Vegetables cultivated in cities, carry the risk of absorbing pollutants generated by anthropogenic activities, increasing the health risk of those who consume them. Heavy metal concentrations (As, Cd, Cr, Ni and Pb) were analyzed in water, soil and in plant parts of lettuce (roots and leaves) and cherry tomatoes (roots, stem, leaves and tomato fruit) lettuce and cherry tomatoes, cultivated in urban gardens in Mexico City. In none of the studied sites was the heavy metal concentration in the soil above the permitted limits. In irrigation water, Cr surpassed the permitted limit in 5 sites and that of Cd in one site. Ni surpassed the established limit for lettuce and cherry tomato in the rank among 0.7 and 31.15 mg kg-1 in 9 sites, Cr exceeded the allowed limits 1.45-38 mg kg-1 in 11 sites, As go beyond the established limits between 5-52 mg kg-1 in 4 sites and to a lesser extent, Pb was above the allowed limits between 15.5 and 26 mg kg-1. According to the translocation factor, lettuce and cherry tomato could be an important phytoextractors of Cr and Cd respectively. The compound risk value for lettuce (TTHQ) was greater than one in three sites for lettuce and five sites for cherry tomato. Also, in gardens A, D, E and J, the Hazard Index (HI) showed a potential risk (HI>1), which reveals the danger of consuming both vegetables given the conditions of soil, water and air and taking into account all paths of exposure.

La horticultura urbana y periurbana (HUP) presenta grandes desafíos, como la poca disponibilidad en el suministro del agua y de tierra. No existen datos sobre la cantidad de agua que se utiliza para la producción de hortalizas en la Ciudad de México (CDMX). Por lo tanto, el objetivo del presente estudio fue la estimación de los rendimientos y la productividad del agua para dos hortalizas representativas en jardines urbanos: lechuga romana y tomate cherry en 13 huertos urbanos de la CDMX. Asimismo, se estimó el volumen de agua de lluvia que se puede almacenar en cada huerto, utilizando la estación meteorológica más cercana. El rendimiento y la eficiencia del uso del agua para la producción de lechuga oscilaron entre 0.10 y 1.20 kg m-2, así como de 0.21 a 2.93 kg m-3 de agua, respectivamente. Estos indicadores, para el caso de tomate cherry, estuvieron entre 0.25 y 3.40 kg m-2 y entre 0.32 y 5.52 kg m-3 de agua, respectivamente. El riego en los huertos se hace de manera empírica y utilizando agua potable; el exceso en el suministro de agua puede ir de 0.27 a 0.4 m3 m-2 para un ciclo completo de lechuga y tomate cherry, respectivamente. Utilizando una probabilidad de excedencia del 80 %, se estimó la captación de agua de lluvia para todo el año, generando un almacenamiento acumulado entre 0.261 y 0.5215 m3 m-2 en los huertos urbanos. Esta cantidad de agua podría suplir los requerimientos hídricos de lechuga (de 0.128 a 0.389 m3 m-2) o tomate cherry (de 0.145 a 0.569 m3 m-2) para un ciclo completo.

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop.

La evapotranspiración de referencia (ETo) es una variable hidrológica de gran importancia en el manejo del riego. Su estimación se realiza con la ecuación de Penman-Montieth (PM), que requiere de muchas variables meteorológicas, las cuales, a veces, no se encuentran disponibles. Dado que la ETo es una variable no lineal y compleja, en los últimos años han surgido métodos alternativos para su estimación, como las redes neuronales artificiales (RNA). El objetivo del presente trabajo fue estimar la evapotranspiración de referencia (ETo) usando la ecuación de Penman-Montieth, a fin de desarrollar modelos de redes neuronales artificiales (RNA) que permitan predecir la ETo en regiones con información climatológica limitada, y su vez comparar el desempeño de tres modelos de RNA: FFNN, ERNN y NARX. Se utilizó información diaria durante el periodo 1 de enero de 2007 al 31 de diciembre de 2018, de las estaciones meteorológicas ENP8 y ENP4 de la CDMX. Se realizó un análisis de correlación y el análisis de sensibilidad de Garson para estudiar 2 casos (red estática FFNN y redes dinámicas: ERNN y NARX) usando 3 modelos de RNA: 1) RNA con 6 entradas: radiación solar (Rad), temperatura máxima y mínima (Tmax, Tmin), humedad relativa máxima y mínima (HRmax, HRmin) y velocidad del viento (u); y 2) RNA con 2 entradas (Rad y Tmax). La variable de salida fue la ETo calculada con la ecuación de PM. En todos los casos, las 3 RNA fueron muy parecidas, la diferencia más notable es que las redes dinámicas (ERNN y NARX) requieren de menor número de iteraciones para llegar al desempeño óptimo. Las RNA entrenadas, únicamente con Rad y Tmax como entradas, fueron capaces de predecir la ETo en el largo plazo, durante 440 d, en otra estación meteorológica cercana (ENP4), con eficiencias mayores al 90 %.

The Crop Environment Resource Synthesis-Maize (CERES-Maize) mechanistic model, included in the Decision Support System for Agrotechnology Transfer (DSSAT), is a useful and powerful tool that simulates the growth and yield of maize in different environments. The qualitative and quantitative information provided to the CERES-Maize model guarantees reliability in the simulations obtained. However, it requires a lot of information such as soil characteristics, daily climate, crop characteristics and management, as well as six genetic coefficients. This research assessed a non-destructive methodology for estimating the six required parameters (genetic coefficients): P1, P2, P5, G2, G3 and PHINT, based on the maize physiology, measured from the Growing Degree Days (GDD) base 10. An experiment was established at the experimental station of the International Maize and Wheat Improvement Center (CIMMYT) in Tlaltizapan, Morelos, Mexico, where 27 white maize hybrids and 14 yellow maize hybrids were manually sown in an irrigation conservation tillage system. Once the simulations of maize growth and yield were obtained with CERES-Maize model, the genetic coefficients were calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE). After calibration of the six genetic coefficients for all hybrids, an average values of P1, G2 and G3 were within the typical range, while P2 and P5 were greater than the typical range, and PHINT was below typical range. However, the simulation model showed good performance after calibration, according to the average R2 of 0.9809 and 0.9730 between measured and simulated yields for white and yellow hybrids respectively. The coefficients estimated in this study can be used in the CERES-Maize model to simulate maize yields in different regions of the country for the hybrids used here.

Bean production is at risk due to climate change, declining water resources, and inadequate crop management. To address these challenges, dynamic models that predict crop growth and development can be used as fundamental tools to generate basic and applied knowledge such as production management and decision support. This study aimed to calibrate and evaluate the SIMPLE model under irrigation conditions for a semi-arid region in north-central Mexico and to simulate thermal time, biomass (Bio), and grain yield (GY) of common beans cv. ‘Pinto Saltillo’ using experimental data from four crop evapotranspiration treatments (ETct) (I₅₀, I₇₅, I₁₀₀, and I₁₂₅) applied during the 2020 and 2021 growing seasons. Both experiments were conducted in a randomized complete block design with three replicates. Model calibration was carried out by posing and solving an optimization problem with the differential-evolution algorithm with 2020 experimental data, while the evaluation was performed with 2021 experimental data. For Bio, calibration values had a root-mean-square error and Nash and Sutcliffe’s efficiency of 0.93, respectively, while the corresponding evaluation values were 0.89, respectively. The I₅₀ and I₁₀₀ ETct had better fit for calibration, while I₅₀ and I₇₅ had better fit in the evaluation. On average, the model fitted for the predicted GY values had estimation errors of 37% and 22% for the calibration and evaluation procedures, respectively. Therefore, an empirical model was proposed to estimate the harvest index (HI), which produced, on average, a relative error of 6.9% for the bean-GY estimation. The SIMPLE model was able to predict bean biomass under irrigated conditions for these semi-arid regions of Mexico. Also, the use of both crop Bio and transpiration simulated by the SIMPLE model to calculate the HI significantly improved GY prediction under ETct. However, the harvest index needs to be validated under other irrigation levels and field experiments in different locations to strengthen the proposed model and design different GY scenarios under water restrictions for irrigation due to climate change.