Search Results (44)

Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. Among semantic segmentation models, DeepLabV3+ had the best performance in PV extraction, and the Mean Intersection over Union, precision, and F1-score were 91.97%, 89.02%, 89.2%, and 89.11%, respectively, with accuracies close to 100% after manual correction. Subsequent land surface temperature inversion and spatial buffer analysis quantified the thermal environmental effects of PV installation. Localized cooling patterns may be influenced by albedo and vegetation dynamics, though further validation is needed. The total PV site area in Ningxia expanded from 59.62 km² to 410.06 km² between 2015 and 2024. Yinchuan and Wuzhong cities were primary growth hubs; Yinchuan alone added 99.98 km² (2022–2023) through localized policy incentives. PV installations induced significant daytime cooling effects within 0–100 m buffers, reducing ambient temperatures by 0.19–1.35 °C on average. The most pronounced cooling occurred in western desert regions during winter (maximum temperature differential = 1.97 °C). Agricultural zones in central Ningxia exhibited weaker thermal modulation due to coupled vegetation–PV interactions. Policy-driven land use optimization was the dominant catalyst for PV proliferation. This study validates “remote sensing + deep learning” framework efficacy in renewable energy monitoring and provides empirical insights into eco-environmental impacts under “PV + ecological restoration” paradigms, offering critical data support for energy–ecology synergy planning in arid regions. Full article

This study systematically investigates the mechanistic effects of multifactor interactions (including soil properties, climatic conditions, and cultivation practices) on the productivity parameters (grain yield, stover yield, dry biomass, harvest index) of maize cultivars of different maturity groups in the arid region of Xinjiang, China. Twelve representative maize-growing counties were selected as study sites, where we collected maize samples to measure HI, grain yield, stover yield, and soil physicochemical properties (e.g., organic matter content, total nitrogen, and available phosphorus). Additionally, climate data (effective accumulated temperature) and agronomic parameters (planting density) were integrated to comprehensively analyze the interactive effects of multiple environmental factors on HI using structural equation modeling (SEM). The results demonstrated significant varietal differences in HI across maturity periods. Specifically, early-maturing cultivars showed the highest average HI (0.58), significantly exceeding those of medium-maturing (0.55) and late-maturing varieties (0.54). Environmental analysis further revealed that soil phosphorus content (both available and total phosphorus), elevation, and organic matter content significantly positively affected HI, whereas soil bulk density and electrical conductivity exhibited negative impacts. Notably, HI exhibited a strong negative correlation with stover yield (R² = 0.49), but remained relatively stable across different dry matter (DM) and grain yield levels. Despite the strong positive correlation between DM and grain yield (R² = 0.81), the relative stability of HI suggests that yield improvement requires balanced optimization of both DM and partitioning efficiency. This study provides crucial theoretical foundations for optimizing high-yield maize cultivation systems, regulating fertilizer application rates and their ratios, and improving the configuration of planting density in arid regions. These findings offer practical guidance for sustainable agricultural development in similar environments. Full article

This paper introduces an innovative, environmentally sustainable, and climatic study analysing the impact of overhang depths on heating and cooling building energy demands in the Mediterranean Basin via dynamic energy simulations of a south-oriented reference residential building zone. The adopted bioclimatic approach aims at increasing building sustainability and suggests, for representative Köppen–Geiger climate subtypes, optimal overhang depths and climate-correlated depth domains. The definition of a large geoclimatic study based on 80 locations and the classification of results based on climate subtypes are two novelties introduced in this work. From the energy point of view, overhangs can reduce local building cooling needs by, on average, 27%, while decreasing the total final energy needs (Q_TOT) by 17%. A new approach is also introduced: comparing the energy reduction due to the addition of an overhang to commonly applied envelope retrofitting solutions, such as wall insulation or window substitutions. Overhangs show great potential in sites with arid climate subtypes and are more effective than other solutions in several locations. This study underlines the need to increase the adoption of passive cooling solutions by local retrofitting regulations in places with a Mediterranean climate, following a bio-regionalist approach able to increase the local buildings’ sustainable development. Full article

The arrival of psittacine in North America is well known but undefined. It is widely accepted that these birds originated in South America, and it has been suggested that different factors have promoted the biodiversity of birds in Mexico. However, in general, for North American psittacine, there are no proposed divergence times, and the possible influence of different geological events on these processes is unknown. In this study, phylogenetic relationships, divergence times, and ancestral areas of the genera Aratinga, Eupsittula, and Psittacara and related genera were estimated to propose hypotheses of the origin, diversification, and dispersal of groups under a Bayesian inference framework based on mitochondrial molecular markers. Of seven monophyletic clades within the Arini tribe, four coincided with the genera Psittacara, Eupsittula, Rhynchopsitta, and Pyrrhura, while Aratinga was grouped with Conuropsis and Cyanopsitta. Diversification of the analyzed genera probably occurred during the Miocene and around the Miocene–Pliocene boundary. The results suggest that the most likely origin of these genera is the Amazonian or Chaco regions. The diversification of these groups seems to be related to geoclimatic events associated with the uplift of the central and northern portions of the Andes and the closure of the Isthmus of Panama. We propose routes from south to north in the Neotropics and the use of the Greater and Lesser Antilles as a northward path. Full article

Exploring the gene flow and its causes in complex habitats of forest trees is valuable for understanding species’ adaptive evolution. Pinus yunnanensis var. tenuifolia (PYT) is mainly distributed in the dry–hot valleys along the Nanpan-Hongshui rivers in southwest China, an ecologically fragile area. In this study, we analyzed 1056 seeds from eleven natural populations of PYT across its range using twelve cpSSR markers to explore haplotype polymorphisms and correlations with environmental factors. The results revealed a high genetic diversity (H_E = 0.83), with the private haplotypes significantly exceeding the shared haplotypes. A genealogical structure was observed among the populations, with a moderate differentiation (F_ST = 0.162). The population clustering and haplotype network demonstrated localized areas of pollen exchange, especially in the middle and lower reaches of the river. Redundancy analysis showed that, as the populations were closer to the river, genetic diversity tended to decrease significantly, implying that the pollen dispersal is restricted by the foehn effect in the valley. Variability in genetic differentiation among the offspring populations was primarily influenced by geographic factors, such as mountains and rivers, which, along with local environmental adaptations, shaped the pollen distribution pattern. These findings may facilitate the sustainable management and conservation of PYT and other species under similar habitats. Full article

Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time series data, there is limited evidence of their application in predicting disease outbreaks in Africa. In recent times, the literature has reported several applications of machine learning in facilitating intelligent decision-making within the healthcare sector and public health. However, there is a scarcity of information regarding the utilization of the XGBoost model for predicting disease outbreaks. Within the provinces of Kenya, the incidence of Rift Valley fever was more prominent in the Rift Valley (26.80%) and Eastern (20.60%) regions. This study investigated the correlation between the occurrence of RVF (rapid vegetation failure) and several climatic variables, including humidity, clay content, elevation, slope, and rainfall. The correlation matrix revealed a modest linear dependence between different climatic variables and RVF cases, with the highest correlation, a mere 0.02903, observed for rainfall. The XGBoost model was trained using these climate variables and achieved outstanding performance measures including an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. The analysis of feature importance revealed that rainfall was the most significant predictor. These findings align with previous studies demonstrating the significance of weather conditions in RVF outbreaks. The study’s results indicate that incorporating advanced machine learning models that consider several climatic variables can significantly enhance the prediction and management of RVF incidence. Full article

Elaeagnus angustifolia L. is a highly adaptable urban ornamental plant, playing a key role in dry land and saline-alkali protective forests. The diverse geographical and climatic conditions in Gansu Province have resulted in variations in its distribution and growth. This study assesses the phenotypic diversity of fruits and seeds, and the seed germination characteristics of 82 E. angustifolia plants from nine populations in Gansu Province, exploring their relationship with geographical and climatic factors. We measured 12 phenotypic traits and five germination indices. This study included germination tests under standard conditions, statistical analysis of phenotypic differences, and Pearson and Spearman correlation analyses to examine relationships between traits and geo-climatic factors. Principal component and cluster analyses were also performed to identify key traits and classify populations. The findings were as follows: (1) Significant differences were observed in phenotypic traits and germination characteristics among populations. Single fruit weight showed the highest variation (27.56%), while seed transverse diameter had the lowest (8.76%). The Lanzhou population exhibited the greatest variability (14.27%), while Linze had the lowest (6.29%). (2) A gradient change pattern in traits was observed, primarily influenced by longitude and a combination of geographical and climatic factors. Seed germination was positively correlated with altitude, annual precipitation, and relative humidity, but negatively affected by latitude and traits such as fruit weight. (3) Principal component analysis identified germination rate, germination index, seed shape index, and fruit shape index as primary factors, contributing 27.4%, 20.6%, and 19.9% to the variation, respectively. Cluster analysis grouped the 82 plants into four clusters, not strictly based on geographical distance, suggesting influence from factors such as genotype or environmental conditions. In conclusion, this study lays a foundation for understanding the genetic mechanisms behind the phenotypic diversity and germination characteristics of E. angustifolia. It offers insights into how geo-climatic factors influence these traits, providing valuable information for the species’ conservation, cultivation, and management. Full article

Sea surface salinity (SSS) observed by satellite has been widely used since the successful launch of the first salinity satellite in 2009. However, compared with other oceanographic satellite products (e.g., sea surface temperature, SST) that became operational in the 1980s, the SSS product is less mature and lacks effective validation from the user end. We employed an unsupervised machine learning approach to classify the Level 3 SSS bias from the Soil Moisture Active Passive (SMAP) satellite and its observing environment. The classification model divides the samples into fifteen classes based on four variables: satellite SSS bias, SST, rain rate, and wind speed. SST is one of the most significant factors influencing the classification. In regions with cold SST, satellite SSS has an accuracy of less than 0.2 PSU (Practical Salinity Unit), mainly due to the higher uncertainty in the cold environment. A small number of observations near the seawater freezing point show a significant fresh bias caused by sea ice. A systematic bias of the SMAP SSS product is found in the mid-latitudes: positive bias tends to occur north (south) of 45°N(S) and negative bias is more common in 25°N(S)–45°N(S) bands, likely associated with the SMAP calibration scheme. A significant bias also occurs in regions with strong ocean currents and eddy activities, likely due to spatial mismatch in the highly dynamic background. Notably, satellite SSS and in situ data correlations remain good in similar environments with weaker ocean dynamic activities, implying that satellite salinity data are reliable in dynamically active regions for capturing high-resolution details. The features of the SMAP SSS shown in this work call for careful consideration by the data user community when interpreting biased values. Full article

Sesquiterpene lactones are specialized plant metabolites with promising pharmacological activities. These metabolites are characteristic marker compounds for the aerial parts of Ambrosia artemisiifolia. Numerus sesquiterpene lactones have been isolated from ragweed; however, there is no information on their bioproduction and quantification throughout the life cycle of the plant. The sesquiterpene lactone content of ragweed samples collected in Szeged and Nyíri was analyzed using HPLC. Significant differences in the amount and bioproduction rhythm of sesquiterpene lactones were found between the two sets of samples. The samples collected near Szeged contained significantly lower amounts of the investigated compounds compared to the Nyíri samples. Sesquiterpene lactone production in the samples peaked at the end of July or in August; the trend of the change in sesquiterpene lactones might correlate with precipitation and temperature. Geographical location and geoclimatic factors might exert significant influence on the production of sesquiterpene lactones in ragweed. Full article

Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage. Full article

We analyzed the two main drivers (climate and land uses) shaping the composition of small mammal communities at 16 localities situated in the confluence of the Mediterranean and Eurosiberian regions (Barcelona, Spain). The study area represents a land use and land cover gradient showing urbanization and crop intensification in the lowlands and forest encroachment in mountain areas. We identified 2458 small mammal individuals of 12 different species from barn owl (Tyto alba) pellets. Three open-land species (Microtus duodecimcostatus, Crocidura russula, and Mus spretus) and one forest/generalist species (Apodemus sylvaticus) were dominant in the diet, accounting for 93% of prey. In order to disentangle the effects of both main drivers on the small mammal community, we used partial constrained ordination techniques, which allowed us to determine the pure effects (and shared effects) of the environmental factors. Land use predictors explained 33.4% of the variance (mostly crops), followed by 23.4% of the variance explained by the geo-climatic variables (mostly rainfall), and an additional 24.8% of the variance was shared by both groups of predictors, totaling 81.6% of environmental variance. The remaining 18.4% of variance was unexplained by environmental matrices. This pattern was consistent with expected associations of species and biotic influences at small spatial scales and highlighted that the number of species increased from the crops in the lowlands towards the highlands covered by deciduous and coniferous forests. Full article

The favourable geo-climatic conditions in Portugal have made it highly conducive to the development of Dirofilaria immitis in dogs, leading to its identification as an endemic region. This nematode is rapidly spreading across Europe, particularly in northeastern countries. The objective of this study was to provide an updated assessment of the prevalence of this disease in Portuguese dogs, analysing the results in relation to epidemiological and geo-environmental factors, and to identify potential risk factors. A total of 1367 dogs from all continental and insular districts were included in the study and tested for D. immitis antigens. The overall prevalence was found to be 5.9%. It was observed that the disease is spreading northward, with previously unaffected districts now reporting cases, and that the prevalence in coastal districts exceeded that of inland ones. Notably, the Aveiro district exhibited a significant increase in D. immitis prevalence, while in certain districts such as Setúbal, Santarém, Madeira, or Faro, a stabilisation or decrease in prevalence was noted. Furthermore, outdoor and older dogs were found to be at a higher risk of infection. No positive cases were detected in the Azores. Most of the infected dogs were located in areas of high and medium risk of infection. This study underscores the importance of implementing pharmacological prophylaxis, vector control strategies, and public awareness programs to control the spread of this zoonotic disease. Full article

The incidence of paracoccidioidomycosis (PCM) varies in Latin America, and it is influenced by environmental factors. This study evaluated the distribution of PCM acute/subacute form (AF) cases and their correlation with geoclimatic factors in the Mato Grosso do Sul (MS) state. The study included 81 patients diagnosed with the PCM/AF at the University Hospital of the Federal University of Mato Grosso do Sul between January 1980 and February 2022. Geographic coordinates, health microregion of patient’s residence, compensated average temperature, relative air humidity (RH), El Niño Southern Oscillation (ENSO), and average global temperature were analyzed. The highest incidence was observed in the Aquidauana (7/100,000 inhabitants), while Campo Grande, the state’s capital, had the highest number (n = 34; 42.4%) and density (4.4 cases/km²) of cases. The number of cases increased during extended periods of the El Niño phenomenon. A positive correlation was found between higher RH and PCM/AF cases. Most PCM/AF cases were found in areas with loamy soils and RH ranging from 60.8 to 73.6%. In MS, the health microregions of PCM/AF patients are characterized by deforestation for agricultural and pasture use, coupled with loamy soils and specific climatic phenomena leading to higher soil humidity. Full article

Studying the variation and correlation of traits among provenances is of great significance for the selection of excellent provenances and the interpretation of the acclimation mechanisms of different provenances in the context of climate change. The photosynthetic characteristic parameters and leaf functional traits of 18 Cunninghamia lanceolata provenances in a common garden were measured. Redundancy analysis combined with Pearson analysis was used to analyze the relationship among leaf photosynthetic characteristics, functional traits, and geo-climatic conditions. The results showed the following: (1) Significant differences in functional traits and photosynthetic parameters among provenances were observed, and the gsw and LDMC have the greatest variation as photosynthetic indicators and functional traits, respectively, because of the acclimation ability. (2) Leaf functional traits can better reflect the variation of photosynthetic characteristic parameters. The correlation between most photosynthetic characteristic parameters and functional traits reached a significant level (p < 0.05), and the leaf dry weight (LDW) and specific leaf area (SLA) are key trait factors that determine photosynthetic characteristic parameters. (3) Precipitation appeared to be a key factor that influences intraspecific leaf traits’ variability compared to temperature. This study can explain how provenances acclimate to the environment and which provenances are more suitable for planting in the study area under the context of climate change from a mechanistic perspective. Full article

Karst basins have a relatively low capacity for water retention, rendering them very vulnerable to drought hazards. However, karst geo-climatic features are highly spatially heterogeneous, making reliable drought assessment challenging. To account for geo-climatic heterogeneous features and to enhance the reliability of drought assessment, a framework methodology is proposed. Firstly, based on the history of climate (1963–2019) from the Global Climate Model (GCM) and station observations within the Chengbi River karst basin, a multi-station calibration-based automated statistical downscaling (ASD) model is developed, and the Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) are selected as performance metrics. After that, future climate (2023–2100) under three GCM scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are obtained by using the ASD model. Finally, the Standardized Precipitation Evapotranspiration Index (SPEI), calculated by future climate is applied to assess drought conditions. The results indicate that the multi-station calibration-based ASD model has good performance and thus can be used for climate data downscaling in karst areas. Precipitation mainly shows a significant upward trend under all scenarios with the maximum variation (128.22%), while the temperature shows a slow upward trend with the maximum variation (3.44%). The drought condition in the 2040s is still relatively severe. In the 2060s and 2080s, the basin is wetter compared with the historical period. The percentage of drought duration decreases in most areas from the 2040s to the 2080s, demonstrating that the future drought condition is alleviated. From the SSP1-2.6 scenario to the SSP5-8.5 scenario, the trend of drought may also increase. Full article