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IPM > IPM exam #1 > Flashcards

IPM exam #1 Flashcards

1
Q

Define IPM.

A

(FAO). A pest management system that, in the context of associated environment and population dynamics of the pest species, utilizes all suitable techniques and methods to maintain pest populations at levels below those causing economic injury.

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2
Q

4 eras in pest control history.

A

Pre-scientific era (Pre-1880)
Biological control era (1880-1939)
Chemical era (1939-1962) (DDT)
IPM (1962-present)

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3
Q

3 principles of IPM.

A

Economic;
Ecological; and
Environmental principals.

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4
Q

Differences between IPM and traditional pest control.

A

IPM is not a single pest control method but, rather, a series of pest management evaluations, decisions, and controls.

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5
Q

Discuss the 3 principles of IPM.

A

Economic: reducing pest population (below EIL).
Ecological: to manage the agroecosystem in a way that considers the crops (not pests) as the core of the ecosystem.
Environmental principals: minimizing impact on the environment.

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6
Q

Denife experimental factors.

A

Main effects to be tested

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7
Q

Define experimental unit.

A

An entity the forms the material on which an experiment is performed.

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8
Q

Define Treatment.

A

Something that researchers administer to experimental units.

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9
Q

Define Treatment combination.

A

Combinations of the treatment levels of two or more factors.

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10
Q

Define Observation unit. An entity from which response is recorder (data are recorded).

A

An entity from which response is recorded.

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11
Q

Define Replication.

A

The repetition of an experiment or observation in the same or similar conditions.

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12
Q

Define Fixed effect.

A

Once all treatments are included in the experiment, inferences cannot be made to a larger experiment. If rate is a fixed effect, we cannot make inferences for other rates.

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13
Q

Define Random effect.

A

Treatments are a sample of the population to which you can make inferences toward a larger population. If rate is a random effect, we can make inferences about other rates not used in the experiment.

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14
Q

Completely random design.

A

Design in which experimental units are essentially homogeneous. Treatments are assigned to experimental units completely at random.

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15
Q

Define Randomized completely block design.

A

Design in which experimental subjects are not homogeneous and are first divided into homogeneous blocks. Treatments are assigned to the experimental units in each block at random. Each block contains all treatments only once.

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16
Q

Define Split-plot design.

A

SPD involves two experimental factors, A and B. Levels of factor A are randomly assigned to whole plots and levels of factor B are randomly assigned to subplots within each plot. (SPD provides more precise information about factor B than factor A).

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17
Q

Why do we need “block” in the randomized block design? Give some examples.

A

Blocks reduce variability within treatment conditions and make it more homogeneous.

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18
Q

Define Population.

A

A group to be studied.

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19
Q

Define Samples.

A

A subset of the population.

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20
Q

Define Sampling.

A

Select a proportion of the population to infer the attributes of the population based on the information (estimation) generated from the samples.

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21
Q

Define Sampling universe.

A

The whole population from which samples are collected.

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22
Q

Define Sample unit.

A

A portion of the sampling universe from which insect count is taken.

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23
Q

Define Random sampling.

A

Selection of one sample unit does not influence the choice of another sample unit.

24
Q

Define Non-random sampling.

A

Selection of one unit influences the selection of another sample unit.

25
Q

Define Stratified sampling.

A

Stratifies the sampling universe into smaller areas called strata and then random collect sample units within the strata.

26
Q

Define Two-stage random sampling.

A

A number of primary sample units are random selected, and from these units a number of secondary sample units are random examined for the pest.

27
Q

Define Unbiased and biased samplings.

A

Biased: Difference between the true value (μ) and expectation (m) of an estimate ≠ 0
(μ – m) ≠ 0
Unbiased: (μ – m) = 0 (B = 0)

28
Q

Define Precision.

A

A measure of how close an estimate is to its expectation.

29
Q

Define Accuracy.

A

A measure of how close an estimate is to what is estimating (true value).

30
Q

Define Absolute estimates.

A

An estimate of population size expressed as actual number of insects per unit of volume.

31
Q

Define Relative estimate.

A

An estimation of population size based on the type of sampling technique.

32
Q

Define Random distribution.

A

An equal probability of an individual occupying any point in space. The presence of one individual does not influence the distribution of another.

33
Q

Define Poisson distribution.

A

A type of random distribution. The probability (p) of finding a certain number of individuals (x) in a sample from a given population mean (μ).

34
Q

Define Uniform distribution.

A

Repulsion among individuals which tends to equalize the number of individuals per sample unit.

35
Q

Define Binomial distribution.

A

A type of uniform distribution with two possible outcomes.

36
Q

Define Aggregated distribution.

A

Sample universe in which some samples contain few or no individuals of a particular species while come sample may contain high numbers of individuals.

37
Q

Define Negative binomial distribution.

A

A type of aggregated distribution..

38
Q

Define Mean crowding.

A

The number of other insects per individual in the same sample unit.

39
Q

List seven common insect sampling techniques.

A 
Knockdown
Indirect techniques
Direct Count
Netting
Attractive traps
Passive traps
Extraction from soil
40
Q

List major factors needed to be considered in insect sampling.

A 
Goals
Estimated information
Sample universe
Type of sampling
Sampling techniques
Size of a sample unit
Cost.
41
Q

Based on the rate of mean and variance, what are the three possible distributions of a population in the field?

A

Uniform, random, and aggregated.

42
Q

What is the lwao’s mean-mean crowding relationship?

A

Linear relationship between mean crowding and mean density.

43
Q

What is the Taylor’s power law?

A

A power law function to describe the relationship between the mean and variance.

44
Q

What are the two directions of the modern sampling programs?

A

Iwao’s X-x regression model and Taylor’s power law.

45
Q

Procedures for finding IWAO’s α and β values.

A

1 - Select certain number of fields.
2- Random select a certain number of samples from each field.
3 - Compute sample mean (X) and sample variance (S) for each field.
4 - Compute mean crowding (X)
5 - Find the linear regression equation using SAS to get the intercept (α) and the slope (β).

46
Q
  1. Procedures for finding Taylor’s a and b values.
A

1 - Select certain number of fields.
2 - Random select a certain number of samples from each field.
3 - Compute sample mean (X) and sample variance (S) for each field.
4 - Transform s² to ln(s²) (α) and the slope (β) and x to ln(x). The transformation can be done by SAS using log function.
5 - Find the linear regression equation using SAS to get the intercept (a = exp(intercept)) and and slope (b).

47
Q
  1. Define binomial sampling and sequential sampling, and discuss their major differences and advantages compared to conventional sampling plans.
A

Binomial sampling is an estimation based on the presence/absence of insects, it is a sampling plan where the information collected from a sample unit is only whether or not the number of insects on the sample is greater than a predetermined number of insects (T = 0, 1, 2…). Sequential sampling is a sampling plan in which sample units are collected one by one. As each sample unit is collected, the data are accumulated and compared with present boundary conditions to decide whether to stop and make a classification decision, or to continue sampling. In comparison to conventional samplings, these sampling plans are more beneficial in terms of reducing sampling time, number of samples, and cost.

48
Q

Define Injury.

A

The effect of pest activities on host physiology that is usually deleterious.

49
Q

Define Damage.

A

A measurable loss of hosts, yield, quality, or aesthetics.

50
Q

Define Economic damage.

A

The amount of injury that will justify the cost of control measures.

51
Q

Equilibrium position (EP).

A

Long-term population levels of organisms.

52
Q

Define ET (Economic Threshold).

A

The density of a pest population at which control measures are justified to prevent an increasing pest population from reaching the economic injury level.

53
Q

Define EIL.

A

The lowest population density that will cause economic damage. The density of a pest population at which the cost of pest control equals the amount of crop loss caused by the pest.

54
Q

Based on values of EIL and EP, what are the four groups of pests?

A

Non-pests; occasional, frequent, and severe pests.

55
Q

EIL and ET are dynamic and influenced by many factors. Discuss the major factors that influence EIL and ET.

A 
Plant growth and development
Insect population dynamics
Populations of natural enemies
Environment conditions; market price of control Materials and commodities
Ecological consideration
56
Q

Describe the four types of ET.

A

Nominal threshold: based on partition’s experience, not based on calculation.
Fixed ET: set at fixed percentage of EIL.
Dynamic ET: based on predicted future growth in plant injury levels, need to predict population growth in the future.
ET for single species vs species complex.

57
Q

Describe the 4 experimental techniques for developing insect injury (density)/crop yield relationships.

A

Observe natural densities of pest populations.
Modify natural densities of pest populations.
Artificial infestations.
Injury simulation – mimic feeding, for defoliators picking leaves or punching holes in leaves.

IPM (1 decks)

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