Search Results (8,520)

In recent years, some new “Qi-Nan” clones of Aquilaria sinensis with the characteristics of easy induction and high-quality agarwood have been obtained, through the cultivation and propagation of grafted seedlings. These clones are used for the intensive production of high-quality agarwood. The speed of resin formation and yield are crucial for the development of the agarwood industry. The differences in yield and chemical composition among different Qi-Nan clones and induction times are worth investigating. While the chemical composition differences between Qi-Nan and ordinary A. sinensis have been extensively studied, the effects of induction time coupled with different Qi-Nan clones on the chemical composition of Qi-Nan agarwood remain insufficiently explored. This study compared the changes in the chemical composition of four “Qi-Nan” clones of A. sinensis after 6, 12, and 24 months of induction through GC-QTOF-MS, the chemical composition and structure types of the four “Qi-Nan” clones were mainly 2-(2-phenylethyl)chromone derivatives (PECs) and Sesquiterpenoids (SESs), with the prolongation of induction time, the content of SESs increased, while the content of PECs decreased. Both the differences among clones and the induction time of “Qi-Nan” agarwood influence its chemical composition, which in turn affects the quality of the agarwood. Among these factors, induction time has a greater impact on the production of PECs in agarwood. The prolongation of induction significantly enhanced the yield of “Qi-Nan” agarwood and exhibited an inducing effect on the production of 2-(2-phenylethyl) chromone and 2-(2-4 phenylethyl)chromone. Compared with the agarwood obtained after 6 and 12 months of induction, the agarwood of “Qi-Nan” after 24 months of induction exhibited superior quality. The induction time for high-quality agarwood from the XGY clone was shorter (12 months) compared to the RH, YYZ, and AS clones (24 months). The study underscores that optimizing induction time and selecting suitable “Qi-Nan” clones can significantly enhance agarwood yield, quality, and production efficiency. Full article

Intercropping with legume forages is recognized as an effective strategy for enhancing nitrogen levels in agroforestry, while mowing may influence nitrogen fixation capacity and yield. This study investigated the rooting, nitrogen fixation, nutritive value, and yield of alfalfa (Medicago sativa L.) under intercropping and varying mowing frequencies (CK, 2, and 3) from 2021 to 2023, using walnut (Juglans regia L.) and alfalfa as experimental subjects. The results indicated that intercropping suppressed root growth, whereas increased mowing frequency stimulated root development in the topsoil (0–20 cm). Specifically, the average root length density, root surface area, and root volume from the twice- and thrice-mowed treatments increased by 18.26, 17.45, and 4.15%, respectively, in comparison to the control. The δ¹⁵N values of the intercropped alfalfa were significantly lower than those of the monocropped alfalfa (p < 0.05), with the δ¹⁵N values of the mowing-thrice treatment increasing by an average of 38.61% compared to the control. Intercropping suppressed alfalfa yield but did not affect the total nitrogen content in the leaves or the nutritive value, and all mowing treatments resulted in land equivalent ratios (LERs) greater than 1. Furthermore, increased mowing frequency enhanced both the nutritive value and yield of alfalfa. Our study suggests that intercropping with walnut can improve biological nitrogen fixation in alfalfa, and that adopting a mowing-thrice regime can optimize yield and nutritive value. Full article

The utilization of nitrogen (N) is crucial for the optimal growth and development of plants. As the dominant form of nitrogen in temperate soil, nitrate (NO₃⁻) is absorbed from the soil and redistributed to other organs through NO₃⁻ transporters (NRTs). Therefore, exploration of the role of NRTs in response to various NO₃⁻ conditions is crucial for improving N utilization efficiency (NUE). Here, we present a comprehensive genome-wide analysis and characterization of the NRT gene family in Korean pine, an invaluable tree species cultivated extensively in northeastern China. A total of 76 PkNRTs were identified in Korean pine and further divided into three subfamilies (NRT1/NPF, NRT2, and NRT3) based on phylogenetic analysis. All PkNRTs were distributed on 11 chromosomes, with multiple tandem duplications observed. The tissue-specific expression analysis indicated that most PkNRTs showed differential expression in six vegetative tissues. Furthermore, a significantly greater number of lateral roots was observed in seedlings under nitrogen-deficient conditions, accompanied by an increase in both total root biomass and root length. The temporal expression profiles of 16 PkNRTs in seedling roots revealed that four PkNRTs, PkNPF5.6, PkNPF5.13, PkNPF6.1, and PkNPF6.2, exhibited significantly upregulated expression under the NO₃⁻ deficiency condition, whereas robust induction was observed for PkNPF1.1, PkNRT2.6, and PkNRT3.3 upon the NO₃⁻ sufficiency condition. The expression patterns of the PkNRTs suggest their potential diverse roles as key participants in root NO₃⁻ uptake under varying NO₃⁻ conditions during root development. These findings would provide a theoretical foundation for further investigations into the functions of PkNRTs in Korean pine. Full article

Water use efficiency (WUE) plays a pivotal role in connecting the carbon and water cycles and represents the amount of water used by plants or ecosystems to achieve carbon sequestration. The response of WUE to climate warming and its underlying mechanisms remain unclear. Here, we examined the effects of varying levels of warming on carbon fluxes, water fluxes, and WUE in an alpine peatland, with Blysmus sinocompressus and Carex secbrirostris as dominant species. Open-top chambers were utilized to simulate two levels of warming: low-level warming (TL) and high-level warming (TH). The carbon dioxide and water fluxes were monitored over a growing season (June to September). Gradient warming significantly decreased both gross primary productivity (GPP) and net ecosystem carbon exchange (NEE); GPP was 10.05% and 13.31% lower and NEE was 21.00% and 30.00% lower in the TL and TH treatments, respectively, than in the control. Warming had no significant effect on soil evaporation, and plant transpiration and evapotranspiration were 36.98% and 23.71% higher in the TL treatment than in the control, respectively; this led to decreases of 31.38% and 28.17% in canopy water use efficiency (WUEc) and ecosystem water use efficiency (WUEe), respectively. Plant transpiration was the main factor affecting both WUEe and WUEc in response to warming. The findings underscore the essential function of water fluxes in regulating WUE and enhance our understanding of carbon–water coupling mechanisms under climate change. Full article

Superoxide dismutase (SOD) enzymes are essential for reducing oxidative damage resulting from overabundant reactive oxygen species under abiotic stress. While the SOD gene family has been extensively studied in many species, research focusing on Cymbidium species remains limited. In this study, a comprehensive analysis of the SOD gene family in three Cymbidium genomes was conducted. A total of 23 SOD genes were identified, with nine SODs in C. sinense, eight in C. ensifolium, and six in C. goeringii. These SOD genes were categorized into three clades: Cu/Zn-SOD, Fe-SOD, and Mn-SOD, with the Cu/Zn-SOD being the most abundant in these three types. This classification was supported by analyses of conserved domains, motifs, and phylogenetic relationships. Cis-element prediction showed that stress-responsive elements were identified in most SODs. Transcriptomic data revealed that seven CsSODs exhibited a border expression in all sequenced tissues, while two exhibited undetectable expression levels. Further qRT-PCR analysis showed that all CsSODs were upregulated under salt stress, with some exhibiting significant changes in expression. These findings all highlight the crucial role of CsSODs in the salt stress response and provide valuable insights for further breeding salt-tolerance varieties of C. sinense. Full article

Fatty acids (FAs) are a group of organic compounds that are regulated by polygenic and environmental factors and affect the taste, nutritional value, and quality of meat. Lamb meat is rich in FAs required by the human body, which has directed more attention to sheep research and meat production. The fatty acid-binding protein 4 (FABP4) gene is considered a candidate gene that can affect FA composition in livestock. Therefore, the aim of this study was to screen for genetic polymorphisms of FABP4 and confirm the association between these polymorphisms and FAs, chemical composition, and carcass traits in Sonid lambs. The results of the association study showed that g.57764667T>C, g.57764436T>G, g.57764242G>A, and g.57757988A>G were associated with the composition of certain long-chain FAs, and g.57764242G>A, g.57764436T>G, and g.57758026G>A were associated with free amino acid levels. In addition, g.57764667T>C and g.57757988A>G were associated with carcass weight and live weight in Sonid lambs. Therefore, the polymorphisms of the FABP4 gene are expected to be a genetic selection marker for superior traits in Sonid sheep breeding, which also provides new insights into how the ovine FABP4 gene affects traits of lamb quality. Full article

The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (S_GPP and S_ER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in S_GPP and S_ER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in S_GPP and S_ER. Spatially, S_GPP and S_ER were generally strongly correlated. Among different ecosystems, the correlation between S_GPP and S_ER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, S_GPP and S_ER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for S_GPP, whereas needleleaf forest showed the highest values for S_ER. Temporally, S_ER demonstrated more pronounced changes than S_GPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both S_GPP and S_ER. Forest, on the other hand, maintained stable S_GPP but displayed a downward trend in S_ER. Additionally, S_GPP and S_ER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, S_ER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns. Full article

The cultivation of saline–alkali-tolerant peanut (Arachis hypogaea L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress. Full article

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production. Full article

The Yellow River Basin is a critical region for ecological environment protection and social and economic development in China. It is of great significance to study vegetation dynamics for the high-quality development of the Yellow River Basin. In this study, based on the data of NDVI and precipitation datasets in the growing season (June to September) from 2000 to 2019, we used a Sen+Mann–Kendall trend analysis and other methods to study the spatial and temporal evolution characteristics of precipitation and vegetation cover in the Shaanxi section of the Yellow River Basin and to assess the regional vegetation quality change characteristics based on estimating the rain-use efficiency (RUE). The results show the following: (1) The precipitation in the study area showed a spatial distribution pattern of more in the south and less in the north, in which Yulin City had the lowest precipitation overall, but it was an area with significant increasing precipitation. (2) The NDVI value of the Shaanxi section of the Yellow River Basin showed an overall upward trend from 2000 to 2019, with a growth rate of 0.327/10a. The vegetation cover showed the spatial characteristics of high in the south and low in the north, which showed that the vegetation growth condition was poor in the wind-sand grassland area at the southern edge of the Mu Us Sandland in the northwestern part of Yulin City and the construction areas in the Guanzhong Plain. Meanwhile, the vegetation grew well in Yan’an City and the area close to the Qinba Mountains. Moreover, the NDVI of the study area increased with the increase in precipitation. (3) The vegetation quality in the study area showed fluctuating interannual changes and a weak upward trend. More than 80% of the vegetation in the study area was in a state of improvement, and the areas with more significant improvement were mainly located in the northern part of the study area, while the vegetation was degraded in the urban and urban suburb areas in the Guanzhong Plain. The results of this study are of great practical significance for promoting the socio-economic development of the Yellow River Basin in coordination with ecological environmental protection. Full article

This study aimed to identify the predominant factors that are important for environmental sustainability in wetland restoration in the Kushiro Wetlands, Hokkaido, Japan, where a nature restoration project was implemented over a decade ago. Field surveys of topography and vegetation, as well as laboratory soil tests, were conducted in the meandering-restored section of the Kushiro River, where alder trees have flourished, and in the reference section, where wetland grasslands have been maintained. We then applied correlation analysis to the data to examine the relationship between the peat soil characteristics and alder tree size. The results showed a significant positive correlation between organic matter and the water content ratio in all the survey sections (correlation coefficient: 0.88; p-value < 0.05). The reference section had 24.1 ± 11.1% organic matter, indicating well-developed peat with sufficient moisture retention, while the re-meandering section had 13.1 ± 3.8%, indicating underdeveloped peat with limited moisture retention. Furthermore, no correlation was found between the relative elevation and water content ratio (correlation coefficient: −0.01; p-value > 0.05), nor was there any difference in nutrient concentrations between the survey sections. Therefore, it is possible that the difference in alder tree sizes between the sections depended on the soil moisture retention capacity based on the degree of peat soil development. These results suggest that peat soil restoration is crucial in human sustainable development for suppressing alder proliferation and restoring original peat grasslands. Full article

Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R² > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data. Full article

To investigate the impacts of vegetation change on deep soil water recharge, it is essential to identify the sources of deep soil water and deep drainage. The combination of stable and radioactive water isotopes is an effective method for studying deep vadose zones, though it has been rarely applied in complex gully areas. In this study, we measured δ²H, δ¹⁸O, and ³H in soil water under long-term natural grassland and C. korshinskii on the same slope. Both natural grassland and C. korshinskii plots received deep soil water from rainfall during the rainy season; however, the replenishment thresholds for soil water at depths of 2–10.4 m differed between the two vegetation types, corresponding to rainfall intensities of ≥20 mm and ≥50 mm, respectively. Following the conversion of natural grassland to C. korshinskii vegetation, the rate of soil water storage deficit increased by 46.4 mm yr⁻¹, and deep drainage shifted from 39.6 mm yr⁻¹ to 0 mm yr⁻¹. Deep-rooted vegetation significantly depletes soil water to meet transpiration demands, thus hindering rainfall recharge. These findings have important implications for water and land resource management, especially in areas undergoing significant vegetation changes. Full article

Extreme climate events have become more frequent under global warming, significantly affecting vegetation phenology and carbon cycles in Central Asia. However, the mediating effects of intensity of compound drought and heat events (CDHEs) and compound moisture and heat events (CMHEs) on grassland phenology and their trends in the relative contributions to grassland phenology over time have remained unclear. Based on the calculation results of grassland phenology and compound events (CEs), this study used trend analysis, partial least squares regression structural equation modeling (PLS-SEM), and ridge regression analysis to investigate the mediating effect and the temporal trend in relative contribution of CEs to grassland phenology in Central Asia, and the magnitude of sensitivity of grassland phenology to CEs. This study revealed that the start of season (SOS) was advanced by 0.4 d·a⁻¹, end of season (EOS) was delayed by 0.5 d·a⁻¹, and length of season (LOS) extended by 0.8 d·a⁻¹ in 1982–2022. The duration of the CDHEs (0−37 days) was greater than that of the CMHEs (0−9 days) in Central Asia. The direct effects of CDHEs and CMHEs on grassland phenology were generally negative, except for the direct positive effect of CDHEs on LOS. The indirect effects of temperature and precipitation on grassland phenology through CDHEs and CMHEs were greater than their direct effects on phenology. The relative contribution of CDHEs to grassland phenology was consistently greater than that of CMHEs, and both the relative contribution curves showed a significant upward trend. The sensitivity of grassland phenology to CDHEs was higher than its sensitivity to CMHEs at 0.79 (SOS), 1.18 (EOS), and 0.72 (LOS). Our results emphasize the mediating effects of CDHEs and CMHEs on grassland phenology. Under the influence of CDHEs and CMHEs, the LOS will further lengthen in the future. Full article

Effects of climate warming on nutrition quality and storage of alpine grasslands are still controversial, which is not conducive to the management and utilization of alpine grasslands. A long-term warming experiment (with open-top chambers used to elevate temperature) was conducted at three elevations (relatively low, mid-, and high elevations with 4313, 4513, and 4693 m) of Northern Tibet in 2010 to compare the differences in forage nutritional quality and storage response to warming among three elevations and to explore the relationships between forage nutritional quality and production. In 2019, community surveys, observations of forage biomass and nutrition quality, and soil physicochemical properties were carried out. Forage nutrition quality included crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), ether extract (EE), crude ash (Ash), and water-soluble carbohydrate (WSC) content. Warming did not affect community aboveground biomass (AGB) at the three elevations. Warming improved community nutrition quality by increasing community CP content by 25.80% and decreasing community NDF content by 15.51% at the low elevation. In contrast, warming reduced community nutrition quality by increasing community CP, ADF, and NDF contents by 13.45%, 23.68%, and 17.43%, respectively, and decreasing Ash content by 39.50% at the high elevation. Warming did not affect community CP, ADF, NDF, EE, Ash, or WSC contents at the mid-elevation. Warming increased community nutrition storage by increasing community CP, ADF, and NDF storges by 74.69%, 88.18%, and 79.71%, respectively, at the high elevation. Warming did not affect community nutrition storages at the low or mid-elevations. Overall, forbs had higher CP, EE, Ash, and WSC contents and lower ADF and NDF contents compared with graminoids. Community EE content increased with community AGB, but community CP, ADF, NDF, EE, Ash, and WSC contents were not related to community AGB. Therefore, from the low to high elevation, the effects of warming on forage nutrition quality gradually changed from improving to inhibiting. Warming altered rangeland quality by affecting forage nutrition quality rather than forage production. There were no trade-offs between forage nutrition quality and forage production. Full article