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Based on the theory of ecological crop nutrient deficiency and compensation effect, the nitrogen (N) deficiency at tillering stage and N compensation at young panicle differentiation stage in rice ( L.) was selected to study. Four N treatments were treated, and the effects of N deficiency and compensation were investigated on grain yield, N uptake and utilization and the physiological characteristics of rice. The results showed that the yield per plant presented an equivalent compensatory effect. Double N compensation led to superiority in the number of effective panicle per plant, increased the activity of nitrate reductase and glutamine synthetase. The content of endogenous growth-inhibitory hormone abscisic acid (ABA) decreased in the leaves, photosynthesis was enhanced, and the number of tillers per plant increased after double N compensation. During maturation stage, the panicle dry weigh in T1 (double N compensation at young panicle differentiation stage, after N deficiency at tillering stage) was higher than that in CK1 (constant supply of N throughout different stages of growth) and the biomass per plant in T1 increased by 1.47% compared with CK1. N contents in all organs, N accumulation, and total N content were all higher in T1 during maturation stage. Moreover, N agronomic efficiency, N physiological efficiency, and N partial factor productivity were optimized for T1 and CK2 (constant N compensation at young panicle differentiation stage, after N deficiency at tillering stage) compared with CK1. This study contributes to the understanding of the physiological mechanisms underlying the compensation of N deficiency in rice.

Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: The tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially with a large sample of catchments, is currently lacking in the literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long‐term baseflow signatures for 1,100 catchments in the Contiguous United States. Our results suggest that SEC is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless of the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution. Plain Language Summary River flow can be broken down into two components: fast flow and slow flow. The latter is usually known as baseflow, and it represents the stable portion of river flow that comes from stored water sources, such as groundwater or snowpack. It is crucial to understand the proportion of baseflow in river flow for effective water resource management. A commonly used method to separate baseflow from river flow is by filtering streamflow data with empirical baseflow filters. These filters contain some parameters that are often optimized using geochemical data, such as specific electrical conductance (SEC) and turbidity, to ensure reasonable performance of baseflow separation. This study examined how SEC and turbidity can be used to optimize four empirical baseflow filters for quantitative assessment of baseflow contribution to streamflow. Our analysis of 1,100 catchments across the Contiguous United States revealed that SEC is a more reliable indicator of baseflow than turbidity. Interestingly, the choice of empirical baseflow filter had minimal impact, though some filters produced inconsistent results for the quantification of baseflow contribution. This research enhances our ability to accurately estimate baseflow, aiding in water resource planning and management. Key Points Evidence suggests that specific electrical conductance is a better tracer for baseflow separation compared to turbidity Using monthly extreme values to describe tracer signature in flow components is better than using a power function relationship The smooth minima method provides the most consistent estimation of baseflow contribution across various combinations

Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for optimizing soil moisture sensor network deployment at the catchment scale. The framework was validated using Sentinel-1 SAR-derived soil moisture data within a humid catchment in southern China. Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. The optimized sensor network achieved a 52.45% reduction in RMSE compared to random placement. Moreover, the optimal number of sensors varied with seasonal dynamics: the wet season required 11 sensors due to increased precipitation-induced spatial variability, whereas the dry season could be adequately monitored with only six sensors. The proposed optimization approach offers a cost-effective strategy for collecting reliable in situ data, which is essential for improving the accuracy and applicability of remote sensing products in catchment-scale soil moisture monitoring.

The subtropical forests in China play a pivotal part in the global and regional carbon–water cycle and in regulating the climate. Ecosystem water-use efficiency (WUE) is a crucial index for understanding the trade-off between ecosystem carbon gain and water consumption. However, the underlying mechanisms of the WUE in forest ecosystems, especially the different subtropical forests, have remained unclear. In this paper, we developed a simple framework for estimating forest WUE and revealing the underlying mechanisms of forest WUE changes via a series of numerical experiments. Validated by measured WUE, the simulated WUE from our developed WUE framework showed a good performance. In addition, we found that the subtropical forest WUE experienced a significant increasing trend during 2001–2018, especially in evergreen and deciduous broadleaf forests where the increasing rate was greatest (0.027 gC kg−1 H2O year−1, p < 0.001). Further analysis indicated that the atmospheric CO2 concentration and vapor pressure deficits (VPD), rather than leaf area index (LAI), were the dominant drivers leading to the subtropical forest WUE changes. When summed for the whole subtropical forests, CO2 and VPD had an almost equal spatial impact on annual WUE change trends and accounted for 45.3% and 49.1% of the whole study area, respectively. This suggests that future forest management aiming to increase forest carbon uptake and protect water resources needs to pay more attention to the long-term impacts of climate change on forest WUE.

To project the distribution of three major forest types in the northeastern USA in response to expected climate change. The New England region of the United States. We modelled the potential distribution of boreal conifer, northern deciduous hardwood and mixed oak-hickory forests using the process-based BIOME4 vegetation model parameterized for regional forests under historic and projected future climate conditions. Projections of future climate were derived from three general circulation models forced by three global warming scenarios that span the range of likely anthropogenic greenhouse gas emissions. Annual temperature in New England is projected to increase by 2.2-3.3 °C by 2041-70 and by 3.0-5.2 °C by 2071-99 with corresponding increases in precipitation of 4.7-9.5% and 6.4-11.4%, respectively. We project that regional warming will result in the loss of 71-100% of boreal conifer forest in New England by the late 21st century. The range of mixed oak-hickory forests will shift northward by 1.0-2.1 latitudinal degrees (c. 100-200 km) and will increase in area by 149-431% by the end of the 21st century. Northern deciduous hardwoods are expected to decrease in area by 26% and move upslope by 76 m on average. The upslope movement of the northern deciduous hardwoods and the increase in oak-hickory forests coincide with an approximate 556 m upslope retreat of the boreal conifer forest by 2071-99. In our simulations, rising atmospheric CO₂ concentrations reduce the losses of boreal conifer forest in New England from expected losses based on climatic change alone. Projected climate warming in the 21st century is likely to cause the extensive loss of boreal conifer forests, reduce the extent of northern hardwood deciduous forests, and result in large increases of mixed oak-hickory forest in New England.

Chemical weathering of carbonate (CWC) minerals plays an important role in global carbon cycle, as it bridges the atmospheric, lithospheric and hydrospheric carbon pools. However, data limitations have hindered an accurate estimation of the carbon sink flux induced by global CWC (CSF CWC ), as well as its response to environmental change. Conventional hydrochemical methods, which infer CSF CWC indirectly from riverine hydrochemistry, provide only catchment-integrated signals, yet cannot resolve the specific contribution of CWC across different climate, pedological, and ecological settings within a catchment. Here, we synthesize 2,444 globally-distributed in-situ CWC rates measured by rock tablet tests, and investigate the magnitude, spatiotemporal variation and controlling factors of CSF CWC using a machine learning model. We find that soil physicochemical properties (e.g., pH and moisture) play a more important role in determining global CWC spatial variation than climate, hydrology and vegetation factors. The machine-learning model developed in this study explains 68% of the variance in globally observed CWC-induced carbon sink flux values. Global application of our model indicates that CWC generates a carbon sink of 0.27 Pg C yr -1 worldwide, which is comparable to previous catchment-integrated estimates derived using different approaches, accounting for approximately 8% of the total terrestrial carbon sink. Over the past two decades, global greening has significantly accelerated global total carbon sink induced by CWC, with this acceleration particularly pronounced in Asia. Overall, this study provides a benchmark estimate of global CSF CWC and advances the mechanistic understanding of carbonate weathering. Our findings contribute to improving existing weathering models and reducing uncertainties in future projections of the terrestrial carbon sink.

The normal methods of agricultural production worldwide have been strongly affected by the frequent occurrence of drought. Rice rhizosphere microorganisms have been significantly affected by drought stress. To provide a hypothetical basis for improving the drought resistance and N utilization efficiency of rice, the study adopted a barrel planting method at the heading stage, treating rice with no drought or drought stress and three different nitrogen (N) levels. Untargeted metabolomics and 16S rRNA gene sequencing technology were used to study the changes in microorganisms in roots and the differential metabolites (DMs) in rhizosphere soil. The results showed that under the same N application rate, the dry matter mass, N content and N accumulation in rice plants increased to different degrees under drought stress. The root soluble protein, nitrate reductase and soil urease activities were improved over those of the no-drought treatment. Proteobacteria, Bacteroidota, Nitrospirota and Zixibacteria were the dominant flora related to N absorption. A total of 184 DMs (98 upregulated and 86 downregulated) were identified between low N with no drought (LN) and normal N with no drought (NN); 139 DMs (83 upregulated and 56 downregulated) were identified between high N with no drought (HN) and NN; 166 DMs (103 upregulated and 63 downregulated) were identified between low N with drought stress (LND) and normal N with drought stress (NND); and 124 DMs (71 upregulated and 53 downregulated) were identified between high N with drought stress (HND) and NND. Fatty acyl was the metabolite with the highest proportion. KEGG analysis showed that energy metabolism pathways, such as D-alanine metabolism and the phosphotransferase system (PTS), were enriched. We conclude that N-metabolism enzymes with higher activity and higher bacterial diversity have a significant effect on drought tolerance and nitrogen uptake in rice.

Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value > 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.

What are the main findings? * Two 500 m, 8-day ET products with matching spatiotemporal resolution, MOD16 and PML-V2, were intercompared, and PML-V2 showed better agreement with ChinaFLUX observations for subtropical China. * Annual ET in subtropical China increased significantly during 2001–2020, with clear south–north and coast–inland gradients; SWDown and LAI were the dominant controls, with northern subregions mainly energy-limited and southern subregions jointly regulated by vegetation and temperature. Two 500 m, 8-day ET products with matching spatiotemporal resolution, MOD16 and PML-V2, were intercompared, and PML-V2 showed better agreement with ChinaFLUX observations for subtropical China. Annual ET in subtropical China increased significantly during 2001–2020, with clear south–north and coast–inland gradients; SWDown and LAI were the dominant controls, with northern subregions mainly energy-limited and southern subregions jointly regulated by vegetation and temperature. What are the implications of the main findings? * The dominant factors for ET changes can vary from south to north in subtropical China, suggesting the significance of weighing different variables in modelling ET and managing water resources in this region. * Residual ET concentrated in urban and cropland areas may partly reflect anthropogenic influence, whereas in regions such as karst landscapes or complex terrain, it likely reflects unmodeled natural processes. The dominant factors for ET changes can vary from south to north in subtropical China, suggesting the significance of weighing different variables in modelling ET and managing water resources in this region. Residual ET concentrated in urban and cropland areas may partly reflect anthropogenic influence, whereas in regions such as karst landscapes or complex terrain, it likely reflects unmodeled natural processes. Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr[sup.−1] from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution.

Background Rice ECQ (eating and cooking quality) is an important determinant of rice consumption and market expansion. Therefore, improvement of ECQ is one of the primary goals in rice breeding. However, ECQ-related quantitative trait loci (QTL) have not yet been fully revealed. The present study aimed to identify a major effect QTL for rice taste, an important component of ECQ via genotyping-by-sequencing, to reveal the associated molecular mechanisms, and to predict key candidate genes. Results A population of F 9 recombinant inbred lines resulting from a cross between R668 (national standard of high-quality third class) and R838 (common edible rice) was used to construct a high-density genetic map (2,295.062 cM). The map comprises 639,504 markers distributed on 12 linkage elements with an average genetic distance of 0.004 cM. We detected a major taste-related QTL, qECQ8, which explained 41.4% of phenotypic variance and had LOD values of 4.42–7.73. Using a five-generation NIL population from the backcross of “Ganxiangzhan No. 1” carrying qECQ8 with the recurrent parent R838 (without qECQ8), we narrowed qECQ8 to a 187.5 kb interval between markers M33 and M37 on Chr8. Comparative transcriptomic analysis revealed that photosynthesis, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, and alpha-linolenic acid metabolism were induced in developing seeds of lines containing qECQ8. Furthermore, we identified two candidate genes in the qECQ8 region, including LOC_Os08g30550 (zinc knuckle family protein), a major candidate for genetic-assisted breeding of high-quality rice. Conclusion Our findings provide important genetic resources for targeted improvement of rice taste quality and may facilitate the genetic breeding of rice ECQ.