scholarly journals Competitive ability of canola (Brassica napus var. oleifera) hybrids with black oat (Avena strigosa) in a subtropical environment

2021 ◽  
Vol 53 (2) ◽  
pp. 119-131
Author(s):  
Leandro Galon ◽  
Germani Concenço ◽  
Luciane Renata Agazzi ◽  
Felipe Nonemacher ◽  
Thais Stradioto Melo ◽  
...  
Keyword(s):  
Competitive Ability ◽  
Total Population ◽  
Co2 Concentration ◽  
Replacement Series ◽  
Data Set ◽  
Avena Strigosa ◽  
Internal Co2 ◽  
Infrared Gas Analyzer ◽  
Better Than ◽  
Subtropical Environment

The objective of this study was to assess the competitive ability of canola (Brassicanapus var. oleifera) hybrids in competition with black oat (Avena strigosa) in a subtropical environment. The experiments were conducted in a greenhouse where canola hybrids ‘Hyola 61,’ ‘Hyola 76,’ ‘Hyola 433,’ and ‘Hyola 571’ were tested individually for their competitive performance with black oat. The plant proportion between black oat and the canola hybrid was changed (100%:0%; 75%:25%; 50%:50%; 25%:75%; and 0%:100%) while keeping the total population of plants constant (20 plants plot‑1). Photosynthesis rate (μmol m-2 s-1), internal CO2 concentration (μmol mol-1), and  transpiration rate (mol H2O m-2 s-1) were assessed using an infrared gas analyzer 55 days after emergence. Leaf area (m2 m-2) and dry matter (g m-2) were also assessed on the same day. The data set was analyzed by the replacement series method for competition studies. There was evidence of intense competition between canola and black oat, independent of plant proportion. The competitive ability of canola was distinct among hybrids; Hyola 571 performed better than the others in the competition against black oat. Choosing the most competitive hybrid, such as Hyola 571, helps maintain high canola grain yield levels in areas infested with black oat. Highlights: There is difference in competition among canola hybrids against black oat; Hyola 571 performed better in the competition; Preference should be given for most competitive canola genotypes against weeds, and weed control should be carried out early in the critical period of interference; Aggressiveness is the most preponderant parameter in determining canola genotypes with superior ability in competition against weeds.

The Influence of Increasing Carbon Dioxide and Temperature on Competitive Interactions Between a C3 Crop, Rice (Oryza sativa) and a C4 Weed (Echinochloa glabrescens)

1996 ◽  
Vol 23 (6) ◽  
pp. 795 ◽  
Author(s):  
AMP Alberto ◽  
LH Ziska ◽  
CR Cervancia ◽  
PA Manalo
Keyword(s):  
Elevated Co2 ◽  
Growth Temperature ◽  
Competitive Ability ◽  
Relative Yield ◽  
Co2 Concentration ◽  
The Philippines ◽  
Replacement Series ◽  
Lower Growth ◽  
C4 Species ◽  
Lower Growth Temperature

Many of the most troublesome weeds in agricultural systems are C4 plants. As atmospheric CO2 increases it is conceivable that competitive ability of these weeds could be reduced relative to C3 crops such as rice. At the International Rice Research Institute (IRRI) in the Philippines, rice (IR72) and one of its associated C4 weeds, Echinochloa glabrescens, were grown from seeding to maturity using replacement series mixtures (100:0, 75:25, 50:50, 25:75, and 0:100, % rice:%weed) at two different CO2 concentrations (393 and 594 μL L-1) in naturally sunlit glasshouses. Since increasing CO2 may also result in elevated growth temperatures, the response of rice to each CO2 concentration was also examined at daylnight temperatures of 27/21 and 37/29�C. At 27/21�C, increasing the CO2 concentration resulted in a significant increase in above ground biomass (+47%) and seed yield (+55%) of rice when averaged over all mixtures. For E. glabrescens, the C4 species, no significant effect of CO2 concentration on biomass or yield was observed. When grown in mixture, the proportion of rice biomass increased significantly relative to that of the C4 weed at all mixtures at elevated CO2. Evaluation of changes in competitiveness (by calculation of plant relative yield (PRY) and replacement series diagrams) of the two species demonstrated that, at elevated CO2, the competitiveness of rice was increased relative to that of E. glabrescens. However, at the higher growth temperature (37/29�C), growth and reproductive stimulation of rice by elevated CO2 was reduced compared to the lower growth temperature. This resulted in a reduction in the proportion of rice:weed biomass present in all mixtures relative to 27/21�C and a greater reduction in PRY in rice relative to E. glabrescens. Data from this experiment suggest that competitiveness could be enhanced in a C3 crop (rice) relative to a C4 weed (E. glabrescens) with elevated CO2 alone, but that simultaneous increases in CO2 and temperature could still favour a C4 species.

scholarly journals RELATIVE COMPETITIVENESS OF GOOSEGRASS BIOTYPES AND SOYBEAN CROPS

2017 ◽  
Vol 30 (2) ◽  
pp. 271-277 ◽  
Author(s):  
JADER JOB FRANCO ◽  
DIRCEU AGOSTINETTO ◽  
ANA CLAUDIA LANGARO ◽  
LAIS TESSARI PERBONI ◽  
LEANDRO VARGAS
Keyword(s):  
Competitive Ability ◽  
Total Population ◽  
Dry Weight ◽  
Replacement Series ◽  
Genetically Modified Soybean ◽  
Eleusine Indica ◽  
Soybean Crop ◽  
Shoot Dry Weight ◽  
Control Failure ◽  
Level Resistance

ABSTRACT The goosegrass (Eleusine indica (L.) Gaertn) is an annual plant that has a low-level resistance to glyphosate (LLRG), resulting in control failure in genetically modified soybean crops for resistance to this herbicide. Alleles related to resistance may cause changes in the plant biotype, such as inferior competitive ability. Thus, the objective of this work was to evaluated the competitive ability of soybean crops and susceptible and resistant (LLRG) goosegrass biotypes. Replacement series experiments were conducted with soybean crops and goosegrass biotypes. The ratios of soybean to susceptible or resistant (LLRG) goosegrass plants were 100:0, 75:25, 50:50, 25:75 and 0:100, with a total population of 481 plants m-2. The leaf area, plant height and shoot dry weight were evaluated at 40 days after emergence of the soybean crops and weeds. The soybean crop had superior competitive ability to the susceptible and resistant (LLRG) goosegrass biotypes. The soybean crop showed similar competitive ability in both competitions, either with the susceptible or resistant (LLRG) goosegrass biotypes. The intraspecific competition was more harmful to the soybean crop, while the interspecific competition caused greater damage to the goosegrass biotypes competing with the soybean crop.

scholarly journals Gas exchange of the umbu tree under semi-arid conditions

2004 ◽  
Vol 26 (2) ◽  
pp. 206-208 ◽  
Author(s):  
José Moacir Pinheiro Lima Filho
Keyword(s):  
Stomatal Conductance ◽  
Gas Exchange ◽  
Rainy Season ◽  
Exchange Process ◽  
Co2 Concentration ◽  
Co2 Assimilation ◽  
Internal Co2 ◽  
Gas Exchange Process ◽  
Infrared Gas Analyzer ◽  
Dry And Rainy Seasons

A study was carried out at Embrapa Semi-Árido, Petrolina-PE, Brazil, aiming to understand the gas exchange process of the umbu tree (Spondias tuberosa Arr. Cam.) in the dry and rainy seasons. Stomatal conductance, transpiration, photosynthesis and internal CO2 concentration were obtained with a portable infrared gas analyzer (IRGA). During the dry season the umbu tree showed a much lower stomatal conductance early in the morning, as soon as the vapor pressure deficit increased, apparently affecting CO2 assimilation more than transpiration. The highest values were detected around 6:00 am but decreased to the lowest points between 10:00 am and 2:00 pm. During the rainy season, however, stomatal conductance, transpiration and photosynthesis were significantly higher, reaching the highest values between 8:00 and 10:00 am and the lowest around 2:00 pm. It was also observed at 4:00 pm, mainly during the rainy season, an increase on these variables indicating that the umbu tree exhibits a two-picked daily course of gas exchange.

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

scholarly journals Competitiveness of alexandergrass or Bengal dayflower with soybean

2010 ◽  
Vol 28 (3) ◽  
pp. 515-522 ◽  
Author(s):  
A.C.R. Dias ◽  
S.J.P. Carvalho ◽  
L.W. Marcolini ◽  
M.S.C. Melo ◽  
P.J. Christoffoleti
Keyword(s):  
Competitive Ability ◽  
Dry Mass ◽  
Total Density ◽  
Weed Species ◽  
Replacement Series ◽  
Commelina Benghalensis ◽  
Randomized Design ◽  
Full Flowering ◽  
Completely Randomized Design ◽  
Soybean Plants

Weeds compete with field crops mainly for water, light and nutrients, and the degree of competition is affected by the weed density and the intrinsic competitive ability of each plant species in coexistence. The objective of this research was to compare the competitiveness of alexandergrass (Brachiaria plantaginea) or Bengal dayflower (Commelina benghalensis) in coexistence with soybean, cv. M-Soy 8045. A factorial experiment (2 x 5) with two weed species and five competition proportions was carried out in a completely randomized design with four replicates. Proportions were based on a replacement series competition design, always maintaining the total density of four plants per 10 L plastic pots, which corresponded to 60 plants m ². The weed-crop proportions were: 0:4; 1:3; 2:2; 3:1; 4:0; that corresponded to the proportion of 100, 75, 50, 25 and 0% of soybean plants and the opposite for weeds, B. plantaginea or C. benghalensis plants. Leaf area, shoot dry mass of the weeds and soybean and number of soybean trifoliate leaves were evaluated when the soybean reached the phenologic stage of full flowering. B. plantaginea was a better competitor than soybean plants. Otherwise, C. benghalensis revealed a similar competitive ability that of the soybean. In both cases, there were evidences that intraspecific competition was more important.

scholarly journals Initial growth and competitive ability of yellow nutsedge and irrigated rice

2013 ◽  
Vol 31 (4) ◽  
pp. 813-821 ◽  
Author(s):  
N.R. Westendorff ◽  
D. Agostinetto ◽  
A.R. Ulguim ◽  
A.C. Langaro ◽  
L. Thürmer
Keyword(s):  
Leaf Area ◽  
Competitive Ability ◽  
Growth Cycle ◽  
Rice Crop ◽  
Initial Growth ◽  
Rice Cultivars ◽  
Replacement Series ◽  
Irrigated Rice ◽  
Yellow Nutsedge ◽  
Dry Biomass

Weeds cause significant reduction in the irrigated rice crop yield. Cyperus esculentus (yellow nutsedge) is adapted to irrigate environment. Information on the competitive ability of the weed to the culture, and their environmental adaptation, are scarce. In this study, we sought to determine the initial growth and competitive ability of yellow nutsedge and irrigated rice, as a function of cultivar growth cycle. Initial growth and competition studies were conducted in a randomized complete design in a greenhouse in the agricultural year 2010/11. For the initial growth study, the treatments consisted of a factorial combination of a biotype of yellow nutsedge and two rice cultivars in the function of the vegetative cycle (BRS Querência: early cycle - IRGA 424: intermediate cycle) and six evaluation times (10, 20, 30, 40, 50, and 60 days after emergence). Were evaluated: plant height, leaf area, aboveground dry biomass and root dry biomass. In the competitive ability study in the replacement series, the cultivar BRS Querência (early cycle) and yellow nutsedge were utilized and tested in different proportions of competition (100:0, 75:25, 50:50, 25:75, and 0:100). Were evaluated leaf area and aboveground dry biomass. In general, rice cultivars have an adaptive value equivalent to yellow nutsedge. IRGA 424 cultivar has less height than weed, becoming the weed control more important in this cultivar. For rice crop, intraspecific competition is more important, whereas for the weed, interspecific competition is the most pronounced.

scholarly journals Fast effect size shrinkage software for beta-binomial models of allelic imbalance

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2024
Author(s):  
Joshua P. Zitovsky ◽  
Michael I. Love
Keyword(s):  
Allelic Imbalance ◽  
Real Data ◽  
Shrinkage Estimators ◽  
Data Set ◽  
Bayesian Shrinkage ◽  
In Cis ◽  
Posterior Estimation ◽  
Binomial Models ◽  
Better Than ◽  
Diploid Organism

Allelic imbalance occurs when the two alleles of a gene are differentially expressed within a diploid organism and can indicate important differences in cis-regulation and epigenetic state across the two chromosomes. Because of this, the ability to accurately quantify the proportion at which each allele of a gene is expressed is of great interest to researchers. This becomes challenging in the presence of small read counts and/or sample sizes, which can cause estimators for allelic expression proportions to have high variance. Investigators have traditionally dealt with this problem by filtering out genes with small counts and samples. However, this may inadvertently remove important genes that have truly large allelic imbalances. Another option is to use pseudocounts or Bayesian estimators to reduce the variance. To this end, we evaluated the accuracy of four different estimators, the latter two of which are Bayesian shrinkage estimators: maximum likelihood, adding a pseudocount to each allele, approximate posterior estimation of GLM coefficients (apeglm) and adaptive shrinkage (ash). We also wrote C++ code to quickly calculate ML and apeglm estimates and integrated it into the apeglm package. The four methods were evaluated on two simulations and one real data set. Apeglm consistently performed better than ML according to a variety of criteria, and generally outperformed use of pseudocounts as well. Ash also performed better than ML in one of the simulations, but in the other performance was more mixed. Finally, when compared to five other packages that also fit beta-binomial models, the apeglm package was substantially faster and more numerically reliable, making our package useful for quick and reliable analyses of allelic imbalance. Apeglm is available as an R/Bioconductor package at http://bioconductor.org/packages/apeglm.

scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

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