Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment

Turan, İ̇nci Demirağ; Dengi̇z, Orhan; Özkan, Barış

doi:10.1016/j.compag.2019.104933

Cited by 53 publications

(24 citation statements)

References 26 publications

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“…The total data set were the following soil properties: soil texture, pH, OC, BD, PO, EC, MWD, CEC, CCE, SMR, ESP, TN, AK, AP, AFe, AMn, AZn, and ACu. These soil properties were selected based on previous literature [5,15,16,17,22,24,26,27,33,50,51,52,53,54]. Initially, the soil quality indicators that did not demonstrate a significant correlation with wheat yields were removed and were not included in the MDS [6].…”

Section: Assessment Of Soil Quality Index (Sqi) 241 Determining Thmentioning

confidence: 99%

“…This is the most frequently used process for assessing soil quality. Soil quality indicators generate SQIs in an easy and flexible format based on an integrated valuation of SQIs and their weights [14][15][16][17]. SQIs have been successfully applied at various scales for different purposes such as assessing the impacts of agricultural practices, soil management, salinization and alkalization on soil quality and crop production [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Nabiollahi¹,

Heshmat²,

Mosavi³

et al. 2020

Preprint

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Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA) and multiple linear regression (MLR) method are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0-20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Mean wheat grain yield for the two years for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators taken in the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQI-M) and the rainfed wheat yield (r=0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQI-F) (r=0.58). Results showed that the means of both SQI-M and SQI-F and rainfed wheat yield for Inceptisols were higher than for Entisols although these differences were not statistically significant. Both SQI-M and SQI-F showed that areas with Entisols had lower proportions of good soil quality grades (Grade I and II), and higher proportions of poor soil quality grades (Grade IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientific integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields be promoted.

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Section: Assessment Of Soil Quality Index (Sqi) 241 Determining Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Nabiollahi¹,

Heshmat²,

Mosavi³

et al. 2020

Preprint

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“…Land degradation is a significant phenomenon on the world particularly arid, semi-arid and semi-humid terrestrial ecosystem environments due to variety of factors such as through unsustainable land management, and through physical and climatic characteristics. One of the main indicators and commonly used in the land degradation and desertification is the soil quality level (Demirağ Turan et al, 2019). Therefore, the quantity and quality of soil is an important indicator of the sustainable development of each country, which feeds and provides livelihoods for the people (Bekturova and Romanova, 2007).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of using the rangeland hydrology and erosion model for assessing the degradation of pastures and forage lands in Aydarly, Kazakhstan

Kussainova

Spaeth

Zhaparkulova

2020

Eurasian Journal of Soil Science (Ejss)

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This study examined the use of a novel web-tool for Rangeland Hydrology and Erosion Model (RHEM) as a prediction runoff and erosion as a function of vegetation structure and behavior of different plant community phases and the amount of coverage for the different states in the Aydarly village of Jambul district of Almaty province. US Department of Agriculture experts and Kazakhstani scientists jointly conducted this study, where, based on the results, they received recommendations on improving rangeland. Results suggested that the model could be further improved with additional measured experimental data on infiltration, runoff, and soil erosion within key ecological sites in order to better quantify model parameters to reflect ecosystem changes and risk of crossing interdependent biotic and abiotic thresholds. These additions were further improved and implemented in other regions of Kazakhstan on other projects.

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“…In other words, an evaluation approach involving multiple criteria would be more appropriate for land evaluation analysis studies. Today, besides the current techniques such as remote sensing and Geographic Information Systems (GIS), these challenges can be overcome by using approaches such as Multi-Criteria Decision Analysis (MCDA) to make rational analyses and evaluations [7][8][9][10][11][12] . Analytic Hierarchy Process 13 (AHP) assigning weights to evaluation criteria belongs to often used MCDA methods.…”

mentioning

confidence: 99%

“…Identification of potential land suitability classes in the MCDA approach is usually developed using a fourstage process. These stages are (i) indicator selection, (ii) indicator categorization and scoring, (iii) weighting the indicators according to their significance, and (iv) calculating the scores according to a selected model 5,12,26,27 . However, the developed land quality indexes generally applicable under certain purposes and environmental conditions on a limited scale 28 .…”

mentioning

confidence: 99%

Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem

Özkan

Dengi̇z

Turan

2020

Sci Rep

Self Cite

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The main purpose of this study is to identify suitable potential areas for agricultural activities in the semi-arid terrestrial ecosystem in the Central Anatolia Region. MCDA was performed in fuzzy environment integrated with GIS techniques and different geostatistical interpolation models, which was chosen as the basis for the present study. A total of nine criteria were used, as four terrain properties and five soil features to identify potential sites suitable for agriculture lands in Central Anatolia which covers approximately 195,012.7 km2. In order to assign weighting value for each criterion, FAHP approach was used to make sufficiently sensitive levels of importance of the criteria. DEM with 10 m pixel resolution used to determine the height and slope characteristics, digital geology and soil maps, CORINE land use/land cover, long-term meteorological data, and 4517 soil samples taken from the study area were used. It was identified that approximately 30.7% of the total area (59,921.8 ha) is very suitable and suitable for potential agriculture activities on S1 and S2 levels, 42.7% of the area is not suitable for agricultural uses, and only 27% of the area is marginally suitable for agricultural activities. Besides, it was identified that 34.8% of the area is slightly suitable.

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Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment

Cited by 53 publications

References 26 publications

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Efficiency of using the rangeland hydrology and erosion model for assessing the degradation of pastures and forage lands in Aydarly, Kazakhstan

Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem

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