A problem in post-harvest of avocado (Persea americana Mill.) is the heterogeneity in fruit ripening, due to differences in the time of fruit set and the inability to ripen on the tree, a situation that causes inconsistencies in quality and differences in the response to preservation and processing technologies. In postharvest, the application of ethylene gas in hermetic chambers has been used to advance ripening; however, the use of ethylene releasers in liquid form (ethephon) has been proposed as an alternative, mainly for the treatment of low volumes of fruit. The present work was carried out in the production zone of Salvador Escalante (Michoacán, Mexico) with the objective of evaluating the effect of the application of two concentrations of ethephon on the time and homogenization of fruit ripening of avocado cultivars (cv.) Hass and Méndez. Fruits with 23.4% (cv. Hass) and 24% (cv. Méndez) of dry matter were harvested; one group was immersed in a solution of ethephon 500 mg/L and the other in 1,000 mg/L, both for 5 minutes; the treated fruits plus a control were stored at 20 °C for 11 days. Changes in respiration, ethylene production, weight loss, firmness, epicarp and pulp color, total phenol, chlorophyll and total carotenoid concentrations were evaluated. The results showed that ethephon doses of 1,000 mg/L in cv. Hass and 500 mg/L in cv. Méndez presented a ripening process 2 days earlier than the control.

This study explores the feminization of poverty and the dynamics of the care economy in rural areas, focusing on the municipality of Génova, Quindío, Colombia. The novelty of this study lies in its analysis of the compounded effects of the COVID-19 pandemic on women’s economic participation and care responsibilities in a rural context, offering insights relevant to Latin America. This study addresses the critical problem of how increased caregiving responsibilities and labor informality during the pandemic have disproportionately impacted economically active women, exacerbating gender inequalities. The objective is to analyze the relationship between the care economy and feminization of poverty, providing policy recommendations for post-pandemic recovery in rural settings. The methodology consisted of a two-stage approach. In the first stage, a probabilistic stratified sampling design was applied using data from the Colombian National Population and Housing Census and the Génova, Quindío, and Colombia Municipal Panel. In the second stage, fieldwork was conducted with a sample of 347 women using the RedCap application for data collection. The results indicate a significant increase in unpaid domestic and caregiving work during the pandemic, particularly for the elderly, disabled, and children. Additionally, labor informality increased, further limiting economic opportunities for women. The key conclusion is that public policies aimed at reducing gender disparities in rural labor markets must prioritize caregiving support and formal employment opportunities for women. These findings suggest that addressing the care economy is crucial for closing gender gaps and fostering equitable economic recovery in rural Latin American areas.

This study analyzes the role of innovation in the development of smart cities in Latin America. It focuses on how emerging technologies and sustainable strategies are being integrated into urban planning and urban development. In this sense, this study seeks to contribute to the smart city literature by answering the following research questions: (i) To what extent smart city innovative initiatives have been addressed in Latin America? and (ii) To what extent scholars have addressed sustainable innovation strategies in the smart city literature? To this end, this is the first comprehensive bibliometric analysis of smart city research in Latin America, with a structured and systematized review of the available literature. This methodological approach allows cluster visualization and detailed analysis of inter-node relationships using the VOSViewer software. The research comprises 4 stages: (a) search criteria; (b) selection of documents; (c) software and data extraction; and (d) analysis of results and trends. Results indicate that studies on the Latin America region began to develop in 2012, with Brazil as a leader in this field and the tourism sector as the most relevant. Nevertheless, strong international collaboration was identified in co-authoring studies, underscoring a cooperative approach to solving common urban problems. The most active research area is technological innovation and sustainability, with focus on solutions for urban mobility, quality of life and smart governance. Finally, this work underlines the need to continue exploring the integration of technology in urban development, suggesting an agenda to guide future research to evaluate the sustainability and long-term impacts of smart city initiatives in Latin America. From the policy perspective, smart city initiatives need to be human-centered to boost smart solutions adoption and to guarantee long term local impacts.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.