The HortSyst model is a new discrete time model for describing the dynamics of photo-thermal time (PTI), total dry matter production (DMP), N uptake (Nup), leaf area index (LAI), and evapotranspiration (ETc) for greenhouse crops. The first three variables are considered as state variables and the latter two are conceptualized as output variables. This model was developed as a tool for decision support systems in Mexican greenhouses for the application of N and water in tomato (Solanum lycopersicum L.) production. The HortSyst has 13 parameters. It was used to calibrate the model and estimate the correct parameter values for the crop season. An experiment was carried out to test model predictions in a greenhouse during the autumn-winter season in Chapingo, Mexico. Tomato 'CID F1' was grown in a hydroponic system and plants were distributed with a density of 3.5 plants m-2. The tomato crop was transplanted on 21 August 2015. A weather station was installed inside the greenhouse to measure temperature, relative humidity, and global radiation. The HortSyst model provides an excellent predictive quality for DMP, Nup, LAI, and ETc according to the statistics. Values for bias (BIAS) were DMP (-3.897), Nup (-0.071), LAI (0.026), and ETc (3.647), values for root mean square error (RMSE) were DMP (14.543), Nup (0.500), LAI (0.100), and ETc (39.330), and values for modeling efficiency (EF)were DMP (0.996), Nup (0.991), LAI (0.998), and ETc (0.815). The model proposed and described in this paper can be integrated as a decision support tool for N supply and irrigation management in greenhouse production systems.

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Wetting pattern geometry is useful in determining the spacing between emitters and the irrigation time in drip irrigation systems. This research aimed to generate an empirical model to estimate the width and depth of the wetting front in surface drip irrigation based on experimental tests in a cube-shaped container with transparent walls in soils with a sandy clay loam texture, with hydraulic conductivities from 2.316 to 3.945 cm h−1, and organic matter contents from 1.7 to 2.8%, and different irrigation conditions: discharge rates of 1.44, 2.90, 3.00, 3.75, and 4.00 L h−1, initial moisture levels between permanent wilting point and field capacity, and irrigation times from 0.58 to 9.50 h. The experimental conditions and the strategy for measuring the wetting front and soil moisture are detailed so the experiment is verifiable. The proposed model performed better than five other empirical models, with average values of 3 cm for the root mean square error and 0.88 for the Nash and Sutcliffe efficiency coefficient. The generated model is efficient and simple and can be a very useful tool for the design and operation of surface drip irrigation systems in soils with conditions similar to those of this study.

Sensitivity analysis is the first step in elucidating how the uncertainties in model parameters affect the uncertainty in model outputs. Calibration of dynamic models is another issue of considerable interest, which is usually carried out by optimizing an objective function. The first aim of this research was to perform a global sensitivity analysis (GSA) with Sobol’s method for the 16 parameters of the new HORTSYST nonlinear model that simulates photo–thermal time (PTI), daily dry matter production DMP, nitrogen uptake (Nup), leaf area index (LAI), and crop transpiration (ETc). The second objective was to carry out the calibration of the HORTSYST model by applying a differential evolution (DE) algorithm as the global optimization method. Two tomato (Solanum lycopersicum L.) crops were established during the autumn–winter and spring–summer seasons under greenhouse and soilless culture conditions. Plants were distributed with a density of 3.5 plants m−2. Air temperature and relative humidity were measured with an S-THB-M008 model sensor. Global solar radiation was measured with an S-LIB-M003 sensor connected to a U-30-NRC datalogger. In the sensitivity analysis run in the two growth stages, it was observed that a greater number of parameters were more important at the beginning of fructification than at the end of crop growth for 10% and 20% of the variation of the parameters. The sensitivity analysis came up with nine parameters (RUE, a, b, c1 , c2, A, Bd, Bn, and PTIini) as the most important of the HORTSYST model, which were included in the calibration process with the DE algorithm. The best fit, according to RMSE, was for LAI, followed by Nup, DMP, and ETc for both crop seasons; the RMSE was close to zero, indicating a good prediction of the model’s performance.

Rainfall interception plays a role in the hydrological cycle and is a critical component of water balances at the basin level, which is why understanding it is very important; as a result, in recent years, various authors have proposed different models to explain this process and identify which of them adapts better to each forest species. In this context, the aim of this research was to evaluate the Gash model and the sparse Gash analytical model in Pinus hartwegii Lindl. and Abies religiosa (Kunth) Schltdl. et. Cham., using measurements from 20 precipitation events recorded in May and June 2018 at the Zoquiapan Experimental Forest Station, Mexico. The evaporation rate was calculated using the Penman–Monteith method (PM) and Gash’s calculation procedure. The canopy parameters were also calculated with two methods, a graphical one (A) and a method proposed in this research (B), which is based on point cloud generated with drone photogrammetry. For P. hartwegii, the most accurate model to estimate interception per rainfall event was the Gash model with the A and Gash methods, which were used to calculate the canopy parameters and evaporation rates, respectively; for accumulated interception, the sparse Gash analytical model with the B and PM methods was used. For A. religiosa, the best fit for individual events was presented by the sparse Gash analytical model with the B and PM methods, and for accumulated interception, it was the Gash model with the B and Gash methods. The results allow concluding that the B method proposed in this research is a good alternative for the calculation of rainfall interception, since it tends to improve its estimation, shortening the time for acquiring information about the parameters of the canopy structure and thus minimizing the costs involved.