The wide distribution of the common beech (Fagus sylvatica) in Europe reveals its great adaptation to diverse conditions of temperature and humidity. This interesting aspect explains the context of the main objective of this work: to carry out a dendroclimatic analysis of the species Fagus sylvatica in the Polaciones valley (Cantabria), an area of transition with environmental conditions from a characteristic Atlantic type to more Mediterranean, at the southern limit of its growth. The methodology developed is based on the analysis of 25 local chronologies of growth rings sampled at different altitudes along the valley, generating a reference chronology for the study area. Subsequently, the patterns of growth and response to climatic variations are estimated through the response and correlation function, and the most significant monthly variables in the annual growth of the species are obtained. Finally, these are introduced into a Geographic Information System (GIS) where they are cartographically modeled in the altitudinal gradient through multivariate analysis, taking into account the different geographic and topographic variables that influence the zonal variability of the species response. The results of the analyses and cartographic models show which variables are most determinant in the annual growth of the species and the distribution of its climatic response according to the variables considered.

A total of 25 SSR primers were screened on 37 putative F₁s derived from the five different crosses. Identified cross specific highly informative SSRs primers, i.e., 14 for the first cross, 10 for the second, 12 for the third and 6 each for fourth and fifth crosses. For the first cross Bhagwa × Daru 17, four primers (HvSSRT_375, NRCP_SSR9, NRCP_SSR12 and NRCP_SSR92) were found to be highly informative with higher 100% hybrid purity index (HPI), PIC (~0.52), and observed heterozygosity (Ho, range 0.87–0.93) values, and two F₁s namely H₁ and H₂ were found to be highly heterotic with a heterozygosity index (HI) of 92.85%. Similarly, for Bhagwa × Nana, three primers (HvSSRT_375, HvSSRT_605 and NRCP_SSR19) had higher HPI (70%–100%), PIC (0.52–0.69), and Ho (0.75–0.33) values, and three F₁s H₁, H₂, and H₄ had 70% (HI). For Bhagwa × IC318712, four SSRs (HvSSRT_254, HvSSRT_348, HvSSRT_826 and NRCP_SSR95) had higher Ho (~0.83), HPI (100%) and PIC (~0.52) values, and four F₁s H₂, H₇, H₉, and H₁₀ showed 91.66% (HI). For Bhagwa × Nayana, HvSSRT_605, HvSSRT_826, and HvSSRT_432, and for Ganesh × Nayana, HVSSRT_375, HVSSRT_605, and HvSSRT_826 were found informative. These markers will be highly useful in developing maps of populations.

This study investigates the role of agricultural exports as a potential engine of economic growth in South Africa, employing a cointegration and error correction model (ECM) framework on time series data from 1980 to 2023. The results confirm a long-run equilibrium relationship between agricultural exports and economic growth, with lagged total exports and employment significantly influencing GDP growth in the short run. However, other factors like foreign direct investment, gross capital formation, and population growth did not exhibit a statistically significant impact. These findings underscore the importance of agricultural exports in driving South Africa’s economic growth. To further enhance this potential, the study recommends establishing a consistent and transparent policy environment to foster investor confidence and long-term planning in the agricultural sector, expanding the range of agricultural exports to reduce vulnerability to external shocks and enhance overall economic resilience and streamlining customs procedures, reducing trade barriers, and improving logistics to enhance the competitiveness of South African agricultural exports in the global market. These policy recommendations, grounded in empirical evidence, offer a roadmap for harnessing the full potential of agricultural exports to drive sustainable economic growth in South Africa.

Purpose: Religiosity as an intrinsic principle affects the sustainable behavior of consumers. Studies have been undertaken to discover the impact of religiosity on sustainable consumer behavior in various contexts, cultures, and countries. The current bibliometric study focused on religiosity and sustainable consumer behavior in Gulf Corporation Council (GCC) countries who has similar religions and cultures so that the research trend, contribution, and gap through thematic and content analysis could be investigated and future direction could be suggested. The literature for this study was solicited from 2016 to June 2024. Methodology: Bibliometrics and content analysis were used to study the existing literature on religiosity and sustainable consumption behavior in GCC countries. The VOS viewer was used to visualize literature and understand the network landscape of the research topic and their interconnectivity. Additionally, Scopus analytics and Microsoft Excel were used to review and analyze the religiosity of consumers regarding the sustainable consumption of products and services. Finding: The descriptive analysis revealed trends, prolific countries, and researchers in this area along with their affiliation. The co-occurrence analysis showed 3 main clusters of co-occurrences with various link strengths. The content analysis looked at the 6 clusters depicted by the coupling function and compared them against co-occurrence analysis to uncover related themes. This analysis produced 4 related themes for content analysis. Contribution: This research contributed to understanding the current themes, challenges, and the need for marketing strategies and action so that sustainable consumption could be encouraged. As such this research will fill the void in the current literature left in this research area. This research has practical and policy implications for businesses, organizations, and policymakers as they try to capture consumers for sustainable products and services in GCC countries.

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.