الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Soil characteristics play a major role in determining the cause and effects of crop selection and yield production. The soil morphology shows potential worrisome soil properties. Precision farming aim to manage fields according to topography, water consumption and soil types in different areas and its effect on crop yield. The current study aimed to use advanced techniques of remote sensing as a tools to solve the challenges facing the new reclaimed areas, especially in arid lands such as water scarcity and soil problems. Precision agriculture aims to reach the highest output and most appropriate production using lowest inputs while maintaining the safety of the surrounding environment and managing fields based on soil types in different areas. Moreover, the study discusses the impact of precision farming techniques on crop productivity and highlights the application of GPS techniques to adjust fertilizer nutrition according to Available Phosphorus, Available Potassium and soil Micro nutrients distribution and yield goals set by decision makers.
The integrated management achieved using remote sensing and GIS techniques by producing of soil topography, various soil maps such as soil physical characteristics, EC, pH, CaCO3, Available phosphorus, Available potassium and Micro nutrients (Fe, Mn and Zn) linked to productivity crop of the study soil locations. Furthermore, study the relationship of plant spectral characteristics and yield response and to use the variable irrigation rate in irrigation scheduling precisely 5*5 meters. To achieve the main goal of the current study, Landsat satellite data were selected. The imagery information of the Landsat OLI provide visible reflective bands, shortwave infrared bands at 30 meter and thermal infrared bands resampled to 30-meter resolution, also the revisit time every eight days, allowing continuous monitoring of crop growth and the amount of water consumption by the presence of thermal bands. The advanced resolution merge techniques were used to increase high spatial resolution from 30 meter in Landsat sensors to 5 meters using Rapideye imagery which specially designed for precision agriculture service where it can be daily acquired and at a reasonable price and accurately spatial five meters. In this study we applied image fusion using Principal Component Spectral Sharpening (PCSS) method to integrate NDVI and plant water consumption calculated from Landsat satellite data.
Furthermore, the ultra-multi-spectral devices has been used as a kind of new remote sensing modern techniques to study the vegetation characteristics and monitor vegetation healthy using narrow bands vegetation indices which easily can be linked to crop productivity. The global GPS system had a major role in locating training samples location, spectral measurements locations and revisiting the same places to take spectral measurements during different stages of crop growth. Collecting information on soil analyzes from previous studies allow identifying different soil units of the study area. Furthermore, the analysis of the soil gives a reasonable idea of the level of productivity in different soil units and also helps in developing new strategies to resolve the problems of soil to reach the highest productivity.
The GIS techniques and geo-statistical models helped in the production of various soil maps for the study area, identifying the degree of soil fertility and adding the optimal fertilizer units, also GIS helps in producing yield map, soil samples grid system.
The results showed that the use of Geographic Information Systems (GIS) and ground truth points determined by GPS has effectively contributed to a more efficient way a symmetric random sampling in the study area. Also, the Electrical Conductivity of water (ECw) was 0.85 dS m-1 which has not any side effects on peanut and olive growth. Using GIS and predictive statistical models we produced various soil maps, which included maps of salinity, the proportion of calcium carbonate, texture, pH, DEM, Slope, soil saturation percentage, ESP. Texture was sandy clay and sandy clay loam soils in salhiya pivot; also texture generally was loamy sand in olive field at east of Beni-Suef site. In salhiya site a small percentage of calcium carbonate was found in some areas and moderate in other areas so this land are not considered calcareous soil; however in Beni-Suef site calcium carbonate was found with high percent. The pH value ranged from 7.1: 8.2 and the soil tend to be alkaline in both sites before and after cultivation. The results obtained from this study indicate that the integration of RS-GIS and application of Spatial Multi-Criteria Evaluation (SMCE) could provide a good database and production guide map for decision makers considering crop substitution in order to achieve better agricultural production. After analyzing soil maps, we can conclude that these maps are useful as first step to build the soil database it need to be updated. SMCE criteria estimated based on salinity elements effect on the production, but this data needs to be updated annually, because soil parameters change continuously by mineral fertilizers and organic matter percent. Organic matter has a major role in improving soil characteristics where it increases soil fertility and modify pH range accompanied with decrease soil salinity and increase cation exchange capacity. On the other hand, by the time the salinity of water increases and the only solution of that adding water availability to the final suitable crop distribution map. SMCE could be done to other crops, also it’s recommended to combine these criteria with water availability, Crop economics, and agriculture machine availability. The results of Mapping of Vegetation Indices (of EVI, NDVI and SAVI) showed that the highest values for evidence of vegetation derived from remote sensing data at 60 days of plant age while she was with low values at 90 days from sowing and 30 days from sowing, which is due to the overlapping of reflections spectral soil with reflections spectral plant. Methodology for predicting nutrients status from satellite data, based on vegetation indices has been successfully tested with the measured and predicted data of yield. The results showed that the use of Field Spectroradiometer device as one of the advanced remote sensing techniques which give one nanometer spectral resolution and very accurate data about vegetation healthy, estimated yield and linked to soil productivity. The study indicated that in case of the use of this device, the manager can obtain precise information with nanometer accuracy about the healthy status of the plants in various stages of growth and give an idea of the quality and quantity of the final production. Moreover, in the range of 350–1000 nm, the red-edge (705-750 nm) is the most sensitive spectral region for assessing LAI, for peanut spectral. However, the degree of importance is determined by the specific band formation of the hyperspectral sensor as well as the crop. The results of Tukey’s HSD showed that blue, green and NIR spectral zones are more sufficient in the discrimination between peanut growth stages than red, SWIR-1 and SWIR-2 spectral zones.
The results showed that yield estimations have a significant correlation coefficient with field measurements. Also, both soil suitability and peanut growth models were successfully employed to simulate plant indices effect on canopy structure and final yield
The results of empirical equations showed high coefficient of determination (R2) value = 0.90 as well as the adjusted coefficient of determination (Adjusted R2) were 0.85 for the soil properties and yield. The results of the analysis also showed the calculated values and projected production for both expected and calculated for the peanut crop that the mid-season stage was higher accuracy than other stages of growth. Compared with the crop growth models, the soil suitability model provided better detection of small areas referred to soil properties, such as Calcareous areas and saline soils.
The study indicated those modern techniques of remote sensing and its promising opportunity to achieve integrated management and sustainable development of the agriculture sector in Egypt, especially the newly reclaimed areas, or arid lands where a DROP of water equal to wealth treasure. Moreover, this study faced many difficulties and challenges; such as clouds in some Landsat 8 imagery so we recommend the use of radar data because it is not affected by clouds. Moreover, Rapideye data is available daily however you cannot acquire area less than one thousand square kilometers in a time which represents a high cost, non-economic in Egypt so we recommend working on launching an Egyptian satellite with suitable spatial and spectral accuracy to help in the development of the agricultural strategies. Furthermore, the ASD field spectroradiometer although nanometer accuracy, but it too expensive and does not have devices available for commercial use or logistical. Fifthly field work always going to be very expensive, so the study recommends using farm records to record seasonal activities, productivity and the results of soil analyses and nutrients and infected areas using GPS and GIS, a relatively low-cost and easy-to-learn techniques. Study recommends applying the new models and new techniques used during the study for managing the new reclaimed areas and for other economic crops also, preparing training workshops for methods and modern techniques used during the study where the future of agriculture in Egypt is the integrated management of precision agriculture using advanced remote sensing techniques for arid areas.