Phenological Stages Research Articles

Evaluation of herbicides for Geyer larkspur (Delphinium geyeri) control

Abstract Geyer larkspur is a native perennial forb toxic to cattle. Herbicide control of Geyer larkspur is variable and most likely attributable to the growth stage of the plant when herbicide is applied. The objectives of this study were to (1) evaluate aminopyralid, aminopyralid + florpyrauxifen-benzyl, aminopyralid + 2,4-D, aminopyralid + metsulfuron-methyl, metsulfuron-methyl, triclopyr, and triclopyr + 2,4-D for efficacy in controlling Geyer larkspur; (2) determine if plant growth stage (vegetative or flowering) at the time of herbicide application influences herbicide effectiveness; and (3) determine whether herbicide treatment alters norditerpenoid alkaloid content of Geyer larkspur. Plots were established in eastern Wyoming (2021) and northern Colorado (2022). Herbicide application at the different phenological stages did not affect Geyer larkspur density at the Wyoming site (P = 0.1065; data not shown). Geyer larkspur density at the Wyoming site was reduced by all herbicide treatments 1 year after treatment (YAT) at the vegetative stage and by all herbicides except for triclopyr 2 YAT (P = 0.0249). At the flowering stage, Geyer larkspur density was reduced by all herbicides except metsulfuron-methyl, triclopyr, and triclopyr + 2,4-D at 1 YAT and triclopyr and triclopyr + 2,4-D at 2 YAT. In contrast, there were no differences in Geyer larkspur density across treatments at the Colorado site (P = 0.9621). Precipitation was below average several months prior to herbicide application, which may have affected herbicide effectiveness. The metsulfuron-methyl treatment resulted in the highest total alkaloid concentrations of Geyer larkspur at the vegetative stage and the lowest concentrations at the flowering stage at the Wyoming site. Control efforts of Geyer larkspur in semiarid rangelands can be effectively accomplished with aminopyralid herbicides across vegetative and flowering growth stages provided environmental conditions prior to herbicide application are sufficient for plant growth and uptake of the herbicide.

Evaluation of resistance to charcoal rot in different phenological stages of the common bean crop

Evaluation of resistance to charcoal rot in different phenological stages of the common bean crop

Characterization of Shrub Fuel Structure and Spatial Distribution Using Multispectral and 3D Multitemporal UAV Data

Shrubland vegetation plays a crucial role in ecological processes, but its conservation is facing threats due to climate change, wildfires, and human activities. Unmanned Aerial Vehicles (UAVs), or ‘drones’, have become valuable tools for detailed vegetation mapping, providing high-resolution imagery and 3D models despite challenges such as legal restrictions and limited coverage. We developed a methodology for estimating vegetation height, map vegetation classes, and fuel models by using multitemporal UAV data (imagery and point clouds from the imagery) and other ancillary data to provide insights into habitat condition and fuel characteristics. Two different random forest classification methods (an object- and a pixel-based approach) for discriminating between vegetation classes and fuel models were developed and compared. The method showed promise for characterizing vegetation structure (shrub height), with an RMSE of less than 0.3 m and slight overestimation of taller heights. For discriminating between vegetation classes and fuel models, the best results were obtained with the object-based random forest approach, with overall accuracies of 0.96 and 0.93, respectively. Although some difficulties were encountered in distinguishing low shrubs and brackens and in distinguishing low-height fuel models due to the spatial mixture, accurate results were obtained for most classes. Future improvements include refining terrain models by including data acquired with UAV aerial scanners and exploring different phenological stages and machine learning approaches for classification.

Influence of sowing dates on the vegetation period and productivity of common bean under the conditions of the forest-steppe of the Priobye region

This study of the adaptability of common bean in the forest-steppe region of Priobye analyzes the differences in the duration of the growing season and productivity between early and late sowing. The object of the research is the medium-early variety “Solnyshko.” The work was conducted at the experimental field “Sad Michurintsev” of Novosibirsk State Agrarian University from 2022 to 2024. During the growing season, the dates of phenophase onset, meteorological factors (average daily temperature, sum of active temperatures, and precipitation), and productivity elements (mass and number of pods per plant, mass of one pod, yield of green beans) were recorded. IBM SPSS Statistics software was used for variance analysis and correlation analysis. The conducted research showed that the medium-early variety “Solnyshko” is suitable for sowing in the third decade of May in the forest-steppe region of Priobye. If the sowing period is delayed, the growing season is shortened, with the coefficient of variation for the duration of the “sowing to technical maturity” period being 52.6%. The average daily temperature negatively correlates (r = -0.685) with the duration of the phenological stage in the early growing season of beans, while it positively correlates (r = 0.584–0.682) with the duration of the phenological stage in the late period. The sum of active temperatures and precipitation has a positive correlation with the duration of phenological periods. The mass and number of pods per plant, as well as the yield of green beans, decreased in the third sowing period by 35.9–38.4, 28.0–34.8, and 36.0–37.8 % (p < 0.001 between sowing dates, p < 0.05 between years). The stage “bud formation – flowering” may be a key period influencing the yield of beans.

Monitoring the Progression of Downy Mildew on Vineyards Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Data

Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness.

Seasonal dynamics of non-structural carbohydrates in new twigs and old branches of Vitex negundo Var. heterophylla under three densities of Robinia pseudoacacia forests

Non-structural carbohydrates (NSCs) are vital for plant growth, with their levels influenced by light intensity and seasonal changes. However, research on how varying light conditions due to forest density and seasons affect carbon allocation in new twigs and old branches is scarce. Vitex negundo var. heterophylla is a leading shrub species in the warm temperate zone’s shrub layer. In this study, we conducted a detailed sampling of V. negundo var. heterophylla branches, differentiating new twigs and old branches across phenological stages under three densities of Robinia pseudoacacia forests. Our sampling schedule was as follows: March (dormant period), May (sprouting period), July (leaf spreading period), September (flowering and fruiting period), and December (deciduous period). The results showed that the seasonal patterns of carbon allocation in the new twigs and old branches were largely in harmony. The starch concentration in the old branches under the high density was significantly lower than in the other two densities during the growing season, but the NSC concentration in December remained at a high level and did not significantly decrease. These indicated even though the light environment was unfavorable to understory V. negundo var. heterophylla during the growing season, cold tolerance in December was not inhibited. And the concentrations of soluble sugars and starch in the new twigs were typically higher than those found in the old branches. This dynamic suggests a strategic prioritization of resources to fuel the growth and development of the plant during the current year. Findings from this study not only contribute to our understanding of carbon allocation strategies in V. negundo var. heterophylla but also provide critical insights for managing and predicting the resilience of warm temperate shrub ecosystems to environmental change.

Ecological behavior of Drechslera/Helminthosporium spores in a wheat crop in NW Spain

Abstract The objective of this study was to determine the presence of Drechslera/Helminthosporium in the air of a wheat crop in the NW Iberian Peninsula and its relationship with meteorological variables. Research was conducted in A Limia, Ourense, during the wheat growing seasons from 2021 to 2023. The Lanzoni VPPS-2010 spore trap was used to collect the spores, which were identified by optical microscopy. The phenological stages of the wheat crop were classified according to the BBCH scale, and the spore concentrations in each phenophase were analyzed. Meteorological variables were provided by a meteorological station located near the spore trap. In 2021, monitoring was done for 94 days, in 2022 for 89 days and in 2023 for 78 days. Spores were detected during most phenological stages, mainly during 11–20 h. Temperature and sunshine hours were the variables that positively influenced the variations in Drechslera/Helminthosporium concentrations in the air in 2021 and 2023, while in 2022, relative humidity, rainfall and leaf moisture were the factors that had the most influence. The maximum temperature and leaf moisture of the previous days were the variables that best predicted the concentrations of these spores for 2022. This is the first investigation in Spain that reports the presence of the sporal type Drechslera/Helminthosporium in the air during all phenological stages of wheat cultivation, and provides useful information for the development of strategies to prevent the diseases they cause.

Influence of row spacing and harvest date on Cephalaria joppensis performance as a forage crop

Abstract Cephalaria joppensis Rchb. (CJ) has recently been domesticated as a new forage crop in Israel. Feeding trials with dairy cows and sheep have shown that CJ silage can replace wheat silage for these animals. The current work tested the effect of row spacing on the growth, yield, chemical composition and digestibility of CJ for forage. Two field trials were performed at a research farm in Acre to test three row spacings: (T1) 16 cm between rows (narrow row spacing), (T2) pairs of adjacent (16 cm apart) rows with 32 cm spacing between pairs (alternated row spacing) and (T3) 32 cm between rows (wide row spacing). T1 row spacing had the advantage of a better area coverage rate, which may be beneficial at the beginning of growth, reflected by the rapid establishment of the plants in the field, as well as in the competition with weeds. The growth rate was more significant in T2 and T3 row spacings. In the first year, a higher yield was obtained for T2 and T3 row spacings; in the second year, no differences in yield were found among the row spacing treatments. Row spacing did not affect the plant’s digestibility or chemical nutrition for ruminants. There was no uniformity in plant quality among the cultivars between the two years. Thus, we should define the phenological stages of Cephalaria joppensis and their relationship to nutritional quality at different sites having different climate conditions, in both small and semi-commercial plots.

Olive production in the 21st century will be threatened by water stress and declining solar activity

The production of olive oil, a cornerstone of Mediterranean diet, faces important threats from recurring droughts and the anticipated decline in cyclical solar activity. These escalating challenges cast uncertainty on the long-term sustainability of olive-growing regions. Here we draw on 8000 years of olive pollen data - closely related to flowering, fertilization and fruit production - to identify the key long-term determinants influencing olive yields. Our findings indicate that pollen production, and therefore yields, are primarily driven by photosynthesis, which is influenced not only by temperature and CO2 levels, but also by precipitation during olive phenological stages and changes in the radiation balance, rather than by variations in solar energy alone. Changes in photosynthetic activity emerge as a critical determinant of the future of the Mediterranean olive oil economy and have major implications for food security in the region.

On the quality of USDA gridded crop condition layers

AbstractPrecise, accurate, and reliable crop condition data continues to be in demand for farmers, agribusiness, government agencies, agroclimatologists, and research institutions. This study evaluated the data quality of four major United States field crops: corn (Zea mays L.), cotton (Gossypium hirsutum L.), soybeans (Glycine max L.), and winter wheat (Triticum aestivum L.) from the USDA National Agricultural Statistics Service's (NASS) Gridded Crop Progress and Condition dataset. Upon aggregating the weekly 9 km gridded data to the county level (and further to the state and national level) over the 2015–2023 period, no statistically significant differences emerged between the gridded condition data and the tabular condition data from the USDA NASS Crop Progress and Condition Report (CPCR). In line with state and national‐level analyses, a strong linear relationship between crop conditions and yield existed at the county scale. County‐level crop condition ratings were a statistically significant covariate of yield during the critical reproduction period through harvest for 90% of corn, 78% of cotton, 90% of soybean, and 96% of winter wheat‐producing counties. In addition, intramonthly county‐level crop conditions changed accordingly based on the magnitude of temperature and precipitation anomalies during certain phenological stages. In at least 80% of counties for each respective crop, temperatures and precipitation were statistically significant covariates for crop condition changes. The relationships between USDA NASS gridded crop condition data, CPCR data, yield, and climate substantiate the utility and fidelity of this dataset as a representation of confidential crop condition reports, supporting its practical application in research and operational decision‐making.

Foliar application of microalgae extracts impacted the seed yield of white lupine (Lupinus albus)

ABSTRACT Algae are known for their rich nutrients, which can enhance productivity of agricultural crops, making them valuable biostimulants. However, the use of microalgae for this purpose is still in its early stages, and there have been no studies conducted on lupine crops in Brazil so far. This study aimed to investigate the impact of microalgae extracts on the growth and productivity of white lupine. The experiment included four different microalgae extracts (Limnospira platensis, Nannochloropsis oculata, Tetradesmus obliquus, and Chaetoceros muelleri), applied either once or twice, along with a control group. The results showed that there was no interaction between the number of applications and the type of microalgae extract applied via foliar application in white lupine crops. The microalgae extracts did not have a significant impact on the 100-grain weight and yield of white lupine crops. The average height of white lupine plants at harvest was 117.04 cm, regardless of the treatment. However, there was a significant increase (13.12%) in the number of seeds per pod of white lupine with a single application of microalgae extract, regardless of the specific extract used. The height of the white lupine plants was not affected by the treatments during both the vegetative and reproductive phases. Further research is needed to optimise the bioactive extraction process, identify metabolites with biostimulant potential in the extracts, and determine the most effective application method (soil, seed, or foliar), as well as the optimal timing and frequency of foliar applications based on the crop's phenological stages. Highlights First report of microalgae extracts used in Lupinus albus cultures in Brazil. Extracts of microalgae have no effect on the grain mass and productivity of white lupine. A single application of any microalgae extract enhanced the seed yield per white lupine pod.

A Spatial Multi-Criteria Decision-Making Approach to Evaluating Homogeneous Areas for Rainfed Wheat Yield Assessment

Rainfed wheat plays a vital role in global food security, particularly in regions where water availability is a limiting factor. Identifying homogeneous areas with a similar yield potential is essential for optimizing resource allocation, improving agricultural sustainability, and enhancing water resource management. Unlike previous studies that primarily focused on cropland suitability, this study presents an integrated approach to delineate homogeneous areas for the rainfed wheat yield using advanced mechanistic analysis and multi-criteria decision-making techniques. Additionally, it examines the homogeneity of these areas in terms of the actual yield relative to the potential yield. Kurdistan province in Iran was selected as the study area. Key phenological stages of wheat growth—germination, flowering, and seed filling—were determined using a day-growth model. A set of four primary criteria—precipitation, temperature, soil properties, and topography—along with twenty sub-criteria were selected based on expert knowledge and previous research. The Fuzzy-AHP method was employed to assign weights to each factor, and a weighted linear combination approach was used to generate a final classification map. The results categorized the study area into five suitability classes: currently unsuitable (N2 and N1), somewhat suitable (S3), moderately suitable (S2), and very suitable (S1), in accordance with the FAO standard framework. These classifications highlighted significant yield variations among the zones. The findings revealed that the highest and lowest average rainfed wheat yields were observed in classes S1 and N2, respectively, with yield-to-potential yield ratios ranging from 75% in S1 to 20% in N2. This research underscores the potential of spatial analysis in enhancing precision agriculture and water resource management, contributing to more resilient food production systems in water-scarce regions.

Widespread Advances in Corn and Soybean Phenology in Response to Future Climate Change Across the United States

AbstractCrop phenology regulates seasonal carbon and water fluxes between croplands and the atmosphere and provides essential information for monitoring and predicting crop growth dynamics and productivity. However, under rapid climate change and more frequent extreme events, future changes in crop phenological shifts have not been well investigated and fully considered in earth system modeling and regional climate assessments. Here, we propose an innovative approach combining remote sensing imagery and machine learning (ML) with climate and survey data to predict future crop phenological shifts across the US corn and soybean systems. Specifically, our projected findings demonstrate distinct acceleration patterns—under the RCP 4.5/RCP 8.5 scenarios, corn planting, silking, maturity, and harvesting stages would significantly advance by 0.94/1.66, 1.13/2.45, 0.89/2.68, and 1.04/2.16 days/decade during 2021–2099, respectively. Soybeans exhibit more muted responses with phenological stages showing relatively smaller negative trends (0.59, 1.08, 0.07, and 0.64 days/decade under the RCP 4.5 vs. 1.24, 1.53, 0.92, and 1.04 days/decade under the RCP 8.5). These spatially explicit projections illustrate how crop phenology would respond to future climate change, highlighting widespread and progressively earlier phenological timing. Based on these findings, we call for a specific effort to quantify the cascading effects of future phenology shifts on crop yield and carbon, water, and energy balances and, accordingly, craft targeted adaptive strategies.

Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods

Plant moisture content (PMC) serves as a crucial indicator of crop water status, directly affecting agricultural productivity, product quality, and the effectiveness of precision irrigation. Conventional methods for PMC assessment predominantly rely on destructive sampling techniques, which are labor-intensive and impede real-time monitoring. This study investigates silage maize cultivated in the Hexi region of China, leveraging multispectral data acquired via an unmanned aerial vehicle (UAV) to estimate PMC across different phenological stages. A stacked ensemble learning framework was developed, integrating Back Propagation Neural Network (BPNN), Random Forest Regression (RFR), and Support Vector Regression (SVR), with Partial Least Squares Regression (PLSR) employed for feature fusion. The findings indicate that incorporating vegetation indices into spectral variables significantly improved prediction performance. The standalone models demonstrated coefficient of determination (R2) values ranging from 0.43 to 0.69, with root mean square error (RMSE) spanning 0.61% to 1.43%. In contrast, the ensemble model exhibited superior accuracy, achieving R2 values between 0.61 and 0.87 and RMSE values from 0.54% to 1.38%. This methodology offers a scalable, non-invasive alternative for PMC estimation, facilitating data-driven irrigation optimization in regions facing water scarcity.

Enhancing Soybean Physiology and Productivity Through Foliar Application of Soluble Monoammonium Phosphate

Phosphorus (P) is essential for crop growth, but its complex behavior in tropical soils necessitates alternative management strategies, such as foliar supplementation. Foliar-applied nutrients act as biostimulants, enhancing stress tolerance and plant productivity. This study assessed the physiological responses of soybean to foliar application of soluble monoammonium phosphate (MAP; at a rate of 5 kg ha−1 each application) at different phenological stages (two during vegetative stages V4 and V6 and two during reproductive stages R1 and R3 or all four stages) across two growing seasons in tropical field conditions. Key parameters analyzed included leaf nutrient content, photosynthetic pigments, Rubisco activity, carbohydrate content, gas exchange (photosynthetic rate, stomatal conductance, transpiration, water use efficiency, and carboxylation efficiency), oxidative stress markers, and productivity indicators (100-grain weight and grain yield). MAP application improved all parameters, particularly at R1 and R3. Total chlorophyll increased by 29.2% at R1 and 30.0% when applied at all four stages, while the net photosynthetic rate rose by 15.8% and 18.4%, respectively. Water use efficiency improved by 20.0% at R1 and all four stages, while oxidative stress indicators, such as H2O2 levels, decreased. Rubisco activity increased most at R3 (46.0%) and all four stages (59.9%). Grain yield was highest with MAP spread at all four stages (12.3% increase), though a single application at R1 still boosted yield by 7.4%, compared to the control treatment.

Determination of Floristic Characteristics and Bulb Yield of Alternative Fragrant Narcissus Varieties under Bayındır Ecological Conditions

This research was conducted in the trial fields of Ege University Bayındır Vocational Training School from 2021 to 2023. The study investigated the adaptation of 13 different fragrant daffodil cultivars to the Bayındır region. Fifty bulbs were planted in each plot using a randomized plot experimental design. Phenological developmental stages, floristic characteristics, and bulb yield parameters were evaluated. The findings revealed significant variations among the cultivars. ‘Grand Soleil d’Or’ was the earliest emerging and flowering variety in both growing seasons. This cultivar also had the longest flowering period, while ‘Minnow’ and ‘White Cheerfulness’ exhibited the shortest. ‘Babymoon’ and ‘Cornish Dawn’ produced the highest number of flowers, while ‘Tripartite’ showed the lowest. In floral characterization, ‘Avalanche’ had the highest number of florets per stem, and ‘Rosemoor Gold’ had the largest flower diameter. ‘Geranium’, ‘Grand Soleil d’Or’, and ‘Cornish Dawn’ excelled in flower length and stem thickness. ‘Avalanche’ showed the highest performance in bulb yield, while ‘Tripartite’, ‘White Cheerfulness’, ‘Hillstar’, ‘Quail’, ‘Babymoon’, and ‘Minnow’ had low yields. In bulb number analysis, ‘Tripartite’ produced the highest count, though all bulbs remained small. Based on these results, ‘Cornish Dawn’ and ‘Garden Opera’ are recommended for both cut flower and bulb cultivation in the Bayındır region. The ‘Babymoon’ cultivar has potential for use in landscaping and as a potted ornamental plant due to its high flower count.

Vorax biofertilizer on off-season maize

The search for more sustainable agriculture has stimulated interest in organic fertilizers. The rising costs of chemical fertilizers and the many benefits identified by the use of biofertilizers have motivated this trend. However, there are many options on the market and assessing the impact of each on yield and crop development is therefore a key aspect in making the best choice. Vorax® Biostimulant is composed of glutamic acid, a source of carbohydrate absorbed by the plant. Therefore, the aim of this work was to evaluate the use of this biostimulant, extracted from Corynebacterium glutamicum, on corn for the off-season. Two experiments were carried out using Vorax® biofertilizer in different combinations of dose, number of applications and phenological stages in a randomized block design with four replications. It was found that when Vorax® is applied at V8 and at lower doses, with a maximum of two applications, there is a (non-significant) gain in yield and ear weight.

Spatial Distribution Pattern of Forests in Yunnan Province in 2022: Analysis Based on Multi-Source Remote Sensing Data and Machine Learning

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making.

Exploring the Hemp Virome and Assessing Hemp Germplasm for Resistance to an Emerging Pathogen

Hemp ( Cannabis sativa L., <0.3% tetrahydrocannabinol) as a crop has been widely adopted throughout the United States. However, there is little support or guidance on disease identification and management for growers. This study aimed to describe the diversity and distribution of viruses infecting hemp in Colorado and determine if there is genetic resistance to pathogens in hemp. In 2021 and 2022, leaf tissues from symptomatic and asymptomatic plants were sampled during three phenological stages (early vegetative, advanced vegetative, and mature flowering) in four regions of Colorado. High-throughput sequencing revealed a total of seven viruses across both years from 48 pooled samples: alfalfa mosaic virus, beet curly top virus (BCTV), cannabis cryptic virus, cannabis sativa mitovirus 1, grapevine line pattern virus, opuntia umbra-like virus, and tomato bushy stunt virus. These viruses had >97% nucleotide identity to their nearest GenBank accessions. As the growing season progressed, viral incidence and the number of viral species increased. We observed similar and unique viromes between individual cultivars from the same field. Given the prevalence of BCTV in the virome and its prevalence across the western United States, 13 genotypes of hemp were screened for resistance to this pathogen. Two genotypes (4587 and 4710) had a lower BCTV log copy number and disease index than others. Our study provides initial evidence of the diversity of viral communities in hemp across Colorado and provides impetus for developing accurate detection methods and screening for host resistance. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Variation Patterns and Climate-Influencing Factors Affecting Maximum Light Use Efficiency in Terrestrial Ecosystem Vegetation

Accurately understanding the changes in global light-response parameters (i.e., maximum light use efficiency, LUEmax) is essential for improving the simulation of terrestrial ecosystem’s photosynthetic carbon cycling under climate change, but a comprehensive understanding and assessments are still lacking. In this study, LUEmax was quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The results show that the LUEmax values of the same vegetation type varied across different climate types. More variable climates (e.g., polar and alpine climates) are associated with more significant fluctuations in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show more consistent LUEmax values. Within the same climate type, evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) generally exhibited higher LUEmax values in temperate and continental climates, whereas the LUEmax values of wetlands (WET) were relatively high in polar and alpine climates. The mechanisms driving variations in LUEmax across different vegetation types exhibited significant disparities under diverse environmental conditions. For ENF and DBF, LUEmax is predominantly influenced by temperature and radiation. In contrast, the LUEmax of GRA, WET, and croplands is more closely associated with vegetation indices and temperature factors. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems.