scholarly journals A Physically Based Theoretical Model of Spore Deposition for Predicting Spread of Plant Diseases

2016 ◽  
Vol 106 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Scott A. Isard ◽  
Marcelo Chamecki
Keyword(s):  
Theoretical Model ◽  
Resistance Coefficient ◽  
Plant Diseases ◽  
Disease Spread ◽  
Source Distribution ◽  
Rust Disease ◽  
Modeling Framework ◽  
Area Source ◽  
Physically Based ◽  
Spore Deposition

A physically based theory for predicting spore deposition downwind from an area source of inoculum is presented. The modeling framework is based on theories of turbulence dispersion in the atmospheric boundary layer and applies only to spores that escape from plant canopies. A “disease resistance” coefficient is introduced to convert the theoretical spore deposition model into a simple tool for predicting disease spread at the field scale. Results from the model agree well with published measurements of Uromyces phaseoli spore deposition and measurements of wheat leaf rust disease severity. The theoretical model has the advantage over empirical models in that it can be used to assess the influence of source distribution and geometry, spore characteristics, and meteorological conditions on spore deposition and disease spread. The modeling framework is refined to predict the detailed two-dimensional spatial pattern of disease spread from an infection focus. Accounting for the time variations of wind speed and direction in the refined modeling procedure improves predictions, especially near the inoculum source, and enables application of the theoretical modeling framework to field experiment design.

scholarly journals Impact of Xylella fastidiosa subspecies pauca in European olives

2020 ◽  
Vol 117 (17) ◽  
pp. 9250-9259 ◽  
Author(s):  
Kevin Schneider ◽  
Wopke van der Werf ◽  
Martina Cendoya ◽  
Monique Mourits ◽  
Juan A. Navas-Cortés ◽  
...  
Keyword(s):  
Xylella Fastidiosa ◽  
Plant Diseases ◽  
Disease Spread ◽  
Economic Damage ◽  
Climatic Suitability ◽  
Case Scenario ◽  
Worst Case ◽  
Ongoing Research ◽  
Phytosanitary Measures ◽  
The Impact

Xylella fastidiosa is the causal agent of plant diseases that cause massive economic damage. In 2013, a strain of the bacterium was, for the first time, detected in the European territory (Italy), causing the Olive Quick Decline Syndrome. We simulate future spread of the disease based on climatic-suitability modeling and radial expansion of the invaded territory. An economic model is developed to compute impact based on discounted foregone profits and losses in investment. The model projects impact for Italy, Greece, and Spain, as these countries account for around 95% of the European olive oil production. Climatic suitability modeling indicates that, depending on the suitability threshold, 95.5 to 98.9%, 99.2 to 99.8%, and 84.6 to 99.1% of the national areas of production fall into suitable territory in Italy, Greece, and Spain, respectively. For Italy, across the considered rates of radial range expansion the potential economic impact over 50 y ranges from 1.9 billion to 5.2 billion Euros for the economic worst-case scenario, in which production ceases after orchards die off. If replanting with resistant varieties is feasible, the impact ranges from 0.6 billion to 1.6 billion Euros. Depending on whether replanting is feasible, between 0.5 billion and 1.3 billion Euros can be saved over the course of 50 y if disease spread is reduced from 5.18 to 1.1 km per year. The analysis stresses the necessity to strengthen the ongoing research on cultivar resistance traits and application of phytosanitary measures, including vector control and inoculum suppression, by removing host plants.

Predictions of ICHAZ Cyclic Thermomechanical Response in GTAW Process for 9Cr Steels

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Padraig Mac Ardghail ◽  
Richard A. Barrett ◽  
Noel Harrison ◽  
Sean B. Leen
Keyword(s):  
Constitutive Model ◽  
Heat Affected Zone ◽  
Volume Fraction ◽  
Cyclic Performance ◽  
Modeling Framework ◽  
Thermomechanical Response ◽  
Welding Simulation ◽  
Physically Based ◽  
Gtaw Process

This work is concerned with the development of a modeling framework to predict the effects of tempered–untempered martensite heterogeneity on the thermomechanical performance of welded material. A physically based viscoplasticity model for the intercritical heat-affected zone (ICHAZ) for 9Cr steels (e.g., P91, P92) is presented in this work, with the ICHAZ represented as a mixture of tempered and untempered martensite. The constitutive model includes dislocation-based Taylor hardening and damage for different material phases. A sequentially coupled thermal–mechanical welding simulation is conducted to predict the volume fraction compositions for the various weld-affected material zones in a cross-weld (CW) specimen. The out-of-phase cyclic thermomechanical (25 °C to 600 °C) performance of notched and plain samples is comparatively assessed for a range of different tempered–untempered martensitic material heterogeneities. It is shown that the heterogeneity in a simulated CW material is highly detrimental to thermal cyclic performance.

scholarly journals Eco-Epidemiological Uncertainties of Emerging Plant Diseases: The Challenge of Predicting Xylella fastidiosa Dynamics in Novel Environments

2020 ◽  
Vol 110 (11) ◽  
pp. 1740-1750
Author(s):  
Flavia Occhibove ◽  
Daniel S. Chapman ◽  
Alexander J. Mastin ◽  
Stephen S. R. Parnell ◽  
Barbara Agstner ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Disease Risk ◽  
Xylella Fastidiosa ◽  
Management Strategies ◽  
Plant Diseases ◽  
Disease Spread ◽  
Future Research ◽  
Bacterial Strains ◽  
Novel Environments ◽  
Epidemiological Characteristics

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in a number of European countries, we review the most pertinent epidemiological uncertainties concerning the dynamics of this bacterium in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterizing infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritize when developing pest risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations. [Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

scholarly journals Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

2013 ◽  
Vol 17 (9) ◽  
pp. 3371-3387 ◽  
Author(s):  
C. Lepore ◽  
E. Arnone ◽  
L. V. Noto ◽  
G. Sivandran ◽  
R. L. Bras
Keyword(s):  
Soil Moisture ◽  
Soil Column ◽  
Hydrologic Model ◽  
Richards Equation ◽  
Landslide Risk ◽  
Modeling Framework ◽  
Soil Moisture Dynamics ◽  
Physically Based ◽  
Spatially Distributed ◽  
Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.

scholarly journals Geostatistical Analysis of the Spatiotemporal Dynamics of Powdery Mildew and Leaf Rust in Wheat

2009 ◽  
Vol 99 (8) ◽  
pp. 974-984 ◽  
Author(s):  
Jonas Franke ◽  
Steffen Gebhardt ◽  
Gunter Menz ◽  
Hans-Peter Helfrich
Keyword(s):  
Spatial Analysis ◽  
Powdery Mildew ◽  
Disease Control ◽  
Leaf Rust ◽  
Temporal Dynamics ◽  
Growth Parameters ◽  
Spatiotemporal Dynamics ◽  
High Variability ◽  
Plant Diseases ◽  
Disease Spread

Plant diseases are dynamic systems that progress or regress in spatial and temporal dimensions. Site-specific or temporally optimized disease control requires profound knowledge about the development of each stressor. The spatiotemporal dynamics of leaf rust (Puccinia recondite f. sp. tritici) and powdery mildew (Blumeria graminis f. sp. tritici) in wheat was analyzed in order to evaluate typical species-dependent characteristics of disease spread. During two growing seasons, severity data and other relevant plant growth parameters were collected in wheat fields. Spatial characteristics of both diseases were assessed by cluster analyses using spatial analysis by distance indices, whereas the temporal epidemic trends were assessed using statistical parameters. Multivariate statistics were used to identify parameters suitable for characterizing disease trends into four classes of temporal dynamics. The results of the spatial analysis showed that both diseases generally occurred in patches but a differentiation between the diseases by their spatial patterns and spread was not possible. In contrast, temporal characteristics allowed for a differentiation of the diseases, due to the fact that a typical trend was found for leaf rust which differed from the trend of powdery mildew. Therefore, these trends suggested a high potential for temporally optimized disease control. Precise powdery mildew control would be more complicated due to the observed high variability in spatial and temporal dynamics. The general results suggest that, in spite of the high variability in spatiotemporal dynamics, disease control that is optimized in space and time is generally possible but requires consideration of disease- and case-dependent characteristics.

scholarly journals Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea

2022 ◽  
Vol 19 (2) ◽  
pp. 824
Author(s):  
Youngbin Lym ◽  
Hyobin Lym ◽  
Keekwang Kim ◽  
Ki-Jung Kim
Keyword(s):  
South Korea ◽  
Unobserved Heterogeneity ◽  
Local Level ◽  
Explanatory Power ◽  
Temporal Correlation ◽  
Disease Spread ◽  
Modeling Framework ◽  
Safety Level ◽  
Latent Effects ◽  
Local Safety

This study aims provide understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

scholarly journals Identifying uncertainties in simulated streamflow from hydrologic model components for climate change impact assessments

2019 ◽  
Author(s):  
Dongmei Feng ◽  
Edward Beighley
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Hydrologic Model ◽  
Process Models ◽  
Runoff Generation ◽  
Modeling Framework ◽  
Total Uncertainty ◽  
Physically Based ◽  
Model Components

Abstract. Assessing the impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used in the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties in the simulated hydrologic variables, yet the identification and quantification of such uncertainties is rarely studied. Here, a modeling framework is developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches: runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically-based, infiltration excess) and simple-TOPMODEL (STP, physically-based, saturation excess), are coupled with Hillslope River Routing model to simulate streamflow. A case study conducted in Santa Barbara County, California, reveals that the median changes are 1–10 % increases in mean annual discharge (Qm) and 10–40 % increases in annual maximum daily discharge (Qp) and 100-yr flood discharge (Q100). The Bayesian Model Averaging analysis indicates that the probability of increase in streamflow can be up to 85 %. However, the simulated discharge uncertainties are large (i.e., 230 % for Qm and 330 % for Qp and Q100) with general circulation models (GCMs) and emission scenarios accounting for more than half of the total uncertainty. Hydrologic process models contribute 10–30 % of the total uncertainty, while uncertainty due to hydrologic model parameterization is almost negligible (

scholarly journals Improving Management Strategies of Plant Diseases Using Sequential Sensitivity Analyses

2019 ◽  
Vol 109 (7) ◽  
pp. 1184-1197 ◽  
Author(s):  
Loup Rimbaud ◽  
Sylvie Dallot ◽  
Claude Bruchou ◽  
Sophie Thoyer ◽  
Emmanuel Jacquot ◽  
...  
Keyword(s):  
Net Present Value ◽  
Management Strategies ◽  
Optimal Solution ◽  
Plant Diseases ◽  
Control Measures ◽  
Disease Suppression ◽  
Simultaneous Optimization ◽  
Disease Spread ◽  
Sensitivity Analyses ◽  
Plum Pox Virus

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and cost-effective strategies for disease management.

scholarly journals Spatio-temporal predictive modeling framework for infectious disease spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sashikumaar Ganesan ◽  
Deepak Subramani
Keyword(s):  
Predictive Modeling ◽  
Dimensional Space ◽  
Scalar Function ◽  
Disease Spread ◽  
High Dimensional ◽  
Modeling Framework ◽  
High Dimensional Space ◽  
Susceptible Population ◽  
Infected Population ◽  
Spatio Temporal

AbstractA novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from a non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across duration of disease, infection severity and age of the population. These insights could be used for science-informed policy planning.

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