الفهرس 
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Abstract
Aim of the study: This study was conducted to assess the impact of allocating irrigation water under limited water conditions, in an effort to support attempts to increase water productivity by relying on improving planning and allocating irrigation water, with integration of precision agriculture techniques. Methodology and Approach: First part: A limited field experiment was conducted on two consecutive seasons during summer seasons 2018 and 2019 to determine the rice yield response factor to water stress in addition to evaluating the influence of deficit irrigation scenarios. The experiment outline was set up as Randomized Complete-blocks Design under factorial scheme (3 × 4 + 1): four randomized test blocks for the full growth period (ALL) and the main growth stages of rice: vegetative (VEG.), reproduction (PRO.), and ripening (RIP.) which divided into three different treatment plots representing water stress levels that determined as 90, 75, and 60 % of Readily Available Water (RAW). In addition, the additional treatment plot (CONTROL) representing the full irrigation at 100% of RAW. Second part: A simple ArcGIS toolbox was created for assessment irrigation water allocation using ArcGIS Pro 2.7. The toolbox includes three toolsets, the first toolset is to create the required parameters for calculations, second toolset to produce raster datasets representing the calculations, and the third toolset to complete and editing the parameters raster datasets. Third part: A case study was conducted to the study area to reallocate irrigation water based on the experiment results and the GIS based model for irrigation water allocation assessment using Genetic Algorithms. Results and Discussion: Results of first Part: The average (Ky) to water stress for paddy rice during seasons 2018 and 2019 are: as 1.016, 1.16, 0.65, and 1.04, in addition, regression coefficient (R2) values are found as 0.99, 0.98, 0.99, and 0.97 for vegetative, reproduction, ripening growth stages, and full growth period respectively. The growth stage that suffered the least impact of water stress is ripening stage, on the other hand, the highest impact occurred during the reproductive stage. The best results obtained from the treatment RIP90: applying 90% water deficit level during the ripening growth stage, in opposite to the treatment PRO 60: applying 60% deficit irrigation level during the reproductive growth stage. Results of second Part: The toolbox was conducted to the study area at summer season of 2019 based on the obtained Ky values from the previous experiment. The important obtained results are as follows: 1. The map of study area classification shows that, the area of rice fields was 73.3662 km2 which represented 45% of the total area. However, the area of buildings, bare soil, and other crops were 16.044, 16.460, 57.988 km2 represented 10,10,35% of the total area. 2. The average NDVI values for vegetative reproductive, and ripening stages range from (0.18 to 0.85), (0.85 to 0.89), and (0.89 to 0.3). the results indicated that the highest value of NDVI is during the reproductive stage. 3.Values of Kc for vegetative reproductive, and ripening stages range from (0.65 to 1.07), (1.07 to 1.09), and (1.09 to 0.8). Kc value for the reproductive stage is the highest values during the season likewise NDVI. The duration of each growth stage is approximately 73 ,24, and 32 for Vegetative, reproductive, and Ripening. The longest growth stage is vegetative stage. 4. The obtained Kc values are validated compared to the adjusted FAO Kc values to the local climatic condition for the growth stages, vegetative, reproductive, and ripening. The validation of the simple regression equation yielded a coefficient of determination (R2) = 0.9552 and Root Mean Square Error (RMSE) = 0.08 which indicates to the high accuracy of the estimation. 5. The highest levels of water stress occurred during the vegetative stage of the rice crop fields. Otherwise, the lowest level of water stress occurs during the reproductive stage. The agreement between CWSI and Ks for evaluating water stress throughout individual growth stages. On the other hand, CWSI is not an accurate index for water stress throughout the season because it is based on land surface temperature which affected by the growth stage of the plant. 6. The spatial distribution of yield production(ton/ha) indicates that, the estimated total yield production of the study area for 2019 season is 27588 ton. 7. The outputs of the ArcToolbox were validated by the corresponding output from the limited experiment to estimate rice yield response to water stress. the output values of model-based WP and ETWP for ripening stage were used for the validation compared to the similar experiment- based values. The statistical analysis illustrated the convergence between the model-based and experiment-based outputs, which proved the accuracy of the toolbox for estimating water consumption, water stress level, yield production, water productivity, and evapotranspiration water productivity. Results of third part: The new spatial distribution of growth stages which could be concluded as follows: 7.95%, 6.55%, and 85.49% for the vegetative, ripening growth stages and full season water stress. reproductive growth stage didn’t distribute due to the high sensitivity to water stress. The total new potential yield production is 28383 ton, which higher than the real production by 11.88% (3014 ton) under the same amount of available water. Conclusion and Recommendations: • It is recommended to use the rice yield response factors to water stress for various growth stages for accurate estimation of actual yield production. •The most tolerant growth stage of rice which is recommended to apply water stress is ripening stage, on the other hand, the highest impact of water stress occurs during the reproductive stage. • Applying 90% of readily available water during the ripening growth stage is recommended under water stress conditions while, applying 60% deficit of RAW during the reproductive growth stage causes a reduction in yield production. • It is recommended to use the (ArcGIS Toolbox for irrigation water allocation assessment) to estimate water stress, yield production and water productivity accurately, using daily data throughout the season. • Using genetic algorithms is recommended to reallocate irrigation water based on deficit irrigation methods.