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Abstract The Tibetan Plateau (TP) is crucial for global climate change and China’s ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change.

Climate change has a substantial influence on the end of the growing season (EOS). The time-lag and cumulative effects are non-negligible phenomena when studying the interactions between climate and vegetation. However, quantification of the temporal effects of climatic factors on the EOS in the context of changing hydrothermal patterns remains scarce. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR), this study first inverted the EOS of typical steppe vegetation in a semi-arid region of China and then quantified the time-lag and cumulative effects of monthly total precipitation (PRE) and monthly average temperature (TEM) on the EOS during 2003–2022. The results showed that a turning point occurred in 2011, when the EOS displayed an advancing trend until 2011, followed by a delayed trend. Accordingly, the climatic background has changed from warming and drying conditions during 2003–2011 to warming and wetting conditions during 2011–2022. The time-lag scales of PRE and TEM on the EOS decreased from 2- and 4-month scales during 2003–2011, respectively, to 1- and 2-month scales during 2011–2022, respectively. The time-lag degree of the hydrothermal factors on the EOS weakened with increased precipitation. The cumulative time scales of the EOS response to PRE and TEM were mainly concentrated within 1-month during different time periods, but the EOS was more sensitive to short-term precipitation. The time lag and cumulative partial correlation coefficient of PRE to EOS changed from mainly negative regulation during 2003–2011 (39.2% and 50.0%, respectively) to mainly positive regulation during 2011–2022 (67.8% and 93.7%, respectively). The time-lag and cumulative effects of TEM on the EOS were positive with the precipitation and temperature gradient under a warming and wetting climate, which indicated that increased precipitation was a prerequisite for temperature to induce a delayed EOS in the semi-arid study region. This study emphasizes the important role of precipitation in regulating the EOS response to hydrothermal factors in semi-arid regions.

IntroductionSoil contamination with copper (Cu) threatens ecological security and human health. Rapeseed demonstrates potential in remediating copper-contaminated soil, and biochar-assisted phytoremediation is increasingly being employed to improve remediation efficiency. However, the combined application of them has not been thoroughly studied in terms of the synergistic effects and the mechanisms of their interaction.MethodsIn this regard, this study conducted a pot experiment to evaluate biochar-assisted remediation under Cu-contaminated soil with varying biochar application rates; Furthermore, the plant physiological mechanism and soil physicochemical properties involved in the biocharrapeseed system was explored.ResultsOur results showed that the exchangeable pool of copper in soil decreased by 10.0% and 12.3% with adding 5% biochar (BC1) and 10% biochar (BC2) relative to control (BC0), respectively, prior to rapeseed cultivation. The rapeseed cultivation for one season further reclaimed 4.9%, 9.0%, and 13.6% of the available copper in this soil by root extraction under the BC0, BC1, and BC2 treatments, respectively. The overall copper concentration in plants decreased by 23.7% under BC2 and 13.3% under BC1 compared to BC0. However, the plant’s dry biomass at BC1 and BC2 treatments increased by 1.7-fold and 2.7-fold relative to BC0, which offset the negative impact of the decreased copper concentration on phytoremediation. Physiological analysis showed adding 10% biochar decreased the MDA content by 36% in the leaf and 49% in the root, compared to BC0. The transmission electron microscopy for cell wall ultrastructure in root tips showed that biochar addition in Cu-contaminated soil increased the mechanical strength of the celL wall, explicitly increasing the thickness of the secondary cell wall. Further cell wall components analysis revealed a remarkable increment of the pectin content in BC2 relative to BC0, increased by 56% in the leaf and 99% in the root, respectively. Additionally, 10% biochar application led to a roughly 2-fold increase in seed yield via ameliorating the soil physicochemical properties and increasing the rapeseed growth.DiscussionThese findings offer insights into synergistic rapeseed-biochar use for Cu-contaminated soil remediation.

Abstract The growth of newly formed particles through new particle formation (NPF) contributes a significant fraction to the cloud condensation nuclei, yet the driving mechanisms remain unclear, especially for polluted environments. To investigate the potential species contributing for nanoparticle growth in environments with significant anthropogenic influences, we measured the hygroscopicity of newly formed particles at 20–40 nm at a rural observational site in the North China Plain during winter 2018. Our results demonstrate that these particles were not very hygroscopic, with the mean hygroscopicity parameter κ of 0.13 ± 0.09. Clear differences in the inferred κ of the growing material responsible for the growth were observed among different events, indicating that even at the same region, the compounds driving particle growth may not be identical. This may be synergistically influenced by the NPF precursors, oxidants and meteorological conditions, suggesting complex mechanisms might co‐exist behind nanoparticle growth in polluted environments.

Qinghai-Tibet Plateau lakes are important carriers of water resources in the ‘Asian’s Water Tower’, and it is of great significance to grasp the spatial distribution of plateau lakes for the climate, ecological environment, and regional water cycle. However, the differences in spatial-spectral characteristics of various types of plateau lakes, and the complex background information of plateau both influence the extraction effect of lakes. Therefore, it is a great challenge to completely and effectively extract plateau lakes. In this study, we proposed a multiscale contextual information aggregation network, termed MSCANet, to automatically extract Plateau lake regions. It consists of three main components: a multiscale lake feature encoder, a feature decoder, and a Multicore Pyramid Pooling Module (MPPM). The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes. The MPPM module aggregated the contextual information of various lakes globally. We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data; additionally, comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity. Finally, we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

Vegetation phenology serves as a sensitive indicator of climate change. However, the mechanism of the hydrothermal role in vegetation phenology changes is still controversial. Utilizing the data on the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from MODIS and meteorological data, the study employed the dynamic threshold method to derive the end of the growing season (EOS). The research delved into the spatiotemporal patterns of the EOS for typical steppe vegetation in the semi-arid region of Inner Mongolia spanning the period from 2003 to 2022. Furthermore, the investigation scrutinized the response of EOS to temperature and precipitation dynamics. The results showed that (1) the dynamic threshold method exhibited robust performance in the EOS of typical steppe vegetation, with an optimal threshold of 45% and a Root Mean Square Error (RMSE) of 5.5 days (r = 0.81); (2) the spatiotemporal patterns of the EOS of typical steppe vegetation in the semi-arid region experienced a noteworthy reversal from 2003 to 2022; (3) the lag effects of precipitation and temperature on the EOS were found, and the lag time scales were mainly 1 month and 2 months. The increase in precipitation in August was the key reason for the reversal of the EOS, and satisfying the precipitation was a prerequisite for the temperature to delay the EOS. The study emphasizes the important role of water availability in regulating the response of the EOS to hydrothermal factors and highlights the utility and reliability of FPAR in monitoring the EOS of typical steppe vegetation.

The effects of temperature, and photoperiod on autumn phenology are well established for many species. However, the impact of multiple environmental factors and their interactions on regulating autumn phenology remains insufficiently explored. A large-scale controlled experiment in an artificial climate chamber was conducted from April to October 2021 at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Hebei Province. This study aimed to investigate the interactive effects of temperature [T1.5, (1.5 °C above the control), T2, (2 °C above the control)], photoperiod [LP, long photoperiod (4 h photoperiod above the control), SP, short photoperiod (4 h photoperiod below the control)], and nitrogen addition [LN, low nitrogen, (nitrogen at 5 g N·m−2·a−1), MN, medium nitrogen, (nitrogen at 10 g N·m−2·a−1), HN, high nitrogen, (nitrogen at 20 g N·m−2·a−1), control for temperature and photoperiod was the mean monthly temperature and average photoperiod (14 h) from 1989–2020 for Stipa krylovii, while the control for nitrogen treatment was without nitrogen addition] on leaf senescence in Stipa krylovii. A three-way analysis of variance (ANOVA) revealed significant effects of temperature, photoperiod, and nitrogen addition on leaf senescence (p < 0.01), with effects varying across different levels of each factor. Increased temperature notably delayed leaf senescence, with delays averaging of 4.0 and 6.3 days for T1.5 and T2, respectively. The LP treatment advanced leaf senescence by an average of 4.0 days, while the SP treatment delayed it by an average of 6.2 days; nitrogen addition advanced leaf senescence, with the effect intensifying as nitrogen levels increased, resulting in average advancements of 1.5, 1.9, and 4.3 days for LN, MN, and HN, respectively. Additionally, we observed that temperature altered the sensitivity of leaf senescence to the photoperiod, diminishing the advancement caused by LP at 2 °C and amplifying the delay caused by SP. These findings underscore the differential impacts of these three factors on the leaf senescence of Stipa krylovii and provide critical insights into plant phenology in response to varying environmental conditions.

Drought stress is a major abiotic stress that poses serious threats to the yield and quality of rapeseed. Understanding the mechanisms underlying the drought tolerance of rapeseed is of great importance for the breeding of drought-resistant rapeseed varieties. Here, we compared the phenotypic characteristics, photosynthetic physiology, specific metabolites and related metabolic pathways between the drought-tolerant genotype (Q2) and drought-sensitive genotype (Q8) of rapeseed under drought stress using gas chromatography and liquid chromatography–mass spectrometry. The results indicated that drought stress decreased the total biomass and photosynthesis of both genotypes, particularly those of Q8, and the reduction was substantially lower in the root surface area, root volume and root diameter of Q2 than in those of Q8 under drought stress. The adverse effect on Q2 could be improved by increasing the specific leaf area, root length, and non-photochemical quenching. Furthermore, four metabolic pathways (galactose metabolism, TCA cycle, aminoacyl-tRNA biosynthesis as well as nicotinate and nicotinamide metabolisms) were greatly activated in Q2 compared with those in Q8 under drought stress. Metabolic profiling revealed 17 differential metabolites between the two genotypes, including carbohydrates, amino acids and organic acids and the increase in tagatose was accompanied by the accumulation of fumaric acid in the roots of Q2 under drought stress. The drought resistance of Q2 could be attributed to the accumulation of more metabolites, such as galactose, tagatose, glycerone and fumaric acid. Our results provide valuable insights into the growth characteristics, physiological changes and metabolic regulation mechanism of rapeseed under drought stress as well as a theoretical basis for the breeding of drought-resistant rapeseed varieties.

Abstract Accurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, restricting their applicability on a national scale and under future climate change scenarios. We present a robust framework leveraging machine learning and multiple data sets to estimate IWU across China. Forced with an ensemble of climate and socio‐economic projections, we appraise future trends and additional costs of IWU. Our model shows high accuracy in reproducing IWU, with coefficient of determination (R2) ranging from 0.86 to 0.91 and root mean square error from 0.261 to 0.361 km3/yr when compared to reported values in Chinese prefectures. Independent validation at 11 cropland sites further confirms the model's predictive power (R2 = 0.67). Under different emissions scenarios, China's IWU is projected to increase by 8.5%–17.1% (6.8%–34.8%) by 2050s (2100s) compared to the historical period (1981–2010), with higher emissions leading to more significant increases. This rise in IWU by 2050s (2100s) comes with an estimated additional cost of US $1.65–3.91 ($2.28–6.5) billion/year, highlighting the urgency for sustainable water management. Our study provides an effective approach for estimating current and future IWU using machine learning techniques, transferable to other countries facing increasing irrigation demands.

A variable future climate would lead to changes in the biomass allocation pattern of plant communities, but its divergent changes under different environments have not been clearly explored, greatly limiting a better understanding of the responsess of grassland ecosystems to environmental changes. Herein, we conducted field samplings at 215 grassland sites in the Qinghai-Tibetan and Inner Mongolian Plateaus to analyze community biomass allocation patterns (specifically, the ratio of root to shoot, R/S) in various grasslands along environmental gradients. We found a gradual increase in R/S from humid to semi-arid and arid environments across both plateaus. In humid environment, soil factor had a significant effect on the R/S of alpine humid grassland, while climate factor had a significant effect on the R/S of humid grassland. In semi-arid environment, both climate and plant factors had significant effects on R/S in semi-arid grassland. In arid environments, climate and plant factors had significant effects on R/S in arid grassland. These findings emphasize the multifaceted role of environmental factors in shaping biomass allocations across diverse ecosystems, enriching our understanding of ecological dynamics. The current results are timely in advancing the fundamental understanding of the mechanisms of biomass allocation in grasslands and are useful for global research on grasslands in different environments.

High levels of copper released in the soil, mainly from anthropogenic activity, can be hazardous to plants, animals, and humans. The present research aimed to estimate the suitability and effectiveness of rapeseed (Brassica napus L.) as a possible soil remediation option and to uncover underlying adaptive mechanisms A pot experiment was conducted to explore the effect of copper stress on agronomic and yield traits for 32 rapeseed genotypes. The copper-tolerant genotype H2009 and copper-sensitive genotype ZYZ16 were selected for further physiological, metabolomic, and transcriptomic analyses. The results exhibited a significant genotypic variation in copper stress tolerance in rapeseed. Specifically, the ratio of seed yield under copper stress to control ranged from 0.29 to 0.74. Furthermore, the proline content and antioxidant enzymatic activities in the roots were greater than those in the shoots. The accumulated copper in the roots accounted for about 50% of the total amount absorbed by plants; thus, the genotypes possessing high root volumes can be used for rhizofiltration to uptake and sequester copper. Additionally, the pectin and hemicellulose contents were significantly increased by 15.6% and 162%, respectively, under copper stress for the copper-tolerant genotype, allowing for greater sequestration of copper ions in the cell wall and lower oxidative stress. Comparative analysis of transcriptomes and metabolomes revealed that excessive copper enhanced the up-regulation of functional genes or metabolites related to cell wall binding, copper transportation, and chelation in the copper-tolerant genotype. Our results suggest that copper-tolerant rapeseed can thrive in heavily copper-polluted soils with a 5.85% remediation efficiency as well as produce seed and vegetable oil without exceeding food quality standards for the industry. This multi-omics comparison study provides insights into breeding copper-tolerant genotypes that can be used for the phytoremediation of heavy metal-polluted soils.

Multi-rotor unmanned aerial vehicles (UAVs) are increasingly prevalent due to technological advancements. During rapeseed’s seedling stage, UAV-generated airflow, known as wind-induced response, affects leaf movement, tied to airflow speed and distribution. Understanding wind-induced response aids early rapeseed lodging prediction. Determining airflow distribution at various UAV heights is crucial for wind-induced response study, yet lacks theoretical guidance. In this study, Computational Fluid Dynamics (CFD) was employed to analyze airflow distribution at different UAV heights. Fluid–solid coupling simulation assessed 3D rapeseed model motion and surface pressure distribution in UAV downwash airflow. Validation occurred via wind speed experiments. Optimal uniform airflow distribution was observed at 2 m UAV height, with a wind speed variation coefficient of 0.258. The simulation showed greater vertical than horizontal leaf displacement, with elastic modulus inversely affecting displacement and leaf area directly. Discrepancies within 10.5% in the 0.5–0.8 m height range above the rapeseed canopy validated simulation accuracy. This study guides UAV height selection, leaf point determination, and wind-induced response parameter identification for rapeseed seedling stage wind-induced response research.

Abstract The response of terrestrial water storages to dryland vegetation growth remains poorly understood. Using multiple proxies from satellite observations and model outputs, we show an overall increase (decrease) in vegetation growth (terrestrial water storages) across drylands globally during 1982–2016. Terrestrial water storages in greening drylands correlate negatively with vegetation growth, particularly for cropland-dominated regions, and such response is pronounced when the growth rate of vegetation productivity is high. Reduction in terrestrial water storage is dominated by precipitation and evapotranspiration variability rather by than runoff. We predict reduction in terrestrial water storage of 41–84% by 2100, accompanying expansion of drylands by 4.1–10.6%. Our findings, which indicate sustained adverse response of terrestrial water storage to vegetation growth in drylands, highlight the need for concerted planning for balanced ecological restoration, agricultural management, and water resource utilization that will affect 5.17 billion people, 64% of whom live in developing countries.

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO2 sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios.

Solar radiation triggers atmospheric nitrous acid (HONO) photolysis, producing OH radicals, thereby accelerating photochemical reactions, leading to severe secondary pollution formation. Missing daytime sources were detected in the extensive HONO budget studies carried out in the past. In the rural North China Plain, some studies attributed those to soil emissions and more recent studies to dew evaporation. To investigate the contributions of these two processes to HONO temporal variations and unknown production rates in rural areas, HONO and related field observations obtained at the Gucheng Agricultural and Ecological Meteorological Station during spring and autumn were thoroughly analyzed. Morning peaks in HONO frequently occurred simultaneously with those of ammonia (NH3) and water vapor both during spring and autumn, which were mostly caused by dew and guttation water evaporation. In spring, the unknown HONO production rate revealed pronounced afternoon peaks exceeding those in the morning. In autumn, however, the afternoon peak was barely detectable compared to the morning peak. The unknown afternoon HONO production rates were attributed to soil emissions due to their good relationship to soil temperatures, while NH3 soil emissions were not as distinctive as dew emissions. Overall, the relative daytime contribution of dew emissions was higher during autumn, while soil emissions dominated during spring. Nevertheless, dew emission remained the most dominant contributor to morning time HONO emissions in both seasons, thus being responsible for the initiation of daytime OH radical formation and activation of photochemical reactions, while soil emissions further maintained HONO and associated OH radial formation rates at a high level, especially during spring. Future studies need to thoroughly investigate the influencing factors of dew and soil emissions and establish their relationship to HONO emission rates, form reasonable parameterizations for regional and global models, and improve current underestimations in modeled atmospheric oxidation capacity.

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

Seed germination is one of the complex traits in the plant life cycle, which is accomplished by the interaction of various complex processes including seed reserve mobilization and utilization under various environmental conditions. Since seed reserves can influence seed germination, the quantitative and qualitative differences in seed reserves may relate to the germination characteristics of rapeseed seeds. Therefore, we hypothesized the positive relationship between seed germination and mobilization process of seed reserves during the seed imbibition of two rapeseed cultivars with high and low oil contents under normal and drought stress conditions. Herein, we measured the initial absorption of water and seed microstructure during seed imbibition, besides, we also determined seed germination parameters, osmolytes, enzymatic activity, lipid peroxidation, and fatty acid composition in both rapeseed cultivars. Our results displayed that the high oil content cultivar (QY1) showed a higher germination percentage with higher water absorption during the seed imbibition under drought stress conditions. Moreover, seed microstructural analysis indicated that oil bodies (OBs) were surrounding the protein storage vacuoles (PBs) compartment and cell periphery, the gap between OBs was increased to some extent, and boundaries became blurred in QY1 under drought stress conditions. Besides, the number of oil bodies was higher, while their area was lower, which might provide more energy for the germination process. Furthermore, the active metabolism of protein, sugar, and proline in QY1 was higher than ZYZ108 under normal and drought stress conditions, which might be closely related to the accretion of osmoregulatory substances that improved water uptake, thereby improving seed germination. Additionally, unsaturated fatty acids were higher in QY1 than ZYZ108, which provide greater protection against drought stress damages. Besides, QY1 exhibited high efficiency of defense system, which reduced malonaldehyde (MDA) content, hence ameliorating the oxidative damage due to drought stress. Taken together, this investigation provides new insight into the role of seed reserves as energy resources in the germination process in high oil content seeds of rapeseed cultivars, especially under drought stress conditions.

Abstract Background Climatic warming is increasing regionally and globally, and results concerning warming and its consequent drought impacts have been reported extensively. However, due to a lack of quantitative analysis of warming severities, it is still unclear how warming and warming-induced drought influence leaf functional traits, particularly how the traits coordinate with each other to cope with climatic change. To address these uncertainties, we performed a field experiment with ambient, moderate and severe warming regimes in an arid ecosystem over 4 years. Results Severe warming significantly reduced the specific leaf area and net photosynthetic rate with a relatively stable change and even enhancement under moderate warming, especially showing species-specific performance. The current results largely indicate that a coordinated trade-off can exist between plant functional traits in plant communities in a dryland ecosystem under ambient temperature conditions, which is strongly amplified by moderate warming but diminished or even eliminated by severe warming. Based on the present findings and recent results in the relevant literature, we advance the ecological conceptual models (e.g., LES and CSR) in the response to climatic warming in arid grassland communities, where the few key species play a crucial role by balancing their functional performances to cope with environmental change. Conclusion Our results highlight the importance of coordination and/or trade-off between leaf functional traits for understanding patterns of climatic change-induced vegetation degradation and suggest that the plant community composition in these drylands could be shifted under future climate change.

In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.

The impact of changing meteorological conditions on crop quality has become a trending topic in current agriculture research. In this study, we analyzed the combined effects of both meteorological conditions and key stages on the nutritional quality of maize based on the data of field-staged sowing trials from 2018 to 2022. The results are as follows: (1) The key stage of meteorological conditions affecting the content of major nutritional qualities of maize is from 6 d before to 35 d after flowering. (2) The maximum temperature from 6 d before to 8 d after flowering, average temperature from 5 d before to 20 d after flowering, and minimum temperature from 9–20 d after flowering have significant positive effects on protein, fat, and essential amino acids, respectively; the daily difference in temperature from 9 to 35 d and 24 to 35 d after flowering have a significant negative effect on crude fiber and essential amino acids, respectively, and the daily difference in temperature from 24 to 35 d and the minimum temperature from 5 d before to 35 d after flowering have a significant effect on non-essential amino acids. (3) When the maximum temperature during the key stage of nutritional quality is 31.2 °C, the average temperatures are 24.9 °C and 22.4 °C, the minimum temperature is 18.9 °C, and the daily difference in temperature is 15.0 °C, the contents could reach the optimal values of 9.66% (protein), 4.80% (fat), 4.97% (crude fiber), 40.39 g·kg−1 (essential amino acids), and 58.96 g·kg−1 (non-essential amino acids), respectively. The findings provide a basis for adjusting the sowing period to improve the nutritional quality of maize in the context of climate change.

This study aimed to investigate whether presoaking with hemin (5 μmol·L−1) could alleviate NaCl stress during rapeseed seedlings’ growth and its role in the regulation of photosynthesis. In this experiment, ‘HUAYOUZA 62 (HYZ 62)’ and ‘HUAYOUZA 158R (158R)’ were used as materials for pot experiments to study the morphology, photosynthetic characteristics, antioxidant activity, and osmoregulatory factors of seedlings under different salt concentrations, as well as the regulatory effects of hemin-presoaked seeds. Our findings revealed that, compared the control, NaCl stress inhibited the growth of two rapeseed varieties, decreased the seedling emergence rate, and increased the content of malondialdehyde (MDA), the electrolyte leakage rate (EL) and antioxidant enzyme activity. Hemin soaking alleviated the adverse effects of salt stress and increased plant height, root elongation and dry matter accumulation. Compared with all NaCl treatments, hemin significantly enhanced photosynthetic indexes, including a percent increase of 12.99–24.36% and 5.39–16.52% in net photosynthetic rate (Pn), 17.86–48.08% and 8.6–23.44% in stomatal conductivity (Gs), and 15.42–37.94% and 11.09–19.08% in transpiration rate (Tr) for HYZ62 and 158R, respectively. Moreover, hemin soaking also increased antioxidant enzyme activities, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX), reducing the malondialdehyde, and thus resulting in the alleviation of oxidative damage caused by NaCl stress. Furthermore, hemin stimulated the formation of soluble protein, which effectively regulated the osmo-protective qualities. The current findings strongly elucidate that hemin soaking could effectively alleviate the negative impacts of NaCl stress by regulating the morphological, photosynthetic, and antioxidant traits. This study provides a new idea regarding the effect of Hemin on the salt tolerance of rapeseed, and provides a basis for the practical application of Hemin in saline–alkali soil to improve the salt tolerance of cultivated rapeseed.

The study of the response of vegetation phenology in the Qinghai Tibet Plateau to various climatic variables is paramount to unveiling the reaction of alpine ecosystems to worldwide climate alterations. Nonetheless, the lagged and cumulative effects of various climatic variables on vegetation phenology in the Qinghai Tibet Plateau remain unclear. Therefore, based on MODIS NDVI data, we extracted vegetation phenological parameters from 2001 to 2020, including the start of the vegetation growing season (SOS) and the end of the vegetation growing season (EOS), and then analyzed the response mechanisms of vegetation phenology to pre-seasonal air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) in the Qinghai Tibet Plateau on the basis of an investigation of the lag and cumulative effects. The results showed that: (1) the multiyear mean values of the SOS mainly occurred from 120 to 160 days, accounting for 86.17% of the study area, while the multiyear mean values of the EOS were mainly concentrated between 260 and 280 days, accounting for 77.05% of the study area; (2) air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) had different degrees of lagging effects on the SOS and the EOS. Among them, the time lag effect of precipitation on vegetation phenology was more pronounced; (3) different climatic variables had distinct cumulative effects on vegetation phenology. In contrast to the insignificant cumulative effects of temperature and nighttime surface temperature on the SOS and the EOS, the cumulative effects of precipitation and daytime land surface temperature on the SOS were more pronounced than those on the EOS; (4) the SOS and air temperature, precipitation, and NLST were mainly negatively correlated, in which the proportion of the negative correlation between SOS and NLST was up to 68.80%, and SOS and DLST were mainly positively correlated with a positive correlation proportion of 73.27%, EOS and air temperature, precipitation, and NLST were positively correlated with a positive correlation proportion of EOS and precipitation of up to 71.52%, and EOS and DLST were mainly negatively correlated with a negative correlation ratio of 55.87%.

Biological yield indicates the potential for increasing yield. Leaf carbon metabolism plays an important role in the biomass accumulation of rapeseed (Brassica napus L.). Field experiments with the hybrid HZ62 (with a conventional plant architecture) grown in 2016–2017, and HZ62 and accession 1301 (with a compact plant architecture) grown in 2017–2018 were conducted to characterize the physiological and proteomic responses of leaf photosynthetic carbon metabolism to density and row spacing configurations. The densities were set at 15×104 ha−1 (D1), 30×104 ha−1 (D2), and 45×104 ha−1 (D3) (main plot), with row spacings of 15 cm (R15), 25 cm (R25), and 35 cm (R35) (subplot). Individual and plant population biomass accumulation was greatest at R25, R15, and R15 for D1, D2, and D3, respectively, for both genotypes. In comparison with D1R25, the individual aboveground biomass of HZ62 decreased by 60.2%, whereas the population biomass increased by 31.9%, and the individual biomass of genotype 1301 decreased by 54.0% and the population biomass increased by 53.9% at D3R15. Leaf carbon metabolic enzymes varied between genotypes at flowering stage. In contrast to D1R25, at D3R15 the activities of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and sucrose phosphate synthase (SPS) and the contents of starch, sucrose and soluble sugars in leaves were significantly decreased in HZ62 and increased in genotype 1301. The activities of fructose-1,6-bisphosphatase (FBPase) decreased, in consistency with the abundance of fructose-bisphosphate aldolase in HZ62. In contrast, sucrose synthase (SuSy) activity appeared to decrease in both genotypes, but a significant increase in abundance of a protein with sucrose synthase was found in the 1301 genotype by proteomic analysis. With increased density and reduced row spacing, the expression of most key proteins involved in carbon metabolism was elevated, and enzyme activity and carbon assimilate content were increased in 1301, whereas HZ62 showed the opposite trend, indicating that the compact plant type can accumulate more population biomass with denser planting.

Understanding the effect of environmental factors on the net ecosystem CO2 exchange (NEE) and the response of NEE to rainfall events is of great significance for an accurate understanding of the carbon cycle for desert steppe ecosystems. Based on the long-term (2011–2018) eddy covariance flux data of a temperate desert steppe in Inner Mongolia, China, this study used path analysis to analyze the combined impact of the environmental factors on NEE. The results showed that during the growing season, vapor pressure deficit (VPD) and soil water content (SWC) was the most prominent environmental factor for the daytime NEE and nighttime NEE, respectively. NEE responds differently to individual environmental factors among multi-year climatic conditions. The size of rainfall event has significant impacts on NEE, it can effectively promote the CO2 uptake of the desert steppe ecosystem when rainfall event size is greater than 5 mm, and the NEE response increased with the rainfall event size. Moreover, NEE peaked approximately 1–3 days after a 5–10 mm rainfall event, while the rainfall event size >10 mm, it would take 3–5 days for NEE to reach a peak value; and yet, small rainfall events (< 5 mm) slightly increased CO2 emissions. During the growing season, carbon uptake increased with monthly rainfall, except in May. Our results are important for understanding the carbon cycle and its control mechanisms in the temperate desert steppe of Inner Mongolia.

Abstract Selenium nanoparticles (SeNPs) have attracted considerable attention globally due to their significant potential for alleviating abiotic stresses in plants. Accordingly, further research has been conducted to develop nanoparticles using chemical ways. However, our knowledge about the potential benefit or phytotoxicity of bioSeNPs in rapeseed is still unclear. Herein, we investigated the effect of bioSeNPs on growth and physiochemical attributes, and selenium detoxification pathways compared to sodium selenite (Se (IV)) during the early seedling stage under normal and salt stress conditions. Our findings showed that the range between optimal and toxic levels of bioSeNPs was wider than Se (IV), which increased the plant’s ability to reduce salinity-induced oxidative stress. BioSeNPs improved the phenotypic characteristics of rapeseed seedlings without the sign of toxicity, markedly elevated germination, growth, photosynthetic efficiency and osmolyte accumulation versus Se (IV) under normal and salt stress conditions. In addition to modulation of Na+ and K+ uptake, bioSeNPs minimized the ROS level and MDA content by activating the antioxidant enzymes engaged in ROS detoxification by regulating these enzyme-related genes expression patterns. Importantly, the main effect of bioSeNPs and Se (IV) on plant growth appeared to be correlated with the change in the expression levels of Se-related genes. Our qRT-PCR results revealed that the genes involved in Se detoxification in root tissue were upregulated upon Se (IV) treated seedlings compared to NPs, indicating that bioSeNPs have a slightly toxic effect under higher concentrations. Furthermore, bioSeNPs might improve lateral root production by increasing the expression level of LBD16. Taken together, transamination and selenation were more functional methods of Se detoxification and proposed different degradation pathways that synthesized malformed or deformed selenoproteins, which provided essential mechanisms to increase Se tolerance at higher concentrations in rapeseed seedlings. Current findings could add more knowledge regarding the mechanisms underlying bioSeNPs induced plant growth. Graphical Abstract

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

As a key indicator of agricultural production capacity, crop production potential is critical to evaluate the impacts of climate variability on agriculture. However, less attention has been paid to the pattern of biomass accumulation in response to crop climatic production potential and its relation to grain yield formation at an intra-seasonal time scale, especially under evolving soil water limitation. In this study, we combined a mechanism-based empirical model with field experiments conducted at different growth stages of maize on the Northern China Plain (NCP) to assess the dynamic response of biomass accumulation to climatic production potential and its relation to grain yield. The results showed that the ability of climatic production potential to estimate biomass was significantly improved when a vapor pressure deficit (VPD) was incorporated, with the root mean square error (RMSE) reduced by 33.3~41.7% and 45.6~47.2% under rainfed and evolving soil water limitation conditions, respectively. Drought significantly decreased biomass accumulation mainly by decreasing the intrinsic growth rate and delaying the occurrence timing of maximum growth. Moreover, grain yield shared a nonlinear and saturating relationship with biomass across rainfed and water deficit conditions. The results imply that evolving soil water limitation changes the process of biomass accumulation but not its relationship with grain yield. These findings provide useful information to estimate crop production potential under abiotic stresses and improve the accuracy of crop yield prediction.

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m2d in December 2014 to 26.5 MJ/m2d in July 2015, with an average value of 20.6 MJ/m2d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau.

Boreal forests, the second continental biome on Earth, are known for their massive carbon storage capacity and important role in the global carbon cycle. Comprehending the temporal dynamics and controlling factors of net ecosystem CO2 exchange (NEE) is critical for predicting how the carbon exchange in boreal forests will change in response to climate change. Therefore, based on long-term eddy covariance observations from 2008 to 2018, we evaluated the diurnal, seasonal, and interannual variations in the boreal forest ecosystem NEE in Northeast China and explored its environmental regulation. It was found that the boreal forest was a minor CO2 sink with an annual average NEE of -64.01 (± 24.23) g CO2 m-2 yr-1. The diurnal variation in the NEE of boreal forest during the growing season was considerably larger than that during the non-growing season, and carbon uptake peaked between 8:30 and 9:30 in the morning. The seasonal variation in NEE demonstrated a “U” shaped curve, and the carbon uptake peaked in July. On a half-hourly scale, photosynthetically active radiation and vapor pressure deficit had larger impacts on daytime NEE during the growing season. However, temperature had major control on NEE during the growing season at night and during the non-growing season. On a daily scale, temperature was the dominant factor controlling seasonal variation in NEE. Occurrence of extreme temperature days, especially extreme temperature events, would reduce boreal forest carbon uptake; interannual variation in NEE was substantially associated with the maximum CO2 uptake rate during the growing season. This study deepens our understanding of environmental controls on NEE at multiple timescales and provides a data basis for evaluating the global carbon budget.

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.

In response to the new concept of the impact of total climate production factors on plant phenology, this study verifies the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plants from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors have a significant impact on plant phenological changes and can be effectively utilized for simulating phenology and determining triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plants, and their triggering thresholds depend on the parameters of climate resource changes and the characteristics of plant biology, which are (0.085, −5.363) and (0.086, −27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plants, and its triggering thresholds also depend on the parameters of climate resource changes and the characteristics of plant biology, which is (394.632, −38,026.268). Furthermore, the results support the viewpoint that abrupt changes to the climate determine the beginning and ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

Understanding how plant ecophysiological traits of coexisting species within a community respond to environmental changes could help to predict the shift in plant community structure and function, but it remains limited in the scenarios of co-occurring precipitation variations and N deposition. A two-year field experiment was conducted to explore the effects of large precipitation changes (reduced and increased precipitation amount by 25% and 50% relative to ambient control) and high N deposition (10 g N m−2 yr−1) on a series of leaf ecophysiological traits of three dominant species (Stipa tianschanica, a C3 grass; Cleistogenes squarrosa, a C4 grass; and Artemisia capillaris, a C3 forb) in a desert steppe in Northern China. Increasing precipitation significantly linearly promoted the leaf light-saturated photosynthesis rate (Asat) and N use efficiency of the two C3 species, irrespective of N addition. The rises in Asat of both C3 species were mainly caused by increased soil moisture, which strongly induced increases in leaf stomatal conductance (gs) and declines in quantum yield of photosystem II (ΦPSII). However, the Asat of the two C3 species was weakly correlated with their specific leaf area and leaf N concentration, whereas the Asat of the C4 grass was negatively related to its leaf N. Moreover, the Asat, height, and aboveground biomass of the forbs were much more water-sensitive than those of both grass species, with a consequence of the most dominant species turning from grass to forbs as precipitation increased. Our findings highlight that water limitation, rather than N deficit, is the largest factor controlling plant growth in drylands, and plant species-specific ecophysiological responses to precipitation fluctuations will cause a substantial shift in the production and composition of plant community.

Clarifying the response of plant phenology to drought duration is helpful for accurately interpreting and predicting carbon sinks in ecosystems. Based on the response of different phenological periods of the dominant species Stipa krylovii to monthly, seasonal, and semiannual time scale drought in the typical steppe of Inner Mongolia from 1983 to 2018, the results revealed that (1) the start of the growing season (SOS) was characterized by an advance–delay–advance pattern, and the heading stage (HOS), flowering stage (FOS), and end of the growing season (EOS) all showed consistent advanced trends, which provided additional insight into the conclusions of previous studies that found the SOS was advanced in arid and semiarid regions. (2) The response mechanism of the SOS to the timing of drought was not consistent. Among the response mechanism, the SOS was delayed because of the January–February drought at different time scales but advanced because of the April drought at different time scales. The HOS/FOS was delayed by the June–July drought at different time scales, and the EOS was advanced by the August–September drought at different time scales. (3) More importantly, the SOS, HOS, FOS, and EOS were affected by predrought and its persistence, and the effects were greater the closer to the phenological periods that drought occurred. (4) At the monthly scale, droughts in January, June, and August were the critical drought periods affecting the SOS, HOS/FOS, and EOS, while seasonal and semiannual scale droughts in February, June–July, and September were the critical periods affecting the SOS, HOS/FOS, and EOS. The results of this study enrich our understanding of how drought at different time scales affects different phenological periods, providing a basis for improving plant phenological models.

Timely sowing and harvesting play important roles in agricultural production. The appropriate management decisions are necessary to cope with climate change and ensure high and stable crop yields. This study analyzed the effects of sowing date on the growth process of winter wheat and quantified the effects of climate resources and photothermal potential yield on theoretical yield at different stages of winter wheat. The analysis was based on the data from winter wheat interval sowing experiments conducted at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station (Gucheng station) in north China (115°40′ E, 39°08′ N) during 2017–2019. The results showed that: (1) with the delay in sowing date, the growth process of winter wheat significantly advanced, the proportion of vegetative growth period significantly reduced (0.19% for per day delay), the proportion of reproductive growth period (RGP) significantly increased (0.12% for per day delay), and the prewintering light and temperature resources significantly reduced (12.2 °C·d accumulated temperature and 19.0 MJ·m−2 solar radiation for per day delay); (2) the theoretical yield of winter wheat showed a significant exponential relationship with the photothermal potential yield of the whole growth period: the minimum photothermal potential for yield formation was 26.6 t·ha−1, and the maximum theoretical yield was 12.6 t·ha−1; and (3) the wheat yield and yield stability were highest when the RGP photothermal potential yield was 16.0 t·ha−1 and the prewintering active accumulated temperature was 400 °C·d. This study also proposed a method to estimate the suitable sowing and harvesting dates to achieve high and stable yield of winter wheat, showing that the suitable sowing dates of winter wheat at Gucheng station from 1997 to 2021 ranged from 1 to 15 October, with no significant interannual variation; the suitable harvesting period ranged from 5 June to 10 July and showed a trend of gradual advance with the delay of the year. The results of the study provide a reference for sowing date adjustment of crops to adapt to climate change.

Improving seed yield and quality and optimizing plant architecture to adapt to mechanized harvesting are essential strategies for rapeseed industry development in the Yangtze River basin. The present study selected 24 elite rapeseed varieties from the middle region of the Yangtze River basin as materials to investigate the growth period, plant architecture characteristics, lodging resistance, yield, and seed quality across 2 growing seasons. The results showed that plant biomass, silique number per plant, and seed yield showed a significant positive correlation with each other. A high plant growth rate was the prerequisite for early maturity varieties to achieve high yield. The path analysis illustrated that plant architecture can directly affect the seed yield (direct path efficiency = 0.17) or indirectly affect the yield through lodging (indirect path efficiency: −0.37 × 0.30 = −0.11). Therefore, modifying the plant architecture needs to balance the tradeoff between seed yield and lodging. The comprehensive performance of selected genotypes was evaluated by combining D-CRITIC (Distance-based inter-criteria correlation) and membership function methods. From the comprehensive performance across two cropping seasons, the varieties V24, V23, V22, V21, V12, V17, V19, and V20 had substantial potential for mechanized harvesting with high yield and good seed quality. These results provide a theoretical basis for farmers’ decisions and breeding of rapeseed suitable for mechanized harvesting in the Yangtze River basin.

Most plants are sensitive to salt-alkali stress, and the degree of tolerance to salt-alkali stress varies from different species and varieties. In order to explore the salt-alkali stress adaptability of Brassica napus, we collected the phenotypic data of 505 B. napus accessions at seedling and mature stages under control, low and high salt-alkali soil stress conditions in Inner Mongolia of China. Six resistant and 5 sensitive materials, respectively, have been identified both in Inner Mongolia and Xinjiang Uygur Autonomous Region of China. Genome-wide association studies (GWAS) for 15 absolute values and 10 tolerance coefficients (TCs) of growth and agronomic traits were applied to investigate the genetic basis of salt-alkali tolerance of B. napus. We finally mapped 9 significant QTLs related to salt-alkali stress response and predicted 20 candidate genes related to salt-alkali stress tolerance. Some important candidate genes, including BnABA4, BnBBX14, BnVTI12, BnPYL8, and BnCRR1, were identified by combining sequence variation annotation and expression differences. The identified valuable loci and germplasms could be useful for breeding salt-alkali-tolerant B.napus varieties. This study laid a foundation for understanding molecular mechanism of salt-alkali stress adaptation and provides rich genetic resources for the large-scale production of B. napus on salt-alkali land in the future.

Abstract Background Net primary productivity (NPP) in forests plays an important role in the global carbon cycle. However, it is not well known about the increase rate of China’s forest NPP, and there are different opinions about the key factors controlling the variability of forest NPP. Methods This paper established a statistics-based multiple regression model to estimate forest NPP, using the observed NPP, meteorological and remote sensing data in five major forest ecosystems. The fluctuation values of NPP and environment variables were extracted to identify the key variables influencing the variation of forest NPP by correlation analysis. Results The long-term trends and annual fluctuations of forest NPP between 2000 and 2018 were examined. The results showed a significant increase in forest NPP for all five forest ecosystems, with an average rise of 5.2 gC·m− 2·year− 1 over China. Over 90% of the forest area had an increasing NPP range of 0–161 gC·m− 2·year− 1. Forest NPP had an interannual fluctuation of 50–269 gC·m− 2·year− 1 for the five major forest ecosystems. The evergreen broadleaf forest had the largest fluctuation. The variability in forest NPP was caused mainly by variations in precipitation, then by temperature fluctuations. Conclusions All five forest ecosystems in China exhibited a significant increasing NPP along with annual fluctuations evidently during 2000–2018. The variations in China’s forest NPP were controlled mainly by changes in precipitation.

Adjusting the sowing dates of crops is an effective measure for adapting them to climate change, but very few studies have explained how the optimum sowing dates can be determined. In this study, we used the sowing date field data from 2018 to 2021 from Hebei Gucheng Agricultural Meteorology National Observation and Research Station to analyze the effects of the sowing date on growth, development, and yield of maize, and to quantify the impact of light-temperature potential productivity on different stages of the yield formation. The results showed that delayed sowing decreased the vegetative growth period (VGP) and increased the reproductive growth period (RGP) of maize. The light-temperature potential productivity of the whole growth (WG) period had an exponential relationship with the theoretical yield. At least 14,614.95 kg ha−1 of light-temperature potential productivity was needed to produce grain yield. The maximum theoretical yield was approximately 18,052.56 kg ha−1, as indicated by the curve simulation results. The influence of light-temperature potential productivity on theoretical yield was as follows: VGP > RGP > vegetative and reproductive period (VRP). Accordingly, a method for determining the sowing time window based on VGP was established, and the optimal sowing dates were estimated for 1995–2021 and the SSP2-4.5 scenario in CMIP6 in the middle of this century (2030–2060). The simulation results showed that the optimum sowing date of maize “Lianyu 1” at the study site was 20–25 May in 1995–2021. In the middle of this century, the optimal sowing time of maize was ahead of schedule and the suitable sowing window was increased slightly. We conclude that advancing the sowing date of maize is a practical strategy for enhancing yield in the context of climate warming, and this strategy will provide a meaningful reference for scientific optimization of sowing dates to adapt maize to climate change.

Accurate identification and extraction of lake boundaries are the basis of the accurate assessment of lake changes and their responses to climate change. To reduce the effects of lake ice and snow cover, mountain shadows, cloud and fog shielding, alluvial and proluvial deposits, and shoals on the extraction of lake boundaries on the Tibetan Plateau, this study developed an RNSS water index to increase the contrast between the lake and similar surface objects of the spectral curve, and constructed a new method flow for lake extraction on the Tibetan Plateau based on image synthesis, topographic-spectral feature indexes, and machine learning algorithms. The lake extraction effects of three common machine learning classification algorithms were compared: the Cart decision tree, random forest (RF), and gradient boosting decision tree (GBDT). The results show that the new lake extraction method based on topographic-spectral characteristics and the GBDT classification method had the highest extraction accuracy for Tibetan Plateau lakes in 2016 and 2021. Its overall accuracy, Kappa coefficient, user’s accuracy, and producer’s accuracy for 2016 and 2021 were 99.81%, 0.887, 83.55%, 94.67% and 99.88%, 0.933, 89.18%, 98.24%, respectively, and the total area of lake extraction was the most consistent with the validation datasets. The three classification methods can effectively extract lakes covered by ice and snow, and the extraction effect was ranked as GBDT > RF > Cart. The lake extraction effect under mountain shadow was ranked as Cart > GBDT > RF, and the lake extraction effect under alluvial deposits and shoals was ranked as GBDT > RF > Cart. The results may provide technical support for extracting lakes from long time series and reveal the impact of climate change on Tibetan Plateau lakes.

The advent of the nanotechnology era offers a unique opportunity for sustainable agriculture, and the contribution of nanoparticles (NPs) to ameliorate abiotic stresses became the new area of interest for researchers due to their special physiochemical characteristics in the biological system. Salinity is a key devastating abiotic factor that hinders the development and yield of rapeseed. On the flip side, the impact of nanoparticles on plant hormones upon salt stress during seed imbibition and germination has been poorly understood. Hence, we aimed to study the influence of nanopriming on plant hormones and germination processes using selenium and zinc oxide nanoparticles (SeNPs and ZnONPs) during seed imbibition and the early seedling stage upon salinity stress. Nanopriming showed a positive effect on final germination percentage, germination rate, seed microstructure, and antioxidant enzyme activity of two rapeseed cultivars under salt stress. Moreover, nano-treatment decreased the expression of abscisic acid related genes BnCYP707A1, 3, and 4 during the priming time and after sowing, where the levels of BnCYP707A1, and 3 genes showed a slightly significant difference between the nanopriming and hydropriming, which gave an evidence that the nanopriming influenced the ABA levels then elevated the seed germination with SeNPs and ZnONPs. Likewise, nanoparticles significantly elevated the expression levels of BnGA20ox, BnGA3ox and BnCPS genes during the germination stage, especially at 24 h after being sown in salt stress. That confirms the positive role of SeNPs and ZnONPs in regulating gibberellic acid level, which increases the germination in primed seeds as compared to unprimed seeds and hydroprimed seeds. Additionally, our results demonstrated that nanopriming regulated the expression level of BnCAM and BnPER during priming time and after sowing, along with the various levels of expression remarkably in BnEXP4 and BnRAB28, especially at 24 h of being sown under salt stress, which promoted seed germination and early seedling growth. Overall, this work provides new insights into mechanisms underlying the interactions of SeNPs and ZnONPs with plant hormones during the seed imbibition and early seedling stage, consequently enhanced plant growth and development. Additionally, these findings portrayed that the application of SeNPs and ZnONPs could be a new strategy and useful approach to enhance tolerance against salinity in rapeseed plants.

The maximizing of water use efficiency (WUE) and radiation use efficiency (RUE) is vital to improving crop production in dryland farming systems. However, the fundamental question as to the association of WUE with RUE and its underlying mechanism under limited-water availability remains contentious. Here, a two-year field trial for maize designed with five progressive soil drying regimes applied at two different growth stages (three-leaf stage and seven-leaf stage) was conducted during the 2013–2014 growing seasons. Both environmental variables and maize growth traits at the leaf and canopy levels were measured during the soil drying process. The results showed that leaf WUE increased with irrigation reduction at the early stage, while it decreased with irrigation reduction at the later stage. Leaf RUE thoroughly decreased with irrigation reduction during the progressive soil drying process. Aboveground biomass (AGB), leaf area index (LAI), a fraction of absorbed photosynthetically active radiation (fAPAR), and light extinction coefficient (k) of the maize canopy were significantly decreased by water deficits regardless of the growth stages when soil drying applied. The interrelationships between WUE and RUE were linear across the leaf and canopy scales under different soil drying patterns. Specifically, a positive linear relationship between WUE and RUE are unexpectedly found when soil drying was applied at the three-leaf stage, while it turned out to be negative when soil drying was applied at the seven-leaf stage. Moreover, the interaction between canopy WUE and RUE was more regulated by fAPAR than LAI under soil drying. Our findings suggest that more attention must be paid to fAPAR in evaluating the effect of drought on crops and may bring new insights into the interrelationships of water and radiation use processes in dryland agricultural ecosystems.

Glacier changes on the Tibetan Plateau are of great importance for regional climate and hydrology and even global ecological changes. It is urgent to understand the effect of climate warming on both clean and debris-covered glaciers on the Tibetan Plateau. This study used the double RF method and Landsat series images to extract clean glaciers and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 and analyzed their temporal and spatial changes under the background of climate change. The total area of glaciers on the Tibetan Plateau showed a retreating trend from 1985 to 2020, with an average retreat rate of −0.5 % yr−1. The area of clean glaciers showed a significant retreating trend, with a retreat rate of −0.55 % yr−1. The area of debris-covered glaciers showed an expanding trend, with an expanding rate of 0.62 % yr−1. The clean glaciers retreated faster in the southeast and slower in the northwest, while the debris-covered glaciers expanded in most basins. The debris-covered glaciers were generally located at lower elevation areas than those of the clean glaciers. The slopes of clean glaciers were mainly in the range of 0–50°, while the slopes of debris-covered glaciers were mainly in the range of 0–30°. Climate warming was a main driver of glacier change. The clean glacier area was correlated negatively with average temperature in summer and positively with average precipitation in winter, while the debris-covered glacier area was correlated positively with both. The results of the study may provide a basis for scientific management of glaciers on the Tibetan Plateau in the context of climate warming.

Timely harvest can effectively guarantee the yield and quality of rapeseed. In order to change the artificial experience model in the monitoring of rapeseed harvest period, an intelligent prediction method of harvest period based on deep learning network was proposed. Three varieties of field rapeseed in the harvest period were divided into 15 plots, and mobile phones were used to capture images of silique and stalk and manually measure the yield. The daily yield was divided into three grades of more than 90%, 70–90%, and less than 70%, according to the proportion of the maximum yield of varieties. The high-dimensional features of rapeseed canopy images were extracted using CNN networks in the HSV space that were significantly related to the maturity of the rapeseed, and the seven color features of rapeseed stalks were screened using random forests in the three color-spaces of RGB/HSV/YCbCr to form a canopy-stalk joint feature as input to the subsequent classifier. Considering that the rapeseed ripening process is a continuous time series, the LSTM network was used to establish the rapeseed yield classification prediction model. The experimental results showed that Inception v3 of the five CNN networks has the highest prediction accuracy. The recognition rate was 91% when only canopy image features were used, and the recognition rate using canopy-stalk combined features reached 96%. This method can accurately predict the yield level of rapeseed in the mature stage by only using a mobile phone to take a color image, and it is expected to become an intelligent tool for rapeseed production.

Crop quality is directly related to national food security and people’s living standards, and it is also key to the improvement of agricultural quality and efficiency. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), climate change has already exerted a negative impact on crop quality. To address climate change scientifically, this paper reviews the latest progress in studies on the impact of climate change on crop quality, and points out limitations of existing studies: (1) Climate factors affecting crop quality are not yet clearly identified; (2) The climate change influencing mechanism and disaster-inducing critical meteorological conditions for crop quality are not clearly established; and (3) No climatic suitability model for crop quality has been constructed to reflect the synergy of multiple climate factors. To ensure food quality and security, and to adjust and optimize the industrial planning of grain crops, promote a high crop quality and yield, and address climate change scientifically, this paper argues that subsequent studies should discuss the following topics, focusing on the climatic suitability of crop quality and resistance mechanisms: (1) changing laws involving the climate sensitivity of crop quality in the context of climate change; (2) response mechanisms of crop quality to climate change; (3) crop quality monitoring and assessment modelling; (4) climatic suitability zoning of crop quality; (5) spatiotemporal evolution trends of crop quality and its responses; and (6) crop quality and its legal measures in the world.

Global warming has changed plant phenology and induced variations in the productivity of terrestrial ecosystems. Recent studies have shown inconsistent results regarding the influence of phenological changes on plant production. We carried out a three-year in situ experiment in Inner Mongolia and used Stipa krylovii as an example to examine the phenological changes and their importance to plant production under changes in temperature and precipitation. We found that precipitation, temperature, and their interactions had no significant impact on the start of the growing season (SOS) or vegetative growth length (VGL). Precipitation had significant impacts on the end of the growing season (EOS), the length of the growing season (LOS), and reproductive growth length (RGL). The precipitation addition treatments of T2.0W + 50% (2 °C warming and 50% precipitation addition) and T1.5W + 50% (1.5 °C warming and 50% precipitation addition) significantly delayed the EOS by 6.7 d and 5.4 d, and significantly prolonged the LOS by 9.3 d and 9.3 d, respectively. Precipitation significantly changed the net CO2 assimilation rate (Pn) of the heading stage. There was no significant difference in the dry mass among all the treatments. The SOS and VGL had significant negative impacts on the dry mass of Stipa krylovii, while temperature, precipitation, and the EOS had no significant direct effect on it. Our results imply that the SOS was more important than the EOS in regulating the plant production of Stipa krylovii. This study can facilitate the understanding of the response of productivity to phenological dynamics and improve the accuracy of simulating the terrestrial ecosystem carbon budget.

Leaf area index (LAI), which is an important structural parameter, plays a vital role in evaluating crop growth and yield. In this study, we used the canopy coverage (CC) derived from unmanned aerial vehicle (UAV) images as a correction parameter in the PROSAIL model coupled with a neural network (NN) to improve the accuracy of LAI inversion of rapeseed plots. CC had a significantly positive impact on the accuracy of LAI inversion especially in sparse canopy structure with the 22.24% decrease in the entire dataset and 35.76% decrease in the sparse canopy dataset. We then compared the inversion performances of an empirical statistical model (ESM) based on a vegetation index and the PROSAIL model incorporating CC correction for 2016 and 2018 datasets. The ESM performed better in modeling the 2016 dataset, but its accuracy was much lower for the 2018 dataset (2016: NRMSE = 0.131; 2018: NRSME = 0.348). Overall, the PROSAIL model was more robust over these two datasets (2016: NRMSE = 0.152; 2018: NRMSE = 0.168). In addition, the original-resolution images were resampled to six coarse resolutions to evaluate the influence of image resolution on the LAI inversion performance of the PROSAIL model. When pixel size increased to more than 10 cm, the inversion accuracy began to decrease dramatically. In conclusion, introducing a canopy coverage correction parameter in the PROSAIL model improved its performance in retrieving rapeseed LAI.

Abstract Climate change, characterized by warming and precipitation variability, restricted the growth of plants in arid and semiarid areas, and various functional traits are impacted differently. Comparing responses of functional traits to warming and precipitation variability and determining critical water threshold of dominate steppe grasses from Inner Mongolia facilitates the identification and monitoring of water stress effects. A combination of warming (ambient temperature, +1.5°C and +2.0°C) and varying precipitation (−30%, −15%, ambient, +15%, and +30%) manipulation experiments were performed on four Stipa species (S. baicalensis, S. bungeana, S. grandis, and S. breviflora) from Inner Mongolia steppe. The results showed that the functional traits of the four grasses differed in their responses to precipitation, but they shared common sensitive traits (root/shoot ratio, R/S, and specific leaf area; SLA) under ambient temperature condition. Warming increased the response of the four grasses to changing precipitation, and these differences in functional traits resulted in changes to their total biomass, with leaf area, SLA, and R/S making the largest contributions. Critical water thresholds of the four grasses were identified, and warming led to their higher optimum precipitation requirements. The four steppe grasses were able to adapt better to mild drought (summer precipitation decreased by 12%–28%) when warming 1.5°C rather than 2.0°C. These results indicated that if the Paris Agreement to limit global warming to 1.5°C will be accomplished, this will increase the probability for sustained viability of the Stipa steppes in the next 50–100 years.

Plant phenological variations depend largely on temperature, but they cannot be explained by temperature alone in arid and semi-arid regions. To reveal the response mechanisms of grassland phenology to climate change, the effects of temperature, moisture and light at the start (SOS), peak (POS) and end (EOS) of the growing season for Stipa krylovii (S. krylovii) in Inner Mongolian grassland was analysed from 1985–2018 with partial least squares (PLS) regression. The results showed that the SOS was significantly delayed at a rate of 5.4 d/10a (change over 10 years), while POS and EOS were insignificantly advanced, which were inconsistent with the existing understanding that climate warming advances the SOS and delays the EOS. The vapor pressure deficit (VPD) in July, maximum air temperature (Tmax) in September of the previous year, diurnal temperature range (DTR) from mid-February to mid-March, and Tmax from late March to mid-April of the current year were the critical factors and periods triggering the SOS, which contributed to 68.5% of the variation in the SOS. Additionally, the minimum air temperature (Tmin) occurred from mid-December to late December, and precipitation (PRE) occurred from mid-June to late July for POS, which could explain 52.1% of POS variations. In addition, Tmax from late August to early September influenced the EOS with an explanation of 49.3%. The results indicated that the phenological variations in S. krylovii were the result of the combined effects of climatic conditions from the previous year and the current year. Additionally, an increase in the preseason DTR delayed the SOS, and excessive summer precipitation induced an earlier POS, while warming in early autumn induced an earlier EOS, reflecting the adaptation mechanism of the perennial dense-cluster herbaceous plants in semi-arid regions to climate change. These findings could enrich the understanding of plant phenology in response to climate change.