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 $\left(PTI\right)$, daily dry matter production $\left(DMP\right),$ nitrogen uptake $\left(Nup\right)$, leaf area index $\left(LAI\right)$, and crop transpiration $(E{T}_c)$. 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 ($RUE$, $a$, $b$,$c_{1 }$,$c_2$,$A$, $B_d$, $B_n$, 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 $E{T}_c$ for both crop seasons; the $RMSE$ was close to zero, indicating a good prediction of the model’s performance.

Modernizing the drying process will reduce agricultural product waste and environmental pollution. The aim of this study was to design a monitoring system based on the internet of things (IoT), temperature and relative humidity for a solar dryer. This system consists of a data collection module that gathers data regarding temperature (Ta), external relative humidity (RH) and on/off time of the solar dryer exhaust fans; a communication module that transmits Ta and RH information via LoRa and Wifi to ThingSpeak for monitoring on a mobile device; and a power module providing electrical power for system operation by solar energy. The operation of the IoT monitoring system was evaluated in three drying experiments of Dominican bananas (Musa paradisiaca var sapientum), in which system performance was satisfactory, allowing the user to visualize graphically in a web and mobile interface the behavior of Ta and RH inside the dryer. The data measured by the system were used to predict banana moisture content with an autoregressive model with exogenous variables (ARX) identified online. The mathematical model found predicted the behavior of moisture content with a good goodness of fit, with values of R2 = 0.99, MSE = 1.2910-5 and MAE = -5.0310-6. The solar dryer allowed reducing the moisture content of bananas up to 19.84 % wet basis (w.b.) in a period of 4 days and by 20.03% w.b. for 5 days in the presence of rainfall.

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.

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.

El monitoreo continuo de variables ambientales y fisicoquímicas es esencial para optimizar el rendimiento productivo y la estabilidad operativa de los sistemas acuapónicos, particularmente en esquemas de producción intensiva. El objetivo de este estudio fue desarrollar y evaluar un sistema de monitoreo basado en tecnologías IoT para la medición en tiempo real de variables críticas en un sistema acuapónico de producción de lechuga (Lactuca sativa) en balsas flotantes. Para ello, se diseñó e implementó un sistema autónomo de adquisición de datos que integra sensores ambientales y de calidad del agua, empleando comunicación inalámbrica LoRa mediante módulos TTGO LoRa, con transmisión y visualización de la información en la plataforma ThingSpeak. El sistema fue evaluado durante 105 días, correspondientes a cuatro ciclos de producción, con una frecuencia de muestreo de 10 minutos, registrándose 15,120 mediciones por variable. Los resultados evidenciaron una operación estable del sistema, permitiendo identificar rangos adecuados de temperatura, conductividad eléctrica, pH, oxígeno disuelto y radiación fotosintéticamente activa, coherentes con los requerimientos fisiológicos del cultivo y de la tilapia. Asimismo, la arquitectura mostró confiabilidad en la transmisión de datos y respaldo mediante almacenamiento local, garantizando la integridad de la información ante posibles fallas de conectividad. Sistema viable y replicable ampliamente.