Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time...Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation.展开更多
Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to ...Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to re gional climate remains elusive.Using long-term remote sensing observations and Weather Research and Fore casting(WRF)model simulations,we investigated vegetation phenology changes from 2003 to 2020 and quan tified their biophysical controls on the regional climate in Northeast China.Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests,while advanced green-up and delayed dormancy extended the growing season in croplands.This prolonged presence and increased maximum green cover in tensified climate-vegetation interactions,resulting in more significant surface cooling in croplands compared to forests.Surface cooling from forest phenology changes was prominent during May’s green-up(-0.53±0.07°C),while crop phenology changes induced cooling throughout the growing season,particularly in June(-0.47±0.15°C),July(-0.48±0.11°C),and September(-0.28±0.09°C).Furthermore,we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model,with aero dynamic resistance emerging as the dominant factor.Crucially,our findings underscored that the land surface temperature(LST)sensitivity,exhibited substantially higher values in croplands rather than temperate forests.These strong sensitivities,coupled with the projected continuation of phenology shifts,portend further growing season cooling in croplands.These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature,emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.展开更多
Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimat...Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.展开更多
Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depic...Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.展开更多
[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopt...[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopted to conduct com- parative analysis of shoot histomorphology and phenological phases between two Io- quat varieties, ＇Chuanzao Loquat＇ and ＇Zaozhong 6＇. [Result] ＇Chuanzao Loquat＇ branched out and unfolded leaves about half to a month earlier than ＇Zaozhong 6＇; both the flowering and fruiting phases of ＇Chuanzao Loquat＇ were three months earlier than a precocious variety, ＇Zaozhong 6＇; the proportions of epidermis, cortex parenchyma, vascular tissue and medulla were 3.7%, 14.5%, 15.9% and 65.9%, re- spectively, in spdng shoots of ＇Chuanzao Loquat＇, and 3.1%, 42.5%, 6.9% and 47.5%, respectively, in ＇Zaozhong 6＇. [Conclusion] In terms of phenological phases, ＇Chuanzao Loqua＇ is earlier than ＇Zaozhong 6＇, a currently widely planted precocious variety, and thus is an important germplasm resource of Ioquats.展开更多
In order to provide directionally genetically improved breeding materials of poplar by exploring the phenological traits genetic variation level and its develop- ment potential of Populus deltoides and the resource ev...In order to provide directionally genetically improved breeding materials of poplar by exploring the phenological traits genetic variation level and its develop- ment potential of Populus deltoides and the resource evaluation was carried out; 8 phenological phases in seedling period were observed and analyzed of 60 Populus deltoids clones introduced from America. The results showed that： （1） there was obvious difference in phonological character among clones, especially in leaf-spread- ing peak stage and the end term of leaf-falling stage, with the largest variation co- efficient of 14.97% and the minimum of 3.83% respectively. （2） Leaf-spreading peak stage scattered but the end term of leaf-falling stage concentrated the most. The phonological character in early stage of seedling growth was the main factor influ- encing the length of growing season. （3） By principal component analysis, pheno- logical phases were classified into 3 typical periods, including germination stage, leaf-spreading peak stage and leaf-falling stage. （4） Totaling 60 clones were classi- fied into 4 types by using clustering analysis in phenological time variables of clones.展开更多
Through 5 years of phenological observations on Larix principis-rupprechtii Mayr. in primary seed orchard and studies on population and individuals of clones, the annual periodic phenological laws were revealed and th...Through 5 years of phenological observations on Larix principis-rupprechtii Mayr. in primary seed orchard and studies on population and individuals of clones, the annual periodic phenological laws were revealed and the annual phe-nological periodic table was drawn up. The correlation between various phenophases, the air temperature and active accumu-lated temperature were analyzed and expounded. The authors also analyzed the similarities and differences of phenophases among clonal individuals as well as the blooming properties of male and female flowers at the same time. This study could pro-vide theoretical reference for working out the production plan of improved varieties and other management measures in seed orchard of Larix principis-rupprechtii.展开更多
By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological c...Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological characteristics between tobacco and other crops. The spectral characteristics of tobacco and corn in luxuriant growth stage are very similar, which makes them difficult to be distinguished using a single-phase remote sensing image. Field film after tobacco seedlings transplanting can be used as significant sign to identify tobacco. Remote sensing interpre- tation map based on the fusion image of TM and CBERS02B's High-Resolution (HR) camera image was used as stan- dard reference material to evaluate the classification accuracy of Spectral Angle Mapper (SAM) and Maximum Like- lihood Classifier (MLC) for time series image based on full samples test method. SAM has higher classification accu- racy and stability than MLC in dealing with time series image. The accuracy and Kappa of tobacco coverage extracted by SAM are 83.4% and 0.692 respectively, which can achieve the accuracy required by tobacco coverage measurement in a large area.展开更多
Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI...Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.展开更多
Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on th...Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.展开更多
Proline has been proposed to be an osmoprotector and scavenger of reactive oxygen species in plants subjected to water deficit. The aim of this work was to study the effects of drought on each wheat phenological stage...Proline has been proposed to be an osmoprotector and scavenger of reactive oxygen species in plants subjected to water deficit. The aim of this work was to study the effects of drought on each wheat phenological stage （tillering, booting, heading, flowering and grain-filling） using stress parameters such as the relative water content （RWC）, membrane stability index （MSI）, lipid peroxidation through malondialdehyde levels （MDA） and determination of proline content （PRO）. The Brazilian commercial elite cultivar Triticum aestivum cv. CD 200126 was submitted to eight days of water deficit stress at each stage. The perception of stress was low at tillering and high at the final stages of growth, as verified by the reduction in the MSI and RWC. However, an increase in the MDA was clearly observed. We observed a high proline accumulation when stress was applied, although it was not sufficient to prevent damages. These results indicate that the relevant stages to evaluate the effect of water shortage during wheat plant development are booting, heading and flowering.展开更多
Rice grain yield and quality declines are due to unsuitable temperatures from wide regions and various sowing dates.This study aimed to evaluate the effects of temperature on rice yield and quality at different phenol...Rice grain yield and quality declines are due to unsuitable temperatures from wide regions and various sowing dates.This study aimed to evaluate the effects of temperature on rice yield and quality at different phenological periods and obtain suitable temperatures for phenological periods in the Yangtze River Basin,China.This study conducted experiments on different sowing dates under different areas in the Yangtze River Basin to observe and compare the differences in rice growth,yield,and quality,controlling for regional varieties.The results showed significant differences in rice growth,yield,and quality among sowing dates and areas,which were related to the average daily temperature during the vegetative period(VT)and the first 20 days of the grain-filling period(GT20).In addition,there was a smaller variation in the average daily temperature in the reproductive period(RT)than in the two phenological periods.Therefore,according to the VT and GT20 thresholds of different yields and qualities,the experimental results were divided into four scenarios(Ⅰ,Ⅱ,Ⅲ,andⅣ)in this study.In Scenario I,high head rice production(rice grain yield multiplied by head rice rate)and rice quality could be obtained.The head rice production of ScenariosⅢandⅣwas lower than that of ScenarioⅠ,by 30.1 and 27.6%,respectively.In Scenario II,the head rice production increased insignificantly while the chalky grain rate and chalkiness were 50.6 and 56.3%higher than those of Scenario I.In conclusion,the Scenario I combination with VT ranges of 22.8-23.9℃and GT20 ranges of 24.2-27.0℃or the combination with VT ranges of 23.9-25.3℃and GT20 ranges of 24.2-24.9℃,which can be obtained by adjusting sowing date and selecting rice varieties with suitable growth periods,is recommended to achieve high levels of rice grain yield and quality in the Yangtze River Basin.展开更多
Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions...Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions controlling the phenological traits using the line×tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions.The results interpreted via multiple analysis(mean performance,correlations,principal component,genetic analysis,heterotic and heterobeltiotic potential)disclosed highly significant differences among germplasm.The phenological waxiness traits(glume,boom,and sheath)were strongly interlinked.Flag leaf area exhibits a positive association with peduncle and spike length under drought.The growing degree days(heat-units)greatly influence spikelets and grains per spike,however,the grain yield/plant was significantly reduced(17.44 g to 13.25 g)under drought.The principal components based on eigenvalue indicated significant PCs(first-seven)accounted for 79.9%and 73.9%of total variability under normal irrigation and drought,respectively.The investigated yield traits showed complex genetic behaviour.The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant.The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa.The magnitude of dominance effects for phenological and yield traits,i.e.,leaf twist,auricle hairiness,grain yield/plant,spikelets,and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance.However,the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought.We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.展开更多
Discrimination among grapevine varieties based on quantitative traits,such as flowering,veraison and ripening dates is crucial for variety selection in the context of climate change and in breeding programs.These trai...Discrimination among grapevine varieties based on quantitative traits,such as flowering,veraison and ripening dates is crucial for variety selection in the context of climate change and in breeding programs.These traits are under complex genetic control for which 6 linked SSR loci(VVS2,VVIn16,VMC7G3,VrZAG29,VMC5G7,and VVIB23)have been identified.Using these markers in HRM-PCR analysis,we assessed genetic diversity among a large collection of 192 grapevine varieties.The grapevine germplasm used encompasses the majority of Greek vineyard with 181 varieties,3 prominent foreign varieties and 11 varieties of Palestinian origin.The SSR markers used were highly polymorphic,displaying unique melting curves for unusually higher number of samples than generally observed in SSR analysis.This prompted us to examine sequence composition for selected samples and found that variation present as SNPs in the flanking sequences of SSR motifs was responsible for the observed polymorphism.Hence,HRM-PCR proved to be a tool of higher analytical power to distinguish genotypes surpassing the discrimination power of conventional gel-based SSR analysis.The study provides a better understanding of genetic variation of SSR marker loci associated to phenological traits in grapevine varieties,signifying an analytical methodology that may be of higher discrimination power in detection of polymorphism for utilization in breeding programs.展开更多
Morphological characterization and phenological modeling were carried out on genotypes of <i>Jatropha platyphylla</i> collected from the states of Sinaloa and Durango, Mexico. The morphological characteriz...Morphological characterization and phenological modeling were carried out on genotypes of <i>Jatropha platyphylla</i> collected from the states of Sinaloa and Durango, Mexico. The morphological characterization evidenced the existence of monoecious plants, finding individuals with male and female flowers in the same inflorescence. Fruit with four seeds was also found. The phenological study was divided into two phases and calculated in thermal requirement (<span style="font-family:;" "="">°D): Vegetative [seedtime (0), germination (24), emergence (98), cotyledons (87), second (302) and fourth (524) true leaves, end of vegetative growth (302)] and reproductive [flowering (303), fructification (342), maturation (126), defoliation and senescence (450)]. The thermal constant (2558) was similar in all eight genotypes. The phenological stages and the accumulated degree days were adjusted with a third-degree polynomial (Stage = -0.0041<i>x</i><sup>3</sup> + 0.7446<i>x</i><sup>2</sup> - 8.6808<i>x</i> + 6.2448) (R<sup>2</sup> = 0.99%) stage. The development of phenological models facilitates the prediction of the flowering date for the selection of varieties with high oil and protein content.</span>展开更多
The main objective of this study was to evaluate the process of parameters such as mean temperature;total precipitation on phenology and phenological stages of apple golden type in Razavi Khorasan. For this reason, lo...The main objective of this study was to evaluate the process of parameters such as mean temperature;total precipitation on phenology and phenological stages of apple golden type in Razavi Khorasan. For this reason, long-term data of absolute minimum daily temperature, precipitation, humidity, as well as Digital Elevation Model (DEM) was used. After collecting data on phenology and Growing Degree Days (GDD) for golden apple, to pass each phenological stage at different growth stages, the start and end dates, phenological stages of the locations were identified. Then, regression equations with variable longitude, latitude and altitude on SPSS software at level of 50% and 95%, respectively were used, and finally phenological stages and spatial distribution maps of temperature and precipitation variables based on these equations were drawn in ARC GIS software. The analysis of the phenological stages showed that Torbate Heydarieh station has a decreasing trend which is significant at 1% in all stages of phenology and Ghoochan station does not show any significant increase or decrease trend at all stages of phenology.展开更多
This paper reports the phenological response of forest vegetation to climate change(changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI...This paper reports the phenological response of forest vegetation to climate change(changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky–Golay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows:(1) The start of the growing season(SOS) of the forest vegetation mainly concentrated in day of year(DOY) 105–120, the end of the growing season(EOS) concentrated in DOY 285–315, and the growing season length(GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively.(2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region.(3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.展开更多
Many plants have high ornamental value during specific phenophases, and plant phenology correlates highly with seasonal vegetation landscape. Determination of the span and spatiotemporal patterns of the tourism season...Many plants have high ornamental value during specific phenophases, and plant phenology correlates highly with seasonal vegetation landscape. Determination of the span and spatiotemporal patterns of the tourism season for ornamental plants could provide tourism administrators and the tourists themselves with a theoretical basis for making travel arrangements. Based on data derived from on-the-ground observations of three phenophases, specifically first leafing date, full flowering date, and end of leaf coloring date, and corre- sponding meteorological data at 12 sites in China, we divided the tourism season into its starting date, peak （best date） and end date for ornamental plants by computing frequency distributions of these phenophases. We also determined how the peak of this tourism season changed during the course of the past 50 years. We found that： （1） The peak of the tourism season ranged from March 16 （in Guilin） to May 5 （in Harbin） for first leafing, from April 3 （in Kunming） to May 24 （in Mudanjiang） for full flowering, and from October 1 （in Mudanjiang） to November 30 （in Shanghai） for leaf coloring. As might be expected, the peaks of both the first leafing and full flowering tourism seasons were positively associated with latitude, while for leaf coloring it was negatively correlated with latitude. （2） The ideal tourism season for first leafing and full flowering advanced by more than 0.16 days/year over the past 50 years in Beijing and Xi＇an, while the peak of the tourism season for leaf coloring became significantly delayed （by 0.16 days/year in Beijing and 0.21 days/year in Xi＇an）. （3） The tourism season was significantly associated with temperature across related phenological observation sites. The ideal time for first leafing and full flowering was determined to have advanced, respectively, by 4.02 days and 4.04 days per 1℃ increase in the spring （March-May） temperature. From September to November, the best time for leaf coloring correlated significantly and positively with average temperature, and the spatial sensitivity was 2.98 days/℃.展开更多
The paper had studied phenological phase of three wild plants in Haishi Park at limestone mountainous area of Chongqing which were Berchemia polyphylla var.leioclada,Caesalpinia decapetala and Bauhinia glauca subsp.hu...The paper had studied phenological phase of three wild plants in Haishi Park at limestone mountainous area of Chongqing which were Berchemia polyphylla var.leioclada,Caesalpinia decapetala and Bauhinia glauca subsp.hupehana.Samples were selected based on terrain,and it had selected 30 plants of Berchemia polyphylla var.leioclada and Caesalpinia decapetala and 7 plants of Bauhinia glauca subsp.hupehana which were different in shape but similar in size.Fixed point observation was conducted on them and observation results were recorded.It had explored ornamental value of the three wild plants and identified their corresponding ornamental periods,so as to provide scientific reference and suggestions for gardening and application of the three wild plants.展开更多
基金supported by grants from the National Key Research and Development Program of China(2022YFD2001103)the National Natural Science Foundation of China(42371373)。
文摘Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation.
基金supported by the Strategic Pri-ority Research Program(A)of the Chinese Academy of Sciences(Grant No.XDA28080503)the National Natural Science Foundation of China(Grant No.42071025)+1 种基金the Youth Innovation Promotion Associa-tion of Chinese Academy of Sciences(Grant No.2023240)the Pacific Northwest National Laboratory which is operated for DOE by Battelle Memorial Institute under Contract DE-A06-76RLO 1830.
文摘Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to re gional climate remains elusive.Using long-term remote sensing observations and Weather Research and Fore casting(WRF)model simulations,we investigated vegetation phenology changes from 2003 to 2020 and quan tified their biophysical controls on the regional climate in Northeast China.Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests,while advanced green-up and delayed dormancy extended the growing season in croplands.This prolonged presence and increased maximum green cover in tensified climate-vegetation interactions,resulting in more significant surface cooling in croplands compared to forests.Surface cooling from forest phenology changes was prominent during May’s green-up(-0.53±0.07°C),while crop phenology changes induced cooling throughout the growing season,particularly in June(-0.47±0.15°C),July(-0.48±0.11°C),and September(-0.28±0.09°C).Furthermore,we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model,with aero dynamic resistance emerging as the dominant factor.Crucially,our findings underscored that the land surface temperature(LST)sensitivity,exhibited substantially higher values in croplands rather than temperate forests.These strong sensitivities,coupled with the projected continuation of phenology shifts,portend further growing season cooling in croplands.These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature,emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFD2300700)the Open Project Program of the State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute (Grant No.2023ZZKT20402)+1 种基金the Agricultural Science and Technology Innovation Program, the Central Public-Interest Scientific Institution Basal Research Fund, China (Grant No.CPSIBRF-CNRRI-202119)the Zhejiang ‘Ten Thousand Talents’ Plan Science and Technology Innovation Leading Talent Project, China (Grant No.2020R52035)。
文摘Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.
基金supported by the National Natural Science Foundation of China[grant number 42001386,42271407]within the ESA-MOST China Dragon 5 Cooperation(ID:59313).
文摘Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
文摘[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopted to conduct com- parative analysis of shoot histomorphology and phenological phases between two Io- quat varieties, ＇Chuanzao Loquat＇ and ＇Zaozhong 6＇. [Result] ＇Chuanzao Loquat＇ branched out and unfolded leaves about half to a month earlier than ＇Zaozhong 6＇; both the flowering and fruiting phases of ＇Chuanzao Loquat＇ were three months earlier than a precocious variety, ＇Zaozhong 6＇; the proportions of epidermis, cortex parenchyma, vascular tissue and medulla were 3.7%, 14.5%, 15.9% and 65.9%, re- spectively, in spdng shoots of ＇Chuanzao Loquat＇, and 3.1%, 42.5%, 6.9% and 47.5%, respectively, in ＇Zaozhong 6＇. [Conclusion] In terms of phenological phases, ＇Chuanzao Loqua＇ is earlier than ＇Zaozhong 6＇, a currently widely planted precocious variety, and thus is an important germplasm resource of Ioquats.
基金Supported by National Special Funds for Forest Research in the Public Interest(201004004)~~
文摘In order to provide directionally genetically improved breeding materials of poplar by exploring the phenological traits genetic variation level and its develop- ment potential of Populus deltoides and the resource evaluation was carried out; 8 phenological phases in seedling period were observed and analyzed of 60 Populus deltoids clones introduced from America. The results showed that： （1） there was obvious difference in phonological character among clones, especially in leaf-spread- ing peak stage and the end term of leaf-falling stage, with the largest variation co- efficient of 14.97% and the minimum of 3.83% respectively. （2） Leaf-spreading peak stage scattered but the end term of leaf-falling stage concentrated the most. The phonological character in early stage of seedling growth was the main factor influ- encing the length of growing season. （3） By principal component analysis, pheno- logical phases were classified into 3 typical periods, including germination stage, leaf-spreading peak stage and leaf-falling stage. （4） Totaling 60 clones were classi- fied into 4 types by using clustering analysis in phenological time variables of clones.
文摘Through 5 years of phenological observations on Larix principis-rupprechtii Mayr. in primary seed orchard and studies on population and individuals of clones, the annual periodic phenological laws were revealed and the annual phe-nological periodic table was drawn up. The correlation between various phenophases, the air temperature and active accumu-lated temperature were analyzed and expounded. The authors also analyzed the similarities and differences of phenophases among clonal individuals as well as the blooming properties of male and female flowers at the same time. This study could pro-vide theoretical reference for working out the production plan of improved varieties and other management measures in seed orchard of Larix principis-rupprechtii.
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
基金Under the auspices of China Postdoctoral Science Foundation (No. 20080430586, 20070420018)National Natural Science Foundation of China (No. 40801161, 40801172)Sino US International Cooperation in Science and Technology (No. 2007DFA20640)
文摘Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological characteristics between tobacco and other crops. The spectral characteristics of tobacco and corn in luxuriant growth stage are very similar, which makes them difficult to be distinguished using a single-phase remote sensing image. Field film after tobacco seedlings transplanting can be used as significant sign to identify tobacco. Remote sensing interpre- tation map based on the fusion image of TM and CBERS02B's High-Resolution (HR) camera image was used as stan- dard reference material to evaluate the classification accuracy of Spectral Angle Mapper (SAM) and Maximum Like- lihood Classifier (MLC) for time series image based on full samples test method. SAM has higher classification accu- racy and stability than MLC in dealing with time series image. The accuracy and Kappa of tobacco coverage extracted by SAM are 83.4% and 0.692 respectively, which can achieve the accuracy required by tobacco coverage measurement in a large area.
基金supported by the National Key Research and Development Program of China (2019YFE0125300)the Shandong Provincial Key R&D Plan (2021LZGC026)the China Agriculture Research System (CARS-03)。
文摘Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.
基金supported by the National Natural Science Foundation of China (51909228)the Postdoctoral Science Foundation of China (2020M671623)the ‘‘Blue Project” of Yangzhou University。
文摘Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.
文摘Proline has been proposed to be an osmoprotector and scavenger of reactive oxygen species in plants subjected to water deficit. The aim of this work was to study the effects of drought on each wheat phenological stage （tillering, booting, heading, flowering and grain-filling） using stress parameters such as the relative water content （RWC）, membrane stability index （MSI）, lipid peroxidation through malondialdehyde levels （MDA） and determination of proline content （PRO）. The Brazilian commercial elite cultivar Triticum aestivum cv. CD 200126 was submitted to eight days of water deficit stress at each stage. The perception of stress was low at tillering and high at the final stages of growth, as verified by the reduction in the MSI and RWC. However, an increase in the MDA was clearly observed. We observed a high proline accumulation when stress was applied, although it was not sufficient to prevent damages. These results indicate that the relevant stages to evaluate the effect of water shortage during wheat plant development are booting, heading and flowering.
基金supported by the Science and Technology Plan Project of Hubei Province,China(2012BLB228)the National Key Research and Development Program of China(2017YFD0301402)+2 种基金the National Natural Science Foundation of China(31701359)the Fundamental Research Funds for the Central Universities,China(2662017JC007)the China Postdoctoral Science Foundation(2017M612477)。
文摘Rice grain yield and quality declines are due to unsuitable temperatures from wide regions and various sowing dates.This study aimed to evaluate the effects of temperature on rice yield and quality at different phenological periods and obtain suitable temperatures for phenological periods in the Yangtze River Basin,China.This study conducted experiments on different sowing dates under different areas in the Yangtze River Basin to observe and compare the differences in rice growth,yield,and quality,controlling for regional varieties.The results showed significant differences in rice growth,yield,and quality among sowing dates and areas,which were related to the average daily temperature during the vegetative period(VT)and the first 20 days of the grain-filling period(GT20).In addition,there was a smaller variation in the average daily temperature in the reproductive period(RT)than in the two phenological periods.Therefore,according to the VT and GT20 thresholds of different yields and qualities,the experimental results were divided into four scenarios(Ⅰ,Ⅱ,Ⅲ,andⅣ)in this study.In Scenario I,high head rice production(rice grain yield multiplied by head rice rate)and rice quality could be obtained.The head rice production of ScenariosⅢandⅣwas lower than that of ScenarioⅠ,by 30.1 and 27.6%,respectively.In Scenario II,the head rice production increased insignificantly while the chalky grain rate and chalkiness were 50.6 and 56.3%higher than those of Scenario I.In conclusion,the Scenario I combination with VT ranges of 22.8-23.9℃and GT20 ranges of 24.2-27.0℃or the combination with VT ranges of 23.9-25.3℃and GT20 ranges of 24.2-24.9℃,which can be obtained by adjusting sowing date and selecting rice varieties with suitable growth periods,is recommended to achieve high levels of rice grain yield and quality in the Yangtze River Basin.
文摘Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions controlling the phenological traits using the line×tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions.The results interpreted via multiple analysis(mean performance,correlations,principal component,genetic analysis,heterotic and heterobeltiotic potential)disclosed highly significant differences among germplasm.The phenological waxiness traits(glume,boom,and sheath)were strongly interlinked.Flag leaf area exhibits a positive association with peduncle and spike length under drought.The growing degree days(heat-units)greatly influence spikelets and grains per spike,however,the grain yield/plant was significantly reduced(17.44 g to 13.25 g)under drought.The principal components based on eigenvalue indicated significant PCs(first-seven)accounted for 79.9%and 73.9%of total variability under normal irrigation and drought,respectively.The investigated yield traits showed complex genetic behaviour.The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant.The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa.The magnitude of dominance effects for phenological and yield traits,i.e.,leaf twist,auricle hairiness,grain yield/plant,spikelets,and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance.However,the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought.We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.
文摘Discrimination among grapevine varieties based on quantitative traits,such as flowering,veraison and ripening dates is crucial for variety selection in the context of climate change and in breeding programs.These traits are under complex genetic control for which 6 linked SSR loci(VVS2,VVIn16,VMC7G3,VrZAG29,VMC5G7,and VVIB23)have been identified.Using these markers in HRM-PCR analysis,we assessed genetic diversity among a large collection of 192 grapevine varieties.The grapevine germplasm used encompasses the majority of Greek vineyard with 181 varieties,3 prominent foreign varieties and 11 varieties of Palestinian origin.The SSR markers used were highly polymorphic,displaying unique melting curves for unusually higher number of samples than generally observed in SSR analysis.This prompted us to examine sequence composition for selected samples and found that variation present as SNPs in the flanking sequences of SSR motifs was responsible for the observed polymorphism.Hence,HRM-PCR proved to be a tool of higher analytical power to distinguish genotypes surpassing the discrimination power of conventional gel-based SSR analysis.The study provides a better understanding of genetic variation of SSR marker loci associated to phenological traits in grapevine varieties,signifying an analytical methodology that may be of higher discrimination power in detection of polymorphism for utilization in breeding programs.
文摘Morphological characterization and phenological modeling were carried out on genotypes of <i>Jatropha platyphylla</i> collected from the states of Sinaloa and Durango, Mexico. The morphological characterization evidenced the existence of monoecious plants, finding individuals with male and female flowers in the same inflorescence. Fruit with four seeds was also found. The phenological study was divided into two phases and calculated in thermal requirement (<span style="font-family:;" "="">°D): Vegetative [seedtime (0), germination (24), emergence (98), cotyledons (87), second (302) and fourth (524) true leaves, end of vegetative growth (302)] and reproductive [flowering (303), fructification (342), maturation (126), defoliation and senescence (450)]. The thermal constant (2558) was similar in all eight genotypes. The phenological stages and the accumulated degree days were adjusted with a third-degree polynomial (Stage = -0.0041<i>x</i><sup>3</sup> + 0.7446<i>x</i><sup>2</sup> - 8.6808<i>x</i> + 6.2448) (R<sup>2</sup> = 0.99%) stage. The development of phenological models facilitates the prediction of the flowering date for the selection of varieties with high oil and protein content.</span>
文摘The main objective of this study was to evaluate the process of parameters such as mean temperature;total precipitation on phenology and phenological stages of apple golden type in Razavi Khorasan. For this reason, long-term data of absolute minimum daily temperature, precipitation, humidity, as well as Digital Elevation Model (DEM) was used. After collecting data on phenology and Growing Degree Days (GDD) for golden apple, to pass each phenological stage at different growth stages, the start and end dates, phenological stages of the locations were identified. Then, regression equations with variable longitude, latitude and altitude on SPSS software at level of 50% and 95%, respectively were used, and finally phenological stages and spatial distribution maps of temperature and precipitation variables based on these equations were drawn in ARC GIS software. The analysis of the phenological stages showed that Torbate Heydarieh station has a decreasing trend which is significant at 1% in all stages of phenology and Ghoochan station does not show any significant increase or decrease trend at all stages of phenology.
基金National Natural Science Foundation of China,No.41671090National Basic Research Program(973 Program),No.2015CB452702
文摘This paper reports the phenological response of forest vegetation to climate change(changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky–Golay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows:(1) The start of the growing season(SOS) of the forest vegetation mainly concentrated in day of year(DOY) 105–120, the end of the growing season(EOS) concentrated in DOY 285–315, and the growing season length(GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively.(2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region.(3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.
基金National Natural Science Foundation of China, No.41171043 National Basic Research Program of China, No.2012CB955304+1 种基金 Major National Research Program of Scientific Instruments, No.41427805 National Natural Science Foundation of China, No.41030101
文摘Many plants have high ornamental value during specific phenophases, and plant phenology correlates highly with seasonal vegetation landscape. Determination of the span and spatiotemporal patterns of the tourism season for ornamental plants could provide tourism administrators and the tourists themselves with a theoretical basis for making travel arrangements. Based on data derived from on-the-ground observations of three phenophases, specifically first leafing date, full flowering date, and end of leaf coloring date, and corre- sponding meteorological data at 12 sites in China, we divided the tourism season into its starting date, peak （best date） and end date for ornamental plants by computing frequency distributions of these phenophases. We also determined how the peak of this tourism season changed during the course of the past 50 years. We found that： （1） The peak of the tourism season ranged from March 16 （in Guilin） to May 5 （in Harbin） for first leafing, from April 3 （in Kunming） to May 24 （in Mudanjiang） for full flowering, and from October 1 （in Mudanjiang） to November 30 （in Shanghai） for leaf coloring. As might be expected, the peaks of both the first leafing and full flowering tourism seasons were positively associated with latitude, while for leaf coloring it was negatively correlated with latitude. （2） The ideal tourism season for first leafing and full flowering advanced by more than 0.16 days/year over the past 50 years in Beijing and Xi＇an, while the peak of the tourism season for leaf coloring became significantly delayed （by 0.16 days/year in Beijing and 0.21 days/year in Xi＇an）. （3） The tourism season was significantly associated with temperature across related phenological observation sites. The ideal time for first leafing and full flowering was determined to have advanced, respectively, by 4.02 days and 4.04 days per 1℃ increase in the spring （March-May） temperature. From September to November, the best time for leaf coloring correlated significantly and positively with average temperature, and the spatial sensitivity was 2.98 days/℃.
基金Supported by Funds of National Spark Program(2006EA105025)Funds of Haishi Park of Chongqing City~~
文摘The paper had studied phenological phase of three wild plants in Haishi Park at limestone mountainous area of Chongqing which were Berchemia polyphylla var.leioclada,Caesalpinia decapetala and Bauhinia glauca subsp.hupehana.Samples were selected based on terrain,and it had selected 30 plants of Berchemia polyphylla var.leioclada and Caesalpinia decapetala and 7 plants of Bauhinia glauca subsp.hupehana which were different in shape but similar in size.Fixed point observation was conducted on them and observation results were recorded.It had explored ornamental value of the three wild plants and identified their corresponding ornamental periods,so as to provide scientific reference and suggestions for gardening and application of the three wild plants.
