Search Results (8)

Soil quality assessment, which considers numerous physical, chemical, and biological indicators, has long been a challenge for monitoring soil functions and ensuring sustainable resource use in agriculture. In this study, different indicator selection and weighting methods were compared to derive a reliable Soil Quality Index (SQI) in semi-arid agroecosystems. A total of 117 topsoil samples were taken from the Ap horizon within a 14,200 ha area of the Honam sub-catchment, southwestern Iran. Twenty-one soil indicators were measured and analyzed to assess the overall SQI. Soil indicator selection was performed using Principal Component Analysis (PCA), considering standard and norm value strategies, as well as component rotation. Four weighting approaches, including PCA, Coefficient of Variation (CV), correlation score (r), and Expert Opinion (EO), were applied to the Minimum Dataset (MDS) and Total Dataset (TDS) to compute the Integrated Quality Index (IQI), Nemoro (NQI), simple additive (IQIa), and Fuzzy Fertility Index (FFI). The performance of the SQI models was evaluated using the Sensitivity Index (SI) and their relationships with crop yield. The results showed that the combination of the norm value approach without component rotation was more effective in selecting the influential indicators for SQI determination. The Structural Stability Index (SSI), which integrates soil organic carbon and textural soil properties, was the key indicator with the highest contribution, ranging between 6.3% and 37.5% in most of the models. Among the evaluated approaches, the IQI-CV-MDS showed the highest sensitivity (SI = 6.8) and the strongest correlation (r = 0.53) with rainfed barley yield. The majority of the samples exhibited moderate SQI values, indicating a general risk of soil quality decline in the study area. The findings of this study highlight that appropriate indicator selection and weighting strategies are essential for improving the reliability of SQI assessments in semi-arid environments with diverse mountainous topography. Full article

Corn is a globally important crop, requiring extensive soils and intensive practices to meet the growing human and animal consumption demand. However, intensive agriculture has caused soil deterioration and fertility loss. In response, the Mexican government established the National Soil Strategy for Sustainable Agriculture (ENASAS, acronym in Spanish) to ensure food security and maintain soil fertility. This study develops “Soil Quality Indexes” ($SQI$) to monitor soil quality under corn cultivation using four methodologies (additive ($SQ{I}_a$), weighted ($SQ{I}_w$), unified weighted ($SQ{I}_u$), and Nemoro ($SQ{I}_n$)) in the Bajio region of Guanajuato, Mexico. Twenty-four physicochemical indicators were analyzed, with four (CLY, WHC, Na, and C/N) identified as key indicators of soil quality and fertility through principal component analysis. Among these, $SQ{I}_a$ was the most sensitive and efficient (SI = 2.32, ER = 50) in assessing soil quality, showing values from very low to low ($SQ{I}_a=0.13$ and $SQ{I}_a=0.39$ respectively). Aligned with the ENASAS program, $SQ{I}_a$ can help monitor and improve soil quality under corn cultivation, supporting food security through soil conservation. Moreover, $SQ{I}_a$ performed similarly to the globally recognized Soil Management Assessment Framework (SMAF), making it a valuable tool for managing and improving agricultural soil quality under similar conditions in both Mexico and worldwide. Full article

This study addresses the escalating global concern surrounding land degradation (LD) and its far-reaching implications on water and nutrient availability, as well as on human health and well-being. Focused on the southeastern Caspian Sea region, this research employs a novel remote sensing geo-pedological methodology to comprehensively assess soil and land quality dynamics, particularly influenced by salts, and investigates the intricate relationship between LD and soil development. The study area, marked by a susceptibility to seawater level fluctuations and diverse landforms (lagoons, barriers, and coastal plains) offers a unique opportunity for geopedologic analysis. Utilizing particle size distribution data, six distinct landforms are identified, providing insights into the region’s complex sedimentary history. A soil quality assessment is conducted remotely through the calculation of two indexes—the Integrated Quality Index (IQI) and the Nemoro Quality Index (NQI)—employing both Total Data Set (TDS) and Minimum Data Set (MDS) methodologies. The investigation highlights the role of soluble salts in shaping soil quality, thereby influencing LD and development dynamics. The differentiation of landforms significantly enhances classification accuracy, providing a more nuanced understanding of the multifaceted factors governing LD. The study’s implications extend beyond the southeastern Caspian Sea region, and demonstrate that the potential for incorporating a geopedologic approach when assessing soil and land quality dynamics in arid regions globally. Our analytic approach can inform policymakers and land managers when making decisions to combat LD and foster sustainable land development. This research also contributes towards advancing knowledge in geopedology by providing a robust foundation for future studies aimed at enhancing land management practices in the face of ongoing environmental challenges. Full article

The increasing production of biosolids (BS) as a result of urban wastewater treatment generates pollution problems in their management and final disposal, and a better management is needed for their disposal. The composting of BS is an alternative process for obtaining a product with potential application as an organic amendment in the recovery of agricultural soils. As a biotechnological contribution, this study analyzed a composting process with BS, bovine manure (BM) and rice husks using four treatments T1 (C/N = 24); T2 (C/N = 34); T3 (C/N = 44); T4 (C/N = 54) for 120 days, in order to develop compost quality indexes (CQIs) through the analysis of 18 physicochemical, biological and ecophysiological indicators. Subsequently, three methodologies—successfully used on soils—were implemented for the development of the CQIs called “unified”, “additive” and “nemoro”. The indicators that comprised the CQIs were nitrification index (NI) and synthetic enzymatic index (SEI). The CQIs made it possible to differentiate the quality of the compost according to the treatments applied. The treatments used resulted in composts considered phytonutritious whose average quality value depending on the CQI developed was considered high (CQI_w = 0.62), moderate (CQI_a = 0.56) and low (CQI_n = 0.30). The developed CQIs can be applied to determine the quality of BS composting systems reducing the cost of monitoring. Full article

Monitoring the soil quality (SQ) in agricultural ecosystems is necessary for using sustainable soil and land resources. Therefore, to evaluate the SQ variation in an arid environment in the Bajestan region, northeastern Iran, two soil quality indices (weighted additive soil quality index-SQI_w and nemoro soil quality index-SQI_n) were applied. SQIs were assessed in two datasets (total data set-TDS and minimum data set-MDS) by linear (L) and nonlinear (NL) scoring methods. Physicochemical properties of 223 surface soil samples (0–30 cm depth) were determined. The random forest (RF) model was used to predict the spatial variation of SQIs. The results showed the maximum values of the SQIs in areas with saffron land covers, while the minimum values were acquired in the north of the study area where pistachio orchards are located due to higher EC and SAR. The environmental variables such as topographic attributes and groundwater quality parameters were the main driving factors that control SQIs distribution. These findings are beneficial for identifying suitable locations sites to plan agricultural management and sustainable usage of groundwater resources strategy to avoid further increase of soil salinity. Full article

In the last two decades, as the importance of soil has been recognized as a key component of any ecosystem, there has been an increased global demand to establish criteria for determining soil quality and to develop quantitative indices that can be used to classify and compare that quality in different places. The preliminary estimation of the attributes involved in soil quality was made taking into account the opinion of the experts and our own experience in a semi-arid ecosystem. In this study, 16 soil properties have been selected as potential indicators of soil quality, in a region between Campo de Montiel and Sierra de Alcaraz (Spain): sand and clay percentage, pH, electrical conductivity (EC), soil organic carbon (OC), extractables bases of change (Na, K, Ca and Mg), cationic exchange capacity (CEC), carbonate calcium equivalent (CCE), bulk density (BD), water retention at 33 kPa field capacity and 1500 kPa permanent wither point (GWC33 kPa and GWC1500 kPa), coefficient of linear extensibility (COLE) and factor of soil erodibility (K). The main objective has been to develop an adequate index to characterize the quality of the soils in a semi-arid Mediterranean ecosystem. The preliminary estimation of the attributes involved in soil quality was made considering the opinion of the experts and our own experience in semi-arid ecosystems. Two indicator selection approaches have been used to develop the Soil Quality Index (SQI) (total data set -TDS- and minimum data set -MDS-), scoring functions (linear -L- and nonlinear -NL-) and methods (additive -A-, additive weighted -W- and Nemoro -N-. The quality indices have been calculated, considering the properties of the soil control section (between 0 and 100 cm depth), using 185 samples, belonging to horizons A, B and C of 51 soil profiles. The results have shown that the election of the soil properties, both of the topsoil and subsoil, is an important help in establishing a good relationship between quality, soil functions and agricultural management. The Kriging method has been used to determinate the spatial distribution of the soil quality grades. The indices that best reflect the state of soil quality are the TDS-L-W and TDS-L-A should go as sub-indices, as they are the most accurate indices and provide the most consistent results. These indices are especially indicated when carrying out detailed or semi-detailed studies. However, the MDS-L-W and MDS-L-A should go as sub-indices, which use only a limited number of indicators, are best for large-scale studies. The indicators with the greatest influence on soil quality for different land uses and those developed on different rocks, using linear scoring functions, are the following: (Clay), (GWC1500 kPa) and (Ca). These results can also be expressed as follows: the best soils in this region are deep soils, with a clay texture, with high water retention and a neutral or slightly basic pH. However, the indicators with the greatest influence on soil quality, using nonlinear scoring functions, are: (OC Stock), (Ca) and (CaCO₃). In other words, the most important indicator is the organic carbon content, which is not logical in the case of a region in which the soils have an excessively low SOC content (0.86%). Full article

A precise evaluation of soil quality (SQ) is important for sustainable land use planning. This study was conducted to assess soil quality using multivariate approaches. An assessment of SQ was carried out in an area of Dakhla Oasis using two methods of indicator selection, i.e., total data set (TDS) and minimum data set (MDS), and three soil quality indices (SQIs), i.e., additive quality index (AQI), weighted quality index (WQI), and Nemoro quality index (NQI). Fifty-five soil profiles were dug and samples were collected and analyzed. A total of 16 soil physicochemical parameters were selected for their sensitivity in SQ appraising to represent the TDS. The principal component analysis (PCA) was employed to establish the MDS. Statistical analyses were performed to test the accuracy and validation of each model, as well as to understand the relationship between the used methods and indices. The results of principal component analysis (PCA) showed that soil depth, gravel content, sand fraction, and exchangeable sodium percentage (ESP) were included in the MDS. High positive correlations (r ≥ 0.9) occurred between SQIs calculated using TDS and/or MDS under the three models. Moreover, the findings showed highly significant differences (p < 0.001) among SQIs within and between TDS and MDS. Approximately 80 to 85% of the total study area based on TDS, as well as 70 to 75%, according to MDS, were identified as suitable soils with slight limitations on soil quality grade (Q3, Q2, and Q1), while the remaining 20 to 30% had high to severe limitations (Q4 and Q5). The highest sensitivity (SI = 2.9) occurred by applying WQI using MDS and indicator weights based on the variance of PCA. Furthermore, the highest linear regression value (R² = 0.88) between TDS and MDS was recorded using the same model. Because of its high sensitivity, such a model could be used for monitoring SQ changes caused by agricultural practices and environmental factors. The findings of this study have significant guiding implications and practical value in assessing the soil quality using TDS and MDS in arid areas critically and accurately. Full article

Dwindling water resources have drawn global attention to the reuse of treated wastewater (TWW) for irrigation. However, the impact of continuous TWW applications on soil quality and the proper quantification and monitoring frameworks have not been well-understood. This study aims to provides an insight into the impact of flood irrigation of urban TWW on soil nutritional-chemical attributes and the potential application of multiple soil quality indices for a corn cropping system. To achieve that goal, we pursued the Total Data Set (TDS) and Minimum Data Set (MDS) approaches, as well as the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI) models. A total of 17 soil nutritional-chemical indicators (0–50 cm depths) were determined for the soils irrigated with TWW (five sites) and well water (one site as control) in West Azerbaijan province in northwestern Iran. Results revealed a significant difference in the majority of soil nutritional-chemical attributes, IQI-TDS, NQI-TDS, IQI-MDS, NQI-MDS, and corn yield between the TWW-irrigated and well-irrigated soils. Irrigation with TWW resulted in a significant increase in the amount of organic matter and cation exchange capacity by 9–17% and 17–26%, respectively, macronutrients (N, P, K, Ca, and Mg) by 22–164%, and the majority of trace metals (Fe, Mn, Zn, and Cu) by 17–175%, suggesting an improvement in soil nutrients and an increase in productivity. Comparing to the soil in control sites, the TWW irrigation caused a notable increase in the values of IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models ranging 14.6–29.5%, 19.1–25.5%, 21.7–33.3%, and 18.4–23.7%, respectively. This implies that soil quality was ameliorated to a significant extent with TWW irrigation. These improvements resulted in a remarkable increase in corn yield ranging from 12.5% to 28.1%. The regression equations revealed that up to 78%, 47%, 72%, and 36% of the variance in the IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models, respectively, could be captured by corn yield. The results of the regression and correlation analyses showed that the IQI-MDS model was more accurate than the other models in assessing soil quality and predicting crop yield. These findings may be an effective and practical tool for policy making, implementation, and management of soil irrigated with TWW. Full article