽  
2007 ◽  
Vol 45 (2) ◽  
pp. 129 ◽  
Author(s):  
Teklu Erkossa ◽  
Fisseha Itanna ◽  
Karl Stahr
Keyword(s):  
Soil Quality ◽  
Land Management ◽  
Crop Production ◽  
Microbial Biomass Carbon ◽  
Block Design ◽  
Permanent Plots ◽  
Available Phosphorus ◽  
Soil Quality Index ◽  
Preparation Methods ◽  
Land Preparation

Soil quality indexing is a new approach in spatial and temporal evaluation of land management systems effects on soils’ capacity to function. A field experiment was conducted at Caffee Doonsa (2400 m a.s.l., 08°57′N, 39°06′E) for 6 years (1998–2003) to compare the effects of land preparation methods on soil quality (SQ) and to test the use of the Soil Management Assessment Framework (SMAF) in assessing SQ under the Ethiopian Central Highlands conditions. Four methods of land preparation [broad bed and furrows (BBF), green manure (GM), ridge and furrows (RF), and reduced tillage (RT)] were arranged in a randomised complete block design with 3 replications on permanent plots (22 m by 6 m). Physical, chemical, and biological SQ indicators were determined and scored, and a soil quality index (SQI) was developed using the SMAF procedures. Seven SQ indicators including microbial biomass carbon (MBC), bulk density, aggregate stability (AGG), soil organic carbon (Corg), pH, available water capacity (AWC), and available phosphorus were selected as a minimum dataset. The scored values of the indicators ranged from 0.21 for AGG and 0.97 for pH, both under BBF. Compared with RF (control), all the alternatives (GM, BBF, and RT) increased the scores of Corg and MBC. Moreover, BBF and GM increased the score values of AWC and AGG, respectively. Consequently, there was a non-significant increase in SQI due to the use of GM, BBF, and RT compared with the control. As a result, the land preparation methods may be preferred in a decreasing order GM ≥ BBF ≥ RT ≥ RF for the management goal of crop production. The study indicated that SMAF could be a robust tool to assess the performance of land management methods on soil quality in the study area, but some modifications may be required to fit to the prevailing cropping system and soil characteristics.

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 Assessment of soil quality indicators under rice ecosystem of Assam, India

2020 ◽  
Vol 41 (6) ◽  
pp. 1655-1664
Author(s):  
A. Gayan ◽  
◽  
D.J. Nath ◽  
B. Bhattacharyya ◽  
N. Dutta ◽  
...  
Keyword(s):  
Soil Quality ◽  
Rice Yield ◽  
Maximum Yield ◽  
Principal Component ◽  
Exchange Capacity ◽  
Biological Parameters ◽  
Quality Indices ◽  
Soil Quality Index ◽  
Rice Soils ◽  
Rice Ecosystem

Aim: To assess the soil quality indices and its impact on rice yield in Upper Brahmaputra Valley Zone of Assam. Methodology: Seventy-three numbers of geo referenced soil samples were collected from the rice ecosystems and analysed for twenty-one soil physical, chemical and biological parameters. The soil quality indices (SQI) were developed using statistical tools like principal component analysis (PCA) techniques and expert opinion (EO). Relative soil quality index (RSQI) was also developed for grouping the soils into categories. Correlation matrices were drawn between different soil quality indices. The optimum values of soil quality indices were computed to sustain 80% or more of the existing in field maximum rice yield (5.20 t ha-1). Results: Multivariate statistics showed that four biological parameters viz., fluorescein di-acetate activity, phosphate solubilising bacteria, total bacterial population and collembolan population and three chemical parameters viz., cation exchange capacity, electrical conductivity? and diethylene tri amine penta acetic acid-Zinc could explain 70.2% of the cumulative variance. RSQI demonstrated that >50% and >30% of soils belonged to medium and good category. The regression of percent relative rice yield obtained from farmers field, illustrated that soil functions based EO-SQI could explain high degree of relationship (R2=0.289; r=0.537*), followed by RSQI (R2=0.284;r=0.532*) and PCA-SQI (R2=0.143; r=0.378*) to explain the variability of soils. The optimum value indicates that the rice soils having PCA-SQI value >0.55 were likely to give 80% or more of the maximum yield of UBVZ of Assam. Interpretation: Approaches of rating of soil quality based on PCA-SQI may be a useful tool, and there is need of more extensive investigations to validate its usefulness for assessment of soil quality in different cropping sequences of Assam.

scholarly journals Quality Assessment and Suitability Evaluation of Soils under Tuber-based Cropping System in Katsina Ala Local Government Area, Benue State, Nigeria

2020 ◽  
Vol 24 (8) ◽  
pp. 1341-1350
Author(s):  
O.D. Adeyolanu ◽  
G.A. Oluwatosin ◽  
A.O. Denton ◽  
A.O. Adelana ◽  
K.S. Are ◽  
...  
Keyword(s):  
Local Government ◽  
Soil Quality ◽  
Crop Production ◽  
Crop Yields ◽  
Cropping Systems ◽  
Soil Management ◽  
Local Government Area ◽  
Long Term Effects ◽  
Soil Quality Index ◽  
Suitability Evaluation

Crop yields together with economic and social benefits of farming depend in part on land management and soil quality. Soil management and  cropping systems have long-term effects on agronomic and environmental functions. This study aimed at assessing soils under yam-based  cropping systems for quality and suitability so as to enhance sustainable production. The study was carried out in Katsina Ala local government area of Benue state where yam is a major crop. Sixteen modal profile were dug, described for characterization and suitability evaluation. Ten cluster locations were selected and twenty soil samples randomly collected within each cluster. The soils were subjected to laboratory analyses and results subjected descriptive statistics. Suitability of the soils for yam, citrus and groundnut were evaluated using parametric approach and soil quality of the area was assessed using Relative Soil Quality Indices (RSQI). The soils encountered are sandy to silty in nature with some having plinthite at depth. The soils, classified as Alfisol, Entisol and Inceptisol are moderately (S2) to highly suitable S1) for the three crops and have moderate to high quality for crop production with percentage soil quality index ranging from 60. 37 to 74.31 %. Soils of the study site are of good quality and are suitable for production of yam, citrus and groundnut. However, because yam is a great feeder and tropical soils are fragile making them prone todegradation, there is need for maintenance of soil fertility through organic matter management for sustainable use. Keywords: soil quality, suitability, yam, cropping systems, soil management

CROPPING SYSTEM EFFECTS ON SOIL QUALITY FOR THREE AGRO-ECOSYSTEMS IN INDIA

2013 ◽  
Vol 50 (3) ◽  
pp. 321-342 ◽  
Author(s):  
NISHANT K. SINHA ◽  
USHA KIRAN CHOPRA ◽  
ANIL KUMAR SINGH
Keyword(s):  
Soil Quality ◽  
Crop Production ◽  
Cropping Systems ◽  
Soil Layer ◽  
Cropping System ◽  
Scoring Functions ◽  
Soil Quality Index ◽  
Data Set ◽  
Non Linear ◽  
Semi Arid

SUMMARYSoil quality integrates the effects of soil physical, chemical and biological attributes. Some of them are dynamic in nature and behave differentially in various agro-ecosystems (AESs) and are quantified in terms of a soil quality index (SQI). An attempt has been made in this paper to develop an SQI based on a minimum data set (MDS), which could be used to evaluate the sustainability of the crop production in three varying AESs in India, namely sub-humid, semi-arid and arid. Thirteen indicators were utilized to develop the SQI from the properties measured from the surface soil layer (0–15 cm). Each indicator of the MDS was transformed into a dimensionless score based on scoring functions (linear and non-linear) and integrated into four SQIs. The weighted non-linear index (WNLI) was identified as the most sensitive for all the AESs and was recommended as an index for future assessments. Based on this index, the quantification of soil quality under several cropping systems was carried out for sub-humid, semi-arid and arid AESs and the most suitable cropping system was identified. WLNI was positively and significantly correlated (R2= 0.79,p< 0.01) with wheat equivalent yield for all the cropping systems. This clearly indicated that the index may be used satisfactorily for quantifying soil quality.

scholarly journals A Study of the Spatial Difference of the Soil Quality of The Mun River Basin during the Rainy Season

2019 ◽  
Vol 11 (12) ◽  
pp. 3423 ◽  
Author(s):  
Chunsheng Wu ◽  
Qingsheng Liu ◽  
Guoxia Ma ◽  
Gaohuan Liu ◽  
Fang Yu ◽  
...  
Keyword(s):  
Soil Quality ◽  
River Basin ◽  
Rainy Season ◽  
Crop Production ◽  
Soil Condition ◽  
Soil Conditions ◽  
Eastern Region ◽  
Data Set ◽  
Main Period

The Mun River basin is one of the main grain-producing areas of Thailand, and the rainy season is the main period for crop planting after being idle during the dry season. However, the soil conditions are variable, so an assessment of soil quality during the rainy season is necessary for improving soil condition and crop production. The aim of this study was to conduct a soil quality assessment based on soil samples. To attain that, a minimum data set theory was used to screen evaluation indicators and geographically weighted regression was performed to obtain spatial interpolations of indicators, while the fuzzy logic model was used to determine the soil quality results. The results showed that the contents of indicators had similar spatial trends as their contents declined from the western to the eastern region of the basin. The soil quality results showed that the poor soil was in the middle of the basin, where the main land use is paddy fields, and the good soil was in the southwest of the basin, where forests and dry fields are widely distributed. The results indicated that the soil quality in the Mun River basin varied greatly, especially for farmland, so these findings will be helpful for improving soil conditions and grain production in the Mun River basin.

scholarly journals Evaluation of soil salinity level through NDVI in Syrdarya province, Uzbekistan

2021 ◽  
Vol 258 ◽  
pp. 03017
Author(s):  
Abdulla Djuraev ◽  
Dilmurod Mirdjalalov ◽  
Alisher Nuratdinov ◽  
Tuychi Khushvaktov ◽  
Yunus Karimov
Keyword(s):  
Soil Quality ◽  
Soil Salinity ◽  
Quality Index ◽  
Arable Land ◽  
Land Resources ◽  
Soil Quality Index ◽  
Gis And Remote Sensing ◽  
Earth Observation Satellite ◽  
The Republic ◽  
Tools And Techniques

Traditional soil salinity assessments have been doing by collecting of soil samples and laboratory analyzing of collected samples for determining TDS and electro conductivity, but, GIS and Remote Sensing technologies provides more efficient, economic and rapid tools and techniques for soil salinity assessment and soil salinity mapping. Main goals of this research are to map soil salinity of Syrdarya province, to show relation of its result with soil quality index (arable land validity point) values of this field. The soil quality index data and map of 2019 year were digitized and transferred to ArcMap software format and investigated the soil quality index score. As a source of satellite images has been used Landsat OLI 8 Earth-observation satellite. Syrdarya province, every arable land validity point of different locations were measured by State Commite of the Republic of Uzbekistan of Land Resources, Geodesy, Cartography and State Cadastre was compared to our research conducted on satellite sensor and it can be said that the study have done correctly.

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