Hussein M. Al-Ghobari

Estimation of reference evapotranspiration for southern region of Saudi Arabia.

Abstract The reference crop evapotranspiration (ETr) for four areas in Saudi Arabia was estimated using five different methods: FAO-Penman, Jensen-Haise, Blaney & Criddle, pan evaporation, and calibrated FAO-Penman under local conditions (Penman-SA). Comparison was also made between the estimated ETr and the measured ETr of alfalfa grown in lysimeters in the Riyadh area. Regression analysis revealed that estimated ETr values were highly correlated with measured ETr values. In addition, linear regression relationships between ETr values estimated by the Penman-SA method and other methods were determined. The results of this study indicated that the calibrated Penman-SA method can be transferred successfully to other locations, and this method could be used for the estimation of ETr values in all areas in the southern region of Saudi Arabia.

Calibration of Soil Water Content Data from EnviroSCAN System Using Artificial Neural Network

Irrigation is one of the essential issues in agriculture in developing countries. Usually, in the developing countries, traditional farmers are likely to use more water than the required for crop production, thus wasting water. Hence, soil water sensors are typically needed in such situations to alert the farmer when the field needs irrigation and when it does not. One of these sensors is the EnviroScan system. It has the potential to monitor and estimate the soil water content continuously at various soil depths. Calibration is important to obtain accurate results. In this study, the volumetric soil water content and scaled frequencies from the EnviroScan system were recorded in a 60cm soil profile. An artificial neural network (ANN) was used to calibrate the soil water content compared with a regression analysis using field data at different soil depths in sandy clay loam soil. Several ANN architectures were employed in order to determine the optimum architecture. The Original Research Article Al-Ghobari et al.; AJEA, 12(5): 1-11, 2016; Article no.AJEA.26237 2 coefficients of determination (R) of a regression calibration equation of scaled frequency against the gravimetric soil water content were 0.9225, 0.9623, and 0.9593 for 0–20 cm, 20–30 cm, and 30–60 cm soil depths. The R 2 between gravimetric soil water content and the estimated by ANN model was 0.9928 for a 0–20 cm soil depth, 0.9809 for a 20–30 cm soil depth, and 0.9878 for a 30– 60 cm soil depth. Using the data set for the entire 60-cm soil profile for calibration by ANN model, the R value was 0.9715.