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422 results for “Weed”
Figure 1 in Critical period of weed control in an interseeded system of corn and alfalfa
Figure 1. Interseeded corn silage dry biomass yield as a percentage of the weed-free interseeded corn and alfalfa control over the critical duration of weedy treatments with differing leaf architecture, pendulum (black circles) or upright (green triangles), for 2019 (A) and 2020 (B). In weedy treatments, weeds emerged with the crop and were then removed at different dates,creating the critical timing of weed removal (CTWR;dashed line).In weed-free interseeded treatments,weeds were added later in the crop,creating the critical weed-free period (CWFP; solid line). An interseeded untreated and a weed-free check were included within these treatments. The CTWR based on a 5% acceptable yield loss, averaged over hybrids, is denoted by the dashed vertical line (black); the boxes denote the SEs of those estimates. The CWFP estimates are not shown, because they were greater than the harvest date. Points represent observed mean values; lines represent the fitted models calculated using the DRC package in R (R Core Team 2020).
Figure 1 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses; Page et al. 2021) flow diagram showing the stepwise procedure used for selecting 35 studies for meta-analysis.
Figure 2 in Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression
Figure 2. Schematic diagram representing the workflow process of the area planting optimization model. The graph on the bottom left corresponds to low-density planting yields of maize (red circles, solid line, y = 288.5 − 2.07x), cotton (gray triangles, dashed line, y = 176 − 1.58x), and soybean (blue squares, dotted line, y = 86.5 − 0.70x) in g seed−1.
Figure 4 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 4. The effect of narrow row spacing (<76 cm) on (A) weed density, (B) weed biomass, (C) weed control, and (D) weed seed production as explained by the subgroups of crop,tillage,weed type,weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect.The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 7 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 7. Density plots show the distribution of individual effect sizes (log of response ratios [ln(RR)]) of weed density,biomass,control, weed seed production, and crop yield.
Figure 3 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 3. Changes in the number of seeds m−2 (A, D), the probability of seed capture by harvest weed seed control (HWSC) (B, E), and changes in days to first flower (DFF) (C, F) under weed management system P; HWSC efficacy (up to 20 yr) was increased from 75% (___) to 95% (-○-) (A–C); the level of fruit abscission (up to 20 yr) was changed from low (37%) (-○-) to high (74%) (—) (D–F). Note the variation between replicates was small as long as seed numbers are above 1 m−2; below that level, genetic changes in one or two plants had a more significant effect on the results.
Figure 1 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 1. Showing the number of seeds produced by weeds from each cohort (y axis), dependent on the evolved days to first flower (DFF) (x axis). The peak of each curve indicates the ideal DFF, with earlier cohorts taking longer to flower. (A) The standard farming system, before introduction of harvest weed seed control (HWSC). The range in ideal DFF across the different cohorts is approximately 15 d for Figure 1A. (B–D) How the various weed management systems affect the number of live seeds (avoiding HWSC) for weed management systems E (B), D (C), and P (D). In Figure 1B–D, the range in ideal DFF across the different cohorts is approximately 50 d.
Figure 2 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 2. Cumulative percent shatter over four time periods (maturity, maturity + 2 wk, maturity + 3 wk, maturity + 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 2: Grass species
Figure 3. Cumulative percent seed shatter for all species from planting date to soybean physiological maturity (black vertical line) across the participating states in 2016 and 2017.
Figure 1 in Integrating cover crops for weed management in the semiarid U.S. Great Plains: opportunities and challenges
Figure 1. Map of the Great Plains showing three main regions: (1) Northern Great Plains (marked by purple line), (2) Central Great Plains (marked by red line), and (3) Southern Great Plains (marked by light blue line). Adapted from Center for Great Plains Studies, University of Nebraska–Lincoln.
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 2: Grass 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 physiological 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 +2 wk on October 24 in the state that year. Hence only data from maturity +3 wk are for Arkansas for 2017.)
Figure 3. Designing a in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 3. Designing a metabolomics study. (A) The various approaches for performing a metabolomics experimental study. GC-MS, gas chromatography–mass spectrometry; HILIC-LC-MS/MS, hydrophilic interaction chromatography for liquid chromatography–tandem mass spectrometry; LC-MS/MS, liquid chromatography–tandem mass spectrometry. (B) The general metabolomics workflow. It involves formulating a biological question, setting up an experimental design to test the hypothesis, sample treatment and harvest, metabolite extraction, clean-up, chromatographic separation, identification, statistical validation, and functional interpretation.
Figure 2 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 2. Sustainability of the programs with stacked HT traits or residual herbicides, as influenced by application time (PRE and POST) and number of herbicide SOAs on (A) weed density and (B) resistance evolution. Resistance evolution is presented as % individuals that are resistant to at least one of the herbicides excluding H, either in the form of single or multiple resistance.The populations consist of 80% individuals resistant to H initially. Herbicide scenarios are detailed in Table 2. The simulations were set to stop when weed density exceeded 1 plant m−2, hence the incomplete lines of scenario EWM(i).
Figure 1 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 1. Sustainability of the POST-only programs,as influenced by the number of herbicide SOAs and the initial level of quantitative resistance to herbicide H. Cross-resistance between herbicides H and X is included in D–F. Results are presented as the year of weed control failure; bars represent the mean, and error bars represent the range of 100 replicates. Herbicide scenarios are detailed in Table 2. r-HX, correlation coefficient between phenotypic values of H and X.
Figure 1 in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 1. Classical systems biology concept and omics organization. The central dogma of molecular biology covers the progressive functionalization of the genotype to the phenotype. The omics techniques track and capture various molecular entities across the biological system.
Fig. 2 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 2. Influence of field variables and crop groups on weed density, common and rare species richness in 2013 and 2014. Constrained analysis (RDA) with five constraining variables: Year (2013 or 2014), N_group (N1 0-50, N2 50- 100, N3 100-140, N4>140 kg ha–1 pure nitrogen per hectar), pH and crop group (c.s. – spring cereals, c.w. – winter cereals, S o.s.r. – spring oilseed rape, W o.s.r. – winter oilseed rape, Other – other crops, including maize, grassland, root crops and legumes). The proportion of constrained variation was 33%, the overall analysis and each of the factors were significant (p <0.05), significance tested with permutation tests.
Fig. 1 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 1. Generalized linear models (Poisson) of the total species richness against species density in 2014 and 2013. Model coefficients were 2.41 (p <0.0001) in 2014 and 2.40 (p <0.0001) in 2013. Residual deviance / residual d.f. ratio was used to estimate model overdispersion (1.62 in 2014 and 1.35 in 2013).
Figure 1 in Phytochemical screening and evaluation of antioxidant, total phenolic and flavonoid contents in various weed plants associated with wheat crops
Figure 1. DPPH Assay for Convolvulus arvensis, Chenopodium murale, Avena fatua, Phalaris minor extracts in different solvents.
Figure 3. H 2O2 in Phytochemical screening and evaluation of antioxidant, total phenolic and flavonoid contents in various weed plants associated with wheat crops
Figure 3. H 2O2 Scavenging assay for Convolvulus arvensis, Chenopodium murale, Avena fatua, Phalaris minor extracts in different solvents.
Figure 1. A in Preliminary assessment of weed population in vegetable and fruit farms of Taif, Saudi Arabia
Figure 1. A map of Taif region, Kingdom of Saudi Arabia (https://images.app.goo.gl/SLRH8ep1mKncRHkm7).
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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