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Figure 4 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 4. The log response ratio for germination and seedling radicle length of broadleaf (green dots/line) and grass (red dots/line) weed species as a function of water-stress intensity. Water stress increased as solution osmotic potential (ψsolution) decreased and vice versa.The subgroups for germination are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −0.8, −0.8 to −1.0, −1.0 to −1.4, and <−1.4 MPa, while the subgroups for radicle length are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −1.0, and <−1.0 MPa. Only ψsolution-based studies were used in this analysis. For each subgroup, the solid dots and lines represent mean effect sizes and their corresponding 99% confidence intervals (CIs).The mean effect sizes were considered significantly different when their 99% CIs did not include zero. Similarly, the water-stress effects were significantly different for each subgroup and among weed types only when their 99% CIs did not overlap with one another. The fitted lines represent a four-parameter logistic regression model, and the coefficients of the models are presented in Table 2.
Figure 3 in New directions in weed management and research using 3D imaging
Figure 3. Data pipeline for calculating canopy height and estimating biomass in the field using red, green, and blue (RGB) images and depth data.
Figure 1 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Page and McKenzie 2021) flow diagram highlighting the selection procedure of 86 scientific published papers included in the meta-analysis.
Figure 8 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 8. Results from the sensitivity analysis depicting variations in the overall effect size estimates (mean ± 95% confidence intervals [CIs]) of water-stress effects on (A) branches/tillers per plant, (B) leaves per plant, (C) inflorescences per plant, (D) seeds per plant, (E) total biomass, (F) root biomass, (G) shoot biomass, and (H) root:shoot ratio, when a particular study is omitted from the analysis. The vertical black solid and dashed lines represent overall effect sizes (mean ± 95% CIs) with all studies included.
Figure 6 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 6. Density plots depicting the distribution of the individual effect sizes for all 12 response variables considered in this meta-analysis: (A) weed seed germination/emergence; (B) radicle/root length, plant height, and leaf area; (C) branches/tillers per plant, leaves per plant, inflorescences per plant, and seeds per plant; and (D) total biomass, root biomass, shoot biomass, and root:shoot ratio.
Figure 2 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 2. Overall water-stress effects on weed germination/emergence, growth characteristics, and seed production. The vertical black dashed line represents zero effect. The black dots are overall mean effect sizes, and the black lines are 95% confidence intervals (CIs). The values in parentheses are the number of observations followed by the number of studies for each pair-wise comparison. The mean effect sizes were considered significantly different when their 95% CIs did not include zero.
Figure 2 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 2. Cumulative percent shatter over four time periods (soybean physiological maturity, maturity þ 2 wk,maturity þ 3 wk, maturity þ 4 wk) for each species.The darker the bar, the greater percent of sampled site-years that corresponded to the percent shatter value. This normalizes across species with different sampling efforts. Species sampled in just a single site-year are indicated by a single black square, which represents 100% of the sampling effort. Species are denoted by their EPPO codes.
Figure 3 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 3. Cumulative percent seed shatter for all species from planting date to soybean physiological maturity (black vertical line) for each state in 2016 and 2017. Species are denoted by their EPPO codes.
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity þ2 wk on October 24 in the state that year. Hence only data from maturity þ3 wk are for Arkansas for 2017.)
Figure 5 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 5. The log response ratio for weed growth characteristics (plant height, leaf area, branches/tillers per plant, leaves per plant,root biomass, shoot biomass, and root:shoot ratio) and seed production (inflorescences per plant and seeds per plant) as a function of water-stress intensity. Water stress increased as soil moisture (% field capacity) decreased and vice versa. The green and red dots represent broadleaf and grass weed species, respectively. The solid black points and the lines represent mean effect sizes and their 99% confidence intervals (CIs) for low (>60%), moderate (30%–60%), and severe (<30% field capacity) water-stress subgroups. The mean effect sizes were considered significantly different when their 99% CIs did not include zero. Similarly, the water-stress effects were significantly different for each subgroup and among weed types only when their 99% CIs did not overlap with one another.
Figure 2 in New directions in weed management and research using 3D imaging
Figure 2. Red,green,and blue (RGB) image of soybeans and weeds (A) and corresponding 3D point cloud reconstruction (B). Lower panels show point cloud reconstructions from different angles,including a top view (C), top view offset 45° from vertical (D), front view (E), under canopy and offset 45° (F), directly under canopy (G), facing canopy from behind (H), facing canopy offset 45° right (I), side view (J), and facing canopy offset 45° left (K).
Figure 1 in New directions in weed management and research using 3D imaging
Figure 1. Use of images taken from different angles to create a 3D reconstruction in structure-from-motion (SfM; top) vs. stereo-vision photogrammetry (bottom).
Figure 2 in Exploring the potential of electric weed control: a review
Figure 2. Schematic representation of electric weed control technology using the continuous electrode–plant contact method; produced by Guanhao Cheng and adapted from Vigneault and Benoit (2001) and Bauer et al. (2020). The process starts when the electrode initially contacts the plant (ti). Electricity is then transferred through the plant's foliage and into the roots and soil before returning to the machine via a ground-contact device, forming a complete electrical circuit. Each object through which the current passes is depicted as having individual resistance, such as the target vegetation (Rv), soil and machinery (Rs), or parallel objects (Rp). The circuit continues over time until the final point of electrode–plant contact (tf). The efficacy of weed control depends on contact time (tc), which is the duration of the electrode's contact with the plant. Contact time is determined by the electrode's effective contact surface, the distance traveled while the electrode is in contact with the plant (Se), which will always be greater than the electrode's actual contact surface (Sa).
Figure 3 in Exploring the potential of electric weed control: a review
Figure 3. Representative diagram of the theoretical relationship between electrical flow and plant electrical resistance (Rv) when using electric weed control measures. This diagram is not to scale and was produced by Guanhao Cheng from the information presented in Diprose et al. (1980) and Diprose and Benson (1984).
Figure 4 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 4. Effects of the interaction of weed establishment time and distance from the crop row on Amoronthus polmeri dry weight before soybean harvest. Vertical bars represent ± standard error of the mean (SE2014 = 1.27; SE2015 = 0.74) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 3 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 3. Effects of the interaction of weed establishment time and distance from the crop on Amoronthus polmeri plant height at harvest. Vertical bars represent ± standard error of the mean (SE2014 = 4.68; SE2015 = 3.14) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 7 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 7. Relationship between ground cover and extinction coefficient for each sampling date (n = 12 plots) throughout the 2014 growing season. WAE, weeks after soybean emergence.
Figure 2 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 2. Soybean and Amoronthus polmeri (AMAPA) height (averaged across distance from the crop) at 0, 1, 2, 4, 6, and 8 wk after soybean emergence (WAE) (i.e., AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8, respectively). Vertical bars represent ± standard error of the mean from the analysis for comparisons within each sampling date (i.e., n = 12 for 0 WAE, 24 for 1 WAE, etc.).
Figure 9 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 9. Effects of weed establishment time on soybean yield averaged across Amoronthus polmeri distances from the crop row. Dashed lines indicate the confidence intervals at 95% confidence level (sample size n = 72). WAE, weeks after soybean emergence.
Figure 1 in Weed Seedbank Management: Revisiting How Herbicides Are Evaluated
Figure 1. Scatter plot of principal component analysis indicating the (dis) associations between visual rating, weed seed production, weed biomass and density as a result of meta-analysis of selected research articles in Weed Science and Weed Technology.
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.