Developing a GIS-based agro-land suitability map for the Faria agricultural catchment, Palestine. Sameer M. Shadeed* and Atta M.E.

Transcription

1 190 Int. J. Global Environmental Issues, Vol. 16, Nos. 1/2/3, 2017 Developing a GIS-based agro-land suitability map for the Faria agricultural catchment, Palestine Sameer M. Shadeed* and Atta M.E. Abboushi Water and Environmental Studies Institute, An-Najah National University, Nablus, West Bank, Palestine sshadeed@najah.edu maher-aboushi@hotmail.com *Corresponding author Mohammad N. Almasri Civil Engineering Department, College of Engineering, An-Najah National University, Nablus, West Bank, Palestine mnmasri@najah.edu Abstract: This study utilises the analytic hierarchy process (AHP) to develop an agro-land suitability map to optimally utilise the available land to achieve best agricultural production. Method applicability was demonstrated on the fertile Faria catchment (320 km 2 ), located in the north-eastern part of the West Bank, Palestine. GIS was employed to combine spatial weighted factors for different criteria including topography, soil, landuse, accessibility, climate, water availability, and groundwater vulnerability. Results indicate that about 49% and 6% of the total area of Faria catchment are moderately and highly suitable for agricultural practices, respectively. Whereas marginally and unsuitable areas represent about 41% and 4%, respectively. Agricultural investment in such areas is not feasible and will negatively affect the sustainable agricultural practices in the catchment. The implementation of the obtained results is envisaged to support any governmental policy shifts towards wide spread adoption of sustainable agriculture in Palestine. Keywords: Faria agricultural catchment; agro-land suitability; GIS; analytic hierarchy process; AHP; groundwater vulnerability; sustainable agriculture practices; agricultural production; West Bank; Palestine. Reference to this paper should be made as follows: Shadeed, S.M., Abboushi, A.M.E. and Almasri, M.N. (2017) Developing a GIS-based agro-land suitability map for the Faria agricultural catchment, Palestine, Int. J. Global Environmental Issues, Vol. 16, Nos. 1/2/3, pp Copyright 2017 Inderscience Enterprises Ltd.

2 Developing a GIS-based agro-land suitability map 191 Biographical notes: Sameer M. Shadeed is an Assistant Professor of Water Resources at the Water and Environmental Studies Institute (WESI) at An-Najah National University, Nablus, Palestine. Since 2008, he has been an active researcher and Lecturer in the field of water resources modelling and management. He conducted several researches (e.g., hydrological modelling, rainwater harvesting, climate change, water quality, agricultural best management practices and integrating microwave link data for analysis of precipitation variability (e.g., hydrological modelling, rainwater harvesting, climate change, water quality, agricultural best management practices and integrating microwave link data for analysis of precipitation variability). Moreover, he supervised the work of several graduate students in the field of water and environmental engineering. His teaching includes graduate and undergraduate aspects of water resources quantity and quality, particularly: hydrological processes and systems, natural resources management, engineering hydrology, introduction to environmental engineering, irrigation and drainage, fluid mechanics, and GIS. Finally, he worked as individual freelance consultant for several national and international consultation companies and firms in the fields of water, environment and strategic planning. Atta M.E. Abboushi is currently the engineer in charge of the treatment processes and systems projects at Dubai Municipality, Dubai Government. He received his Master degree in Water and Environmental Engineering from the Water and Environmental Studies Institute (WESI) at An-Najah National University, Nablus, Palestine. He had worked for three years ( ) as a researcher assistant at WESI. He was involved in water and environmental related projects among which groundwater quality, surface-subsurface interaction and agricultural best management practices. His key qualifications focus on hydrological modelling, water resources management, solid waste management, water and wastewater quality analysis and modelling. Mohammad N. Almasri is currently an Associate Professor in the Department of Civil Engineering at An-Najah National University, Nablus, Palestine and an Adjunct Professor at Utah State University, USA where he graduated from with a PhD degree in Civil and Environmental Engineering. His key qualifications focus on modelling and management of groundwater resources, environmental pollution, assessment of water-related projects, environmental impact assessment studies, and the design of water distribution networks and sewer systems. He coordinated and managed development projects in the field of water and wastewater in the past few years and supervised and co-supervised a total of 18 master students. This paper is a revised and expanded version of a paper entitled Developing a GIS-based agro-land suitability map for the Faria agricultural catchment, Palestine presented at Second Conference of PADUCO: Palestinian-Dutch Academic Cooperation Program on Water, The Hague, The Netherlands, 28 October Introduction Land use suitability mapping and analysis is one of the most important applications of GIS for sustainable agricultural purposes (Malczewski, 2004). However, performing land use suitability assessment is a vital step towards sustainable agriculture (Vargahan

3 192 S.M. Shadeed et al. et al., 2011). Various multi-criteria decision analysis (constraints analysis) approaches were used in agricultural land use suitability assessment (Parakash, 2003). Among which is the AHP approach (Feizizadeh and Blaschke, 2012). The AHP is a multi-criteria decision-making approach which enables users to use simple and straightforward principles in analysing multi-criteria decision problems (Nyeko, 2012). A key output in this paper is the development of an agro-land suitability map for the Faria catchment. The produced map is considered as a good assessment tool for investigation of a certain part of land s appropriateness to agriculture (Rabia and Terribile, 2013). GIS-Basedagro-land suitability maps were developed in many regions around the world to contribute in achieving the sustainable agriculture and eventually the food security (e.g., Krishna and Regil, 2014; Akıncı et al., 2013; Rabia, 2012; Jafari and Zaredar, 2010; Carr and Zwick, 2005). The main theoretical framework of developing such a map can be summarised as classifying the catchment under study into certain categories of agricultural suitability, depending on the resolution of the available data (Ritung et al., 2007). As such, weighted criteria, measurement indicators, and subjectively scored sub-criteria should be determined and manipulated under the GIS environment. Developing of agro-land suitability map aims to facilitate its use by non-technical stakeholders, and thus to enhance its uses at a wider scale. In addition, it can help in the development of the agricultural best management practices and guidelines to manage the uncontrolled agricultural activities that are being practiced in the agricultural areas such that groundwater deterioration is controlled while socio-economic conditions are intensified. The developed agro-land suitability map for the Faria catchment is intrinsic one, which indicates different levels of land suitability to agriculture in general. It is worth to mention that the main concept, in which the agricultural land use suitability map depends on, is the diversity of the products that can be cultivated in the area that was taken into consideration. In other words, pieces of land become more suitable for agricultural purposes as they allow more number of species that could be cultivated in these areas (Akıncı et al., 2013). The main beneficiary stakeholders from developing such a map are the catchment farmers (the key players) by adopting proper agricultural practices and management options that contribute in achieving the sustainable agricultural development and thus increasing their income. Additionally, the developed map can help the decision makers (e.g., the Ministry of Agriculture, the Palestinian Water Authority and the Environmental Quality Authority) to come up with some policies and guidelines to support any governmental policy shifts towards wide spread adoption of sustainable agriculture in Palestine. This in turn, can benefit the local communities by halting the deterioration of groundwater quality and thus sustain the supply of good quality water for drinking purposes for the current and upcoming generations. 2 Study area Faria is a 320 km 2 stressed agricultural catchment, which is located in the north eastern part of the West Bank, Palestine (see Figure 1). The catchment is considered as the food basket of the West Bank (Shadeed, 2008). It suffers from both water shortage and water quality concerns (Abboushi, 2013). The groundwater system is the only utilisable

4 Developing a GIS-based agro-land suitability map 193 resource for both domestic and agricultural purposes in the catchment. Most of the wells in the catchment are classified as agricultural and were drilled in the vicinity of the main wadi as depicted in Figure 1 (Abboushi et al., 2015). Figure 1 Location map of the Faria catchment, the distribution of wells and springs, the main wadi and land use classes (see online version for colours) The main wadi of Faria consists mainly from springs discharge, runoff generated from winter storms, untreated wastewater effluent from the eastern part of Nablus City, and the return flow from the adjacent agricultural areas (Abboushi et al., 2015). Note that the agricultural return flow consists mainly of agricultural contaminants such as agro-chemicals, manures, and pesticides that may leach to the groundwater directly through surface infiltration, or discharge to the main wadi and then end up in the most upper underlying aquifer through transmission losses. Those aforementioned quality problems and others will directly and indirectly affect the sustainable agricultural

5 194 S.M. Shadeed et al. production in the catchment. As depicted in Figure 1, about 22% of the land use classes are classified as agricultural areas which refer to irrigated (e.g., green houses) and supplementary irrigated areas (e.g., citrus and open field vegetables). Whereas 41% of the total Faria catchment area occupied by rain-fed agricultural areas (e.g., olive and sparsely vegetated hill slopes). Figure 2 illustrates a typical example of agricultural areas in the Faria catchment. Figure 2 Typical example of agricultural areas in the Faria catchment (see online version for colours) 3 Materials and methods The chart of Figure 3 depicts the overall methodology utilised in this research work for developing a GIS-based agro-land suitability map for the Faria catchment. Firstly, the best available datasets at national level of topography, soil, land use, availability of groundwater wells, climate, road network and political zones were first obtained and compiled in a GIS-based database (MoP, 1997; Shadeed, 2008). Out of that, twelve of constraints (criteria) were determined and used, among which the agricultural vulnerability that was developed based on the DRASTIC approach (Abboushi et al., 2014). Different suitability values (weights) were assigned for each criterion by adopting the AHP approach through constructing a pairwise comparison matrix and using a scoring system based on a preference scale which ranges from (1 to 9) (Saaty, 1980) (see Table 1). The weight of each layer reflects its importance in agro-land suitability potential. Once performing pairwise comparisons of criteria, the AHP provides decision makers with an effective way of checking and improving consistency. The consistency ratio (CR) is calculated using the following formula (Saaty, 1980): CI CR = (1) RI

6 Developing a GIS-based agro-land suitability map 195 λ n CI = (2) n 1 where CR consistency ratio CI consistency index RI random consistency index λ n the normalised principal eigenvector the number of constraints (criteria). If the value of the CR is smaller or equal to 0.1, this means that the judgements (scores assigned for the used criteria) show a sufficient degree of consistency, and the assessment can be continued. On the other hand, if the CR is above 0.1, then the judgements are considered inconsistent and the pairwise comparison needs to be revised (Saaty, 1996, 2000). In the pairwise comparison matrix shown in Table 1, the CI was calculated as and RI was chosen as 1.54 for n = 12. Thus, the CR was found to be 0.098, which is smaller than 0.1; this means that the judgements made are acceptable. Figure 3 General conceptual methodology Setting up the objectives of the landuse suitability analysis Excel Determining the most influencing criteria Determining the sub-criteria (Measurement indicators) Processing the sub-criteria data to have them in GIS-based format AHP: Pairwise comparison matrix Assigning weights for the criteria GIS Assigning scores for the sub-criteria Subjective Scores within a scale of (1-10) Rasterization Weighted overlay summation process Landuse suitability map (LSM) Reclassification of the LSM according to FAO Agro-land suitability map

7 196 S.M. Shadeed et al. Table 1 Pairwise comparison matrix used in the development of the agro-land suitability map of the Faria catchment Measurement indicator Slope Soil texturepractices Soil textureproperties Soil depth Soil erosion Rainfall PET Landuse Political Availability of groundwater wells Proximity to roads Groundwater vulnerability Weight Topography: slope Soil: texture-practices Soil: texture-properties Soil: depth Topography-soil: erosion Climate: rainfall Climate: total PET Landuse: landuse classes Accessibility: political constraints Availability of groundwater wells Accessibility: proximity to roads Groundwater vulnerability Sum

8 Developing a GIS-based agro-land suitability map 197 Table 2 The scores assigned for the sub-criteria classifications # Criteria Measurement indicator 1 Topography Slope 2 Soil Texture-practices 3 Soil Texture-properties 4 Soil Depth 5 Soil/ topography Erosion 6 Climate Rainfall 7 Climate Total PET Sub-criteria with classifications Score <3%: flat (most suitable) %: gently sloping %: rolling/sloping %: hilly %: mountainous %: steep mountainous 2 >60%: very steep mountainous (least suitable) 1 Sandy loam: medium to light (most suitable) 9 Loamy: medium 7 Clay loam: heavy to medium 5 Clay: heavy (Least suitable) 3 Clay loam: high water holding capacity, nutrient 8 holding capacity, and high organics content (most suitable) Clay: high to medium 7 Sandy loam: medium 5 Loamy: low (least suitable) 3 <20 cm: very shallow (least suitable) cm: shallow/regosols cm: moderately deep 6 >75 cm: deep/brown rendzinas and pale renzinas and terra rossas, brown rendzinas and pale renzinas (most suitable) 0 8%: very weak (most suitable) %: weak %: moderate %: high 3 >60%: very high (least suitable) 1 <200 mm/yr: (least suitable) mm/yr mm/yr 6 >400 mm/yr: (most suitable) ,062 mm/yr: (most suitable) 6 1,495 mm/yr 5 1,540 mm/yr: (least suitable) 4 8

9 198 S.M. Shadeed et al. Table 2 The scores assigned for the sub-criteria classifications (continued) # Criteria Measurement indicator 8 Land use Land use classes 9 Availability Groundwater wells 10 Accessibility Proximity to roads 11 Accessibility Political constraints: authority limits 12 Groundwater vulnerability Descriptive vulnerability zones Sub-criteria with classifications Score Agricultural areas: (most suitable) 9 Sparsely vegetated hill slopes 8 Olive plantations 7 Scattered olive plantations 6 Natural forests 5 Natural grassy hill slopes 3 Residential agricultural communities 2 Bare rocks: (least suitable) 1 % of wells in agricultural areas = 77% 9 (most suitable) % of wells in natural grassy hill slopes = 8% 6 % of wells in sparsely vegetated hill slopes = 7% 5 % of wells in scattered olive plantations = 6% 4 % of wells in residential agricultural 2 communities = 2% % of wells in the other categories = 0% (least suitable) % of roads sections that are within 100 m from the agricultural areas = 31% (most suitable) % of roads sections that are within 100 m from the residential agricultural communities = 21% % of roads sections that are within 100 m from the natural grassy hill slopes areas = 19% % of roads sections that are within 100 m from the sparsely vegetated hill slopes areas = 15% % of roads sections that are within 100 m from the scattered olive plantations areas = 7% %of roads sections that are within 100 m from the olive plantations areas = 5% % of roads sections that are within 100 m from the natural forests areas = 2% % of roads sections that are within 100 m from 1 the bare rocks areas = 0% (least suitable) Zone A (most suitable) 9 Zone B 7 Zone C (least suitable) 5 Low (most suitable) 9 Moderate 5 High (least suitable) 2 User-specified cell values (sub-criteria suitability) of each layer were subjectively scored from 1 to 10 (see Table 2 and Figures 4 through 7). Higher scores were given to the

10 Developing a GIS-based agro-land suitability map 199 sub-criteria that positively affect the agricultural production. While, lower scores were given to those that negatively affect the number of products that can be cultivated in the study area. To customise the development of the agro-land suitability map, the conversion to grid theme (rasterisation) and the reclassification of sub criteria was processed by the ArcMap 10.1, which enables a weighted overlay summation process (WOSP) of thematic datasets. The WOSP allows the combination of data from several input grids by converting their cell values to a common scale, assigning a weight to each grid, and then aggregating the weighted cell values together. The WOSP, also known as the multi-criteria evaluation is a weighted linear method commonly used in GIS-based decision-making analysis (Store and Jokimäki, 2003). Each layer is multiplied by its weight and the results are summed according to the following equation (Malczewski, 1999): n A = W S (3) j i ij i= 1 where A j is the final cell suitability score index, S ij is the suitability of the i th cell with respect to the j th layer, and the weight W i is a normalised weight so that W i = 1. The weights enable the solution to reflect the importance of the input layer relative to each other. Figure 4 (a) The rated raster grids of the slope (b) The soil texture practices (c) The soil texture properties (see online version for colours) (a) (b) (c) Figure 5 (a) The rated raster grids of the soil depth (b) The soil erosion (c) The rainfall (see online version for colours) (a) (b) (c)

11 200 S.M. Shadeed et al. Figure 6 (a) The rated raster grids of the total pet (b) The land use classes (c) The availability of the groundwater wells (see online version for colours) (a) (b) (c) Figure 7 (a) The rated raster grids of the proximity to roads (b) The political-administrative zones (c) The vulnerability zones (see online version for colours) (a) (b) (c) 4 Results and discussion Based on the theoretical framework of the land use suitability analysis, and after performing the WOSP, the agro-land suitability map was produced for the Faria catchment. The map was reclassified into four classes of equal ranges according to the land use suitability classifications of FAO (1976). They are highly, moderately, and marginally suitable classes, in addition to the unsuitable one as shown in Figure 8. The developed agro-land suitability map indicates that the highly suitable areas for agriculture are mainly concentrated in the upper part of the catchment. This can be attributed to the high rainfall values, availability of groundwater wells, low potential evapotranspiration, fertile soil, and the low to moderate groundwater vulnerability. Highly suitable areas are sparsely located in the middle and lower parts of the catchment where the irrigated/supplementary irrigated agricultural areas exist. The moderate and marginal agro-land suitability classes for agriculture make up the largest areas in the Faria catchment. The areas that are considered as unsuitable for agricultural activities

12 Developing a GIS-based agro-land suitability map 201 according to the developed map are sparsely distributed, with much concentration in the middle part of the catchment. This is mainly due to the high groundwater vulnerability to contamination in these areas. Figure 9 presents the percentages of the agro-land suitability areas in the Faria catchment. From the figure, it can be concluded that 49% (157 km 2 ) and 6% (19 km 2 ) of the total area of Faria catchment are moderately and highly suitable for agricultural practices, respectively. However and from the given land use map; the total irrigated/supplementary irrigated agricultural areas occupy about 71 km 2 of the total catchment. The developed agro-land suitability map revealed that 53% (38 km 2 ) of these areas are considered suitable for agriculture, whereas the low agricultural suitability areas and the unsuitable areas make up 45% (32 km 2 ) and 2% (1 km 2 ) of the irrigated/supplementary irrigated agricultural areas respectively. This in turn indicates a potential increase of about 138 km 2 of the suitable irrigated/supplementary irrigated agricultural areas in the catchment if some agricultural best management practices are adopted. As such, the socio-economic conditions in the catchment will be optimised and accordingly self-sufficiency in one of the major food baskets in Palestine will be improved. Whereas land having limitations for sustainable agriculture (marginally suitable) form 41% (131 km 2 ). In such areas, agricultural productivity and thus revenues reduced unless intensive agricultural management options/practices are enhanced. Finally, unsuitable areas for agricultural purposes form about 4% (13 km 2 ) of the total area of Faria catchment. Such areas have limitations for sustainable agricultural purposes (FAO, 1976). Figure 8 Agro-land suitability map of the Faria catchment (see online version for colours)

13 202 S.M. Shadeed et al. Figure 9 Percentages of agro-land suitability areas in the Faria catchment (see online version for colours) 5 Conclusions This study dealt with constraint analysis of land suitability for agriculture in the Faria catchment using GIS based on the available data. The major advantage of employing GIS in developing agro-land suitability map is that a high degree of customisability can be attained. It enables the user to add, remove layers and change the relative importance weights of the layers. It should be noted that determining the weights is subjective. In this context, it is advisable to perform a sensitivity analysis by varying the weights in order to provide insights into the generated agro-land suitability map. Although the resolution of the available datasets is coarse, yet the work provides an overall picture about the degree of agricultural suitability in the catchment. This in turn indicates that even under conditions of improper data yet much can be performed to assist the decision makers (for instance the Ministry of Agriculture and the Palestinian Water Authority). In general, the developed agro-land suitability map delineates areas for sustainable agricultural expansion in the Faria catchment. In particular, other factors such as socio-economic, marketing and pricing may have potential impacts on agro-land suitability mapping. Finally, the developed agro-land suitability map for the Faria catchment shed light on how the WOSP can guide sustainable strategy development so that, the socio-economic conditions are maximised, and simultaneously, the groundwater quality deterioration is minimised. Acknowledgements This work was performed within the framework of the Palestinian Dutch Academic Cooperation Program on Water (PADUCO), funded by the Netherlands Representative Office (NRO) in Palestine. The financial support is gratefully acknowledged.

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