Search Results (2,383)

Lightning poses localized risks to signaling, communication, and traction-power systems along high-speed railways, yet most nowcasting products are generated on regular grids rather than operational line segments. This study developed a grid-to-segment machine-learning framework for next-hour lightning warning along the Jinan West–Tai’an corridor. Ground-based lightning observations, hourly ERA5 fields, temporal variables, and engineered historical lightning features and spatial-neighborhood features were organized on a 0.25° grid. Data from 2014 to 2018 were used for training, 2019 for validation, and 2020 for independent testing. Grid probabilities were converted into warnings for six railway segments using 10 km buffers and maximum-probability aggregation. In 2020, the full extreme gradient-boosting (XGBoost) model, a tree-based ensemble-learning algorithm, achieved grid-level probability of detection (POD), false-alarm ratio (FAR), and critical success index (CSI) values of 0.55, 0.50, and 0.36; segment-level verification yielded 0.60, 0.39, and 0.43. To examine transfer to forecast-driven application, the trained model and threshold were fixed, and ERA5 meteorological inputs were replaced by short-lead ECMWF HRES forecasts for July 2025. POD decreased from 0.85 to 0.80 and CSI from 0.60 to 0.57, while FAR remained nearly unchanged. Under a predefined non-zero rule, HRES litota1 achieved 0.62, 0.71, and 0.25. ML-HRES therefore showed higher CSI and lower FAR than the direct litota1 baseline. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

Understanding the interactions among mountain snow resources, river discharge, and irrigated agriculture is essential for sustainable water management in transboundary river basins of Central Asia. This study evaluated the relationships among high-elevation snow conditions, hydroclimatic factors, summer river discharge, and agricultural vegetation dynamics in the transboundary Chu River Basin during 2001–2025 using MODIS snow-cover, snow-persistence, and NDVI products together with meteorological and hydrological observations. The results showed that snow cover was widespread during winter but declined substantially during spring, while the Mann–Kendall analysis indicated that no statistically significant trend predominated across most of the high-elevation snow zone, although significant decreases in snow persistence occurred across 12.83% of the analyzed area. For summer cropland NDVI, 61.15% of the analyzed area showed no significant trend, while significant decreases and increases occurred across 26.24% and 12.62%, respectively. Spring temperature exhibited a significant negative correlation with snow persistence (r = −0.64, p < 0.001), indicating the sensitivity of mountain snow resources to warming conditions. Summer river discharge was strongly correlated with summer precipitation (r = 0.77, p < 0.0001), vegetated cropland area (r = 0.76, p < 0.0001), and mean summer NDVI (r = 0.69, p < 0.001). In addition, snow persistence showed a significant positive relationship with summer river discharge (r = 0.63, p < 0.001), vegetated cropland area (r = 0.52, p = 0.008), and mean summer NDVI (r = 0.42, p = 0.036), suggesting a linkage between high-elevation snow conditions, seasonal water availability, and downstream agricultural vegetation. Canonical correlation analysis further revealed a strong multivariate association between hydroclimatic and hydroagricultural variables (Rc = 0.900, Rc² = 0.810). Overall, the findings indicate significant statistical linkages among climate conditions, snow resources, river discharge, and irrigated agriculture, providing a scientific basis for water-resource management and climate-change adaptation in transboundary mountain-fed basins. Full article

The yield, consumer safety, and storage stability of potato tubers depend on the interaction between the genotypic characteristics of the cultivar, its intended use, the cultivation technology applied, and storage conditions. The aim of this study was to comprehensively assess the effects of seed potato dressing with a fungicide and a biostimulant, applied individually or in combination, on total and starch yield, the content of undesirable nitrogen compounds, and the storage stability of tubers. A field experiment was conducted over three growing seasons (2021–2023) at the Agricultural Experimental Station of the University of Warmia and Mazury in Olsztyn, using a randomized split-block design with three replications. The field experiment was conducted over three growing seasons (2021–2023) at the Agricultural Experiment Station in Tomaszkowo (53°42′ N, 20°26′ E). Three potato cultivars intended for chip processing were evaluated using a randomized split-block design with three replications. The storage experiment and laboratory analyses were conducted at Bydgoszcz University of Science and Technology. Analyses were performed immediately after harvest and after six months of storage under controlled conditions (8 °C and 95% relative humidity). The novelty lies in the comprehensive evaluation of the effects of fungicide and biostimulant seed treatments on potato yield, starch production, nitrate and nitrite accumulation, and storage losses. Potato genotype had a significant effect on tuber yield and the proportion of marketable tuber yield. The highest total and marketable tuber yields were obtained from the cultivar with the longest growing season. The study demonstrated variation in total tuber yield, marketable tuber yield, and the proportion of marketable tuber yield depending on the study year, reflecting differences in meteorological conditions among growing seasons. The cultivation technology did not significantly affect total tuber yield. Numerically, the highest total tuber yield (35.81 t ha⁻¹) was recorded following the combined application of fungicide and biostimulant, while the highest marketable tuber yield was observed after treatment with fungicide (23.40 t ha⁻¹). The potato cultivars intended for chip processing were characterized by low nitrate and nitrite contents (49.56 and 0.49 mg kg⁻¹ FM, respectively), not exceeding 200 mg kg⁻¹ limit for food intended for children. After six months of storage, the contents of these harmful nitrogen compounds decreased by an average of 8%, while natural storage losses remained low, averaging 3%. The effects of the cultivation factors applied during the growing season on nitrate and nitrite contents after storage were consistent with the trends observed immediately after harvest. These findings indicate that the cultivation technology evaluated in this study can be recommended for the production of potatoes intended for chip processing. However, further research involving a larger number of cultivars, including those intended for French fry processing and table use, is needed to confirm the broader applicability of these results. Full article

Rice is a staple grain crop central to China’s food security. As the core production region of single-cropping rice, the middle and lower reaches of the Yangtze River face escalating high daytime and nighttime temperatures and compound drought–heat stress amid global warming. The accurate assessment of heat hazards is therefore pivotal for regional yield stability and disaster mitigation. Based on meteorological, remote-sensing, and soil data, together with county-level rice yield statistics from 150 major producing counties spanning 1991 to 2024 (5009 county-year calibration units), we first constructed a composite heat damage index (CHI) by integrating daytime harmful accumulated temperature (Ha), nighttime harmful accumulated temperature (HNa), and the Vegetation Health Index (VHI). We then implemented a gradient boosting decision tree (GBDT) machine learning framework in which yield loss was imposed as a physical constraint. This framework was benchmarked against convolutional neural network (CNN), random forest (RF), and support vector machine (SVM) models, with the Shapley additive explanations (SHAP) method used for attribution analysis and an independent temporal partitioning strategy applied for model validation. The results indicate the following: (1) compared to the single daytime heat damage index, the CHI elevated the yield correlation coefficient from 0.52 to 0.63; (2) with yield constraint calibration, the model attained a balanced accuracy of 92.6% and 94.0% consistency with historical disaster records; (3) regional heat hazard presents a spatial pattern of “high in inland areas and low in coastal areas,” with the heading–flowering stage as the critical sensitive period; and (4) high nighttime temperature accounts for approximately 20% of the model’s relative importance, with higher discriminative sensitivity for high-grade hazards, while the amplifying effect of water deficit on heat stress maintains a stable relative importance of around 16%. In this study, the coupled optimization of traditional assessment paradigms and data-driven approaches is achieved, providing a methodological reference for refined growth stage–specific heat hazard assessment. Its cross-regional portability and independent predictive validity require further validation. Full article

The case of common ragweed (Ambrosia artemisiifolia) stands as a prime example of an invasive plant that links global change, biological invasion, aeroallergen exposure, and allergic airway disease. However, evidence remains fragmented across invasion biology, aerobiology, molecular allergology, and respiratory medicine. By separating plant occurrence, pollen abundance, molecular allergen dose, sensitisation, and airway disease, this review develops an integrated invasion–exposure–disease logic for interpreting ragweed-related health risk. We examine how climate change, land-use disturbance, repeated introductions, rapid adaptation, and air pollution influence plant distribution, flowering phenology, pollen production, airborne allergen load, and respiratory outcomes. Attention is given to Amb a 1 as the principal marker of genuine ragweed sensitisation, cross-reactivity with Artemisia and other weed pollens, allergen-bearing respirable particles, and the diagnostic limitations of extract-based immunoglobulin E (IgE) testing. The literature indicates that ragweed sensitisation follows a pronounced hotspot–gradient pattern in Europe, whereas patterns in other invaded regions remain more heterogeneous and incompletely characterised. Clinically relevant exposure depends not only on pollen concentration but also on airborne allergen load, pollen allergen potency, atmospheric transport, respirable particle fractions, meteorological conditions, and pollution. Ragweed-related airway disease is mediated by IgE-dependent type 2 immunity and amplified by epithelial danger signals, oxidative stress, protease activity, and innate immune pathways. Based on current evidence, we propose that an integrated surveillance framework linking plant distribution, pollen and airborne-allergen exposure, molecular sensitisation, symptoms, lung function, and asthma outcomes could strengthen risk forecasting, source attribution, prevention, and invasion control of the common ragweed. Full article

The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change, including more frequent and intense heatwaves, droughts, and floods. Consequently, reliable climate information and decision-support tools are essential for strengthening resilience and promoting sustainable management. To address these needs, the Portuguese Institute for the Sea and Atmosphere (IPMA) developed two complementary climate service platforms for mainland Portugal: AgroClima and DataClima. The first provides observations from IPMA’s meteorological network, ECMWF forecasts, and agroclimatic indicators such as temperature, precipitation, soil water, and so-called agroclimatic warnings. The second offers historical climate information including WRFv4.2 simulations dynamically downscaled from ERA5 (1981–present), in situ observations (1941–present), and climate normals. Evaluation of the WRFv4.2 regionalization against IPMA observations shows a systematic underestimation of precipitation and air temperature, while mean wind speed is generally overestimated. Despite these biases, the downscaled WRFv4.2 dataset demonstrates sufficient accuracy to support operational climate services, providing valuable help for environmental monitoring, climate adaptation, and decision-making in agriculture and water resource management across Portugal. Full article

Compound heat and drought events (CHDEs) increasingly threaten crop production, yet conventional assessments rarely account for phenological changes in crop heat sensitivity and water demand. Here, we developed a phenology-aware daily framework for summer maize in the Huang–Huai–Hai (HHH) Plain, China, integrating stage-specific heat thresholds with a crop-coefficient-adjusted standardized precipitation evapotranspiration index (SPEI_K_C) and a fixed cultivation distribution. Using daily meteorological observations from 1980 to 2020, CHDEs were characterized across the sowing-to-jointing, jointing-to-tasseling, and tasseling-to-maturity stages. Across the growing season, CHDE frequency and mean duration increased significantly, whereas mean intensity declined. Stage-specific responses differed markedly: frequency increased across all stages, while the tasseling-to-maturity stage showed the fastest increase in frequency and a significant lengthening of duration. Potential exposure also became progressively concentrated over crop development and was highest during tasseling-to-maturity in major maize-producing areas. By incorporating phenological variation into both heat and drought characterization, this framework resolves within-season differences in compound stress that are obscured by uniform-threshold approaches and provides a crop-relevant basis for stage-targeted monitoring and adaptation. Full article

This study investigates multi-horizon forecasting of solar-based green hydrogen (H₂) production in Southern Morocco (Dakhla: 23.7° N, 15.9° W; Laâyoune: 27.2° N, 13.2° W; and Guelmim: 29.0° N, 10.1° W) using daily meteorological time series (2010–2025) derived from NASA POWER and PVGIS. Daily H₂ production (kg/day) is estimated through a PV-to-hydrogen conversion model assuming a 100 MW PV plant, a performance ratio of 0.75, and a specific electricity consumption of 50 kWh/kg-H₂. We formulate a supervised learning problem to predict H₂ at multiple horizons (J + 1, J + 3, and J + 7), combining calendar features, physically motivated variables, and lagged/rolling statistics. Models are trained on 2010–2023 and evaluated on 2024–2025 using R², RMSE, and sMAPE. CatBoost, Random Forest, and LSTM are compared; additionally, a physically interpretable two-step framework is proposed. For J + 1, the best results reach R² values of 0.863 in Dakhla, 0.795 in Laâyoune, and 0.669 in Guelmim. At the J + 7 horizon, predictive performance remains robust with R² values of 0.792, 0.759, and 0.556, respectively. The proposed two-step approach yields comparable accuracy (e.g., Dakhla J + 1 R² = 0.862) while improving physical consistency. Full article

Climate change is increasing the frequency of heat stress events in dairy farming, adversely affecting milk production, milk composition, cow behavior, and animal health. However, many previous studies relied on distant weather-station data and low-frequency milk recording systems, limiting the assessment of short-term and delayed heat stress responses. Therefore, the aim of this study was to combine dense longitudinal data from automatic milking systems (AMS) with continuously recorded on-farm meteorological measurements to investigate the immediate and lagged effects of heat stress on Holstein dairy cows. The study included 386,587 AMS visit records from 790 cows on three commercial dairy farms in Germany, corresponding to up to 127,310 cow-day records collected between August 2022 and August 2025. Temperature–humidity index (THI) values were calculated based on dense on-farm temperature and relative humidity records and were evaluated for multiple lag periods prior to AMS recordings. Linear mixed models were applied to infer the effects of THI on production, physiological, behavioral, and milking process traits. Increasing THI was associated with reduced daily milk yield, altered milk fat and protein percentages, decreased AMS visit frequency, prolonged milking intervals, and increased milk temperature. For contemporaneous THI, an increase from THI 50 to THI 70 corresponded to model-estimated declines of −0.86 kg in daily milk yield, −0.20% in milk fat content and −0.06% in milk protein content, −0.12 daily AMS visits, and +1.14 °C in milk temperature. The strongest associations were generally observed for prompt and short-term lagged THI windows. In contrast, longer lag periods were associated with weaker and less distinct trait responses. Rather than merely confirming the established decline in milk yield under heat stress, the integrated and temporally resolved analysis revealed trait-specific response patterns across production, behavioral, physiological, health-related, and milking-process traits. In particular, milk temperature and voluntary AMS attendance showed pronounced associations with contemporaneous and short-term THI, demonstrating the value of combining AMS-derived phenotypes with high-resolution on-farm climate data for heat stress monitoring. Full article

Coastal green tides may alter nearshore bioaerosols through coupled marine, atmospheric, and meteorological processes, yet their effects on airborne microbial communities remain poorly resolved. Atmospheric samples were collected in Aoshan Bay, Qingdao, China, during five phases of the Ulva prolifera green tide in 2019 (pre-bloom, 19 April; early bloom, 15 June; middle bloom, 15 July; late bloom, 6 August; post-bloom, 30 August); seawater samples were collected at one nearshore site on each of the five sampling dates, with microbial sequencing performed for the middle-bloom (15 July) and late-bloom (6 August) phases. Bacterial and fungal communities were characterized; although bioaerosols may also contain microalgae and viruses, this study profiled only the bacterial and fungal fractions, using bacterial 16S rRNA gene (V3-V4 region) and fungal internal transcribed spacer (ITS2) amplicon sequencing and evaluated together with meteorological variables, air-pollutant concentrations, and 72-h backward air-mass trajectories. Proteobacteria dominated the airborne bacterial assemblages (81.28–97.83%), with Sphingomonas as the most abundant genus (47.85–89.84%). Basidiomycota and Ascomycota dominated the fungal assemblages, whereas Cryptococcus and Alternaria were the major fungal genera. Community richness and composition varied across bloom phases. Chytridiomycota was undetected before the bloom (0%), appeared after bloom onset, and reached its highest relative abundance during the middle phase (8.19%). Spatial patterns indicated joint terrestrial and marine influences, although bacterial communities in seawater and air remained highly dissimilar. Temperature, relative humidity, particulate matter, ozone, and air-mass origin were associated with changes in microbial diversity and composition. These findings provide an observational baseline for coastal bioaerosol dynamics during a macroalgal green tide, extending the HAB–bioaerosol literature—which has focused predominantly on cyanobacterial blooms—to a large green macroalga. Bacteria and fungi showed contrasting environmental responses: bacterial richness increased with temperature, whereas fungal diversity declined. Greater compositional similarity between seawater and air for fungi than for bacteria suggests differential environmental filtering at the air–sea interface and implies that multiple source pathways—direct aerosolization, sea-surface release, and in-situ atmospheric production—may differentially shape the two domains. Given the single-date-per-phase sampling design, the absence of sequenced laboratory contamination controls, and the lack of absolute abundance data, these results should be regarded as preliminary and hypothesis-generating, underscoring the need for ASV-level source tracking, controlled chamber experiments, and replicated multi-year designs in future assessments of bloom–atmosphere interactions. Full article

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we develop a physically informed random forest framework for global 1 km land AOD retrieval from MODIS. The framework integrates multispectral TOA reflectance, surface properties, observation geometry, meteorological conditions, topography, and physically informed aerosol–surface features. Long-term Aerosol Robotic Network (AERONET) observations from 2001 to 2017 were collocated with MODIS and ancillary datasets for model development and evaluation. Two physically informed features were introduced to improve retrieval robustness across diverse aerosol and surface conditions, including minimum AOD derived from long-term AERONET observations and time-series clear-sky reflectance (TSCR) in the blue, red, and shortwave-infrared bands derived using the 6S radiative-transfer model. Independent retrieval evaluation for 2013–2014 showed good agreement with AERONET observations, with R = 0.81, MAE = 0.063, RMSE = 0.096, and 74.93% of matched samples falling within the MODIS land expected-error envelope, although increasing underestimation was observed at high aerosol loading (AOD > 1). The proposed retrievals also showed better agreement with AERONET than the MOD04 Dark Target and Deep Blue products. These results demonstrate the value of incorporating physically interpretable aerosol-background and surface-reflectance information into data-driven retrievals for AOD over land surfaces. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

This study evaluates the potential and limitations of open/model-derived hydroclimatic data for multi-horizon river discharge forecasting in Babahoyo, Ecuador. A quantitative, applied, retrospective longitudinal design used daily data for 2017–2026. The discharge target is the GloFAS v4 seamless product—reanalysis until July 2022, archived operational forecast thereafter; meteorological predictors are ERA5/ERA5-Land/IFS reanalysis, not forecasts. The framework combined leakage-aware feature engineering, temporal validation, rolling-origin backtesting, naïve baselines, machine-learning regression, conformal prediction intervals, and high-flow classification. Performance was strongest at one day, where models reproduced the signal closely (R² = 0.909), although persistence remained highly competitive. Skill deteriorated at t + 7 and t + 14, where peak timing and magnitude became unreliable. Interval coverage was near-nominal at t + 1 but unreliable at longer horizons. The high-flow classifier identified most q90 cases, yet moderate precision and the absence of gauge validation prevent operational warning claims. Because the target is a 5 km grid simulation of a channel 100–150 m wide, the metrics quantify agreement with GloFAS, not with the physical river, and are reported to three significant digits. Overall, the study is a conservative benchmark for open hydroclimatic data in data-limited tropical floodplains: useful for exploratory monitoring and uncertainty diagnosis, but not a substitute for local hydrometric validation. Full article

Türkiye’s strategic geographical location offers an exceptional opportunity for solar energy harvesting, yet optimizing large-scale investments requires rigorous pre-assessment methodologies. This study presents a comprehensive multi-criteria techno-economic analysis and real-world validation of a 13.18 MWp solar power plant (SPP) located in Kirsehir, a region characterized by high solar irradiance (1750 kWh/m²). Utilizing PVsyst software, four distinct configurations—monofacial and bifacial modules at 21° and 25° tilt angles—were systematically simulated and evaluated across varying equity-to-loan ratios using key financial indicators (NPV, IRR, PI, and Payback Period). The simulation results identified the 21° bifacial configuration, enhanced by the innovative integration of high-albedo industrial calcite (CaCO₃) waste as ground cover, as the optimal engineering solution. Crucially, the accuracy of this optimization was evaluated against 12 months of field data. While the raw measured annual production was recorded as 22,793,323 kWh, the validation was strictly based on the production adjusted for grid outages (23,499,604 kWh). Comparing this adjusted value with the simulated annual generation (22,816,114 kWh) yielded a total annual discrepancy of only 3% and a volumetrically weighted average error of 5.07%. Furthermore, to isolate model fidelity from inter-annual meteorological variability, the validation was assessed using the Performance Ratio (PR). The adjusted volumetrically weighted PR (87.43%) demonstrated a remarkably close alignment with the simulated PR (87.48%), exhibiting a marginal deviation of merely 0.05%. These performance metrics indicate a general consistency between the simulation model and operational field records across the evaluated period. Environmentally, the maximized energy yield of the 21° bifacial system facilitates the avoidance of approximately 6507.58 tonnes of CO₂ emissions annually. This research not only establishes the viability of scalable, low-cost calcite ground covers but also provides a highly robust, de-risked decision-support framework for utility-scale PV investments in similar geographic latitudes. Full article