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The Study of Plant Disease Epidemics
By Laurence V. Madden, Gareth Hughes, and Frank van den Bosch
2007; 8½� X 11� hardcover; 432 pages; 170 black and white illustrations; (3 lbs.)
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Information
| Product Code | 007051 |
| ISBN Code | 9780890543542 |
| Publisher | APS PRESS |
Description
The Study of Plant Disease Epidemics
By Laurence V. Madden, Gareth Hughes, and Frank van den Bosch
Plant disease epidemics, caused by established and invasive pathogen species, continue to impact a world increasingly concerned with the quantity and quality of its primary food supply. The Study of Plant Disease Epidemics is a comprehensive manual that introduces readers to the essential principles and concepts of plant disease epidemiology. This useful reference and textbook provides a detailed exposition on how to describe, compare, analyze, and predict epidemics of plant disease for the ultimate purposes of developing and testing control strategies and tactics.
The authors have synthesized the research advances from the last four decades, with a special emphasis on research done in the last 15 years, to produce a useful framework for:
*
Measuring plant disease
*
Quantifying and modeling disease development in time and space
*
Quantifying patterns of disease and sampling for disease in populations
*
Determining decision thresholds for control interventions
*
Characterizing the relationship between disease development and crop loss
This new reference introduces a coherent theory of disease development in plant host populations over time and space, coupled with detailed explanations of the components of diseases in crops and forests. This theory demonstrates how different levels of mathematical complexity can lead to unifying principles of disease invasion, persistence, and rates of temporal increase and disease expansion from foci. In addition, the book shows how disease control strategies are intricately related to fundamental population-biology parameters.
The information on modeling and statistical analysis provides the needed tools and procedures for researchers to help them properly measure and analyze collected epidemiological data and maximize its value. The methods and principles described throughout the book explain how to translate this valuable data and utilize it to make informed disease management decisions.
The Study of Plant Disease Epidemics is the highly anticipated original work by three of the leading plant disease epidemiologists of the last quarter century. This manual is an essential tool intended for graduate students, researchers, and teachers of plant pathology, as well as crop consultants and those in disease management positions. It will be an excellent teaching tool for courses in Plant Disease Epidemiology, Plant Disease Management, Invasive Species Risk Assessment, and Plant Pathogen Ecology.
Contents
Chapter 1: Introduction
Plant Disease Epidemics
Some Concepts
Epidemics
Epidemiology
Epidemic versus epiphytotic
Some Historical Developments
Up to 1963
After 1963
Some conferences and books, starting in 1963
Final thoughts on the review of historical developments
Prelude to the Rest of the Book
Possible Course Outlines
Suggested Readings
Chapter 2: Measuring Plant Diseases
Introduction
Plant Disease Intensity
Concepts
Severity versus incidence: some considerations
Measurement Levels and Random Variables
Measurement level
Random variables
Plant disease variables
Assessing Disease Intensity
Incidence, counts, and severity: some general comments
Visual assessment of disease severity
Direct estimation
Direct estimation with use of disease diagrams
Estimation with use of disease scales
Estimation with use of ordinal rating scales
Random variables for severity of disease
Remote-sensing and electronic assessment of disease severity
Spectral signature
Multispectral radiometry
Image analysis
Indirect measurement of severity
Reliability, Accuracy, Agreement
General concepts
Reliability
Accuracy
Ordinal and binary data
Improving disease measurements
Attributes and Properties of the Crop
Some useful static and dynamic properties
Leaf area index
Conclusion and Prelude to Following Chapters
Suggested Readings
Chapter 3: Introduction to Modeling in Epidemiology
Introduction
Models
Definition and general classification
Quantitative (mathematical) models - some general concepts
Probability distributions
Is the model linear?
Methods of Model Development
Fitting of Linear Models to Data
Introduction
Least squares regression - general concepts
Distributional results
Model evaluation
Model adjustments
Other considerations
Fitting of Nonlinear Models to Data
General considerations
Nonlinear least squares
Linearized models
From nonlinear to linear
Model fitting
Where is the error additive?
Nonlinear or linearized statistical models?
Applications
Disease intensity in relation to inoculum density
The cumulative response
Maximum Likelihood
Discussions and Prelude to Later Chapters
Suggested Readings
Chapter 4: Temporal Analysis I: Quantifying and Comparing Epidemics
Introduction
General Concepts
Notation and introduction to models
Disease progress curves
How Does an Epidemic Occur?
Contact of inoculum with the crop host
Epidemic classification
Nuances of classification of epidemics
Models
Exponential model
Monomolecular model
Logistic model
Some other population dynamics models
Gompertz model
Richards model
Model comparisons
Calculations with the models
Control
Control strategies for polycyclic diseases
Calculations for polycyclic diseases
Control for monocyclic diseases
Summary of disease control strategies
Model Fitting
Choosing a model
Estimating parameters and assessing model fit - linear least squares
Estimating parameters-nonlinear least squares
Parameter estimation-generalized linear models for disease incidence
Comparing Disease Progress Curves
Simple comparison of epidemics
Epidemics in designed experiments
Choosing a disease progress model
Fitting one or more disease progress models
Comparing models with different error (residual) variance- covariance structures
Summary of model fitting and comparisons
General repeated measures analysis
Area under the disease progress curve
Some other approaches
Models with Maximum Disease Intensity as a Parameter
General concepts
Choosing a model
Parameter estimation
Time-Varying Rate Term
Concluding Comments and Prelude to Advanced Topics
Suggested Readings
Chapter 5: Temporal Analysis II: The Components of Disease
Introductions
Terminology
Disease Progress Models with Fixed Density
A simple discrete-time model
Model derivation
Model simulation
The threshold for epidemic development
Initial disease increase
Concluding remarks
The H-I-R epidemic model
Model derivation
Model simulations
The threshold for epidemic development
Initial disease increase
Final disease level
Concluding remarks
The H-L-I-R epidemic model
Model derivation
Model simulations
The threshold for epidemic development
Initial disease increase
Final disease level
Some concluding remarks
Recapitulation of the model equations - role of latent and infectious periods
The Vanderplank model
Model derivation
The threshold for epidemic development
Initial disease increase
Final disease level
Concluding remarks
The Kermack and McKendrick model
The sporulation curve
Model derivation
The exponential growth rate and derived R0
The exponential growth rate for sporulation curve 5.50
Final disease level
Concluding remarks
Conclusions
Chapter 6: Temporal Analysis III: Advanced Topics
Introduction
Models with Crop Growth
Continuous crop growth
Model derivation
Model simulations
The removed category
Steady states and thresholds for epidemic development
Initial disease increase
Threshold of epidemic development of model equations 6.5
Concluding remarks
Seasonal cropping
Model derivation
Model simulations
Threshold for epidemic development
Concluding remarks
The Role of Primary Infections
Model derivation
Model simulations
Discussion
Epidemics with Vector Transmission
Model derivation
Model simulations
Steady states and thresholds for epidemic development
Some notes on disease management
Concluding remarks
Transitional Dynamics and Other Complexities
Models considered so far
More complicated models
Computer simulation modeling?
Stochasticity
Parameter Estimation
Estimating parameters without direct curve fitting
Fitting models to data
Suggested Readings
Chapter 7: Spatial Aspects of Epidemics � I: Pathogen Dispersal and Disease Gradients
Introduction
Dispersal Gradients, Disease Gradients, and Disease Spread
Concepts
Inoculum sources
Models
Exponential
Power model
Power versus exponential model
Contact distributions
Some other dispersal models
Some calculations
Model Fitting
Choosing a model
Estimating parameters � linear methods
Estimating parameters � nonlinear methods
Disease Gradients � Correcting for Maximum Intensity
Simple adjustment
Generalizations of the exponential and power models
Other models
Model fitting
General comments
Example � graphical evaluation
Example � linear regression
Example � comparing parameter estimates
Spatio-Temporal Dynamics of Disease Spread
General comments
Two spatio-temporal models
∂s/∂t
Isopaths
Two models
Other models
Analysis
Disease Management
Concluding Comments and a Prelude to the Following Chapters
Selected Readings
Chapter 8: Spatial Aspects of Epidemics � II: A Theory of Spatio-Temporal Disease Dynamics
Introduction
Large scale spread: the case of potato light blight
Small scale, focus expansion
Common features of spatial disease expansion
Models for Spatial Populations Expansion
Introduction
Model derivation
Rates of expansion in relation to contact distributions
Gaussian contact distribution
Double exponential contact distribution
Root contact distribution
Modified power law contact
Comparisons
Some Extensions
One dimensional versus two dimensional epidemic expansion
Continuous time and more
Model and simulations
Disease expansion rates � traveling waves
Disease expansion rates � dispersive traveling waves
Multi-seasonal epidemic expansion
Disease expansion with monocyclic diseases
Multiple foci and temporal dynamics
An Application
Concluding Remarks
Selected Readings
Chapter 9: Spatial Aspects of Epidemics � III: Patterns of Plant Disease
Why We Look at Spatial Patterns
Terminology
Spatial Plant Disease Data
Data collection
Analysis of Sparsely-Sampled Incidence Data
Summary statistics
The binomial distribution
The index of dispersion
Intra-cluster correlation
The beta-binomial distribution
The index of dispersion revisted
A power law relationship between variances
How the power law is related to statistical probability distributions
Unequal size sampling units
Two-stage sampling
Analysis of Sparsely-Sampled Count Data
Summary statistics
The Poisson distribution
The negative binomial distribution
The index of dispersion for counts
Taylor�s power law
Relationships between Distributions
Spatial Hierarchies
Disease incidence in a spatial hierarchy
Counts in a spatial hierarchy
Sparsely-Sampled Disease Severity Data
The severity-incidence relationship � regression models
The severity-incidence relationship � a mathematical model
Another regression model
Overview of the severity-incidence relationship
Analysis of Intensively-Mapped Disease Data
Join-count statistics
The cross-product statistic
Spatial autocorrelation
Semivariance
Spatial analysis by distance indices
Spatial patterns and Dispersal Functions
Simulation models
Inference of dispersal from pattern using stochastic models
Distance-Based Methods
Events and intervals
Neighbors
The K(distance) function
Conclusions
Suggested Readings
Chapter 10: Estimating Plant Disease by Sampling
Why We Sample of Epidemiological Data
Sampling Preliminaries
Terminology
Sample size
Sample design
Variability
Population size
Reliability of the estimated sample mean
Simple Random Sampling for Disease Incidence Data
Sample size calculations
Inspection errors
Exact binomial confidence intervals
Simple Random Sampling for Count Data
The Poisson distribution
The negative binomial distribution
Taylor�s power law
Sample size calculations
Exact Poisson confidence intervals
Cluster Sampling for Disease Incidence Data
The binomial distribution
The beta-binomial distribution
The power law
Sample size calculations
Exact confidence intervals for cluster sampling data
Regression Analysis of Disease Incidence Data
Logistic regression
Beta-binomial regression
Logistic regression with deff-transformed data
Fitting statistical probability distributions
Regression Analysis of Count Data
Poisson and negative binomial regression
Group Testing with Incidence Data
The estimator
Choice of group size
Sample size calculations
Exact confidence intervals
Group testing using generalized linear models
Binomial Sampling for Count Data
Binomial sampling based on probability distributions
Binomial sampling based on empirical models
Estimation of Disease Severity
Inverse Sampling for Disease Incidence
How many positives?
Exact confidence intervals
The geometric series
Sequential Estimation of Disease
Sequential estimation of disease incidence from simple random sampling
Sequential estimation for count data
Sequential estimation of disease incidence from cluster sampling
Conclusions
Suggested Readings
Chapter 11: Decision-Making in the Practice of Plant Disease Management
Decision-Making Disease Management
Acceptance Sampling Preliminaries
Probability and likelihood
Thresholds
The operating characteristic curve
The binomial distribution
The hypergeometric distribution
Inspection errors in simple random sampling
Designing a Sampling Plan with a Specified Curve
Plans based on the producer�s and consumer�s risks
Plans based on the indifference quality level
Finding a sampling plan by iteration
Zero Acceptance Number Sampling Plans
The operating characteristic curve
Sample size calculations
The mailroom problem
Sequential Sampling for Classification
Sequential classification with simple random sampling data
Sequential classification with cluster sampling data
The need for simulation
Risk Algorithms as a Basis for Decisions-Making
Risk factors
Risk algorithms
The receiver operating characteristic curve
Sensitivity and specificity as conditional probabilities
Likelihood ratios
Predicting the Need for Treatment
Bayes� theorem
Predicting unusual events is problematic
Conclusion
Suggested Readings
Chapter Twelve: Epidemics and Crop Yield
Introductions
Definitions and Concepts
Yield
Impacts of disease on crops
Data and Relationships
Graphs of yield and disease
Obtaining data from a range of epidemics
Experimental and sampling units
Planned experiments
Surveys
Yield per unit area
Expert opinion
Modeling Yield in Relation to Disease
Notation and general concepts
Single point models
Linear models
Nonlinear models
Model fitting
Some considerations regarding the response and predictor variables in single-point (and other)
models
Multiple-point models
Integral models
Other predictor variables in empirical models
An Example Analysis
Mechanistic Approaches to Crop Loss Assessment
General considerations based on crop physiology
Radiation interception and yield
Characterizing crop losses in relation to HAA and RUE
Virtual lesionsns
Type I and Type II curves
Time of infection
Discussion
Spatial Heterogeneity
General concepts
Models
An approximation (but a good one)
Discussion and Conclusions
Suggested Readings
References
Index
2007; 8½� X 11� hardcover; 432 pages; 170 black and white illustrations; (3 lbs.)
By Laurence V. Madden, Gareth Hughes, and Frank van den Bosch
Plant disease epidemics, caused by established and invasive pathogen species, continue to impact a world increasingly concerned with the quantity and quality of its primary food supply. The Study of Plant Disease Epidemics is a comprehensive manual that introduces readers to the essential principles and concepts of plant disease epidemiology. This useful reference and textbook provides a detailed exposition on how to describe, compare, analyze, and predict epidemics of plant disease for the ultimate purposes of developing and testing control strategies and tactics.
The authors have synthesized the research advances from the last four decades, with a special emphasis on research done in the last 15 years, to produce a useful framework for:
*
Measuring plant disease
*
Quantifying and modeling disease development in time and space
*
Quantifying patterns of disease and sampling for disease in populations
*
Determining decision thresholds for control interventions
*
Characterizing the relationship between disease development and crop loss
This new reference introduces a coherent theory of disease development in plant host populations over time and space, coupled with detailed explanations of the components of diseases in crops and forests. This theory demonstrates how different levels of mathematical complexity can lead to unifying principles of disease invasion, persistence, and rates of temporal increase and disease expansion from foci. In addition, the book shows how disease control strategies are intricately related to fundamental population-biology parameters.
The information on modeling and statistical analysis provides the needed tools and procedures for researchers to help them properly measure and analyze collected epidemiological data and maximize its value. The methods and principles described throughout the book explain how to translate this valuable data and utilize it to make informed disease management decisions.
The Study of Plant Disease Epidemics is the highly anticipated original work by three of the leading plant disease epidemiologists of the last quarter century. This manual is an essential tool intended for graduate students, researchers, and teachers of plant pathology, as well as crop consultants and those in disease management positions. It will be an excellent teaching tool for courses in Plant Disease Epidemiology, Plant Disease Management, Invasive Species Risk Assessment, and Plant Pathogen Ecology.
Contents
Chapter 1: Introduction
Plant Disease Epidemics
Some Concepts
Epidemics
Epidemiology
Epidemic versus epiphytotic
Some Historical Developments
Up to 1963
After 1963
Some conferences and books, starting in 1963
Final thoughts on the review of historical developments
Prelude to the Rest of the Book
Possible Course Outlines
Suggested Readings
Chapter 2: Measuring Plant Diseases
Introduction
Plant Disease Intensity
Concepts
Severity versus incidence: some considerations
Measurement Levels and Random Variables
Measurement level
Random variables
Plant disease variables
Assessing Disease Intensity
Incidence, counts, and severity: some general comments
Visual assessment of disease severity
Direct estimation
Direct estimation with use of disease diagrams
Estimation with use of disease scales
Estimation with use of ordinal rating scales
Random variables for severity of disease
Remote-sensing and electronic assessment of disease severity
Spectral signature
Multispectral radiometry
Image analysis
Indirect measurement of severity
Reliability, Accuracy, Agreement
General concepts
Reliability
Accuracy
Ordinal and binary data
Improving disease measurements
Attributes and Properties of the Crop
Some useful static and dynamic properties
Leaf area index
Conclusion and Prelude to Following Chapters
Suggested Readings
Chapter 3: Introduction to Modeling in Epidemiology
Introduction
Models
Definition and general classification
Quantitative (mathematical) models - some general concepts
Probability distributions
Is the model linear?
Methods of Model Development
Fitting of Linear Models to Data
Introduction
Least squares regression - general concepts
Distributional results
Model evaluation
Model adjustments
Other considerations
Fitting of Nonlinear Models to Data
General considerations
Nonlinear least squares
Linearized models
From nonlinear to linear
Model fitting
Where is the error additive?
Nonlinear or linearized statistical models?
Applications
Disease intensity in relation to inoculum density
The cumulative response
Maximum Likelihood
Discussions and Prelude to Later Chapters
Suggested Readings
Chapter 4: Temporal Analysis I: Quantifying and Comparing Epidemics
Introduction
General Concepts
Notation and introduction to models
Disease progress curves
How Does an Epidemic Occur?
Contact of inoculum with the crop host
Epidemic classification
Nuances of classification of epidemics
Models
Exponential model
Monomolecular model
Logistic model
Some other population dynamics models
Gompertz model
Richards model
Model comparisons
Calculations with the models
Control
Control strategies for polycyclic diseases
Calculations for polycyclic diseases
Control for monocyclic diseases
Summary of disease control strategies
Model Fitting
Choosing a model
Estimating parameters and assessing model fit - linear least squares
Estimating parameters-nonlinear least squares
Parameter estimation-generalized linear models for disease incidence
Comparing Disease Progress Curves
Simple comparison of epidemics
Epidemics in designed experiments
Choosing a disease progress model
Fitting one or more disease progress models
Comparing models with different error (residual) variance- covariance structures
Summary of model fitting and comparisons
General repeated measures analysis
Area under the disease progress curve
Some other approaches
Models with Maximum Disease Intensity as a Parameter
General concepts
Choosing a model
Parameter estimation
Time-Varying Rate Term
Concluding Comments and Prelude to Advanced Topics
Suggested Readings
Chapter 5: Temporal Analysis II: The Components of Disease
Introductions
Terminology
Disease Progress Models with Fixed Density
A simple discrete-time model
Model derivation
Model simulation
The threshold for epidemic development
Initial disease increase
Concluding remarks
The H-I-R epidemic model
Model derivation
Model simulations
The threshold for epidemic development
Initial disease increase
Final disease level
Concluding remarks
The H-L-I-R epidemic model
Model derivation
Model simulations
The threshold for epidemic development
Initial disease increase
Final disease level
Some concluding remarks
Recapitulation of the model equations - role of latent and infectious periods
The Vanderplank model
Model derivation
The threshold for epidemic development
Initial disease increase
Final disease level
Concluding remarks
The Kermack and McKendrick model
The sporulation curve
Model derivation
The exponential growth rate and derived R0
The exponential growth rate for sporulation curve 5.50
Final disease level
Concluding remarks
Conclusions
Chapter 6: Temporal Analysis III: Advanced Topics
Introduction
Models with Crop Growth
Continuous crop growth
Model derivation
Model simulations
The removed category
Steady states and thresholds for epidemic development
Initial disease increase
Threshold of epidemic development of model equations 6.5
Concluding remarks
Seasonal cropping
Model derivation
Model simulations
Threshold for epidemic development
Concluding remarks
The Role of Primary Infections
Model derivation
Model simulations
Discussion
Epidemics with Vector Transmission
Model derivation
Model simulations
Steady states and thresholds for epidemic development
Some notes on disease management
Concluding remarks
Transitional Dynamics and Other Complexities
Models considered so far
More complicated models
Computer simulation modeling?
Stochasticity
Parameter Estimation
Estimating parameters without direct curve fitting
Fitting models to data
Suggested Readings
Chapter 7: Spatial Aspects of Epidemics � I: Pathogen Dispersal and Disease Gradients
Introduction
Dispersal Gradients, Disease Gradients, and Disease Spread
Concepts
Inoculum sources
Models
Exponential
Power model
Power versus exponential model
Contact distributions
Some other dispersal models
Some calculations
Model Fitting
Choosing a model
Estimating parameters � linear methods
Estimating parameters � nonlinear methods
Disease Gradients � Correcting for Maximum Intensity
Simple adjustment
Generalizations of the exponential and power models
Other models
Model fitting
General comments
Example � graphical evaluation
Example � linear regression
Example � comparing parameter estimates
Spatio-Temporal Dynamics of Disease Spread
General comments
Two spatio-temporal models
∂s/∂t
Isopaths
Two models
Other models
Analysis
Disease Management
Concluding Comments and a Prelude to the Following Chapters
Selected Readings
Chapter 8: Spatial Aspects of Epidemics � II: A Theory of Spatio-Temporal Disease Dynamics
Introduction
Large scale spread: the case of potato light blight
Small scale, focus expansion
Common features of spatial disease expansion
Models for Spatial Populations Expansion
Introduction
Model derivation
Rates of expansion in relation to contact distributions
Gaussian contact distribution
Double exponential contact distribution
Root contact distribution
Modified power law contact
Comparisons
Some Extensions
One dimensional versus two dimensional epidemic expansion
Continuous time and more
Model and simulations
Disease expansion rates � traveling waves
Disease expansion rates � dispersive traveling waves
Multi-seasonal epidemic expansion
Disease expansion with monocyclic diseases
Multiple foci and temporal dynamics
An Application
Concluding Remarks
Selected Readings
Chapter 9: Spatial Aspects of Epidemics � III: Patterns of Plant Disease
Why We Look at Spatial Patterns
Terminology
Spatial Plant Disease Data
Data collection
Analysis of Sparsely-Sampled Incidence Data
Summary statistics
The binomial distribution
The index of dispersion
Intra-cluster correlation
The beta-binomial distribution
The index of dispersion revisted
A power law relationship between variances
How the power law is related to statistical probability distributions
Unequal size sampling units
Two-stage sampling
Analysis of Sparsely-Sampled Count Data
Summary statistics
The Poisson distribution
The negative binomial distribution
The index of dispersion for counts
Taylor�s power law
Relationships between Distributions
Spatial Hierarchies
Disease incidence in a spatial hierarchy
Counts in a spatial hierarchy
Sparsely-Sampled Disease Severity Data
The severity-incidence relationship � regression models
The severity-incidence relationship � a mathematical model
Another regression model
Overview of the severity-incidence relationship
Analysis of Intensively-Mapped Disease Data
Join-count statistics
The cross-product statistic
Spatial autocorrelation
Semivariance
Spatial analysis by distance indices
Spatial patterns and Dispersal Functions
Simulation models
Inference of dispersal from pattern using stochastic models
Distance-Based Methods
Events and intervals
Neighbors
The K(distance) function
Conclusions
Suggested Readings
Chapter 10: Estimating Plant Disease by Sampling
Why We Sample of Epidemiological Data
Sampling Preliminaries
Terminology
Sample size
Sample design
Variability
Population size
Reliability of the estimated sample mean
Simple Random Sampling for Disease Incidence Data
Sample size calculations
Inspection errors
Exact binomial confidence intervals
Simple Random Sampling for Count Data
The Poisson distribution
The negative binomial distribution
Taylor�s power law
Sample size calculations
Exact Poisson confidence intervals
Cluster Sampling for Disease Incidence Data
The binomial distribution
The beta-binomial distribution
The power law
Sample size calculations
Exact confidence intervals for cluster sampling data
Regression Analysis of Disease Incidence Data
Logistic regression
Beta-binomial regression
Logistic regression with deff-transformed data
Fitting statistical probability distributions
Regression Analysis of Count Data
Poisson and negative binomial regression
Group Testing with Incidence Data
The estimator
Choice of group size
Sample size calculations
Exact confidence intervals
Group testing using generalized linear models
Binomial Sampling for Count Data
Binomial sampling based on probability distributions
Binomial sampling based on empirical models
Estimation of Disease Severity
Inverse Sampling for Disease Incidence
How many positives?
Exact confidence intervals
The geometric series
Sequential Estimation of Disease
Sequential estimation of disease incidence from simple random sampling
Sequential estimation for count data
Sequential estimation of disease incidence from cluster sampling
Conclusions
Suggested Readings
Chapter 11: Decision-Making in the Practice of Plant Disease Management
Decision-Making Disease Management
Acceptance Sampling Preliminaries
Probability and likelihood
Thresholds
The operating characteristic curve
The binomial distribution
The hypergeometric distribution
Inspection errors in simple random sampling
Designing a Sampling Plan with a Specified Curve
Plans based on the producer�s and consumer�s risks
Plans based on the indifference quality level
Finding a sampling plan by iteration
Zero Acceptance Number Sampling Plans
The operating characteristic curve
Sample size calculations
The mailroom problem
Sequential Sampling for Classification
Sequential classification with simple random sampling data
Sequential classification with cluster sampling data
The need for simulation
Risk Algorithms as a Basis for Decisions-Making
Risk factors
Risk algorithms
The receiver operating characteristic curve
Sensitivity and specificity as conditional probabilities
Likelihood ratios
Predicting the Need for Treatment
Bayes� theorem
Predicting unusual events is problematic
Conclusion
Suggested Readings
Chapter Twelve: Epidemics and Crop Yield
Introductions
Definitions and Concepts
Yield
Impacts of disease on crops
Data and Relationships
Graphs of yield and disease
Obtaining data from a range of epidemics
Experimental and sampling units
Planned experiments
Surveys
Yield per unit area
Expert opinion
Modeling Yield in Relation to Disease
Notation and general concepts
Single point models
Linear models
Nonlinear models
Model fitting
Some considerations regarding the response and predictor variables in single-point (and other)
models
Multiple-point models
Integral models
Other predictor variables in empirical models
An Example Analysis
Mechanistic Approaches to Crop Loss Assessment
General considerations based on crop physiology
Radiation interception and yield
Characterizing crop losses in relation to HAA and RUE
Virtual lesionsns
Type I and Type II curves
Time of infection
Discussion
Spatial Heterogeneity
General concepts
Models
An approximation (but a good one)
Discussion and Conclusions
Suggested Readings
References
Index
2007; 8½� X 11� hardcover; 432 pages; 170 black and white illustrations; (3 lbs.)
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| Product Code | 007051 |
| ISBN Code | 9780890543542 |
| Publisher | APS PRESS |
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