Search Results (807)

High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios. Full article

Since the 1980s, earlier European springs have led to the earlier arrival of migrant passerines. We predict that arrival is related to a suite of climate indices operating during the annual cycle (breeding, autumn migration, wintering, spring migration) in Europe and Africa over the year preceding arrival. The climate variables include the Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO). Furthermore, because migrants arrive sequentially from different wintering areas across Africa, we predict that relationships with climate variables operating in different parts of Africa will change within the season. We tested this using daily ringing data at Bukowo, a spring stopover site on the Baltic coast. We calculated an Annual Anomaly (AA) of spring passage (26 March–15 May, 1982–2024) for four long-distance migrants (Blackcap, Lesser Whitethroat, Willow Warbler, Chiffchaff). We decomposed the anomaly in two ways: into three independent main periods and nine overlapping periods. We used multiple regression to explore the relationships of the arrival of these species at Bukowo. We found sequential effects of climate indices. Bukowo is thus at a crossroads of populations arriving from different wintering regions. The drivers of phenological shifts in passage of wide-ranging species are related to climate indices encountered during breeding, wintering, and migration. Full article

Spodoptera eridania (Stoll, 1781) (Lepidoptera: Noctuidae) is an important pest with a broad host range and growing relevance due to its high dispersal capacity, recent invasions into Africa and Asia, and documented resistance to biological insecticides. Here, we assessed S. eridania flight phenology and seasonal dynamics in the Florida Panhandle, using pheromone trapping data to evaluate population trends and environmental drivers. Moths were collected year-round, showing consistent patterns across six consecutive years, including two distinct annual flight peaks: an early crop season flight around March, and a more prominent flight peak during September–October. Moth abundance followed a negative quadratic relationship with temperature, with peak activity occurring between 15 °C and 26 °C. No significant relationship was found with precipitation or wind. These results underscore the strong influence of abiotic factors, particularly temperature, on seasonal abundance patterns of this species. Our findings offer key insights by identifying predictable periods of high pest pressure and the environmental conditions that drive population increases. Understanding the flight phenology and behavior of this species provides an ultimate contribution to the development of effective IPM and insect resistance management (IRM) programs, promoting the development of forecasting tools for more effective, timely pest management interventions. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

Mango is highly sensitive to heat stress, which directly affects the yield and quality. The extreme heat waves of 2024, with temperatures reaching 41–47 °C over 25 days, caused significant impacts on sensitive cultivars. The impact of heat waves on ten commercial cultivars from subtropical regions viz.,‘Dashehari’, ‘Langra’, ‘Chausa’, ‘Bombay Green’, ‘Himsagar’, ‘Amrapali’, ‘Mallika’, ‘Sharda Bhog’, ‘Kesar’, and ‘Rataul’, and thirteen selected elite hybrids H-4208, H-3680, H-4505, H-3833, H-4504, H-1739, H-3623, H-1084, H-4264, HS-01, H-949, H-4065, and H-2805, is reported. The predominant effects that were observed include the following: burning symptoms or blackened tips, surrounded by a yellow halo, with premature ripening in affected parts and, in severe cases, tissue mummification. Among commercial cultivars, viz., ‘Amrapali’ (25%), ‘Mallika’ (30%), ‘Langra’ (30%), ‘Dashehari’ (50%), and ‘Himsagar’ and ‘Bombay Green’ had severe impacts, with ~80% of fruits being affected, followed by ‘Sharda Bhog’. In contrast, mid-maturing cultivars like ‘Kesar’, ‘Rataul’, and late-maturing elite hybrids, which were immature during the stress period, showed no symptoms, indicating they are tolerant. Biochemical analyses revealed significantly elevated total soluble solids (TSS > 25 °B) in affected areas of sensitive genotypes compared to non-affected tissues and tolerant genotypes. Aroma profiling indicated variations in compounds such as caryophyllene and humulene between affected and unaffected parts. The study envisages that the phenological maturity scales are indicators for the selection of climate-resilient mango varieties/hybrids and shows potential for future breeding programs. Full article

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions. Full article

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental stresses remains limited. This study includes an analysis of selected meteorological and flowering data for Olea europaea L. “Istrska belica” to evaluate the use of a chilling and forcing model for a better understanding of flowering time dynamics under a changing climate. The flowering process is influenced by high diurnal temperature ranges (DTRs) during the pre-flowering period, resulting in earlier flowering. Despite annual fluctuations due to various climatic factors, an increase in DTRs has been observed in recent decades, although the mechanisms by which olive trees respond to high DTRs remain unclear. The chilling requirements are still well met in the region (1500 ± 250 chilling units), although their total has declined over the years. According to the Chilling Hours Model, chilling units—referred to as chilling hours—represent the number of hours with temperatures between 0 and 7.2 °C, accumulated throughout the winter season. Growing degree hours (GDHs) are strongly correlated with the onset of flowering. These results suggest that global warming is already affecting the synchrony between olive tree phenology and environmental conditions in the northern Mediterranean and may be one of the reason for the green drop. Full article

Understanding how flowering phenology varies across spatial scales is essential for assessing plant responses to environmental heterogeneity under climate change. In this study, we investigated the flowering phenology of the plant species Taraxacum officinale across five sites in an agricultural region of Wanju, Republic of Korea. Each site contained five 1 m × 1 m quadrats, where the number of flowering heads was recorded at 1- to 2-day intervals during the spring flowering period (February to May). We applied the nlstimedist package in R to model flowering distributions and to estimate key phenological metrics including flowering onset (5%), peak (50%), and end (95%). The results revealed substantial variation in flowering timing and duration at both the intra-site (quadrat-level) and inter-site (site-level) scales. Across all sites, the mean onset, peak, end, and duration of flowering were day of year (DOY) 89.6, 101.5, 117.6, and 28.0, respectively. Although flowering onset showed relatively small variation across sites (DOY 88 to 92), flowering peak (DOY 97 to 108) and end dates (DOY 105 to 128) exhibited larger differences at the site level. Sites with dry soils and regularly mowed Zoysia japonica vegetation with minimal understory exhibited shorter flowering durations, while those with moist soils, complex microtopography, and diverse slope orientations showed delayed and prolonged flowering. These findings suggest that microhabitat variability—including landform type, slope direction, soil water content, and soil temperature—plays a key role in shaping local flowering dynamics. Recognizing this fine-scale heterogeneity is essential for improving phenological models and informing site-specific climate adaptation strategies. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. The SFSIs were derived from composites of S2 multispectral images obtained with Google Earth Engine (GEE), processed using different techniques, for periods of 30, 60 and 90 days. Field verification was conducted through stratified random sampling by severity class on 100 circular plots of 707 m², where immediate post-fire effects were evaluated for five strata, including the canopy scorch in overstory (OCS)—divided in canopy (CCS) and subcanopy (SCS)—understory (UCS) and soil burn severity (SBS). Best fits were obtained with relative, phenologically corrected indices of 60–90 days. For canopy scorch percentage prediction, the indices RBR3c and RBR5n, using NIR (bands 8 and 8a) and SWIR (band 12), provided the best accuracy (R² = 0.82). SBS could be best mapped from RBR1c (using 11 and 12 bands) with relatively acceptable precision (R² = 0.62). Our results support the feasibility to separately map OCS and SBS from S2, in relatively open oak–pine seasonally dry forests, potentially supporting post-fire management planning. Full article

Habitat manipulation through non-crop vegetation management is a strategy in conservation biological control, and using native plants is attractive because they can also help in biodiversity conservation. The potential for nectar provision of 13 flowering species native to Chile, and two introduced, was evaluated considering Mastrus ridens (Hymenoptera: Braconidae). Nectar availability was studied through flower phenology, accessibility through flower and parasitoid morphology, and suitability through longevity when exposed to nectar solutions or cut flowers. Most species had long flowering periods, potentially making nectar available when adults are active, but they differed in nectar accessibility and profitability. Of the 13 native species, nectar was easily accessible for M. ridens in Cistanthe grandiflora, Sphaeralcea obtusiloba, Andeimalva chilensis, and Lycium chilense. None of the nine native species tested with nectar solutions increased longevity, but with cut flowers, parasitoids lived longer with the natives Teucrium bicolor and S. obtusiloba, and the introduced Fagopyrum esculentum, making them candidates for M. ridens conservation. Females lived longer with cut flowers of T. bicolor and S. obtusiloba than with their nectar solutions. In conclusion, using the native flowering species Teucrium bicolor and Sphaeralcea obtusiloba in agroecosystems can serve biological control and biodiversity conservation. Full article

Blueberry is a high-value fruit crop in the United States, with Georgia and Florida serving as important early-season production regions. In these areas, several thrips species (Thysanoptera: Thripidae), including Frankliniella tritici (Fitch), Frankliniella bispinosa (Morgan), and Scirtothrips dorsalis (Hood), have emerged as economically significant pests. While F. tritici and F. bispinosa primarily damage floral tissues, S. dorsalis targets young foliage. Their rapid reproduction, high mobility, and broad host range contribute to rapid population buildup and complicate the management programs. Species identification is often difficult due to overlapping morphological features and requires the use of molecular diagnostic tools for accurate identification. Although action thresholds, such as 2–6 F. tritici per flower cluster, are used to guide management decisions, robust economic thresholds based on yield loss remain undeveloped. Integrated pest management (IPM) practices include regular monitoring, cultural control (e.g., pruning, reflective mulch), biological control using Orius insidiosus (Say) and predatory mites, and chemical control. Reduced-risk insecticides like spinetoram and spinosad offer effective suppression while minimizing harm to pollinators and beneficial insects. However, the brief flowering period limits the establishment of biological control agents. Developing species-specific economic thresholds and phenology-based IPM strategies is critical for effective and sustainable thrips management in blueberry cropping systems. Full article

Urban environments pose challenges for pollinators due to habitat loss and limited floral resources. However, green infrastructure, particularly street and ornamental trees, can play a critical role in supporting urban pollinator communities. In this study, we evaluated nectar volume, sugar content, and amino acid composition across eight urban tree species commonly planted in South Korea. Using standardized productivity metrics at the flower, tree, and hectare scales, we compared their nutritional contributions. Our results revealed substantial interspecific differences in nectar quantity and composition. Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda exhibited high nectar yields or amino acid productivity, whereas species such as Cornus kousa, though lower in nutritional yield, may offer complementary value due to their distinct flowering periods or other phenological traits. These findings underscore the importance of selecting tree species not only for aesthetic value but also for ecological function, providing an evidence-based approach to pollinator-friendly urban biodiversity planning and landscape management. Full article

Flowering represents the most important process in the reproductive stage of fruit trees, including olive trees. Previous studies have demonstrated that the genotype–environment interaction (GEI) has a considerable influence on olive flowering time. This study investigated the GEI and genetic parameters influencing olive flowering phenology in Southwestern China (a non-Mediterranean region), using multi-trait-based stability selection methods. Sixteen olive cultivars from five countries were evaluated over two years in two distinct climatic regions of Southwestern China. Flowering phenology was assessed based on three parameters: full-bloom date (FBD), flowering-period length (FP), and full-bloom-period length (FBP). In the analyses, the best linear unbiased prediction (BLUP) to predict genetic value and genotype + genotype by environment interaction (GGE) biplot methods to visualize and assess stability and performance were employed across four environments. The results showed that genotype, environment, and GEI had highly significant effects on flowering traits, with GEI accounting for 54.12% to 89.62% of the variance. Heritability values were low (0.0589 to 0.262), indicating that genetic factors had limited control over flowering phenology compared to environmental factors. A stability analysis using a mean performance and stability (MPS) index identified genotypes with earlier flowering dates and longer flowering periods. Multi-trait selection using a multi-trait mean performance and stability (MTMPS) index further highlighted six superior genotypes with high performance and stability across environments. The findings emphasize the critical role of environmental factors on olive flowering phenology, highlighting the challenges in breeding for stable flowering traits. This study demonstrates the effectiveness of multi-trait selection methods in identifying genotypes with superior performance and stability under different environmental conditions. These results provide valuable insights for olive breeding programs, particularly in non-Mediterranean regions, suggesting that targeted selection and multi-trait evaluation could enhance the adaptability and productivity of olive cultivars under changing climatic conditions. Full article

Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. Using phenological information and multi-temporal feature-selected imagery, this paper proposed a large-scale apple orchard mapping method based on the AOCF-SegNet model. First, to distinguish apples from other crops, phenological information was used to divide time periods and select optimal phases for each spectral feature, thereby obtaining spectral features integrating phenological and temporal information. Second, semantic segmentation models (FCN-8s, SegNet, U-Net) were com-pared, and SegNet was chosen as the base model for apple orchard identification. Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. The results from mapping the Yantai apple orchards indicate that AOCF-SegNet achieved strong segmentation performance, with an overall accuracy of 89.34%. Compared to the SegNet, U-Net, and FCN-8s models, AOCF-SegNet achieved an improvement in overall accuracy by 3%, 6.1%, and 9.6%, respectively. The predicted orchard area exhibited an approximate area consistency of 71.97% with the official statistics. Full article