A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran

Nosrati, Kazem; Collins, Adrian L.

doi:10.1007/s11629-019-5567-8

Cited by 45 publications

(22 citation statements)

References 76 publications

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“…Another interesting result of the present research is the non-significant differences of the measured biological parameters between dry and irrigated farming. Similar to our results, Nosrati and Collins [31] also found there were not any significant differences between soil biological differences in dry and irrigated farming. Although the availability of water can affect the activity of soil microbes, they are able to be functional at a minimum level of water, which may not result in a significant difference in the biological indices of dry and irrigated farming.…”

Section: Discussionsupporting

confidence: 92%

Soil chemical and microbial properties affected by land use type in a unique ecosystem (Fars, Iran)

Baligh

Honarjoo

Jalalian

et al. 2021

Biomass Conv. Bioref.

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Land use type is an important parameter, affecting soil physicochemical and biological properties. Accordingly, it is important to find how land use type may affect soil properties in an ecosystem to avoid further environmental degradation. The objective was to investigate how different land use types including forest, rangeland, and dry and irrigated farming may alter soil chemical and biological properties including organic carbon (C), total nitrogen (N), potentially mineralizable N (PMN), particulate organic matter (POM), microbial respiration (MR), soil basal respiration (BR), and microbial biomass C (MBC) and N (MBN). The research was conducted in a unique ecosystem (having all types of the abovementioned land use), namely the Zanian region (17,000 ha, altitude of 1952-2657 m) of Fars province, Iran. A total of 130 samples (0-10 cm), collected from different stations across the research area, were analyzed. Geographical properties of the region including topography, slope, and altitude were also determined and presented by maps. POM, PMN, MBC, MBN, MR, and BR averaged to 0.48%, 53.44, 65.72 mg kg −1 soil, 8.85 mg N kg −1 soil, 0.118 mg CO 2 g −1 soil day −1 , and 41.22 mg CO 2 day −1 g −1 soil, respectively. Land use change from forest to farming (dry and irrigated) significantly decreased soil chemical and biological activities. Following forest, rangeland was the second most chemically and biologically active type of land. There were significant differences between rangeland and irrigated farming. Just POM was not different under rangeland and dry and irrigated farming. Land use change can profoundly alter soil chemical and biological properties.

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Section: Discussionsupporting

confidence: 92%

Soil chemical and microbial properties affected by land use type in a unique ecosystem (Fars, Iran)

Baligh

Honarjoo

Jalalian

et al. 2021

Biomass Conv. Bioref.

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“…Training on Physical Index Based on Deep Learning [12]. e working principle of the feature extractor is that the convolutional layer separately calculates the feature map output after the activation function of the local features of the input image through the convolution calculation, and the classifier output after the feature map is compressed.…”

Section: Evaluation Methods Of the Influence Of Sportsmentioning

confidence: 99%

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Wang

2021

Security and Communication Networks

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With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

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“…Therefore, monitoring of soil quality parameters will help to simplify and increase awareness of the reasons and impacts of climate change and the responses required [17]. This is because soil quality is a measure of soil capacity to function to maintain productivity, to maintain environmental quality, to limit ecosystems and land use, and to promote plant and animal health [18].…”

Section: Introductionmentioning

confidence: 99%

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

et al. 2021

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Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

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A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran

Abstract: The output can be accessed at: https://repository.rothamsted.ac.uk/item/96z09/a-soilquality-index-for-evaluation-of-degradation-under-land-use-and-soil-erosion-categoriesin-a-small-mountainous-catchment-iran.

Cited by 45 publications

References 76 publications

Soil chemical and microbial properties affected by land use type in a unique ecosystem (Fars, Iran)

Soil chemical and microbial properties affected by land use type in a unique ecosystem (Fars, Iran)

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

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