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Increasing water and fertilizer productivity stands as a relevant challenge for sustainable agriculture. Alternate furrow irrigation and surface fertigation have long been identified as water and fertilizer conserving techniques in agricultural lands. The objective of this study was to simulate water flow and fertilizer transport in the soil surface and in the soil profile for variable and fixed alternate furrow fertigation and for conventional furrow fertigation. An experimental data set was used to calibrate and validate two simulation models: a 1D surface fertigation model and the 2D subsurface water and solute transfer model HYDRUS-2D. Both models were combined to simulate the fertigation process in furrow irrigation. The surface fertigation model could successfully simulate runoff discharge and nitrate concentration for all irrigation treatments. Six soil hydraulic and solute transport parameters were inversely estimated using the Levenberg-Marquardt optimization technique. The outcome of this process calibrated HYDRUS-2D to the observed field data. HYDRUS-2D was run in validation mode, simulating water content and nitrate concentration in the soil profiles of the wet furrows, ridges and dry furrows at the upstream, middle and downstream parts of the experimental field. This model produced adequate agreement between measured and predicted soil water content and nitrate concentration. The combined model stands as a valuable tool to better design and manage fertigation in alternate and conventional furrow irrigation.

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Alternate furrow fertigation has shown potential to improve water and fertilizer application efficiency in irrigated areas. The combination of simulation and optimization approaches permits to identify optimum design and management practices in furrow fertigation, resulting in optimum cost, irrigation performance or environmental impact. The objective of this paper is to apply 1D surface and 2D subsurface simulation-optimization models to the minimization of nitrate losses in two types of alternate furrow fertigation: a) variable alternate furrow irrigation; and b) fixed alternate furrow irrigation. For comparison purposes, optimizations are also reported for conventional furrow irrigation. The model uses numerical surface fertigation and soil water models to simulate water flow and nitrate transport in the soil surface and subsurface, respectively. A genetic algorithm is used to solve the optimization problem. Four decision variables (inflow discharge, cutoff time, start time and duration of fertilizer solution injection) were optimized to minimize the selected objective function (nitrate loss) for two fertigation events performed during a maize growing season. The simulation-optimization model succeeded in substantially reducing the value of the objective function, as compared to the field conditions for all irrigation treatments. In the experimental conditions, optimization led to decreased inflow discharge and fertilizer injection during the first half of the irrigation event. This was due to the high potential of the field experiment to lose water and nitrate via runoff. In the optimum conditions, alternate furrow fertigation strongly reduced water and nitrate losses compared to conventional furrow irrigation. The simulation-optimization model stands as a valuable tool for the alleviation of the environmental impact of furrow irrigation.

The saturation percentage (SP) of soils is an important index in hydrological studies. In this paper, artificial neural networks (ANNs), multiple regression (MR), and adaptive neural-based fuzzy inference system (ANFIS) were used for estimation of saturation percentage of soils collected from Boukan region in the northwestern part of Iran. Percent clay, silt, sand and organic carbon (OC) were used to develop the applied methods. In additions contributions of each input variable were assessed on estimation of SP index. Two performance functions, namely root mean square errors (RMSE) and determination coefficient (R 2 ), were used to evaluate the adequacy of the models. ANFIS method was found to be superior over the other methods. It is, then, proposed that ANFIS model can be used for reasonable estimation of SP values of soils.

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The computer simulation hydrologic model, DRAINMOD and modified DRAINMOD (Luo et al., 2000) for cold condition, was used to predict the performance of surface drainage and yield of canola as post-rice cultivation during the wet growing season of [2004][2005] in Rasht, Iran.Performance of surface drainage system treatments with 2 m spacing and 15 cm drain depth as compared with plots having no drainage (4 Â 10 m 2 ), were used to evaluate the models. Evaluation of predicted daily groundwater table depths as compared with measured values shows that the root mean square error (RMSE) was about 10.2 and 9.9 cm for DRAINMOD versus 11.0 and 11.5 for modified DRAINMOD in 2 m drain spacing and no drainage treatment respectively. Original DRAINMOD and modified DRAINMOD predictions for groundwater table depth was on average about 4 and 7% less than measured values for the surface drainage treatment and 22 and 14% greater for the no drainage treatment, whereas the standard error of original DRAINMOD was 1.4 and 1.51 (cm), respectively. The average absolute deviations were 8.67 and 9.22 (cm). However, by using the modified DRAINMOD these values were decreased to 1.34, 1.