Search Results (10)

Fatty acids tend to undergo dynamic changes during the growth and development of fruits. In this study, we analyzed the variations in fruit morphology and kernel fatty acid fractions and contents at seven post-flowering stages in the fruit of ‘A4’ and ‘OC’, two main macadamia cultivars in Yunnan, China. The single fruit weight and longitudinal and transverse diameters showed a ‘fast–slow–stable’ growth trend, and the fruit shape index gradually decreased with fruit development. A total of 13 saturated fatty acids, 18 monounsaturated fatty acids, and 10 polyunsaturated fatty acids were detected in macadamia kernels at seven developmental stages. The total fatty acid content in ‘OC’ and ‘A4’ tended to first increase and then decrease. The fatty acid content accounted for 8.81% and 6.33% of the total fatty acids at 50 days after flowering (DAF), and peaked at 95 DAF and 125 DAF (the fatty acid content accounted for 25.61% and 20.69% of the total fatty acids), indicating that these two periods are critical for fatty acid accumulation in the two cultivars. In addition, oleic acid, palmitoleic acid, cis-Vaccenic acid, and hexadecenoic acid were determined as the main fatty acids. This study reveals the dynamic changes in fatty acid composition and content in ‘OC’ and ‘A4’ during fruit development, providing a scientific basis for determining the appropriate harvesting time for macadamia nuts. Full article

Pollination is a determining factor in achieving economic yield in hazelnut cultivation, and together with variable climate conditions, this requires the use of artificial pollination. This study evaluated the efficiency of artificial pollination performed with a manual sprayer using pollen from three pollinizer cultivars on the ‘Tonda Francescana^®’ commercial orchard and the effect of different pollen sources on nuts. Dry pollens were applied by a Pollen Blower machine twice during female blooming. The pollen of ‘Nocchione’ determined the highest fruit set and yield per tree, even if it did not determine the highest blank seed percentage. The open pollinizers exhibited a lower sphericity and shape index (NSI), ‘Camponica’ pollen was associated with the biggest nut and kernel; ‘San Giovanni’ pollen showed higher nut elongation. Artificial pollination turned out to be a good tool to increase yield, but its efficiency is strongly influenced by the pollen used. Full article

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies. Full article

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R² coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R² diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times. Full article

Population-dense urban areas often concentrate high commercial and industrial activity and intricate transportation systems. In crowded cities, extreme events can be even more damaging due to the high population they affect and the social inequalities that are likely to emerge. Extreme heat is a climate hazard that has been linked to high morbidity and mortality, especially in cities with high population densities. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors and other socio-demographic parameters. In this article, we quantify and locate vulnerabilities of populations to extreme heat in order to formulate policy and practice recommendations that will make communities resilient and will shape the transition to a more sustainable future. This work contributes towards the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities—by developing the tools to make cities and settlements resilient and sustainable. To this end, we analyze socioeconomic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals, and a methodology is developed for constructing this variable based on socioeconomic data available in public databases. The independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly individuals, retirees, unemployed persons, renters, those living alone, those residing in smaller houses, those living in older houses and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also facilitates a comparison between vulnerability scores across different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health. Full article

High-density urban development is promoted by both global and local policies in response to socio-economic and environmental challenges since it increases mobility of different land uses, decreases the need for traveling, encourages the use of more energy-efficient buildings and modes of transportation, and permits the sharing of scarce urban amenities. It is therefore argued that increased density and mixed-use development are expected to deliver positive outcomes in terms of contributing to three pillars (social, economic, and environmental domains) of sustainability in the subject themes. Territorial quality of life (TQL)—initially proposed by the ESPON Programme—is a composite indicator of the socio-economic and environmental well-being and life satisfaction of individuals living in an area. Understanding the role of urban density in TQL can provide an important input for urban planning debates addressing whether compact development can be promoted by referring to potential efficiencies in high-density, mixed land use and sustainable transport provisions. Alternatively, low-density suburban development is preferable due to its benefits of high per capita land use consumption (larger houses) for individual households given lower land prices. There is little empirical evidence on how TQL is shaped by high-density versus low-density urban forms. This paper investigates this topic through providing an approach to spatially map and examine the relationship between TQL, residential expansion, and densification processes in the so-called NUTS2 (nomenclature of terrestrial units for statistics) regions of European Union (EU) member countries. The relative importance of each TQL indicator was determined through the entropy weight method, where these indicators were aggregated through using the subject weights to obtain the overall TQL indicator. The spatial dynamics of TQL were examined and its relationship with residential expansion and densification processes was analysed to uncover whether the former or the latter process is positively associated with the TQL indicator within our study area. From our regression models, the residential expansion index is negatively related to the TQL indicator, implying that high levels of residential expansion can result in a reduction in overall quality of life in the regions if they are not supported by associated infrastructure and facility investments. Full article

In recent years, although life expectancy has increased significantly, non-communicable diseases (NCDs) continue to pose a significant threat to the health of the global population. Therefore, eating habits have been recognized as key modifiable factors that influence people’s health and well-being. For this reason, it is interesting to study dietary patterns, since the human diet is a complex mixture of macronutrients, micronutrients, and bioactive compounds, and can modulate multiple physiological processes, including immune function, the metabolism, and inflammation. To ensure that the data we acquired were current and relevant, we searched primary and secondary sources, including scientific journals, bibliographic indexes, and databases in the last 15 years with the most relevant articles. After this search, we observed that all the recent research on NCDs suggests that diet is a critical factor in shaping an individual’s health outcomes. Thus, cardiovascular, metabolic, mental, dental, and visual health depends largely on the intake, habits and patterns, and nutritional behaviors. A diet high in processed and refined foods, added sugars, and saturated fats can increase the risk of developing chronic diseases. On the other hand, a diet rich in whole, nutrient-dense foods, such as vegetables, fruits, nuts, legumes, and a high adherence to Mediterranean diet can improve health’s people. Full article

The article has an empirical nature. The subject of detailed analysis is the variations in spatial distribution of human capital in rural areas of Poland and the analysis of the correlation of this phenomenon with local structures of the socio-economic development process. The diagnosis and assessment of variations in the spatial distribution of human capital were performed based on an author’s indicator—a synthetic measure of human capital level (HCI—human capital index). The characterisation of local socio-economic structures was based on the typology of rural areas according to Rural Development Monitoring (RDM 2014 and 2023). The study was conducted for rural areas in Poland defined by the Main Statistical Office based on the administrative criteria of rural and rural-urban municipalities. A total of 2172 municipalities were covered by the study. The data analysis was conducted spatially at the NUTS 5 level and comparatively at the NUTS 2 level for the years 2013–2018. The assumption was verified that the processes of human capital concentration in rural areas in Poland are related to local socio-economic structures of development processes, and the local structure factor that influences the existing differences is the degree of use of agricultural functions. The results of statistical tests positively verify this relationship as statistically significant. Moreover, the article provides strong arguments for shaping regional and rural policy and its implementation. The assumption about the need to change the approach to the study of rural space was positively verified; it is suggested to move away from analyses conducted at the level of the NUTS 2 region to the level of the NUTS 5 municipality. Research on rural areas makes sense only from a local perspective; it allows for a more accurate illustration of the specificity of local communities, revealing their development potentials and barriers, and, as a result, more effective programming of instruments supporting local development, dedicating specific support programs individually for each municipality, while the regional approach presents the state of differences too generally and may often lead to incorrect interpretation. In the empirical part of the article, taxonomic methods of hierarchy (patternless) and classification of multi-featured objects were used. As a result, each object (municipality) was assigned a synthetic measure—the relative human capital level index (HCI). Based on the HCI index, an ex-post hierarchical classification of municipalities was carried out. The main sources of data (diagnostic variables) for the construction of the HCI index were the Local Data Bank of the Central Statistical Office (BDL GUS), the national census of NSP 2011, the Ministry of Finance, the Ministry of Family and Social Policy, and District Examination Boards. The source of data on local socio-economic structures expressed based on the typology of rural areas according to the Rural Development Monitoring (RDM) methodology was the European Fund for the Development of Polish Villages (EFRWP). Full article

Genetic erosion of shea trees, which has been on-going at an alarming rate, has necessitated urgent conservation attentions. Owing to the vast geographical distribution of the species across Ghana, in situ germplasms conservation was established by tagging and monitoring selected trees annually. Technologies have been developed that enable shea germplasms to be grafted, allowing for the development of germplasm banks at the research station of the Cocoa Research Institute of Ghana, Bole. However, before these materials could be used in crop improvement programs, there is a need to evaluate them for key fruit traits relevant to the global shea business. This experiment was carried out to evaluate the tagged in situ shea trees for fruit and nut traits. Freshly harvested shea fruits were evaluated for their brix, pulp yield and kernel size properties to see if there was the needed diversity for crop improvement gains. Eight key traits were studied, with all showing significant difference, with high broad sense heritability and genetic advance for all the traits, indicating the potential for genetic gains in breeding programs. Qualitative analysis classified the fruits into five shapes, ellipsoid fruit shape was the most frequent observation (69.5%), while oblong was the least represented (1%). Fruit surface pubescence indicated that the surfaces without hairs (smooth) were slightly higher in number (52.6%) than the surface with hairs (rough), which were 47.4%. Pearson correlation studies showed a positive significant relationship between kernel weight and fruit weight (0.68), fruit length (0.48), fruit width (0.51), pulp weight (0.5) and shell weight (0.77). Key components responsible for total variations observed were decomposed from the first two principal components (PC), which cumulatively explained 78.4% of the total observed variation within the materials. PC1 alone contributed 46.4%, while PC2 contributed 32%. Fruit weight, fruit length, fruit width, pulp weight, nut weight, shell weight and kernel weight were contributing traits to variations observed in PC1, while brix and percent pulp contributed to the variations observed in PC2. Percent kernel to nut ratio contributed to the variations observed in PC3. Clustering of the germplasms showed no regular pattern based on location or any particular trait, indicating a high level of diversity at 58% of the Pearson dissimilarity index. Full article

Perception of fat taste, aroma, and texture are proposed to influence food preferences, thus shaping dietary intake and eating behaviour and consequently long-term health. In this study, we investigated associations between fatty acid taste, olfaction, mouthfeel of fat, dietary intake, eating behaviour, and body mass index (BMI). Fifty women attended three sessions to assess oleic acid taste and olfaction thresholds, the olfactory threshold for n-butanol and subjective mouthfeel ratings of custard samples. Dietary intake and eating behaviour were evaluated using a Food Frequency and Three-Factor Eating Questionnaire, respectively. Binomial regression analysis was used to model fat taste and olfaction data. Taste and olfactory detection for oleic acid were positively correlated (r = 0.325; p < 0.02). Oleic acid taste hypersensitive women had significantly increased n-butanol olfactory sensitivity (p < 0.03). The eating behaviour disinhibition and BMI were higher in women who were hyposensitive to oleic acid taste (p < 0.05). Dietary intake of nuts, nut spreads, and seeds were significantly correlated with high olfactory sensitivity to oleic acid (p < 0.01). These findings demonstrate a clear link between fatty acid taste sensitivity and olfaction and suggest that fat taste perception is associated with specific characteristics of eating behaviour and body composition. Full article