Identification of Land Suitability for Agricultural Use by Applying Morphometric and Risk Parameters Based on GIS Spatial Analysis

Abstract

Agricultural land is one of the main resources for the development of rural communities and the peripheries of urban centres. An area of 936 km 2 , belonging to Intercommunity Association for Development Alba-Iulia, Transylvania region, Romania, was analysed in order to identify suitable land for agricultural use. This approach represents the stage preceding the identification of crops favourability for agricultural land, thus reducing the time and resources needed for the proper land evaluation mark. The extension of suitable surfaces for agricultural crops was realized using a GIS model based on spatial analysis, taking into account morphometric parameters (slope, altitude, slope orientation, the density of fragmentation) and the risk factors (probability of landslides, flooding, temperature and rainfall). The outcome of the case study was an agricultural land suitability map of the investigated area, which provides valuable information regarding areas suitable for crops. By applying this model, a better management of agricultural lands can be assured, representing an alternative to the classic method of evaluation marks. The proposed model was validated by comparing the results with the grades of crop suitability, method achieved through the land evaluation mark.Keywords: agricultural land, database, favourable crops, GIS model, maps cultivation, having as main sales market the polarizing urban centres.The GIS model shown in the present article was intended to facilitate the agricultural land evaluation mark; the main purpose of was to reduce the time provided for the identification of favourability for some agricultural crops by imposing restrictiveness among the territory involved in the research. Using GIS technology with the purpose of identifying these territories has provided remarkable results for land evaluation mark (Dumitru et al., 2010;Bhandari et al., 2013;Halder, 2013; Roşca et al., 2015).Achieving the spatial analysis model relies on the morphometric and risk parameters of the studied territory, parameters that have the greatest influence on land suitability and on the arrangement of different types of crops.

Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area

“…In order to establish landslides probability classes for the studied territory, it was applied a statistical model that takes into account the principles of statistical bivariate analysis so that the landslides become the dependent variables for determining the statistical influence of the causal and triggering coefficients which represent the independent variables. This type of analysis has provided good results for territories of different sizes in different study regions previously performed [27][28][29][30].…”

“…The analysis of the land-use highlights the agricultural character of the study area, so the largest area of land is occupied with arable land not irrigated (295.41 km 2 ), meadows (248.12 km 2 ) and deciduous forests (191.35 km 2 ) (Table 10). Agricultural work, ploughing and sowing operations, which do not meet the technical requirements (ploughing and sowing on hill slope direction), cause the rainwater to flow faster into the soil, thus creating favourable conditions for excessive soil wetness and sliding land for areas with clay and semi-clay geological substrate [29,31]. In the analysed area, the deciduous forests play a dual role in the landslides: they primarily have the role of stabilizing and mitigating surface erosion for the low slopes of the low hills, and secondly they play a destabilizing role for inclined slopes due to overloading (Figures 11 and 12).…”

Land Use Favourability Assessment Based on Soil Characteristics and Anthropic Pollution. Case Study Somesul Mic Valley Corridor, Romania

“…Taking this into consideration, to achieve the purpose of the research, a complex GIS model of spatial analysis was developed (based on qualitative and quantitative analysis), composed of secondary models developed for nine crops: barley, corn, potato, wheat, beetroot, soy, green peas, beans and sunflower. The model is based on a digital database formed by raster and vector structures that are managed through geoinformatic software [9][10][11]. The result is represented by raster and vector databases, which spatially identify the territorial areas with the best quality score for the development of agricultural land [12][13][14][15] or to identify favourable or restrictive conditions for different plant [16,17] or forest species [18,19].…”

Bioremediation of Oil Contaminated Soil and Restoration of Land Historically Polluted with Oil Products in the Agricultural Circuit in the Plain and Western Hills, Romania

“…The time required for bioremediation of oil-polluted soils within the study area varies between 17 and 36 weeks (i.e., 119 and 252 days), a higher rate than in other case studies in similar articles, 45 days in the case of bioremediation using vermicompost [43], 49 days in the case of bioremediation with Ricinus communis L. enzymes [44], 187 days in the case of remediation by persulphate oxidation coupled with microbial degradation [45] but the differences are due to the technology applied and the degree of initial soil pollution. Because hydrocarbons stay in the soil for a very long time, biostimulation and bioaugmentation techniques must be used to dispose of them [46][47][48].…”