High-altitude Regions Research Articles

Elucidating the leaf-based heavy metal bioindication in two significant plant taxa from Caucasus

The present study was aimed at assessing the potential of two important plants of the Transcaucasia – Betula medwediewii Regel (Caucasian birch) and Veratrum album L. (white hellebore) - as bioindicators of pollution of the environment. Betula medwediewii is a subalpine and alpine deciduous tree species that is significant for soil and habitat development. Veratrum album is found in the high altitude regions, is also known to withstand extreme conditions and has been used in herbal medicine. Elemental analysis was conducted on the plant leaves for total heavy metals such as aluminium, cadmium, cobalt, lead, and zinc. Results of the study showed that, in the leaves of B. medwediewii, aluminium, cadmium, and zinc heavy metals were found to be within permissible levels but levels of cobalt and lead were above permissible levels. For V. album cadmium and zinc also remained within the normal range but aluminum, cobalt and lead levels were notably high. The results of the study indicate the distinct accumulation characteristics that these species possess a unique behaviour in the presence of heavy metal pollution in the Transcaucasian environment and both plants have a potential capability as bio indicators.

A prospective, self-controlled study of sub-plateau heart rate variability in healthy adults

BackgroundThe low-pressure, hypoxic environment characteristic of high-altitude regions significantly affects the cardiovascular and autonomic nervous system functions of individuals, consequently impairing their sleep quality. Heart rate variability, a non-invasive indicator of autonomic nervous system activity and balance within the cardiovascular system, has not been thoroughly investigated in terms of its patterns during acclimatization and de-acclimatization phases for individuals traveling to and residing in high-altitude areas and its relationship with sleep stability.MethodsData was collected from 22 medical staff members who traveled from Chengdu to Yecheng, with measurements taken before leaving Chengdu, 1 week in Yecheng, 3 months in Yecheng, and 1 week after returning to Chengdu. The study analyzed changes in heart rate variability during acclimatization and de-acclimatization at 1,400 m above sea level. It also examined arrhythmia and sleep disorders based on circadian groups.Results1. Following 1 week of acclimatization to the sub-plateau environment of Yecheng, significant decreases were observed in SDANN, SDNN and SD2 indices compared to departure from Chengdu (P < 0.05). After 3 months of sub-plateau acclimatization, these indices significantly increased (P < 0.05). Upon returning to Chengdu and undergoing de-acclimatization for 1 week, these indices further significantly increased (P < 0.05). 2. During the period of sub-plateau acclimatization and de-acclimatization, significant changes were noted in average heart rate and minimum heart rate (P < 0.05), with the average heart rate showing a continuous decrease and the minimum heart rate exhibiting an initial increase followed by a decrease. No significant changes were observed in maximum heart rate or the incidence of arrhythmias (P > 0.05). 3. When stratified by day and night, the trends for SDANN, RMSSD, and TP were consistent with the overall trend at night (P < 0.05), but opposite during the day (P < 0.05). 4. During the sub-plateau acclimatization period, stable sleep duration was significantly reduced, and increased markedly after de-acclimatization, although it did not return to pre-acclimatization levels (P < 0.05).ConclusionAcclimatization to the sub-plateau environment of Yecheng affects the autonomic nervous system, heart rate, and sleep in healthy adults. De-acclimatization can ameliorate these effects. Furthermore, the impact of sub-plateau acclimatization on the autonomic nervous system exhibits a distinct circadian rhythmicity.

Study on Soil Freeze–Thaw and Surface Deformation Patterns in the Qilian Mountains Alpine Permafrost Region Using SBAS-InSAR Technique

The Qilian Mountains, located on the northeastern edge of the Qinghai–Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai–Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze–thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze–thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east–west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai–Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Optimization of Ecosystem Service Bundles from a Trade-Off and Synergistic Perspective: A Case Study of Qinghai Province

Ecosystem services, encompassing the provision of food, water, air purification, climate regulation, and disease mitigation, form the bedrock upon which human well-being and socio-economic development are anchored. The preservation of these services is instrumental in safeguarding human survival and fostering progress. Qinghai Province, located on the Qinghai–Tibetan Plateau, holds a pivotal role as a crucial ecological barrier within China. The optimization of its ecosystem services is vital for the effective management of ecologically fragile areas. This study focuses on Qinghai Province. By integrating natural geographic, social, and Points of Interest (POI) big data, we utilized tools like InVEST, CASA, and advanced algorithmic optimizations to analyze ecosystem services and their trade-off synergies in Qinghai from 2000 to 2020. Based on these synergies, we developed a novel optimization algorithm to generate spatial bundles that amplify synergistic interactions while minimizing costs. Our findings indicate that the following: (1) Between 2000 and 2020, recreational services, water yield, and habitat quality in Qinghai Province generally exhibited an upward trend, whereas carbon sequestration showed a decline; (2) Notable synergies were evident between carbon sequestration and habitat quality and between recreation services and habitat quality. Conversely, significant trade-offs were observed between water yield and habitat quality and between water yield and cultural services, with these trade-off synergy effects varying markedly across different regions; (3) We constructed ecosystem service bundles characterized by “strong synergy–weak trade-off” based on these relationships. Following optimization, regions demonstrating significant synergies expanded, while those showing significant trade-offs contracted, thereby adapting to the ecological heterogeneity of high-altitude areas. This study advances the optimization of ecosystem service bundles in ecologically sensitive zones through a lens of trade-off synergies. The results offer a scientific foundation for formulating effective ecological protection and restoration strategies, providing valuable insights for ecosystem service research in other high-altitude regions globally.

Effect of double-layer formwork curing on the early temperature field and properties of precast concrete components in environments with large diurnal temperature variations

An efficient double-layer formwork curing method using an “inner supporting formwork + outer insulation formwork” was proposed in this study to address the early cracking of precast concrete components in high altitude regions. Steel and plywood formwork were designed as inner support formwork, while polystyrene (PS) and polyurethane (PU) were used as outer insulation formwork. Indoor experiments and two finite element methods (The complete simulation method focuses on computational accuracy, and the equivalent simulation method emphasizes computational efficiency) were employed to analyze the evolution of the concrete temperature field under different double-layer formwork curing methods throughout the curing period, combined with compressive strength and pore structures testing. The results show that steel + 5-mm-thick PU insulation formwork curing method can significantly mitigate the impact of large diurnal temperature variations on the internal temperature of concrete. Unlike traditional steam-curing, this method does not deteriorate the pore structure or compressive strength of the concrete. This study is of great significance in addressing the problem of early cracking of precast concrete components exposed to large diurnal temperature vriations in high altitude regions.

Comparative Analysis on Policy Frameworks of High-Altitude Mineral Resource Management: Implications for Sustainable Development Goals (SDGs)

As the global demand for minerals critical to clean energy and technological innovation continues to rise, the sustainable exploitation of mineral resources in high-altitude regions becomes increasingly essential for global sustainable development. Employing SWOT analysis, deep learning, and heatmap techniques, this study delves into the mineral resource policies of China, the United States, Canada, and Chile, assessing their alignment with and impact on Sustainable Development Goals (SDGs). Despite distinct policy frameworks, a shared focus on technological innovation and environmental sustainability is evident. China’s strategic resource allocation and stringent regulations drive a green, low-carbon shift, aligning with SDG 13 (Climate Action). The United States fosters SDG 9 (Industry, Innovation, and Infrastructure) through market-driven technological advancements. Canada’s collaborative approach, emphasizing indigenous rights, underpins SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production). Chile’s national governance and international collaboration mix bolsters the mining industry’s efficiency and sustainability, supporting SDG 7 (Affordable and Clean Energy). The study underscores a trend toward sustainable practices in mineral resource management and stresses the critical need for international cooperation. The study advocates for global collaboration and sharing of green mining technologies to accelerate the industry’s transition to a sustainable and responsible future and boost SDG achievements worldwide.

Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China.

The alpine wetlands in western Sichuan are distributed along the eastern section of the Qinghai-Tibet Plateau (QTP), where the ecological environment is fragile and highly sensitive to global climate change. These wetlands are already experiencing severe ecological and environmental issues, such as drought, retrogressive succession, and desertification. However, due to the limitations of computational models, previous studies have been unable to adequately understand the spatiotemporal change trends of these alpine wetlands. We employed a large sample and composite supervised classification algorithms to classify alpine wetlands and generate wetland maps, based on the Google Earth Engine cloud computing platform. The thematic maps were then grid-sampled for predictive modeling of future wetland changes. Four species distribution models (SDMs), BIOCLIM, DOMAIN, MAXENT, and GARP were innovatively introduced. Using the WorldClim dataset as environmental variables, we predicted the future distribution of wetlands in western Sichuan under multiple climate scenarios. The Kappa coefficients for Landsat 8 and Sentinel 2 were 0.89 and 0.91, respectively. Among the four SDMs, MAXENT achieved a higher accuracy (α = 91.6%) for the actual wetland compared to the thematic overlay analysis. The area under the curve (AUC) of the MAXENT model simulations for wetland spatial distribution were all greater than 0.80. This suggests that incorporating the SDM model into land change simulations has high generalizability and significant advantages on a large scale. Furthermore, simulation results reveal that between 2021 and 2100 years, with increasing emission concentrations, highly suitable areas for wetland development exhibit significant spatial differentiation. In particular, wetland areas in high-altitude regions are expected to increase, while low-altitude regions will markedly shrink. The changes in the future spatial distribution of wetlands show a high level of consistency with historical climate changes, with warming being the main driving force behind the spatiotemporal changes in alpine wetlands in western Sichuan, especially evident in the central high-altitude and northern low-altitude areas.

Potential drivers of plant diversity and composition in high-altitude alpine regions of Himalaya

Potential drivers of plant diversity and composition in high-altitude alpine regions of Himalaya

Source apportionment and driving factor identification for typical watersheds soil heavy metals of Tibetan Plateau based on receptor models and geodetector

The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Engineering cold resilience: implementing gene editing tools for plant cold stress tolerance.

This paper highlights the need for innovative approaches to enhance cold tolerance. It underscores how genome-editing tools can deepen our understanding of genes involved in cold stress. Cold stress is a significant abiotic factor in high-altitude regions, adversely affecting plant growth and limiting crop productivity. Plants have evolved various mechanisms in response to low temperatures that enable resistance at both physiological and molecular levels during chilling and freezing stress. Several cold-inducible genes have been isolated and characterized, with most playing key roles in providing tolerance against low-temperature stress. However, many plants fail to survive at low temperatures due to the absence of cold acclimatization mechanisms. Conventional breeding techniques, such as inter-specific or inter-genic hybridization, have had limited effectiveness in enhancing the cold resistance of essential crops. Thus, it is crucial to develop crops with improved adaptability, high yields and resistance to cold stress using advanced genomic approaches. The current availability of gene editing tools offers the opportunity to introduce targeted modifications in plant genomes efficiently, thereby developing cold-tolerant varieties. This review discusses advancements in gene editing tools, including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)/Cas12a(Cpf1), prime editing (PE) and retron library recombineering (RLR). We focus specifically on the CRISPR/Cas system, which has garnered significant attention in recent years as a groundbreaking tool for genome editing across various species. These techniques will enhance our understanding of molecular interactions under low-temperature stress response and highlight the progress of genome editing in designing future climate-resilient crops.

Fishway assessment and monitoring for endemic migratory fish using multiple techniques in high-altitude river systems: A case study from the Yarlung Zangbo River, Southeastern Tibetan Plateau

The Yarlung Zangbo River (YZR) on the Tibetan Plateau is a prime hotspot for hydropower development in China, yet this inevitably poses risks to river connectivity and the survival of its unique endemic fish species. Fishway installations are often adopted to mitigate the impacts on fish, but their effectiveness especially in high-altitude regions remains inadequately studied. Our study offers the first comprehensive evaluation of a fishway installed on a primary tributary of the YZR from 2019 to 2021, using multiple methods including fish sampling, video monitoring, hydroacoustic detection, and passive integrated transponder telemetry technology. The below-dam fish community was dominated by cold-water rheophilic Schizopygopsinae, Schizothoracinae, and Nemacheilidae. Fish movements exhibited distinct seasonal and diel patterns, with activity rising in April and peaking in May. Approximately 75.0 % of fish movements within the fishway were recorded during daytime (7:00–19:00), whereas the fish assemblage near the fishway entrance primarily occurred during nighttime (20:00–6:00). Telemetric trials on five endemic species revealed upstream- and downstream passage efficiencies of 31.0 % and 61.7 %, respectively. Passage efficiency, transit time, and speed varied substantially among species and individuals, primarily affected by environmental factors such as fishway discharge and water temperature. Our findings provide the essential information on fish behavior and movement patterns for designing more effective fishways in plateau regions and underscore the importance of continuous monitoring and evaluation to optimize fishway performance, ensuring that the conservation of endemic fish species is achieved successfully amid expanding hydropower development in the region.

Spatiotemporal Pattern of Vegetation Coverage and Its Response to LULC Changes in Coastal Regions in South China from 2000 to 2020

Analyzing vegetation coverage and land-use and land cover (LULC) characteristics helps to understand the interaction between human activities and the natural environment. The coastal regions of the Guangdong Province are economically active areas with frequent human activities, located in the advantageous natural environment of South China. This study analyzed the spatiotemporal characteristics of the normalized difference vegetation index (NDVI) and LULC from 2000 to 2020, to explore the response of NDVI changes to LULC changes. The results show that (1) the overall NDVI is relatively high, with a proportion of 85.37% to 89.48% of areas with higher coverage and above categories, mainly distributed in the east and west. Vegetation coverage showed an increasing trend. (2) The LULC in this area is mainly composed of forest land (46.5% to 47.5%) and cultivated land (30.7% to 33.4%), with forest land mainly distributed in relatively high-altitude regions and cultivated land mainly distributed in the plains. The changes in LULC from 2015 to 2020 were relatively significant, mainly due to the mutual transfer of cultivated land and forest land. In addition, built-up land continued to expand from 2000 to 2020, mainly in the Pearl River Delta. (3) The NDVI decreases come from the transfer of various types of land to built-up land, mainly in the Pearl River Delta region, while the NDVI increase comes from the stability and mutual transfer of cultivated land. The net contribution rate of forest land change to vegetation cover change is the most significant (−38.903% to 23.144%). This study has reference significance for the spatiotemporal characteristics of vegetation cover changes in coastal areas and their response to land-use changes, as well as coastal management and sustainable development.

Late Cretaceous glaciations in a hyper-arid plateau desert of the South China Coastal Mountains

Increasing evidence indicates the existence of a cryosphere during the Cretaceous supergreenhouse. However, current understanding of a potential link between lithosphere dynamics and cryospheric processes in the Cretaceous plateau desert successions of China remains limited. We report the occurrence of ice-rafted dropstones and diamictites from the Upper Cretaceous Chishan Formation of the Subei Basin at the East Asian continental margin. Our provenance results indicate that fluvial deposits of the Lower Chishan Formation were mainly derived from the Sulu Orogen to the north and the Zhangbaling Uplift to the west, whereas aeolian deposits of the Upper Chishan Formation were largely recycled from the two sources with an additionally notable contribution from the post-Cretaceous basement of the Yangtze Block. Combined with previous evidence, provenance analysis indicates that Late Cretaceous collision between the Okhotomorsk Block and the East Asian continent led to the growth of the South China Coastal Mountains via crustal thickening, which generated an arid, high-altitude basin region that experienced desertification and paleohydrological variability, and that was supplied with additional clastic sediment sources from the basement of the South China Block. Our results provide evidence of Late Cretaceous cryospheric processes in a continental mid-latitude plateau desert linked to the northwestward subduction and collision of the paleo-Pacific realm. Global cooling from the late Turonian to Maastrichtian drove the establishment of glaciers in high-altitude mountains leading to the development of ice-related deposits in the plateau deserts, as recorded in the Subei desert basin of the South China Coastal Mountains. The record of ice-rafted debris and the provenance signature reveal an active Cretaceous plateau cryosphere linked to lithosphere dynamics.

Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau

Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species.

Identifying Alpine Lakes with Shoreline Features

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

The Multifaceted Soils of Himachal Pradesh, India: Types and Characteristics

The soils of Himachal Pradesh face a multitude of pressing issues that threaten their health and agricultural productivity. The diverse geographical features, temperature regimes and biological zones of Himachal Pradesh have resulted into a vast range of soil types in the state. Alpine, forest, alluvial and sub-mountain soils are the four major soil categories into which the state's soils can be generally divided. Only a particular species and adapted plants can thrive in the extreme weather circumstances of alpine soils, which are located at higher elevations and possess soils having thin, stony and acidic in reaction. The mid-hill areas are dominated by forest soils which are distinguished by their high levels of organic matter and nutrients. These soils support luxuriant vegetation and make effective agricultural methods possible. Alluvial soils, which are mostly found in river valleys and are deposited by ravine systems are extremely rich and perfect in fertility and suitable for growing a range of crops including vegetables, rice and maize. Located in the foothills, sub-mountain soil exhibits a blend of traits from alluvial and forest soils, thus providing a range of uses for horticulture and agriculture. In addition to supporting a diverse array of plant and animal life, Himachal Pradesh's varied soil types are vital to the region's agricultural economy. One of the biggest problems is soil erosion, which is made worse by the steep terrain and abundant rainfall in the area. This results in the loss of topsoil and decreased fertility. Deforestation, overgrazing, and unsustainable farming methods all contribute to land degradation, which further reduces biodiversity and soil quality. Furthermore, crop development is impacted by soil acidity in high-altitude regions, which restricts nutrient availability. However, these issues need efficient conservation measures and soil management techniques. In order to maintain Himachal Pradesh's rich biodiversity and agricultural potential for future generations, sustainable agricultural development and environmental protection in this ecologically sensitive area depend on an understanding of the complex soil dynamics. Addressing these interconnected issues requires a comprehensive approach, emphasizing sustainable land management practices, community engagement, and restoration efforts to preserve the vital soil resources of this ecologically sensitive region.

Spatiotemporal variation in grain production performance and efficiency of the cultivated landscapes in Upper Blue Nile Basin of Ethiopia: the impact of residual moisture-based farming on water and food security

Analysis of grain production performance can provide reference information to explore multiple cropping options and further improve the resource use efficiency of farming methods. This study investigated the spatiotemporal dynamics of grain production performance and efficiency of major crop production systems (CPS) in the Ethiopia’s Blue Nile Basin. The results show that only 39% of the basin is currently cultivated, although a significant cropland expansion (10%) was recorded between 1985 and 2020. The study identified 11 major CPS, mostly practiced in the basin. Of these, single cropping based on the main rainy season (Meher-Only) covers the largest area (26%), followed by Meher-Residual-Intermittent (12%) and Meher-Belg-Dependable (11%). Extended-Meher, Meher-Residual-Dependable, Meher-Residual-Intermittent, and Meher-Belg-Dependable are the four more powerful CPS with higher efficiency. Comparatively, CPS practiced in Wet-Woyna-Dega and Wet-Dega have better overall performance. Findings confirm that agricultural space management (land) and green-water (rainfall) utilization are the most influential factors, followed by land use planning and land use systems (CPS) invention. As landscape suitability for grain production governs future performance, in the low elevation and flood plains parts of the basin, the possibility of creating additional space into the food system is very high. In mountainous and high-altitude regions, the efficiency of grain production will decrease because incorporating additional arable land into the food system is trivial. In the last three decades, in BNB, only 10% of arable land (equivalent to 30 million quintals of food) has been added to the good system, which can support approximately 6 million people. Compared to the population growth of the basin (12 million 1985–2020), its contribution to the food system was less than 50%. This confirms that multiple cropping systems, such as Residual moisture-based CPS, have played a significant role in boosting the food system in the basin. Therefore, improving grain production performance/efficiency requires targeted investments, including the invention of more adaptable crop varieties, efficient cropping practices, and the introduction of advanced agricultural space and water management technologies. The results of the study will help identify important policy gaps and suggest possible options to enhance residual farming and other multiple cropping systems.

Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region.

The Phenology of Coffea arabica var. Esperanza L4A5 Under Different Agroforestry Associations and Fertilization Conditions in the Caribbean Region of Costa Rica

This study focused on the phenology of Coffea arabica var. Esperanza L4A5, an F1 interspecific hybrid obtained by crossing commercial varieties with wild genotypes from Ethiopia and Sudan. Most phenological studies on C. arabica have been conducted in traditional high-altitude regions, leaving a gap in the understanding of its behavior in non-traditional areas such as the Caribbean region of Costa Rica. To establish a baseline on the phenological behavior of the Esperanza L4A5 hybrid in this region, we conducted a four-year study examining the effects of different agroforestry associations: (1) Albizia saman; (2) Hymenaea courbaril and Erythrina poeppigiana; (3) Anacardium excelsum and Erythrina poeppigiana; and coffee plots under full sun. Additionally, the phenology of the coffee plants was evaluated under differentiated fertilizations (physical, chemical, and without fertilization), considering meteorological factors such as temperature, humidity, and rainfall. The observed variables included the development of floral nodes, pre-anthesis, anthesis, and fruiting stages. To analyze the relationships between environmental factors, tree cover, fertilization, and the phenological stages, we employed multiple linear regression (MLR), which revealed that both tree cover and physical and chemical fertilizations had significant effects on the presence of developed floral nodes and, consequently, on fruit production. Furthermore, the random forest (RF) model was applied to capture complex interactions between variables and to rank the importance of meteorological factors, tree cover, and fertilization practices. These analyses demonstrated that the Esperanza L4A5 hybrid exhibited viable phenological development under the atypical conditions of the Caribbean region of Costa Rica, suggesting its potential to adapt and thrive in non-traditional coffee-growing areas.