Wetlands are transitional lands between terrestrial and aquatic systems that provide various ecosystem services. The objective of this study was to evaluate the change in wetlands in the Chimborazo Wildlife Reserve (CR) in the period 1986–2022 using geographic information systems (GISs), multitemporal satellite data, and field data from the 16 wetlands of the reserve. Images from Landsat satellite collections (five from Thematic Mapper, seven from Enhanced Thematic Mapper, and eight from Operational Land Imager and Thermal Infrared Sensor) were used. Image analysis and processing was performed, and the resulting maps were evaluated in a GIS environment to determine the land cover change and growth rate of hydrophilic opportunistic vegetation (HOV) according to hillside orientation. The results show that there are negative annual anomalies in the water-covered areas, which coincide with the increase in HOV. This shows that the constancy or increase in the rate of increase in HOV, which varies between 0.0018 and 0.0028, causes the disappearance of these ecosystems. The importance of the study lies in its potential contribution to the decision-making process in the management of the CR.

This paper analyses Land Use and Land Cover (LULC) change trends in the Llanganates-Sangay Connectivity Corridor (CELS) from 2018 to 2022 and predicts trends through 2030. MapBiomas LULC maps reveals annual change rates (2018–2022) of -0.37 %/year (-1147.33 ha) for Forest Formation, -1.17 %/year (-30.01 ha) for Non-Forest Natural Formation, 2.21 %/year (906.19 ha) for Agriculture and Livestock Areas, 8.50 %/year (250.84 ha) for Non-Vegetated Areas, and 0.17 %/year (30.31 ha) for Water Bodies. The higher annual change rate inside Forest Formation is -0.58 %/year (-990.35 ha) occurring in areas not designated under any conservation status. Projections for 2030 were made using the MOLUSCE tool, combining an Artificial Neural Network (ANN) model with Cellular Automata simulations. The ANN model was trained on five explanatory variables and LULC maps from 2018 and 2020, achieving a training error of 8.46 %. Predictive accuracy was assessed by comparing the simulated 2022 LULC map with the 2022 MapBiomas map, resulting in a Kappa coefficient of 0.95, indicating excellent predictive accuracy. Additionally, LULC simulations from 2022 to 2030 predict annual rates of change of –0.27 %/year (-1628.97 ha) for Forest Formation, -1.39 %/year (-63.49 ha) for Non-Forest Natural Formation, 1.92 %/year (1778.26 ha) for Agriculture and Livestock Areas, 0.97 %/year (30.38 ha) for Non-Vegetated Areas, and 0.63 %/year (-146.18 ha) for Water Bodies. The findings show that annual rates of deforestation will remain low and protected areas will have less deforestation than nonprotected areas.

This paper analyses Land Use and Land Cover (LULC) change trends in the Llanganates-Sangay Connectivity Corridor (CELS) from 2018 to 2022 and predicts trends through 2030. MapBiomas LULC maps reveals annual change rates (2018–2022) of -0.37 %/year (-1147.33 ha) for Forest Formation, -1.17 %/year (-30.01 ha) for Non-Forest Natural Formation, 2.21 %/year (906.19 ha) for Agriculture and Livestock Areas, 8.50 %/year (250.84 ha) for Non-Vegetated Areas, and 0.17 %/year (30.31 ha) for Water Bodies. The higher annual change rate inside Forest Formation is -0.58 %/year (-990.35 ha) occurring in areas not designated under any conservation status. Projections for 2030 were made using the MOLUSCE tool, combining an Artificial Neural Network (ANN) model with Cellular Automata simulations. The ANN model was trained on five explanatory variables and LULC maps from 2018 and 2020, achieving a training error of 8.46 %. Predictive accuracy was assessed by comparing the simulated 2022 LULC map with the 2022 MapBiomas map, resulting in a Kappa coefficient of 0.95, indicating excellent predictive accuracy. Additionally, LULC simulations from 2022 to 2030 predict annual rates of change of –0.27 %/year (-1628.97 ha) for Forest Formation, -1.39 %/year (-63.49 ha) for Non-Forest Natural Formation, 1.92 %/year (1778.26 ha) for Agriculture and Livestock Areas, 0.97 %/year (30.38 ha) for Non-Vegetated Areas, and 0.63 %/year (-146.18 ha) for Water Bodies. The findings show that annual rates of deforestation will remain low and protected areas will have less deforestation than nonprotected areas.

El objetivo del estudio fue generar superficies de interpolación de la variable temperatura en la provincia de Chimborazo en el periodo 2010-2020. Para la interpolación se trabajó con métodos geoestadísticos recopilando información de tres fuentes de datos, obteniendo información de 30 estaciones meteorológicas de diferentes años. Con su ubicación se descargó los valores de la temperatura en la base de datos TerraClimate, se evaluó la correlación de los valores observados y las estimaciones satelitales (ESat) utilizando los coeficientes de correlación de Pearson y Nash Sutcliffe (NSE), con un buen coeficiente de correlación se rellenó los datos faltantes y se obtuvo más puntos obteniendo un total de 201. El método de interpolación empleado fue Kriging Ordinario (KO) para cada uno de los años. En la evaluación se obtuvieron valores de R de 0,96 y 0,881, demostrando que entre los valores observados y ESat existe una alta correlación, se tuvo que la temperatura aumentando a través de los periodos llegando hasta 24,63 °C en el cantón Cumandá en el periodo 2018-2020, mientras que la temperatura baja fue de (3,25 – 5,39 °C) en el periodo 2010-2013 en los cantones Guano y Riobamba. Los datos de la variable son importantes y la existencia de estaciones meteorológicas, sin embargo, se puede hacer uso de ESat que son proporcionadas por modelos climáticos globales.

El texto de este artículo presenta las reflexiones acerca de los rasgos teóricos y prácticos que involucran la sustentabilidad social en el marco de la agroecología, con una referencia empírica a la realidad de las familias de productores que se dedican al cultivo del tomate riñón, en el Cantón Riobamba, Ecuador. Se parte de la importancia que han adquirido las buenas prácticas agrícolas, como necesidad para la preservación de un ambiente sano y la calidad de vida en zonas de productores agrícolas. Así, la producción del tomate riñón y otras hortalizas son básicas para la industria agrícola ecuatoriana, por lo cual, debe corresponderse con un proceso de producción socialmente responsable. Metodológicamente, el artículo es producto de una investigación mixta que contempla, por un lado, el análisis de la información especializada sobre el tema, encontrada en bases de datos reconocidas, y la aplicación de un cuestionario sobre la sustentabilidad social en la producción del tomate riñón a un grupo de agricultores dedicados a este rubro en el Cantón Riobamba. El estudio concluye certificando la necesidad de socializar la importancia de la sustentabilidad con indicadores claves como: prácticas laborales justas, participación comunitaria y responsabilidad socio ambiental.

Soil degradation in high-altitude livestock systems—driven by acidification, compaction, low water retention and nutrient loss—reduces forage productivity and limits the sustainability of grazing-based production. These constraints highlight the need for spatial tools capable of prioritising soil interventions and guiding more efficient land management. The objective of this study was to develop a spatial analysis model to identify and rank soil management priorities in a high-altitude livestock farm. A total of 441 georeferenced observations were collected using portable sensors to measure pH, electrical conductivity, water retention capacity and soil compaction. The data were processed through GIS interpolation, cartographic overlay and reclassification techniques to assign intervention levels across the landscape. The results indicated that 70% of the area presented moderately acidic soils, 32% required improvements in water retention, and 67% exhibited moderate compaction. The proposed model is replicable, operationally simple and suitable for site-specific decision-making. Overall, this study provides a technical tool that supports extension programmes, territorial planning and sustainable livestock management.