Search Results (33)

Climate change is disrupting marine ecosystems, necessitating a deeper understanding of environmental and fishing-related impacts on exploited species. This study examines the effects of physical factors (temperature, thermal anomalies, salinity, seabed conditions), biogeochemical elements (pH, oxygen levels, nutrients, primary production), and fishing pressure on the biomass of commercially important species in the Alboran Sea from 1999 to 2022. Data were sourced from the Copernicus observational program, focusing on the geographical sub-area 1 (GSA-1) zone across three depth ranges. Generalized Additive Models were applied for analysis. Rising temperatures and seasonal anomalies have largely negative effects, disrupting species’ physiological balance. Changes in water quality, including improved nutrient and oxygen concentrations, have yielded complex ecological responses. Fishing indices highlight the vulnerability of small pelagic fish to climate change and overfishing, underscoring their economic and ecological significance. These findings stress the urgent need for ecosystem-based management strategies that integrate climate change impacts to ensure sustainable marine resource management. Full article

Water, the lifeblood of our planet, sustains ecosystems, economies, and communities. However, climate change and increasing hydrological variability have exacerbated global water scarcity, threatening livelihoods and economic stability. According to the United Nations, over 2 billion people currently live in water-stressed regions, a figure expected to rise significantly by 2030. To address this urgent challenge, this study proposes an AI-driven anomaly detection framework for smart water metering networks (SWMNs) using machine learning (ML) techniques and data resampling methods to enhance water conservation efforts. This research utilizes 6 years of monthly water consumption data from 1375 households from Location A, Windhoek, Namibia, and applies support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (kNN) models within ensemble learning strategies. A significant challenge in real-world datasets is class imbalance, which can reduce model reliability in detecting abnormal patterns. To address this, we employed data resampling techniques including random undersampling (RUS), SMOTE, and SMOTEENN. Among these, SMOTEENN achieved the best overall performance for individual models, with the RF classifier reaching an accuracy of 99.5% and an AUC score of 0.998. Ensemble learning approaches also yielded strong results, with the stacking ensemble achieving 99.6% accuracy, followed by soft voting at 99.2% and hard voting at 98.1%. These results highlight the effectiveness of ensemble methods and advanced sampling techniques in improving anomaly detection under class-imbalanced conditions. To the best of our knowledge, this is the first study to explore and evaluate the combined use of ensemble learning and resampling techniques for ML-based anomaly detection in SWMNs. By integrating artificial intelligence into water systems, this work lays the foundation for scalable, secure, and efficient smart water management solutions, contributing to global efforts in sustainable water governance. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models. Full article

According to the climate projections, drought will increase in frequency and severity. Since water stress (WS) impacts a grapevine’s physiology and yield negatively, the evaluation and selection of tolerant genotypes are needed. To analyse the WS effects on the morphology and cell division of three grapevines (Vitis vinifera L.) varieties, “Touriga Franca” (TF), “Touriga Nacional” (TN) and “Viosinho” (VS), in vitro-grown plants were exposed to 10% polyethylene glycol 6000 (PEG) (−0.4 MPa) or 20% PEG (−0.8 MPa), incorporated in the culture medium, for four weeks. Control plants were kept in culture media without PEG. The VS and TN plants showed the highest mean numbers of nodes, shoots and leaves and average mitotic indexes under 20% PEG. The TF and TN plants showed the lowest frequencies of mitotic anomalies under 10% PEG. The VS plant growth was less affected by WS, but TF and TN presented more regular mitosis under moderate WS. Globally, in vitro culture constitutes a cost-effective experimental system for studying grapevine responses to WS and the preliminary selection of resilient genotypes. These approaches could be applied to study plant responses to other abiotic stresses based on additional evaluation techniques (e.g., transcriptional analyses or genome-wide association studies). Full article

Vegetation indices are widely used to assess vegetation dynamics. The Normalized Vegetation Index (NDVI) is the most widely used metric in agriculture, frequently as a proxy for different physiological and agronomical aspects, such as crop yield or biomass, crop density, or drought stress. Much effort has therefore been directed to NDVI forecasting, which is usually correlated with precipitation. However, in Mediterranean and arid climates, the relationship is more complex due to prolonged dry periods and sparse precipitation events. In this study, we forecast the NDVI 7 and 30 days ahead for Mediterranean permanent grasslands using a machine learning Random Forest (RF) model for the period from 2015 to 2022. The model compares two soil moisture products as predictors: simulated soil moisture values based on in situ soil moisture sensor observations and remote sensing-derived observations of Soil Water Index (SWI) values. We further analyzed the anomalies of the predicted NDVI using the z-score. The results show that both products can be used as reliable predictors for permanent grasslands in Mediterranean areas. Predictions at 7 days are more accurate and better forecast the negative effect of drought on vegetation dynamics than those at 30 days. This study shows the potential of using a simple methodology and readily available data to predict the grassland growth dynamics in the Mediterranean area. Full article

The cultivation of walnuts (Juglans regia L.) has become increasingly popular worldwide due to the nutritional value of the nuts. Plant biostimulants (PBs) and nutrient-rich products have been increasingly used in agriculture to improve yield, quality, and abiotic stress tolerance. However, farmers need fast laboratory studies to determine the most suitable treatment per crop or ecosystem to take full advantage of these products. Evaluating nucleolar activity and protein content can provide clues about the most appropriate treatment. This study aimed to determine how five commercial products, four PBs based on seaweed extract and/or free amino acids and one boron-enriched fertiliser used as foliar sprays, affect walnut cv’s nucleolar activity and protein content. “Franquette” from an orchard located in NE Portugal was compared to untreated (control) plants. All treatments brought a low leaf mitotic index. The control showed the smallest nucleolar area, highest protein content, and highest frequency of nucleolar irregularities. Fitoalgas Green^®, Sprint Plus^®, and Tradebor^® showed the highest nucleolar area and lowest frequencies of nucleolar irregularities. The recruitment of proteins/enzymes for response against abiotic stresses may explain the high protein content in the control. Hence, the enhanced abiotic stress tolerance of the treated trees explains their lower protein content and frequency of nucleolar anomalies. Globally, the Fitoalgas Green^®, Sprint Plus^®, and Tradebor^® seem better suited for “Franquette” walnut trees under the edaphoclimatic conditions where trials were conducted. Full article

The Shuangwang Au deposit (with a gold resource of approximately 70 t Au), is located in the Fenxian-Taibai fore-arc basin in the West Qinling Orogen of central China. Igneous intrusions in the region include the Xiba granitic pluton and granite porphyry and lamprophyre dykes. The Xiba pluton is composed of granodiorite and monzonite granite. The granodiorite is typical I-type granite, and it yields a crystallization age of 221.1 ± 1.2 Ma and a two-stage Hf model age of 1432–1634 Ma. The monzonite granite shows a transitional characteristic between I-type and A-type granite, and it yields a crystallization age of 214.8 ± 1.2 Ma and a two-stage Hf model age of 1443–1549 Ma. The granitoid was derived mainly from a crust–mantle mixed source. The ages indicate that the granodiorite and monzonite granite formed during two different stages. The REE distribution patterns of the Xiba granitoid exhibit significant fractionation between LREE and HREE, showing right-dipping curves, with an enrichment of LREE and a deficit of HREE. The granodiorite displays a light negative Eu anomaly, while the monzonite granite displays an obvious negative Eu anomaly. The granite porphyry dikes are distributed in the No. I breccia and Jiupinggou granite porphyry, and they yield crystallization ages of 219.9 ± 1.5 Ma and 213.1 ± 0.89 Ma, respectively, and two-stage Hf model ages of 1382–1501 Ma and 1373–1522 Ma, respectively. The lamprophyre dikes in the deposit yield a crystallization age of 214.4 ± 2.7 Ma. After the collision event between the Yangtze and the North China Plates along the Qinling orogenic belt, at approximately 220 Ma in the Late Triassic, the detachment of the slab produced the upwelling of the asthenosphere material. Under conditions of mantle heat and tectonic stress, widespread partial melting of the subducted continental crust and the upper lithosphere mantle occurred, forming granitoids with various degrees of adakite characteristics. Full article

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R² between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino’s effects and the reduced maize yields in Zimbabwe. The R² between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers’ livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. Full article

Climate-change-induced variations in temperature and rainfall patterns are a serious threat across the globe. Flooding is the foremost challenge to agricultural productivity, and it is believed to become more intense under a changing climate. Flooding is a serious form of stress that significantly reduces crop yields, and future climatic anomalies are predicted to make the problem even worse in many areas of the world. To cope with the prevailing flooding stress, plants have developed different morphological and anatomical adaptations in their roots, aerenchyma cells, and leaves. Therefore, researchers are paying more attention to identifying developed and adopted molecular-based plant mechanisms with the objective of obtaining flooding-resistant cultivars. In this review, we discuss the various physiological, anatomical, and morphological adaptations (aerenchyma cells, ROL barriers (redial O₂ loss), and adventitious roots) and the phytohormonal regulation in plants under flooding stress. This review comprises ongoing innovations and strategies to mitigate flooding stress, and it also provides new insights into how this knowledge can be used to improve productivity in the scenario of a rapidly changing climate and increasing flood intensity. Full article

Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. However, acquiring a large amount of properly annotated hyperspectral data on stressed crops requires extensive biochemical experiments and specialized knowledge. This limitation poses a challenge to the construction of large-scale datasets for crop stress analysis. Meta-learning is a learning approach that is capable of learning to learn and can achieve high detection accuracy with limited training samples. In this paper, we introduce meta-learning to hyperspectral imaging and crop detection for the first time. In addition, we gathered 88 hyperspectral images of drought-stressed tomato plants and 68 images of freeze-stressed tomato plants. The data related to drought serve as the source domain, while the data related to frost damage serve as the target domain. Due to the difficulty of obtaining target domain data from real-world testing scenarios, only a limited amount of target domain data and source domain data are used for model training. The results indicated that meta-learning, with a minimum of eight target domain samples, achieved a detection accuracy of 69.57%, precision of 59.29%, recall of 66.32% and $F_1$-score of 62.61% for classifying the severity of frost stress, surpassing other methods with a target domain sample size of 20. Moreover, for determining whether the plants were under stress, meta-learning, with a minimum of four target domain samples, achieved a detection accuracy of 89.1%, precision of 89.72%, recall of 93.08% and $F_1$-score of 91.37% outperforming other methods at a target domain sample size of 20. The results show that meta-learning methods require significantly less data across different domains compared to other methods. The performance of meta-learning techniques thoroughly demonstrates the feasibility of rapidly detecting crop stress without the need for collecting a large amount of target stress data. This research alleviates the data annotation pressure for researchers and provides a foundation for detection personnel to anticipate and prevent potential large-scale stress damage to crops. Full article

The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought. Full article

Under the background of global warming, seasonal drought has become frequent and intensified in many parts of the world in recent years. Drought is one of the most widespread and severe natural disasters, and poses a serious threat to normal sugarcane growth and yield. However, a deep understanding of sugarcane responses to drought stress remains limited, especially at a large spatial scale. In this work, we used the traditional vegetation index (enhanced vegetation index, EVI) and newly downscaled satellite solar-induced chlorophyll fluorescence (SIF) to investigate the impacts of drought on sugarcane in a major sugarcane-planting region of China (Chongzuo City, Southwest China). The results showed that Chongzuo City experienced an extremely severe drought event during the critical growth periods of sugarcane from August to November 2009. During the early stage of the 2009 drought, sugarcane SIF exhibited a quick negative response with a reduction of approximately 2.5% from the multiyear mean in late August 2009, while EVI was not able to capture the drought stress until late September 2009. Compared with EVI, sugarcane SIF shows more pronounced responses to drought stress during the later stage of drought, especially after late September 2009. SIF anomalies can closely capture the spatial and temporal dynamics of drought stress on sugarcane during this drought event. We also found that sugarcane SIF can provide earlier and much more pronounced physiological responses (as indicated by fluorescence yield) than structural responses (as indicated by the fraction of photosynthetically active radiation) to drought stress. Our results suggest that the satellite SIF has a great potential for sugarcane drought monitoring in a timely manner at a large spatial scale. These results are important for developing early warning models for sugarcane drought monitoring, and provide reliable information for developing measures to relieve the negative impacts of drought on sugarcane yield and regional economics. Full article

This work estimates yield losses due to drought events in the mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between the leaf area index (LAI) and grassland yield, we exploit the LAI as a proxy for yield. We estimate the LAI by using the Sentinel-2 biophysical processor and compare different gap-filling methods, including time series interpolation and fusion with Sentinel-1 SAR data. We derive the grassland production index (GPI) as the growing season cumulate of the daily product between the LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as an anomaly of the GPI. The validation of the Sentinel-2 LAI with ground measurements showed an RMSE of 0.92 [m² m⁻²] and an R² of 0.81 over all the measurement sites. A comparison between the GPI and yield showed, on average, an R² of 0.56 at the pixel scale and an R² of 0.74 at the parcel scale. The developed prototype GPI index was used at the end of the growing season of the year 2022 to calculate the payments of an experimental insurance scheme which was proposed to a group of farmers in Trentino-South Tyrol. Full article

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km${}^2$) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018. Full article