The study examines the factors shaping inflation in 2022–2023 and explores why inflation in the Hungarian economy has increased more sharply than in neighboring countries with similar structures. The research hypothesis suggests that the inflationary surge, which is notable both globally and within the European Union, is not solely due to market economy mechanisms, but also to specific circumstances in Hungary, including the state’s radical interventions aimed at curbing inflation. The study seeks to highlight these effects and provide recommendations for economic policymakers to develop a more resilient inflation policy. Additionally, it focuses on analyzing inflation in the agricultural sector. The results indicate that, alongside global inflationary pressures, several country-specific factors have driven up the inflation rate in Hungary. Energy prices have risen sharply, and some supply chains from the East have been disrupted. The country under study is less productive, and the impact of the energy price shock on the energy-intensive food industry is higher than in surrounding countries. Consequently, the exchange rate volatility in 2022–2023, combined with short- and medium-term factors, has had a significant impact on food inflation, causing substantial deviations from long-term equilibrium. The research concludes that, in addition to increasing food self-sufficiency, special attention should be given to the domestic development of the agricultural supply chain.

Global warming is a problem that affects humanity; hence, crisis management in the face of natural events is necessary. The aim of the research was to analyze the passage of Hurricane Otis through Acapulco from the theoretical perspective of crisis management, to understand the socio-environmental, economic, and decision-making challenges. For data collection, content analysis and hemerographic review proved useful, complemented by theoretical contrastation. Findings revealed failures in communication by various government actors; the unprecedented growth of Hurricane Otis led to a flawed crisis management. Among the physical, economic, environmental, and social impacts, the latter stands out due to the humanitarian crisis overflow. It is the first time that Acapulco, despite having a tradition in risk management against hydrometeorological events, faces a hurricane of magnitude five on the Saffir-Simpson scale. Ultimately, the city was unprepared to face a category five hydrometeorological event; institutional responses were overwhelmed by the complexity of the crisis, and the community came together to improve its environment and make it habitable again.

Alfalfa is considered the most used forage crop in the world, its main use is for cattle feeding, due to its high nutritional value, specifically in protein and digestible fiber. Currently, the trend in agriculture is to reduce the application of chemicals and among them are fertilizers that pollute soil and water, so the adoption of new technologies and other not so new is becoming a good habit among farmers. Nanotechnology in the plant system allows the development of new fertilizers to improve agricultural productivity and the release of mineral nutrients in nanoforms, which has a wide variety of benefits, including the timing and direct release of nutrients, as well as synchronizing or specifying the environmental response. Biofertilizers are important components of integrated nutrient management and play a key role in soil productivity and sustainability. While protecting the environment, they are a cost-effective, environmentally friendly and renewable source of plant nutrients to supplement chemical fertilizers in the sustainable agricultural system. Nanotechnology and biofertilization allow in a practical way the reduction in the application of chemicals, contributing to the sustainability of agriculture, so this work aims to review the relevant results on biofertilization, the use of nanotechnology and the evaluation of the nutritional composition of alfalfa when grown with the application of biofertilizers.

The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day⁻¹ are generated in 2016, 4.37 Tn/day⁻¹ in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m⁻³ in 2016 and 172.69 kg/m⁻³ in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

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.