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173 results for “sugarcane”
Fig. 1 in Sharing of termites (Blattodea: Isoptera) between sugarcane matrices and Atlantic Forest fragments in Northeast Brazil
Fig. 1. Non-metric multidimensional scaling for termite assemblages of two Atlantic Forest fragments and adjacent sugarcane plantations. Usina são João (A) and Usina São José (B). •, Atlantic Forest. Δ, sugarcane field.
Fig. 5 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 5. Mean ± SE photosynthetic rates (μmol CO2 m−2 s−1) of resistant (TX-7000 and KS-585) and susceptible (TX-2783 and DKS-37-07) sorghum cultivars grown under either conventional or light-emitting diodes. All plants were measured at 15 d afer infestation with sugarcane aphids. Bars with different letters are significantly different (Kruskal-Wallis ANOVA, df = 3; H> 27.14; P <0.01).
Fig. 7 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 7. Mean ± SE chlorophyll loss at 15 d afer infestation under lightemitting diode and conventional lights (control-infested)/control.Different letters represent significant differences (P <0.001) with a Kruskal-Wallis ANOVA followed by Dunn's multiple comparison test (H = 62.629; df = 7).
Fig. 3 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 3. Susceptible sorghum variety KS-585 across 4 treatments: (A) control under light-emitting diodes; (B) infested under light-emitting diodes; (C) control under conventional lights; (D) infested under conventional lights. Plants were infested with sugarcane aphids and assessed 15 d post infestation.
Fig. 1. Neighbor-joining tree generated under the Kimura 2 in Cotesia flavipes (Hymenoptera: Braconidae) as a biological control agent of sugarcane stem borers in Colombia's Cauca River Valley
Fig. 1. Neighbor-joining tree generated under the Kimura 2-parameter (K2P) nucleotide substitution model. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches. Abbreviations for sugarcane mills in Colombia's Cauca River Valley are as follows: Manuelita (MN), Mayagüez (MY), Pichichí (PC), Providencia (PV), Riopaila (RP), Risaralda (RS), Sancarlos (SC). GeneBank C. flavipes accessions:Uganda - JQ396735.1, Brazil - DQ232320.1, India - DQ232336.1, Kenya - DQ232317, Thailand - DQ232340.1, USA - DQ232330.1, South Pakistan - JQ396714.1, Jamaica - DQ232321.1, Pakistan - DQ232335.1, Sri Lanka - DQ232327.1, Indonesia - DQ232337.1, Mauritius - DQ232319.1, Reunion - DQ232329.1, Papua New Guinea - DQ232316.1.
Fig. 2 in Cotesia flavipes (Hymenoptera: Braconidae) as a biological control agent of sugarcane stem borers in Colombia's Cauca River Valley
Fig. 2. Distribution of Cotesia flavipes in different sugarcane mills of Colombia's Cauca River Valley.
Fig. 2 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 2. Resistant sorghum variety TX-2783 across 4 treatments: (A) control under light-emitting diodes; (B) infested under light-emitting diodes; (C) control under conventional lights; (D) infested under conventional lights. Plants were infested with sugarcane aphids and assessed 15 d post infestation.
Fig. 1 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 1. Light emission spectrum of the 9 band 60-watt light-emitting diode grow panels over the visible spectrum and into the near infrared.
Fig. 6 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 6. Mean ± SE stomatal conductance (mol H2O m−2 s−1) at 15 d af- ter infestation under light-emitting diode and conventional lights. Bars with different letters are significantly different (Kruskal-Wallis ANOVA, df = 3; H> 24.13; P <0.01).
Fig. 4 in The effects of light-emitting diode and conventional lighting on sorghum physiology and sugarcane aphid interaction
Fig. 4. Mean ± SE number of sugarcane aphids per plant 15 d afer infestation when grown for resistant (TX-2783 and DKS-37-07) and susceptible (TX-7000 and KS-585) sorghum cultivars grown under either conventional or light-emitting diodes. P-values represent results of a Student's t-test (df = 22) for each variety.
Fig. 2 in Use of chemical inducers as a resistance trigger in Brachiaria grasses and sugarcane
Fig. 2. Mean (± SE) of dry matter in Brachiaria shoots in relation to cultivar (A), inducer (B), and the interaction between cultivar and inducer in Brachiaria roots (C), in sugarcane shoots in relation to the interaction between cultivar and inducer (D), and in sugarcane roots in relation to cultivar (E) and inducer (F). Bars with the same lowercase letter comparing cultivars and bars with uppercase letters comparing inducers do not differ by the Scott Knott test (P <0.05).
Fig. 1 in Use of chemical inducers as a resistance trigger in Brachiaria grasses and sugarcane
Fig. 1. Mean (± SE) of total phenolic compounds in Brachiaria shoots in relation to cultivar (A), inducer (B), and the interaction between cultivar and inducer in roots (C), in sugarcane shoots in relation to cultivar (D), and inducer (E), and in sugarcane roots in relation to cultivar (F) and inducer (G). Bars with the same lowercase letter comparing cultivars and bars with uppercase letters comparing inducers do not differ by the Scott Knott test (P <0.05).
Fig. 1 in Dispersal records of the sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), through the Midwest Suction Trap Network
Fig. 1. Seasonal population dynamics of the sugarcane aphid, Melanaphis sacchari, collected between 2015 and 2017 from selected states in the Midwest Suction Trap Network.
Figure 3 in Instances for "Sugarcane Harvest Logistics in Brazil"
Figure 3. - Linear regressions of morphometrics on total length (TL) for the eighteen Narcine bancrofti presenting normal pigmentation (black circles) as well as the single leucistic individual (open circle) included in this study. Abbreviations for measurements are as described in De Carvalho and Séret (2002).
Fig. 1 in Acrotomopus atropunctellus (Coleoptera: Curculionidae) preference for large sugarcane shoots mitigates damage to sugarcane crop
Fig. 1. Mean numbers (± SE) of feeding punctures made by Acrotomopus atropunctellus on sugarcane shoots of different sizes. Different letters represent significant differences between means (P <0.05) (Fisher's LSD test).
Fig. 2 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment
Fig. 2. Growth characteristics of grain sorghum grown under conventional lighting (A) from within an environmental chamber, fitted with a W2238 LED grow panel (B and C, see Fig. 1 for light spectrum measured), and for sorghum cv MORHC 858, DKS 37-07, TX 2783, and WSH117 afer 21 d in a growth chamber fitted with a W2238 LED grow panel.
Fig. 3 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment
Fig. 3. Number of true leaves on 4 different sorghum entries grown under conventional and LED light sources.
Fig. 4 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment
Fig. 4. Plant height (cm) for 2 different sorghum entries grown under conventional and LED light sources.
Fig. 1 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment
Fig. 1. Light emission spectrum of the W2238 LED grow panel over the visible spectrum and into the near infrared. The inset spectrum is zoomed vertically to show details of any weaker emissions.
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
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International Brain Laboratory public data
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OpenNeuro
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