Contribution of Different Land Evaluation Systems to Assess Land Capability and Suitability of Some Coastal Soils in Egypt

Land capability and suitability assessment become a necessary process for defining the potential capabilities of the land under different uses for sustainable land management. Scientifically each specific land unit should be utilized for an application which is suitable for that application (FAO, 1976). Agricultural land suitability is a very significant piece of information in agriculture development and planning (Chiranjit and Kishore, 2018; Ramamurthy et al., 2019).  Therefore, there is an intense need for land evaluation studies to select the superior land use (Zhang et al., 2015; Sabareeshwari et al., 2018). Combination of geographic information system with soil survey and land methods were developed and adopted to evaluate soil suitability for different crops (Bhaskar, et al., 2015). Many systems have been designed and developed for land evaluation assessment such as Storie Index (Storie, 1973), land capability classification system (Klingebiel and Montgomery, 1961), FAO Framework for Land Evaluation (FAO 1976), Soil Productivity Index (Delgado 2003), Land Use Suitability Evaluation Tool (LUSET) (Yen et al., 2006), Modified Storie Index (UCDAVIS, 2008), Microcomputer Land Evaluation Information System (MicroLEIS) (De La Rosa et al., 2009) and the Agriculture Land Evaluation System (ALESarid and modified ALESarid-GIS) (Ismail et al., 2005). Land evaluation systems could be either qualitative or quantitative methods. The qualitative approach gives the results in qualitative terms where the quantitative approach involves more parametric techniques which allow various statistical analyses to be performed. This study focused on the comparison of modified Storie Index (UCDAVIS, 2008) and Cervatana model of MicroLEIS (De la Rosaet al., 2004) and also comparison between (LUSET) (Yen et al., 2006) and of Almagra model of MicroLEIS (De la Rosaet al., 2004) as land suitability evaluation systems. Many researchers used these systems or built up their own systems depend on the methodology of the soil science (Xingwu et al., 2015). Modified Storie Index was used in several studies to evaluate the land capability in the northwestern coast of Egypt and in many other areas (Sawy, et al., 2013; Abd El-Aziz, 2018; Yousif, 2018; Yousif and Ahmed, 2019). Cervatana and Almagra models of MicroLEIS were applied to assess the land capability and suitability evaluation in many areas in Mediterranean region (Abd El-Aziz, 2018; Abd-Elmabod et al., 2019; Mahmoud et al., 2019; Yousif, 2019). LUSET is a utility tool of land suitability Evaluation for multiple crops. It is programmed in Microsoft Excel and the calculation in  LUSET was coded using Visual basic for Application (VBA), (Yen et al., 2006) and depends on the land evaluation framework of the FOA (FAO, 1976). LUSET tool was used to assess land suitability evaluation in many different areas (Aldabaa, 2018; Yousif, 2018). The objectives of the current study were to (1) characterize the soils of the area extended from El-Kasaba village to Paghoush village and located at the east of Matrouh city, Egypt. (2) Assess land capability using modified Storie Index and Cervatana model of MicroLEIS. (3) Assess land suitability by LUSET and Almagra model of MicroLEIS.

Study area
 
The study area is located in the east of Matrouh city by about 25 km and it is extended from El-Kasaba village to Paghoush village. It occupies an area of 197.22 km² (46957.14 Fadden) and located between longitudes 27° 25' 24" to 27° 47' 50" E and latitudes 31° 7' 26" to 31° 13' 41" N (Fig 1).  The elevation ranged between 11-120 m ASL. Flat to nearly level, gently sloping and sloping are the dominant slope classes (Fig 2). Normalized Difference Vegetation Index (NDVI) showed that some areas covered by scattered vegetation with maximum value of 0.5 (Fig 3). The investigated area is characterized by dry hot summer where the mean monthly temperature ranged between 14.5 to 26.7°C and almost rainy winter where the annual rainfall ranged between 87.10 and 274.50 mm year^-1 with an average of 145.06 mm year^-1 (E.M.A., 2014). It is dominated by a sedimentary rock varying from Tertiary (Miocene) to Quaternary period (El Shazly et al., 1975).
 

 

 
GIS and remote sensing
 
Using ArcGIS 10.5, Landsat 8 OLI image (path 179, row 38) captured in 2018 and 3D presentation created from digital elevation model (DEM) were used to distinguish and delineate the different physiographic units.
 
Field work and laboratory analysis
 
To represent all physiographic units, twenty-seven soil profiles were dug and morphologically described according to FAO (2006). Soil analyses were done according to USDA (2017). Soils were classified according to Soil Survey Staff (2014).
 
Land Evaluation Methods
 
Land capability classification
 
1. MicroLEIS Cervatana model, De la Roza, 2000 (Table 1).
2. Modified Storie Index Rating, UCDVVIS, 2008 (Table 2).
 

 

 
Land suitability classification
 
1. MicroLEIS, De la Roza (2000), Almagra model (Table 3).
2. Land Use Suitability Evaluation Tool (LUSET), Yen et al., 2006 (Table 4).
 

 

Each landform was represented by some soil profiles as shown in Fig 4 and Table 5 and soils were characterized as the following:
 

 

 
Soils of upper slope unit
This unit is located in the southern part of the studied area and occupies an area of 51.77 km² and represented by eight soil profiles (Table 5). Results reveled that most of soils are very deep soil except profile 6 and 7 which have moderately depth with 50 and 65 cm respectively. EC ranged between 0.59 and 10.22 dSm^-1. Calcium carbonate (CaCO₃) content was to the tune of 939.26 g kg^-1 and 93.93% the soils were considered as extremely calcareous. The soils were classified as Typic Torripsamments (12.50%), Typic Haplocalcids (25%), Typic Torriorthents (37.50%) and Lithic Torriorthents (25%) as shown in Table 6.
 

 
Soils of lower slope unit
 
This unit is the largest unit and located in the middle part of the area and occupies an area of 99.88 km² and represented by twelve soil profiles (Table 5). Soil depth varied from shallow to very deep (40 to 150 cm). EC values ranged between 0.44 and 14.81 dSm^-1 except soil profile 22 with EC value of 68.3 dSm^-1 (extremely saline). Soils are extremely calcareous where calcium carbonate reached up to 913.57 g kg^-1 (91.36 %). Soils of this unit were classified as Typic Haplocalcids cover (50 %), Typic Torriorthents cover (33.34 %), Lithic Torriorthents cover (8.33 %) and Calcic Haplosalids cover (8.33 %) as illustrated in Table 6.
 
Soils of alluvial fans unit
 
This unit is located in the northern part of the area covers (26.07 km²) and represented by five soil profiles (Table 5). Most of soils are very deep soil except profile 20 which is shallow (60 cm). EC ranged between 0.8 and 62.50 dSm^-1. CaCO₃ content reached up to 860.62 g kg^-1 (86.06%). Soils of this unit were classified as Typic Haplosalids (20%), Typic Torriorthents (40%) and Typic Haplocalcids (40%) as shown in Table 6.
 
Soils of oolitic longitudinal sand dunes unit
 
This unit is located in the northern part of the investigated area parallel with shore line. It occupies an area of 3.32 km² and represented by two soil profiles (Table 5). Soils of this unit classified as a very deep soil. EC values varied between 0.46 and 0.60 dSm^-1. CaCO₃ content reached up to 997.50 g kg^-1 (99.75 %). Soils of this unit were classified as Typic Torripsamments (Table 6).
 
Land Capability of investigated soils
 
Modified Storie Index
 
The area could be classified into four capability classes (Fig 5; Table 7).  Grade 3 occupied an area of 98.98 km² (50.19%) while grade 4 had an area of 58.57 km² (29.70%) as illustrated in Table 8. The common limiting factors are soil salinity, shallow soil depth and coarse of texture class.
 

 

 

 
MicroLEIS Cervatana model
 
The studied area could be classified into three capability classes viz S2, S3 and N (Fig 6; Table 10). Lands with good capability (S2) have a topographic or climatic limitation which in turn restrict the choice for possible crops and their productivity. Land capability (S3) having the limitations of topographic or climatic factors cause limit of potential crops capability of productivity.  S3 class includes three sub capability classes S3lr, S3r and S3l. Marginal land (N) as non-productive land is not recommended for cultivation and may be used for a pasture or forestry. Nl sub-class occupied an area of 16.45 km² (8.34%) and affected by salinity and soil depth and some physical limitations (Table 9).
 

 

 

 
Land suitability assessment
 
MicroLEIS Almagra model
 
The investigated soils are classified into four suitability classes vis high suitable (S2), moderate suitable (S3), marginal suitable (S4) and not suitable (S5). Land suitability analysis indicated that 4.71% of the studied area is S2 for wheat, soya, sunflower, alfalfa and citrus whereas 14.82% of the study area is S2 for olives (Table 11). The common limitations in theses soils are calcium carbonate, salinity and soil texture. About 31.78% of the study area is S3 for wheat, maize, peach, citrus, cotton, sunflower and alfalfa. About 36.5% of the study area is S3 for watermelon and about 52% of the study area is S3 for olives. About 40% is S4 for most of crops evaluated. Soil salinity, excess of calcium carbonate, drainage and soil texture were the most common limiting factors in these soils (Fig 7; Table10).
 
@figure7
 

 
LUST model
 
The investigated soils are classified into three suitability classes as highly suitable (S1), moderately suitable (S2) and marginally suitable (S3) (Table 12; Fig 8). Land suitability analysis indicated that 5.85, 3.73 and 2.11% of the area are S1 for wheat, cotton and olives respectively (Table 13). About 31% of the study area is S2 for citrus, peach and soya where 63.86% is moderately suitable for alfalfa and sunflower. About 85% of the area is moderately suitable for wheat and melon. Finally, about 55% of the study area is moderately suitable for potato and cotton while 75% is moderately suitable for maize and olives. About 30% of the area is S3 for most of the selected crops (Table 13). Soil salinity, excess of calcium carbonate, drainage and soil texture were the most common limiting factors in the studied soils.
 
@figure8
 

 

It may be concluded that Storie Index categorized 50% of the area as S3. Cervatana model showed that 52.5% of the soils were classified as S3. The main land capability limitations were erosion risk, excess of soil salinity and shallow soil depth. Land suitability analysis by Almagra model showed that about 4.71% and 31.78% of the area are S2 for wheat and citrus respectively. Olives had an area of 14.82% and 52% as S2 and S3 respectively. LUST results showed that about 5.85, 3.73 and 2.11 % of the area are S1 for wheat, cotton and olives respectively. About 31% of the study area is S2 for citrus, peach and soya whereas 63.86% is S2 for alfalfa and sunflower and 85% of the area is S2 for wheat and melon. The main limitation factors were soil salinity, calcium carbonate, drainage and texture. Thus MicroLEIS application either Cervatana or Almagra to predict land suitability and land capability respectively is not recommended as these applications evaluates the land based on the minimum limiting factor. LUSET application for land suitability or Modified Storie index for land capability is recommended where all soil parameters share together for assessing the soil suitability rate by calculating the average methods.