Soil Quality Assessment under Different Land Use Types: A Tool for Supporting Soil Management

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Soil-Sediment-Water Systems".

Deadline for manuscript submissions: closed (7 April 2023) | Viewed by 14088

Special Issue Editors


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Guest Editor
Department of Environmental Biological and Pharmaceutical Sciences and Technologies, University of Study of Campania, 81100 Caserta, Italy
Interests: soil quality; soil health; ecosystem services; soil microbial biomass and activity

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Guest Editor
Rain Forest Research Institute, Jorhat 785001, Assam, India
Interests: soil quality monitoring; carbon sequestration

Special Issue Information

Dear Colleagues,

Soil is the most basic and yet most complex component of terrestrial ecosystems. It regulates most of the ecosystem processes and hosts a large part of the earth's biodiversity, providing the physical basis for many human activities. Healthy and productive soils underpin the long-term sustainability of ecosystems and provide key ecosystem services needed for human well-being. Land use could directly affect the main soil ecosystem services and consequently compromise their functioning. Intensification and competing uses of soil for cropping, pasture, forestry and urbanization are increasingly affecting the provisioning, regulating and supporting ecosystem services (e.g., food, fibre, wood, nutrient cycling, climate regulation, water filtration and purification), threatening the health and the quality of soils worldwide. Thus, maintaining and/or restoring soil quality and health should become a key task for land management planning and policies. The assessment of soil quality is based on the study of soil physical, chemical, and biological soil properties and the processes related to the ability of the soil to function effectively as a component of a healthy ecosystem. The choice of these properties can be complex and varies among different land uses and management systems, also differing on spatial and temporal scales. In this sense, soil quality assessment is a proxy for suggesting sustainable management of soil resources. Within this special issue, we welcome the submission of all types of contributions (original research, reviews, and meta-analyses) providing state-of-the-art and new insights to assess soil quality and soil health across a wide range of land uses and land management systems. More specifically, we are interested in studies investigating the direct impact of broad land uses on soil quality as evaluated by field trials, as well as contributions focusing on geostatistical, remote sensing, modeling methods and multivariate approaches.

Contributions may include, but are not limited to, the following topics:

  1. Soil quality assessment and monitoring
  2. Advanced approaches for soil quality evaluation
  3. New insights into minimum data-sets and indexing approaches
  4. Geostatistical, remote sensing, molecular biology and soil imaging approaches for soil quality monitoring
  5. Impact of different land use types on soil quality
  6. Land use changes effects on soil quality
  7. Assessment of soil quality under different soil management systems

Dr. Rossana Marzaioli
Dr. Gaurav Mishra
Guest Editors

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Keywords

  • Soil Ecosystem Services
  • Soil Quality
  • Soil Health
  • Land Use
  • Land Management
  • Minimum Data Set
  • Soil Quality Index
  • Geographical Information System
  • PCA

Published Papers (6 papers)

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Research

17 pages, 4679 KiB  
Article
Interacting Effects of Land Use Type, Soil Attributes, and Environmental Factors on Aggregate Stability
by Haoye Li, Lei Chang, Yuyu Wei and Yuefen Li
Land 2023, 12(7), 1286; https://0-doi-org.brum.beds.ac.uk/10.3390/land12071286 - 26 Jun 2023
Cited by 2 | Viewed by 1385
Abstract
Soil erosion and surface pollution near reservoirs can adversely affect water quality and safety. Soil aggregate stability is an important predictor of soil water loss and erosion resistance that is strongly influenced by land use. This study therefore aimed to identify factors affecting [...] Read more.
Soil erosion and surface pollution near reservoirs can adversely affect water quality and safety. Soil aggregate stability is an important predictor of soil water loss and erosion resistance that is strongly influenced by land use. This study therefore aimed to identify factors affecting soil aggregate stability near reservoirs to provide empirical and theoretical insights that could guide the development of management measures to increase land quality, optimize land use, and maximize sustainability. This study focuses on the land around the Shitoukoumen Reservoir in China and examines the effects of six land use types, eleven soil physicochemical properties, and five environmental factors. Ninety-four sets of soil samples were collected in 2021 for analysis of soil aggregates and properties. Particle size classification of soil aggregates was carried out using the wet sieve method and four indicators were calculated to evaluate the effects of land use, soil physicochemical properties, and environmental factors on soil aggregate stability: water stable aggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD), and fractal dimension (D). Descriptive statistics and geostatistics were used to explore the spatial distributions of soil aggregate stability around the reservoir and the influence of soil properties was studied using correlation analysis and path analysis. The conclusion indicates that land use type significantly affects aggregate stability. The most stable aggregates were found in paddy fields (WSA = 0.77, MWD = 0.76, GMD = 0.57) and forests (WSA = 0.75, MWD = 0.76, GMD = 0.55), followed by an orchard, irrigated land, and grassland. Aggregate stability was worst in upland sites (WSA = 0.61, D = 2.28), where soil aggregates were highly fragmented. There were clear spatial correlations between all four stability indicators. The environmental factors and soil physicochemical characteristics with the strongest influence on aggregate stability were soil organic matter, pH, soil clay content, total nitrogen, and temperature changes. Path analysis revealed that some soil properties affect aggregate stability indirectly, with particularly complex relationships between clay, soil organic matter, and pH. In conclusion, land use type, soil organic matter, pH, soil clay content, total nitrogen, these soil physicochemical properties, and environmental factors, especially temperature, significantly affect soil aggregate stability around reservoirs. In the future, it is necessary to appropriately change upland into paddy land, increase forest land, and appropriately add organic fertilizer to improve soil quality. Full article
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18 pages, 3058 KiB  
Article
Combining Fuzzy, Multicriteria and Mapping Techniques to Assess Soil Fertility for Agricultural Development: A Case Study of Firozabad District, Uttar Pradesh, India
by Anuj Saraswat, Shri Ram, Mohamed A. E. AbdelRahman, Md Basit Raza, Debasis Golui, Hombegowda HC, Pramod Lawate, Sonal Sharma, Amit Kumar Dash, Antonio Scopa and Mohammad Mahmudur Rahman
Land 2023, 12(4), 860; https://0-doi-org.brum.beds.ac.uk/10.3390/land12040860 - 11 Apr 2023
Cited by 4 | Viewed by 1829
Abstract
Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be [...] Read more.
Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country. Full article
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11 pages, 405 KiB  
Article
Comparative Analysis of Soil Quality Assessment and Its Perception by Rice Farmers
by Shakeel Ahmad Mir, Nasir Bashir Naikoo, Fehim Jeelani Wani, M. H. Chesti, Inayat Khan, Eajaz Ahmad Dar, Bodiga Divya, Navaneet Kumar, Prashant Kaushik, Hamed A. El-Serehy and Muntazir Mushtaq
Land 2022, 11(9), 1401; https://0-doi-org.brum.beds.ac.uk/10.3390/land11091401 - 26 Aug 2022
Cited by 1 | Viewed by 2112
Abstract
The present study was conducted in three villages of district Budgam in the union territory of Jammu and Kashmir, to find out how farmers differentiate the quality of soils and to determine the level of concurrence between farmers perception and scientific assessment of [...] Read more.
The present study was conducted in three villages of district Budgam in the union territory of Jammu and Kashmir, to find out how farmers differentiate the quality of soils and to determine the level of concurrence between farmers perception and scientific assessment of soil quality. Five fields in each village were selected and ranked on the basis of soil quality indices computed from the minimum data set of indicators, including plant available nutrients N, P, K, Ca, Mg, S, OC, BD, WHC (water holding capacity), CEC (cation exchange capacity) as well as microbial count. The respondents ranked the same 5 selected fields on the bases of their experience and perceptions of soil quality. The study reveals that 58% of farmers ranked the best soils correctly whereas, the percentage of farmers who ranked 2nd, 3rd, 4th and 5th soils correctly was 40, 30, 40, and 45%, respectively. The study found that a greater number of farmers from the remotest village Dalwash were able to judge the soils properly, thereby indicating more profound knowledge and better cognitive abilities to understand soils in the local context. The results divulged by the current study highlight the remarkable local soil knowledge of the farmers and therefore, linking this knowledge system with scientific concepts would prove valuable for sustained land-use management. Full article
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18 pages, 5919 KiB  
Article
Investigating Plant Response to Soil Characteristics and Slope Positions in a Small Catchment
by Tibor Zsigmond, Péter Braun, János Mészáros, István Waltner and Ágota Horel
Land 2022, 11(6), 774; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060774 - 25 May 2022
Cited by 3 | Viewed by 1843
Abstract
Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil–plant interaction using field measurements of plant reflectance, soil water content, [...] Read more.
Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil–plant interaction using field measurements of plant reflectance, soil water content, and selected soil physical and chemical parameters. Particular emphasis was placed on sloping transects. We further compared ground- and Sentinel-2 satellite-based Normalized Vegetation Index (NDVI) time series data in different land use types. The Photochemical Reflectance Index (PRI) and NDVI were measured concurrently with calculating the fraction of absorbed photochemically active radiation (fAPAR) and leaf area index (LAI) values of three vegetation types (a grassland, three vineyard sites, and a cropland with maize). Each land use site had an upper and a lower study point of a given slope. The NDVI, fAPAR, and LAI averaged values were the lowest for the grassland (0.293, 0.197, and 0.51, respectively), which showed the highest signs of water stress. Maize had the highest NDVI values (0.653) among vegetation types. Slope position affected NDVI, PRI, and fAPAR values significantly for the grassland and cropland (p < 0.05), while the soil water content (SWC) was different for all three vineyard sites (p < 0.05). The strongest connections were observed between soil physical and chemical parameters and NDVI values for the vineyard samples and the selected soil parameters and PRI for the grassland. Measured and satellite-retrieved NDVI values of the different land use types were compared, and strong correlations (r = 0.761) between the methods were found. For the maize, the satellite-based NDVI values were higher, while for the grassland they were slightly lower compared to the field-based measurements. Our study indicated that incorporating Sentinel-derived NDVI can greatly improve the value of field monitoring and provides an opportunity to extend field research in more depth. The present study further highlights the close relations in the soil–plant–water system, and continuous monitoring can greatly help in developing site-specific climate change mitigating methods. Full article
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19 pages, 5134 KiB  
Article
Assessment of Potentially Toxic Elements’ Contamination in the Soil of Greater Cairo, Egypt Using Geochemical and Magnetic Attributes
by Ahmed Saleh, Yehia H. Dawood and Ahmed Gad
Land 2022, 11(3), 319; https://0-doi-org.brum.beds.ac.uk/10.3390/land11030319 - 22 Feb 2022
Cited by 6 | Viewed by 2593
Abstract
Enhanced soil’s magnetic susceptibility reflects particles of anthropogenic/natural origin; therefore, it can be utilized as an indication of soil contamination. A total of 51 different land-use soil samples collected from Greater Cairo, Egypt, were assessed integrally using potentially toxic elements content (PTEs), magnetic [...] Read more.
Enhanced soil’s magnetic susceptibility reflects particles of anthropogenic/natural origin; therefore, it can be utilized as an indication of soil contamination. A total of 51 different land-use soil samples collected from Greater Cairo, Egypt, were assessed integrally using potentially toxic elements content (PTEs), magnetic susceptibility, and statistical and spatial analysis. PTE concentrations were compared to the world average, threshold, and screening values set by literature. Various environmental indices were estimated to assess soil contamination with these elements. Spatial distribution maps of PTEs and environmental indices were constructed to provide decision makers with a certain identification of riskier areas. In general, the concentrations of the analyzed PTEs showed variation with land-use types and follows a pattern of: Industrial > Agricultural > Urban. The distribution of PTEs in Greater Cairo was influenced by several anthropogenic sources, including traffic emission, industrial activity, and agricultural practices. The measured magnetic susceptibility values indicate magnetically enhanced soil signals dominated by multi-domain or pseudo-single-domain superparamagnetic particles of anthropogenic origin. A significant association was observed between magnetic susceptibility values and Co, Cr, Cu, Ni, and V, and the calculated environmental indices. It can be concluded that magnetic susceptibility is of proven effectivity in the assessment of soil contamination. Full article
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22 pages, 2565 KiB  
Article
Soil Quality Assessment Using Multivariate Approaches: A Case Study of the Dakhla Oasis Arid Lands
by Salman A. H. Selmy, Salah H. Abd Al-Aziz, Raimundo Jiménez-Ballesta, Francisco Jesús García-Navarro and Mohamed E. Fadl
Land 2021, 10(10), 1074; https://0-doi-org.brum.beds.ac.uk/10.3390/land10101074 - 12 Oct 2021
Cited by 11 | Viewed by 2391
Abstract
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 [...] Read more.
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
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