Favorable soil conditions are essential to the proper growth and production of any crop worldwide. Only 9 % of the Ecuadorian Amazon Region (EAR) territory is suitable for agriculture and/or livestock farming, due to limitations imposed by physio-edaphoclimatic factors. This study evaluated the soil potential suitability for coffee, cocoa and tropical pastures to determine optimal, moderate and marginal areas in three provinces in the EAR (Orellana, Sucumbios and Pastaza) under different agroecological conditions. On-site field work was carried out to analyze the soil profile to determine the mineral composition. Eight environmental variables were evaluated through the integration of GIS-based modeling to determine the agroecological zoning of the crops. The results showed that the main determining factors for optimal areas of soil suitability within the three provinces included soil depth (30 cm), organic matter content (5.3 %), concentration of macronutrients (2,700 mg kg^–1), and slope (< 10 %), whereas annual temperature (18-26 °C), annual precipitation (1200-3000 mm), altitude (0-500 m), and pH (4.38) were included for moderate and marginal areas. Depending on the crop and the province, soil suitability was classified into two or three classes (optimal, moderate, and marginal areas), relying on the agroecological conditions specific to each crop. This study highlights that all crops showed mostly optimal soil suitability for proper development under the agroecological conditions analyzed, which contributes to the provision of valuable information to decision-makers about key limiting factors, land-use planning and mapping soil suitability when considering the best location for a specific crop.

Desde el año 2018 en Ecuador se observa un crecimiento exponencial en el cometimiento del Homicidio Intencional (HI), por el accionar de Grupos Criminales Organizados (GDO), lo que ha situado al país entre los más violentos de la región. Por eso, el presente estudio que estima un modelo de aprendizaje automático basado en bosque aleatorio, permite predecir el cometimiento de este tipo de violencia en base a otros tipos penales. Esto permitirá a instituciones del sector justicia intervenir de forma óptima y sustentada en información con el objetivo de reducir la criminalidad y garantizar niveles adecuados de seguridad y convivencia ciudadana.

Trajectory tracking remains one of the main challenges in mobile robotics, particularly when robots operate under real-world disturbances and modeling uncertainties. Model Predictive Control (MPC) has become one of the most effective solutions for this problem because of its ability to optimize future control actions while explicitly handling system constraints. However, despite the variety of predictive models reported in the literature, there is still limited experimental evidence regarding how predictive-model selection influences the closed-loop behavior of mobile robots. This work presents an experimental comparison between two MPC implementations: a Kinematic MPC and a multiple-input multiple-output MPC (MIMO-MPC) based on an experimentally identified state-space model. Both controllers were implemented on the same four-wheel differential-drive mobile robot running Robot Operating System 2 (ROS 2) and were configured with identical prediction horizons, weighting matrices, constraints, reference trajectories, and disturbance sequences, enabling a direct experimental comparison of the influence of predictive-model selection on closed-loop performance. Performance was assessed through trajectory-tracking accuracy, disturbance recovery, and control smoothness metrics under both nominal and externally perturbed operating conditions. The experimental evaluation was performed on real hardware at 50 Hz using a closed-loop trajectory of approximately 20 m. Under nominal conditions, the kinematic MPC achieved a lower lateral root mean square error (RMSE) (0.1031 m versus 0.1187 m) and maintained 93.23% of the trajectory within the $\pm 0.20$ m tolerance band. Under external perturbations, however, the MIMO-MPC reduced the heading RMSE from 0.7617 rad to 0.6503 rad, shortened the recovery time after the two largest disturbances by up to 35.6%, and decreased the angular-velocity rate root mean square (RateRMS) and jerk root mean square (JerkRMS) by approximately 24.7% and 24.8%, respectively. These results demonstrate that predictive-model selection has a significant influence on the transient behavior of MPC. While a conventional kinematic model provides excellent nominal tracking performance, an experimentally identified MIMO predictive model offers faster recovery and smoother control under disturbed conditions. The proposed experimental methodology provides practical evidence that can assist the selection of predictive models for MPC-based mobile robots according to the expected operating conditions.