Phenological Periods Research Articles

Rhizobacterial communities and crop development in response to long-term tillage practices in maize and soybean fields on the Loess Plateau of China

Conservation tillage, that is, reduced or zero tillage, can change the soil ecological environment and rhizosphere bacterial communities. Here, we investigated temporal variations in rhizosphere bacterial communities during crop development and their relationships with crop growth characteristics under three tillage practices (Plow tillage (PT), chisel plough tillage (CPT), and zero tillage (ZT)) in maize and soybean fields on the Loess Plateau of China. Conservation tillage (CPT and ZT) practices significantly increased the contents of soil moisture (3.62–13.13%), soil organic carbon (29.19–39.67%), and nitrogen (38.69–60.23%) and decreased soil pH (1.15–3.76%). The overall responses of rhizosphere bacterial community composition to tillage practice and phenological period were taxonomically consistent across maize and soybean fields, and changes were predominantly observed in the relative abundances of dominant bacterial phyla. Co-occurrence network analysis indicated that the average connectivity degree among 16S operational taxonomic units was higher under PT (38.92% and 73.60%) practice than under CPT and ZT practices. CPT practice tended to increase the total biomass and nitrogen accumulation of maize and soybean compared with PT practice, whereas ZT practice significantly (P < 0.05) decreased them. Partial least squares path modeling analysis showed that temporal variations in the rhizosphere bacterial communities (explained variations in maize and soybean fields were 94.7% and 92.7%, respectively) were regulated by tillage practices and phenological periods, and partly explained the changes in growth dynamics of maize and soybean (0.161 and 0.031, respectively). Overall, CPT practice could improve the soil ecological environment and promote crop growth in the China’s Loess Plateau. Therefore, we recommend that CPT practice should be adopted in semi-arid agricultural areas to improve the potential relationships between the underground and aboveground in agroecosystems.

Contributions of phenological groups of wild bees as an indicator of food availability in urban wastelands

Protection and increasing of biodiversity in urban areas should be a priority for urban authorities, managers of green areas, and the society. This objective can be reached by proper shaping of vegetation structure in green spaces, including urban wastelands, on the basis of e.g. investigation of relations between bees and their food plants. We evaluated the attractiveness of vegetation to the Apiformes, as an indicator of food availability in wastelands, in the city of Bydgoszcz and its suburbs. In respect of species richness and abundance, early-spring bees (emergence in March-April) and spring bees (emergence in May) prevailed in suburban habitats. In contrast, in urban wastelands, summer species of bees (emergence in June) were the richest and most abundant. This was linked with analogous differences in species richness of bee food plants starting to flower in those periods, and various plant life-forms played important roles in individual phenological periods. In the case of early-spring and spring bee species, the differences concerned primarily polylectic and cleptoparasitic bees, which prevailed in suburban habitats. Polylectic bees that emerged in summer were more abundant in urban habitats. No such a relationship was observed for oligolectic species, whose occurrence depends on the presence of a narrow group of bee food plant species, irrespective of their location and degree of human disturbance. We also found that trees and shrubs were major sources of food for the dominant bee species in early spring and spring. Among native plants, the most important role was played by apophytes (i.e. native plants typically growing on disturbed land) starting to flower in spring. Our results can be used by managers of urban landscape to shape urban vegetation so that it provides a continuous supply of food for bees and other pollinating insects.

Effects of wood ash and soil water potential on vegetative development of mung bean (Vigna radiata L.)

This research aimed to evaluate the amending potential of eucalyptus’s wood ash on soil chemical properties and soil-water potential. The experiment was conducted in a greenhouse at the Federal University of Mato Grosso, campus in Rondonópolis. The experimental design was composed of randomized blocks in a 5x5 factorial scheme, including five soil-water potentials (‒4, ‒8, ‒16, ‒32 and ‒64 kPa), and five wood ash doses (0; 8; 16; 24 and 32 g dm-3). The soil samples were collected from the top layer of an Oxisol under natural Cerrado vegetation. Mung bean (Vigna radiata L.) growth variables (plant height, numbers of leaves, stem diameter, and SPAD index) were analyzed at three different phenological periods. In general, the wood ash doses increased soil pH, eliminated the exchangeable aluminum, and improved soil essential nutrients availability. As a result, mung bean plants responded positively to wood ash, achieving superior results at doses ranging from 24 to 26 g dm-3. The interaction between wood ash doses and soil water potential was not significant. However, drier soil conditions constrained plant growth severely. According to our experimental conditions, plant growth variables achieved higher performance at soil water potential of -4 kPa

Effect of Skipping Irrigation in Different Phenological Periods on Yield and Some Physiological Parameters of Corn (Zea mays L.)

This study was carried out in Southeastern Anatolia Region in Turkey between 2017 and 2018 to determine the effect of irrigation skipping in different phenological periods on yield and some physiological properties of corn (Zea mays L.) in second crop conditions. The trial is designed as randomized complete block design with three replications. DKC-6664 hybrid corn variety having classified as moderate maturity in the FAO (650-700) maturity classes were used in the study. Four irrigation skipping periods which are full irrigation treatments (S1, control), irrigation skipping in vegetative growth period (S2), irrigation skipping in pollination period (S3), and irrigation skipping in generative development period (S4) were studied in the research. According to the two-year averages; there were statistically significant differences in hybrid corn variety in terms of investigated characteristics. According to results, grain yield (GY), chlorophyll content (CC, spad), water use efficiency (WUE, mm), crop water stress index (CWSI) and plant water consumption (ETa, mm) were ranged from 12761.5 (S2)-14021.3 (S1) kg ha-1, 39.10 (S4)-44.50 (S1) spad, 1.902 (S1)-2.114 (S3) mm, 0.18 (S3)-0.33 (S4) and 6752 (S3)-7712.0 (S1) mm respectively. Because of drier weather conditions in 2017 compared to 2018, while GY, CC, and WUE parameters decreased, CWSI and ETa parameters increased. In the first year of the trial when dry weather conditions prevailed, the corn variety consumed more water and was more stressed. As a result, yield losses were experienced. There were positive and significant correlations between GY and CC and WUE, and negative and significant correlations between CWSI. The results of the study indicated that irrigation should not be skipped during the entire vegetative period for economical and profitable corn production in semi-arid regions.

Classification and Observed Seasonal Phenology of Broadleaf Deciduous Forests in a Tropical Region by Using Multitemporal Sentinel-1A and Landsat 8 Data

Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis Miq. (S. siamensis), Shorea obtusa Wall. ex Blume (S. obtusa), and Dipterocarpus tuberculatus Roxb. (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.

Quantitative Assessment of the Influences of Snow Drought on Forest and Grass Growth in Mid-High Latitude Regions by Using Remote Sensing

Global climate change, especially the snow drought events, is causing extreme weather events influencing regional vegetation growth and terrestrial ecosystem stability in a long-term and persistent way. In this study, the Sanjiang Plain was selected, as this area has been experiencing snow drought in the past two decades. Logistic models, combined with multisource remote sensing and unmanned aerial vehicle (UAV) data, as well as the meteorological data over the past 20 years, were used to calculate sixteen phenological periods and biomass. The results show that (1) over the past two decades, snow drought has been based on the snow accumulation and has been occurring more frequently, wider-ranging and more severely; (2) snow drought has advanced the forest start of season (SOS)/end of season (EOS) by 6/5 days, respectively; (3) if the snowfall is greater than 80% of a normal year, the SOS/EOS of grass is postponed by 8/6 days; conversely, if it is less than 80%, the SOS/EOS are advanced by 7/5 days; and (4) biomass decreased approximately 0.61%, compared with an abundant snowfall year. Overall, this study is the first to explore how snow drought impacts the phenological period in a mid-high latitude area, and more attention should be paid to these unknown risks to the ecosystem.

Phenology of the Genus Indigofera L. (Fabaceae-Papilionoideae) of Holalkere and Hosadurga thaluks of Chitradurga District, Karnataka, India

Importance of phenology has increased in view of global climate change. Reproductive phenology is responsible for success in seed production and thereby perpetuation of a species. Phenological studies were not given much importance,especially in the region of Holalkere and Hosadurga of chitraduga district of Karnataka state. Hence, attempt was made to study the phenological aspects of the genus Indigofera L. of sub family papilionoideae in the study area. The phenophases viz., leafing, flowering and fruiting and leaf fall are observed for 10 recorded species of the genus Indigofera. The regular Phenological period of the majority of genus was studied,variation in the phenophases of some small herbs and undershrubs was noticed with respect to its ecology. This investigation gives phenological database of the economically important genus Indigofera of the study area. This database can be also utilized, besides this importance, in future, in planning and management of conservational measures in the region.

Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China

Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China’s grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p &gt; 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands.

Bionic Technology and Deep Learning in Agricultural Engineering: Current Status and Future Prospects

As one of the most important production activity of mankind, agriculture plays an important role in social development. With the development of science and technology, agricultural technology has constantly been explored and researched. By learning and imitating the characteristics of creatures in nature, bionic technology has been applied to the improvement of agricultural machinery and farm implements. In recent years, as an extension of bionic technology, machine vision and deep learning have been widely used in agricultural production. The application of bionic technology and deep learning in agricultural engineering are reviewed in this study. In traditional agricultural engineering, many bionic farming tools were developed to reduce soil resistance and multiple bionic cutting cutters were designed to improve work efficiency and save energy. Machine vision and neural networks were widely used in crop classification, sorting, phenological period recognition and navigation. Deep learning methods can promote the intelligentization of agricultural engineering and has obvious advantages in crop classification, disease and pest identification, growth status evaluation and autonomous robots. Agricultural engineering that integrates bionic technology, machine vision and deep learning will develop toward more automation and intelligence.

Effects of Grazing Enclosures on Species Diversity, Phenology, Biomass, and Carrying Capacity in Borana Rangeland, Southern Ethiopia

A grazing enclosure (GE) is one of the most effective techniques for restoring degraded rangelands by modifying the composition, abundance, and diversity of species. However, the effect of GEs on different grazing intensities and durations compared to open-grazing (OG) rangelands is not well known. We aimed to assess the effect of GE on the characteristics of plant species. We established five plots in a short-term enclosure, a long-term enclosure, and an OG treatment to examine the effect of GE on vegetation species’ height, abundance, diversity, phenology, biomass, heterogeneity, and the carrying capacity (CC) of rangeland under grazed and enclosed conditions. We found that GE significantly (P&amp;lt; 0.05) increased vegetation height, abundance, biomass, CC, phenological period, and species diversity. However, the increase in height, diversity, and phenological periods were not consistent with enclosure duration, and higher increments were recorded at the short-term enclosure site. The highest Shannon index value recorded at StGE (2.45) was 28.6 and 12.2% greater than the LtGE (1.75) and OG (2.15) sites, respectively. The advanced phenological periods were higher at the StGE site and showed 22.6 and 60.3% higher values than the LtGE and OG sites, respectively. The highest carrying capacities of 23.4 and 114.3% for livestock were observed at the LtGE compared to the StGE and OG grazing sites. In addition, the heterogeneity of the plants’ community was significantly higher in the long-term GE, due to the decrease in species’ diversity. In conclusion, this paper further contributes to the development of the theoretical basis on the effects of GEs and recommends strategies like rotational grazing and reseeding to be used in tandem with a GE for sustainable management of rangelands.

Abiotic disturbances affect forest short-term vegetation cover and phenology in Southwest China

Forest disturbances significantly affect the global carbon cycle by, for example, vegetation loss or changing forest phenology. However, the lack of historical disturbance events constitutes a challenge for in-depth temporal and spatial analysis. Available remote sensing time series and combined climate data may have great potential to quickly and consistently detect and identify forest disturbances events. We employed time-series data (2001–2014) of a vegetation index (normalized difference vegetation index, NDVI) and a change detection algorithm (the breaks for additive seasonal and trend, BFAST) to detect forest disturbances in a sub-tropical area located in Southwest China. Remote sensing and meteorological data were combined to distinguish among the typical forest disturbances: fires, extreme cold events in winter (ECE), and droughts. With the reconstructed historical disturbance events, post-disturbance vegetation loss, short-term vegetation cover, and phenology changes were analyzed. Our results show that fires and droughts caused severe damage to forest cover (NDVI anomalies can reach up to −1.84 and −1.11, respectively). Fire changed the regular phenological periods which last 3–4 years, and it also took 1–2 years for vegetation greenness to recover after ECE and droughts, which triggered carbon emissions and reduced forest stocks. Warmer areas were vulnerable to ECE effects as well and should be paid more attention. Post-disturbance effects show complex patterns: characteristics of disturbances, climatic conditions, and multiple events overlaying contribute to modifying forest vegetation. Hence, forest disturbances cannot be neglected but should be emphasized in future forest ecosystem modeling or analyzing. The approach used in the study can be a crucial step in detecting and assessing the effects of various disturbances on forest vegetation and phenology and, thereby, contributes to improved risk analysis and management in forestry.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China.

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

The Effect of Different Phenological Periods and Harvest Times on the Essential Oil Ratio and Components of Basil Genotypes

The basil is an annual medicinal plant that can be harvested more than once in the growing season. This study aimed to examine the variation of essential oil ratios and components of five different basil genotypes harvested in different phenological periods (pre-flowering, full-flowering, and post-flowering periods.). When the basil was harvested in the pre-flowering period, it could be harvested three times. In parcels left to full-flowering, basil genotypes have reached full-flowering twice in one vegetation period. The parcels left to the post-flowering period were harvested once. Essential oils were obtained separately from the flowers and leaves of the plants by hydrodistillation. Essential oil ratios in flowers and leaves for all phenological periods varied between 0.28-3.19 %. The ratio of essential oil in the parcels with multiple harvests was higher in the second and third harvests compared to the first harvest. It was determined that the ratio of essential oil in the parcels with multiple harvests was higher in the second and third harvests compared to the first harvest. The main components of the basil genotypes were determined as linalool, methyl cinnamate, eugenol, g-muurolene, and eucalyptol in all harvesting periods. In the post-flowering period harvested once, the linalool ratio of the basil genotypes ranged from 20.32-54.63 %. On the other hand, methyl cinnamate could not be detected in some genotypes like in the two and three times harvests, the highest ratio was found as 46.10 %. The linalool ratio of genotypes harvested twice in the full flowering period varied between 24.20-65.91 %, while the methyl cinnamate in some genotypes ranged from 0.04 to 53.26 %. The linalool ratio in the parcels with three times harvests in the pre-flowering period varied between 22.47-59.0 %. The methyl cinnamate ratio varied between 0.04-51.38 %. It was determined that the proportions of the main components decreased in the second and third harvests in the parcels harvested more than once.

Automatic Identification Algorithm of the Rice Tiller Period Based on PCA and SVM

The tillering period of rice is the crucial phenological period for the cultivation of high-quality and high-yield rice. Currently, human inspection is mainly used for identification, but it is time-consuming, laborious, and prone to mistakes. To efficiently and accurately identify the start date of rice tillering in the monitoring area, this paper proposed a new algorithm called rice tiller period recognition combining principal component analysis (PCA) and a support vector machine (SVM) (RTR-CPS). This algorithm characterizes the problem of identifying the rice tiller stage as a binary classification problem of rice either entering or not entering the tiller stage. To improve the image segmentation quality of traditional visual segmentation methods, the algorithm was designed to extract five image feature values to describe the rice tiller stage in a multi-featured way, reducing the impact of single feature value bias on the identification of the rice tiller stage. To improve the performance of the rice tiller stage recognition model, the algorithm selects ideal principal features in limited sample data by the PCA algorithm and optimizes SVM classification model hyper-parameters by combining 5-fold cross-validation. The experimental results showed that the accuracy of the algorithm for identifying the tillering date of potted rice was as high as 97.76%, which is significantly higher than other competitive methods, and the maximum error between the detection results and human inspection of potted rice tillering period was no more than 2 days. The rice tillering stage recognition model was applied to the field, and the images of field rice planted by two different methods were tested, which verified that the algorithm proposed in this paper is generalizable. These results fully demonstrate the feasibility and superiority of the algorithm in this paper.

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In 2020, waters and territories of the eastern part of Emerald Network object “Samarskyi Lis – UA0000212” were studied by biology specialists. The main purpose of the research was to identify rare species of plants and animals. We found species of flora and fauna protected by European Red List, Red Data Book of Ukraine, Additions to the Bern Convention and Regional Red Data Book. The list of plant species subjected to special protection contains 42 species, including Allium savranicum, Agropyron dasyanthum, and Jurinea cyanoides. It is noted that in the eastern part of the Emerald Network object “Samarskyi Lis ‒ UA0000212” the presence of other rare plant species is assumed (Senecio borysthenicus (D&amp;c.) Andrz., Agropyron dasyanthum Ledeb, Viola lavrenkoana Klokov., Allium savranicum Besser). This matter requires monitoring in various phenological periods of the year. Concerning entomofauna in the eastern part of the Emerald Network object “Samarskyi Lis – UA0000212” it was found 7 species listed in the Red Data Book of Ukraine (2009): Lucanus cervus, Papilio machaon, Iphiclides podalirius, Zerunthia polyxena, Xylocopa valla, Scolіa maculata, Catocala sponsa, as well as 2 species of insects protected by the provisions of the Bern Convention ‒ Lucanus cervus, Lycaena dispar. The ichthyofauna of the eastern part of the Emerald Network object “Samarskyi Lis ‒ UA0000212” includes 4 species that are listed in the Bern Convention: Leucaspius delineatus, Rhodeus amarus, Cobitis taenia, Sykhorn abotter. Analysis of terrestrial vertebrates revealed the presence of 95 species to be protected. There are 5 species of amphibians (three of them are included in the International Red List – Lissotriton vulgaris, Hyla arborea, Bufo bufo), as well as 6 species of reptiles, among which one is protected by IUCN (Emys orbicularis), two species listed in the Red Data Book of Ukraine (Coronella austriaca, Vipera renardi). 74 species of birds were found in the study area. Three of them (Coracias garrulus, Ciconia nigra and Buteo rufinus) are listed in the Red Data Book of Ukraine. In the eastern part of the Emerald Network object “Samarskyi Lis ‒ UA0000212” there are 10 species of mammals, of which nine are protected by the provisions of the Bern Convention, and two species (Eptesicus serotinus, Nyctalus noctula) are listed in the Red Data Book Of Ukraine. It is proposed to conduct further monitoring studies in the eastern part of the Emerald Network “Samarskyi Lis ‒ UA00002122”

Examining phenological variation of on-year and off-year bamboo forests based on the vegetation and environment monitoring on a New Micro-Satellite (VENµS) time-series data

ABSTRACT Moso bamboo is an evergreen plant that extensively distributes in subtropical regions. Comparing to other forest types, Moso bamboo forest has some unique characteristics: high growth rate, short harvesting rotation, and on/off-year phenomenon. Plant phenology plays an important role in regulating carbon sequestration of the bamboo forest ecosystem. However, it is a challenge task to capture the phenological features of Moso bamboo forests on a regional scale due to frequent change of canopy structures and lack of high spatiotemporal remotely sensed data. The Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) data with high spatiotemporal resolution provide the potential to examine the seasonal change of Moso bamboo forests. This research employs the VENµS time-series data (from January 2018 to December 2019) to analyse the spectral characteristics of on-year/off-year Moso bamboo forests and other two evergreen forest types (i.e., broadleaf forest and coniferous forest). The optimal spectral ranges for examining the seasonal variation of bamboo forests were determined. Three red-edge-based vegetation indices were reconstructed using the Harmonic analysis of time series (Hants) and compared. Red-edge position index (REPI) was selected to identify different phenological periods of Moso bamboo forests and other evergreen forest types. The results show that the spectral range of 730–920 nm in the VENμS data is sensitive to seasonal variation of Moso bamboo forests. The REPI can more effectively identify the two-year growing cycle of the bamboo forests than other vegetation indices, especially the bamboo shoots period. The start of the growing season of the off-year bamboo forest is approximately 50 to 60 days earlier than on-year bamboo forest. The results provided time-series phenological datasets of on-year and off-year Moso bamboo forests, which is valuable for local governments to conduct better ecological management and decision-making.

How do the features of individual gardens affect bird diversity in rural-suburban areas?

Demographic data show booming metropolitan populations and the related rapid spread of urbanized areas. Cities already harbor numerous diverse habitats, including gardens, varying greatly both in terms of floristics and architectural attributes. The way gardens are designed and managed is of crucial importance for the birds associated with them. The present work provides a comprehensive overview of garden features, which were used in cluster analysis to distinguish several garden types. The birds occurring in gardens located on the outskirts of the city in southern Poland were analyzed in three phenological periods (breeding season, autumn, winter).Depending on garden type, bird density ranged from 10.8 to 57.4 breeding pairs per ha, with 0.8–8.3 species per garden. The species similarity Jaccard index of bird assemblages was from 0.10 to 0.75. The evenness index revealed some garden types with very even species distributions, as well as some types that were strongly dominated by a single species. Garden features also affected proportions between groups of species with different nesting preferences (cavity nesters, canopy nesters, birds nesting in building, and those nesting in shrubs and near the ground). The function of gardens as food sources increased from spring to winter. The garden attributes that significantly affected the occurrence of breeding birds were high tree density and hedges, building walls and attics available for birds, as well the presence of chicken coops. In terms of feeding, of importance was not only lush vegetation, but also places where different types of food were provided for birds, poultry, and domestic and farm animals.As the unfolding changes in urban and suburban areas are likely to result in the disappearance of some garden features and the associated niches, it will be necessary to compensate for them with a view to conserving bird species such as the house sparrow. To that end, appropriate educational and advisory programs should be offered to garden owners.

Ecological meteorology: Origin, concept, and prospects

Ecological meteorology is an emerging earth science subject in response to the current environmental crisis and has become a secondary subject of atmospheric sciences. It encompasses meteorological science and technological support for national ecological civilization construction, especially national environmental protection and sustainable development. Accelerating ecological meteorology monitoring and research and establishing an operational brand is critical. The origin, concept, and primary research areas of ecological meteorology are hereby reviewed. Ecological meteorology studies the relationship between ecosystem and meteorological conditions (the core of multiple earth system sphere interactions) and serves the harmonious development of humans and nature. It includes six main aspects: (1) The patterns and laws of meteorologically induced ecosystem types and their geographical distribution; (2) the measurement of ecosystem temporal and spatial changes due to variable meteorological conditions; (3) the meteorological contribution and attribution analyses to ecological changes; (4) the feedback of ecosystem changes to the earth system; (5) the numerical modeling of ecological meteorology; (6) the interaction between meteorological conditions and ecosystems, and the principles that promote the harmonious development of humans and Nature. Compared with ecology and meteorology, ecological meteorology research has the following characteristics: (1) The time scale ranges from seconds to millennia; (2) the spatial scale ranges from the stomatal scale to the global scale; (3) the driving forces include weather, climate, atmospheric composition, climate change, and ecological and environmental changes; (4) the content emphasizes multiple earth system sphere interactions and disaster risk management; (5) the methods and technologies focus on satellite-ground integration monitoring systems and data-model fusion analyses; (6) the goal is to understand the relationship between humans and nature based on the sustainable development of the earth system. Ecological meteorology observations come from typical ecosystem research stations and regional-scale field investigations, and the operational services include assessing the ecological carrying capacity of climate resources and monitoring, evaluating, and warning for ecological meteorology disasters. This paper explains the relationship between ecological meteorology and ecological civilization construction, disaster prevention and mitigation, and how to address climate change. The primary and urgent ecological meteorology research tasks are extracting relevant information for analysis using big data and artificial intelligence, investigating ecosystem adaptations to climate change and their change attributions, exploring an ecosystem’s main meteorological disaster processes and critical meteorological conditions, analyzing the eco-physiological mechanisms of key terrestrial ecosystem phenological periods and developing simulation models of them based on multiple environmental factors, developing an eco-meteorological numerical model coupling the biological, physical, and chemical, management processes, and understanding how terrestrial ecosystem changes and sustainable development countermeasures affect the climate system. Ecological meteorology is a new and promising field that is expected to provide valuable information for the future.

Bioecological Properties Of Helianthus Tuberosus In Introduction Conditions

Bio ecological features of helianthus tuberoses in soil-climatic conditions of Surkhandarya region were studied. The research obtained data on flowering phenology and flowering period under the conditions of introduction, the germination of plant seeds was determined.

NMR AND UPLC-QTOF/MS-BASED METABOLOMICS OF DIFFERENT DEVELOPMENTAL STAGES OF CYNOMORIUM SONGARICUM

Cynomorium songaricum Rupr., a holoparasitic plant that grows in the desert and has important dietetic and medical value. However, the medicinal efficacy of C. songaricum collected in different seasons varies greatly, and the difference in the transformation and accumulation of its major metabolites is still unclear. In this paper, UPLC-QTOF/MS and NMR were used to study the metabolomics of C. songaricum in different growth stages, so as to explore the metabolic differences and regularity of C. songaricum in different phenological periods during one cycle. The results showed that there were thirty and sixteen compounds with significant differences based on UPLC-QTOF/MS and NMR, respectively, which were distributed in flavonoids, organic acids, sugars and amino acids. Among them, the content of secondary metabolites such as catechins and procyanidins accumulated more in the Unearthing (U), and Maturing (M) stages, while other differential compounds accumulated more in the Tubercle (T), Sprouting (S) and Atrophy (A) stages. The differential metabolic pathways of C. songaricum in different stages involved in flavonoids, sugar, amino acid and other pathways. This provides scientific basis for understand of metabolites accumulation, quality evaluation and use as medicinal materials for C. songaricum.