Phenological Periods Research Articles

Diyarbakır ili pamuk alanlarındaki Lygus (Hemiptera: Miridae) türleri, yayılış alanları ve popülasyon gelişimi

Many insect pests infest cotton (Gossypium hirsutum L.) crop, resulting in significant economic losses. The tarnished plant bugs (Lygus species) greatly damage the cotton crop during all growth stages. This study determined Lygus species infesting cotton in Diyarbakır province, their distribution, and population dynamics during 2020 and 2021. Lygus species samples were collected from 244 fields in 7 districts of the province by using either D-vac or sweep net based on the phenological period of cotton. Population dynamics of the species were monitored weekly by using sweep net and D-vac from two fields situated in the Sur (Gencan village) and Çınar (Şükürlü village) districts of the province during both years. Two species including Lygus gemellatus (Herrich-Schäffer) and L. pratensis (Linnaeus) were identified during the study. Lygus gemellatus was the most common and abundant species observed in 91.2% and 74.7% of the surveyed fields during 2020 and 2021, respectively. The species were recorded at the end of May (before flowering) during both years, and their populations increased afterward. The peak population of both species was observed during the boll maturation period. The D-vac trapped a statistically higher number of Lygus bugs than the sweep net at Gencan (p

Dragonflies of Moldova: an updated checklist of the Odonatofauna

The present research compiles all known records of dragonflies in Moldova, including Transnistria (from the beginning of the 20th century until present), based on all known literature and other sources, and also the personal observations of the author in 2005, 2009, and 2011. Brief history of Moldovan odonatology is given with the gap of more than 50 years of odonatological studies in Moldova in the middle of the last century. The analysis shows that the checklist of Odonata in Moldova comprises 37 species. The presence of several species in Moldova needs confirmation. The probable absence of Nehalennia speciosa in the territory of Moldova is discussed in the article based on the opinion of other authors and the lack of favourable habitats in the area. The map presented illustrates the areas in Moldova with the lack of data on the presence of dragonflies. This elucidated the understudied regions. Unfortunately, some records from the literature sources are given without any coordinates, so they could not be mapped. This analysis will help in planning further research into dragonflies of Moldova. Moldova still remains poorly studied from an odonatological point of view. Further investigations will undoubtedly fill in the ‘white spots’ on the distribution of species in the territory of Moldova and will help in the development of modern recommendations of habitat protection, underline urgent conservation measures in the country and highlight priority species. We expect that 10 to 20 species of dragonflies remain to be discovered in Moldova. Also, field research in different phenological periods will presumably help to add some new species. The data about dragonflies of Moldova were amended after three field trips of the author in 2005 (the area near Saharna) and in 2009 and 2011 (the route covered the territory of Moldova from south to north). Based on the author’s field research, three species of dragonflies are listed for the first time for the territory of Moldova: Lestes macrostigma, Coenagrion ornatum, and Orthetrum brunneum. For one species, Coenagrion scitulum, the exact locations are given for the first time by the author. The presence of the species of European concern in Moldova include Lestes macrostigma and Coenagrion ornatum, and Gomhus flavipes might be considered as well by odonatologists from the neighbouring countries.

FORAGE YIELD AND QUALITY CHARACTERISTICS OF WINTER LEGUME / GRASS MIXTURES HARVESTED AT DIFFERENT PHENOLOGICAL STAGES

Winter forage crop mixtures are very important sources of quality forage as well as the benefits they bring to the soil and the main crop. In areas where year-round production is carried out, leaving the soil fallow during the winter months and producing maize-maize or cotton-cotton causes fatigue in the soil and facilitates the increase of pests. For this purpose, forage yield and quality characteristics of 12 different winter forage crop applications (4 pure and 8 mixtures) were investigated between 2014-2016 in Büyük Menderes basin. The experiment was designed according to the split-plot experimental design in randomized blocks and mowing operations were carried out in 2 different phenological periods with 4 replications. Hay yield (kg da-1), ADF (%), NDF (%), ADL (%), crude protein ratio (%), crude protein yield (kg da-1) and relative feed value averages were measured. According to the results obtained from the experiment, there are differences between crude protein ratio and relative feed values. When analyzed in line with the production purpose, 75% forage pea + 25% oat mixtures stand out in terms of crude protein yield, while 55% forage pea + 45% annual ryegrass stands out in terms of relative feed value. Due to the changes in mowing times depending on the characteristics, it is thought that it would be ideal to perform mowing operations between 50%-100% flowering.

THE EFFECTS OF DIFFERENT SOWING TIMES ON THE PHENOLOGICAL CHARACTERISTICS AND SEED YIELD OF THE PEA

The pea is a legume with a high protein content and high nutritional value. In recent times, it has come to be
 consumed as a frozen and canned food as well as fresh. It has a special place among legumes because of its
 suitability for crop rotation. In Turkey, it is mostly grown as a winter crop in the western regions. Sowing
 takes place in the fall for the winter growing season. Determining the best sowing time is vital to avoid cold
 damage. This study was conducted over two years in 2020-21 and 2021-22 growing seasons in the field crops
 trial fields of Aydin Adnan Menderes University Faculty of Agriculture. During the study, the dry matter
 values and GDD (growing degree days) of four pea cultivars (Giresun, Mayer, Local 1 and Local 2) were
 calculated at four phenological periods (emergence, flowering, pod set and maturity) for three different sowing
 times (November 1, November 20 and December 10). In addition, the yield and yield characteristics and
 protein content were analyzed. Among the four varieties, Mayer (1.95t ha-1) recorded the highest yield value,
 while Giresun (25.6%) achieved the highest protein content. The number of days to maturity, dry matter
 content and GDD values of the varieties at the different phenological periods varied with the sowing time.
 Higher GDD and dry matter content levels were measured in early sowing. The later the sowing, the shorter
 the period from pod set to maturation and the lower the dry matter content.

Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Sarı Pas Hastalığının Fenolojik Dönemlere göre Hastalık-Verim İlişkisinin Çok Bantlı (Hiperspektral) Veriler Kullanılarak Araştırılması

The aim of this study was to evaluate the severity of yellow rust in different phenological periods by 
 subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period 
 (Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was 
 determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques.

The Influences of Soil and Meteorological Factors on the Growth and Fruit Quality of Chinese Jujube (Ziziphus jujuba Mill.).

Chinese jujube (Ziziphus jujuba Mill.) is attracting more and more attention worldwide due to their tasty and nutritious fruit with extremely high contents of vitamin C (Vc) and soluble sugar. In order to find out the main factors that influence jujube growth and reproductive adaptability, the phenological periods, vegetative growths, fruiting abilities, and fruit qualities of eight newly released cultivars were compared and comprehensively analyzed in three representative ecological sites of the three main jujube-producing regions including Fuping (Hebei), Taigu (Shanxi), and Alar (Xinjiang) in China. Our results showed that the characteristics of jujube cultivars were significantly affected by soil and meteorological factors. The fruit number per bearing shoot was much more affected by temperature, light, and rainfall. The fruit number per bearing shoot, contents of soluble solids, and soluble sugar and Vc contents in fruits were influenced more by meteorological factors. The content of flavonoids was affected by both soil and meteorological factors. A principal component analysis (PCA) indicated that cultivars suitable for planting in Fuping (Hebei) were Yuhong and Lengbaiyu. Zaocuimi, Fucuimi, and Zaoqiuhong were suitable to be cultivated in Taigu (Shanxi), while Zaocuimi, Yuhong, Yulu, Luzao 2, and Yueguang behaved better in Alar (Xinjiang). This study provides insights of the environmental factors on jujube yield and quality and therefore provides references for highly efficient jujube cultivation.

Forage senescence and disease influence elk pregnancy across the Greater Yellowstone Ecosystem

AbstractFor various temperate ungulate species, recent research has highlighted the potential for spring vegetation phenology (“green‐up”) to influence individual condition, with purported benefits to population productivity. However, few studies have been able to measure the benefit on vital rates directly, and fewer still have investigated the comparative influence of other phenological periods on ungulate vital rates. In this study, we tracked phenological changes throughout the duration of the growing season and examined how their timing affected the probability of pregnancy in an ungulate population. We did this for elk (Cervus canadensis) across the Greater Yellowstone Ecosystem (GYE) by sampling 1106 adult females in winter at 25 sites over a 13‐year period and assessing sources of variation in pregnancy using a Bayesian hierarchical model. Pregnancy rates were generally high across the GYE (82.4%), and the primary influences on probability of pregnancy were the timing of vegetation senescence (“brown‐down”) in autumn and exposure to the reproductive disease brucellosis. Earlier forage brown‐down in fall negatively influenced the probability of pregnancy of elk aged 6–9 years by an estimated 17.2% within the range (ca. 32 days) of observed brown‐down end dates. While summer habitat quality has been inferred to influence elk pregnancy previously, our findings specify the key influence of foraging conditions later in the seasonal cycle, immediately before the breeding season. The reproductive disease brucellosis was also an important factor, reducing the probability of pregnancy by 12.4% in elk in the 6‐ to 9‐year age class. Because pregnancy was tested before most disease‐induced abortions occur, the apparent mechanism for this effect is a prolonged reduction in fertility beyond the period of initial exposure in which fetal mortality is typically expected. Our results prompt greater scrutiny of the combined effects of late‐season phenology and disease on reproductive rates and population productivity in temperate ungulates.

A novel habitat adaptability evaluation indicator (HAEI) for predicting yield of county-level winter wheat in China base on multisource climate data from 2001 to 2020

The effective collaboration between crop habitat adaptability evaluation and yield prediction is of great significance. Climate factors occupy an indispensable role, but the contributions of different climate factors to crop growth present spatiotemporal heterogeneity and confusion, amplifying the challenges associated with large-scale dynamic evaluations. Additionally, the mounting input parameters in yield prediction compound the uncertainty and intricacy of modeling. To address these challenges, a climate-driven dynamic habitat adaptability evaluation indicator (HAEI) was developed, capable of forecasting county-level winter wheat yields in China. First, the distribution characteristics and matching relationship between climate and yield variability were explored from multi-source data in the long time series, and a novel method of multiple-factor adaptive matching habitat membership degree was proposed. Second, considering the interaction and contribution differences between multiple-factor at different phenological periods, a comprehensive HAEI suitable for the entire growth period of winter wheat is constructed. The results showed that HAEI can integrate climate information that has a greater impact on yield variability and has a significant correlation with yield in various regions and periods, with an average correlation of 0.70. Remarkably, the predictive models incorporating HAEI consistently outperformed other yield prediction algorithms, demonstrating superior accuracy (R2 = 0.62–0.76, nRMSE = 0.1517–0.2031). Even in the least favorable scenario, involving a linear model with HAEI input, satisfactory results were achieved. This comprehensive framework effectively mitigates the adverse consequences of widespread agricultural climate heterogeneity and evaluates the habitat adaptability and yield status of wheat at the county level in China.

Spatial and temporal variations of vegetation phenology and its response to urbanization in central Yunnan urban agglomeration, Southwest China

Vegetation phenology is an important sensor that responds to environmental changes. Based on MOD13Q1 EVI data, we used the dynamic threshold method to extract vegetation phenological parameters of the central Yunnan urban agglomeration from 2001 to 2020, namely the start of growing season, the end of growing season, and the length of growing season, aiming to reveal the spatiotemporal variations in vegetation phenology and urban-rural differences. The results showed that vegetation phenology of the central Yunnan urban agglomeration from 2001 to 2020 generally showed a phenomenon of delayed start of growing season, delayed the end of growing season (0.66 days per year), and prolonged growing season. Compared with suburban and rural areas, growing season in urban areas in the past 20 years had started earlier (1.05 days per year), ended later (0.91 days per year), and thus growing season had been prolonged (1.79 days per year). Vegetation phenology showed significant difference on the gradient of urban, suburban, and rural areas. The start and the end of growing season of urban vegetation were the earliest, and the length of growing season was the longest, with the most significant changes in the urban areas and within the range of 0-2 km outward. The start of growing season in urban area was significantly earlier, the end of growing season was significantly delayed, and length of growing season was prolonged significantly with the increase of population density, per capita GDP, and the proportion of built-up area. The sensitivity of different phenological periods of vegetation and their duration to environmental changes varied on the gradient of urban, suburban and rural areas. Population density and proportion of built-up area in the study area played an important role in delaying the end of growing season of vegetation in the central Yunnan urban agglomeration.

Population structure and phenological attributes of Adansonia digitata L. (baobab) in Northwestern lowland area of Ethiopia

Adansonia digitata (baobab), a multipurpose and highly valued tree species, is facing threats due to anthropogenic factors like shifting cultivation practices and fire. The aim of this study was to examine the population structure and phenological attributes of baobab in three districts (i.e. Kafta Humera, Tselemt, and Quara district) in Northwestern Ethiopia. The study was carried out by establishing 17 plots 1 km long and 100 m wide covering a total area of 170 ha in the Quara district and five plots covering 50 ha each in the Kafta Humera and Tselemt districts. Further, plots were subdivided into 25 × 25 m and 5 × 5 m sub-plots for recording other woody species and their regeneration status, respectively. Thirty reproductively matured trees with easily visible crowns were selected to record phenological characteristics and fruit yield. The findings revealed that baobab population was significantly higher in the Tselemt district (3.15 ± 0.15) as compared to Quara (1.43 ± 0.43) and Kafta Humera (1.30 ± 0.23) sites. A bell-shaped diameter distribution was observed in the Quara district and irregular-shaped distributions were observed in Kafta Humera and Tselemt districts. Phenological periodicity and fruit production of baobab did not vary significantly among the three study sites. On average, 404 fruits per tree were recorded with a maximum of 559 fruits in mid-diameter size class trees. Due to livestock browsing, shifting cultivation practices, and uncontrolled fire, the recruitments are limited in the study areas. An in-situ conservation strategy through the plantation and proper management practices are needed to sustain baobab tree species.

Projecting the impact of climate change and elevated CO2 concentration on rice irrigation water requirement in China

Climate change and elevated CO2 concentrations significantly affect rice growth and water consumption. Understanding the specific impacts of climate change and elevated CO2 concentrations on rice physiological phenology, crop water demand (ETC), and irrigation water requirement (IR) is of great significance for the sustainable utilization of water resources and food security. This is particularly true in China, the world's largest rice producer. In this study, with the help of two rice phenological models, the modified Penman-Monteith equation, and the paddy water balance model, we project the changes in rice phenological period, ETC, and IR in four main rice-producing regions of China in the period 2015–2100 based on the 11 GCM outputs. The results show that the rice growing period is shortened in most rice-producing regions, except for the parts of the middle and lower reaches of the Yangtze River. Meanwhile, the trend of ETC and IR of rice varies slightly among regions in the future scenario, with almost all regions decreasing yearly except for the middle and lower reaches of the Yangtze River, where the trend is increasing. The progressively increasing atmospheric CO2 concentration has a “fertilization effect” on the crop, which can reduce the water requirements of rice. In the SSP585 scenario, the “ CO2 fertilization effect” can reduce up to 8.87 × 108 m3 of ETC and 6.94 × 108 m3 of IR in the middle and lower reaches of the Yangtze River in the period of 2090s. This study provides beneficial references to understand the response of rice ETC and IR to future climate change and CO2 concentration elevation in China and highlights that the simulation in terms of crop irrigation must account for the “CO2 fertilization effect”.

A Method of Chestnut Forest Identification Based on Time Series and Key Phenology from Sentinel-2

Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF > DT > SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user’s accuracy (UA) and producer’s accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field.

Tracking photosynthetic phenology using spectral indices at the leaf and canopy scales in temperate evergreen and deciduous trees

Vegetation photosynthetic phenology is an important indicator to characterize the biological responses of terrestrial carbon cycle to climate change. Remote sensing-derived spectral indices have been used to estimate photosynthetic phenology of terrestrial ecosystems, yet there are large uncertainties due to differences in leaf structure and canopy function between evergreen and deciduous trees. In this study, we used in-situ measured meteorology, the maximum rate of leaf-level CO2 assimilation (Amax), chlorophyll fluorescence and canopy color changes in evergreen and deciduous species to evaluate and compare the abilities of spectral indices for photosynthetic phenology modeling. Seasonal variation in Amax is used as a proxy for photosynthetic phenology. Several indices were used, including the normalized difference vegetation index (NDVI), near-infrared reflectance vegetation index (NIRv), photochemical reflectance index (PRI), chlorophyll/carotenoid index (CCI), green-red vegetation index (GRVI), and green chromaticity coordinates (Gcc). We found that NIRv and PRI estimated phenological periods for Cedrus deodara and three deciduous trees all differed from Amax by less than 5 days, and all were linearly related to Amax (R2 > 0.6). CCI had less potential for Amax modeling, while NDVI had the largest error in predicting Amax. Gcc was a more accurate proxy for photosynthetic phenology than GRVI at the canopy scale. In addition, we found that the variability of different spectral indices in tracking photosynthetic phenology of evergreen and deciduous species was related to non-photochemical quenching processes. Especially for the fraction of absorbed light quenched by dynamic NPQ (ɸNPQ) and the fraction of absorbed light quenched by sustained NPQ (ɸf,D), they contribute more than 40 % to the variations of the NIRv and PRI spectral signals. Therefore, NIRv and PRI provide powerful tools for monitoring photosynthetic phenology, and future exploration of NIRv and PRI at larger spatial scales will favor the global plant phenology and carbon uptake modeling.

Effects of Altitude on Phenological Periods with Some Quality Features in Raisins and Molasses

Effects of Altitude on Phenological Periods with Some Quality Features in Raisins and Molasses

Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes

Images of key phenological periods play a vital role in agricultural applications as they capture the unique spectral characteristics of crops. Unfortunately, acquiring high-spatial-resolution images of the key phenological period from a single satellite platform remains challenging due to its short duration and synchronization with the rainy season. Spatiotemporal fusion (STF) is an effective tool for the prediction of missing high spatial resolution images on the required date, however, most STF methods assume a uniform relationship between the reflectance on the base date and the predicted date for the same land-cover type, which fails to hold in agricultural scenarios because of the diverse phenological changes among different crop types or different growth processes, even within a single crop type. To address these challenges, we propose a novel spatiotemporal fusion method for agricultural scenarios called Agri-Fuse. Agri-Fuse emphasizes change information and classifies change types based on the difference image derived from the fine image on the base date and the coarse image on the predicted date. Considering the various regression relationships between reflectance on the base date and the predicted date for different change types, a category-based linear regression model was built, and we introduced an unmixing model to solve these regression coefficients. We validated the performance of Agri-Fuse at two experimental sites where diverse phenological changes occurred during the key phenological periods of the same and different crop types. Compared to the four typical fusion methods (FSDAF, RASDF, STARFM, and Fit-FC), Agri-Fuse achieved the best performance in preserving both spectral fidelity and spatial details. Ablation experiments further confirmed the effectiveness of the proposed change-type classification and category-based regression in capturing diverse phenological change information. Thus, Agri-Fuse is expected to precisely reconstruct high-spatial-resolution images of key phenological periods necessary for agricultural applications.

A dataset of ecological observations of soybean and foxtail millet during different phenological periods at Ansai Station from 2005 to 2020

As important grain crops of the typical farmland ecosystem in the hilly areas of the Loess Plateau, soybean and foxtail millet are of great significance to ensuring regional food security. Ecological observations of crops during various phenological periods over successive years can offer crucial data support for researching the impact of climate change on farmland ecosystems. As a member of China Ecosystem Research Network (CERN) and Chinese National Ecosystem Research Network (CNERN), the National Field Scientific Observation and Research Station for the Farmland Ecosystem in Ansai, Shaanxi (Ansai Station) is a long-term field observation platform for farmland ecosystems focused on the hilly areas of arid and semi-arid Loess Plateau. This dataset includes the phenology, leaf area index and aboveground biomass during different phenological periods and the annual yield of soybean and foxtail millet at Ansai Station from 2005 to 2020. This dataset is expected to serve as a scientific foundation and provide essential data for the establishment of crop growth model, dynamic monitoring of crop growth, and also hold great significance for the dynamic management of agricultural production in this study area.

Climate Variation and Its Influence on the Cold Tolerance and Phenology Periods of Pear Cultivars in Jilin, China

ABSTRACT This study primarily analyzed the response of cold tolerance and phenology periods of four pear cultivars to climate differences in central (Gongzhuling) and eastern (Longjing) Jilin, which could provide a theoretical basis for the regional division and cultivation. It showed that there was a significant difference in the cold tolerance of the same cultivars between the two regions. Soluble sugar and soluble starch contents are the most important physiological indicators that determine the cold tolerance of the pear cultivars in Gongzhuling is stronger than that in Longjing. In phenology, only the cessation of terminal shoot extension and growing season length of pear cultivars were significantly related to their contents of soluble sugar and soluble starch. Furthermore, the gray correlation analysis identified the district’s effective accumulated temperature as the key meteorological factors affecting the cessation of terminal shoot extension. The precipitation in the growing season was another major meteorological factor affecting the growing season length. Therefore, in this study, the influence of the main district meteorological factors on the key phenology periods of four pear cultivars, which decided the different accumulation of sugar and starch, eventually lead to the difference in cold tolerance of the same pear cultivar in two regions.

GC-MS Analysis of Essential Oil and Volatiles from Aerial Parts of Peucedanum tauricum M.B. during the Phenological Period

Widespread worldwide Peucedanum plants (Apiaceae) have been used for centuries as plant medicines. The polymorphism of this genus is consistent with chemotaxonomically and therapeutically significant differences in the composition of secondary metabolites. GC-MS of Peucedanum tauricum M.B. volatiles from the headspace (HS) and hydrodistilled essential oil (HD), both obtained simultaneously from flowers (FL), immature and ripe fruits (IF, MF) and leaves (L1-L3) collected at the time of harvesting of generative organs, show differences in the chemical profiles of HS and HD from the same parts of the plant, and between organs (FL, IF, MF vs. L1-L3). The presented studies on the variability of biometabolites in the phenological period indicated the optimal harvesting time, focused on two molecular chemotaxonomic markers of PT; guaia-1(10),11-diene and guaia-9,11-diene (in generative organs iHD at 25.5–32.1% and 26.8–33.6%; and in their HS at 29.4–41.3% and 25.0–29.4%, respectively). This is the first report on the analysis of fresh aerial parts of Peucedanum sp. in which GC-MS of HS and HD was performed simultaneously during the vegetation period. The importance, with possible limitations, of GC-MS analysis of HS and HD as an evaluation tool useful in the chemotaxonomy of Peucedanum plants was also discussed.

Effects of drip irrigation timing and water temperature on soil conditions, cotton phenological period, and fiber quality under plastic film mulching

Effects of drip irrigation timing and water temperature on soil conditions, cotton phenological period, and fiber quality under plastic film mulching

Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.