Search Results (65)

Biochar has garnered considerable attention as a soil amendment due to its unique physicochemical properties. Its application not only enhances soil carbon sequestration but also improves nutrient availability. Incorporating biochar into soil is regarded as a promising strategy for mitigating global climate change while delivering substantial environmental and agricultural benefits. In this study, biochar was extracted from Siraitia grosvenorii and subsequently modified through alkali treatment. A laboratory incubation experiment was conducted to assess the effects of unmodified (JMC) and modified (GXC) biochar, applied at different rates (1%, 2%, and 4%), on organic carbon mineralization and soil nutrient dynamics. Results indicated that, at equivalent application rates, JMC-treated soils exhibited lower CO₂ emissions than those treated with GXC, with emissions increasing alongside biochar dosage. After the incubation, the 1% JMC treatment exhibited a mineralization rate of 17.3 mg·kg⁻¹·d⁻¹, which was lower than that of the control (CK, 18.8 mg·kg⁻¹·d⁻¹), suggesting that JMC effectively inhibited organic carbon mineralization and reduced CO₂ emissions, thereby contributing positively to carbon sequestration in Siraitia grosvenorii farmland. In contrast, GXC application significantly enhanced soil nutrient levels, particularly increasing available phosphorus (AP) by 14.33% to 157.99%. Furthermore, partial least squares structural equation modeling (PLS-SEM) identified application rate and pH as the key direct factors influencing soil nutrient availability. Full article

Effective management of straw in rice (Oryza sativa L.)–rape (Brassica napus L.) rotation systems is essential for optimising resource efficiency and improving soil quality. This two-year study investigated the impact of seven straw treatment methods on soil organic carbon (SOC) dynamics. The treatments examined were as follows: (1) control (CK); (2) rice straw (SF); (3) rapeseed straw (YF); (4) rice-straw-derived biochar (SB); (5) rapeseed-straw-derived biochar (YB); (6) mixed straw (YSF); (7) mixed biochar (YSB). Soil properties, enzyme activities and carbon fractions were subsequently analysed. During the canola growing season, the application of rice straw biochar increased oxidisable carbon (ROC), dissolved organic carbon (DOC) and microbial biomass carbon (MBC) by 25.7%, 61.7% and 67.2%, respectively, compared to the control. Notably, SB was more effective than unprocessed rice straw (SF) at increasing SOC and ROC. Furthermore, SB demonstrated superior performance in enhancing ROC (56.4%), MBC (36.0%) and DOC (12.2%) compared to hybrid biochar (YSB). SB consistently exhibited a higher carbon accumulation trend than the rapeseed-derived treatments (YF, YB and YSB). The results of the study indicated that applying rice straw biochar during the oilseed rape growing season was effective in increasing variable carbon pools and soil organic carbon accumulation. Full article

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. Full article

The Japanese archipelago as a continental island of the Eurasia continent and harboring high levels of plant species diversity provides an ideal geographical setting for investigating vicariant allopatric speciation due to the sea-level fluctuations associated with climatic oscillations during the Quaternary. In this study, three chloroplast DNA regions and 14 nuclear loci were sequenced for 31 individuals from three populations of Torreya nucifera var. nucifera and 52 individuals from three populations of T. nucifera var. radicans. Population genetic analyses (Network, STRUCTURE and phylogeny) revealed that the genetic boundaries of the two varieties are distinct, with high genetic differentiation (F_ST) of 0.9619 in chloroplast DNA and 0.6543 in nuclear loci. The relatively ancient divergence times between the two varieties were estimated to 3.03 Ma by DIYABC and 1.77 Ma by IMa2 when dated back to the late Pliocene and the early Pleistocene, respectively. The extremely weak gene flow (2Nm = 0.1) between the two varieties was detected by IMa2, which might be caused by their population expansion since the early Pleistocene (~2.0 Ma) inferred in the Bayesian skyline plots and DIYABC. Niche modeling showed that the two varieties had significant ecological differentiation (p < 0.001) since the Last Interglacial even earlier. These results demonstrate that vicariant allopatric speciation due to sea-level fluctuations may be a common mode of speciation in the Japanese archipelago. This finding provides insights into the understanding of species diversification in the Japanese Archipelago and even East Asian flora under climatic oscillations during the Quaternary. Full article

Exhaust emissions from ships are significant threats to the environment and human health, necessitating effective control measures and treatment technologies. In response to the increasing stringency of emission regulations set by the International Maritime Organization (IMO) and national governments, the shipping industry must adopt advanced techniques to mitigate these emissions. The study focuses on the current status of exhaust pollution prevention and control on the Lijiang River and describes the latest progress in ship emission management. It summarizes the sources and hazards of nitrogen oxides (NO_X), sulfur oxides (SO_X), and particulate matter (PM) emitted from ships. The study introduces and compares several exhaust treatment key technologies for desulfurization, denitrification, and integrated desulfurization and denitrification to emphasize their principles, processes, and characteristics. It also demonstrates the future prospects for controlling exhaust gas pollution on inland ships and advocates for the development of integrated technologies that are efficient, space-saving, and cost-effective. The research aims to provide a valuable reference for inland ship exhaust pollution prevention and control. Full article

Passion fruit (Passiflora edulis), a tropical crop of significant economic value, exhibits temperature-sensitive floral development. Here, we identified 23 TALE transcription factors (PeTALEs) and characterized their roles in floral organogenesis and thermal adaptation. Phylogenetic analysis classified PeTALEs into KNOX and BELL subfamilies, with conserved domain architectures and cis-regulatory elements linked to stress and hormone signaling. Spatiotemporal expression profiling revealed PeTALE21 as a key regulator of corona initiation, while PeTALE17 dominated in later floral stages. Temperature stress assays demonstrated cold-induced upregulation of PeTALE15/16/19/22 and heat-mediated suppression of PeTALE10/18/21. Yeast two-hybrid assays uncovered functional interactions between PeTALE3/16/18/22/23, highlighting a network governing floral thermoresilience. This study provides the first genome-wide analysis of PeTALEs, offering insights for breeding climate-resilient passion fruit varieties. Full article

As a typical karst landform region, the Lijiang River Basin, located in Southwest China, is characterized by both soil erosion and ecological fragility. The transformation of land use, driven by long-term intensive human activities, has exacerbated the degradation of ecosystem services, threatening the region’s carbon sink function. To clarify the coupling mechanism between land use and land cover change (LUCC) and carbon storage, this paper integrates complex network theory with the PLUS-InVEST model framework. Based on land use data from five periods, i.e., 2001, 2006, 2011, 2016, and 2021, the key transformation types are identified, and the evolution of carbon storage from 2021 to 2041 is simulated under three scenarios, namely, inertial scenario, ecological protection scenario, and urban development scenario. The paper finds that (1) land use transformation in the basin exhibits spatial heterogeneity and network complexity, as evidenced by a significant negative correlation between the node clustering coefficient and the average path length, revealing that land type transitions possess small-world network characteristics. (2) The forested land experienced a net decrease of 196.73 km² from 2001 to 2021, driving a 3.03% decline in carbon storage. This highlights the inhibitory effect of unregulated urban expansion on carbon sink capacity. (3) Scenario simulations indicate that the carbon storage under the ecological protection scenario will be 1.0% higher than under the inertial scenario and 1.5% higher than under the urban development scenario. These suggest that restricting impervious land expansion and promoting forest and grassland restoration can enhance carbon sink capacity. Therefore, this paper provides a quantitative basis for optimizing territorial spatial planning and coordinating the “dual carbon” goals in karst regions. Full article

Despite the increasing adoption of machine learning and data-driven models for predicting regional groundwater potential (GWP), exploration geoscientists have recognized that these models still face various challenges in their predictive precision. For instance, the stochastic uncertainty associated with incomplete groundwater investigation inventories and the inherent non-transparency characteristic of machine learning models, which lack transparency regarding how input features influence outcomes, pose significant challenges. This research constructs a bagging-based learning framework that integrates Positive–Unlabeled samples (BPUL), along with ex-post interpretability, to map the GWP of the Lijiang River Basin in China, a renowned karst region. For this purpose, we first aggregated various topographic, hydrological, geological, meteorological, and land conditional factors. The training samples were enhanced with data from the subterranean stream investigated in the study area, in addition to conventional groundwater inventories such as wells, boreholes, and karst springs. We employed the BPUL algorithm with four different base learners—Logistic Regression (LR), k-nearest neighbor (KNN), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)—and model validation was conducted to map the GWP in karst regions. The findings indicate that all models exhibit satisfactory performance in GWP mapping, with the hybrid ensemble models (RF-BPUL and LightGBM-BPUL) achieving higher validation scores. The model interpretation of the aggregated SHAP values revealed the contribution patterns of various conditional factors to groundwater distribution in karst zones, emphasizing that lithology, the multiresolution index of valley bottom flatness (MRVBF), and the geochemical element calcium oxide (CaO) have the most significant impact on groundwater enrichment in karst zones. These findings offer new approaches and methodologies for the in-depth exploration and scientific prediction of groundwater potential. Full article

Global climate change and changes in land use structures during rapid urbanization have profoundly impacted ecosystem carbon storage. Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. The findings are as follows: (1) From 2000 to 2020, cultivated and construction land increased, while forest land significantly decreased, lowering from 4331.404 km² to 4111.936 km². This land use change mainly manifests in the significant transformation of forest land into cultivated and construction lands. Under different climate scenarios, the cultivated and construction lands will continue to expand, the forest land will decrease, and the grassland area will increase. (2) Total carbon storage decreased significantly from 2000 to 2020, with forest carbon storage changing the most significantly, for a total reduction of 5,540,612.13 tons, followed by grassland and water area. Regardless of the future scenario, the total carbon storage in the Lijiang River Basin will experience a decreasing trend; the decline in carbon reserves is most significant in the SSP585 scenario and smallest in the SSP126 scenario, with slight increases even appearing in some regions. (3) From the perspective of land use change, the large-scale expansion of construction land in the process of rapid urbanization has occupied a large amount of ecological land, such as forests and grasslands, and this is the main reason for the reduction in total carbon storage in the basin. From the perspective of climate change scenarios, a global temperature increase caused by a high-emission scenario (SSP585) may exceed the optimal growth temperature for some plants, inhibit the carbon absorption capacity of vegetation, and thus reduce the carbon fixation capacity of forest land and grassland. Therefore, to maintain long-term climate goals and sustainable development, the SSP126 scenario should be prioritized to strengthen the protection of forest resources in the northern and central regions of the Lijiang River Basin, balance the relationship between ecological protection and urbanization, avoid the occupation of ecological land by excessive urbanization, and improve the carbon sink potential of the basin. These research results can provide a scientific basis for the optimization of land spatial patterns, ecological restoration and protection, and the enhancement of carbon sink potential in the Lijiang River Basin under the “double carbon” goal. Full article

Nitrogen (N)-fixing plants are commonly employed in the restoration of degraded terrestrial ecosystems due to their ability to increase soil N capital and boost ecosystem productivity. Given the close coupling between N and phosphorus (P) in soil, the effects of N-fixing plants on soil P fractions and availability in karst forests remain largely unexplored. Herein, we compared soil P pools, fractions, and availability in the rhizosphere and non-rhizosphere soils of N-fixing and non-N-fixing plants, and explored associated drivers, such as soil, microbial, and plant properties, in a subtropical karst forest. The results showed that the N-fixing plants increased total P, inorganic P, and available P in both the rhizosphere and non-rhizosphere soils. The nitrogen-fixing plants increased soil labile P (LP) and non-labile P (NLP), but decreased moderately labile P (MLP), particularly in the rhizosphere soils, due to transformations among different soil P fractions. Soil P fractions were primarily influenced by soil inorganic P, root and leaf N, and microbial biomass N in the N-fixing plant treatment, whereas soil inorganic P, dissolved organic carbon (DOC), and dissolved organic N (DON) were the key factors in the non-N-fixing plant treatment. Consequently, soil properties, microbial attributes, plant nutrients, and soil P fractions collectively exerted both direct and indirect effects to increase soil P availability in the N-fixing plant treatment. In contrast, soil P fractions directly and soil properties indirectly influenced soil P availability in the non-N-fixing plant treatment. Our results revealed the unique role of N-fixing plants in driving soil P availability in subtropical karst forests. These findings are essential for developing effective strategies for P nutrient management and guiding the selection of appropriate plant species for vegetation restoration in karst regions. Full article

Revealing the multidimensional value and spatial distribution of cultivated land (CL) resources is crucial for formulating scientific and reasonable CL protection policies and regional ecological supplements. This study takes the Pearl River–Xijiang Economic Belt (PRXJEB), China, as the study area, calculates the economic, social, ecological, and comprehensive values of CL resources based on the county scale, and analyzes the spatial distribution characteristics and influencing factors of CL resource value so as to provide a reference for protecting CL resources in the PRXJEB and promoting the healthy and scientific development of the economic belt. The income reduction method, alternative market method, and equivalent factor method were applied to calculate the economic, social, and ecological values of CL resources in 88 counties in 2021, respectively. Furthermore, spatial autocorrelation analysis was employed to reveal their distribution characteristics and influencing factors. The results show that (1) there are significant differences in the value of CL resources in the counties of the PRXJEB, and the value of CL resources is generally low. (2) The value of CL resources in the PRXJEB generally follows a distribution pattern of “higher in the east and lower in the west”. (3) The value of CL resources in the PRXJEB is mainly affected by natural conditions and the level of socio-economic development. Therefore, in view of the low value of CL resources and its spatial differentiation characteristics in the PRXJEB, it is recommended to strengthen land consolidation and improve the quality of CL, improve CL infrastructure and strengthen the introduction of agricultural science and technology, enhance policies to benefit farmers, and adjust farmers’ behaviour. Full article

Camphora Fabr. is a genus in the family Lauraceae, comprising over 20 tropical and subtropical tree species. Since the genera Camphora and Cinnamomum Schaeff. were described, there has been a long-lasting controversy regarding the phylogenetic relationships among taxa in both genera. In particular, phylogenetic inferences derived from plastid data remain debated, with varying hypotheses proposed and occasional disputes concerning the monophyly of Camphora taxa. To further investigate the relationships, We analyzed plastomes and nuclear ribosomal cistron sequences (nrDNA) of 22 Camphora taxa, 15 Cinnamomum taxa, and 13 representative taxa of related genera. The Camphora plastomes range from 152,745 to 154,190 bp, with a GC content of 39.1% to 39.2%. A total of 128 genes were identified in the Camphora plastomes, including 84 protein-coding genes, 8 rRNA genes, and 36 tRNA genes. A total of 1130 SSR loci were detected from plastomes of Camphora, and A/T base repeats looked like the most common. Comparative analyses revealed that the plastomes of Camphora exhibit high similarity in overall structure. The loci ycf1, ycf2, trnK (UUU), psbJ-psbL, and ccsA-ndhD were identified as candidate DNA barcodes for these taxa. Plastome phylogenetic analysis revealed that Camphora is not monophyletic, whereas the nrDNA dataset supported the monophyly of Camphora. We propose that intergeneric hybridization may underlie the observed discordance between plastid and nuclear data in Camphora, and we recommend enhanced taxonomic sampling and precise species identification to improve phylogenetic resolution and accuracy. Full article

The Soil and Water Assessment Tool (SWAT) model is extensively used globally for hydrological and water quality assessments but encounters challenges in karst regions due to their complex surface and groundwater hydrological environments. This study aims to refine the delineation of hydrological response units within the SWAT model by combining geomorphological classification and to enhance the model with an epikarst zone hydrological process module, exploring the accuracy improvement of SWAT model simulations in karst regions of Southwest China. Compared with the simulation results of the original SWAT model, we simulated runoff and nutrient concentrations in the Mudong watershed from January 2017 to December 2021 using the improved SWAT model. The simulation results indicated that the modified SWAT model responded more rapidly to precipitation events, particularly in bare karst landform, aligning more closely with the actual hydrological processes in Southwest China’s karst regions. In terms of the predictive accuracy for monthly loads of total nitrogen (TN) and total phosphorus (TP), the coefficient of determination (R²) value of the modified model increased by 10.3% and 9.7%, respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) increased by 11.3% and 9.9%, respectively. The modified SWAT model improves prediction accuracy in karst areas and holds significant practical value for guiding non-point source pollution control in agricultural watersheds. Full article

Detecting spatiotemporal changes in ecological environment quality (EEQ) is of great importance for maintaining regional ecological security and supporting sustainable economic and social development. However, research on EEQ detection from a remote sensing perspective is insufficient, especially at the basin scale. Based on two indices, namely, the Ecological Index (EI) and the Remote Sensing Ecological Index (RSEI), we established a dual model, combining the remote sensing ecological comprehensive index (RSECI) and its differential change model, to study the spatiotemporal evolutionary characteristics of EEQ in the Lijiang River Basin (LRB) from 2000 to 2020. The RSECI combines the following five indicators: greenness, wetness, heat, dryness, and aerosol optical depth. The results of this study show that the area of good and excellent EEQ in the LRB decreased from 3676.22 km² in 2000 to 2083.89 km² in 2020, while the area of poor and fair EEQ increased from 80.81 km² in 2000 to 1375.91 km² in 2020. From 2000 to 2020, the change curve of the EEQ difference in the LRB first rose, fell, and then rose again. The wetness and greenness indicators had positive effects on promoting EEQ, while the heat, aerosol optical depth, and dryness indicators had restraining effects. The results of stepwise regression analysis showed that, among the selected indicators, wetness and greenness were the key factors for improving the EEQ in the LRB during the study period. The RSECI approach and the difference change model proposed in this study can be used to quantitatively evaluate the EEQ and facilitate the analysis of the spatial and temporal dynamic changes and difference changes in EEQ. Full article

The hydrological processes in karst watersheds are influenced by various factors, including climate characteristics, underlying surface properties, and human activities. Existing watershed hydrological models primarily rely on theoretical concepts or empirical function relationships for simulation, resulting in insufficient accuracy in hydrological process analysis for study areas with limited data. The structure of artificial neural networks is similar to the hydrological process structure in karst watersheds. Based on the hydrological characteristics of the Lijiang River, a BP neural network model is configured with structural parameters set to 13-9-1. Using hydrological data from the Lijiang River from 1995 to 2020 as the foundational dataset, the network is trained and tested for prediction accuracy. The results show that the coefficient of determination for the monthly runoff model in the Lijiang River basin, based on the BP neural network, is 0.942. This suggests that it is feasible to use historical data to predict future flow changes in the Lijiang River basin, assuming that the changes are due exclusively to precipitation and evapotranspiration, but no significant changes occur in the land uses. The findings hold significant importance for water resource management in typical karst watersheds. Full article