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E-grāmata: Sampling and Monitoring in Crop Protection: The Theoretical Basis for Developing Practical Decision Guides [CABI E-books]

(Cornell University, USA), (Sub-department of Theoretical Production Ecology, Wageningen Agricultural University, Netherlands), (Agriculture and Agri-Food Canada, Ottawa, Canada)
  • Formāts: 304 pages
  • Sērija : Cabi Publishing
  • Izdošanas datums: 04-Jan-2000
  • Izdevniecība: CABI Publishing
  • ISBN-13: 9780851993478
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  • CABI E-books
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Sampling and Monitoring in Crop Protection: The Theoretical Basis for Developing Practical Decision GuidesPalielināt
  • Formāts: 304 pages
  • Sērija : Cabi Publishing
  • Izdošanas datums: 04-Jan-2000
  • Izdevniecība: CABI Publishing
  • ISBN-13: 9780851993478
Citas grāmatas par šo tēmu:
Wiley OnlineMore info about CABI e-books
This book covers the statistical concepts of sampling in agricultural pest management. These can be summarised as how to obtain sample data from the field and how to use the data in decision-making. Options may include introducing natural enemies, spraying with pesticide, or adopting a wait-and-see approach. Some prior knowledge of pests and how they interact with crops is required of the reader, but only minimal mathematical background is assumed. Worked examples using the mathematical software program Mathcad are also included.
Preface vii
Acknowledgements xi
Basic Concepts of Decision-making in Pest Management
1(16)
Introduction
1(1)
Agriculture, pest management and the role of information
1(2)
Injury, damage, loss and threshold concepts
3(3)
The effect of uncertainty on the decision process: probabilities of management action
6(2)
The sampling effort: the OC and ASN functions
8(1)
On fuzziness and satisfaction
9(2)
Monitoring a system through time
11(1)
The formal design of methods for sampling and monitoring: Why and how?
12(1)
On learning by doing and farmer empowerment
13(1)
Summary
14(3)
Basic Concepts of Sampling for Pest Management
17(24)
Introduction
17(1)
Randomness
17(2)
Bias, precision and accuracy
19(3)
Dealing with bias
22(1)
Criteria for a trustworthy sample
23(1)
Spatially explicit data versus data lists
24(1)
Simulation of the sampling process
25(2)
The variance of sample means
27(2)
The distribution of sample means -- the Central Limit Theorem
29(4)
What does this mean for decision-making?
33(1)
Calculating a probability of decision (OC) function
34(5)
Summary
39(2)
Classifying Pest Density
41(20)
Introduction
41(1)
Classification versus estimation
41(5)
Estimation
42(2)
Choosing the sample size, n, to achieve specified precision
44(1)
Classification
45(1)
Reducing sampling costs: classification by sampling in batches
46(7)
Two batches
46(6)
More than two batches: batch sequential sampling plans
52(1)
Variance--mean relationships
53(7)
Summary
60(1)
Distributions
61(36)
Introduction
61(1)
Spatial pattern, frequency distribution and probability distribution
61(2)
Random patterns and the Poisson probability distribution
63(3)
Fitting a distribution to data using the maximum likelihood principle
66(1)
Testing the goodness of fit of a probability distribution
67(4)
Aggregated spatial patterns and the negative binomial distribution
71(7)
Estimating the negative binomial k from a variance--mean relationship
77(1)
The effect of sample size on fitting distributions
78(2)
The mechanics of fitting distributions to data
80(2)
The binomial distribution
82(1)
The beta-binomial distribution
83(6)
Relationships among the distributions
89(2)
On the choice of a probability distribution model
91(1)
Summary
91(6)
Computer generation of aggregated spatial patterns
94(3)
Sequential Sampling for Classification
97(34)
Introduction
97(1)
From batch to sequential sampling
97(1)
Iwao's sequential procedure
98(10)
Wald's sequential probability ratio test (SPRT)
108(8)
Converging lines
116(4)
Green's stop boundary for estimation
120(2)
Which stop boundary?
122(1)
Summary
123(8)
Calculating aggregation parameters for use in the simulations
127(4)
Enhancing and Evaluating the Usefulness of Sampling Plans
131(24)
Introduction
131(1)
The cyclical process of learning, improvement and design
132(2)
Proposing or modifying a sampling method (phase 1)
133(1)
Determining and evaluating the performance of the proposed method (phase 2)
133(1)
Implementing the decision guide (phase 3)
133(1)
Design ingredients of sampling plans that affect usefulness
134(4)
The size of the management unit
134(1)
The definition of the sample unit
135(1)
When and what to sample
136(1)
The sample path in the crop
137(1)
Procedures for recording data
137(1)
Evaluation by users, field tests and simulation
138(3)
Field evaluation
139(1)
Simulation, sensitivity and robustness
140(1)
Critical density
140(1)
Sensitivity with respect to sampling distribution
140(1)
Size and representativeness of sample
141(1)
The economic value of sample information
141(12)
Best control strategy with no sampling
142(1)
Calculating the value of sample information
143(1)
The value of sample information in practice
144(9)
Summary
153(2)
Binomial Counts
155(28)
Introduction
155(1)
What is binomial sampling?
156(1)
Presence--absence binomial sampling with the Poisson distribution
156(2)
Tally numbers other than zero
158(7)
The tally number and precision
162(3)
Binomial sampling based on the negative binomial distribution
165(5)
Incorporating a variance--mean relationship
170(3)
Binomial sampling using an empirical model
173(4)
Estimation
177(1)
Summary
178(5)
Variance and bias of binomial count estimates of the mean
180(3)
Multiple Sources of Variation
183(22)
Introduction
183(1)
Nested or multistage sampling
184(1)
Analysis of variance and two-stage sampling
185(6)
TPL and the application of ANOVA in decision-making
187(1)
Use of the ANOVA to test for correlation within psu
187(1)
Use of the ANOVA to estimate optimum values for ns
188(3)
Patchy environments
191(1)
Predictable patches
192(1)
Unpredictable patches -- VIS
192(10)
A VIS look-up chart
198(3)
OC and ASN functions for VIS plans
201(1)
Summary
202(3)
The variance of the sample mean in two-stage sampling
204(1)
Resampling to Evaluate the Properties of Sampling Plans
205(22)
Introduction
205(1)
Principles of resampling
206(2)
Typical properties of OC and ASN functions estimated by resampling
208(4)
Collecting data for resampling: the number and size of the basic samples
212(4)
Sequential sampling
216(1)
Binomial sampling
217(4)
A comparison between resampling and model-based methods
221(2)
When is resampling a better method to evaluate sampling plans?
223(1)
Summary
224(3)
Confidence intervals for negative binomial k estimated by TPL
226(1)
Sampling over Time to Classify or Estimate a Population Growth Curve
227(18)
Introduction
227(1)
Time-sequential classification of a population trajectory
228(8)
Time-sequential estimation of a population growth curve
236(6)
Bayesian and regression approaches
236(2)
Implementation of the regression approach
238(4)
A comparison between the methods and other considerations
242(1)
Summary
243(2)
Monitoring Pest Populations through Time
245(22)
Introduction
245(1)
Terminology of monitoring
246(1)
Tripartite classification sampling plans
246(3)
Principles for quantitative evaluation of monitoring protocols
249(4)
Direct calculation based on PC and ASN functions and the trajectory
250(1)
Calculation based on simulation and the trajectory
251(1)
Principles for designing monitoring protocols
252(1)
Population trajectories for use in evaluating monitoring protocols
253(2)
Evaluation of monitoring protocols based on cascaded tripartite classification
255(8)
Further considerations
263(1)
Summary
264(3)
Epilogue
267(6)
Glossary 273(8)
Index 281
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