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A Desertification Impact on Siwa Oasis: Present and Future Challenges. Abo-Ragab Samy Desert Research Center Cairo, Egypt.
Introduction
Siwa Oasis is a natural depression about 23m below sea level. It covers an area of about (250,000 Feddans); of which about 87.95 km2 (20940 Feddans) are currently cultivated. The total numbers of inhabitants in Siwa were 21482 residents in 2006 (Siwa Information Center, 2009). The main activity in Siwa oasis is agriculture which depends on the groundwater that outflows from about 1199 wells and springs (Ministry of irrigation, 2008), giving a total annual discharge of about 255 million cubic meters. From this, about 222 million cubic meters are lost as evaporation and evapotranspiration, while the remainder goes to the natural lakes of Siwa Oasis. Thus, the annual surplus groundwater – based on monitoring in 1997 and under present conditions – reaches 33 million cubic meters (Gad, 2000). This has led to a continuous rise in the water table level (4.5 cm/ year) causing water logging, soil salinization as a result of improper management and uncontrolled water flow from wells and springs and inefficiencies in the system of drainage water (Al-Kadi, 2003), and consequently, deterioration in land productivity, which in turn results in lowering Agriculture income (Abo-Ragab, 2008). Agriculture represents the basis of the Siwan economy (International Union for Conservation of Nature, (IUCN), 2000); The present study focuses on investigating the economic impact of the environmental problem in the present and future; using the Autoregressive Integrated Moving Average models (ARIMA) and geographic information systems that will lead to the quantitative assessment of desertification processes, its impact on agriculture to identify priorities for development beginning with areas of high development potential and less vulnerable to desertification processes. 2. The Environmental Problem in Siwa Oasis. The Siwa Oasis suffers many environmental problems such as; water logging; soil salinization; increase in the surface area of the saltwater lakes, Marshes and the rise of soil water levels by 4.5 cm/year. The results of these problems are deterioration in land productivity and which in turn results in lowering Agriculture income (Abo-Ragab, 2008). 3- Objectives: The research aims to identify the concept of quantitative measurement of desertification processes on agriculture production and income in the present and future, through the development of mathematical models (Milles, 1993), the Autoregressive Integrated Moving Average, (ARIMA) with integration of Geographic Information Systems (GIS) and Remote Sensing (RS) that lead to the quantitative assessment of desertification processes in the present and future, as well as its impact on agriculture production and income to identify. The primary goal is to be achieved through the following sub-goals: 1) Dealing with the space images, topographic maps and other available data to determine the environmental characteristics of Siwa Oasis. 2-Monitoring the present and future desertification phenomena in Siwa Oasis, it's impact on resources, land use (Su, 2000) and development plans. 3) Building integrated geographical information systems model of environmental and economical to identify the impact of desertification on the productivity and income on present and future. 4. Methodology In order to achieve the objectives, the principal research methodologies are: 4-1- Due to lack of data, the study adopted the RS techniques and some published and unpublished data to provide the layers of Land Use / Land Cover Maps (Gupta, 1991) that lead to tracking human activities and the development of desertification problems in Siwa Oasis for the period 1990 to 2008. The most relevant data needed to assess different Land Use/ Land Covers year the probability of different Land Use / Land Covers occurs has been categorized below. Reference topic group 1 – Administrative boundaries Reference topic over zero contour Reference topic group 2 – Topography maps. Reference topic 2.1 - Satellite images (Campbell, 1996) TM Year 1990 Projection UTM 35, Multispectral Image 7 Band. TM Year 1992 Projection UTM 35, Multispectral Image 7 Band. TM Year 1994 Projection UTM 35, Multispectral Image 7 Band and spot X3. TM Year 1998 Projection UTM 35, Multispectral Image 7 Band. ETM+ Year 2000 Projection UTM 35, Multispectral Image 8 Band. STM Year 2000 Projection UTM 35, DEM panchromatic mono Band. ETM+ Year 2002 Projection UTM 35, Multispectral Image 8 Band. TM Year 2004 Projection UTM 35, Multispectral Image 7 Band. TM Year 2006 Projection UTM 35, Multispectral Image 7 Band. TM Year 2008 Projection UTM 35, Multispectral Image 7 Band. Quick bird year 2008 UTM 35, Multispectral Image 3 Band. Reference topic 2.6 - Cross-shore profiles Reference topic group 3 – DCW, for different data layers. Reference topic 3.1 - Land use /Land covers Reference topic 3.2 - Infrastructure Reference topic 3.3 - Terrestrial elevation Reference topic 3.3 – Urban area. Reference topic group 4- Demography Reference topic 4-2- The study has adopted the qualitative and quantitative analysis using the method of the standard functions of the regression trend for the time-series forecasting models, Maximum Likelihood Estimation (MLE) which maximize logs probabilities of the Autoregressive Integrated Moving Average (ARIMA), where the model contains a Autoregressive [AR (p)], a Moving Average [MA (q)], and the degree of Difference (d), then integrated forecasting data in Geographical Information Systems (GIS) models to project the forecast data in the real world coordinate systems via applying GIS Layers (previous Land Use, the elevation layer, Slope Layer, Dem Layers, and prospect development plans) to produce potential land use layers in 2028 to evaluate trends (positive or negative) 4-3- Conducting a questionnaire survey with farmers to provide information to identify the impact of desertification on the productivity and income of the Siwa population on present and future. 4-4-Building integrated environmental and economical models using GIS techniques to identify the impact of desertification on the productivity and income of the Siwa population on present and future. 5. RESULTS AND DISCUSSION 5.1 Land Use Maps: Comparison of 1990 and 2008. A comparison between the different lakes, Marshes, Cultivated Land, and Urban during the period 1990 to 2008 bounded by the zero contour line, as indicated on Different satellite Image, topographic maps and other available data (for the years 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006 and 2008). 5.2 The Total Surface Area of the Different Lakes in Siwa Oasis (1990 to 2008). i. Data in Fig1 shows 6 Lakes inside the concerned area. The lakes areas estimated 60.40, 61.28, 62.80, 54.20, 54.60, 58.10, 60.20, 65.50, 69.70 and 75.54 km2 in 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006 and 2008, respectively. Siwa Lake is the largest lake. The Aghormi and Ma’asser &Timera pools are the smallest lakes. ii. The increase in surface area is more pronounced with respect to the western lakes while there is more or less a natural balance for the Timera and El Ma'asser lakes. On the other hand, the substantial increase in surface area of Siwa Lake, from 21.32 to 38.435 km2 during the period 1990 to 2008, reflects the effect of over discharge of the great number of hand dug wells characteristic of this locality. iii. The decrease in Lakes surface area from 62.80 km2 to 58.10 km2 during the period 1994 to 2000 was due to an improved irrigation and drainage system, and the closure of the same well which contributed to a decreased surface area for the Siwa Lake (the Ministry of Irrigation for 1996). The increase to 75.54 km2 by 2008 was due to increased reclamation. The Maximum lakes area estimated 75.54 km2 of the total area in 2008; while the minimum lakes area estimated 54.20 km2 in 1996. (Figures 1 and Table 1). 
Source: from Imagine8.3 and Arc/Gis9.2 software using Different Land Use/ Land Covers Maps Fig.1. 5.3 The Total Surface Area of the Different Marshes in Siwa Oasis (1990 to 2008). The Marshes area estimated 335.00, 355.70, 374.31, 389.61, 401.23, 415.70, 438.30, 447.20, 453.80 and 469.52 km2 in 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006 and 2008, respectively. The maximum Marshes area estimated 469.52 km2 in 2008; while the minimum marshes estimated 335 km2 in1990. Based on these available data, the largest Marshes surrounds the El Zaitoon pool (207.5 km2 in surface area) while the smallest one (0.5 km2) is located SW of Naqab Abdel Hady. See Fig.1 and Table 1. 5.4 The Total Surface Area of Agricultural Activities in Siwa Oasis (1990 to 2008). i. The Cultivated area estimated 53.00, 55.80, 58.41, 62.35, 67.84, 69.62, 71.76, 77.47, 81.70 and 87.95 km2 in 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006 and 2008, respectively. The maximum cultivated area was 87.95 km2 in 2008; while the minimum was 53.00 km2 in1990. (Fig.1 and Table 1). 5.5 The Total Surface Area of the Different Urban in Siwa Oasis (1990 to 2008). The Urban area estimated 6.70, 6.90, 7.10, 7.50, 7.80, 8.00, 8.30, 8.70, 9.00 and 10.00 km2 in 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006 and 2008, respectively. The maximum urban area estimated 10.00 km2 in 2008; on other hand the minimum was 6.7 km2 in1990. (Fig.1 and Table 1). 
6. Forecasting Environmental changes and its impact on the Land Use/Land covers, 2028. Monitoring future environmental changes using estimation, the ARIMA model and integration with GIS and RS for dynamic forecasting Siwa problem in the future. So this study will depend on forecasting the impact of dynamic environmental problems on the resources available and activities in Siwa oasis, forecasting the Lakes, cultivated area, Marshes, Urban, and productivity of olives, dates palm, crops in Siwa Oasis. ARIMA fits a Box-Jenkins ARIMA model to a time series (Abdel-Fattah, 2008). ARIMA stands can be used to model patterns that may not be visible in plotted data. Estimation [ARIMA (p, d, q)] model through the four stages (Ragab, 2008) can be explained as follows 6-1- Stage Identification The data transferred to logarithms to stabilize the variance and to get the data normally distributed. In other word log or power transfer used to induce constant amplitude in the series over time so that the residuals from fitted will have a constant variance. The Graph (1-A, 1-B, 1- C and 1-D) for the data of the lakes, agricultural areas, Marches and Urban areas during the studied period, show that there are non static in terms variance, it means that we must remove this pattern of non-stationary and phase of a general trend has been to take the differences (D) of this series which led to the elimination of a general trend of the series. Graph (2-A, 2-B, 2-C and 2-D) show the standarized values of the differencing log of Lake, Marches, Cultivated area and Urban. The series seems stationarity. Graph 3 show Graph 3-A: Olive and Dates palm affected by desertification indicated that the series seems stationarity, Graph 3-B: Productivity of Olive and Dates Palm unaffected by desertification indicated that there are not static in terms variance, it means we must remove this pattern of non-stationary and phase of a general trend has been to take the differences (D) of this series which led to the elimination of a general trend of the series. Graph 3-C, after Different to remove the pattern of non-stationary for Productivity of Olive and Date Palm unaffected by desertification 
Autocorrelations Function (ACF) and Partial Autocorrelation functions (PACF) for lake, Marches, Cultivated area, Urban, productivity of olive and date palm affected by desertification and productivity of olive and date palm unaffected by desertification. 
The collerogram for Lakes, indicated that the ACF for 8 lag where ACF rang from 0.45 to -0.2 is significantly different from zero the corresponding t-statistic rang from ?.35 to -0.47, thus it is smaller than 2. This pattern is typical to a MA process of order one. The PACF for 8 lag where PACF Ranges from 0.45 to -0.069 is significantly different from zero the corresponding t-statistic rang from 1.35, -0.12 thus it is smaller than 2. This pattern is typical to an MA process of order one. 
The collerogram for Marches indicated that the ACF for 8 lag where ACF rang from 0.68 to -0.41 is significantly the corresponding t-statistic rang from 1.96 to -0.78. This pattern is typical to a MA. The PACF for 9 lag where PACF rang from 0.68 to -0.17 is significantly. The corresponding t-statistic rang from 1.96, -0.54. This pattern is typical to an MA 
The collerogram for cultivated area indicated that the ACF for 9 lag where ACF rang from 0.67 to -0.42 is significantly the corresponding t-statistic rang from 1.93 to -0.83. This pattern is typical to a MA. The partial autocorrelation for 9 lag where PACF Rang from 0.68 to -0.22 is significantly the corresponding t-statistic rang from 1.93, -0.69. This pattern is typical to an MA. 
The collerogram for urban area indicated that the ACF for 9 lag where ACF rang from 0.29 to -0.33 is significantly the corresponding t-statistic rang from 0.93 to -0.87. This pattern is typical to a MA. For the PACF for 8 lag where PACF Rang from 0.29 to -0.21 is significantly the corresponding t-statistic rang from 0.93, -0.68 thus it is small than 2. This pattern is typical to an MA. 
The collerogram for productivity of olive affected by desertification indicated that the ACF for 8 lag where ACF rang from 0.55 to -0.52 is significantly the corresponding t-statistic rang from 1.66 to -1.11. This pattern is typical to a MA. For the PACF for 8 lag where PACF Rang from 0.29 to -0.21 is significantly the corresponding t-statistic rang from 0.55, -0.48. This pattern is typical to an MA process. 
The collerogram, productivity of date palm affected by desertification indicated that the ACF for 8 lag where ACF rang from 0.43 to -0.27 is significantly the corresponding t-statistic rang from 1.30 to -0.68. This pattern is typical to a MA process. The PACF for 8 lag where PACF Rang from 0.43 to -0.28 is significantly the corresponding t-statistic rang from 1.30, -1.05. This pattern is typical to an MA process. 
The collerogram: productivity of Olive unaffected by desertification indicated that the autocorrelation for 8 lag where ACF rang from 0.608 to -0.38 is significantly the corresponding t-statistic rang from 1.82 to -0.38. This pattern is typical to a MA process. the PACF for 8 lag where PACF rang from 0.60 to -0.23 is significantly the corresponding t-statistic rang from 1.30, -1.05 This pattern is typical to an MA process. Autocorrelation Function: Dates palm unaffected by desertification Partial Autocorrelation Function: Dates palm unaffected by desertification 
The collerogram: productivity of Date palm unaffected by desertification indicated that the ACF for 8 lag where ACF rang from 0.62 to -0.47 is significantly the corresponding t-statistic rang from 1.87 to -0.90. This pattern is typical to a MA process. For the PACF for 8 lag where PACF Rang from 0.62 to -0.41 is significantly the corresponding t-statistic rang from 1.87, -1.25. This pattern is typical to an MA process. The collerograms indicated that the pattern are typical not only ACF but also PACF. PACF can be examined to determine the order of the process that order is equal to the number of significant PACF. And we can induct the ARIMA model. 6-2- Estimation Stage Lake forecasting ARIMA models (0, 2, 1) is the best lake model: Z(C10) = 0.46 + 1.60 MA(1) (7.20)* (2.55)* Where P 0.044 ARIMA Marshes forecasting models (0, 1, 1) The best lake forecasting model was 0, 1, 1. Z(C10) = 15.501 - 0.895 MA(1) (7.61)* (-2.82)* Where P 0.026 ARIMA Cultivated forecasting models (0, 1, 1) The best forecasting model was 0, 1, 1. Z(C10) = 5.028 + 1.196 MA(1) (19.34)* (7.30)* Where P 0.00 ARIMA Urban forecasting models (0, 1, 1) The best forecasting model was 0, 1, 1. Z(C10) = 1.267 - 0.678 MA(1) (1.86)* (-3.00)* Where P 0.02 ARIMA for productivity of olive affected by desertification forecasting model (0, 0, 1). Z(C10) = 4.15 - 1.47 MA(1) (36.41)* (-3.40)* Where P 0.01 ARIMA for productivity of Date palm affected by desertification forecasting model (0, 0, 1) Z(C10) = 4.18 - 1.38 MA(1) (40.47)* (-3.38)* Where P 0.01 ARIMA for productivity of olive unaffected by desertification forecasting model (0, 0, 3) Z(C10) = 6.85- 2.03 MA(1) (114)* (-3.92)* Where P 0.01 ARIMA for productivity of Date palm unaffected by desertification forecasting model (0, 1, 1) Z(C10) = 0.05 + 1.60 MA(1) (30.51)* (2.45)* Where P 0.05 6-3-Diagnostic Stage Significant ARIMA Lakes Models (0, 2, 1), indicate that the T statistic is 2.55 for the MA parameter and the associated p-value is 0.044 since the p-value is small and less than the usually chosen ?-level of 0.05 the test is significant. These result indicate that the model is acceptable. Significant ARIMA Marshes Models (0, 1, 1), indicate that the T statistic is -2.82 for the MA parameter and the associated p-value is 0.026. These result indicated the model is acceptable. Significant ARIMA Cultivated Model (0, 1, 1), indicate that the T statistic is 7.30 for the MA parameter and the associated p-value is 0.00. These result indicated the model is acceptable. Significant ARIMA Urban Models (0, 1, 1), The T statistic is -3.00 for the MA parameter and the associated p-value is 0.02. These result indicated the model is acceptable. Significant ARIMA for productivity of olive affected by desertification forecasting model(0,0,1) , the T statistic is -3.40 for the MA parameter and the associated p-value is 0.01. These result indicated the model is acceptable. Significant ARIMA for productivity of Date palm affected by desertification forecasting model (0, 0, 1) , the T statistic is -3.38 for the MA parameter and the associated p-value is 0.01. These result indicated the model is acceptable. Significant ARIMA for productivity of olive unaffected by desertification forecasting models (0, 0, 3) , the T statistic is -3.92 for the MA parameter and the associated p-value is 0.01. These result indicated the model is acceptable. Significant ARIMA for productivity of Dates palm unaffected by desertification forecasting models (0,1, 1) , the T statistic is 2.45 for the MA parameter and the associated p-value is 0.05. These result indicated the model is acceptable. 6-4- Forecasting Stage (2010 to 2028) - ARIMA Lakes Model ( 0, 2, 1) for the forecasting Lakes in Siwa Oasis. The forecasts lakes data, for the next 20 years are made using the fitted MA model and are displayed next to the corresponding periods with GIS integration to project the forecasting data in the real world coordinated Based on the Contour Siwa layer and Slop with the project the agriculture plane in Siwa additional GIS layers analysis. The location of these lakes areas were shown between -16.7 to -23 m under sea level, The maximum lake area Aghormi and Zitoone estimated 97 km2 in 2028, on the other hand Temmer is minimum by about 16 km2 in 2028, Based on the land use map 2028. The lakes areas estimated 81.69, 87.84, 94.46, 101.54, 109.09, 117.09, 125.56, 134.50, 143.90 and 153.76 km2 of the total Siwa area in 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026 and 2028, respectively. (Fig. 2, and Table 3). - ARIMA Marches Model (0, 1, 1) for the forecasting Marches area in Siwa Oasis. The forecasts Marshes data for the next 20 years, Based on the Contour Siwa layer and Slop with the project agriculture plane in Siwa. We should expect to area Marshes from 556.35 km2 to 845.83 km2 during the period 2010 to 2028. The Marshes areas estimated 556.35, 588.52, 620.68, 653.85, 685.01, 717.17, 749.34, 781.5, 813.67 and 845.83 km2 of the total Siwa area in 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026 and 2028, respectively. The location of these lakes areas were shown between -16.7 to -0.0 m under sea level. (Figures 2 and Table 3). 
Sources: The calculated by Minitab software 15.1.1 and are displayed next to the corresponding periods GIS integration to project the forecasting data in the real world coordinated. 
Source of data: The construction of the Land Use/ Land Covers 2028 results from a three-step process: a baseline Land Use/ Land Covers 2008, using forecasting data from Minitab 15.1.1, using agriculture plan for the Siwa and converted to digital layout, using contour layer and DEM Model, integrate all data to project the forecasting data in the real world coordinate systems with the helping from GIS Software (ARC/GIS 9.2 and Imagine 9 ) - ARIMA Agriculture Models (0, 1, 1) for the forecasted cultivated area in Siwa Oasis. Forecasted Cultivated data, for the next 20 years, Based on the Contour Siwa layer and Slop with the project agriculture plane in Siwa, the largest Cultivable Land is Northeast and Southwest of Siwa Oasis. The cultivated areas estimated 83.97, 89, 94.03, 99.06, 104.09, 109.11, 114.14, 119.17, 124.2 and 129.23 km2 of the total Siwa area in 2010,2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026 and 2028, respectively. (Figure2 and Table 3). -ARIMA Urban Models (0, 1, 1) for the forecasting Urban in Siwa Oasis. Te predicted urban area for the next twenty year. We should expect to area Urban 22.56 km2. The Urban areas estimated 11.16, 12.42, 13.69, 14.96, 16.23, 17.49, 18.76, 20.02, 21.3 and 22.56 km2 of the total Siwa area in 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026 and 2028, respectively. (Figures 2, and Table 3). The forecasts for productivity of olive and date palm affected by desertification, for the next twenty years are made using the fitted MA model, we should expect to 4.15 and 4.18 Tones/Fadden in 2028, respectively .The forecasts for productivity of olive and date palm unaffected by desertification, for the next twenty years are made using the fitted MA model, we should expect to 6.86 and 7.38 Tones/Fadden in 2028. See the trend in Table 4. 
Table (4). Agricultural productivity for the olive and Date palm for the first model (affected by desertification) and the second model (unaffected by desertification) - Production Tons, 2010 -2028 7 - Productivity and economic variables sample study: Data in table (5) shows that variables of productivity and economic of sample survey have been classified into two areas totaled 87.95 km2 (20940 F) in 2008. The first Category was a cultivated area of 15890 feddans with 75.88% from total Siwa area affected by desertification. The second Category was the unaffected cultivated area of 5050 feddans with 24.11% of total Siwa area. Olive and date palm where selected for both categories of about 20059 feddans, with 95.79% of the cultivated area in Siwa oasis. The first category includes olive and date palm of about 15413 feddans representing 97%, namely 9093 and 6319 feddans representing about 59%, 41%, of olive and date palm, respectively. In the Second category, the total area of olive and palm around 4646 feddans, with 92%, while the area of olive and palm reached 2834, 1812 feddans, represent about 61% and 39% of the total area of olive and palm, respectively. The data shows that the total sample was about 205 the size of holders of about 1705 feddans with 8.49% of the total area of olive and date palm in Siwa. The study sample was selected for the farmers by the purposive sample method that is consistent with the nature of the community study. The first Category area covers about 1203 feddans representing about 7.8%, for 127 holders, where 73 olive holders have an area of 655 feddans with about 7.2% of the total area of olives, and the other 54 holders have an area of 548 feddans with 8.6% of the total area of date palm. The second category area covers about 502 feddans representing about 10.8%, for 78 holders, where 47 olive holders have an area of 323 feddans with about 11.39% of the total area of olives, and 31 date palm holders have an area of 179 feddans with 9.8% of the total area of date palm.  Fig 3. Location Map of the farmer observation samples in Siwa Source of data: using Land Use/ Land Covers 2008 a base map and project the samples of holder according to there coordinate system over the map. Using GIS Software (ARC/GIS 9.2) 
Source: collected and calculated from the questionnaires to a sample field study of the production season of 2008 / 2009. Data show that the average productivity was maximum in the second category at an average production was 6.87, 6.85 tones per feddan, with rise of about 1.97, 2.34 tons and the rate of about 71.33%, 65.84% over the average production of olive and date palm to the first category, respectively. As for the second category, farming costs per feddan rose with an average of 8979 and 8140 LE/feddan with an increase of about 686 and 485 LE/feddan of olive and dates palm to the first category, respectively, while the average total income about 21984, 19659.5 LE/feddan for the second group an increase of about 6304, 6715.8 LE/feddan of olive and date palm to the first category, respectively, while the average net income of about 13005, 11519.5 LE/feddan for the second group an increase of about 5579, 6230.8 LE/feddan of olive and date palm to the first category, respectively. Data in table (6) indicate that the test of significance of differences between the averages (Z-Test) of two categories, the first category, of olive cultivated area affected by desertification and the second category, olive unaffected area. It proved that was significant differences values of the total costs, average productivity, total income, net Revenue and LE profit at the significant level of 0.05. The test of significance of differences between the averages (Z-Test) of two categories, the first category, of date palm cultivated area affected by desertification and the second category, date palm unaffected area. It proved that was significant differences values of the total costs, average productivity, total income, net Revenue and LE profit at the significant level of 0.05. 
Source: collected and calculated from the questionnaires to a sample field study of the production season of 2008/2009. 9-Economic impact: Economic impact is a consequences of what had been achieved environmentally, hence the environmental impact has an economic return. To improve environmental conditions in Siwa Oasis is conducive to improving soil characteristics, which leads to increased productivity, and thus increase the income of farmers in Siwa. The rate of change depends on the extent of progress in reduce the problem of groundwater to reach the normal rates and the target achieved. 9-1- Impact of Current Economy Data in table (7) indicate that the first category (Cultivated area affected by desertification) net revenues in first category for olive and date palm, around 67.52, 33.42 million L.E, respectively, a total net revenues of about 100.94 Million LE. The second category (Cultivated area unaffected by desertification) about 36.83, 20.87 million LE for olive and Date palm respectively, 57.73 Million LE in aggregate. The data in table (8) indicate that the cultivation of olives in Siwa oasis loss annually to 50.73 Million LE against 39.37 Million LE of date palm, which amounts to 90.10 Million LE annual result of desertification degradation Oasis, hence a need to improve the environmental situation to the forum, There can be done through intervention by the state, but must contribute to it by farms Sawy. 
Source: collected and calculated forms of field research. From the tables ( 5, 6, 7 ). Thus, when the elimination or reduction of natural resource degradation Oasis expected to be a breakthrough productivity, Oasis not only for crops Date Palm, olives, but for most other crops. In the case of access to productive target, it means increasing productivity at a value lost as a result of degradation and the estimated value of olive and Date Palm level oasis total 90.10 Million LE annually represents 56.89% of net revenues of olive and date palm caused by the deterioration of the crops environmental in the Siwa. 9-2- Future economic impact of desertification on agricultural production in Siwa 2028 Data in table (3) reveals the results of ARIMA models. In the first model productivity is expected to hit 4.15 and 4.18 tones of olives and date palm, respectively in 2028, In the second model productivity is expected to hit 6.86 and 7.38 tones of olives and date palm, respectively. The agricultural area is expected to be 129.23 km2 (30769.05 Feddans) in 2028. It was assumed that cultivated area of olive and dates palm is the same proportion as the base year (2008), with an area of olive and palm around 95.79% of the cultivated area, while the percentage of Olive about 59% and date palm about 41% from the total area of olive and date palm, and thus an area of 17609.13 feddans of olive, and 12236.85 feddans of date palm. The assumption was that the prices increase by about 20% for the base year (2008). Two models were applied to measure the economic impact of desertification on agricultural production of olive and date palm in 2028. 9-2-1- The first model: Studying the economic net revenues for olive and date palm if desertification problems continued in Siwa Oasis in 2028. Data in table (9) reveals that the cultivation of olives in the Siwa oasis achieved annual net revenue of 105.7 Million LE. For 62.25 Million LE of date palm, which amount to 167.95 Million LE in aggregate. 9-2-2- Second model: Studying the economic net revenues for olive and date palm if desertification problems get resolved in Siwa Oasis in 2028. Data in table (9) shows the cultivation of olives in the oasis of Siwa achieved net revenue annually to 307.62 Million LE. in the first mode against 165.58 Million LE. in the second models, which amounts to 437.40 Million LE. The cultivation of olives in the oasis of Siwa loss annually to 202.12 Million LE versus 103.33 Million LE of date palm, which amounts 305.45 Million LE annual result of desertification degradation Oasis, hence a need to improve the environmental situation to the forum, There can be done through intervention by the state, but must contribute to it by farms Sawy. Table (9). Estimate the average earnings achieved as a result of environmental degradation oasis of Siwa, 2028, Million LE. Unrealized profits  Source: collected and calculated forms of field research. From the tables (3, 4, 5, 6). Thus, when the elimination or reduction of natural resource degradation Oasis expected to be a breakthrough productivity, Oasis not only for crops olives, date Palm, but also for most other crops. In the case of access to productive target, it means increasing productivity at a value lost as a result of degradation and the estimated value of crops and olive Date Palm level Siwa Oasis total 305.45 Million LE. annually. 10- Solving the Environmental problem of Siwa, the following steps are suggested:
The research aims to identify the concept of quantitative measurement of desertification processes on agriculture production and income, through the development of mathematical models the Autoregressive Integrated Moving Average (ARIMA) and geographic information systems that will lead to the quantitative assessment of desertification processes present and future, its impact on agriculture production and net revenue present and future to identify priorities for development beginning with areas of high development potential and less vulnerable to desertification processes. Impact of Current Economic 2008, the cultivation of olives in Siwa oasis loss annually to 90.10 Million LE annual as a result of desertification degradation. Future economic impact of desertification on agricultural production in Siwa 2028, Two models were applied to measure the economic impact of desertification on agricultural production of olive and date palm in 2028. The first model: Studying the economic net revenues for olive and date palm if desertification problems continued in Siwa Oasis in 2028. The cultivation of Olive and Date Palm in the Siwa oasis achieved annual net revenue amount to 167.95 Million LE. Second model: Studying the economic net revenues for olive and date palm if desertification problems get resolved in Siwa Oasis in 2028. The cultivation of Olive and Date Palm in the Siwa oasis achieved annual net revenue amount to 437.40 Million LE. The cultivation of olive and date palm in the oasis of Siwa loss annually to 305.45 Million LE annual as a result of desertification degradation Oasis, hence a need to improve the environmental situation to the forum, There can be done through intervention by the state, but must contribute to it by farms Sawy. Solving the Environmental of Siwa problem, the following steps are suggested:
References
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