scholarly journals Peanut Seed Germination and Radicle Development Response to Direct Exposure of Flumioxazin Across Multiple Temperatures

2020 ◽  
Vol 47 (2) ◽  
pp. 89-93
Author(s):  
Nicholas L. Hurdle ◽  
Timothy L. Grey ◽  
C. Pilon ◽  
W. Scott Monfort ◽  
Eric P. Prostko
Keyword(s):  
Seed Germination ◽  
Regression Models ◽  
Field Application ◽  
Linear Regression Models ◽  
Peanut Seed ◽  
Application Rates ◽  
Arachis Hypogaea L ◽  
Direct Exposure ◽  
Table Data ◽  
Development Temperature

ABSTRACT Peanut injury in the field can occur from flumioxazin applied PRE, but this is associated with plants that have emerged, or are about to, emerge from soil. The direct effect of flumioxazin on peanut seed germination and radicle development has not been evaluated. Therefore, research was conducted to determine peanut seed radicle development response to flumioxazin at different concentrations (0.0, 0.01, 0.10, 1.0 and 10.0 ppb) when tested at multiple temperatures (20, 23, 26, and 29 C) in laboratory experiments on a thermogradient table. Data analysis indicated that flumioxazin concentration was not different from the nontreated control (0.0 ppb) for 0.01, 0.1, and 1.0 ppb for peanut germination. Flumioxazin at 10.0 ppb was different from all other treatments and the nontreated control. However, comparing linear regression models for each flumioxazin concentration across all temperatures indicated no differences for slope. These data indicate that when there is direct peanut seed exposure to flumioxazin at field application rates, there is no impact on germination and radicle development. Temperature was noted to affect radicle development greater than field application rates of flumioxazin. As temperature decreased, germination and radicle length was inhibited or decreased, respectively. Nomenclature: Flumioxazin, peanut, Arachis hypogaea (L.), radicle, seed germination

scholarly journals Effect of Oak Tree Sawdust Fermentation Period on Peanut Seed Germination, Seedling Biomass, and Morphology

Horticulturae ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 182
Author(s):  
Junsik Ahn ◽  
Soyeon Oh ◽  
Yang Joo Kang ◽  
KiBum Kim ◽  
Sung-Kwon Moon ◽  
...  
Keyword(s):  
Microbial Community ◽  
Seed Germination ◽  
Germination Rate ◽  
Peanut Seed ◽  
Arachis Hypogaea L ◽  
Vigor Index ◽  
Seedling Biomass ◽  
Oak Tree ◽  
Fermentation Period ◽  
Seedling Characteristics

Peanut (Arachis hypogaea L.) seeds were germinated to investigate the effect of the fermentation period of oak tree sawdust on germination viability and seedling characteristics. Its germination rate, seedling weight, length, and total vigor index were assessed. The seeds were sown in oak tree sawdust fermented for 0, 30, 45, and 60 days. The germination rates of the seeds in fermented sawdust were significantly different. The seeds in the 45-day fermented sawdust produced the heaviest biomass weight (4.6 g) with the longest true leaf (1.7 cm) and hypocotyl (3.4 cm) resulting in the highest total vigor index (925.8). In contrast, seeds in 0-day fermented sawdust had the lowest total vigor index (18.3). Microbiome analysis showed that the microbial community in the sawdust changed as the fermentation progressed, indicating that the microbial community seems to affect seed germination physiology. Taken together, 45-day fermented sawdust is recommended for optimal peanut seed germination and seedling growth.

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.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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