Search Results (2,935)

Among flying insects, Diptera were the main visitors and colonisers of aquatic and terrestrial habitats in an Alpine glacial floodplain (NE Italy) at 2400 m a.s.l. In all, 4317 dipteran adults were collected using different collection techniques in, on, and out of the water: pond and drift nets, and emergence and Malaise traps, with a different periodicity: biweekly and every three hours for four consecutive days, in early and late summer 2015. Thirty-eight families in all, and 56 species within seven Brachycera families, were identified. Specifically, Chironomidae (36%) within Nematocera and Empidoidea families (23%), and Muscidae (9%) within Brachycera, prevailed. Chironomidae seemed to emerge and fly mainly in late morning–early afternoon, while most Brachycera were more active in late afternoon. Some ecological notes are given for seven Brachycera families, including Muscidae as the predominant family of anthophilous dipterans and the most efficient pollinators in mountain habitats and in the deglaciated areas of the proglacial forelands. Three genera of Muscidae were found as the main representatives of these environments: Thricops Rondani, Spilogona Schnabl, and Phaonia Robineau-Desvoidy). Among these genera, noteworthy was the finding of Spilogona triangulifera (Zetterstedt) as being new to the Italian fauna. Full article

Traditional winter pruning in dormancy (BBCH-00) as control (C) and three late pruning treatments, LP1 (wool stage—BBCH-05), LP2 (two leaves folded—BBCH-12), and LP3 (four leaves folded—BBCH-14), were applied on Kékfrankos grapevines. The evolution of the phenological growth stages, grape juice, wine analytical parameters, and phenolic composition were evaluated. The quantitative aspects of the grape berry, bunch, yield, and cane were also assessed. Our goal was to reach a decrease in sugar content and an increase in acidity. Delaying or postponing the phenological phases to bring technological and phenolic ripening closer together was also one of our objectives. These were accomplished, but the negative aspects of late pruning, which resulted in a reduction in the diameter and weight of the canes, should also be taken into account. We also found that, the later the late pruning, the more the yield was reduced. By postponing pruning, the phenological phases were also extended. Full article

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CI_{red edge}). The results revealed that the coefficient values (β₀ and β₁) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β₀ and β₁) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CI_{red edge} were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat. Full article

Lily is one of the world’s important ornamental flowers. Potted Asiatic lily is a further selected dwarf cultivar suitable for indoor or garden planting. However, there is a lack of relevant research on the cultivation adaptability of potted Asiatic lilies cultivars in the Chongqing region which in the southwest of China. This study selected five potted Asiatic lily cultivars, and the phenological period, stem and leaf characteristics, and flowering traits were assessed through statistical observation. The Asiatic lily ‘Tiny Ghost’ and ‘Tiny Double You’ are well-suited for both spring and autumn planting in Chongqing, while ‘Sugar Love’ and ‘Curitiba’ are best planted in the spring. The ‘Tiny Diamond’ is more appropriate for autumn planting due to its low tolerance to high temperature. The application of exogenous substances, including calcium chloride (CaCl₂), potassium fulvic acid (PFA) and melatonin (MT), can mitigate the detrimental effects of high-temperature stress on ‘Tiny Diamond’ by regulating photosynthesis, antioxidant systems, and osmotic substance content. A comprehensive evaluation using the membership function showed that the effect of exogenous CaCl₂ treatment is the best, followed by exogenous PFA treatment. CaCl₂ acts as a positive regulator of heat stress tolerance in Asian lilies, with potential applications in Asian lily cultivation. This study provides reference for cultivation and application of Asian lily varieties in Chongqing region, and also laid the foundation for further research on the mechanism of exogenous substances alleviating heat stress in lilies. Full article

Drosophila suzukii is a pest affecting a wide range of host plants, causing severe damage to small fruits, berries, and grapes. This study analyzed environmental factors influencing its population dynamics in regions where temperature is not a limiting factor. Data were collected in the spring–summer seasons of 2018 and 2019 across three vineyards in northwestern Portugal, examining the relationship between captured D. suzukii females, climatic variables, vine phenological stages, and ecological infrastructures. A stepwise linear model and Pearson correlation matrix were used. In 2020, a winter study was conducted in nine vineyards, focusing on landscape composition and its effect on D. suzukii populations. An ecological infrastructure index was created and correlated with captures data. Results show that vine phenological stages and nearby ecological infrastructures significantly affect population dynamics in spring and summer. Vineyards surrounded by complex landscapes, especially with wild hosts, supported higher D. suzukii populations during winter. These findings highlight the importance of ecological infrastructures in managing D. suzukii populations year-round and suggest their consideration in pest control strategies. Full article

Climate change significantly threatens crops, mainly through drought stress, affecting barley, which is essential for food and feed globally. Ten barley cultivars were evaluated under normal and drought stress conditions during the 2019/20 and 2020/21 seasons, focusing on traits such as days to heading and maturity, plant height, number of spikes m⁻², spike length, 1000-kernel weight, and biological and grain yield. Drought stress significantly reduced most of these traits. The genotypes showed significant differences in their responses to irrigation treatments, with the interaction between seasons and cultivars also being significant for most traits. The grain yield and 1000-kernel weight were among the least affected traits under drought stress, respectively. Notably, Giza138 and Giza126 showed strong drought tolerance, suitable for drought-resilient breeding. In season one, Giza126, Giza134, and Giza138 yielded 13%, 9%, and 11%, respectively, while Giza135 and Giza129 showed higher reductions at 31% and 39%. In season two, Giza126, Giza134, and Giza138 had reductions of 14%, 10%, and 13%, respectively, while Giza135 and Giza129 again exhibited higher reductions at 31% and 38%. These cultivars also showed strong performance across various stress tolerance indices, including the MP, YSI, STI, GMP, and YI. Giza 134 demonstrated the lowest values for the SDI and TOL, indicating superior drought stress tolerance. On the other hand, Giza 129 and Giza 135 were the most sensitive to drought stress, experiencing significant reductions across critical traits, including 6.1% in days to heading, 18.37% in plant height, 28.21% in number of kernel spikes⁻¹, 38.45% in grain yield, and 34.91% in biological yield. In contrast, Giza 138 and Giza 2000 showed better resilience, with lower reductions in the 1000-kernel weight (6.41%) and grain yield (10.61%), making them more suitable for drought-prone conditions. Giza 126 and Giza 132 also exhibited lower sensitivity, with minimal reductions in days to heading (2%) and maturity (2.4%), suggesting potential adaptability to water-limited environments. Giza 126 maintained the highest root lengths and had the highest stomatal conductance. Giza 138 consistently had the highest chlorophyll content, with SPAD values decreasing to 79% under drought. Despite leading in shoot length, Giza 135 decreased to 42.59% under drought stress. In conclusion, Giza 126 and Giza 138 showed adaptability to water-limited conditions with minimal impact on phenological traits. Giza 126 had the longest roots and highest stomatal conductance, while Giza 138 consistently maintained a high chlorophyll content. Together, they and Giza 134 are valuable for breeding programs to improve barley drought tolerance. Full article

Climate change has significant implications for agriculture, especially in viticulture, where temperature plays a crucial role in grapevine (Vitis vinifera) growth. Mendoza’s climate is ideal for producing high-quality wines, but 21st-century climate change is expected to have negative impacts. This study aimed to evaluate the effects of increased temperature on the phenology, physiology, and yield of Malbec, Bonarda, and Syrah. A field trial was conducted over two seasons (2019–2020 and 2020–2021) in an experimental vineyard with an active canopy heating system (+2–4 °C). Phenological stages (budburst, flowering, fruit set, veraison, harvest), shoot growth (SG), number of shoots (NS), stomatal conductance (gs), chlorophyll content (CC), chlorophyll fluorescence (CF), and water potential (ψa) were measured. Additionally, temperature, relative humidity, light intensity, and canopy temperature were recorded. Heat treatment advanced all phenological stages by approximately two weeks, increased SG and NS, and reduced gs and ψa during the hottest months. CC and CF remained unaffected. The treatment also resulted in lower yields, reduced acidity, and increased °Brix in both seasons. Overall, rising temperatures due to climate change advance the phenological phases of Malbec, Syrah, and Bonarda, leading to lower yields, higher °Brix, and lower acidity, although physiological variables remained largely unchanged. Full article

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive phenology metrics from bi-monthly PlanetScope Normalized Difference Vegetation Index (NDVI) data from 2017 to 2024. A repeated measures Analysis of Variance (ANOVA) assessed differences in phenology metrics between species, while a regression analysis modeled the Start of Season (SOS) and End of Season (EOS). The results show significant seasonal and species-level variations in phenology. Brachystegia spiciformis differed from other species in EOS, Length of Season (LOS), base value, and peak value. Surface solar radiation and skin temperature one month before SOS were key predictors of SOS, with an adjusted R-squared of 0.90 and a Root Mean Square Error (RMSE) of 13.47 for Brachystegia spiciformis. SOS also strongly predicted EOS, with an adjusted R-squared of 1 and an RMSE of 3.01 for Brachystegia spiciformis, indicating a shift in the growth cycle of tree species due to seasonal variability. These models provide valuable insights into potential phenological shifts in miombo species due to climate change. Full article

Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing. Full article

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO₂ concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security. Full article

Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3–6 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R² = 0.72–0.84) and Unit 2 (R² = 0.78–0.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R² = 0.56–0.72) and Unit 2 (R² = 0.36–0.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production. Full article

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks. Full article

The advent of precision agriculture has highlighted the necessity for the careful determination of crop phenology at increasingly smaller scales. Although remote sensing technology is extensively employed for the monitoring of crop growth, the acquisition of high-precision phenological data continues to present a significant challenge. This study, conducted in Youyi County, Shuangyashan City, Heilongjiang Province, China, employed time-series spectral index data derived from Sentinel-2 remote sensing images to investigate methodologies for the extraction of pivotal phenological phases during the primary growth stages of maize. The data were subjected to Savitzky–Golay (S-G) filtering and cubic spline interpolation in order to denoise and smooth them. The combination of dynamic thresholding with slope characteristic node recognition enabled the successful extraction of the jointing and tasseling stages of maize. Furthermore, a comparison of the extraction of phenophases based on the time-series curves of the NDVI, EVI, GNDVI, OSAVI, and MSR was conducted. The results showed that maize exhibited different sensitivities to the spectral indices during the jointing and tasseling stages: the OSAVI demonstrated the highest accuracy for the jointing stage, with a mean absolute error of 3.91 days, representing a 24.8% improvement over the commonly used NDVI. For the tasseling stage, the MSR was the most accurate, achieving an absolute error of 4.87 days, with an 8.6% improvement compared to the NDVI. In this study, further analysis was conducted based on maize cultivation data from Youyi County (2021–2023). The results showed that the maize phenology in Youyi County in 2021 was more advanced compared to 2022 and 2023, primarily due to the higher average temperatures in 2021. This study provides valuable support for the development of precision agriculture and maize phenology monitoring and also provides a useful data reference for future agricultural management. Full article

Heat velocity (V_h) is a key metric to estimate sap flow which is linked to transpiration rate and is commonly measured using thermocouples implanted in plant stems or tree trunks. However, measuring transpiration rates in the Gramineae family, characterized by thin and hollow stems, is challenging. Commercially available sensors based on the measurement of heat velocity can be unaffordable, especially in developing countries. In this work, a real-time heat pulse flux monitoring system based on the heat ratio approach was configured to estimate heat velocity in wheat (Triticum durum L.). The heat velocity sensors were designed to achieve optimal performance for a stem diameter smaller than 5 mm. Sensor parameterization included the determination of casing thermal properties, stabilization time, and time to achieve maximum heat velocity which occurred 30 s after applying a heat pulse. Heat velocity sensors were able to track plant water transport dynamics during phenological stages with high crop water demand (milk development, dough development, and end of grain filling) reporting maximum V_h values in the order of 0.004 cm s⁻¹ which scale to sap flow rates in the order of 3.0 g h⁻¹ comparing to reports from other methods to assess sap flow in wheat. Full article

Floral and vegetative responses of the strawberry (Fragaria x ananassa Duch.) to specific light wavelengths are not well documented. LED lights make it feasible for precise exposure to specific wavelengths during a 24 h cycle to alter growth responses regulated by phytochromes and cryptochromes and thereby potentially enhance fruit productivity in both a controlled environment and field systems or to enhance stolon production for controlled environment propagation. This research developed a systematic method to assess the effects of supplemental, low-irradiance LED lighting on strawberry flowering and vegetative biology. Growth of the long-day F1 seed-propagated cultivar ‘Soraya’ was evaluated during and following 6 or 12 weeks of exposure to supplemental red (660 nm), far-red (730 nm), blue (454 nm), or incandescent lighting at various times during the dark period of a 24 h cycle under a 10 h non-inductive photoperiod at non-inductive temperatures (>27/18 °C, day/night). Treatment effects were monitored via flower mapping and phenology during treatment, field and greenhouse production after treatment, and floral scores derived by ranking treatment effects within the evaluation method and then combining them into a single, simple score. The most promising treatment for enhancing the floral nature of plug plants was exposure to far-red + red light as a 5 h night interruption. This treatment increased inflorescence production in the greenhouse by 285% and resulted in multi-branched, floral plants with the potential for enhancing yield in either greenhouse or field production. Greenhouse runner production increased by 483% following exposure to incandescent lighting at the beginning of the dark period; thus, this treatment or one using a spectral distribution similar to incandescent may be suitable for enhancing vegetative propagation in controlled environments. Full article