Dynamic Quality Index for agricultural soils based on fuzzy logic

Rodríguez, Esther; Peche, Roberto; Garbisu, Carlos; Gorostiza, I.; Epelde, Lur; Artetxe, Unai; Irizar, Amaia; Soto, Manu; Becerril, José M.; Etxebarría, Javier

doi:10.1016/j.ecolind.2015.08.016

Cited by 28 publications

(10 citation statements)

References 41 publications

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“…Models of the spatial distribution of soil properties based on landscape attributes have been formalized in the context of digital soil mapping [62][63][64][65]. Fuzzy logic-based approaches have been applied for modeling in several studies [20,38,[66][67][68][69]. To our knowledge, this is the first time that the landscape-scale relationship between soil organisms and humus forms has been evaluated on the basis of spatial modeling.…”

Section: Spatial Distribution Of Enchytraeids and Humus Formsmentioning

confidence: 99%

Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic

Hellwig

Graefe

Tatti

et al. 2017

European Journal of Soil Biology

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The aim of this study was to map the spatial distribution of enchytraeids and humus forms in a study area in the Italian Alps by means of a knowledge-based modeling approach. The modeled area is located around Val di Sole and Val di Rabbi (Trentino, Italy) and includes the forested parts in the range between 1100 m and 1800 m a.s.l. Elevation and slope exposure are considered as environmental covariates. Models were implemented regarding the spatial distribution of three variables at the landscape scale: 1) enchytraeids indicating mull humus forms, 2) enchytraeids indicating moder/mor humus forms, 3) humus forms showing an OH horizon. All three models reveal a consistent trend of an increasing accumulation of plant residues and humus in organic layers from low to high elevations and from south-facing to north-facing slopes. Validation and uncertainty analysis of input data confirm these trends, although some deviations are to be expected (RMSE values from validation sites range from 26.3 to 36.2 percentage points). Effects of additional potentially influencing variables may lead to uncertainties of the model predictions especially at positions with particular landforms (e.g. gullies and ridges). In the high mountains environmental conditions are often quite heterogeneous due to a highly variable topography, which also affects the species composition of the decomposer community and the occurrence of different humus forms.

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Section: Spatial Distribution Of Enchytraeids and Humus Formsmentioning

confidence: 99%

Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic

Hellwig

Graefe

Tatti

et al. 2017

European Journal of Soil Biology

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“…Fuzzy logic theory is a methodology, first proposed by Zadeh in 1965, that is widely applied in various domains today. For example, it has been applied in the following domains: corporate sustainable performance assessment [35], groundwater contamination estimation [36], hydrothermal process assessment [37], habitat ecological integrity and environmental impact assessment [38], renewable energy system assessment [39], and agricultural soil dynamic quality index assessment [40].…”

Section: Delphi Methods and Fuzzy Logic Theorymentioning

confidence: 99%

Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities

2019

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The Industrial Revolution has enabled mechanization to lead the manufacturing industry into a new era of explosive economic growth. Mass production through the use of machines has improved the overall societal prosperity in industrialized countries. The great and continuous demand of mechanization, economic growth, and energy consumption has resulted in the excessive use of natural resources and the mining of natural energy deposits. Challenges such as serious environmental pollution, ecological damage, and climate change have been identified as some of the critical issues for sustainable development. Therefore, this paper proposes a Delphi and Fuzzy (DFuzzy) model in order to institute low-carbon ecocities. DFuzzy is a scientific decision-making model with quantitative multiple attributes and artificial intelligence. Twelve specialists from the Delphi experts were interviewed, including chief executive officers (CEOs) of industries, management committee members, and senior academics, all of whom have 15 years of experience in urban planning practice. After a three-round Delphi process with 17 criteria that were taken from the literature, four criteria of the DFuzzy model were recognized by experts: policy norms, resident cooperation, pollution prevention and control, and ecological reserves. The practical application of the DFuzzy model took three areas as examples: the Baiyun District and Conghua District of Guangzhou City, and the Dahu Community of Kaohsiung City. Through the experts’ consensus in the two-round Delphi process, the four criteria, as the input basis, demonstrated the objective quantitative calculation function of the DFuzzy model and also indicated that the model established in this study provides a reference for evaluating low-carbon ecocities.

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“…In addition, geostatistical models, such as ordinary kriging and cokriging, are commonly used in a small number of spatial research projects [13,14], the accuracies of which are highly dependent on the number of sampling points, with the result maps consistently displaying a "bullseye" phenomenon [15]. Moreover, there are some unreasonable conditions in the threshold setting processes of indicators and assessment results; for example, most research indicators are often standardized or directly graded according to the indicator values [16], and then the grade values are used as evaluation parameters for overlay analysis [17]. This strict grading method has been questioned in term of its reasonability, though there is no recognized classification standard of soil quality [18].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Spatial Difference of the Soil Quality of The Mun River Basin during the Rainy Season

Liu

et al. 2019

Sustainability

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The Mun River basin is one of the main grain-producing areas of Thailand, and the rainy season is the main period for crop planting after being idle during the dry season. However, the soil conditions are variable, so an assessment of soil quality during the rainy season is necessary for improving soil condition and crop production. The aim of this study was to conduct a soil quality assessment based on soil samples. To attain that, a minimum data set theory was used to screen evaluation indicators and geographically weighted regression was performed to obtain spatial interpolations of indicators, while the fuzzy logic model was used to determine the soil quality results. The results showed that the contents of indicators had similar spatial trends as their contents declined from the western to the eastern region of the basin. The soil quality results showed that the poor soil was in the middle of the basin, where the main land use is paddy fields, and the good soil was in the southwest of the basin, where forests and dry fields are widely distributed. The results indicated that the soil quality in the Mun River basin varied greatly, especially for farmland, so these findings will be helpful for improving soil conditions and grain production in the Mun River basin.

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Dynamic Quality Index for agricultural soils based on fuzzy logic

Cited by 28 publications

References 41 publications

Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic

Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic

Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities

A Study of the Spatial Difference of the Soil Quality of The Mun River Basin during the Rainy Season

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