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1. Unsupervised learning analysis of European working condition

Author

Olumide S. Adesina, Adedayo F. Adedotun, Semiu A. Alayande, Emmanuel O. Efe-Imafidon, Tolulope F. Adesina, Hillary I. Okagbue, and Oluwakemi O. Onayemi

Subjects
Europe, working condition, job satisfaction, machine learning, principal component analysis, Huifen (Helen) Cai, Middlesex University Business School, United Kingdom, Business, HF5001-6182, Management. Industrial management, HD28-70
Abstract

AbstractWorkers require good working conditions to enhance their job performance, in this study, we conducted a survey of European working conditions in 2022 and compared the results with that of 2016 using an unsupervised learning approach for exploratory data analysis and determining the relationships. Hence, the Principal Component Analysis (PCA) was adopted. The analyses were in two parts for both the 2016 and 2022 surveys. Following the PCA, the first part shows that European workers are mostly characterized by cheerfulness and good spirits. The second part reveals that European workers are best characterized by enthusiasm in their work. Test statistics showed that the European working condition for the two periods does not differ significantly. The working conditions in Europe have not been altered in the space of six years. This study recommends that the working condition in Europe should be improved so that employers would continue to give their best.

Published
2024
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2. On logistic regression versus support vectors machine using vaccination dataset

Author

Olumide S. Adesina, Adedayo F. Adedotuun, Kayode S. Adekeye, Ogbu F. Imaga, Adeleke J. Adeyiga, and Toluwalase J. Akingbade

Subjects
Logistic regression, Leave out-one validation, Support vectors machine, Prediction, Vaccination, Physics, QC1-999
Abstract

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.

Published
2024
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3. Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data

Author

Olumide S. Adesina, Dawud A. Agunbiade, and Pelumi E. Oguntunde

Subjects
Bayesian, Counts, Mixture models, Dispersion, Medicine, Insurance, Science
Abstract

The need to model count data correctly calls for introducting a flexible yet robust model that can sufficiently handle various types of count data. Models such as Ordinary Least Squares (OLS) used in the past were considered unsuitable. The introduction of the Generalized Linear Model (GLM) and its various extensions was the first breakthrough recorded in modeling count data. Thishis article, propose the Bayesian Dirichlet process mixture prior of generalized linear mixed models (DPMglmm). Metropolis Hasting Monte Carlo Markov Chain (M-H MCMC) was used to draw parameters from target posterior distribution. The Iterated Weighted Least Square (IWLS) proposal was used to determine the acceptance probability in the M-H MCMC phase. Under and over-dispersed count data were simulated, 500 Burn-in was scanned to allow for stability in the chain. 100 thinning interval was allowed so as to nullify the possible effect of autocorrelation in the data due to the Monte Carlo procedure. The DPMglmm and other competing models were fitted to the simulated data, and real-life health insurance claims data sets. The results obtained showed that DPMglmm outperformed MCMCglmm, Bayesian Discrete Weibull, and four other frequentist models. This indicates that DPMglmm is flexible and can fit count data best, either under-dispersed or over-dispersed data.

Published
2021
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4. Timing of weed management and yield penalty due to delayed weed management in soybean

Author

Olumide S. Daramola

Subjects
critical period Glycine max (L.) Merrill, manual weeding, weed control, weed infestation, Biology (General), QH301-705.5, Botany, QK1-989
Abstract

Abstract Background: Weed interference is a major limiting factor for economically viable soybean production. Appropriate timing of weed management would enable farmers to make more efficient use of resources for weed management. Objective: The objective of this study was to determine the critical period for weed competition and appropriate timing of weed management for optimum yield of soybean. Methods: The treatments consisted of periods of weed infestation and weed removal for the first 14, 28, 42 and 56 days after sowing (DAS), and till harvest in a randomized complete block design with three replications in 2016 and 2017. Results: Soybean yields in both years ranged from 914-945 kg ha-1 with no weed control to 1,984-2,127 kg ha-1 in the weed-free plots; a yield loss of 53-56. Weed infestation for the first 14 DAS had no detrimental effect on growth and yield of soybean provided the weeds were subsequently removed. Increasing period of weed interference from 14 to 42 DAS resulted in a steady decline in growth and yield of soybean. Yield losses equivalent of 32-37 kg ha-1 resulted for each day that weed control was delayed between 14 and 42 DAS. Subsequent weed control after 42 DAS did not improve growth and yield significantly, nor obviate yield depression of the crop compared to crop weed-infested till harvest. Conclusions: Results indicated that the critical period of weed competition in soybean was between 14 and 42 DAS. Hence soybean should be maintained weed-free during this period to avoid high yield loss.

Published
2020
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5. Hybrid intelligent model for real time assessment of voice quality of service

Author

Jacob O. Mebawondu, Folasade M. Dahunsi, and Olumide S. Adewale

Subjects
Crowdsourcing, Quality of service, Mobile network operator, Key performance indicator, Intelligent system, Voice calls, Science
Abstract

Mobile telecommunication has made an unprecedented positive impact on every nation's economy and social life. It has allowed users to save time and money in their daily transactions and improved their quality of life. However, network inaccessibility, unreliability, and unsatisfactory voice service have led to many complaints from clients. One of the significant weaknesses inferred from the literature review is the lack of reliable user-based information for monitoring and performance evaluation. This work is aimed at developing a hybrid model for the assessment of voice calls quality of service (QoS) offered by Mobile Network Operators (MNOs) based on standard performance metrics, and to carry out the analyses on developed models. The experimental study is carried out using the network data collected from volunteer's mobile phones using the host-based crowdsourced technique. The collected user-based data for quality of service metrics is modeled based on the combination of the Fuzzy Logic algorithm and Takagi Sugeno Kang inference mechanism of the Neuro-Fuzzy model. The modeling was implemented using MATLAB and Weka analytics as front end and MySQL as backend on Windows 10 operational environment. The performance evaluation of the hybrid model shows the high accuracy of 97.10%; the true positive rate and precision rates were 95.5% and 97.47%, respectively.

Published
2020
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6. Conceptual Agent-Based Model for Optimal Power Utilization in Wireless Sensor Networks

Author

Olumide S. Adewale, Ojonukpe S. Egwuche, and Samuel A. Oluwadare

Subjects
Mobile Agents, Sensor Nodes, Wireless Sensor Networks, Clustering, Electronic computers. Computer science, QA75.5-76.95
Abstract

Wireless Sensor Networks (WSNs) are densely and largely deployed in a variety of environments to sense real-world events. Many approaches have been proposed for collecting sensory data in any wireless sensor networks in a flexible, reliable, and efficient manner. In this study, we design an efficient mobile agent-based model for data gathering in wireless sensor networks to achieve energy and timely collection of sensory information in the target scene. The current focus in wireless sensor networks is placed on energy optimization during data gathering, processing and transmission. Mobile Agents (MAs) as a piece of a program have attracted growing research interest that travels the network from node to node to compute the local data to get useful information globally. Mobile agent’s features such as autonomy, social ability, learning, and more significantly, mobility makes it a chosen technology for information processing in wireless sensor networks and other resource constrained computing environments. The nodes of the sensors are modelled and represented by the states of a Markov chain. The nodes are further grouped into clusters to save the energy of the nodes farther away from the base station.

Published
2016
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7. Protective Effects of Methanol Extract of (.) Leaf on Aspirin-Induced Gastric Ulceration and Oxidative Mucosal Damage in a Rat Model of Gastric Injury

Author

Modinat A. Adefisayo, Rufus O. Akomolafe, Olumide S. Akinsomisoye, Quadri K. Alabi, Laofe Ogundipe, Joseph G. Omole, and Kehinde P. Olamilosoye

Subjects
Therapeutics. Pharmacology, RM1-950
Abstract

This study investigated the quantitative polyphenolic constituents and gastroprotective effects of methanol extract of Vernonia amygdalina leaf (MEVA) against aspirin-induced gastric ulcer in rats. Ulceration was induced by 3 days’ oral administration of aspirin (150 mg/kg body weight). Wistar rats were pretreated with cimetidine (reference drug) at a dose of 100 mg/kg body weight and MEVA at 200, 300, and 400 mg/kg body weight once daily for 28 days prior to ulcer induction. At the end of the experiment, gastric secretions, antioxidant status, and histopathological alteration were evaluated. We observed that the significantly increased ulcer index, gastric volume, free and total acidity, malondialdehyde level, and pepsin activity were effectively reduced following treatment with 200 and 300 mg/kg MEVA. The extract also markedly attenuated the reduced activity of superoxide dismutase and reduced glutathione level as well as pH and mucin content in the ulcerated rats. Administration of the extract also significantly attenuates necrosis of the stomach tissue of the ulcerated rats. The results suggested that the MEVA leaf, preferably at 200 and 300 mg/kg body weight, ameliorated aspirin-induced gastric ulceration via antioxidative and H 2 receptor antagonist.

Published
2018
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19. A Web-Based Virtual Classroom System Model

Author

Adewale, Olumide S., Ibam, Emmanuel O., and Alese, B. K.

Abstract

The population of students all over the world is growing without a proportionate increase in teaching/learning resources/infrastructure. There is also much quest for learning in an environment that provides equal opportunities to all learners. The need to provide an equal opportunity learning environment that will hitherto improve the system of education globally has therefore become imperative. Based on our findings, a mathematical model Web-based Virtual Classroom system (WebVCS) was developed to provide a viable medium through which sound education can be offered in tertiary institutions that can carter for varieties of learners irrespective of their abilities, dispositions and geographical locations. Our system model is developed based on active learning approach that adopts blended learning theory (Constructivist-Cognivist learning approach), incorporating e-pedagogy that supports collaboration among participants in the web-based Virtual learning environment. The key objects used in creating the WebVCS model are: Courses, Students, Instructors and Learning performances. Our system model sets a framework for developers of virtual classrooms and successful implementation of the model leads to students learning by interacting with their peers resulting in the construction of knowledge. (Contains 6 figures.)

Published
2012

21. Peanut (Arachis hypogaea L.) response to low‐rate applications of selected herbicides at vegetative and reproductive growth stages.

Author

Singh, Navjot, Daramola, Olumide S., Iboyi, Joseph E., and Devkota, Pratap

Abstract

Off‐target drift of herbicides can seriously reduce peanut (Arachis hypogea L.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide‐tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed‐free check. However, NDVI values could differentiate between injured and weed‐free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias. Core Ideas: Peanut yield loss from 25% labeled rates of selected herbicides was greater at reproductive than at vegetative stage.Peanut was most susceptible to glyphosate (59%) and expressed the greatest tolerance to lactofen (≤10%).The order of yield reduction from the herbicides was glyphosate > glufosinate = dicamba > paraquat = lactofen. [ABSTRACT FROM AUTHOR]

Published
2024
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24. On COVID-19 Vaccination in Nigeria: An Empirical Study

Author

Olumide S. Adesina, Adedayo F. Adedotun, Nureni O. Adeboye, Tolulope F. Adesina, Hilary I. Okagbue, and Ayobami F. Gboyega

Subjects
General Engineering, General Agricultural and Biological Sciences, General Environmental Science
Published
2023

25. Peanut response to 2,4-D plus glyphosate

Author

Olumide S. Daramola, Prasanna Kharel, Joseph E. Iboyi, and Pratap Devkota

Subjects
Plant Science, Agronomy and Crop Science
Abstract

The commercialization of crops that are resistant to 2,4-D plus glyphosate provided an opportunity to growers to apply the herbicide postemergence. However, the potential drift injury of these herbicides to peanuts grown near crops that are resistant to 2,4-D plus glyphosate is concerning. Field experiments were conducted in 2019 and 2020 to evaluate peanut response when exposed at 25, 50, and 75 d after planting (DAP) corresponding to vegetative, flowering, and pod development stages, respectively, to reduced rates of 1/512×, 1/128×, 1/32×, and 1/8× of the labeled rate of 2,4-D plus glyphosate (i.e., 1,077 + 1,132 g ae ha−1, respectively). 2,4-D plus glyphosate was more injurious to peanuts when exposed at 25 DAP compared with 50 and 75 DAP. Similarly, greater canopy height (12%) and canopy width (16%) reductions were observed at 25 DAP compared with 50 and 75 DAP exposure timings (3% to 9%). This result indicates that peanut is more sensitive to 2,4-D plus glyphosate exposure at the vegetative growth stage than at the flowering and pod development stages. However, yield reductions (13% to 16%) were not different between 25, 50, or 75 DAT exposure timings. Regression analysis indicated a linear response for peanut injury, canopy height, width, and yield reduction with an increasing rate of 2,4-D plus glyphosate. The highest rate of 2,4-D plus glyphosate (1/8× of the label rate) resulted in 38%, 22%, and 23% peanut injury, canopy height, and width reduction at 4 wk after treatment, and 33% yield reduction. There was a correlation between peanut injury and yield reduction, with Pearson’s rho values ranging from 0.70 to 0.73. The findings suggest that peanut injury rating data after 2,4-D plus glyphosate drift can be useful for estimating potential yield losses.

Published
2023

27. Spray water quality and herbicide performance: a review

Author

Olumide S. Daramola, William G. Johnson, David L. Jordan, Gurinderbir S. Chahal, and Pratap Devkota

Subjects
Plant Science, Agronomy and Crop Science
Abstract

Water is the primary carrier for herbicide applications. Spray water qualities such as pH, hardness, temperature, or turbidity can influence herbicide performance and may need to be amended for optimum weed control. Water quality factors can affect herbicide activity by reducing solubility, enhancing degradation in the spray tank, or forming herbicide-salt complexes with mineral cations, thereby reducing the absorption, translocation, and subsequent weed control. The available literature suggests that the effect of water quality varies with herbicide chemistry and weed species. The efficacy of weak-acid herbicides such as glyphosate, glufosinate, clethodim, sethoxydim, bentazon, and 2,4-D is improved with acidic water pH; however, the efficacy of sulfonylurea herbicides is negatively impacted. Hard-water antagonism is more prevalent with weak-acid herbicides, and trivalent cations are the most problematic. Spray solution temperature between 18 C and 44 C is optimum for some weak-acid herbicides; however, their efficacy can be reduced at relatively low (5 C) or high (56 C) water temperature. The effect of water turbidity is severe on cationic herbicides such as paraquat and diquat, and herbicides with low soil mobility such as glyphosate. Although adjuvants are recommended to overcome the negative effect of spray water hardness or pH, the response has been inconsistent with the herbicide chemistry and weed species. Moreover, information on the effect of spray water quality on various herbicide chemistries, weed species, and adjuvants is limited; therefore, it is difficult to develop guidelines for improving weed control efficacy. Further research is needed to determine the effect of spray water factors and develop specific recommendations for improving herbicide efficacy on problematic weed species.

Published
2022

28. Knowledge, Attitude, and Perception of Health and Non-Healthcare Workers Towards COVID-19 Vaccination: Machine Learning Approach

Author

Olumide S. Adesina, Adedayo F. Adedotun, Daniel S. Oladepo, and Tolulope F. Adesina

Subjects
Renewable Energy, Sustainability and the Environment, Geography, Planning and Development, Management, Monitoring, Policy and Law
Abstract

There have been concerns globally as to whether taking COVID-19 vaccination is harmful or not. In this study, we conducted an online survey to measure the knowledge and attitude of people, first about COVID-19, and second about COVID-19 vaccination—various analyses such as descriptive statistics, logistic regression, and support vector regression with k-fold cross-validation. The support vector machine and tuned support vector machine suggest a better fit based on cross-validation error. The results show that immigration requirements significantly explain why an individual would accept the COVID-19 vaccine. This study suggests that people in authority should look into people's concerns regarding taking the COVID-19 vaccine and address them accordingly. The study aims to draw the attention of the people to the concern that surrounds taking COVID-19 vaccination and explored various statistical techniques to draw inference.

Published
2022

31. Peanut (Arachis hypogaeaL.) response to low‐rate applications of selected herbicides at vegetative and reproductive growth stages

Author

Singh, Navjot, Daramola, Olumide S., Iboyi, Joseph E., and Devkota, Pratap

Abstract

Off‐target drift of herbicides can seriously reduce peanut (Arachis hypogeaL.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide‐tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed‐free check. However, NDVI values could differentiate between injured and weed‐free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias. Peanut yield loss from 25% labeled rates of selected herbicides was greater at reproductive than at vegetative stage.Peanut was most susceptible to glyphosate (59%) and expressed the greatest tolerance to lactofen (≤10%).The order of yield reduction from the herbicides was glyphosate > glufosinate = dicamba > paraquat = lactofen. Peanut yield loss from 25% labeled rates of selected herbicides was greater at reproductive than at vegetative stage. Peanut was most susceptible to glyphosate (59%) and expressed the greatest tolerance to lactofen (≤10%). The order of yield reduction from the herbicides was glyphosate > glufosinate = dicamba > paraquat = lactofen.

Published
2024
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33. Count Models Analysis of Factors Associated with Road Accidents in Nigeria

Author

Adedayo F. Adedotun, Olumide S. Adesina, Olanrewaju K. Onasanya, Edeki S. Onos, and Odekina G. Onuche

Subjects
Safety, Risk, Reliability and Quality, General Environmental Science
Abstract

The current state of all Nigerian roads is in poor condition, and reports of accidents have been recorded across the federation. The larger mission of the sustainable development goal is to promote sustainable cities and communities. This research study aims to examine factors responsible for road accidents in Nigeria through the quantitative tool of higher extensions of the Poisson regression model (ZTNPRM). A cross-sectional study design was adopted and secondary data was used within a sample period from the 1st quarter of 2006 to the 2nd quarter of 2020. Due to overdispersion, ZTNPRM indicates human errors contribute to a large proportion (41.4%) of road accidents. Vehicle factors are also statistically and positively related to road accidents. All the factors this model identified that lead to road accidents predicted low road accidents. Hence, the study recommends that Nigerian car users follow all rules and regulations associated with safe driving and make the environment safer for people as the sustainable development goal (SDGs). This study recommends more attention to the area of accident and injury prevention as a strategic objective of the SDGs.

Published
2022

35. E-payment Challenges: The Genesis and Remedies to the Problem

Author

Oluwabunmi. O. Olaniyan, Olumide, S. Adesina, and Daniel, S. Oladepo

Abstract

Switching between internet solutions in an organization can be challenging, mainly if it affects the core operations and running of the organizations, such as e-payment. It becomes problematic when the role of e-government is not properly considered in an organization. In this study, the school fees e-payment project of a Nigerian University and the losses incurred due to the lack of incorporation of ICT- government into the e-payment process was evaluated. This study recommends good outsourcing, corporate and ICT governance practices such as the incorporation of frameworks like the Control Objectives for Information and Related Technology (COBIT) and IT Infrastructure Library (ITIL), can be integrated to minimize such problems. This would help any University to avert likely losses.

Published
2022

39. Bayesian Spatial Analysis of Socio-Demographic Factors Influencing Smoking, Use of Hard Drugs and Its Residual Geographic Variation among Teenagers of Reproductive Age in Nigeria

Author

Adedayo F. Adedotun, Olanrewaju K. Onasanya, Abass I. Taiwo, Olumide S. Adesina, Oluwole A. Odetunmibi, and Onuche G. Odekina

Subjects
Renewable Energy, Sustainability and the Environment, Geography, Planning and Development, Management, Monitoring, Policy and Law
Abstract

The use of hard drugs (Alcohol, cocaine and Nicotine) has remained the censorious issue globally and in Nigeria. The use of hard drugs and tobacco smoking is common in the stage of adolescence and youth life, which is a deterrent to education and career advancement. Hence, this study looks into socio-demographic factors that influence the use of hard drugs and tobacco smoking among teenagers between the ages of 15 years to 19 years. To achieve this objective, a cross-sectional data was used and a secondary data was obtained from DHS - National Demographic and Health Surveys (NDHS) from the survey year 2018. Some Bayesian models were developed and Conditional Autoregressive (CAR) model with random walk 1 (RW1) was the best model. The study unveiled a positive significant association of settlement, previous place of residence, education attainment, religion, ethnicity, literacy with reported use of hard drugs amongst teenagers of reproductive age.

Published
2022

43. Aspiration Risk Factors, Microbiology, and Empiric Antibiotics for Patients Hospitalized With Community-Acquired Pneumonia

Author

Marin-Corral, J, Pascual-Guardia, S, Amati, F, Aliberti, S, Masclans, J, Soni, N, Rodriguez, A, Sibila, O, Sanz, F, Sotgiu, G, Anzueto, A, Dimakou, K, Petrino, R, van de Garde, E, Restrepo, M, Aruj, P, Attorri, S, Barimboim, E, Caeiro, J, Garzon, M, Cambursano, V, Adrian Ceccato, V, Chertcoff, J, Lascar, F, Di Tulio, F, Diaz, A, de Vedia, L, Ganaha, M, Lambert, S, Lopardo, G, Luna, C, Malberti, A, Morcillo, N, Tartara, S, Pensotti, C, Pereyra, B, Scapellato, P, Stagnaro, J, Shah, S, Lotsch, F, Thalhammer, F, Anseeuw, K, Francois, C, Van Braeckel, E, Vincent, J, Djimon, M, Bashi, J, Dodo, R, Nouer, S, Chipev, P, Encheva, M, Miteva, D, Petkova, D, Balkissou, A, Pefura Yone, E, Mbatchou Ngahane, B, Shen, N, Xu, J, Bustamante Rico, C, Buitrago, R, Pereira Paternina, F, Kayembe Ntumba, J, Carevic, V, Jakopovic, M, Jankovic, M, Matkovic, Z, Mitrecic, I, Bouchy Jacobsson, M, Christensen, A, Heitmann Bodtger, U, Meyer, C, Jensen, A, Baunbaek-knudsen, G, Petersen, P, Andersen, S, El-Said Abd El-Wahhab, I, Morsy, N, Shafiek, H, Sobh, E, Abdulsemed, K, Bertrand, F, Brun-Buisson, C, de Montmollin, E, Fartoukh, M, Messika, J, Tattevin, P, Khoury, A, Ebruke, B, Dreher, M, Kolditz, M, Meisinger, M, Pletz, M, Hagel, S, Rupp, J, Schaberg, T, Spielmanns, M, Creutz, P, Suttorp, N, Siaw-Lartey, B, Papapetrou, D, Tsigou, E, Ampazis, D, Kaimakamis, E, Bhatia, M, Dhar, R, D'Souza, G, Garg, R, Koul, P, Mahesh, P, Jayaraj, B, Narayan, K, Udnur, H, Krishnamurthy, S, Kant, S, Swarnakar, R, Limaye, S, Salvi, S, Golshani, K, Keatings, V, Martin-Loeches, I, Maor, Y, Strahilevitz, J, Faverio, P, Battaglia, S, Carrabba, M, Ceriana, P, Confalonieri, M, Monforte, A, Del Prato, B, De Rosa, M, Fantini, R, Fiorentino, G, Gammino, M, Menzella, F, Milani, G, Nava, S, Palmiero, G, Gabrielli, B, Rossi, P, Sorino, C, Steinhilber, G, Zanforlin, A, San Luca, O, Franzetti, F, Carugati, M, Morosi, M, Monge, E, Carone, M, Patella, V, Scarlata, S, Comel, A, Kurahashi, K, Bacha, Z, Ugalde, D, Zuniga, O, Villegas, J, Medenica, M, Mihsra, D, Shrestha, P, Ridgeon, E, Awokola, B, Adefuye Bolanle Olufunlola, O, Olumide, S, Ukwaja, K, Irfan, M, Minarowski, L, Szymon, S, Froes, F, Leuschner, P, Meireles, M, Ferrao, C, Neves, J, Abel, S, Ravara, S, Brocovschii, V, Rusu, D, Toma, C, Chirita, D, Dorobat, C, Birkun, A, Kaluzhenina, A, Almotairi, A, Ali Bukhary, Z, Edathodu, J, Fathy, A, Abdulaziz Enani, A, Mohamed, N, Memon, J, Bella, A, Bogdanovic, S, Milenkovic, B, Pesut, D, Borderias, L, Bordon Garcia, N, Alarcon, H, Cilloniz, C, Torres, A, Diaz-Brito, V, Casas, X, Gonzalez, A, Fernandez-Almira, M, Interna, M, Gallego, M, Gaspar-GarcIa, I, Gonzalez del Castillo, J, Victoria, P, Martinez, E, Malo de Molina, R, Marcos, P, Menendez, R, Pando-Sandoval, A, Aymerich, C, Lacoma de la Torre, A, Garcia-Olive, I, Rello, J, Moyano, S, Rodrigo-Troyano, A, Sole-Violan, J, Uranga, A, van Boven, J, Torra, E, Pujol, J, Feldman, C, Yum, H, Arnauld Attannon Fiogbe, I, Yangui, F, Bilaceroglu, S, Levent Dalar, I, Yilmaz, U, Bogomolov, A, Elahi, N, Dhasmana, D, Feneley, A, Hill, A, Rudran, B, Ruiz-Buitrago, S, Campbell, M, Whitaker, P, Youzguin, A, Singanayagam, A, Hancock, C, Villafuerte, D, Allen, K, Brito, V, Dietz, J, Dysart, C, Kellie, S, Ricardo, A, Meier, G, Gaga, M, Holland, T, Bergin, S, Kheir, F, Landmeier, M, Lois, M, Nair, G, Patel, H, Reyes, K, Rodriguez-Cintron, W, Saito, S, Noda, J, Hinojosa, C, Levine, S, Reyes, L, Angel, L, Whitlow, K, Hipskind, J, Sukhija, K, Totten, V, Wunderink, R, Shah, R, Mateyo, K, Noriega, L, Alvarado, E, Aman, M, Labra, L, Marin-Corral J., Pascual-Guardia S., Amati F., Aliberti S., Masclans J. R., Soni N., Rodriguez A., Sibila O., Sanz F., Sotgiu G., Anzueto A., Dimakou K., Petrino R., van de Garde E., Restrepo M. I., Aruj P. K., Attorri S., Barimboim E., Caeiro J. P., Garzon M. I., Cambursano V. H., Adrian Ceccato V. H. D. C. A., Chertcoff J., Lascar F., Di Tulio F., Diaz A. C., de Vedia L., Ganaha M. C., Lambert S., Lopardo G., Luna C. M., Malberti A. G., Morcillo N., Tartara S., Pensotti C., Pereyra B., Scapellato P. G., Stagnaro J. P., Shah S., Lotsch F., Thalhammer F., Anseeuw K., Francois C. A., Van Braeckel E., Vincent J. L., Djimon M. Z., Bashi J., Dodo R., Nouer S. A., Chipev P., Encheva M., Miteva D., Petkova D., Balkissou A. D., Pefura Yone E. W., Mbatchou Ngahane B. H., Shen N., Xu J. -F., Bustamante Rico C. A., Buitrago R., Pereira Paternina F. J., Kayembe Ntumba J. -M., Carevic V. V., Jakopovic M., Jankovic M., Matkovic Z., Mitrecic I., Bouchy Jacobsson M. -L., Christensen A. B., Heitmann Bodtger U. C., Meyer C. N., Jensen A. V., Baunbaek-knudsen G., Petersen P. T., Andersen S., El-Said Abd El-Wahhab I., Morsy N. E., Shafiek H., Sobh E., Abdulsemed K. A., Bertrand F., Brun-Buisson C., de Montmollin E., Fartoukh M., Messika J., Tattevin P., Khoury A., Ebruke B., Dreher M., Kolditz M., Meisinger M., Pletz M. W., Hagel S., Rupp J., Schaberg T., Spielmanns M., Creutz P., Suttorp N., Siaw-Lartey B., Papapetrou D., Tsigou E., Ampazis D., Kaimakamis E., Bhatia M., Dhar R., D'Souza G., Garg R., Koul P. A., Mahesh P. A., Jayaraj B. S., Narayan K. V., Udnur H. B., Krishnamurthy S. B., Kant S., Swarnakar R., Limaye S., Salvi S., Golshani K., Keatings V. M., Martin-Loeches I., Maor Y., Strahilevitz J., Faverio P., Battaglia S., Carrabba M., Ceriana P., Confalonieri M., Monforte A. D., Del Prato B., De Rosa M., Fantini R., Fiorentino G., Gammino M. A., Menzella F., Milani G., Nava S., Palmiero G., Gabrielli B., Rossi P., Sorino C., Steinhilber G., Zanforlin A., San Luca O., Franzetti F., Carugati M., Morosi M., Monge E., Carone M., Patella V., Scarlata S., Comel A., Kurahashi K., Bacha Z. A., Ugalde D. B., Zuniga O. C., Villegas J. F., Medenica M., Mihsra D. R., Shrestha P., Ridgeon E., Awokola B. I., Adefuye Bolanle Olufunlola O. N. O., Olumide S., Ukwaja K. N., Irfan M., Minarowski L., Szymon S., Froes F., Leuschner P., Meireles M., Ferrao C., Neves J., Abel Salazar, Ravara S. B., Brocovschii V., Rusu D., Toma C., Chirita D., Dorobat C. M., Birkun A., Kaluzhenina A., Almotairi A., Ali Bukhary Z. A., Edathodu J., Fathy A., Abdulaziz Enani A. M., Mohamed N. E., Memon J. U., Bella A., Bogdanovic S. N., Milenkovic B., Pesut D., Borderias L., Bordon Garcia N. M., Alarcon H. C., Cilloniz C., Torres A., Diaz-Brito V., Casas X., Gonzalez A. E., Fernandez-Almira M. L., Interna M., Gallego M., Gaspar-GarcIa I., Gonzalez del Castillo J., Victoria P. J., Martinez E. L., Malo de Molina R., Marcos P. J., Menendez R., Pando-Sandoval A., Aymerich C. P., Lacoma de la Torre A., Garcia-Olive I., Rello J., Moyano S., Rodrigo-Troyano A., Sole-Violan J., Uranga A., van Boven J. F., Torra E. V., Pujol J. A., Feldman C., Yum H. K., Arnauld Attannon Fiogbe I. U., Yangui F., Bilaceroglu S., Levent Dalar I. D., Yilmaz U., Bogomolov A., Elahi N., Dhasmana D. J., Feneley A., Hill A. T., Rudran B., Ruiz-Buitrago S., Campbell M., Whitaker P., Youzguin A., Singanayagam A., Hancock C., Villafuerte D., Allen K. S., Brito V., Dietz J., Dysart C. E., Kellie S. M., Ricardo A. Franco-Sadud C. J., Meier G., Gaga M., Holland T. L., Bergin S. P., Kheir F., Landmeier M., Lois M., Nair G. B., Patel H., Reyes K., Rodriguez-Cintron W., Saito S., Noda J., Hinojosa C. I., Levine S. M., Reyes L. F., Angel L. F., Whitlow K. S., Hipskind J., Sukhija K., Totten V., Wunderink R. G., Shah R. D., Mateyo K. J., Noriega L., Alvarado E., Aman M., Labra L., Marin-Corral, J, Pascual-Guardia, S, Amati, F, Aliberti, S, Masclans, J, Soni, N, Rodriguez, A, Sibila, O, Sanz, F, Sotgiu, G, Anzueto, A, Dimakou, K, Petrino, R, van de Garde, E, Restrepo, M, Aruj, P, Attorri, S, Barimboim, E, Caeiro, J, Garzon, M, Cambursano, V, Adrian Ceccato, V, Chertcoff, J, Lascar, F, Di Tulio, F, Diaz, A, de Vedia, L, Ganaha, M, Lambert, S, Lopardo, G, Luna, C, Malberti, A, Morcillo, N, Tartara, S, Pensotti, C, Pereyra, B, Scapellato, P, Stagnaro, J, Shah, S, Lotsch, F, Thalhammer, F, Anseeuw, K, Francois, C, Van Braeckel, E, Vincent, J, Djimon, M, Bashi, J, Dodo, R, Nouer, S, Chipev, P, Encheva, M, Miteva, D, Petkova, D, Balkissou, A, Pefura Yone, E, Mbatchou Ngahane, B, Shen, N, Xu, J, Bustamante Rico, C, Buitrago, R, Pereira Paternina, F, Kayembe Ntumba, J, Carevic, V, Jakopovic, M, Jankovic, M, Matkovic, Z, Mitrecic, I, Bouchy Jacobsson, M, Christensen, A, Heitmann Bodtger, U, Meyer, C, Jensen, A, Baunbaek-knudsen, G, Petersen, P, Andersen, S, El-Said Abd El-Wahhab, I, Morsy, N, Shafiek, H, Sobh, E, Abdulsemed, K, Bertrand, F, Brun-Buisson, C, de Montmollin, E, Fartoukh, M, Messika, J, Tattevin, P, Khoury, A, Ebruke, B, Dreher, M, Kolditz, M, Meisinger, M, Pletz, M, Hagel, S, Rupp, J, Schaberg, T, Spielmanns, M, Creutz, P, Suttorp, N, Siaw-Lartey, B, Papapetrou, D, Tsigou, E, Ampazis, D, Kaimakamis, E, Bhatia, M, Dhar, R, D'Souza, G, Garg, R, Koul, P, Mahesh, P, Jayaraj, B, Narayan, K, Udnur, H, Krishnamurthy, S, Kant, S, Swarnakar, R, Limaye, S, Salvi, S, Golshani, K, Keatings, V, Martin-Loeches, I, Maor, Y, Strahilevitz, J, Faverio, P, Battaglia, S, Carrabba, M, Ceriana, P, Confalonieri, M, Monforte, A, Del Prato, B, De Rosa, M, Fantini, R, Fiorentino, G, Gammino, M, Menzella, F, Milani, G, Nava, S, Palmiero, G, Gabrielli, B, Rossi, P, Sorino, C, Steinhilber, G, Zanforlin, A, San Luca, O, Franzetti, F, Carugati, M, Morosi, M, Monge, E, Carone, M, Patella, V, Scarlata, S, Comel, A, Kurahashi, K, Bacha, Z, Ugalde, D, Zuniga, O, Villegas, J, Medenica, M, Mihsra, D, Shrestha, P, Ridgeon, E, Awokola, B, Adefuye Bolanle Olufunlola, O, Olumide, S, Ukwaja, K, Irfan, M, Minarowski, L, Szymon, S, Froes, F, Leuschner, P, Meireles, M, Ferrao, C, Neves, J, Abel, S, Ravara, S, Brocovschii, V, Rusu, D, Toma, C, Chirita, D, Dorobat, C, Birkun, A, Kaluzhenina, A, Almotairi, A, Ali Bukhary, Z, Edathodu, J, Fathy, A, Abdulaziz Enani, A, Mohamed, N, Memon, J, Bella, A, Bogdanovic, S, Milenkovic, B, Pesut, D, Borderias, L, Bordon Garcia, N, Alarcon, H, Cilloniz, C, Torres, A, Diaz-Brito, V, Casas, X, Gonzalez, A, Fernandez-Almira, M, Interna, M, Gallego, M, Gaspar-GarcIa, I, Gonzalez del Castillo, J, Victoria, P, Martinez, E, Malo de Molina, R, Marcos, P, Menendez, R, Pando-Sandoval, A, Aymerich, C, Lacoma de la Torre, A, Garcia-Olive, I, Rello, J, Moyano, S, Rodrigo-Troyano, A, Sole-Violan, J, Uranga, A, van Boven, J, Torra, E, Pujol, J, Feldman, C, Yum, H, Arnauld Attannon Fiogbe, I, Yangui, F, Bilaceroglu, S, Levent Dalar, I, Yilmaz, U, Bogomolov, A, Elahi, N, Dhasmana, D, Feneley, A, Hill, A, Rudran, B, Ruiz-Buitrago, S, Campbell, M, Whitaker, P, Youzguin, A, Singanayagam, A, Hancock, C, Villafuerte, D, Allen, K, Brito, V, Dietz, J, Dysart, C, Kellie, S, Ricardo, A, Meier, G, Gaga, M, Holland, T, Bergin, S, Kheir, F, Landmeier, M, Lois, M, Nair, G, Patel, H, Reyes, K, Rodriguez-Cintron, W, Saito, S, Noda, J, Hinojosa, C, Levine, S, Reyes, L, Angel, L, Whitlow, K, Hipskind, J, Sukhija, K, Totten, V, Wunderink, R, Shah, R, Mateyo, K, Noriega, L, Alvarado, E, Aman, M, Labra, L, Marin-Corral J., Pascual-Guardia S., Amati F., Aliberti S., Masclans J. R., Soni N., Rodriguez A., Sibila O., Sanz F., Sotgiu G., Anzueto A., Dimakou K., Petrino R., van de Garde E., Restrepo M. I., Aruj P. K., Attorri S., Barimboim E., Caeiro J. P., Garzon M. I., Cambursano V. H., Adrian Ceccato V. H. D. C. A., Chertcoff J., Lascar F., Di Tulio F., Diaz A. C., de Vedia L., Ganaha M. C., Lambert S., Lopardo G., Luna C. M., Malberti A. G., Morcillo N., Tartara S., Pensotti C., Pereyra B., Scapellato P. G., Stagnaro J. P., Shah S., Lotsch F., Thalhammer F., Anseeuw K., Francois C. A., Van Braeckel E., Vincent J. L., Djimon M. Z., Bashi J., Dodo R., Nouer S. A., Chipev P., Encheva M., Miteva D., Petkova D., Balkissou A. D., Pefura Yone E. W., Mbatchou Ngahane B. H., Shen N., Xu J. -F., Bustamante Rico C. A., Buitrago R., Pereira Paternina F. J., Kayembe Ntumba J. -M., Carevic V. V., Jakopovic M., Jankovic M., Matkovic Z., Mitrecic I., Bouchy Jacobsson M. -L., Christensen A. B., Heitmann Bodtger U. C., Meyer C. N., Jensen A. V., Baunbaek-knudsen G., Petersen P. T., Andersen S., El-Said Abd El-Wahhab I., Morsy N. E., Shafiek H., Sobh E., Abdulsemed K. A., Bertrand F., Brun-Buisson C., de Montmollin E., Fartoukh M., Messika J., Tattevin P., Khoury A., Ebruke B., Dreher M., Kolditz M., Meisinger M., Pletz M. W., Hagel S., Rupp J., Schaberg T., Spielmanns M., Creutz P., Suttorp N., Siaw-Lartey B., Papapetrou D., Tsigou E., Ampazis D., Kaimakamis E., Bhatia M., Dhar R., D'Souza G., Garg R., Koul P. A., Mahesh P. A., Jayaraj B. S., Narayan K. V., Udnur H. B., Krishnamurthy S. B., Kant S., Swarnakar R., Limaye S., Salvi S., Golshani K., Keatings V. M., Martin-Loeches I., Maor Y., Strahilevitz J., Faverio P., Battaglia S., Carrabba M., Ceriana P., Confalonieri M., Monforte A. D., Del Prato B., De Rosa M., Fantini R., Fiorentino G., Gammino M. A., Menzella F., Milani G., Nava S., Palmiero G., Gabrielli B., Rossi P., Sorino C., Steinhilber G., Zanforlin A., San Luca O., Franzetti F., Carugati M., Morosi M., Monge E., Carone M., Patella V., Scarlata S., Comel A., Kurahashi K., Bacha Z. A., Ugalde D. B., Zuniga O. C., Villegas J. F., Medenica M., Mihsra D. R., Shrestha P., Ridgeon E., Awokola B. I., Adefuye Bolanle Olufunlola O. N. O., Olumide S., Ukwaja K. N., Irfan M., Minarowski L., Szymon S., Froes F., Leuschner P., Meireles M., Ferrao C., Neves J., Abel Salazar, Ravara S. B., Brocovschii V., Rusu D., Toma C., Chirita D., Dorobat C. M., Birkun A., Kaluzhenina A., Almotairi A., Ali Bukhary Z. A., Edathodu J., Fathy A., Abdulaziz Enani A. M., Mohamed N. E., Memon J. U., Bella A., Bogdanovic S. N., Milenkovic B., Pesut D., Borderias L., Bordon Garcia N. M., Alarcon H. C., Cilloniz C., Torres A., Diaz-Brito V., Casas X., Gonzalez A. E., Fernandez-Almira M. L., Interna M., Gallego M., Gaspar-GarcIa I., Gonzalez del Castillo J., Victoria P. J., Martinez E. L., Malo de Molina R., Marcos P. J., Menendez R., Pando-Sandoval A., Aymerich C. P., Lacoma de la Torre A., Garcia-Olive I., Rello J., Moyano S., Rodrigo-Troyano A., Sole-Violan J., Uranga A., van Boven J. F., Torra E. V., Pujol J. A., Feldman C., Yum H. K., Arnauld Attannon Fiogbe I. U., Yangui F., Bilaceroglu S., Levent Dalar I. D., Yilmaz U., Bogomolov A., Elahi N., Dhasmana D. J., Feneley A., Hill A. T., Rudran B., Ruiz-Buitrago S., Campbell M., Whitaker P., Youzguin A., Singanayagam A., Hancock C., Villafuerte D., Allen K. S., Brito V., Dietz J., Dysart C. E., Kellie S. M., Ricardo A. Franco-Sadud C. J., Meier G., Gaga M., Holland T. L., Bergin S. P., Kheir F., Landmeier M., Lois M., Nair G. B., Patel H., Reyes K., Rodriguez-Cintron W., Saito S., Noda J., Hinojosa C. I., Levine S. M., Reyes L. F., Angel L. F., Whitlow K. S., Hipskind J., Sukhija K., Totten V., Wunderink R. G., Shah R. D., Mateyo K. J., Noriega L., Alvarado E., Aman M., and Labra L.

Abstract

Background: Aspiration community-acquired pneumonia (ACAP) and community-acquired pneumonia (CAP) in patients with aspiration risk factors (AspRFs) are infections associated with anaerobes, but limited evidence suggests their pathogenic role. Research Question: What are the aspiration risk factors, microbiology patterns, and empiric anti-anaerobic use in patients hospitalized with CAP? Study Design and Methods: This is a secondary analysis of GLIMP, an international, multicenter, point-prevalence study of adults hospitalized with CAP. Patients were stratified into three groups: (1) ACAP, (2) CAP/AspRF+ (CAP with AspRF), and (3) CAP/AspRF- (CAP without AspRF). Data on demographics, comorbidities, microbiological results, and anti-anaerobic antibiotics were analyzed in all groups. Patients were further stratified in severe and nonsevere CAP groups. Results: We enrolled 2,606 patients with CAP, of which 193 (7.4%) had ACAP. Risk factors independently associated with ACAP were male, bedridden, underweight, a nursing home resident, and having a history of stroke, dementia, mental illness, and enteral tube feeding. Among non-ACAP patients, 1,709 (70.8%) had CAP/AspRF+ and 704 (29.2%) had CAP/AspRF-. Microbiology patterns including anaerobes were similar between CAP/AspRF-, CAP/AspRF+ and ACAP (0.0% vs 1.03% vs 1.64%). Patients with severe ACAP had higher rates of total gram-negative bacteria (64.3% vs 44.3% vs 33.3%, P =.021) and lower rates of total gram-positive bacteria (7.1% vs 38.1% vs 50.0%, P <.001) when compared with patients with severe CAP/AspRF+ and severe CAP/AspRF-, respectively. Most patients (>50% in all groups) independent of AspRFs or ACAP received specific or broad-spectrum anti-anaerobic coverage antibiotics. Interpretation: Hospitalized patients with ACAP or CAP/AspRF+ had similar anaerobic flora compared with patients without aspiration risk factors. Gram-negative bacteria were more prevalent in patients with severe ACAP. Despite having similar

Published
2021

44. Bayesian Multilevel Models for Count Data

Author

Olumide S. Adesina

Subjects
Count Data, Health, Insurance, Dispersion, Multilevel Models, Physics, QC1-999, General Mathematics, Multilevel model, Posterior probability, Bayesian probability, General Physics and Astronomy, General Chemistry, Poisson distribution, Data set, symbols.namesake, Statistics, symbols, Poisson regression, Mathematics, Weibull distribution, Count data
Abstract

The traditional Poisson regression model for fitting count data is considered inadequate to fit over-or under-dispersed count data and new models have been developed to make up for such inadequacies inherent in the model. In this study, Bayesian Multi-level model was proposed using the No-U-Turn Sampler (NUTS) sampler to sample from the posterior distribution. A simulation was carried out for both over-and under-dispersed data from discrete Weibull distribution. Pareto k diagnostics was implemented, and the result showed that under-dispersed and over-dispersed simulated data has all its k value to be less than 0.5, which indicate that all the observations are good. Also all WAIC were the same as LOO-IC except for Poisson in the over-dispersed simulated data. Real-life data set from National Health Insurance Scheme (NHIS) was used for further analysis. Seven multi-level models were f itted and the Geometric model outperformed other model.

Published
2021

45. Variability and inheritance in macadamia progenies to Phytophthora cinnamomi and P. multivora the causal agents of root rot and stem canker

Author

Olufemi A. Akinsanmi, Olumide S. Jeff-Ego, Andre Drenth, Bruce Topp, and J. Henderson

Subjects
Canker, biology, Inoculation, fungi, food and beverages, Soil Science, Plant Science, Phytophthora cinnamomi, Heritability, biology.organism_classification, medicine.disease, Horticulture, Seedling, Root rot, medicine, Phytophthora, Cultivar
Abstract

Phytophthora species cause root rot and stem canker in macadamia, resulting in yield reduction and tree death. Differences in disease severity between trees in commercial orchards may be attributed to genetic factors and stochastic variation. Here we examined variability in half-sib, open-pollinated macadamia seedling progenies and their maternal parents to Phytophthora stem, root and leaf infections. Open-pollinated seeds were obtained from trees of five commercial macadamia cultivars. Leaves were obtained from the same trees from which the seeds were collected. Leaf, stem and roots were inoculated separately with P. multivora and P. cinnamomi using in vivo and in planta assays. Disease severity parameters were obtained from the pathogen-inoculated plants and compared with the control plants. Both Phytophthora species caused varied levels of leaf lesion, stem canker and root rot in the macadamia cultivars. Root infections by P. cinnamomi were more severe than P. multivora, whereas, stem infections by both Phytophthora species were similar. A moderate to high narrow-sense heritability (h2) were estimated for root efficiency loss by P. cinnamomi (0.60–0.76) and P. multivora (0.36–0.69), which were higher than the combined h2 estimates for both Phytophthora species. The differential interaction of the disease severity parameters for P. multivora and P. cinnamomi suggests variation in their resistance mechanism(s) in macadamia.

Published
2021

50. Response of peanut (Arachis hypogaeaL.) to sublethal rates of dicamba plus glyphosate at different growth stages

Author

Daramola, Olumide S., Kharel, Prasanna, Iboyi, Joseph E., and Devkota, Pratap

Abstract

The widespread adoption of dicamba‐resistant crops has increased the applications of newer dicamba formulations for weed control. However, there is a concern for potential off‐target injury and yield reduction to peanuts (Arachis hypogeaL.) planted in close proximity to dicamba‐resistant crops. Field experiments were conducted in the summer of 2019 and 2020 to evaluate peanut response to reduced rates (1/512X, 1/128X, 1/32X, and 1/8X the label rate, 564 + 1280 g ae ha−1) of dicamba plus glyphosate (XtendiMax plus Roundup PowerMax) at 25, 50, and 75 days after planting (DAP) which corresponds to vegetative, flowering, and pod development stages, respectively. Peanut exposure to dicamba plus glyphosate at 25 DAP resulted in 1.6–2.3 times greater injury and 3.4–8.5 times greater height and canopy width reductions compared to 50 and 75 DAP exposures. Peanuts suffered greater yield reduction (18%–19%) when exposed to dicamba plus glyphosate at 25 and 75 DAP than at 50 DAP (10%). Regression analysis indicated a significant linear response for peanut injury (except at 8 weeks after treatment), canopy width reduction, and yield reduction with an increasing rate of dicamba plus glyphosate. Dicamba plus glyphosate at 1/512X rate resulted in a 3% peanut yield reduction, whereas a 41% yield reduction was observed at 1/8X rate. Correlation analysis, with Pearson's rho values ranging from 0.81 to 0.86, showed that peanut injury can be a useful predictor for estimating yield reduction. Therefore, extreme care must be taken to prevent drift occurrence or spray‐tank contamination when applying dicamba plus glyphosate on XtendFlex crops near peanut fields. Dicamba plus glyphosate caused greater injury to peanut at vegetative than at flowering or pod development stages.The yield loss was two times greater at the vegetative and pod development stages than at the flowering stage.The 1/8X label rate of dicamba plus glyphosate reduced peanut yield up to 41% compared to the weed‐free control.

Published
2023
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