In Mexico, agriculture has an allowance of 76% of the available water (surface and underground), although the average application efficiencies are below 50%. Despite the fact that in recent years modern pressurized irrigation systems have been the best option to increase the water-use efficiency (WUE), the gravity irrigation system continues to be the most used method to provide water to crops. This work was carried out during the 2014–2019 period in three crops, namely, barley, corn, and sorghum, in an irrigation district, showing the results of a methodology applied to gravity irrigation systems to increase the WUE. The results show that, with an efficient design, by means of irrigation tests, characterization of the plot, and the calculation of the optimal flow through an analytical formula, it was possible to reduce the irrigation times per hectare and the irrigation depth applied. Application efficiencies increased from 43% to 95%, while the WUE increased by 27, 38, and 47% for sorghum, barley, and corn, respectively. With this methodology, farmers are more attentive in irrigation because the optimal flow in each furrow or border is, in general, higher than that applied in the traditional way and they take less time to irrigate their plots. For farmers to adopt this methodology, the following actions are required: (a) be aware that the water that comes from dams is as valuable as the water from wells; (b) increase the irrigation quota; (c) seek government support to increase the WUE; and (d) show them that with less water they can have better yields.

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (K${}_{\mathrm{S}}$) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R${}^2$, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

The aim of this study is the deduction of an analytic representation of the optimal irrigation flow depending on the border length, hydrodynamic properties, and soil moisture constants, with high values of the coefficient of uniformity. In order not to be limited to the simplified models, the linear relationship of the numerical simulation with the hydrodynamic model, formed by the coupled equations of Barré de Saint-Venant and Richards, was established. Sample records for 10 soil types of contrasting texture were used and were applied to three water depths. On the other hand, the analytical representation of the linear relationship using the Parlange theory of infiltration proposed for integrating the differential equation of one-dimensional vertical infiltration was established. The obtained formula for calculating the optimal unitary discharge is a function of the border strip length, the net depth, the characteristic infiltration parameters (capillary forces, sorptivity, and gravitational forces), the saturated hydraulic conductivity, and a shape parameter of the hydrodynamic characteristics. The good accordance between the numerical and analytical results allows us to recommend the formula for the design of gravity irrigation.

Soil water movement is important in fields such as soil mechanics, irrigation, drainage, hydrology, and agriculture. The Richards equation describes the flow of water in an unsaturated porous medium, and analytical solutions exist only for simplified cases. However, numerous practical situations require a numerical solution (1D, 2D, or 3D) depending on the complexity of the studied problem. In this paper, numerical solution of the equation describing water infiltration into soil using the finite difference method is studied. The finite difference solution is made via iterative schemes of local balance, including explicit, implicit, and intermediate methods; as a special case, the Laasonen method is shown. The found solution is applied to water transfer problems during and after gravity irrigation to observe phenomena of infiltration, evaporation, transpiration, and percolation.

In recent years, groundwater levels have been decreasing due to the demand in agricultural and industrial activities, as well as the population that has grown exponentially in cities. One method of controlling the progressive lowering of the water table is the artificial recharge of water through wells. With this practice, it is possible to control the amount of water that enters the aquifer through field measurements. However, the construction of these wells is costly in some areas, in addition to the fact that most models only simulate the well as if it were a homogeneous profile and the base equations are restricted. In this work, the amount of infiltrated water by a well is modeled using a stratified media of the porous media methodology. The results obtained can help decision-making by evaluating the cost benefit of the construction of wells to a certain location for the recharge of aquifers.