Utilizing auxin dwarf genes to optimize seed yield and lodging resistance in rapeseed

Direct-seeding rapeseed production at high plant density raises the risk of lodging.We investigated the use of dwarf genes to improve rapeseed plant architecture to balance yield and lodging.Three genotypes with different plant architectures(dwarf sca^(HS5),semi-dwarf+/sca^(HS5),and tall ^(HS5))were evaluated under varying nitrogen rates(N1,N2,and N3:120,240,and 360 kg N ha^(-1))and plant densities(D1,D2,and D3:15,45,and 75 plants m^(-2))from 2019 to 2022.The results showed that increasing N rate positively influenced yield while decreasing lodging resistance in all genotypes.Increasing plant density(D2-D3)enhanced lodging resistance and yield in sca^(HS5) and+/sca^(HS5),but reduced yield in ^(HS5).Compared to the two parents,+/sca^(HS5) exhibited moderate expressions of IAA3,GH3.15,and SAUR30 in stems under N2D3,resulting in reduced plant height and increased compactness.Additionally,+/sca^(HS5) had a thicker silique layer than ^(HS5) by 14.7%,and it had a significant correlation between branch height/angle and yield.Increasing N rate led to increased lignin and pectin contents,while cellulose content decreased.Increasing plant density resulted in greater stem cellulose content and CSLA3/7 expression in sca^(HS5) and+/sca^(HS5),but decreased in ^(HS5).Compared to ^(HS5),+/sca^(HS5) exhibited higher expressions of ARAD1 and GAUT4,along with a 51.1%increase in pectin content,leading to improved lodging resistance under N2D3.Consequently,+/sca^(HS5) showed a 46.4%higher yield and 38.9%lodging resistance than ^(HS5) under N2D3,while sca^(HS5) demonstrated strong lodging resistance but lower yield potential.Overall,this study underscores the potential of utilizing auxin dwarf genes to optimize the trade-off between yield and lodging resistance in rapeseed and the possibility of maximizing yield potential by optimizing the plant architecture of+/sca^(HS5) through nitrogen reduction and dense planting.

Variation of net primary productivity and its drivers in China’s forests during 2000-2018

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.

Response of leaf carbon metabolism and dry matter accumulation to density and row spacing in two rapeseed(Brassica napus L.)genotypes with differing plant architectures

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 15×10;ha^(-1)(D1),30×10^(4)ha^(-1)(D2),and 45×10^(4)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 D1 R25,the individual aboveground biomass of HZ62 decreased by60.2%,whereas the population biomass increased by 31.9%,and the individual biomass of genotype1301 decreased by 54.0%and the population biomass increased by 53.9%at D3 R15.Leaf carbon metabolic enzymes varied between genotypes at flowering stage.In contrast to D1 R25,at D3 R15 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(Su Sy)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.

Climate Suitability and Vulnerability of Winter Wheat Planting in Gansu under the Background of Global Warming

Winter wheat is the main food crop in China. Gansu Province is a traditional winter wheat growing area, and its planting range is limited by the thermal conditions of winter. The average temperature in Gansu Province increased by 0.28°C per decade, higher than the China’s and global average, and the warming in winter was more obvious. Therefore, it is necessary to study the climate suitability and vulnerability of winter wheat planting in Gansu. In this paper, the maximum entropy model Maxent and Arcgis software are used to select six major climatic factors including annual total radiation, annual precipitation, the warmest monthly average temperature, the coldest monthly average temperature, annual average temperature, and annual extreme minimum temperature, which construct winter wheat planting distribution-climate relationship model that studies the climate suitability and vulnerability of winter wheat during the period 1961-2015. Studies have shown that the average cold weather and annual extreme minimum temperature are the most important climatic factors affecting winter wheat in Gansu, which can reflect the low temperature conditions that winter wheat can tolerate. However, the main winter wheat planting areas in Gansu Province are distributed in arid and semi-arid rain-fed agriculture areas. Precipitation and total annual radiation are also very important constraints. At the same time, climate change has little effect on winter wheat in Gansu Province, and the area of suitable area fluctuates slightly. It shows moderate adaptation in each evaluation period.

Carbon Budget Dynamics over a Rain-Fed Maize Agricultural Ecosystem in Northeast China and Its Regulation

Based on the eddy-covariance observation data over rain-fed maize agricultural ecosystem during 2005-2011, the dynamics of net ecosystem CO2 exchange (NEE) and its control mechanism were analyzed in the present study. We found that the average carbon budget of non-growing season, growing season and annual were 153.16 - 202.03 g C/m2, −689.36 - −488.17 g C/m2, and −316.96 - −487.33 g C/m2, respectively. Maize carbon content of grain yield was −226.6 - −339.94 g C/m2, accounting for 55.4% of carbon budget in the growing season. From sowing to seven-leaf stage, the carbon budget of this ecosystem was characterized by carbon release, with the rate of 0.028 ±0.0056 mg CO2 m−2⋅s−1. From seven-leaf to mature stage, the carbon budget was characterized by carbon absorption, with the rate of −0.256 ±0.0693 mg CO2 m−2⋅s−1. The key meteorological factors affecting annual carbon budget included daily average temperature (R = −0.81, P = 0.03) and saturated vapor pressure deficit (R = −0.64, P = 0.12). At the same photosynthetically active radiation (PAR) level, CO2 assimilation rate was linearly correlated with leaf area index (P 【0.05), and the slopes increased with PAR, indicating the increase in net ecosystem CO2 exchange in growing season was unlikely to be resulted from the extension of growing season. On the contrary, the carbon sink of rain-fed maize ecosystem in growing season might be decreased by extending the growing season ahead of the sowing date.

Review for Porcine Reproductive and Respiratory Syndrome Virus

Porcine reproductive and respiratory syndrome （PRRS） is the severest disease of pigs worldwide, caused by a highly genetically diverse RNA virus, called Porcine reproductive and respiratory syndrome virus （PRRSV）. The research summarized the genome characteristics of PRRSV particles and the most updated knowledge of structure protein function, and introduced the intellectual of PRRSV transmission and host immune response, which is very important for prevention and control for PRRS. A report showed that mass vaccination can stabilize the immunity of the entire herd, and this is the first required step for a PRRS eradication plan. However, the attenuated live vaccines may not achieve a valid prevention. The final goal of the EU project is to develop new generation, efficacious and safe maker vaccines that can be adapted to temporary changes and geographical differences.Robinson reported that broadly antibodies could neutralize all rapidly evolving type Ⅰ and type Ⅱ viruses, while further studies are expected to elucidate mechanisms of neutralizing antibody production and maturation and to investigate conserved epitope targets of cross-neutralization in this rapidly evolving virus.

Effects of Meteorological Factors on Overwintering Ability,Yield and Quality of Forage Rape

In order to investigate the effects of meteorological factors on rape overwintering ability,forage yield and quality of rape in the North China plain,Brassia campestris L.and Brassica napus L.were used in this study.The results showed that compared with the B.napus L.varieties,the growth period of B.campestris L.was shortened by 10-15 d,the overwintering rate(WR)increased by 50.6%,and the density after winter(PD)increased by 41.5%.The fresh forage yield(FFY)and dry forage yield(DFY)of the B.campestris L.type significantly increased by 40.9%and 38.1%compared with the B.napus L.type.,respectively,while the forage quality of the B.napus L.type rape was significantly better than that of the B.campestris L.type.Compared with the B.campestris L.type,the crude protein(CP),fat,ash and total fatty acid(TFA)contents of the B.napus L.type of rape increased by 27.6%,42.9%,23.9%and 52.3%,respectively,and the milk productivity(HM),relative forage value(RFV)and relative forage quality(RFQ)increased by 14.0%,16.2%and 42.1%,respectively.The light and heat resources before wintering increased the WR and PD(P<0.05),and were positively correlated with FFY and DFY(P>0.05),and lower temperature during the wintering period led to lower WR(P<0.01).The light and heat resources during the overwintering period and after regreening were negatively correlated with FFY and DFY(P>0.05).The contents of CP,fat and TFA of rape had an extremely significant negative correlation with the temperature and sunshine hours before wintering,but an extremely significant positive correlation with the temperature during the wintering period and after regreening,as well as the sunshine hours and rainfall during the wintering period;and HM had an extremely significant positive correlation with the temperature,sunshine hours and rainfall during the wintering period,while RFV and RFQ were only extremely significantly positively correlated with the maximum temperature and rainfall.In summary,in the North China Plain,for autumn sowing rape,the B.campestris L.type can be selected to improve the wintering rate,and the B.napus L.type should be the main choice to improve the forage quality of rape.Therefore,the B.napus L.variety HYZ62 can be selected for autumn sowing in the North China Plain.

Exploring HONO formation and its role in driving secondary pollutants formation during winter in the North China Plain

Daytime HONO photolysis is an important source of atmospheric hydroxyl radicals(OH).Knowledge of HONO formation chemistry under typical haze conditions,however,is still limited.In the Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain in 2018,we investigated the wintertime HONO formation and its atmospheric implications at a rural site Gucheng.Three different episodes based on atmospheric aerosol loading levels were classified:clean periods(CPs),moderately polluted periods(MPPs)and severely polluted periods(SPPs).Correlation analysis revealed that HONO formation via heteroge.neous conversion of NO_(2)was more efficient on aerosol surfaces than on ground,highlighting the important role of aerosols in promoting HONO formation.Daytime HONO budget analysis indicated a large missing source(with an average production rate of 0.66±0.26,0.97±0.47 and 1.45±0.55 ppbV/hr for CPs,MPPs and SPPs,respectively),which strongly correlated with photo-enhanced reactions(NO_(2)heterogeneous reaction and particulate nitrate photolysis).Average OH formation derived from HONO photolysis reached up to(0.92±0.71),(1.75±1.26)and(1.82±1.47)ppbV/hr in CPs,MPPs and SPPs respectively,much higher than that from O3 photolysis(i.e.,(0.004±0.004),(0.006±0.007)and(0.0035±0.0034)ppbV/hr).Such high OH production rates could markedly regulate the atmospheric oxidation capacity and hence promote the formation of secondary aerosols and pollutants.

Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change.Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems,although it remains to be tested in areas with dwarf and sparse vegetation,such as the Tibetan Plateau.We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect.We estimated community structural and functional traits,as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices(VIs)derived from hyperspectral images.We quantified the contributions of environmental drivers,VIs,and community traits to community function by structural equation modelling(SEM).Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index,enhanced vegetation index,and simple ratio.Structural equation modelling showed that VIs and community traits positively affected community function,whereas environmental drivers and specific leaf area had the opposite effect.Additionally,VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits.This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes.Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

A new regional vegetation mapping method based on terrain-climate-remote sensing and its application on the Qinghai-Xizang Plateau

Understanding the impact of climate change on vegetation and its evolution trend requires long-term accurate data on regional vegetation types and their geographical distribution.Currently,land use and land cover types are mainly obtained based on remote sensing information.Little research has been conducted on remote sensing interpretation of vegetation types and their geographical distributions in terms of the comprehensive utilization of remote sensing,climate,and terrain.A new region vegetation mapping method based on terrain-climate-remote sensing was developed in this study,supported by the Google Earth Engine(GEE)and the random forest algorithm,which is a new generation of earth science data and analysis application platform,together with optimal vegetation mapping features obtained from the average impure reduction method and out-of-bag error value,using different information from remote sensing,climate,and terrain.This vegetation of Qinghai-Xizang Plateau with 10 m spatial resolution in 2020 was mapped,in terms of this new vegetation mapping method,Sentinel-2A/B remotely sensed images,climate,and terrain.The accuracy verification of vegetation mapping on the Qinghai-Xizang Plateau showed an overall accuracy of 89.5%and a Kappa coefficient of 0.87.The results suggest that the regional vegetation mapping method based on terrain-climate-remote sensing proposed in this study can provide technical support for obtaining long-term accurate data on vegetation types and their geographical distributions on the Qinghai-Xizang Plateau and the globe.

MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

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.

黄河水生态承载力的流域整体性和时空连通性

黄河是中华民族的母亲河,黄河流域是中华文明的重要发育地.在中国5000多年的历史长河中,黄河流域作为全国政治、经济和文化中心占据了3000多年,孕育了河洛文化、河湟文化、关中文化等,分布有西安、郑州、洛阳、开封等古都,诞生了四大发明和《诗经》、《老子》、《史记》等经典著作.黄河流域是连接青藏高原、黄土高原和华北平原的生态廊道,是西北和华北地区的重要水源。

中国陆地生态系统增汇潜力研究展望

碳达峰与碳中和已经成为全球气候治理和生态文明建设的重大需求.厘清陆地生态系统增汇潜力不仅对准确预估未来气候变化对陆地生态系统的影响至关重要,更是人类制定气候变化应对方案的基本前提.本文从陆地生态系统碳储量和碳汇视角,综述了中国森林、灌丛、草原、农田和湿地等主要陆地生态系统碳储量、中国陆地生态系统碳汇及其对不同气候变化情景和人类活动的响应,发现中国陆地生态系统碳储量和碳汇的评价结果因不同的研究方法和资料差异较大,需要不断完善陆地生态系统碳储量和碳汇的评估方法并基于最新资料不断更新评估结果.文章进一步指出,现有研究对未来不同时期、不同排放情景下中国陆地生态系统的碳汇功能,尤其是同一区域不同国家重大生态工程增汇潜力的整合研究仍不足,对氮沉降及其与气候变化、大气CO_(2)浓度变化协同作用的影响,特别是对国家重大生态工程增汇潜力影响的研究仍不够,还没有开展中国陆地生态系统增汇的气候变化风险研究.据此,本文指出,未来中国陆地生态系统增汇研究需要重视国家重大生态工程增汇潜力及其风险评价,重点关注高度空间异质性的区域陆地生态系统碳收支模拟技术和不同气候变化情景下气象资料获取技术的发展,为中国陆地生态系统增汇的系统管理优化方案提供科学支撑,服务于国家碳中和战略.

生态气象:起源、概念和展望

生态气象是应人类面临的生存环境危机而兴起的地球系统科学新兴学科,已经成为大气科学的二级学科.本文阐述了生态气象的学科起源、概念、主要研究内容与特征,指出生态气象是研究生态系统与气象条件之间相互关系的科学,是地球系统多圈层相互作用的核心,服务于人与自然和谐发展;探讨了生态气象观测的主要指标与可能的业务服务产品;阐释了生态气象研究与生态文明气象保障、气象防灾减灾和应对气候变化的关系;指出当前生态气象迫切需要开展的重点研究任务:(1)生态气象长期观测联网研究;(2)基于大数据与人工智能的生态气象信息提取与分析技术;(3)生态系统对气候变化的适应性及其变化归因;(4)生态系统主要气象灾变机制及其致灾临界气象条件;(5)陆地生态系统关键物候期对多环境要素响应的生理生态机制与模拟模型研究;(6)耦合生物-物理-化学-管理过程的生态气象数值模式研发;(7)陆地生态系统变化对气候系统的反馈作用与可持续发展对策研究.

Global change and climate-vegetation classification

Three phrases of the quantitative study of climate-vegetation classification and their characteristics are presented based on the review of advance in climate-vegetation interaction, a key issue of 'global change and terrestrial ecosystems (GCTE)' which is the core project of International Geosphere-Biosphere Programme (IGBP): (i) characterized by the correlation between natural vegetation types and climate; (? characterized by climatic indices which have obviously been restricted to plant ecophysiology; (iii) characterized by coupling both structure and function of vegetation. Thus, the prospective of climate-vegetation classification for global change study in China was proposed, especially the study coupling climate-vegetation classification models with atmospheric general circulation models (GCMs) was emphasized.

Climatic suitability and spatial distribution for summer maize cultivation in China at 1.5 and 2.0 °C global warming

Evaluating climatic suitability of crop cultivation lays a foundation for agriculture coping with climate change scientifically. Herein, we analyse changes in the climatically suitable distribution of summer maize cultivation in China at 1.5℃(GW1.5) and 2.0℃(GW2.0) global warming in the future according to the temperature control targets set by the Paris Agreement. Compared with the reference period (1971- 2000), the summer maize cultivation climatically suitable region (CSR) in China mainly shifts eastwards, and its acreage significantly decreases at both GW1.5 and GW2.0. Despite no dramatic changes in the CSR spatial pattern, there are considerable decreases in the acreages of optimum and suitable regions (the core of the main producing region), indicating that half-a-degree more global warming is unfavourable for summer maize production in China's main producing region. When the global warming threshold increases from GW1.5 to GW2.0, the centres-of-gravity of optimum areas shift northeastward under RCP4.5 and RCP8.5, the centres-of-gravity of both suitable and less suitable areas shift northwestward, though the northward trend is more prominent for the less suitable areas, and the centre-of-gravity of unsuitable areas shifts southeastward. Generally, half-a-degree more global warming drives the cultivable areas of summer maize to shift northward in China, while the west region shows a certain potential for expansion of summer maize cultivation.

Altitudinal patterns of maximum plant height on the Tibetan Plateau

Aims Several studies have shown that plant height changes along environmen-tal gradients.However,altitudinal patterns of plant height across species are still unclear,especially in regions sensitive to climate change.As canopy height decreases dramatically near the tree line in alpine areas,we hypothesize that plant height across all species also decreases with increasing altitude,and distinct thresholds exist along this gradient.Methods Using a large dataset of maximum plant height and elevation range(400 to 6000 m a.s.l.)of 4295 angiosperms from the regional flora of the Tibetan Plateau,we regressed plant height for every 100 m belt against elevation to explore the relationships.To identify the approximate boundaries where dramatic changes in plant height occurs for herbaceous plants,shrubs,trees,woody plants and all angiosperms,we used piecewise linear regression.Phylogenetically independent contrast was used to test the potential evolutionary influences on altitudinal patterns at the family level.Important Findings Results showed that for herbaceous plants,shrubs,trees,woody plants and all angiosperms,plant height decreases significantly as altitude increases.In addition,we found that altitude,a proxy for many environmental factors,had obvious thresholds(breakpoints)dictating patterns of plant height.The results of phylogenetically independent contrast also emphasized the importance of evolution-ary history in determining the altitudinal patterns of plant height for some growth forms.Our results highlight the relative intense filter-ing effect of environmental factors in shaping patterns of functional traits and how this could vary for different ranges of environmental variables.

Analysis of Parameter Sensitivity on Surface Heat Exchange in the Noah Land Surface Model at a Temperate Desert Steppe Site in China

The dominant parameters in the Noah land surface model （LSM） are identified, and the effects of parameter optimization on the surface heat exchange are investigated at a temperate desert steppe site during growing season in Inner Mongolia, China. The relative impacts of parameters on surface heat flux are examined by the distributed evaluation of local sensitivity analysis （DELSA）, and the Noah LSM is calibrated by the global shuffled complex evolution （SCE） against the corresponding observations during May-September of 2008 and 2009. The differences in flux sim- ulations are assessed between the Noah LSM calibrated by the SCE with 27 parameters and 12 dominant parameters. The systematic error, unsystematic error, root mean squared error, and mean squared error decompositions are used to evaluate the model performance. Compared to the control experiment, parameter optimization by the SCE using net radiation, sensible heat flux, latent heat flux, and ground heat flux as the objective criterion, respectively, can ob- viously reduce the errors of the Noah LSM. The calibrated Noah LSM is further validated against flux observations of growing season in 2010, and it is found that the calibrated Noah LSM can be applied in the longer term at this site. The Noah LSM with 12 dominant parameters calibrated performs similar to that with 27 parameters calibrated.

Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.

Projection of Heat Injury to Single-Cropping Rice in the Middle and Lower Reaches of the Yangtze River, China under Future Global Warming Scenarios

Based on simulation results from the 16 CMIP5 model runs under three Representative Concentration Pathways(RCP2.6, RCP4.5, and RCP8.5) in combination with the recent five years of growth-stage data from agrometeorological observation stations in the middle and lower reaches of the Yangtze River, changes in heat injury and spatial distribution patterns of single-cropping rice in China during the early(2016–35), middle(2046–65), and late(2080–99) 21 st century were projected by using quantitative estimations. Relative to the reference period(1986–2005), the occurrence probabilities of heat injury to single-cropping rice under different RCP scenarios increased significantly, showing a trend of mild > moderate > severe. The occurrence probabilities increased with time and predicted emissions, especially the average and maximum occurrence probabilities, which were ～48% and ～80%,respectively, in the late 21 st century under the RCP8.5 scenario. The spatial patterns of the occurrence probabilities at each level of heat injury to single-cropping rice did not change, remaining high in the middle planting region and low in the east. The high-value areas were mainly in central Anhui and southeastern Hubei provinces, and the areas extended to the northwest and northeast of the cultivation area over time. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the total area of heat injury to single-cropping rice showed a significant linear increasing trend of 7.4 × 10~3, 19.9× 10~3, and 35.3 × 10~3 ha yr^(–1), respectively, from 2016 to 2099, and the areas of heat injury were greatest in the late21 st century, accounting for ～25%, ～40%, and ～59% of the cultivation area.