Abstract:Irrigation system design and irrigation management require appropriate match of the soil and type of crop. In Chile, agrological reports are currently being used to determine the homogeneity of soil units and land capability classes: fruit tree production vs orchard design. Agrological studies use a number of soil characteristics, but these do not include hydrophysical properties, which are essential when zoning for irrigation. Therefore, it is necessary to establish a methodology to objectively evaluate soils… Show more
“…The goal offers the objective which demonstrates favored results. Irrigation land suitability analysis is given to the physical and chemical properties of soil in relation to methods of irrigation considered (Kebede and Ademe, 2016;Valdivia-Ceal et al, 2017). Most of the conducted studies have used the parametric method for land suitability analysis for irrigation methods (Albaji et al, 2008(Albaji et al, , 2010(Albaji et al, , 2015Dengiz, 2006;Hoseini and Delavari, 2016) and less frequently applied new methods based on the fuzzy logic.…”
Section: Discussionmentioning
confidence: 99%
“…The goal offers the objective which demonstrates favored results. Irrigation land suitability analysis is given to the physical and chemical properties of soil in relation to methods of irrigation considered (Kebede and Ademe, 2016; Valdivia-Ceal et al, 2017).…”
Land evaluation for irrigation is the process of predicting land use potential on the basis of soil attributes. The Food and Agriculture Organization (FAO) framework for land suitability evaluation is the most commonly used and is based on the biophysical properties of lands. The FAO framework method for land suitability application Boolean approach that has been criticized by some researchers. Because the Boolean representations ignore the continuous nature of soil and uncertainties in measurement and also its inability for overcoming problems related to vagueness in definition and other uncertainties, fuzzy set methodologies have been proposed. In the present study, the qualitative land suitability evaluation for sprinkler irrigation using parametric-based FAO learning and fuzzy inference system was carried out in an area of 5175 ha in Northwest Iran. By overlaying the layers (soil texture, soil depth, lime, electric conductivity, drainage, and slope) and use of spatial data modeler in ArcGIS 9.3, land evaluation maps for sprinkler irrigation were provided for the area under study. Results showed that based on the parametric approach, 1598 ha of the study area were classified as highly suitable (S1 class) for sprinkler irrigation; the area of highly suitable lands in the parametric method was about five times the area of highly suitable lands obtained through the fuzzy method. In addition, the two methods were completely different in determining moderately suitable lands (S2). Accordingly, 787 ha in the area was moderately suitable using the parametric method, which was about two times that obtained through the fuzzy method. This showed the significant difference between two methods applied to evaluate the lands. Moreover, fuzzy approaches accommodate the continuous nature of some soil properties and produce more intuitive distributions of land suitability indexes.
“…The goal offers the objective which demonstrates favored results. Irrigation land suitability analysis is given to the physical and chemical properties of soil in relation to methods of irrigation considered (Kebede and Ademe, 2016;Valdivia-Ceal et al, 2017). Most of the conducted studies have used the parametric method for land suitability analysis for irrigation methods (Albaji et al, 2008(Albaji et al, , 2010(Albaji et al, , 2015Dengiz, 2006;Hoseini and Delavari, 2016) and less frequently applied new methods based on the fuzzy logic.…”
Section: Discussionmentioning
confidence: 99%
“…The goal offers the objective which demonstrates favored results. Irrigation land suitability analysis is given to the physical and chemical properties of soil in relation to methods of irrigation considered (Kebede and Ademe, 2016; Valdivia-Ceal et al, 2017).…”
Land evaluation for irrigation is the process of predicting land use potential on the basis of soil attributes. The Food and Agriculture Organization (FAO) framework for land suitability evaluation is the most commonly used and is based on the biophysical properties of lands. The FAO framework method for land suitability application Boolean approach that has been criticized by some researchers. Because the Boolean representations ignore the continuous nature of soil and uncertainties in measurement and also its inability for overcoming problems related to vagueness in definition and other uncertainties, fuzzy set methodologies have been proposed. In the present study, the qualitative land suitability evaluation for sprinkler irrigation using parametric-based FAO learning and fuzzy inference system was carried out in an area of 5175 ha in Northwest Iran. By overlaying the layers (soil texture, soil depth, lime, electric conductivity, drainage, and slope) and use of spatial data modeler in ArcGIS 9.3, land evaluation maps for sprinkler irrigation were provided for the area under study. Results showed that based on the parametric approach, 1598 ha of the study area were classified as highly suitable (S1 class) for sprinkler irrigation; the area of highly suitable lands in the parametric method was about five times the area of highly suitable lands obtained through the fuzzy method. In addition, the two methods were completely different in determining moderately suitable lands (S2). Accordingly, 787 ha in the area was moderately suitable using the parametric method, which was about two times that obtained through the fuzzy method. This showed the significant difference between two methods applied to evaluate the lands. Moreover, fuzzy approaches accommodate the continuous nature of some soil properties and produce more intuitive distributions of land suitability indexes.
“…Inaccurate estimation of hydraulic properties may result in the poor modelling of the water dynamics and could give misleading overall results from a biosphere model, resulting in inefficient use and management of natural resources [12,13]. Therefore, the existing PTFs developed internationally still need to be calibrated and validated locally against measurements before they can be reliably used to estimate hydraulic properties in new sites that are located in different regions from their origins [12,[44][45][46]. However, the lack of representative information on soil hydraulic properties hinders the validation of the existing PTFs and the development of local PTFs in many countries of the world [12,24,46,47].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the existing PTFs developed internationally still need to be calibrated and validated locally against measurements before they can be reliably used to estimate hydraulic properties in new sites that are located in different regions from their origins [12,[44][45][46]. However, the lack of representative information on soil hydraulic properties hinders the validation of the existing PTFs and the development of local PTFs in many countries of the world [12,24,46,47]. Consequently, the existing PTFs are often applied without any calibration or validation in many regions as the result of data constraints [13,46,48].…”
Section: Introductionmentioning
confidence: 99%
“…However, the lack of representative information on soil hydraulic properties hinders the validation of the existing PTFs and the development of local PTFs in many countries of the world [12,24,46,47]. Consequently, the existing PTFs are often applied without any calibration or validation in many regions as the result of data constraints [13,46,48]. Therefore, the reliability of the estimated soil hydraulic properties from the existing PTFs in regions outside their origins with different geological, climatic and soil bio-physico-chemical properties is questionable.…”
This study was undertaken to develop new pedotransfer functions (PTFs) for the estimation of soil moisture content at field capacity (FC, at −33 kPa) and permanent wilting point (PWP, at −1500 kPa) for South African soils based on easily measurable soil physico-chemical properties. The new PTFs were developed using stepwise multiple linear regressions with the dependent variable (either FC or PWP) against clay, silt, sand and soil organic carbon (SOC) content from a total of 3171 soil horizons as the explanatory variables. These new PTFs were evaluated and compared with five well-established PTFs using a total of 3136 soil horizons as an independent dataset. The coefficient of determination (r2) values for the existing PTFs ranged from 0.65–0.72 for FC and 0.72–0.81 for PWP, whilst those developed in this study were 0.77 and 0.82 for FC and PWP, respectively. The root mean square error (RMSE) values for the well-established PTFs ranged from 0.052–0.058 kg kg−1 for FC and 0.030–0.036 kg kg−1 for PWP, whilst those developed in this study were 0.047 and 0.029 kg kg−1 for FC and PWP, respectively. These findings suggest that PTFs derived locally using a large number of soil horizons acquired from different agro-climatic locations improved the estimation of soil moisture at FC and PWP. Due to the range of conditions and large soil datasets used in this study, it is concluded that these new PTFs can be applied with caution in other regions facing data scarcity but with similar soil types and climatic conditions.
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