The study examines factors affecting sprinkler irrigation system distribution uniformity, including sprinkler type, operating pressure and computer software design. The pressures used were 1.5, 2.1, and 2.5 bar for spray nozzles 4A and 17A, and 1.7, 2, 2.5, and 3 bar for rotor nozzles 1 and 3. The results showed a strong correlation, with (R2) values of 0.996 and 0.973, between SIDUL-Program (Sprinkler Irrigation Distribution Uniformity for Landscape–Program) and Excel program with respect to coefficient of uniformity (CU) and distribution uniformity (DU) respectively, suggesting a strong correlation between the two programs. The HEDIA program validated the SIDUL–Program, revealing a 0.42% difference in CU values between the two programs for rotor nozzle 3, indicating also a strong correlation (R2 = 0.953) between the two programs. The results showed that DU for spray 4A at a pressure of 2.1 bar was 7.4%, higher than DU at a pressure of 1.5 bar; the CU for spray 17A was 11.9% higher, as supported by manufacturer specifications. The study concluded that calculating CU and DU using the SIDUL–Program is critical for assessing sprinkler performance in irrigation systems since it considers factors such as soil type, irrigation 1methods and location; in addition to being easy to use and accurate com-pared to other programs.

The research aims at studying the indicators of Adoption (Rate of Adoption and Degree of Adoption) of sprinkler irrigation technique of farmers growing wheat depend on underground water for irrigation (wells) and studying some of the economical and social factors and others which influence the decision of the wheat farmers in adopting sprinkler irrigation technique and knowing the obstacles which limit adoption of this technique. To fulfill aims of the research, the data were collected from a random sample, (335) farmers from the two zones, the first and second agricultural stability zones from the north region (two provinces of Aleppo & Idlep) by an interview questionnaire in the third and fourth quarters of 2010. The study relied on the following tests and measures: Correlation, binary logistic regression . Results of the research have shown that the farmers adoption rate of sprinkler irrigation technique was 47.8% and the adoption degree of this technique is 50.9%, and the adoption rate of this technique is expected to increase from 47.8% in 2007 to77 % in 2030 with an annual rate of nearly 1.27.   The results of studying the factors which influence the adoption of this technique using binary logistic regression revealed a negative significant relation between the dependent variable " adoption of modern irrigation techniques by wheat farmers" and all the independent variables (Percentage of farming labor force to total family labor force, the availability of labor force, the availability of water for irrigation). While, there has been revealed a positive significant relation between the dependant variable (sprinkler irrigation technique adoption by farmers) and all independent variables (crop area, family participation in agricultural decision making, attitude of the farmer towards the agricultural extension, the percentage of farmers with level of elementary certificate and above). The results also have shown that the factors which influence the decision of wheat farmers adoption of sprinkler irrigation technique varies between the first agricultural stability zone and the second one concerning the effect value and its signification. The results have shown that the great obstacles which limit the sprinkler irrigation adoption was increasing the technique cost of 38.1 % and the ignorance of the loans& facilities which are presented by the transfer project to the modern irrigation in a percentage of 14.2 %.

In this research, a rule-based expert system named EFSIS-ES (Engineering Factors of Sprinkler Irrigation System -Expert System) had been built, verified and validated. Therefore, the aims of this research were to build, verify and validate an expert system for making a decision for the normal user to maximize system efficiency of sprinkler irrigation. The inputs data included location data, climate data, pump data, crop data, soil data and sprinkler system data. The outputs of the expert system were number of the sprinklers on a lateral, number of laterals in the irrigation system, length of each lateral, main line length, irrigation frequency, required pump power, total dynamic head (TDH) and pump discharge output. Results showed high correlation (R2 = 0.9927) between the EFSIS-ES program and the designed spreadsheet in regard to irrigation frequency. It could be indicated high correlation (R2 = 0.9999) between the EFSIS-ES program and the designed spreadsheet regarding pump power. The regression coefficient (R2) was 0.9983 between EFSIS-ES program and SSSDPS Expert regarding total dynamic head (TDH). It could be indicated high correlation (R2 = 0.9979) between EFSIS-ES program and SSSDPS Expert regarding pump discharge output.