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Marey S.A.,King Saud University | Marey S.A.,Agricultural Engineering Research Institute AEnRI
Bulgarian Journal of Agricultural Science | Year: 2015

Design parameters of the ridger furrow opener directly affecting the furrow profile characteristics and the amount of applied water. Furrow-bed irrigation technique is usually used for water conservation, efficient fertilizer use and many other benefits. This study is to evaluate the impact of design parameters of the ridger furrow opener and planting methods on sugar beet yield and water use efficiency. Therefore, field experiments are conducted to (i) investigate the effects of share rake angles (20°, 25° and 30°), opener wing angles (35° and 45°) and wing shape configurations (straight and curved) on the furrow profile characteristics, transverse scattering, draft force, and (ii) evaluate planting methods (i.e. ridges with 50 cm rows spacing and pair of rows on bed with 30, 35 and 40 cm rows spacing), the wing shape and angles on the emergence, sugar percentage, root and sugar yield, applied water and water use efficiency. The results showed that the curved shape and the wing angle of 45° produced wider furrows than those produced by the straight shape and 35° wing angle. Minimum transverse scattering is associated with the curved wing, wing angle of 35° and share rake angle of 20°. Increasing the share rake and wing angles increased the required draft force. The highest average values of root and sugar yields have been achieved at beet planting in beds with 30 cm rows spacing flowed by beds with 35 and 40 cm rows spacing, respectively. The lowest value of the water use efficiency is achieved at planting on ridges compared to the other planting methods. The maximum emergence percentage, root and sugar yields, sugar percentage and water use efficiency are associated with a wing angle of 45° and the curved wing shape. © 2015, National Centre for Agrarian Sciences. All rights reserved. Source

Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI | Alazba A.A.,King Saud University | Zin El-Abedin T.K.,King Saud University | Zin El-Abedin T.K.,Alexandria University
Agricultural Water Management | Year: 2015

An artificial neural network (ANN) was developed for estimating the infiltrated water volume (Z) under furrow irrigation. A feed-forward neural network using back-propagation training algorithm was developed for the prediction. Four variables were used as input parameters; inflow rate (Qo), furrow length (L), waterfront advance time at the end of the furrow (TL) and infiltration opportunity time (To). The Z was the one node in the output layer. The data used to develop the ANN model were taken from published experiments. The ANN model predicted Z over a wide range of the input variables with statistical analysis indicating that it can successfully predict Z with a high degree of accuracy. Performance evaluation criteria indicated that the ANN model was better than the two-point method using a volume balance model. Using testing and validation data sets to compare the ANN model with the two-point method shows that the two-point method had a mean coefficient of determination (R2) value that was about 3.6% less accurate than that from the ANN model. Also, the mean root mean square error (RMSE) value of 0.0135m3m-1 for the two-point method was almost double that of mean values for the ANN model. The relative errors of computed Z values for the ANN model were mostly around ±10%. Therefore, the ANN model is applicable to other soils and to different furrow irrigation hydraulics. © 2014 Elsevier B.V. Source

Yassin M.A.,King Saud University | Alazba A.A.,King Saud University | Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI
Computers and Electronics in Agriculture | Year: 2016

This study investigates the ability of gene expression programming (GEP) in modeling of the infiltrated water volume (Z) under furrow irrigation. Field data were collected in the literature study for modeling Z covering wide range of opportunity time. Five variables were used as input parameters; inflow rate (Qo), furrow length (L), waterfront advance time at the end of the furrow (TL), infiltration opportunity time (To) and cross-sectional area of the inflow (Ao). The following statistical parameters that coefficient of determination (R2), overall index of the model performance (OI), root mean square errors (RMSE) and mean absolute errors (MAE) are used as comparing criteria for the evaluation of the models' performances. The best value of the statistical parameters which range in training, testing and validation processes as the following (R2 = 95-97%; OI = 94-97%; RMSE = 0.013-0.009 m3 m-1; and MAE = 0.011-0.007 m3 m-1) implies that the GEP model provides an excellent fit for the measured data. A comparison is made between the estimates provided by the GEP and the two-point method. The comparison results reveal that the GEP models are superior to two-point method. Furthermore, the remarkable advantage of GEP was that it resulted in an explicit equation for the estimation of the Z under furrow irrigation. © 2016 Elsevier B.V. Source

Abdallah S.E.,Kafr El Sheikh University | Basiouny M.A.,Agricultural Engineering Research Institute AEnRI
AMA, Agricultural Mechanization in Asia, Africa and Latin America | Year: 2012

The research was conducted in one of commercial refrigerators for ripening bananas (Musa Sapientium), in Kafr Elsheikh Governorate, Egypt during the season of 2006/2007. The need was to investigate the behavior of bananas during the ripening process at various temperatures and airflow rates. Both temperature and airflow rate were controlled by air distribution and adjusted inside the ripening room before loading bananas. This enhanced air temperature uniformity in both the vertical and horizontal dimensions. Additionally, this assisted in determining the most important changes in some physical properties of bananas that occured during the ripening process. The deviations in the ripening room temperatures about their mean values were less with the especially designed air distribution duct. It, also, enhanced the uniformity of air distribution inside the ripening room and increased the effectiveness by 458.62% at a ripening room temperature of 21 °C. The shortest periods of banana ripening (shelflife) were obtained at a ripening room temperature of 21 °C and airflow rate of 0.3 m3/s.kg. At an airflow rate of 0.3 m3s.kg, the shelflife of bananas was increased from 12 to 25 days by decreasing ripening room temperature from 21 to 15 °C. The optimum conditions for banana ripening were obtained at an airflow rate of 0.3 m3/s.kg at all the ripening room temperatures under study. Generally, at constant airflow rate, the ripening room temperature of 21 °C could be used to achieve a high rate of banana ripening. On the other hand, a ripening room temperature of 18 and 15 °C could be used to achieve moderate and slow rates of banana ripening, respectively. Therefore, shelf-life of bananas could be considered a function of storage period for marketing or processing. Some physical properties such as ripening stage, mass loss percentage, pulp-to-peel ratio, pulp texture, pulp moisture content and pulp temperature were noticeably changed as the ripening process of banana fruits proceeded. Source

Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI | Alamoud A.I.,King Saud University
Computers and Electronics in Agriculture | Year: 2015

In this paper, we examine the discharge of labyrinth-channel emitters under different operating pressures (P) and water temperatures (T). An artificial neural network (ANN) and multiple linear regression (MLR) model are developed for the emitter flow variation (qvar) and the manufacturer's coefficient of variation (CV). As well as P and T, the structural parameters of the labyrinth emitter are considered as independent variables. The ANN results demonstrate that a feed-forward back-propagation network with five input neurons and 14 neurons in the hidden layer successfully model qvar and CV. The trapezoidal unit spacing and path length of the labyrinth emitter are found to be insignificant. In our ANN model, we use a hyperbolic tangent as the activation function in the hidden layer and the output layer. Statistical criteria indicate that the ANN is better at predicting the hydraulic performance of the labyrinth emitters than MLR. The root mean square errors for qvar and CV are 1.0497 and 0.0044, respectively, for the ANN model, and 2.0703 and 0.0107, respectively, for the MLR model using a test dataset. The relatively low errors obtained by the ANN approach lead to high model predictability and are feasible for modeling the hydraulic performance of labyrinth emitters. © 2015 Elsevier B.V. Source

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