scholarly journals Irrigation Water Potential and Land Suitability Assessment in Kurfa Chele-Girawa Watershed, Wabe Shebelle River Basin, Ethiopia

2020 ◽  
Vol 8 (1) ◽  
pp. 139
Author(s):  
Girma Mideksa ◽  
Tasisa Temesgen Tolossa
Keyword(s):  
Land Cover ◽  
Land Evaluation ◽  
Suitability Analysis ◽  
Stream Flows ◽  
Irrigation Suitability ◽  
Suitability Assessment ◽  
Irrigation Potential ◽  
Slope Land ◽  
Land Suitability Assessment ◽  
Watershed Boundary

Assessing available water and land for irrigation are important for planning their use. In the watershed, stream flows from some of the rivers are not known and potential irrigable areas have not been identified. By delineating watershed boundary, irrigation suitability factors such as soil type, slope, land cover/use, and distance from the water supply were classified based on the FAO guideline for land evaluation in to S1, S2, S3 and N suitability classes independently. The irrigation potential suitability analysis of soil, slope, land cover/use and Euclidean distance indicates that 55.1 %, 95.6 %, 88.6% and 93.7% respectively are in the range of highly to marginally suitable. By weighted overlay analysis, total surface irrigation suitability potential of the study area is 54.6%, from this, the potential irrigable land obtained by multi_ criteria analysis has 45.9%. The total available flow above abstraction site is 335.7m3/s or 2.9-million-meter cube (MMC) annually.

scholarly journals Soil Nutrient Based Land Suitability Analysis for Lentil Crop in Tarakeswar, Hooghly, West Bengal

2020 ◽  
Author(s):  
Kishore Chandra Swain ◽  
Chiranjit Singha
Keyword(s):  
West Bengal ◽  
Pairwise Comparison ◽  
Soil Nutrient ◽  
Exponential Model ◽  
Land Suitability ◽  
Suitability Analysis ◽  
Available Nitrogen ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Gis Software

Background: Land suitability assessment can inform decisions on land uses suitable for maximizing crop yield while making best use, but not impairing the ability of natural resources such as soil to support development. We assessed the suitability of lentil to be produce in 300 ha land of Tarakeswar block of Hooghly district West Bengal. Methods: Suitability criteria included eight criterion, such as: soil texture (ST), electrical conductivity (EC), organic carbon (OC), available nitrogen(N), available phosphorous (P),available potassium(K) and available zinc (Zn). We modiûed and used a novel set of techniques to assess suitability: Analytical Hierarchy Process (AHP) pairwise comparison matrixand Geographic Information System (GIS) software. Result: The exponential model generated by Ordinary Kriging was used to estimate the lentil land use suitability. Suitability distribution of deep layer found nearly 42% area under suitability class, i.e. S1-2 which may be used for growing the crop profitably.

scholarly journals Taking account of uncertainties in digital land suitability assessment

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1366 ◽  
Author(s):  
Brendan P. Malone ◽  
Darren B. Kidd ◽  
Budiman Minasny ◽  
Alex B. McBratney
Keyword(s):  
Land Suitability ◽  
Evaluation Framework ◽  
Land Evaluation ◽  
Irrigation District ◽  
Biophysical Model ◽  
Continuous Variables ◽  
Simulation Approach ◽  
Suitability Assessment ◽  
Significant Difference ◽  
Land Suitability Assessment

Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the ‘error free’ assessment, where a prediction of ‘unsuitable’ was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.

Land suitability assessment in the Namoi Valley of Australia, using a continuous model

Soil Research ◽  
2001 ◽  
Vol 39 (2) ◽  
pp. 273 ◽  
Author(s):  
J. Triantafilis ◽  
W. T. Ward ◽  
A. B. McBratney
Keyword(s):  
Land Use ◽  
Continuous Model ◽  
Land Suitability ◽  
Land Evaluation ◽  
Fuzzy Approach ◽  
Suitability Assessment ◽  
Suitability Maps ◽  
Punctual Kriging ◽  
Land Suitability Assessment ◽  
Membership Score

In an agricultural context, land evaluation is assessment for a specified kind of land utilisation. The final result of agricultural evaluation is a map, which partitions the landscapes into suitable and unsuitable areas for a particular land-use of interest. However, this approach may not represent the continuity of land. Land suitability could be better expressed by a fuzzy approach. In this paper a fuzzy methodology is used to evaluate land suitability in the Edgeroi district for various crops including barley, dryland cotton, oats, pasture, soybean, sorghum, sunflower, and wheat. This is achieved using a membership function to derive a land-suitability membership score ranging from non-suitable (i.e. 0) to suitable (i.e. 1). We express this as continuous land suitability maps using punctual kriging. An expression for overall land suitability (i.e. its versatility) and its capacity with respect to suitability to particular rotations is introduced to highlight the most productive units of soil.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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