The purpose of this paper is to evaluate from a nutritional, phytochemical, and rheological point of view different varieties/lines of oats cultivated in Romania. In this sense, the proximate composition (proteins, lipids, mineral substances, starch, and carbohydrates), the total polyphenols content (TPC) of individual polyphenols and macro and microelements of 20 oat varieties/lines were determined. In order to evaluate the potential for use in the flour industry (pasta, bread, and biscuits)., a variety of oats with optimal nutritional properties and active principles was used to obtain oat/wheat composite flours in different proportions (10–30% oat flour) and subjected to rheological analysis using the Chopin Mixolab test (Chopin Technologies, Paris, France) The results showed a protein content between 14.36–17.08%, lipid content between 6.01–9.03%, starch between 44.13–52.95%, total mineral substances between 0.93–1.65%, carbohydrates between 65.16–69.32% and energy value between 389.81–409.86 kcal/100 g. The TPC content varied between 614.76–1244.58 µgGAE/g, and the average values of individual polyphenols were between 1.87 μg/g (rosmarinic acid) and 28.18 μg/g (gallic acid). Regarding the macroelements content, potassium (K) was the major element (203.19 μg/g), followed by calcium (Ca) (181.71 μg/g) and magnesium (Mg) (105.79 μg/g). The level of microelements varied between 3.02 μg/g for nickel (Ni) and 60.25 μg/g for iron (Fe). Multivariate data analysis PCA was used to correlate the obtained data. The rheological analysis using the MIXOLAB system proved that, from the point of view of technological properties, composite flours from a mixture of 10–30% oat and wheat flour are the best for obtaining biscuit dough, compared to pasta or pan bread. Following the study and based on nutritional, phytochemical and rheological results, line 2511 was selected as optimal for use in obtaining hypoglycemic flour foods for people with diabetes.

The cultivation of alfalfa is crucial for farmers as it is an excellent forage crop with a high nitrogen-fixing capacity, making it indispensable in crop rotations. Breeding programs face challenges in advancing more rapidly in genetic diversity to achieve a higher heterosis effect and, consequently, greater yield. In this study, we used 30 alfalfa varieties, which were used for molecular analyses by 5 ISSR primers and 13 RAPD primers. The results obtained highlighted the greater efficiency of ISSR primers in identifying genetic diversity. On the other hand, the simultaneous use of ISSR + RAPD allowed for clearer clustering of varieties that enabled more efficiently distinguishing the genetic diversity. The most efficient ISSR primer, A17, generated 31 polymorphic bands, while the most efficient RAPD primer, L-07, generated only 21 bands. Varieties such as “Pastoral” and “F1413-02” exhibited low similarity coefficients (0.39), suggesting their potential for enhancing genetic variability through crossbreeding, thereby increasing the potential of achieving a greater heterosis effect. Conversely, varieties with high similarity coefficients, such as ”Cristal” and “Viking” (0.81) are less suited for this purpose. The correlation between specific markers highlights that using both ISSR and RAPD markers together offers a clear understanding of genetic diversity in alfalfa, aiding in more effective selection for crossbreeding in breeding programs.

The landscapes and, implicitly, the surfaces of secondary grasslands in the mountain areas have been intensively modified and transformed by humans. In this context, this paper analyses the spatial and temporal changes of grassland surfaces following the impact of human population reduction. Thus, the study proposes the implementation of the Grassland Anthropic Impact Index (GAII) as a “measurement tool” to functionally link the two components, grassland surface and human population. The spatiotemporal analyses are based on Corine Land Cover data and demographic data, processed via Geographic Information Systems (GIS) methods and the Land Change Modeler (LCM) tool. The research shows that over a period of 28 years, the population, which was continuously decreasing, caused a series of transformations to the grasslands over an area of 33343 ha. The influence of the reduction in the number of inhabitants was also demonstrated by the direction of the changes produced in the grassland surfaces: in the better populated areas, the grasslands expanded over lands with other uses, and in the sparsely populated areas, they were abandoned. GAII values generally increase with the decrease of the population in the target area, meaning that for an inhabitant (potential user) a greater grassland surface is reported, resulting in a greater responsibility for the management of this resource on a space and time scale. Following the evaluation of the trend of the last 28 years, it was observed that the depopulation of mountain areas can be seen as a threat to grassland ecosystems, either through the transition to other categories of use, or through abandonment. The implications of these phenomena are much broader: they produce chain reactions and affect other components of the regional geosystem.

Grasslands represent an essential resource for rural economies and for the provision of ecosystem services, yet they are increasingly affected by anthropogenic pressures, functional land-use changes, and institutional constraints. This study develops a geospatial decision-support framework for assessing grassland suitability in Hunedoara County, Romania, by integrating the Analytic Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) within a GIS environment. The assessment is based on nine criteria thematically grouped into three dimensions: (A) physical-geographical, including topographic suitability, climatic pressure, and hydrological risk exposure; (B) ecological and conservation-related, reflected by ecological conservation value, ecological carrying capacity, and the anthropic pressure index; and (C) socio-economic and functional, represented by spatial accessibility, recreational value, and policy support mechanisms. Suitability is defined as the integrated capacity of grasslands to sustain productive and multifunctional uses compatible with ecological conservation and the existing policy framework. Results indicate that 0.43% of the grassland area exhibits very high suitability (Class 1), 44.51% high suitability (Class 2), and 54.75% moderate suitability (Class 3), while unfavorable areas account for only 0.31% of the total (Class 4). The proposed methodology is reproducible and transferable, providing support for prioritizing management interventions, agri-environmental payments, and rural planning in mountainous and hilly regions.

The Allium test is a cytological method used to monitor the impact of heavy metals. It can be used to evaluate meristematic tissues and highlight abnormalities occurring during mitotic division, with the advantage of being both rapid and economical. Copper and lead are among the most widespread metals in everyday life, mainly due to the worldwide expansion of industrialization, and are present in soil, water, and air. Using Allium sativum as a bioindicator for this study, statistical analysis confirmed significant differences in genotoxicity between the two metals, reflected by the inhibition of mitotic activity (MI) or increased indices of cellular abnormalities (AI). Toxicity was dose- and time-dependent for both metals, with copper exhibiting greater genotoxic effects than lead. Copper caused a significant reduction in MI, even at relatively low concentrations, with the IC₅₀ observed at 0.50 mM after 72 h of exposure. In contrast, for lead, the IC₅₀ was recorded from 0.75 mM after 72 h exposure. The advantages of the Allium test were demonstrated by its simplicity and high sensitivity in detecting abnormalities. In our experiment, chromosome abnormalities such as chromosome bridges, as well as isolated, delayed, or sticky chromosomes, were observed. In addition, at a concentration of 0.25 mM for copper (72 h exposure) and 0.50 mM for lead (72 h exposure), cellular abnormalities, including giant cells and binucleated cells, were identified.

Wheat is a crucial crop for human nutrition, and the demand for high-quality indicators within the “from farm to fork” concept is increasing. Based on this premise, this study examined how, at the farm level, the fertilization system can influence key quality indicators relevant to wheat production and final products. This research was conducted under specific conditions of the Western Plain of Romania at the Agricultural Research and Development Station (ARDS), Lovrin, during 2015–2017. Fertilization involved the autumn application of phosphorus (concentrated superphosphate; 0, 40, 80, 120, 160 kg ha⁻¹ active substance, a.s.) and potassium (potassium chloride; 0, 40, 80, 120 kg ha⁻¹ a.s.). Nitrogen (ammonium nitrate; 0, 30, 60, 90, 120 kg ha⁻¹ active substance) was applied in spring in two stages. The combination of these three fertilizers resulted in 18 fertilized variants (T2 to T19), tested alongside an unfertilized control (T1). The experimental variants were arranged in four randomized replications. Grain quality was assessed based on protein content (PRO, %), gluten (GLT, g 100 g⁻¹), gliadins (Gliad, %), glutenins (Glut, g 100 g⁻¹), high-molecular-weight glutenins (HMW, g 100 g⁻¹), low-molecular-weight glutenins (LMW, g 100 g⁻¹), and the gliadin/glutenin ratio (Gliad/Glut). Compared to the average values for each indicator across the experiment, certain variants produced values above the mean, with statistical significance. Variant T16 stood out by producing values above the mean for all indicators, with statistical confidence. Multivariate analysis showed that five indicators with very strong (PRO, GLT) and strong (HMW, Glut, LMW) influence grouped in PC1, while two indicators (Gliad, Gliad/Glut) with very strong and strong influence grouped in PC2. The analysis revealed varying levels of correlation between the applied fertilizers, with nitrogen (N) showing very strong and strong correlations with most indicators, while phosphorus and potassium showed moderate-to-weak correlations. Regression analysis generated mathematical models that statistically described how each indicator varied in relation to the fertilizers applied.