Search published articles


Showing 3 results for Soil Quality Index

S. Rahimi, M. Afyuni, A. H. Khoshgoftarmanesh, M. Noruzi,
Volume 19, Issue 71 (6-2015)
Abstract

Management of organic and inorganic treatments may have positive or negative effects on soil quality, plant growth and human nutrition. The objectives of this study were to determine the effects of organic and inorganic zinc fertilizer application on soil quality indicators and wheat yield. This research was conducted at Agricultural Research Station Roudasht, Isfahan, Iran. Sewage sludge and cow manure (5 and 10 t/ha), ash rubber (1 t/ha), powder rubber (200 kg/ha), ZnSO4 (40 kg/ha) were applied and wheat was cultivated. Soil samples were collected at tilling and harvest stages. After taking samples and measurements of the soil parameters, we determined the critical limits for each category and class rating for the each soil parameters, and the soil quality index was calculated. The results showed sewage sludge and rubber ash were significantly effective in increasing soil bioavailable Zn compared to other treatments. Application of sewage sludge and cow manure at 10 ton/ha improved soil quality. The expanded soil quality index can help better understand the effect of fertilizers on soil. A positive and significant relationship between soil quality indicators and Zn uptake and wheat yields was also observed. Our results indicate that addition of 10 t/ha sewage sludge as fertilizer can significantly improve soil quality, supplying the necessary amount of Zn for wheat growth.


M. Naderi Khorasgani, R. Amiri, A. Karimi, J. Mohammadi,
Volume 29, Issue 1 (4-2025)
Abstract

The soils of the Shahrekord plain, part of the Beheshtabad watershed subbasin in Shahrekord County, Chaharmahal va Bakhtiari province, have been used for crop production and domestic animal feeding for centuries, yet the soil quality of this plain has been overlooked. Therefore, assessing the quality of Shahrekord plain soil is essential. This research aimed to evaluate the physical soil quality of the plain using soil quality indices such as the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI). A randomized compound sampling strategy was employed, and 106 surficial (0-25 cm) soil samples were collected during intensive fieldwork. Following pretreatments of the soil samples, several key soil characteristics were measured using standard methods, which were compiled into a Total Data Set (TDS) and used to calculate IQITDS and NQITDS. The minimum effective data set (MDS) was selected, and weights for the quality indices were determined using TDS and Principal Component Analysis (PCA). The minimum data set included the soil sand percentage, soil organic matter percentage, mean weighted diameter of aggregates, soil moisture at field capacity, bulk density, soil reaction, and electrical conductivity. The soil quality at each sample site was assessed using the indices and data sets, TDS and MDS. Geostatistical techniques and ordinary kriging methods were utilized to map soil quality. Results indicated that the soil quality of rangelands was significantly higher than that of cultivated soils (irrigated and drylands). Additionally, approximately 71% of the soils were classified as very low, low, and medium quality, highlighting the need for monitoring and managing such soils.

H. Rezazadeh, P. Alamdari, S. Rezapour, M. S. Askari,
Volume 29, Issue 3 (10-2025)
Abstract

Soil quality assessment plays a crucial role in sustainable land management, particularly in degraded areas such as saline and sodic soils. This study aimed to determine the spatial distribution of the Soil Quality Index (SQI) in saline and sodic soils around Lake Urmia using two geostatistical interpolation methods: Kriging and Inverse Distance Weighting (IDW). A total of 82 soil samples were collected from a depth of 0–30 cm, and 24 physical, chemical, and heavy metal properties were analyzed. The Soil Quality Index was calculated based on both linear and non-linear approaches. Principal Component Analysis (PCA) was used to identify a Minimum Data Set (MDS), including: calcium carbonate equivalent, EC, clay percentage, BD, silt percentage, organic carbon, Pb, and cadmium, which explained more than 78% of the total variance. The results indicated that the SQI showed moderate spatial variability across the study area, with a decreasing trend from west to east. Comparison of the interpolation methods revealed that Kriging performed better in the linear model, while IDW showed higher accuracy in the non-linear approach. The best-fitted theoretical model was spherical, with a range of influence varying between 6,130 and 20,610 meters. Overall, integrating the Soil Quality Index with geostatistical methods provides a powerful tool for understanding spatial variability and supporting effective planning in saline and sodic soils.


Page 1 from 1     

© 2025 CC BY-NC 4.0 | Journal of Water and Soil Science

Designed & Developed by: Yektaweb