Search Results (5,995)

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

Soils with an albic horizon (characterized by a bleached, nutrient-poor eluvial layer), classified primarily as Albic Planosols and associated groups (e.g., Albic Luvisols and Retisols) in the World Reference Base for Soil Resources (WRB), are widespread in Northeast China and suffer from inherent poor nutrient availability and low crop productivity. The present study aimed to evaluate the efficacy of novel microbial–enzyme composite biofertilizers in ameliorating Albic soils. This comprehensive assessment investigated their effects on soil nutrient availability, microbial community structure, and the activities of key enzymes involved in nutrient cycling (e.g., dehydrogenase and phosphatase). Concurrently, the impact on maize crop performance was determined by measuring changes in agronomic traits, including chlorophyll content, stem diameter, and final grain yield. A field experiment was conducted in Heilongjiang Province during the 2023 maize growing season using a randomized block design with six treatments: CF (conventional chemical fertilizer, 330 kg·ha⁻¹ NPK), OF (chemical fertilizer + 1500 kg·ha⁻¹ organic carrier), BF1 (OF + 75 kg·ha⁻¹ marine actinomycetes), BF2 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ phytase), BF3 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ mycorrhizal fungi + 45 kg·ha⁻¹ phytase), and BF4 (OF + 75 kg·ha⁻¹ actinomycetes + 45 kg·ha⁻¹ mycorrhizal fungi + 45 kg·ha⁻¹ phytase + 45 kg·ha⁻¹ β–glucosidase). The results showed that biofertilizers significantly increased microbial abundance and enzyme activity. The integrated treatment BF4 notably enhanced topsoil fungal abundance by 188.1% and dehydrogenase activity in the 0–20 cm layer, while also increasing available phosphorus by 92.6% at maturity. Although BF4 improved soil properties the most, BF3 produced the highest maize yield—boosting grain output by 18.3% over CF—and improved stem diameter and chlorophyll content. Strong correlations between microbial parameters and enzyme activities indicated a nutrient-cycling mechanism driven by microorganisms, with topsoil fungal abundance positively linked to alkaline phosphatase activity (r = 0.72) and subsoil bacterial abundance associated with available phosphorus (r = 0.65), demonstrating microbial–mediated carbon–phosphorus coupling. In conclusion, microbial–enzyme biofertilizers, particularly BF4, provide a sustainable strategy for enhancing Albic soil fertility and crop productivity. Full article

This study investigates the vegetation dynamics and phenotypic differentiation of five triticale (×Triticosecale Wittm.) varieties under the region-specific agroecological conditions of Southern Dobruja, Bulgaria, across two growing seasons (2024–2025), with the aim of evaluating how local climatic variability shapes vegetation index patterns. UAV-based multispectral imaging was employed throughout key phenological stages to obtain reflectance indices, including NDVI, SAVI, EVI2, and NIRI, which served as indicators of canopy development and physiological status. NDVI was used as the primary reference index, and a baseline value (NDVIbase), defined as the mean NDVI across all varieties on a given date, was applied to evaluate relative varietal deviations over time. Multiple linear regression analyses were performed to assess the relationship between NDVI and baseline biometric parameters for each variety, revealing that varieties 22/78 and 20/52 exhibited reflectance dynamics most closely aligned with expected developmental trends in 2025. In addition, the relationship between NDVI and meteorological variables was examined for the variety Kolorit, demonstrating that relative humidity exerted a pronounced influence on index variability. The findings highlight the sensitivity of triticale vegetation indices to both varietal characteristics and short-term climatic fluctuations. Overall, the study provides a methodological framework for integrating UAV-based multispectral data with meteorological information, emphasizing the importance of region-specific, time-resolved monitoring for improving precision agriculture practices, optimizing crop management, and supporting informed variety selection. Full article

With the growing demands and expectations associated with professionalised youth sport environments, there is an increasing need for psychosocial development initiatives to support young athletes and their healthy progression. The aim of this study was to extend and investigate the application of the 5Cs framework, a prominent psychoeducational approach in sport psychology, to a youth tennis Talent Development Environment (TDE). Using a collective case study design, five athletes, their parents and two coaches (n = 12) participated in a season-long multimodal 5Cs intervention programme at a British Regional Player Development Centre (RPDC). The 30-week programme was delivered by an embedded sport psychology practitioner (SPP) and incorporated a blocked educational curriculum supported by a range of athletes, coach and parent development strategies. Post-intervention semi-structured interviews were conducted with all participants, with reflexive thematic analysis leading to three overarching themes. Findings highlighted the positive influence of the programme, with perceptions of the framework’s effectiveness associated with its specificity to tennis and individual athlete needs, the collaboration of all stakeholders across the TDE and the use of developmentally accessible and innovative strategies enabling evidence of athlete improvements. Researchers, practitioners and sport organisations are encouraged to consider these impact factors in terms of supporting the development, performance and well-being of athletes and their families in competitive youth sport contexts. Full article

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions. Full article

Polyethylene film (PE) mulching produces substantial “white pollution,” prompting the use of biodegradable film (BF) alternatives, yet their performance in rice systems on Northeast black soils is still uncertain. We compared three BFs with different induction periods (45 d, BF₄₅; 60 d, BF₆₀; 80 d, BF₈₀), PE and a no-film control (CK) to quantify their effects on soil hydrothermal conditions, rice growth, yield, grain quality, irrigation water use efficiency (IWUE) and soil C, N. Results showed that mulching increased soil temperature and soil moisture. Across the growing season, the mean soil temperature at the 0–5 cm depth under PE was 5.5% and 2.2–5.5% higher than that under CK and BFs, respectively. Specifically, compared with CK, PE increased grain yield by 31–77% and IWUE by 75–123%, while BFs improved yield by 25–73% and IWUE by 48–101%. PE only slightly outperformed BF₈₀ in yield (by 2.3% in 2023 and 2.1% in 2024) but achieved higher IWUE (11.0–11.7%). Grain chalkiness and sensory scores under BFs were comparable to PE and better than CK. At 0–20 cm, PE increased SOC (2.3–6.8%) and the C/N ratio (0–0.8%) but reduced total nitrogen (TN) (2.7–3.9%) and total carbon (TC) (2.5–3.1%), whereas BFs increased Org-N by 0.4–4.2%, SOC by 2.9–7.1%, and TN by 0.2–0.7%, with BF₈₀ showing the greatest stimulatory effect. Overall, BFs—particularly BF₈₀—are promising substitutes for PE in black soil rice systems, supporting sustainable rice production with strong application potential. Full article

Climate variability necessitates the optimization of sowing dates for vegetable crops to stabilize yields and mitigate abiotic stress risks. This study aimed to evaluate the effect of sowing dates on the productivity of daikon radish (Raphanus sativus L. convar. acanthiformis Sazon.) cultivars Gulliver and Minowase under medium-podzolic, light loamy soil conditions with a pH (pHKCl) of 6.74 during the period 2022–2024. Field experiments were conducted across four sowing dates (ranging from July to early August), accounting for the hydrothermal conditions of the growing season. Effective air temperatures ranged from 428 to 950 °C, with precipitation levels between 36.9 and 252.3 mm. It was established that the sowing date significantly influenced daikon yield (p < 0.001). A significant positive correlation was identified between yield and precipitation (r = 0.76–0.84; p < 0.05), whereas the correlation between yield and the sum of effective temperatures was weak to moderate and predominantly negative (r = −0.62 to −0.10). The highest yields were achieved with sowing in the third ten-day period of July: 54.6 t ha⁻¹ for the Gulliver cultivar and 58.9 t ha⁻¹ for the Minowase cultivar. The Minowase cultivar consistently outperformed Gulliver in terms of yield and exhibited higher ecological plasticity under fluctuating hydrothermal conditions. These findings confirm the feasibility of optimizing sowing dates as an effective adaptive tool for enhancing the stability of daikon production amidst climate change. Full article

Potato virus Y (PVY) and potato leafroll virus (PLRV) are the most damaging viruses for potato production worldwide. Mixed infections not only result in additive detrimental effects on plant growth and tuber yield but also complicate the development of durable and broad-spectrum viral resistance. Heterologous protection against PVY can be achieved through the expression of the coat protein (CP) of lettuce mosaic virus (LMV) (CPLMV), conferring resistance via a capsid protein-mediated mechanism. On the other hand, we have previously demonstrated that transgenic lines expressing the PLRV ORF2 (RepPLRV) exhibit resistance to different PLRV isolates. In this study, potato transgenic lines of cv. Kennebec expressing CPLMV and RepPLRV were developed to confer dual virus resistance. Transgenic and non-transgenic control plants were molecularly and phenotypically characterized in greenhouse and field conditions. Across multiple growing seasons, two selected transgenic lines consistently displayed robust resistance to both major viruses, without exhibiting yield penalties or noticeable phenotypic alterations. These results constitute a significant advancement, demonstrating that dual resistance to PVY and PLRV can be achieved while preserving the original agronomic performance of the cultivar. This breakthrough not only contributes to long-term crop productivity but also provides a more sustainable strategy for managing viral diseases in potato production. Full article

The adoption of conservation systems in agriculture has been increasingly explored as a strategy to improve soil quality and potentially influence greenhouse gas (GHG) emissions. This study reports the first assessment of GHG emissions within a long-term (14 years) agroecological field experiment evaluating soil management systems for onion (Allium cepa L.) production in a Humic Dystrudept (Cambissolo Húmico Distrófico, Brazilian Soil Classification System) in Southern Brazil. Three management systems based on permanent soil cover and crop diversification were evaluated in an onion–maize rotation: conventional tillage (CT) without cover crops, no-till (NT) without cover crops, and a no-till vegetable system (NTV) with a summer cover crop mixture of pearl millet (Pennisetum americanum), velvet bean (Mucuna aterrima), and sunflower (Helianthus annuus). Short-term GHG emissions were monitored during one onion growing season (106 days), while soil chemical and physical properties reflect long-term management effects. Evaluations included (i) daily and cumulative GHG (N₂O, CH₄, and CO₂) emissions, (ii) soil carbon (C) and nitrogen (N) stocks, (iii) soil aggregation, porosity, and bulk density in different soil layers (0.00–0.05, 0.05–0.10, and 0.10–0.30 m), and (iv) onion yield and cover crop dry matter production. The NTV system improved soil physical and chemical quality and increased onion yield compared to NT and CT. However, higher cumulative N₂O emissions were observed in NTV, highlighting a short-term trade-off between increased N₂O emissions and long-term improvements in soil quality and crop productivity. All systems acted as methane sinks, with greater CH₄ uptake under NTV. Despite higher short-term emissions, the NTV system maintained a positive C balance due to long-term C accumulation in soil. Short-term greenhouse gas emissions were assessed during a single onion growing season, whereas soil carbon stocks reflect long-term management effects; CO₂ fluxes measured using static chambers represent ecosystem respiration rather than net ecosystem carbon balance. These results provide an initial baseline of GHG dynamics within a long-term agroecological system and support future multi-year assessments aimed at refining mitigation strategies in diversified vegetable production systems. Full article

Vegetation within riparian and floodplain undergoes significant alterations driven by climatic factors and human interventions, particularly influenced by cascade hydropower development. This study investigated the dynamics of the Normalized Difference Vegetation Index (NDVI) in riparian and representative floodplains vegetation under cascade hydropower development in the middle reach of Hanjiang River by using Landsat imagery and hydroclimatic station data. The vegetation NDVI of the riparian increased significantly (p < 0.01) during the growing season, and the vegetation NDVI of the riparian and typical floodplains also increased after the hydropower developments. In terms of the key driving factors, the increased annual water level may explain the reduction in most floodplains. Increasing temperature, especially in March, can promote vegetation growth of riparian and typical floodplains. The development of cascade hydropower may result in different contributions of climate and hydrology to vegetation at different periods, and it was found that the climate is the major contributor to the changes in the vegetation NDVI after the construction of the dam. This research will help clarify the impact of cascade hydropower development on vegetation in riparian and floodplain ecosystems. It also provides a scientific basis for vegetation protection and environmental restoration in the basin. Full article

Rainfall is an essential component of boreal forest ecosystems. Aerotechnogenic pollution significantly affects the composition of rainfall. To predict the dynamics of biogeochemical cycles and develop strategies to enhance forest resilience in the Arctic zone, it is necessary to study the composition and characteristics of rainfall. The objective of this study is to evaluate the variation in the chemical composition of stemflow in the most typical pine and spruce forests of Fennoscandia under conditions of aerotechnogenic pollution based on long-term monitoring data from 1999 to 2022. The research was carried out in forests exposed to atmospheric industrial pollution from the largest copper–nickel smelter in northern Europe (Murmansk Region, Russia). The study of rainwater composition was conducted in four microsites: open areas (OA), between crowns (BWC), below crowns (BC) and stemflow (SF). A significant influence of the tree canopy on the rainfall composition was noted. Stemflow was found to have the highest concentration of pollutants, indicating a significant biochemical role of this type of precipitation. The results showed an increase in the concentrations of heavy metals and sulfates in rainwater as we moved closer to the pollution source. Below crowns and in the stemflow of spruce forests, element concentrations are higher compared to pine forests. The highest concentrations of major pollutants in stemflow (Ni, Cu and SO₄²⁻) are observed in June—at the beginning of the growing season. Long-term dynamics reveal a decrease in the concentrations of Cu, Cd and Cr in defoliated forests and technogenic sparse forests. Stemflow volume rises from background to technogenic sparse forests due to deteriorating tree-crown conditions. This is associated with the deteriorating condition of tree stands, as manifested by reductions in tree height, diameter and needle cover. It has been established that under pollution conditions, trees’ assimilating organs actively accumulate heavy metals, thereby altering the composition of precipitation passing through the canopy. Full article

Industrial hemp (Cannabis sativa L.) is a multipurpose crop with growing interest due to its environmental adaptability, low input requirements, and potential contribution to sustainable agricultural systems. This study evaluated the agronomic performance of four industrial hemp varieties grown under the edaphoclimatic conditions of the Alentejo region over two consecutive growing seasons (2024 and 2025) using different sowing dates. Phenological stages, plant height and growth parameters were monitored, complemented by meteorological data obtained from IPMA. The results revealed clear differences between years. The later sowing date in 2024 promoted greater vegetative growth, resulting in taller plants, while the earlier sowing in 2025 extended the vegetative phase and delayed flowering. Varietal differences were also observed, particularly for Fibror 79, which flowered slightly later, suggesting greater photoperiod sensitivity. These patterns confirm that both thermal environment and sowing date play a decisive role in hemp phenological development. The findings also highlight the high plasticity of the crop, which demonstrated strong adaptation to the hot and dry Mediterranean summers. Overall, appropriate selection of variety and sowing date can optimize vegetative and reproductive development, representing an important strategy for sustainable agricultural systems in the Alentejo region. Full article

This paper reports the findings of the first initiative of developing a year-round streamflow forecasting system using the HBV hydrologic model in a data-scarce Ruvu catchment in Tanzania. Considering the importance of the Ruvu catchment as the main source of water to the fast-growing mega city of Dar es Salaam, the researchers in this study made the most of the available data and their joint previous application experience of the modelling framework for the purpose of setting up a reliable operational model. In addition, the researchers adopted a phased approach of developing the streamflow forecasting system, using HBV as a hydrological model, which resulted in a simplified model structure with minimized complexity. For instance, the snow routine was removed as it is not relevant to the study area, and a few parameters were reduced to improve model efficiency. As a measure to demonstrate model performance, in addition to the Nash–Sutcliffe Efficiency (NSE) parameter used for model calibration and verification, several other error functions and graphical displays were used. The model performance values, as measured by NSE for calibration and verification periods, are 0.85 and 0.82 for Ruvu Roadbridge (1H8A), and 0.80 and 0.82 for Kidunda (1H3), respectively, and all are classified as “Very Good”. In addition, the PBIAS of less than ±5% in calibration indicates excellent water balance simulation. Furthermore, the forecast’s performance in this study is evidenced by an annual forecast R² of 0.933, with operational meteorological forecasts improving to 0.962 with “perfect” precipitation; dry season performance with R² of 0.964, demonstrating high skill in baseflow-dominated periods; and the PBIAS for forecasts of 0.866, indicating a slight systematic under-forecasting correctable by a ~15% precipitation adjustment. Although the Ruvu catchment has been characterized by this study as a data-scarce catchment, the results of the operational hydrological forecasting system vary with season and quality of forecast meteorological data, and the model is already launched for operational use. As evidenced by these study findings, the journey from data scarcity to operational forecast provision in the Ruvu catchment demonstrates that the principal barriers are fundamentally institutional and capacity-related. The authors suggest that any future forecasting initiative should put much emphasis on both the understanding of the modelling framework to be used and adequate data collection and analysis, in a synergetic manner with all relevant agencies. And it is also recommended to be vigilant regarding changes in the catchment characteristics and model performance during its life cycle, as the performance of the developed model is only valid under the condition that it was calibrated and validated. Full article

Methane emissions from rice paddies account for over 11% of global atmospheric CH₄, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving greenhouse gas estimation accuracy. This study investigates the backscattering mechanisms of L-band SAR for inundation/non-inundation classification in paddy fields using full-polarimetric ALOS-2 PALSAR-2 data. Field surveys and satellite observations were conducted in Ryugasaki (Ibaraki) and Sekikawa (Niigata), Japan, collecting 1360 ground samples during the 2024 growing season. Freeman–Durden decomposition was applied, and relationships with plant height and water level were analyzed. The results indicate that plant height strongly influences backscatter, with backscattering contributions from the surface decreasing beyond 70 cm, reducing classification accuracy. Random forest models can classify inundated and non-inundated fields with up to 88% accuracy when plant height is below 70 cm. However, when using this method, it is necessary to know the plant height. Volume scattering proved robust to incidence angle and observation direction, suggesting its potential for phenological monitoring. These findings highlight the effectiveness of L-band SAR for water management monitoring and the need for integrating crop height estimation and regional adaptation to enhance classification performance. Full article

The two-spotted spider mite, Tetranychus urticae Koch, is a major agricultural pest whose control is increasingly constrained by resistance to synthetic acaricides. This study evaluated the long-term field efficacy of three commercial entomopathogenic fungal (EPF) biopesticides—Velifer^® (Beauveria bassiana), Metab^® (B. bassiana + Metarhizium anisopliae), and Botanigard^® (B. bassiana)—against larval and protonymph stages of T. urticae on two host plants, Italian cypress (Cupressus sempervirens) and sweet orange (Citrus sinensis). Two foliar applications were conducted during the 2023 growing season (25 May and 25 July), and mite populations were monitored for 140 days after the final application. A randomized complete block design was used, and efficacy was calculated using the Henderson–Tilton formula. All EPF treatments significantly reduced mite populations compared with the untreated control throughout the monitoring period. Velifer consistently achieved the highest suppression of larval populations, particularly on C. sinensis, with efficacy comparable to the chemical standard. Botanigard showed more gradual but sustained population reduction over time, whereas Metab exhibited lower but stable efficacy in all cases. Treatment performance was strongly influenced by host plant species and mite developmental stage, with larvae consistently more susceptible than protonymphs. On C. sinensis, Velifer achieved the highest larval suppression (84.6%), comparable to the chemical standard abamectin, while Botanigard and Velifer were most effective on C. sempervirens. Survival analysis confirmed isolate- and host-dependent differences in hazard effects over time. These results demonstrate that EPF-based products can provide sustained, long-term suppression of T. urticae under field conditions, supporting their integration into integrated pest management programs. Full article