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The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning. The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost). Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship. Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

Application of manure has been recommended as an effective strategy to to mitigate climate change. However, the magnitude of greenhouse gases emission derived by application of manure to agricultural soils across environmental conditions still remains unclear. Here, we synthesized data from 379 observations in China and quantified the responses of soil nitrous oxide (N 2 O), carbon dioxide (CO 2 ) and methane (CH 4 ) emissions to manure (Org-M) in comparison to chemical fertilizers (Min-F) or non-fertilizers (Non-F). The results showed that N 2 O, CO 2 and CH 4 emissions were significantly affected by Org-M compared to Min-F (percentage change: −3, +15 and +60%, P  < 0.05) and Non-F (percentage change: +289, +84 and +83%, P  < 0.05), respectively. However, at the same amount of total N input, Org-M decreased soil N 2 O emission by 13% and CH 4 emission by 12%, and increased soil CO 2 emission by 26% relative to Min-F in upland soils. For paddy soils, N 2 O, CO 2 and CH 4 emissions differed by −3%, −36% and +84% between Org-M and Min-F (i.e., Org-M minus Min-F). Thus, practices such as application of manure instead of chemical fertilizer and decreasing nitrogen input rate need to be highly considered and optimized under different soils and climate conditions to mitigate GHGs emission in China.

The sensitivity of model outputs to parameter variations is crucial for effective model calibration and application. This study assessed the sensitivity of N2O and CH4 emissions to varying weather conditions and fertilization practices in a winter wheat–rice cropping system. Using the Sobol first-order sensitivity index within the SPACSYS model, key parameters and input variables influencing gas emissions were identified. The results showed that the index effectively detected highly sensitive parameters, particularly those related to soil water content, oxygen dynamics and microbial processes. Both N2O and CH4 emissions were sensitive to carbon availability and soil oxygen levels. For N2O emissions, microbial process parameters and soil water content had substantial impacts, whereas CH4 emissions were more responsive to methane consumption, oxygen levels, and carbon substrates. Fertilization, rainfall and temperature showed high sensitivity for N2O emissions, while temperature emerged as the dominant factor controlling CH4 emissions. The identified parameters offer valuable insights for improving model performance and informing strategies to mitigate greenhouse gas emissions.

The wide spread of dry soil layers (DSL) in China’s Loess Plateau region has negative effects on the ecosystem, including soil degradation and vegetation failure. To understand the temporal persistence of DSL, a ca. 860 km south-north transect was established and soil water content of the 0–5 m depth soil layer repeatedly measured for a period of four years. The results indicated that DSL varied with time and had a distribution area over 21.5–47.0% in the 860 km transect during the study period. The DSL could be divided into temporary and permanent types based on the length of period for which the soil remains dry. While temporary DSL is recoverable, permanent DSL (which existed in 47 out of 86 sites) was apparently unrecoverable as it persisted throughout the observation period. Permanent DSL was characterized by high temporal persistence, severe soil desiccation and thick dry layers; all of which suggested severe negative effect on the ecosystem. Non-climatic factors, rather than climate factors, contributed more to the formation of permanent DSL in the study area. Thus, it was suggested that policies and measures should be enacted to control especially permanent DSL formation in the region.

Livestock grazing can strongly determine how grasslands function and their role in the carbon cycle. However, how ecosystem carbon exchange responds to grazing and the underlying mechanisms remain unclear. We measured ecosystem carbon fluxes to explore the changes in carbon exchange and their driving mechanisms under different grazing intensities (CK, control; HG, heavy grazing; LG, light grazing; MG, moderate grazing) based on a 16‐year long‐term grazing experimental platform in a desert steppe. We found that grazing intensity influenced aboveground biomass during the peak growing season, primarily by decreasing shrubs and semi‐shrubs and perennial forbs. Furthermore, grazing decreased net ecosystem carbon exchange by decreasing aboveground biomass, especially the functional group of shrubs and semi‐shrubs. At the same time, we found that belowground biomass and soil ammonium nitrogen were the driving factors of soil respiration in grazed systems. Our study indicates that shrubs and semi‐shrubs are important factors in regulating ecosystem carbon exchange under grazing disturbance in the desert steppe, whereas belowground biomass and soil available nitrogen are important factors regulating soil respiration under grazing disturbance in the desert steppe; this results provide deeper insights for understanding how grazing moderates the relationships between soil nutrients, plant biomass, and ecosystem CO2 exchange, which provide a theoretical basis for further grazing management. We measured ecosystem carbon fluxes and their associations in response to a long‐term (16‐year) grazer manipulation experiment in a desert steppe grassland in Inner Mongolia, China. Our results show that grazers alter patterns of net ecosystem exchange primarily via their negative influence on the biomass of shrubs and subshrubs, which play a prominent role in ecosystem functions.

Three-dimensional root architectural models emerged in the late 1980s, providing an opportunity to conceptualise and investigate that all important part of plants that is typically hidden and difficult to measure and study. These models have progressed from representing pre-defined root architectural arrangements, to simulating root growth in response to heterogeneous soil environments. This was done through incorporating soil properties and more complete descriptions of plant function, moving into the realm of functional-structural plant modelling. Modelling studies are often designed to investigate the relationship between root architectural traits and root distribution in soil, and the spatio-temporal variability of resource supply. Modelling root systems presents an opportunity to investigate functional tradeoffs between foraging strategies (i.e. shallow vs deep rooting) for contrasting resources (immobile versus mobile resources), and their dependence on soil type, rainfall and other environmental conditions. The complexity of the interactions between root traits and environment emphasises the need for models in which traits and environmental conditions can be independently manipulated, unlike in the real world. We provide an overview of the development of three-dimensional root architectural models from their origins, to their place today in the world of functional-structural plant modelling. The uses and capability of root architectural models to represent virtual plants and soil environment are addressed. We compare features of six current models, RootTyp, SimRoot, ROOTMAP, SPACSYS, R-SWMS, and RootBox, and discuss the future development of functional-structural root architectural modelling. Functional-structural root architectural models are being used to investigate numerous root-soil interactions, over a range of spatial scales. They are not only providing insights into the relationships between architecture, morphology and functional efficiency, but are also developing into tools that aid in the design of agricultural management schemes and in the selection of root traits for improving plant performance in specific environments.

Apple production systems are an important component in the Chinese agricultural sector with 1.99 million ha plantation. The orchards in China could play an important role in the carbon (C) cycle of terrestrial ecosystems and contribute to C sequestration. The carbon sequestration capability in apple orchards was analyzed through identifying a set of potential assessment factors and their weighting factors determined by a field model study and literature. The dynamics of the net C sink in apple orchards in China was estimated based on the apple orchard inventory data from 1990s and the capability analysis. The field study showed that the trees reached the peak of C sequestration capability when they were 18 years old, and then the capability began to decline with age. Carbon emission derived from management practices would not be compensated through C storage in apple trees before reaching the mature stage. The net C sink in apple orchards in China ranged from 14 to 32 Tg C, and C storage in biomass from 230 to 475 Tg C between 1990 and 2010. The estimated net C sequestration in Chinese apple orchards from 1990 to 2010 was equal to 4.5% of the total net C sink in the terrestrial ecosystems in China. Therefore, apple production systems can be potentially considered as C sinks excluding the energy associated with fruit production in addition to provide fruits.

C.Y. Wu (Actinidiaceae), an endemic species of Yunnan province in China, exhibits substantial therapeutic importance in daily life. Given its narrow distribution and small population size, complete genome sequence is needed to reveal its phylogenetic position in . In this study, we assembled and annotated the complete chloroplast genome of and conducted the phylogenetic analysis among the genus . The results showed that had a typical quadripartite structure, exhibiting a total length of 156,716 bp and consisting of two inverted repeats (IRs) of 23,805 bp separated by a large single-copy (LSC) and a small single-copy (SSC) of 88,437 bp and 20,669 bp. A maximum-likelihood (ML) phylogenetic tree including and 21 related species indicated that it was close to The study will offer valuable genetic resources and improve the phylogenetic resolution of

Leguminous crops have the ability to fix nitrogen (N) biologically from the atmosphere. This can benefit not only the legumes themselves but also any intercropped or subsequent crops, thus reducing or removing the need to apply N fertilizers. Improved quantification of legume biological nitrogen fixation (BNF) will provide better guidance for farmers on managing N to optimise productivity and reduce harmful losses to the environment. There are many techniques available for the direct quantitative measurement of legume BNF in the field and in controlled environments. However, these are time-consuming and therefore expensive, and generate data relevant only to the time and place of measurement. Alternatively, legume BNF can be estimated by either empirical models or dynamic mechanistic simulation models. Comparatively, simulation by a dynamic model is preferable for quantifying legume BNF, because of its capability to simulate the response of N fixation to a wide range of environmental variables and legume growth status. Currently there is no published review of the approaches used to simulate, rather than measure, legume BNF. This review of peer-reviewed literature shows that most simulation models estimate the N fixation rate from a pre-defined potential N fixation rate, adjusted by the response functions of soil temperature, soil/plant water status, soil/plant N concentration, plant carbon (C) supply and crop growth stage. Here, we highlight and compare the methods used to estimate the potential N fixation rate, and the response functions to simulate legume BNF, in nine widely-cited models over the last 30 years.We then assess their relative strengths in simulating legume BNF with varying biotic and abiotic factors, and identify the discrepancies between experimental findings and simulations. After this comparison, we identify the areas where there is the potential to improve legume BNF simulation in the future. These include; (1) consideration of photosynthetic C supply, (2) refining the various effects of soil mineral N concentration, (3) characterization and incorporation of excess soil water stress and other factors into models, and (4) incorporation of the effects of grazing, coexistence and competition with intercrops and weeds into models to improve their practical relevance to sustainable agricultural systems. This review clarifies, for the first time, the current progress in legume BNF quantification in simulation models, and provides guidance for their further development, combining fundamental experimental and modelling work.

Carotenoids in citrus fruits have health benefits and make the fruits visually attractive. Red-fleshed 'Chuhong' ('CH') and pale green-fleshed 'Feicui' ('FC') pummelo (Citrus maxima (Burm) Merr.) fruits are interesting materials for studying the mechanisms of carotenoid accumulation. In this study, particularly high contents of linear carotenes were observed in the albedo tissue, segment membranes and juice sacs of 'CH'. However, carotenoids, especially β-carotene and xanthophylls, accumulated more in the flavedo tissue of 'FC' than in that of 'CH'. Additionally, the contents of other terpenoids such as limonin, nomilin and abscisic acid significantly differed in the juice sacs at 150 days postanthesis. A dramatic increase in carotenoid production was observed at 45 to 75 days postanthesis in the segment membranes and juice sacs of 'CH'. Different expression levels of carotenogenesis genes, especially the ζ-carotene desaturase (CmZDS), β-carotenoid hydroxylase (CmBCH) and zeaxanthin epoxidase (CmZEP) genes, in combination are directly responsible for the largely different carotenoid profiles between these two pummelo fruits. The sequences of eleven genes involved in carotenoid synthesis were investigated; different alleles of seven of eleven genes might also explain the largely different carotenogenesis observed between 'CH' and 'FC'. These results enhance our understanding of carotenogenesis in pummelo fruits.