We analyzed the relationship between nutrient (N and K) parceling and population density on the severity of onion downy mildew under no-tillage fertigation cultivation in the conditions of Alto Vale do Itajaí (Barzil). For this purpose, field trials were conducted in the years 2017, 2018 and 2019, in Ituporanga (Barzil). The treatments corresponded to four population densities (300, 400, 500 and 600 mil plants ha^-1) subjected to applications of nitrogen (150 kg N ha^-1) and potassium (127.5 kg K₂O ha^-1) distributed throughout the vegetative cycle of the crop via fertigation on a weekly, biweekly and monthly basis, based on the absorption curve of these nutrients for the cultivar Empasc 352-Bola Precoce. In fertigated no-tillage systems, nutrient (N and K) tranches do not influence the severity of downy mildew. The severity of downy mildew increases linearly with increasing population density, especially from 500 mil plants per ha^-1.

Energy systems face serious difficulties due to economic policy uncertainty, which affects consumption trends and makes the shift to sustainability more difficult. While adjusting for economic growth and carbon emissions, this study examines the dynamic relationship between economic policy uncertainty and energy consumption (including renewable and nonrenewable) in China from 1985Q1 to 2023Q4. The research reveals the frequency-specific and time-varying relationships between these variables by employing sophisticated techniques such as Wavelet Cross-Quantile Correlation (WCQC) and Partial WCQC (PWCQC). Economic policy uncertainty and energy consumption do not significantly correlate in the short term; however, over the long term, economic policy uncertainty positively correlates with renewable energy consumption at medium-to-upper quantiles, indicating that it may play a role in encouraging investments in sustainable energy. On the other hand, EPU has a negative correlation with nonrenewable energy usage at lower quantiles, indicating a slow move away from fossil fuels. These results are confirmed by robustness testing with Spearman-based WCQC techniques. The study ends with policy recommendations to maximize economic policy uncertainty’s long-term impacts on renewable energy, reduce dependency on fossil fuels, and attain environmental and energy sustainability in China.

This study investigates the impact of extreme rainfall events on soil erosion in the downstream Parnaíba River Basin, located in the Brazilian Cerrado. The analysis focused on rainfall erosivity (R factor) and soil erodibility (K factor) as key indicators. The average erosivity in the region was 9051 MJ mm h⁻¹ha⁻¹year⁻¹, with a variation between 7943 and 10,081 MJ mm h⁻¹ha⁻¹year⁻¹, suggesting a high erosive potential, mainly in the rainiest months, from December to April. The soils of the studied area, mainly Ultisols and Chernosols, present high to very high erodibility, with K factor values ranging from 0.025 to 0.050 t h MJ⁻¹ mm⁻¹. Furthermore, fieldwork revealed areas, near highways, with apparently fragile soils, as well as rills and gullies, identified through photographs taken during fieldwork. These locations, due to the combination of high erosivity and susceptible soils, were considered prone to the occurrence of erosion processes, representing an additional risk to local infrastructure. The spatialization of R and K factors, along with field observations, showed that much of the area is at high risk of erosion and landslides, particularly in regions with greater topographic variability and proximity to water bodies. These results provide a basis for the development of mitigation strategies, being important for the effective prevention of landslides.

Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model’s insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy’s specific needs and challenges.

India’s economic growth is of significant interest due to its expanding Gross Domestic Product (GDP) and global market influence. This study investigates the interplay between production, trade, carbon dioxide (CO₂) emissions, and economic growth in India using Granger causality analysis. Also, the data from 1994 to 2023 were analyzed to explore the relationships among these variables. The results reveal strong positive correlations among production, trade, CO₂ emissions, and GDP, with production showing significant associations with export, import, and GDP. Co-integration tests confirm the presence of a long-term relationship among the variables, suggesting their interconnectedness in shaping India’s economic landscape. Regression analysis indicates that production, export, import, United States (US)-India trade, manufacturing cost of energy, and CO₂ emissions significantly impact GDP. Moreover, the Vector Error Correction Model (VECM) estimation reveals both short-term and long-term dynamics, highlighting the importance of understanding equilibrium and deviations in economic variables. Overall, this study contributes to a better understanding of the complex interactions driving India’s economic growth and sustainability.