The study aims to evaluate the potential of hyperspectral leaf reflectance data for estimating potassium (K) content in two vegetable crops—sugar beet (Beta vulgaris L., cv. Tapir) and celery (Apium graveolens L., cv. Neon). The research seeks to determine whether this approach can provide a reliable, non-invasive, and cost-effective method for monitoring K levels across different plant species, K concentration ranges, and developmental stages, thereby supporting precision fertilization management. Timely and precise monitoring of nutrient levels in plant foliage is crucial for optimizing fertilizer management, enhancing crop productivity, and ensuring environmental sustainability. Spectral reflectance in the visible and near-infrared ranges was recorded across a wide spectrum of K fertilization levels and three plant developmental stages. A Random Forest (RF) regression model was applied to identify optimal wavelengths for predicting foliar K content, using both species-specific and combined datasets. The RF model using 12 selected wavelengths (425, 443, 479, 599, 631, 662, 798, 863, 897, 921, 1978, and 2053 nm) achieved high prediction accuracy for K content in sugar beet (R² = 0.85), celery (R² = 0.79), and both species combined (R² = 0.81). The study identified key spectral features that correspond with physiological changes related to anthocyanin, carotenoid, chlorophyll b, starch, and protein contents. Spectral reflectance offers a reliable, non-invasive, and cost-effective method for estimating K content in plant leaves. The findings support its potential application in precision fertilization strategies across different vegetable species and growth stages.

This study analyzes 209 dog skeletons from two sites in Wolin (9th–mid-13th century AD) using 100 standard metric variables covering cranial, mandibular, and postcranial elements. Estimated withers height, body mass, age at death, and sex were derived using established methods. The results indicate the presence of at least two to three morphotypes: small spitz-like dogs (40–50 cm, 4–6 kg), medium brachycephalic forms (50–60 cm, 10–15 kg), and larger mesocephalic individuals (up to 65 cm, 20–40 kg). Dogs lived 3–10 years, with both sexes represented. Signs of cranial trauma and dental wear suggest utilitarian roles such as guarding. The size range and morphological diversity point to intentional breeding and trade-based importation. Small dogs likely served as companions or city guards, while medium and large types were used for herding, hunting, or transport. These findings highlight Wolin’s role as a dynamic cultural and trade center, where human–dog relationships were shaped by anthropogenic selection and regional exchange.

The proper functioning and future of rural areas and communities are strictly tied to young people's willingness to be engaged in the farming profession, as farming is an important recourse for vocational rehabilitation of the rural population. The agricultural sector acts as a financial injection to the rural economy and society. In this context, one of the most well devised agricultural policies is young farmers' schemes, aiming at generational renewal in EU rural areas. Since young farmers are the people who directly receive the induced effects of these policies, policy makers have to listen carefully to what “message” young farmers can convey. Nevertheless, after almost four decades of implementing young farmers' schemes, there is still limited information regarding their attitudes, beliefs and perceptions towards the form of the related policy schemes. Using this as a starting point, the present study attempts to identify young farmers' attitudes and beliefs towards the current young farmers' scheme of the Rural Development Program (RDP) with overarching scope to identify the needs of young farmers better and thus inform policy makers about the appropriate policy that should be put forward. Results indicate that the current young farmers' scheme acts as a helpful instrument for a considerable percentage of young farmers. Nevertheless, further streamlining is required to provide incentives, especially to new entrants, to be engaged in the farming profession. Effective policy interventions that will be in line with the actual needs of young farmers could contribute to the direction of the enhancement of the vitality and resilience of the rural systems, and also act towards the prevention of the abandonment process, providing vibrance in rural areas and benefits for the whole economy and society, as well as the ecosystems.

Quantum dots consisting of an axial Zn0.97Mg0.03Te insertion inside a large-bandgap Zn0.9Mg0.1Te nanowire core are fabricated in a molecular-beam epitaxy system by employing the vapor-liquid-solid growth mechanism. In addition, this structure is coated with a thin ZnSe radial shell that forms a type-II interface with the dot semiconductor. The resulting radial electron-hole separation is evidenced by several distinct effects that occur in the presence of the ZnSe shell, including the optical emission redshift of about 250 meV, a significant decrease in emission intensity, an increase in the excitonic lifetime by one order of magnitude, and an increase in the biexciton binding energy. The type-II nanowire quantum dots where electrons and holes are radially separated constitute a promising platform for potential applications in the field of quantum information technology.

Carbon Monoxide (CO) is an important urban pollutant with a relation to transition economies based on emission intensities. In this study, Sentinel-5, MODerate resolution Imaging Spectroradiometer (MODIS), and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them. From the assessed correlations for 932 Iranian cities, it occurred that the assessed indices were all correlated. The highest CO levels were 0.031 in the spring of 2019 and 2020, whereas in 2021 it was equal to 0.030 in both the spring and winter, respectively. In 2019 and 2020 the maximum values of the Enhanced Vegetation Index (EVI) in the spring were 0.181 and 0.183. Exceptionally high Absorbing Aerosol Index (AAI) values of – 0.834 and – 1.0, along with Urban Index (UI) of 0.102 and 0.092, were correlated with recorded spikes in CO level, despite that these seasons’ EVI values were not so abnormal. It was forecasted that in 2030 rises in the CO level by 13.2% in the winter and by 17.5% in the fall are expected, with the simultaneous increase of AAI by 204.5% and 980.2%, and Aerosol Optical Depth (AOD) by 27% and 5% in the winter and spring, respectively.

This paper aims to create supervised classification models of the soil water potential based on hyperspectral data of Polish mineral soils (104 samples) from the Visible and Near-InfraRed (VNIR) and Short-Wave InfraRed (SWIR) range and selected soil physico-chemical properties, such as organic carbon content, or fraction of sand, silt, and clay. Soil water content regression models were also created, which took into account the soil water potential ranging from 98.1 J & BULL;m-3 to 1554249 J & BULL;m-3. Several machine learning algorithms were tested to create models of the soil water potential and the soil water content. It occurred that reflectance characteristics of the soils exhibit a high correlation with soil moisture. Gaussian Processes (GP) model was most suitable for the estimation of the soil water content, regardless if input data contained pure reflectance spectra (R=0.82), or if they were supplemented with selected physico-chemical soil properties and soil water potential (R=0.94). No improvement of the models' accuracies was noticed when only the selected soil physical and chemical properties were included as inputs, which suggested that the soil surface spectral data contained in themselves information, which strictly belonged to specific soil physico-chemical properties. Among classification models of the soil water potential, the LOG method had the highest percentage of correctly classified cases. More than 65% of all cases were correctly classified if the spectral data, moisture, and other properties of the tested material were included, and more than 54% when the independent variables did not include soil moisture. The majority of misclassified cases were by one class. The greatest accuracy was achieved for the two extreme values of pF (0 and 4.2), while the worst one was for pF2.2 and pF2.7.

During the last decade, Common Agricultural Policy (CAP) has prioritised measures focused on sustainability and quality over production. The purpose of the AGRICORE project is to develop a tool based on agent-based modelling to assist policymakers in the design of improved policies. The present study belongs to a use case of this project that measures the environmental and climate impact of Measure 11—Organic agriculture—from the 2014–2020 Rural Development Programme of the Andalusian olive sector. As part of this study, a survey campaign was conducted, which collected data from 189 organic olive farmers in Andalusia. The data were analysed in order to characterise organic olive farmers and their farms. This paper presents the resulting characterisation, covering some information gaps detected as part of the AGRICORE project, such as their acceptance of M11 and willingness to innovate and take risks. The results highlight that most of the respondents are unaware of important aspects, such as exploitation costs and their belonging to environmentally protected areas. Furthermore, it is interesting to note that most farmers are approximately 60 years old, and more than 35% are women. Furthermore, most of those studied do not intend to return to conventional production methods. These results help to provide a current perspective of the organic olive sector in Andalusia, which can be used by policymakers to design improved policies that entail an increase in organic olive production.

A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region.

This paper presents the results of a survey conducted electronically in the years 2020-2022 within the framework of the AGRICORE Horizon project. It concerned the Agri Environment-Climate Measure M10 within the Rural Development Programme 2014-2020 and aimed to quantify the impact of its effects on environmental and climatic policy implementation at a national level according to the perceptions of Polish farmers. The representativeness of the scrutinized population was checked using general data from the Polish Statistics Office. The results of our study show a positive perception of M10 by the participating farmers. The majority of them observed the income progress of their activities despite the increased workload connected with programme implementation and the increased costs associated with some of the declared activities. The innovation activities of the M10 participants were directed mainly at sustainable agriculture and protecting the environment. The respondents who did not decide to participate in M10 most frequently explained themselves by noting a lack of information about the programme, bureaucratic limitations, or doubts concerning the profitability of participation. The results of the study suggest that during the implementation of future EU agri-environmental measures, more attention should be paid to administrative and legal activities at the national level which may improve the perception of the programme.

The conservation of environmental resources is aimed at ensuring the continuity of ecosystem services for future generations and maintaining ecosystem integrity. Given the extensive reliance of agriculture on the environment, it is crucial to identify factors that impact the quality of ecosystem services (ESs), which can be regulated at large and heterogeneous national or European scales. This research, conducted within the Polish use case of the AGRICORE project, aims to demonstrate the feasibility of establishing indicators depicted in three ES categories, which can be shaped under the actions of the Common Agricultural Policy (CAP). The study was conducted based on national sources, mostly the database of the Central Statistical Office. The analyses of regression showed a significant impact of selected agricultural productivity factors on the key performance indicators (KPIs) assessing the level of selected ESs. The yield of cereal grains, which quantitatively expresses the potential of current crop production, depended to the greatest extent (r = 0.81) on a comprehensive indicator of the agricultural production space suitability, as well as on the rise of the level of nitrogen fertilization (r = 0.68), and also on the reduced share of permanent grassland in the agricultural area (r = −0.53). It was proved that in territorial units, in which the level of nitrogen fertilization per 1 ha was greater, the share of soils with favorable pH > 5.5 was also greater. The gross nitrogen balance had a positive and significant correlation with the level of investment subsidies (r = 0.86), the share of agricultural land in the total area (r = 0.67), and the level of nitrogen fertilization (r = 0.66). Notably, there were positive correlations between the level of environmental subsidies and the increase in forestation (r = 0.68) and also between air quality and the share of cereals in the sowing structure (r = 0.86). Additionally, the impact of agricultural productivity factors on cultural eco-services was found, e.g., the share of ecological land had a positive impact on the number of natural monuments, the area of nature reserves, the number of agritourists, and agritourism nights, while the share of cereals in the sowing structure negatively correlated with the most of analyzed cultural indicators. These results are useful for the development of a module for the ABM model that employs the desired environmental parameters to provide different assessments of the impact of selected agricultural productivity factors and ecosystem services on the economic farm status.

Jet streams are atmospheric phenomena that operate on a synoptic scale and can intensify the descending/ascending conditions of the air at the lower levels of the atmosphere. This study aimed to identify the patterns and location of the jet stream in southwest Asia during the days of widespread rainfall in Iran based on two criteria: “highest frequency of stations involved” and “maximum cumulative amount on the day of peak rainfall”. For this purpose, the daily precipitation data for 42 synoptic stations in Iran during the period 2006–2019 from the Meteorological Organization of Iran, the daily data at 500 hPa Geopotential Height (HGT), and U and V wind components at 500 and 300 hPa from NCEP/NCAR were gathered. Synoptic patterns were obtained based on daily precipitation data, daily maps at HGT 500 hPa, and U and V wind components at 500 and 300 hPa. The analysis of patterns showed that the position of precipitation cores is associated with the position and extension of jet stream centers at 300 hPa in winter, spring, and autumn. The main position of jet stream cores during flood-causing rainfall at 300 hPa was over the northern part of Saudi Arabia, the Mesopotamia basin, and southern Iran. This position seems to have provided the conditions for the convergence of the earth’s surface and the divergence of the atmosphere for the easy passage of moisture from the Red Sea, Aden Sea, and the Persian Gulf, and in the second rank, the Mediterranean Sea and the Arabian Sea.

In this study, the impact of vegetation, land surface temperature, and precipitation on changes in water level and area of seven inland lakes (Urmia lake in Iran, Tharthar, Mosul lakes and Hammar 4 wetland in Iraq, and Beyşehir and Erçek lakes in Turkey) is analyzed to evaluate the variability of these lakes due to climate change. The altimetric data from four remote sensing databases (TOPEX/POSEIDON and Jason 1, 2, and 3), the area of the lakes from the images of Landsat OLI and ETM + sensors, the vegetation from MOD13Q1-NDVI 250 m database, land surface temperature from LST-MOD11A1, and precipitation from GPM_3IMERGM product were used in this study to assess the changes occurring in the period of the last 20 years (2000–2019). The results showed that in the analyzed area the values of the land surface temperature and vegetation indices increased, whereas annual precipitation sums decreased. Although temperature and vegetation changes in all three countries were almost consistent with each other, changes in the water level and area of the studied lakes were different. The highest decrease in the water level was observed for Urmia lake. Although decreases in the water level were also observed in other lakes, their water level returned after a time to its initial level (1992). This was not the case for Urmia lake, where the water level after 1999 never returned to the initial value, finally lowering by 7 m. The fluctuations of the water level and area of Iran, Turkey, and Iraq lakes are however caused by factors other than only those related to climate, which needs more investigations to determine more precisely the changes in the water level of these lakes.

Despite the importance of the Amu Darya and Kabul River Basins as a region in which more than 15 million people live, and its vulnerability to global warming, only a few studies addressed the issue of the linkage of meteorological parameters on vegetation for the eastern basins of Afghanistan. In this study, data from the MODIS, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) was used for the period from 2000 to 2021. The study utilized several indices, such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Optical Integrated Drought Index (OIDI). The relationships between meteorological quantities, drought conditions, and vegetation variations were examined by analyzing the anomalies and using regression methods. The results showed that the years 2000, 2001, and 2008 had the lowest vegetation coverage (VC) (56, 56, and 55% of the study area, respectively). On the other hand, the years 2010, 2013, 2016, and 2020 had the highest VC (71, 71, 72, and 72% of the study area, respectively). The trend of the VC for the eastern basins of Afghanistan for the period from 2000 to 2021 was upward. High correlations between VC and soil moisture (R = 0.73, p = 0.0008), and precipitation (R = 0.63, p = 0.0014) were found and also significant correlation was found between VC and drought index OIDI. It was revealed that soil moisture, precipitation, land surface temperature, and area under meteorological drought conditions explained 45% of annual VC variability. It was also found that the orography had a significant influence on both the spatial distribution of the LST and VCI, as well as the spatial correlations between VCI and meteorological parameters.

The accurate recognition of atmospheric circulation patterns is vital for understanding the intricate relationships among various climatic elements. Therefore, the main goal of this study is to comprehensively identify circulation patterns during the occurrence of the summertime Extended Area Precipitation Event (EAPE) in southeastern Iran. The data used in this study encompass precipitation rates from synoptic and rain gauge stations, Geopotential Height (GPH), omega (upward motion speed), u-wind (east-west), and v-wind (north-south) components at different atmospheric levels, along with satellite images from the visible spectrum. In this research, both subjective and objective clustering methods have been utilized to identify synoptic circulation patterns based on 500-hPa GPH data. Summer precipitation was chosen for analysis because its characteristics and relationships with large-scale circulation patterns are less understood compared to those of winter precipitation. Examination of the 500-hPa GPH data for sixty-two identified cases of EAPE over southeast Iran revealed that the causative factors for these events are comprised of five recurring patterns (referred to here for convenience as AP, BP, CP, DP, and EP). Three of these patterns (AP, BP, and DP) significantly contributed to 71% of all EAPE cases. It was evident that the five patterns responsible for creating the EAPE in southeastern Iran had distinct directions.

. This study used NDVI, ET, and LST satellite images collected by moderate resolution imaging spectroradiom-eter and tropical rainfall measuring mission sensors to investigate seasonal and yearly vegetation dynamics, and also the influence of climatological factors on it, in the area of the Caspian Sea Watersheds for 2001-2019. The relationships have been assessed using regression analysis and by calculating the anomalies. The results showed that in the winter there is a positive significant cor-relation between NDVI and ET, and also LST (R = 0.46 and 0.55, p-value = 0.05, respectively). In this season, the impact of pre-cipitation on vegetation coverage should not be significant when LST is low, as was observed in the analysed case. In spring, the correlation between NDVI and ET and precipitation is positive and significant (R = 0.86 and 0.55, p-value = 0.05). In this season, the main factor controlling vegetation dynamics is precipitation, and LST's impact on vegetation coverage may be omitted when precipitation is much higher than usual. In the summer, the correla-tion between NDVI and ET is positive and significant (R = 0.70, p-value = 0.05), while the correlation between NDVI and LST is negative and significant (R = -0.45, p-value = 0.05). In this sea-son, the main factor that controls vegetation coverage is LST. In the summer season, when precipitation is much higher than aver-age, the impact of LST on vegetation growth is more pronounced. Also, higher than usual precipitation in the autumn is the reason for extended vegetation coverage in this season, which is mainly due to increased soil moisture.

Abstract Purpose The accurate and frequent estimation of the leaf plant potassium concentration enabled by hyperspectral imaging techniques has allowed growers to optimize fertilizer applications and reduce the negative impact on the environment. In this study, we examined the feasibility of using leaf spectral data to accurately estimate the potassium content in sugar beet and celery plants. Methods Leaf images in the visible and near infrared region (VNIR: 400–1000 nm) and short-wavelength infrared region (SWIR: 1000-2500 nm) were captured by a hyperspectral camera. The potassium content was measured by ordinary destructive laboratory methods. The correlation-based feature selection (CFS) algorithm was implemented to select important wavelengths that carried the most useful information for predicting the potassium content in plant leaves. Four multivariate regression methods were tested to find a model with strong predictive performance. Results The experimental results showed that the Random Forest (RF) model using 12 bands (425, 443, 479, 599, 631, 662, 798, 863, 897, 921, 1978 and 2053 nm) had the highest accuracy for predicting potassium content in sugar beet, celery and both plant datasets (Rp2 = 0.85, Rp2 = 0.79, and Rp2 = 0.81, respectively). Conclusion The results confirm the universality of the described method. Although further validation studies involving other plant species are needed, it appears that the spectral reflectance technique could be a promising tool for the rapid, noninvasive and cost-effective estimation of K content in plant leaves, contributing to a significant step forward in precision fertilization management.

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.

Horse withers height is frequently estimated based on skeletal remains during archeozoological analyses. The routinely used methods (Vitt and Kiesewalter) do not allow withers height estimation without complete long bone and skull osteometry. The modified Wyrost and Kucharczyk formula is based on the internal dimensions of the cranial cavity. This method can be used even when the neurocranium is the only surviving portion of the skull. Earlier investigation demonstrated that it can be used as a substitute for the Kiesewalter method. The statistical analyses of the results achieved using metapodial bones are strongly correlated with the results achieved using the Wyrost and Kucharczyk method. A unique horse skull assemblage, dating back to medieval times, was unearthed from several archeological sites in Poland (Silesia, Kuyavia, Grater Poland, Western Pomerania, and Eastern Pomerania). This study aimed to estimate the withers height of this assemblage using the modified Wyrost and Kucharczyk method. Subsequently, these estimations were compared with accessible literature data from both medieval Poland and surrounding territories. The literature indicates that the horses from Western Pomerania, Silesia, and Kuyavia were larger than the animals from Eastern Pomerania. Our results show that horses from Western Pomerania were larger than those from Silesia, Kuyavia, and Eastern Pomerania. In both cases, the Western Pomeranian horses are the largest. In general, according to our results and the accessible literature, it seems true that Polish medieval horse populations can be described as medium- and small-sized, according to Vitt's classification. The modified Wyrost and Kucharczyk formula can be used as an additional or alternative method of calculating withers height in routine archeozoological studies.

The variations in vegetation coverage (defined as the area with Normalised Difference Vegetation Index (NDVI) > 0.2) and atmospheric patterns occurring during various vegetation seasons in the Kabul River Basin (KRB) in Afghanistan during 2001–2019 were analyzed. The analysis was done based on the NDVI, land surface temperature (LST), precipitation images from the remote sensing data, and geopotential height and temperature at 500 hPa from the retrospective datasets. The results revealed that the vegetation dynamics in KRB are impacted by both precipitation and LST. In the winter season, the LST has a more substantial role in shaping the vegetation dynamics than precipitation, while it is on contrary during the summer season. Cluster analysis showed that the four atmospheric patterns (e.g., Sub-Tropical High Pressure (STHP), Western European ridge-the Eastern Mediterranean and the Black Sea trough, Caspian Sea ridge (CSR), and the Mediterranean Sea trough-Central to Eastern Iran trough) can be identified and connected with the periods with the highest and the lowest vegetation coverage (VC) anomalies in the study area. The CSR and the Mediterranean Sea trough-the Central to Eastern Iran trough are the patterns responsible for the most positive VC anomalies. At the same time, the STHP and Western Europe ridge-the Eastern Mediterranean and the Black Sea trough are responsible for the most negative VC anomalies. As the atmospheric patterns have a significant role in shaping the vegetation status, a quick alert system to prevent agricultural areas from water or temperature stresses can be developed based on observations of them.