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38 results for “canola”
Commodity canola insect visitation
<p>These data were used in a simulation study to examine how honey bees distribute themselves across foraging landscapes.</p> <p>The ideal-free distribution and central-place foraging are important ecological models that can explain the distribution of foraging organisms in their environment. However, this model ignores distance-based foraging costs from a central place (hive, nest), while central-place foraging ignores competition. Different foraging currencies and cooperation between foragers also create different optimal distributions of foragers, but are limited to a simple two-patch model. We present a hybrid model of the ideal-free distribution that uses realistic competitive effects while accounting for distance-based foraging, and test it using honey bees (<em>Apis mellifera</em> L.) foraging in canola fields (<em>Brassica napus</em> L.). Our simulations show that foragers maximizing efficiency (energy profits/losses) prioritize distance to their aggregation more than those maximizing net-rate (energy profits/time), and that social foragers move to more distant patches to maximize group benefits, meaning that social foragers do not approach an ideal-free distribution. Simulated efficiency-maximizers had a hump-shaped relationship of trip times with distance, spending shorter amounts of time in both nearby and far-away patches. Canola fields were far more attractive to simulated foragers than semi-natural areas, suggesting limited foraging on semi-natural lands during the bloom period of canola. Finally, we found that the observed distribution of honey bees in canola fields most closely resembled the optimal distribution of solitary efficiency-maximizers. Our model has both theoretical and practical uses, as it allows us to model central-place forager distributions in complex landscapes as well as providing information on appropriate hive stocking rates for agricultural pollination.</p>
Canola seedling object detection dataset
<div> <div>This is a single-class object detection dataset containing aerial images of early-season canola fields. The objects of interest are canola seedlings. The images were acquired by a Hasselblad L1D-20c camera mounted on a DJI Mavic 2 Pro Drone, which was flown at a height of 2 metres. The dataset contains 431 images and 77059 canola seedling bounding box annotations.</div> </div>
Figure 3 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 3. The role of EDTA and citric acid on (a) leaf turgor potential and (b) water use efficiency, (c) potassium and (d) sodium at vegetative stage for phytoremediation of Ni by using canola plant.
Figure 4 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 4. The role of EDTA and citric acid on (a) SOD and (b) CAT, (c) POD, (d) total free amino acid, (e) total soluble proteins, (f) total soluble sugars at vegetative stage for phytoremediation of Ni by using canola plant and the role of EDTA and citric acid on Ni contents (mg/ pot) in above ground biomass (g) at vegetative stage for phytoremediation of Ni by using canola.
Figure 1 in Comparative effectiveness of EDTA and citric acid assisted phytoremediation of Ni contaminated soil by using canola (Brassica napus)
Figure 1. The role of EDTA and CA on (a) plant height and (b) shoot fresh weight at the vegetative stage of two canola cultivars (Con-II and Oscar, respectively) in control and Ni treatment and the role of EDTA and citric acid on (c) dry weight and (d) photosynthetic rate at vegetative stage for phytoremediation of Ni by using canola plant.
Figure 2. Interaction between nitrogen x phosphorus for seeds pod-1 in Role of beneficial microbes with nitrogen and phosphorous levels on canola productivity
Figure 2. Interaction between nitrogen x phosphorus for seeds pod-1 (a), phosphorous x beneficial microbes for seeds pod-1 (b), nitrogen x beneficial microbes for grains weight (c), and nitrogen x beneficial microbes for seed yield (kg ha-1) of canola (d).
Figure 1 in Role of beneficial microbes with nitrogen and phosphorous levels on canola productivity
Figure 1. Mean monthly maximum & minimum temperature (°C), solar radiation (), relative humidity (%) and rainfall (mm) of the growing season of canola crop (2,016-2,017).
Commodity canola and seed canola visitation and plant data
<p>Insect-mediated pollination of crops is an important service to agriculture, as increased insect visitation can increase fruit production by increasing pollen deposition. Unfortunately, pollination is often treated as a <span>"</span>black box<span>"</span>, and pollination management suffers from key knowledge gaps that hinder its greater utility, particularly the specific mechanisms underlying the processes of visitation, pollination, and fruit production. We present a causal model that links insect visitation to pollination to three separate components of yield, using field data from two types of canola (<span><em>Brassica</em> <em>napus</em></span>) production systems. Our results demonstrate that yield in commodity canola fields is primarily determined by plant size, and we found no relationship between honey bee (<span><em>Apis</em> <em>mellifera</em></span>) visitation and pollen deposition, or pollen deposition and seed yield. In contrast, yield in seed production canola fields was similarly controlled by plant size, but there was also a strong relationship between alfalfa leafcutting bee (<span><em>Megachile</em> <em>rotundata</em></span>) visitation and pollen deposition, as well as pollen deposition and seed yield. Leafcutting bee visitation in particular strongly increased pollen deposition in seed canola fields, whereas honey bee visitation did not. This model serves as a step towards a dynamic model of pollination services and highlights the relative importance of bee pollination in canola production.</p>
Commodity canola and seed canola visitation and plant data
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Commodity canola insect visitation
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Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Edge effects and pitfall trap design influence spider diversity and assemblages in canola agroecosystems on the Canadian Prairies
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Data from: Effect of wheat straw biochar addition on canola growth in different soils
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Impact of Canola Protein Processing on Plasma Amino Acid Responses
ClinicalTrials.gov study NCT06058403. IPD Sharing: NO. Countries: 1. Publications: 1.
Canola Oil, Fibre and DHA Enhanced Clinical Trial
ClinicalTrials.gov study NCT02091583. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Low Glycemic Index Diet (With Canola Oil) for Type 2 Diabetics
ClinicalTrials.gov study NCT01348568. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Nutritional Evaluation of Canola Protein in Comparison With Soy Protein
ClinicalTrials.gov study NCT01481584. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Canola Oil and Coconut Oil on Postprandial Metabolism in Older Adults With Increased Cardiometabolic Risk
ClinicalTrials.gov study NCT05208346. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Efficacy of High-oleic Canola and Flaxseed Oils for Hypercholesterolemia and Cardiovascular Disease Risk Factors
ClinicalTrials.gov study NCT00927199. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Canola Oil Multicentre Intervention Trial
ClinicalTrials.gov study NCT01351012. IPD Sharing: Not stated. Countries: 1. Publications: 6.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.