Rust (Puccinia emaculata) management and impact on biomass yield on switchgrass

Plant Disease ◽  
2021 ◽  
Author(s):  
Kira L. Bowen ◽  
Austin Hagan ◽  
H. Brad Miller
Keyword(s):  
Regression Models ◽  
Biomass Yield ◽  
Linear Regression Models ◽  
Extended Application ◽  
Dry Biomass ◽  
Disease Rating ◽  
Disease Impact ◽  
Rust Severity ◽  
Leaf Necrosis ◽  
The Impact

Rust, putatively caused by Puccinia emaculata, is a widespread and potentially damaging disease of switchgrass, a crop produced as feedstock for livestock and bioenergy. Azoxystrobin, chlorothalonil, and myclobutanil were applied at 1-, 2-, 3-, or 4-wk intervals for 12 to 14 wks to the vegetatively propagated switchgrass cultivar ‘Cloud Nine’ to assess fungicide selection and application interval for the control of rust as well as the impact of this disease on switchgrass biomass yield. While rust severity significantly differed among study years, azoxystrobin and myclobutanil were often equally and more effective than chlorothalonil at controlling rust, with superior disease control coming at the shorter compared to extended application intervals. Year, product, application interval, and product × interval significantly impacted dry biomass yield, which was greatest in 2016 and lowest in 2014. Dry biomass yield protection was significantly better with azoxystrobin and myclobutanil applications than with chlorothalonil or no fungicide. Linear regression models with the final disease rating, as well as with AUDPC in each year, were significant but coefficients of determination were low to moderate (0.21 < R2 < 0.60), indicating that rust response and subsequent disease impact on dry biomass yield were impacted by other factors. From our models, an estimated 3 to 5% biomass decline was calculated for each 10% increment in rust-related leaf necrosis observed at the final September rating date. With rust-related leaf necrosis > 80% by 1 Sept in each of four study years, biomass yield may be reduced by 24 to 40% if rust problems are not managed in switchgrass crops.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals Impact of foreign direct investment on economic growth in the world during 1975–2015

2018 ◽  
Vol 20 (91) ◽  
pp. 28-32
Author(s):  
B. B. Brychka
Keyword(s):  
Economic Growth ◽  
Literature Review ◽  
Regression Models ◽  
Trade Openness ◽  
Positive Influence ◽  
Linear Regression Models ◽  
Average Value ◽  
Gdp Growth Rate ◽  
The World ◽  
The Impact

The study is concentrated on examination the impact of FDI on economic growth in the World during 1975–2015. The study consists of four consecutive parts, including introduction, literature review, model and methodology, data, empirical results and conclusion. Each part of the study is focused on its own goals. According to the results of the literature review, there is positive influence of FDI on economic growth in various countries. Economic growth is one of the most important goals of any country. The country image on the international level is dependent on its economic power. Economic growth provides an opportunity to improve the living standards in the country. Most researchers conclude that there is a positive influence of FDI on the countries’ economic growth. However, the impact of FDI is strong in developing countries. Moreover, this relationship is stronger in countries with higher educational and technological level, trade openness and development of the countries’ stock markets. Economists often build regression models to estimate the relationship between the variables. In order to find the impact of FDI on economic growth, we are going to apply linear regression models. We take two variables as indicators of the countries’ economic growth, including current GDP expressed in U.S dollars, and annual GDP growth rate. Taking into account that the World’s GDP in current U.S dollar is a factor variable with the mentioned resulting variables, the regression equation looks as follows: The R-squared of the built model is 0.99, indicating that roughly 100% of changes in the World’s GDP is caused by the chosen factors. As it is seen from the SAS output, the residuals of dependent variable and factors variables are distributed normally among its average value. Thus, non-normality is not observed in the model. Taking into account the coefficients of the factor variables, the log GDP is most sensitive to the changes in trade as a percent of GDP. The log GDP is not quite sensitive to the changes in FDI, since the coefficient of 0.000128 means that increasing of FDI by one unit increase the logarithmic value of GDP by $ 0.000128.

scholarly journals The effect of leadership style and job motivation on the performance of teachers

2021 ◽  
Vol 6 (2) ◽  
pp. 482
Author(s):  
Ramsiah Hiranah ◽  
Happy Fitria ◽  
Achmad Wahidy
Keyword(s):  
Leadership Style ◽  
Regression Models ◽  
Qualitative Data ◽  
Teacher Performance ◽  
Sampling Technique ◽  
Linear Regression Models ◽  
Profound Influence ◽  
Job Motivation ◽  
Study Techniques ◽  
The Impact

The main objective of this study is to define and explain the impact on teacher performance of the leadership style of the head of the Madrasah and job motivation simultaneously. Quantitative analysis is this form of study performed in this study. Techniques for qualitative data collection, questionnaires and documentation..“ Using different linear regression models, it was analyzed. With the help of the version 21 SPSS program. A saturated sampling technique was used in this analysis, where all 49 respondents were tested. From the following conclusions, the findings of data processing and interpretation are drawn: The principal's leadership style has a positive and important effect on the performance of teachers. Motivation for work has a positive and meaningful influence on the success of teachers. At the same time, the leadership style of madrasah principals and job motivation have a profound influence on teacher performance.

scholarly journals Evaluation of determinants of the serological response to the split-inactivated influenza vaccine

2021 ◽  
Author(s):  
Shaohuan Wu ◽  
Ted M. Ross ◽  
Michael A. Carlock ◽  
Elodie Ghedin ◽  
Hyungwon Choi ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Vaccine Dose ◽  
Serological Response ◽  
Linear Regression Models ◽  
Negative Effects ◽  
Categorical Response ◽  
One Year ◽  
The Impact

AbstractThe seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from >1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, prevaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for >75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the strongest determinants on seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants, and an effect of vaccination month. BMI played a surprisingly small role, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.

scholarly journals The Impact of the Community Built Environment on the Walking Times of Residents in a Community in the Downtown Area of Fuzhou

2019 ◽  
Vol 11 (3) ◽  
pp. 691
Author(s):  
Lizhen Zhao ◽  
Zhenjiang Shen ◽  
Yanji Zhang ◽  
Yan Ma
Keyword(s):  
Built Environment ◽  
Regression Models ◽  
Education Level ◽  
Linear Regression Models ◽  
Downtown Area ◽  
Point Of Interest ◽  
Independent Variable ◽  
And Gender ◽  
The Impact ◽  
Walking Time

By means of on-site and network investigation, we collected data relevant to residents of communities, point of interest (POI) data, and land-use data of Fuzhou. We set traffic walking time and leisure walking time as an independent variable, built environment as dependent variable, and gender, age, education level and income level as control variables. Six linear regression models were established using Statistical Product and Service Solutions (SPSS). The results showed that in the 5D (i.e., Density, Diversity, Design, Destination and Distance) elements of the built environment, the density was negatively correlated with the traffic walking time, whereas other elements were positively correlated with the walking time, but the degree of influence was different.

scholarly journals Medication Intake Is Associated with Lower Plasma Carotenoids and Higher Fat-Soluble Vitamins in the Cross-Sectional MARK-AGE Study in Older Individuals

2020 ◽  
Vol 9 (7) ◽  
pp. 2072
Author(s):  
Daniela Weber ◽  
Bastian Kochlik ◽  
Wolfgang Stuetz ◽  
Martijn E. T. Dollé ◽  
Eugène H. J. M. Jansen ◽  
...  
Keyword(s):  
Regression Models ◽  
Plasma Concentrations ◽  
Older Individuals ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Medication Intake ◽  
The One ◽  
Fat Soluble Vitamins ◽  
The Impact ◽  
Β Carotene

The regular use of medication may interfere with micronutrient metabolism on several levels, such as absorption, turnover rate, and tissue distribution, and this might be amplified during aging. This study evaluates the impact of self-reported medication intake on plasma micronutrients in the MARK-AGE Project, a cross-sectional observational study in 2217 subjects (age- and sex-stratified) aged 35–75 years from six European countries that were grouped according to age. Polypharmacy as possible determinant of micronutrient concentrations was assessed using multiple linear regression models adjusted for age-group, dietary fruit, vegetables, and juice intake, and other confounders. Younger participants reported taking fewer drugs than older participants. Inverse associations between medication intake and lutein (−3.31% difference per increase in medication group), β-carotene (−11.44%), α-carotene (−8.50%) and positive associations with retinol (+2.26%), α-tocopherol/cholesterol (+2.89%) and γ-tocopherol/cholesterol (+1.36%) occurred in multiple adjusted regression models. Combined usage of a higher number of medical drugs was associated with poorer status of carotenoids on the one hand and higher plasma concentrations of retinol, α- and γ-tocopherol on the other hand. Our results raise concerns regarding the safety of drug combinations via the significant and surprisingly multifaceted disturbance of the concentrations of relevant micronutrients.

The impact of strategy on public sector set-aside programs: the case of the state of Ohio

2018 ◽  
Vol 18 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Ian Y. Blount ◽  
Jay Seetharaman ◽  
Trevor L. Brown
Keyword(s):  
Program Implementation ◽  
Regression Models ◽  
Implementation Strategy ◽  
State Level ◽  
The State ◽  
State Agencies ◽  
Linear Regression Models ◽  
Content Type ◽  
Program Strategy ◽  
The Impact

Purpose The purpose of this study is to examine the impact of program strategy on the implementation of the efficacy of a procurement set-aside program at the state level. Design/methodology/approach This study examines the impact of program implementation strategy across two administrations considering the most compelling alternative arguments for what drives agency purchasing through contracts with MBEs. Findings The results of mixed effects linear regression models on the procurement expenditures of 70 state agencies in Ohio from 2008-2015 show significantly higher rates of procurement expenditures with MBEs under the Kasich administration. Originality/value These results provide support for the argument that changes in program implementation strategy led to substantive increases in the use of MBEs by state agencies in Ohio.

scholarly journals Diversification Strategy and Financial Performance of Nigeria Private Firms

2020 ◽  
Vol 8 (07) ◽  
pp. 1883-1889
Author(s):  
Ada Mac- Ozigbo ◽  
Dr. Cross Ogohi Daniel
Keyword(s):  
Firm Performance ◽  
Financial Crisis ◽  
Regression Models ◽  
Global Financial Crisis ◽  
Private Firms ◽  
Linear Regression Models ◽  
Her Family ◽  
Diversified Firms ◽  
The Impact ◽  
Better Than

The relationship between diversification and firm performance varies among institutions and over time. Less is known about the advantageousness of diversification in economy-wide crises, which have occurred frequently in recent years Using data from a recent survey, we studied nearly 400 Nigeria private firms using two different approaches panel and cross-period comparisons. The findings of both approaches show that diversified firms performed better than focused firms. This was also true during the 2008 global financial crisis. The higher the diversification level, the more positive the firm performance was. We also investigated the influence of ownership structure. Firms that are totally owned by the founding owner and his/her family tend to have unsatisfactory performance under crisis. This finding provides evidence of the increasing attention on management and governance to explain firm. Linear regression models were evaluated to test the effect of diversification on firm performance. Panel A uses profit as the dependent variable, and Panel B uses sales. For each year (2007, 2008, and 2009), two regression models were evaluated: one testing the impact of diversification and the other testing the impact of the diversification level. We found that diversified firms performed better than focused firms during the recent global financial crisis. The diversification level was positively and linearly related to performance, that is, more diversified firms performed better. Moreover, we found that private firms that are totally owned by the founding owner and his/her family performed worse under crisis.

scholarly journals The Analysis of the Impact of GDP, FDI, Minimum Wage on Employment in Indonesia

2019 ◽  
Vol 2 (2) ◽  
pp. 53-62
Author(s):  
Thomson Sitompul ◽  
Yansen Simangunsong
Keyword(s):  
Minimum Wage ◽  
Direct Investment ◽  
Regression Models ◽  
Time Series Data ◽  
Minimum Wages ◽  
Series Data ◽  
Economic Sector ◽  
Linear Regression Models ◽  
The Impact ◽  
Multiple Linear Regression Models

Unlike the previous study in determinant of labor absorption, which focused on economic sector and took up regional scope, this paper examines the impact of Gross Domestic Product, Foreign Direct Investment and Minimum Wages on labor absorption in Indonesia which take the national scope and aggregate labor by using secondary series of time series data (1990-2015). This study contributes to the limited literature on aggregate employment and national scope as the impact of the minimum wage, GDP, FDI in developing countries, especially in Indonesia. By using multiple linear regression models, surprisingly, we find that GDP and Minimum Wages have a positive and significant impact to increase employment while FDI  does not affect employment in Indonesia.

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