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El cambio climático (CC) afecta las condiciones meteorológicas actuales y repercute negativamente en el rendimiento de maíz, particularmente de temporal. Para estimar los efectos del CC en la productividad, se han utilizado modelos de simulación de crecimiento bajo diferentes escenarios de cambio climático. En este artículo se hace una revisión sobre modelos implementados globalmente, durante el periodo 2006 a 2019; a través, de Scopus y Google Académico. Los modelos reportados son mecanicistas, dinámicos y estocásticos, como DSSAT-CERES-Maize, APSIM-Maize, CropSyst, AquaCrop, EPIC-Maize, CropWat InfoCrop y WOFOST. Las simulaciones en diversos escenarios reportan disminución del rendimiento de maíz en África Subsahariana (78%), China (70%), Latinoamérica (61%) y Medio Oriente (45%), e incrementos en la Unión Europea (71%), Cinturón Maicero Estadounidense (57%), Medio Oriente (45%) e India (44.5%). En México, se tienen estimaciones de incremento en los rendimientos de maíz desde 5 a 22% considerando los efectos de la fertilización carbónica, y reducciones de hasta de 49.3% bajo otras condiciones. Se requiere profundizar en estudios sobre efectos de CC en las diferentes regiones del país, e implementar modelos que puedan utilizarse para el diseño de políticas y estrategias de adaptación y mitigación, ante los efectos negativos del CC en la agricultura mexicana.

Introduction: Medium and low technology greenhouses use natural ventilation as a method of temperature and humidity control. However, at certain times of the year, this is insufficient to extract excess heat inside the greenhouse, so devices such as hydrophanes (humidifiers) have been implemented to reduce the temperature. It is necessary to know the behavior of temperature and humidity, since both factors influence the development of crops and, therefore, their yield. Objective: To develop a computational fluid dynamics (CFD) model of a naturally ventilated zenithal greenhouse equipped with hydrophanes to understand the spatial and temporal distribution of temperature and humidity inside the greenhouse. Methodology: The experiment was carried out in a greenhouse equipped with hydrophanes and grown with bell pepper. Temperature and humidity measurements were performed from March 7 to 25, 2014. The ANSYS Workbench program was used for the 3D CFD modeling. Results: The CFD model satisfactorily described the temperature and humidity distribution of the greenhouse, with an error of 0.11 to 3.43 °C for temperature, and 0.44 to 10.80 % for humidity. Limitations of the study: Numerical modeling using CFD is inadequate to model the temporality of the variables. Originality: There are few studies that model humidity behavior with CFD and the use of hydrophanes in Mexico. Conclusions: The CFD model allowed visualizing the distribution of temperature and air humidity inside the greenhouse.

One of the advantages of controlled agriculture is the notable increase in crop yields. However, at high-tech levels, water use productivity has increased, while energy use productivity has decreased. Therefore, the objective of this study was to estimate water, energy and labor use productivity in tomato cultivation, for a low-tech greenhouse. The inputs used in the production process were recorded from April 24 (date of transplant) to October 16, 2016 (date of last harvest). The yields obtained were 19.07 kg∙m-2. Water use productivity was 27.86 kg∙m-3 (35.89 L∙kg-1), which is within the range reported for unheated plastic greenhouses (30-40 L∙kg-1). Energy productivity was 0.331 kg∙MJ-1, and the energy consumed per unit area was 57.61 MJ∙m-2, with Calcium nitrate being the input with the highest energy demand (49.49 %). During the production cycle, 738 working hours were used, 78 % of which were dedicated to cleaning and maintenance, with labor productivity of 3.02 kg∙h-1.

The objectives of this research were two: first to investigate experimentally the behavior of pineappl (Ananas comosus, L.) thin layer drying in a greenhouse-type solar dryer and second to describe the best fitting kinetic and mathematical model taken from literature. A large scale greenhouse dryer designed and installed at Silpakorn University, Nakhon Pathom, Thailand was used to dry slices 1 cm width at temperature range between 25-60 °C with relative humidity between 50-90%. Nine statistical models, either empirical or semi-empirical, were tested in order to validate the experimental data. A non-linear regression analysis conducted by a statistical computer program was applied to evaluate the constants of all the models. The parameter values, root mean square error (RMSE), mean absolute error (MAE) and modelling efficiency (EFF) of the nine models were calculated. Comparison outcomes of two experiments are displayed between the predicted moisture content and the observed pineapple moisture content. Hasibuan and Daud drying model proved to describe the best pineapple solar drying curves. The two experiments were carried out on sunny days, the second experiment on the third day showed cloudiness decreasing the solar radiation. Mathematical models of pineapple drying in a greenhouse dryer have not been found so far in the literature. Drying curves obtained from experiments showed that the constant drying and the falling drying rate periods exist. Nine thin-layer drying models were fitted to two experimental data in order to describe the drying characteristics of pineapple founding that the Hasibuan and Daud model was the best fitting.

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.

Introduction: Mathematical crop modeling allows selecting different simulation environments and analyze the response of different variables over time to estimate, predict and potentiate the growth of a crop. Objective: To simulate and evaluate a dynamic mathematical model to predict tomato growth and nitrogen content in the plant under greenhouse conditions. Methodology: The simulation was performed with the VegSyst model, for which climate variables and greenhouse tomato crop variables were recorded during two cycles, for the evaluation a local sensitivity analysis and non-linear parametric identification were applied. Results: The sensitivity analysis allowed identifying the most sensitive parameters of the model and with the parametric identification the values of the parameters were found, which allowed fitting the simulations to the measurements made in the greenhouse. Limitations of the study: The VegSyst model only allows simulating nitrogen in the plant. In addition, it is necessary to calibrate the model to the climatic conditions of the site for experiments in other areas. Originality: Based on the VegSyst model, equations were proposed and calibrated to estimate height, number of fruits, nodes, dry matter of fruits and dry weight of fruits. Conclusions: The model predicts the growth of the tomato crop and the nitrogen content in the plant under greenhouse conditions.

Control of the environmental conditions inside a greenhouse is fundamental to obtain high yields and product quality. One of the tools to improve this control is the mathematical modeling of the system in question. Therefore, in this work a dynamic energy balance model was developed with the objective of predicting the air temperature inside a greenhouse. The model considers the process of plant transpiration, ventilation and condensation inside a greenhouse, outside climate conditions, crop characteristics (leaf area index, stomatal and aerodynamic resistance), cover properties and greenhouse characteristics. The information used was collected in a greenhouse located at the Universidad Autonoma Chapingo, Mexico (19° 29’ LN and 98° 53’ LW), with a polyethylene cover, natural ventilation and tomato cultivation. The model was calibrated in order to obtain optimal values for six parameters: infiltration coefficient, heat transfer coefficient through the cover, heat transfer coefficient through the soil, crop-specific transpiration and wind effect coefficient. After calibration, the efficiency of the model improved by 33.84 %. The performance of the model was quite acceptable when evaluated with another set of data.

Huitlacoche (Ustilago maydis) is an edible fungus that develops from the cob of corn (Zea mays). This fungus is consumed fresh, but is highly perishable. The modified atmosphere packaging (MAP) technology has been suggested as an alternative that could enlarge shelf life. The objective of the work was to characterize the respiratory activity of huitlacoche under O2 and CO2 concentrations that differ from those of normal air storage, in order to improve handling in MAP. Galls of huitlacoche grown in Tulancingo, Hidalgo, Mexico were used. Batches of 150 g of product were placed in containers with walls impermeable to gases, where changes of O2 and CO2 concentrations were monitored during 15 h at 20°C or during 30 h at 3°C, with relative humidity higher than 95%, and atmospheric pressure of 77.993 kPa. The procedure was repeated in presence of lime to remove CO2 from containers, in order to assess its effect on respiration. A transient mass balance was carried out to describe the concentration change as a function of O2 consumption rate, and this was represented through the Michaelis-Menten (M-M) enzymatic kinetic theory. Equations were programmed in the MatLab® environment and parameters of M-M were calibrated using the nonlinear least square algorithm (lsqnonlin.m function) of the Optimization Toolbox of MatLab®. Temperature affected the maximum oxygen consumption rate, which had values of 27.1×10(-9) kg kg-1 s-1 at 20°C and 4.5×10(-9) kg kg-1 s-1 at 3°C. However, the thermal condition did not affect the M-M constant, which had values of 11.44 kPa (14.67%) and 12.93 kPa (16.58%), respectively. Besides, CO2 did not exert an inhibitory effect on respiration within the limits tested, which was verified through analysis of variance applied to parameters obtained from a Michaelis-Menten model without inhibition and from an uncompetitive model.

In dynamic modeling of the greenhouse climate, prediction errors are a significant issue due to uncertainties in initial state values, input variables, model parameters and model structure, all propagating in time in a nonlinear way. We investigated a data assimilation approach using two non-linear Kalman filters in light of prediction uncertainty. An extended (EKF) and an unscented (UKF) Kalman filters were designed to estimate climate states, and also both the states and model parameters. The states to be estimated were air temperature, absolute humidity and carbon dioxide concentration inside a greenhouse. Year round measurements from a Dutch greenhouse with a rose crop were used. The dynamic model was first calibrated manually by estimating ten of its parameters. Uncertainties of the measurements needed for designing EKF and UKF were specified via literature sources whereas the uncertainties related to the process were tuned. Both filters increased the model predictive power several orders of magnitude with respect to mean squared error (MSE) statistics and one order of magnitude with respect to mean absolute error (MAE) analyzed during autumn-winter and spring-summer seasons when only the model states were estimated. However, no improvement on the one step ahead state predictions were achieved when both states and model parameters were estimated by both nonlinear filters. Results showed that data assimilation based on nonlinear Kalman filters is advantageous over data assimilation that uses only model calibration. Therefore, improved model of the greenhouse climate by data assimilation can be used in controlling and optimizing more efficiently the greenhouse system.

Sun-drying is the most widely used method of preserving agricultural products; however, under hostile climatic conditions it leads to serious losses in product quantity and quality. The use of solar drying technology in developing countries can reduce postharvest losses and significantly improve product quality compared to other methods. The development of mathematical models of solar dryers is necessary to be able to design, control and optimize this type of system. The aim of this study is to provide an overview of the different approaches to modeling and simulating greenhouse solar dryers, both of natural and forced convection. In addition, models that have not been extensively used in this area, such as empirical and numerical ones, are discussed.

Cooling is an important practice that extends the shelf life of fruit and vegetable products. The arrangement of these products for cooling is similar to a macroporous bed, where a cold fluid circulates between packed particles. The study of these systems is essential to estimate characteristic operation variables so that they can be suitably designed. The aim of this study was to evaluate the use of the Ergun equation in estimating the pressure drop in a forced-air cooling unit. As study geometry, a pipe 30.0 cm long and 15.5 cm in diameter, which was filled with Styrofoam spheres and subjected to experimental evaluation of pressure drop (ΔPexp) with various sphere diameters and mass air flux, was used. Void fraction, temperature and air density and viscosity were evaluated in each condition. The pressure drop was calculated with the Ergun equation (ΔPest) and the parameters of this model were calibrated through the MatLab® lsqnonlin.m subroutine, with which the adequate estimation of the packed bed was obtained. Based on geometry of packed objects, the results are applicable to fruit such as apples, oranges, peaches, lemons and plums, among others.

Semi-closed greenhouses are more efficient in water and energy use than conventional ones. However, one of the challenges is the humidity control. This paper is based on experimental data generated during the 2012 tomato production cycle in a semi-closed greenhouse located at the Humboldt University of Berlin. The aim was to generate two models for simulation of the humidity behavior. In the first model, the greenhouse was completely closed and only transpiration and condensation processes were taking place. In this model, after calibration, a mean absolute error (MAE) of 1.94 and a correlation coefficient (r) of 0.87 were obtained, with the boundary layer resistance and the radiation extinction coefficient being the most varying parameters. The second model, in addition to the processes considered in the first, had natural ventilation, so a sensitivity analysis was performed before calibration, resulting in a MAE = 2.17 and r = 0.85, with the latent heat of vaporization and the radiation extinction coefficient being the most sensitive parameters. The developed models allow for a quantitative comparison of the processes involved in the water balance in the open and closed system, and will thus enable us to improve control over this factor. Both models were implemented using Simulink in Matlab®.

Las máquinas sembradoras-fertilizadoras utilizadas en el campo mexicano usan sistemas mecánicos para el accionamiento de los dosificadores. En países desarrollados estos sistemas se están reemplazando por sistemas mecatrónicos implementados en máquinas de dosificación variable. Los sistemas mecatrónicos tienen la ventaja de que su desempeño no se afecta por las condiciones del suelo y cubierta vegetal, además de la facilidad para realizar los cambios de la dosis de aplicación de insumos, al permitir realizarlos de manera continua y en tiempo real. Sin embargo, tienen la desventaja de su elevado costo de adquisición. Este estudio presenta los resultados de prueba de laboratorio de un sistema mecatrónico desarrollado con hardware y software libre de bajo costo y adaptado a la sembradora-fertilizadora Modelo LC-U-N del fabricante Sembradoras del Bajío S. A. de C.V. El dosificador de semilla se probó con semilla de maíz, bajo el estándar de la banda engrasada descrito en la norma ISO 7256-1:1984. Los resultados muestran buena calidad de distribución de la semilla a frecuencias de rotación del disco semillero de 0.524 a 2.199 rad s-1, al obtener índices de calidad de alimentación ≥ 90 %, pérdida de semilla y de multiplicidad inferiores al 6 % y precisión < 18 %. Los dosificadores de fertilizante y pesticida se probaron con base a la norma NMX-O-222-SCFI-2004, con fertilizante comercial UREA, al utilizar cinco frecuencias de rotación en el intervalo de 0.524 a 3.77 rad s-1 y de 1047 a 14,661 rad s-1, respectivamente, con diferentes aberturas de la compuerta de salida. Para el caso del dosificador de fertilizante se obtuvo una dosis de aplicación de 3 a 562 kg ha-1 con el mejor desempeño al utilizar aberturas de la compuerta ≤ 50 %, mientras que el dosificador de pesticida presentó un desempeño excelente, con el cual se obtuvo una dosis de aplicación de 0.004 a 16.4 kg ha-1.

The possibility of improving the accuracy of climate monitoring in greenhouse cultivation by way of model based filtering was explored. The focus was on estimating the average climate inside a greenhouse compartment. Starting point was employing an extended Kalman filter (EKF), combined with a greenhouse climate differential equation model. In two different greenhouses (A and B), temperature and humidity were monitored with a 5-min sampling resolution with a sensor grid. The available data sets spanned 1 and 0.5 years. With the average over all sensors as reference signal, the root mean squared errors (RMSEs) of the unfiltered signals (coming from single sensors) were 0.43 °C and 0.48 g m −3 for greenhouse A, and 0.80 °C and 0.64 g m −3 for greenhouse B. The filter was compared with a moving average (MA) filter, and an unscented Kalman filter (UKF). Overall, monitoring accuracy was not improved by any of the filters, and in most cases it deteriorated. Performance was strongly linked to the choice of filter, where the EKF outperformed the other filters by a considerable difference. The violations on the assumptions of whiteness and normality of the noise were severe but had a moderate effect on the RMSEs (0.11 °C and 0.10 g m −3 for greenhouse A). A clear link was found between model accuracy and monitoring accuracy. A 10–15 fold decrease of state errors was associated with an RMSE reduction down to 0.1 °C and 0.1 g m −3 , the expected equivalent of increasing the number of climate sensors from 1 to 25.

Due to the lack of information about energy consumption in heating and cooling requirements ingreenhouses, the enthalpy was used as an approach of the energy content in the environment. A sensitivity analysis was done through seven Neural Network models for enthalpy prediction. It was found that the most important variables to explain the energy content in the greenhouse are temperature, transpiration, and heating and cooling systems, which correspond to inputs of model 4 with a MSE equal to 0.38 and 1.28 for one and two times ahead, respectively. Temperature and relative humidity data were collected from Santa Rosa, Sinaloa, from 1999 to 2009. The ellipses for the comfort zone were constructed in order to determine how far the external conditions are comparable with the optimal conditions for tomato production, and also to estimate the amount of energy required in each case to reach the comfort zone. Data analysis showed that most of the time those external conditions are below and right from the comfort zone, which pointed out a problem of high temperature and low relative humidity. Besides, the most critical months for tomato production are from July to September because the external conditions are far away from comfort zone, Therefore, it is recommended to dedicated those months for cleaning the greenhouse.

Evolutionary Algorithms, Bio-inspired Algorithms and Hybrid Algorithms, considered as Artificial Intelligence techniques have gained great importance due to their capacity to solve complex and nonlinear problems that conventional methods cannot solve. In the last decade the application of these techniques has been applied to the development of intelligent algorithms to identify, predict, optimize and control the weather for crops in protected environments (greenhouses) and to generate near optimal climatic conditions for biosystems for growth and crop development, and to obtain higher yields and product quality. The current paper shows a review of Artificial Intelligence Techniques applied in agricultural engineering, mainly to modeling and control of greenhouse environment, especially those algorithms inspired by the collective behavior of biological systems, evolution and natural genetics.

The current research was conducted in a greenhouse of 20 m2 area located at the University of Chapingo, Mexico, in hydroponic system (NFT) with recirculating nutrient solution, three treatments were applied (100, 150 and 200 ppm) of calcium concentration to study effects on fruit quality and yield of tomato (Lycopersicum esculentum Mill.). The experiment was conducted from September 30, 2010 to February 4, 2011 and random samples of fruits were collected for three consecutive weeks, where each week were harvested fruits of the first to the third cluster of each plant. Treatment two showed the highest yield and it was obtained in the second cluster with 57.1 %, in treatment one on the third week was 37.5 % and yield treatment three during the second and third week was 38.6 % with respect to the total. The fruits quality was good because tomatoes showed good acidity and salt content as recommended. However the sugar content was low and statistically different compared with previous reported studies. The average yield was low and different in all cases due to the effect of extreme temperatures outside the range of 10-25 °C and these results cannot be asigned to the concentration of calcium because there were not significant differences among the treatments.

A molecular analysis found that poultry litter anaerobic digestion was dominated by hydrogenotrophic methanogens which suggests that bacterial acetate oxidation is the primary pathway in the thermophilic digestion of poultry litter. IWA Anaerobic Digestion Model No. 1 (ADM1) was modified to include the bacterial acetate oxidation process in the thermophilic anaerobic digestion (TAD). Two methods for ADM1 parameter estimation were applied: manual calibration with non-linear least squares (MC-NLLS) and an automatic calibration using differential evolution algorithms (DEA). In terms of kinetic parameters for acetate oxidizing bacteria, estimation by MC-NLLS and DEA were, respectively, km 1.12 and 3.25 ± 0.56 kg COD kg COD(-1)d(-1), KS 0.20 and 0.29 ± 0.018 kg COD m(-3) and Yac-st 0.14 and 0.10 ± 0.016 kg COD kg COD(-1). Experimental and predicted volatile fatty acids and biogas composition were in good agreement. Values of BIAS, MSE or INDEX demonstrate that both methods (MC-NLLS and DEA) increased ADM1 accuracy.

Graphical abstractDisplay Omitted FAST and Sobol' sensitivity analysis methods were compared.Genetic algorithms were performed to calibrate reduced TOMGRO model.Reduced TOMGRO model calibration allowed an adequate goodness-of-fit of the simulations.Sensitivity analysis showed the effect of growing conditions on TOMGRO parameters.Current TOMGRO structure can be used to simulate tomato growth in Central Mexico. One common constraint for using crop models for decision making in precise greenhouse crop management is the need for accurate values of model parameters depending on climate conditions, crop varieties, and management. Estimating these parameters from observed data on the crop, using a crop model, is an interesting possibility. Nevertheless, the accuracy of estimations depends on the sensitivity of the model output variables to the parameters. Therefore, this paper proposes the use of the reduced state variable TOMGRO model which describes nodes, leaf area index, total plant weight, total fruit weight, and mature fruit weight as states variables. The objective of this work was to compare EFAST and Sobol' sensitivity analysis methods to determine the most sensitive parameters for TOMGRO model outputs. A former sensitivity analysis showed that 8 parameters were the most sensitive and they were calibrated using genetic algorithms (GAs) to adapt the model to semi-arid weather conditions of Central Mexico. Genetic algorithms are important heuristic search algorithms for optimization problems and have been used to calibrate non-linear models related to control of greenhouse climate conditions. Simulation and analysis of the TOMGRO model showed that the estimations for the state variables are close to the measured data. The model could be adapted for simulating other greenhouse crops by means of sensitivity analysis and calibration.

The effects of pulsed light based-LEDs at eleven frequencies (0.1, 1, 10, 50, 100, 500 Hz, 1, 5, 10, 50 and 100 kHz) programmed at 50 % duty cycle were analyzed, obtaining important parameters of the fluorescence emission of chlorophyll such as: maximum fluorescence (Fm′), minimum fluorescence, the fluorescence emission in steady state, maximum efficiency of PSII (Fv′/Fm′), the fraction of PSII centers that are open, photochemical quenching, nonphotochemical quenching (NPQ), quantum efficiency of photosystem II (ΦPSII), electron transport rate (ETR) and quantum yield of CO2 assimilation (ϕCO2). For the study and validation of the results obtained in the experiments, the analysis of variance (ANOVA) was applied 0for each parameter with confidence intervals of 95 %. The results show that the frequencies of pulsed light had positive and negative effects on the fluorescence parameters with respect to the control treatment (continuous light). The frequencies that generated the best performance of Fv′/Fm′, NPQ, ΦPSII, ETR, ϕCO2 in tomato plants were 0.1, 1, 100 Hz, and 1 kHz. The increase obtained in these parameters can represent an optimal growth and productivity conditions for optimal energy consumption.

We investigated the effect of model reduction and data assimilation on prediction accuracy of greenhouse climate. For this, a first-principle model was reduced, and calibrated with measurement data. Calibration data consisted of a time series of temperature, humidity, and carbon-dioxide concentration in a rose greenhouse, together with 15 variables related to outside climate and control actions. The results indicate that model reduction does not produce a crucial loss of prediction accuracy. In contrast, data assimilation decreases the size and variance of prediction errors drastically, making predictions much more reliable. A static linear model seems to predict the most essential input-output response for temperature and humidity, but the predictive power for carbon-dioxide concentration is limited. The prediction errors have standard deviations of typically 2°C, for temperature, 5-10% for relative humidity, and 200-300 ppm for CO2. The prediction errors have biases of the same order, which differ per period for which the predictions are made. We believe these results are promising for modelling climate via a static black box approach, in combination with data assimilation. The relatively low computational demand for uncertainty analysis and easy model building provide a suitable starting point for investigating augmented systems, such as plant development based on controlled climate.

Resumen es: Un analisis de incertidumbre para un modelo de crecimiento de un cultivo permite evaluar la variabilidad en sus parametros y deducir una distribucion de ...

This research was conducted in a Plant Science Department greenhouse at the Universidad Autonoma Chapingo, located in Chapingo, Mexico State. We evaluated three irrigation frequencies (3, 4 and 5 irrigations per day), which were applied using an automated drip irrigation system at three depths (7, 14 and 25 mL•day-1) controlled by drippers; tezontle was used as substrate. A factorial arrangement of two factors, each at three levels, was used in a completely randomized design, and Steiner universal solution (90 mL•day-1) was used to provide nutrition for the production of Calla lily cv Reno. Crop behavior was analyzed in terms of number, height and development time of flowers and leaves. The crop was established from September 2010 to January 2011, and data taking was weekly from four to fifteen weeks after planting the rhizomes, when the number of developed leaves and flowers were quantified and subsequently the height of both was measured. With the data obtained, we proceeded to analyze the effect of both factors, which determined that the factor affecting the number, height and development time for Calla lily cv Reno flowers is irrigation frequency. With four and five daily irrigations, a greater number of flowers develops than with three.

The infrared thermometer used to measure crop water stress index (CWSI) is a reliable tool for irrigation scheduling, which, combined with efficient irrigation systems can maximize crop productivity. A study was conducted to determine the crop water stress index in husk tomato, or tomatillo, (Physalis ixocarpa Brot.) under a drip irrigation system, its relationship with irrigation depth and plastic mulch in scheduling irrigation and predicting fruit yield. The experiment design was completely randomized with three replicates Treatments consisted of five irrigation depths (replacement of 40, 60, 80, 100 and 120 % of the reference evapotranspiration estimated by the Penman-Monteith method). CWSI was estimated using infrared radiation gun measurements of canopy temperature, air temperature, and relative humidity, and water vapor pressure deficit was calculated. The equation which defines the lower limit expresses the relationship between vapor pressure deficit (VPD) and temperature difference (crop and air (Tc-Ta)). When the crop transpires, the relationship is: Tc -Ta = 1.21 - 131 DPV (r2 = 0.68, P

This paper presents a comparison of different evolutionary algorithms (EAs), such as Genetic Algorithms (GAs), Evolutionary Strategies (ES) and Evolutionary Programming (EP) to calibrate parameters of a climate model that describes the behaviour of air temperature and relative humidity (RH) within a greenhouse where a tomato crop is being grown. The objective was to determine which method generates parameter values that give the best prediction of the environment of a greenhouse located in the central region of Mexico. Simulation and analysis of the climate model show that the estimations of the inside temperature and RH are closest to the measurements when EP was used to calibrate the parameters of the greenhouse model.

Among the main problems that limit production and productivity of husk tomato are the availability and high cost of irrigation water, as well as inefficient use of this resource. Current production systems are not efficient due to, among other reasons, the large volumes of water supplied. The objectives of this study were to assess the effect of different irrigation water treatments and plastic mulching on husk tomato crop production in a fertigation system and also to optimize the irrigation water in order to improve crop productivity. The experiment consisted of a complete randomized design with three replicates, with a 5 x 2 factorial treatment design consisting of five levels of irrigation: 40, 60, 80, 100 and 120 % of reference evapotranspiration calculated using the Penman-Monteith equation, and two levels of plastic mulching, with and without. The results indicate that there were significant differences between mulching and irrigation levels for fruit yield and water productivity. Using plastic mulching increased fruit yield by 56.6 %, and irrigation water efficiency by 57.3 %; water use was reduced 60 % by calculating the required water volume with the Penman-Monteith equation.

Because of the rapid growth of the greenhouse industry in Mexico and the increasing need for relevant information about structural design and management, the internal microclimate of a naturally ventilated greenhouse in central Mexico was investigated. In order to quantify the ventilation rates resulting from inside and outside environmental conditions, tracer gas (TG) experiments were carried out. Properties of an insect screen were determined by a series of wind tunnel experiments. The same experimental conditions were then used to calculate the air exchange rate by means of computational fluid dynamics (CFD). The results showed a significant correlation between ventilation rate and wind speed (R2 = 0.7079). For instances when only roof vents are kept open, high wind speeds (>4.5 m s-1) are required to provide a recommended air exchange rate of one greenhouse volume per minute (6.64 × 10-2 m3 m-2 s-1). The correlation between CFD-simulated and TG-measured ventilation rates was 0.7065 at wind speeds higher than 2 m s-1.

El objetivo de este escrito es presentar una revisión del estado actual del conocimiento sobre modelos matemáticos (modelos explicativos o mecanicistas) de hortalizas cultivadas bajo ambiente invernadero. Se describen las características más importantes de los modelos propuestos hasta la fecha en la literatura, para los cultivos de jitomate, pepino y lechuga. Se discuten las aplicaciones más relevantes de los modelos de hortalizas cultivadas en ambientes controlados. Además se analizan aquellos aspectos que están pendientes por considerar por los modeladores de hortalizas cultivadas en invernadero

Las temperaturas diurnas del aire durante los meses de verano en los invernaderos de nivel tecnológico bajo y medio en México son mayores a las óptimas de los cultivos y se necesita de enfriamiento para evitar estrés del cultivo debido al exceso de calor. Aunque la ventilación natural es el principal mecanismo para controlar el clima en la mayoría de estos invernaderos, la información sobre su desempeño es escasa. El objetivo del presente estudio fue determinar, mediante el método de balance de energía, las tasas de ventilación natural de un invernadero tipo sierra equipado con malla anti-insectos en las ventanas. Además, un modelo matemático teórico de tasas de ventilación en función de la velocidad de viento exterior fue ajustado a datos experimentales. El invernadero está ubicado en el campo experimental de la Universidad Autónoma Chapingo, en Chapingo, México, y se estudió con un cultivo de melón (Cucumis melo L.) bajo un sistema hidropónico y sin cultivo. Los parámetros del modelo de ventilación se estimaron con el algoritmo de mínimos cuadrados no lineales. Los resultados mostraron que las tasas de ventilación fueron más altas cuando las velocidades del viento fueron mayores y sin cultivo. Las mallas instaladas en las ventanas causan una reducción de 48 % en las tasas de ventilación. El modelo teórico de ventilación predijo de manera aceptable las tasas de ventilación de acuerdo con los índices estadísticos de coeficiente de determinación y raíz cuadrada del error cuadrático medio.

The most extended control in the greenhouse industry across the world is the classical Proportional-Integral-Derivative control (PID).