INTRODUCTION
The landscape is always a result of numerous for-mer successes and failures [Luz 2000]. In order to achieve better results, an assessment of the planning is necessary. The agricultural sector in Egypt is fa-cing major sustainability constraints such as, land and water resources scarcity, environmental degradation, rapid population growth, institutional arrangement including land tenure and farm fragmentation, agri-cultural administration, lack of infrastructure, credit utilization and high interest rates [El-Nahry 2001]. These human enterprises lead to local land-use and land-cover changes have a global-scale impact on cli-mate, hydrology, biogeochemistry, biodiversity and then ability of biological systems to support human needs [Foley et al. 2005]. The sustainable land ma-nagement (SLM) requires the integration of techno-logies, policies and activities in the rural sector, par-ticularly agriculture, in such a way as to enhance eco-nomic performance while maintaining the quality and environmental functions of the natural base. To eva-luate sustainable land management five criteria are needed, these include productivity, security, protec-tion, viability, and acceptability [Dumanski, 1997]. The decision supporting system (DSS) based on the framework of sustainable land management (SLM)
is an expert system technology which used to evalu-ate the current conditions of productivity, security, protection, viability, and acceptability indices, [Smith, Dumanski, 1993]. Geographic data capture systems include remotely sensed imagery, environmental monitoring systems such as intelligent transportation systems, and location-aware technologies GPS that can report location in near-real time.
The main objective of the current work is to eva-luate Sustainable Land-use Management (SLM) thro-ugh bio-physics elements (productivity, security, pro-tection) and socio-economic elements, (economic viability and social acceptability) for the purpose of combating and tackling sustainability constraints that preclude the agricultural development or to reduce them to the acceptable levels of mass production.
STUDY AREA
The study area represents 27000 ha and is located at the north-east part of the Nile Delta, Egypt and bounded by longitudes 31O15² and 32O00² E and
la-titudes 31O30² and 30O00² N, (Fig. 1). Through its
history the Neonile in this region has been continu-ously lowering its course at a rate of 1 m/1000 years, [Said 1993]. Based on the meteorological data and the American Soil Taxonomy [USDA 2006], the soil
A.S.I. HEGAZY1, A.H. EL-NAHRY2, M.S. ABD-ELWAHED1
1 Soils Dept., Fac. Agric., Ain- Shams University, Cairo,Egypt 2 National Authority for Remote Sensing and Space Science, Cairo, Egypt
AGRICULTURAL SUSTAINABILITY APPROACH USING REMOTE
SENSING AND GIS: CASE STUDY IN SOUTH EL-HUSSINIA,
NORTH-EAST OF EGYPT
Abstract: This work aims to evaluate Sustainable Land-use Management (SLM) through bio-physics elements (productivity, security and protection) and socio-economic elements (economic viability and social acceptability). The investigated area, 27000 hectares, lies between 31O15² and 32O 00² E and latitudes 31O30² and 30O00² N. To assess SLM, geomorphology and associated
soils were identified, the land degradation processes were recognized, then a Sustainable Land use Management Spatial Model (SLMSM) was built and used to assess the sustainable land use management in the study area. The area included three landscapes; fluvio-lacustrine plain, marine plain and flood plain. Four SLM classes were outlined; the relatively high decantation basins repre-senting 62.4% of the agricultural area, the relatively low decantation basins (Class II) occupying 22.5% of the agricultural area, overflow basins (Class III) covering 9.5% of the agricultural area, and Class IV that found in clay flats, sandy remnants, overflow mantle and river terraces occupying 5.6% of the agricultural area.
Key words: Sustainable land-use management (SLM), geomorphology, spatial model, soil mapping, GIS, South El-Hussinia, Egypt
Vol. 63 No 3/2012: 816
temperature regime of the studied area is defined as Thermic and soil moisture regime as Torric
MATERIALS AND METHODS
1. Remote sensing data processing
Digital image processing of Landsat 7.0 ETM+ satellite images dated to year 2009 was executed using ENVI* 4.7 software [ITT 2009]. The digital image processing included bad lines manipulation by filling gaps module designed using IDL* language, data ca-libration to radiance according to Lillesand and Kie-fer [2007], image enhancement, image geometric cor-rection and enhancing the ground resolution from 28.5 m to 14.25 m using fusion methodology according to Ranchin and Wald [2000] followed by mosaicking and finally extracting of the images for only the stu-dy area (Fig. 1).2. Geographic information system (GIS)
ArcGIS* 9.3.1 and its ArcGIS Spatial Analyst extension [ESRI* 2009] were used for mapping soilvariables and building a Sustainable Land-use Ma-nagement Spatial Model (SLMSM) with the aid of some thematic maps [Talbot, 1998].
3. Geomorphology and soil mapping
Geomorphologic map was carried out using the Landsat ETM+ image (Figure 2). The different soil units were represented by 27 soil profiles, the mor-phological descriptions of the soil profiles were car-ried out using FAO [2006]. The American Soil taxo-nomy, [USDA 2006] was used to classify the diffe-rent soil profiles to sub great group level. Correlation between the geomorphologic and taxonomic units was executed. The socioeconomic data were obtained from Egyptian Environmental Affairs Agency, [EEAA 2009].4. Soil laboratory analysis
Physical analyses: Particle size distribution and bulk density were determined according to USDA [2004].
Chemical analyses: Electric conductivity (EC),
soluble cations and anions, CaCO3, OM, pH, ESP,
macro nutrients (NPK) and CEC were determined according to Bandyopadhyay, [2007].
5. Evaluation of the sustainable land-use
management
Flowchart for Evaluating Sustainable Land-use Management Spatial Model (SLMSM) was designed (Figure 2). It combines technologies, policies and activities aimed at integrating socio-economic prin-ciples with biophysics and environmental concerns to simultaneously satisfy the five pillars of Sustaina-ble Land-use Management (SLM) i.e., productivity, security, protection, economic viability and social acceptability. SLMSM was designed using the spa-tial geoprocessing tools of ARCGIS 9.3.1 as follows: 1 Creating File geodatabase.
2 Specifying spatial model environment settings. 3 Calculating a series of values for input criteria
resulting in five datasets.
4 Reclassifying each derived dataset to a common measurement scale, giving each range a discre te, integer value between 1 and 4. Higher values were given to attributes within each dataset that are more suitable for sustainability classes. 5 Using conditional expressions to get
sustaina-bility raster classes.
6 Weighting datasets through setting equal influ-ence with different scale values.
*ENVI: Environment for Visualizing Images (image process-ing software; Research Systems, Inc.)
*ESRI: software development and services company *IDL: Interactive Data Language (programming language used for data analysis)
*ArcGIS: software which allows the user to view and manip-ulate spatial data.
7 Converting the sustainability raster classes into sustainability polygons in the geodatabase. 8 Creating sustainability layer.
9 Selecting four sustainability classes by attribu-tes (values).
10 Reating the final layers that represent sustaina-bility classes.
RESULTS AND DISCUSSION
1. Geomorphology and soils
The Geomorphology map (Figure 3) and soil ta-xonomy map (Figure 4) show that three main land-scapes could be identified in the study area include: 1 Fluvio-lacustrine plain with five landforms;
clay flats (high & low elevated), salt flat (dry & wet), swamps, gypsiferrous flats and water bo-dies. The soils in this landform were classified into Vertic Torrifluvents, Gypsic Haplosalids and Typic Aquisalids.
2 Marine plain including sandy remnants (high & low elevated).
3 Flood plain containing overflow mantle (rela-tively high & rela(rela-tively low elevated), over flow basins (relatively high & relatively low eleva-ted) and decantation basins (relatively high & relatively low elevated), river terraces (relati-vely high & relati(relati-vely low elevated) and turtle backs. The soils in this landform were
classi-fied into Vertic Torrifluvents, Typic Torriflu-vents and Typic Natrargids.
The main soil properties are shown in table (1). The area was differentiated into twenty seven soil mapping units. The results show that the organic carbon % va-lues in most soil mapping units were lower than 1% except the units D23, A, O21, H21 and M27 which indicates that these unites contains soluble sodium humate. On the other hand, the pH values ranged be-tween 7.2 and 9.1. The CEC values were higher than 24 meq/ 100g soil in the different units. The available nitrogen ranged between 22.6 and 63.7 ppm. The most mapping units contained moderate concentrations of available phosphorus but some units had low concen-trations represented by (H11, O11, O22, L2, L21, M22, H22, M26, and M27). The available potassium was low in most mapping units except for L, M25, M23, and H22 which contains moderate concentrations. Most of the EC values were lower than 4 dS/m. the ESP was higher than 30 in all units.
2. Sustainable Land-use Management Spatial
Model (SLMSM)
Indicators of the international framework for eva-luating sustainable land management (FESLM) were used as inputs for the designed SLMSM. This model was designed depending upon spatial analysis as shown in Figure (2). To assess sustainable land-use management of the agricultural system, five susta-FIGURE 2. Flowchart for the Sustainable Land-use Management Spatial Model (SLMSM)
FIGURE 3. Geomorphology of south El-Hussinia plain
FIGURE 4. Soil Taxonomy map of south El-Hussinia plain inability indicators (productivity, protection, securi-ty, economic, viability and social acceptability) were modeled.
Calculating a series of values for criteria was ba-sed on specified paython expression resulted in five datasets for each input criteria.
Typic Haplosalids
Typic Torrifluvents & Aquic Torrifluvents Typic Torrifluvents & Typic Aquisalids Typic Torrifluvents & Aquic Haplosalids Typic Torrifluvents & Vertic Torrifluvents Turtle back
Aeolian deposite Decantatioon basin Depriession High clay flat High dried lake bed High river terrace Low clay flat Low dried lake bad Low river terrace Mod. dried lake bed Moderate clay flat Moderately High clay flay Over flow basin Turle back
a. Productivity
Results obtained from the first stage of executing SLMSM (getting productivity index from calculating series of values) indicated that, land productivity in some parts of flood plain represented by the mapping unit (Decantation basin D21) meet sustainability re-quirements (class I) where the productivity index in these areas recorded 0.65 (Table 2), meanwhile the rest of flood plain and Fluvio- lacustrine plain are marginally above the requirements of sustainability where the indices of productivity range between 0.43 and 0.59 representing (class II). On the other hand, marine plain is lying below the requirements of su-stainability (class III) whereas its productivity index ranges between 0.28 and 0.29. The low values of the productivity index are due to the decrease of relative yield, cation exchange capacity, available nitrogen, and increase of salinity as well. The productivity in-dex considering the values of ten indicators for de-termining soil productivity was calculated using the equation:
Productivity index (Prod I)= A/100 ´ B/100 ´ C/100 ´ D/100 ´ E/100 ´ F/100 ´ G/100 ´ H/100 ´ I/100 ´ J/100
Where, A = relative yield %, B = organic carbon %, C = pH, D = CEC (meq/100 g. soil), E =
availa-ble nitrogen (ppm), F = availaavaila-ble phosphorous (ppm), G = available potassium (ppm), H = soil depth (cm), I = EC (dS/m) and J = ESP.
Figure (5) shows the productivity index for the different geomorphological units in the study area.
The highest values were found in (Decantation ba-sin D22, D21, and Over flow baba-sin O21) meanwhile the lowest values were found in Low clay flat L21, L2, and Aeolian deposits A.
b. Security and protection indices
The security indices considered three indicators; moisture availability per month/season (A), EC of irrigation water (B), % Biomass (C), meanwhile pro-tection indices include evidence of erosion indica-tors (D), evidence of submerged areas (E) and crop-ping pattern (F). ti n u g n i p p a M O.C% pH CEC, g 0 0 1 / q e m li o s m p p ,y ti li b al i a v a st n ei rt u N Oxygen y ti li b al i a v a dESC/,m E%SP, N P K ) 1 2 O ( n i s a b w o lf r e v O ) 2 2 O ( n i s a b w o lf r e v O ) 3 2 O ( n i s a b w o lf r e v O ) 1 2 H ( d e b e k al d ei r d h g i H ) 2 2 H ( d e b e k al d ei r d h g i H ) 2 L ( d e b e k al d ei r d w o L ) A ( st i s o p e d n ai l o e A ) 1 2 L ( t al f y al c w o L ) 2 2 L ( t al f y al c w o L ) 1 2 D ( n i s a b n o it a t n a c e D ) 2 2 D ( n i s a b n o it a t n a c e D ) 3 2 D ( n i s a b n o it a t n a c e D ) 1 2 M ( t al f y al c y l e t a r e d o M ) 2 2 M ( t al f y al c y l e t a r e d o M ) 3 2 M ( t al f y al c y l e t a r e d o M ) 1 2 H ( t al f y al c h g i H ) 2 2 H ( t al f y al c h g i H ) 3 2 H ( t al f y al c h g i H ) 4 2 M ( t al f y al c h g i h y l e t a r e d o M ) 5 2 M ( t al f y al c h g i h y l e t a r e d o M ) 6 2 M ( s e c a rr e t r e v ir h g i h y l e t a r e d o M ) 4 2 H ( s e c a rr e t r e v ir h g i H ) 7 2 M ( s e c a rr e t r e v ir y l e t a r e d o M ) 8 2 M ( s e c a rr e t r e v ir y l e t a r e d o M ) L ( s e c a rr e t r e v ir w o L ) D ( n o i s s e r p e D 8 1 . 1 1 8 . 0 6 9 . 0 2 8 . 0 5 7 . 0 6 7 . 0 3 . 1 5 7 . 0 6 9 . 0 6 6 . 0 3 . 1 5 . 1 5 8 . 0 3 6 . 0 2 7 . 0 6 1 . 1 3 8 . 0 5 8 . 0 2 8 . 0 9 7 . 0 4 7 . 0 6 2 . 2 8 9 . 1 8 . 3 1 7 . 1 5 2 . 4 1 . 9 2 1 . 8 2 . 8 5 5 . 8 1 . 9 1 . 9 2 . 7 7 5 . 7 8 5 . 7 9 . 7 4 7 . 8 6 6 . 8 6 7 . 8 2 1 . 8 1 . 8 1 5 . 8 1 3 . 8 1 5 . 8 5 4 . 8 1 4 . 7 8 5 . 8 5 . 8 7 . 8 1 8 . 8 9 6 . 8 5 . 8 3 5 4 . 3 5 5 . 8 4 2 . 7 4 5 . 7 4 2 . 6 5 6 . 9 4 8 . 1 5 7 . 7 5 5 . 3 5 9 5 7 5 5 . 9 4 3 . 4 5 4 . 4 5 6 . 4 5 1 . 7 5 5 . 6 5 4 5 1 . 3 6 4 . 8 5 3 5 7 . 1 6 9 5 3 . 6 5 5 . 9 4 4 . 6 4 6 . 0 2 4 . 9 4 2 . 1 4 7 . 7 5 1 . 6 3 7 . 6 5 7 . 2 2 4 . 6 4 8 . 1 6 5 . 1 5 4 . 6 4 8 . 1 6 6 . 9 1 8 . 5 2 7 . 7 4 2 . 1 2 9 . 0 5 4 . 2 4 4 . 9 5 1 . 7 3 3 . 8 5 3 . 3 2 7 . 7 4 7 . 3 6 3 5 1 2 . 7 5 1 . 5 4 2 . 8 1 2 . 7 4 2 . 8 5 1 . 5 4 2 . 8 5 1 . 5 4 2 . 8 7 2 . 9 1 2 . 7 8 1 . 6 4 2 . 8 2 1 . 4 8 1 . 6 3 4 . 7 3 . 5 9 4 . 8 3 4 . 7 9 4 . 8 3 . 5 9 4 . 8 3 . 5 9 4 . 8 5 5 . 9 3 4 . 7 3 0 1 1 . 2 7 5 1 1 3 0 1 9 2 1 7 . 2 9 4 2 1 7 6 3 0 1 4 2 1 8 1 1 4 2 1 4 4 1 7 . 2 9 4 3 1 6 0 1 3 . 4 7 9 1 1 6 0 1 3 3 1 5 . 5 9 7 2 1 9 6 6 0 1 7 2 1 2 2 1 ) 0 4 1 (l l e w ) 0 2 1 (l l e w ) 0 7 (l l e w . d o m ) 0 2 1 (l l e w ) 0 8 (l l e w . d o m ) 0 7 (l l e w . d o m ) 0 5 (l l e w . d o m ) 0 0 1 (l l e w . d o m ) 0 3 1 (l l e w ) 0 2 1 (l l e w ) 0 1 1 (l l e w ) 0 8 (l l e w . d o m ) 0 2 1 (l l e w ) 0 2 1 (l l e w ) 0 2 1 (l l e w ) 0 3 1 (l l e w ) 0 1 1 (l l e w ) 0 2 1 (l l e w ) 0 2 1 (l l e w ) 0 0 1 (l l e w . d o m ) 0 1 1 (l l e w ) 0 1 1 (l l e w ) 5 3 1 (l l e w ) 5 2 1 (l l e w ) 0 1 1 (l l e w ) 0 2 1 (l l e w 5 . 2 5 . 7 5 . 6 6 . 8 0 8 . 1 5 . 4 5 . 4 5 . 4 5 . 1 2 . 4 5 . 2 5 . 2 8 . 1 7 . 1 7 . 1 2 0 . 2 8 . 1 2 . 4 6 . 4 7 . 1 5 . 2 5 . 2 3 6 . 1 5 . 1 1 3 . 2 2 . 1 9 . 4 7 2 . 7 5 9 . 1 2 2 . 2 8 1 . 6 7 7 . 2 7 5 . 1 7 8 . 0 7 5 . 3 7 7 . 4 6 4 . 8 6 4 . 3 5 4 . 4 8 5 5 2 . 0 8 7 . 1 8 9 . 7 6 4 . 8 7 7 . 7 6 3 . 9 5 1 . 9 7 6 . 7 5 1 . 1 6 4 . 9 6 6 . 8 5 9 . 5 5 TABLE 1. Main soil characteristics of the different soil mapping units.in south El-Hussania plane
s e u l a V Landuse/managementstatus Calss 0 . 1 6 . 0 6 . 0 3 . 0 3 . 0 1 . 0 1 . 0 0 st n e m e ri u q e r y ti li b a n i a t s u s e h t t e e M f o d l o h s e r h t e h t e v o b a t u b y ll a n i g r a M y ti li b a n i a t s u s f o d l o h s e r h t e h t w o l e b t u b y ll a n i g r a M y ti li b a n i a t s u s st n e m e ri u q e r y ti li b a n i a t s u s e h t t e e m t o n o D I II II I V I TABLE 2. Sustainability index and associated values and classes
Security and protection practices in flood plain soils meet the requirements of sustainability ranging between 0.90 and 1.00 and representing (class I). On the other side, security and protection indices of ma-rine plains and fluvio- lacustma-rine plain are marginally above the threshold of sustainability where their in-dices range between 0.44 and 0.50, and between 0.34 and 0.58 respectively representing (class II), that may be due to moisture and biomass stress, erosion ha-zard and the unsuitable cropping system.
Security index was calculated using the following equation:
SI = A/100 ´ B/100 ´ C/100
Meanwhile protection index was calculated accor-ding to the following equation:
Prot. I = D/100 ´ E/100 ´ F/100
The results show that the no flood or erosion ha-zards are represented in the study area; meanwhile there was no distinguished crop rotation in the study area. These results reflected on the values of the two indices where no significant differences were found between the different mapping units for either secu-rity index or protection index.
c. Economic viability
The economic viability index considered the va-lue of seven indicators for determining economic via-bility; benefit cost ratio (A), percentage of off farm income (B), difference between farm gate price and the nearest main market % (C), availability of farm labor man/ 0.4 ha (D), size of farm holding in feddan (E), availability of farm credit % (F) and percentage of farm produce sold in market % (G). Economic via-bility index was calculated as follows
EV = A/100 ´ B/100 ´ C/100 ´ D/100 ´ E/100 ´ F/100 ´ G/100
Results revealed that, the economic viability of the different landforms in the marine plain and Fluvio- la-custrine plain are marginally below the requirements of the sustainability (class III), where the economic viability index in these areas ranges between 0.23 and 0.27. The economic viability in the flood plain are marginally above the threshold of sustainability (class II), where the economic viability index realizes the value of 0.58. The rest of flood plain meets the susta-inability requirements (class I), where the economic viability index ranges between 0.66 and 1.00. The low FIGURE 5. Productivity index for the different geomorphological units in El-Hussinia plane
economic viability in the studied area is due to low benefit to coasts ratio, low availability of farm labor, small farm size, low percentage of farm production sale in markets and low off farm income as well.
d. Social acceptability
The social acceptability index (SI) considered the value of seven indicators of social acceptability, land tenure (A), support for extension services (B), health and education facilities in the village (C), percentage of subsidy for conservation packages (D), training of farmers on soil and water conservation (E), availabi-lity of agro-inputs within 510 km (F) and village roads access to main road (G). Data of social accep-tability were obtained from CAPMAS [2005] and EEAA [2009]. Social acceptability index was calcu-lated as follows:
Social Acceptability Index = A/100 × B/100 × C/100 × D/100 × E/100 × F/100 × G/100
Results indicated that, the areas of marine plain are marginally below the requirements of sustainabi-lity (class III), where the social acceptabisustainabi-lity index in these areas is 0.21, which is rather low. The social acceptability in the fluvio-lacustrine plain and some
landforms of the flood plain are marginally above the threshold of sustainability (class II), where their so-cial acceptability index ranges between 0.34 and 0.48. The social acceptability index in the rest of the flood plain is higher, where it realized the value of 1.00, meeting the sustainability requirements (class I). The low value of the social acceptability index is mainly due to the shortage in health and educational facili-ties in the villages and lack of training allocated for the land users on soil and water conservation.
Sustainability index
Sustainability index were obtained by multiply-ing indices of the five indicators accordmultiply-ing to the fol-lowing formula:
Sustainability Index (SUI) = A × B × C × D × E Where (A) = productivity, (B) = security, (C) = protection, (D) = economic viability and (E) = social acceptability.
Sustainability units were converted from raster to polygon to get the areas of the sustainability units for assessment purposes. Sustainability layers were cre-ated to query sustainable land management classes (Figure 6).
FIGURE 6. Sustainability map for south El-Hussinia plane study area
Soil does not meet the requirement of sustainability Soil below the threshold of sustainability
Soil above the threshold of sustanability Soil meet the requirement of sustainability
Quantitative assessment was executed for SLMSM map products to identify and measure the map errors that derived from the model. In this assessments, map data were compared with ground truth data obtained from two sources field measurements & observations on farming system level and from laboratory analyses that assumed to be 100% correct. The overall accura-cy assessment of thematic maps recorded 88.34%.
In general Land management practices tend to be unsustainable as shown in Figure (7). The results in-dicated that, the studied area includes four sustaina-bility classes:
Class I: Land management practices meet susta-inability requirements with score £ 0.65, occupying 16856.1 ha (62.4 % of the agricultural area).
Class II: Land management practices are margi-nally above the threshold of sustainability with va-lue of 0.59 occupying 6069.6 ha (22.5% of the agri-cultural area)
Class III: Land management practices are marginal-ly below the threshold of sustainability with values ran-ged between (0.14 and 0.0.15) occupying 2559.6 ha (9.5% of the agricultural area).
Class IV: Land management practices do not meet sustainability requirements with values > 0.1 occu-pying 1244.7 ha (5.6% of the agricultural area).
CONCLUSION AND
RECOMMENDATIONS
From sustainability point of view, it is noticed that, only 14.09% of the agricultural or arable lands is sustained or above the threshold of sustainability, meanwhile 84.01% of the total land is unsustainable or below sustainability threshold, so it is worthy to say the study area is facing a great threat.
Recommendations to overcome sustainability con-strains; farm management, infrastructure and social se-rvices should be improved to reach the standards of agricultural sustainability throughout: 1 Improving land and water resources following advanced techni-ques of management and conservation; 2 Improving awareness levels on the sustainable issues of natural resources exploitation and enhancing livelihood options for Land-use rs and suppliers; 3 Persuading decision makers to adopt effective rules to regulate marketing processes and ensure effective monitoring and flexible mechanisms; 4 Persuading businessmen to insist on the traceability of the resources they pro-cure from various middlemen, thereby, forcing all in-termediary stakeholders to also comply with sustaina-bility standards; 5 Innovations in the materials and methods of production, appropriate technological
terventions and the introduction of strong backward linkages with suppliers are some of the measures that can reduce demand-driven pressure on sustainability.
SUMMARY
The main aim of this paper is to evaluate Sustaina-ble Land-use Management (SLM) through both bio-physics and socio-economic elements for the purpose of fighting and undertaking sustainability constraints that preclude the agricultural development or at least reduce them to the acceptable levels of mass produc-tion. Geomorphology and associated soils in the study area, which occupied about 27000 ha and is located at the north-east part of the Nile Delta, Egypt, were iden-tified. The land degradation processes were recogni-zed then a Sustainable Land use Management Spatial Model (SLMSM) was built and finally the model was used to assess the sustainable land use management in the study area. The area included three landscapes; fluvio-lacustrine plain, Marine plain and flood plain. Four SLM classes were outlined; the relatively high decantation basins (Class I) representing 62.4% of the agricultural area, the relatively low decantation basins (Class II) occupying 22.5% of the agricultural area, overflow basins (Class III) covering 9.5% of the agri-cultural area and Class IV that found in clay flats, san-dy remnants, overflow mantle and river terraces occu-pied 5.6% of the agricultural area. Only 14.09% of the lands is above the threshold of sustainability, in the meantime 84.01% of the total land is below sustaina-bility threshold, so it is worthy to say the study area is facing a great threat. In order to overcome sustainabi-lity constrains it is recommended that farm manage-ment, infrastructure and social services should be im-proved to reach the standards of agricultural sustaina-bility in the study area.
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Prof. Dr. Abdel Samad Salem Ismail Hegazy, Prof. of Soil Sci, Faculty of Agric. Ain Shams University, Cairo Egypt
P.O.Box 68 Hadayk Shobra, Shobra, Cairo Egypt,
