4.4. | Economic threshold | ||||||
4.4.1 | Intuitively we can get a feel for some kind of threshold pest population | ||||||
4.4.1.1. | If the pest population (and the resulting damage) is low enough, it does not pay to take control measures | ||||||
4.4.1.2. | As the pest population continues to rise, it reaches a point where the resulting damage would justify taking control measures | ||||||
4.4.2. | Unfortunately, our intuitive notion of an "economic threshold" has been corrupted by many rather loose and completely different definitions of the term in the early IPM literature: | ||||||
4.4.2.1. | "The maximum pest population that can be tolerated at a particular time and place without a resultant economic crop loss" | ||||||
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4.4.2.2. | "The density of a pest population below which the cost of applying control measures exceeds the losses caused by the pest". (Glass, 1975) | ||||||
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4.4.2.3. | "That point at which the incremental cost of pest control is equal to the incremental return resulting from pest control" (Thompson and White, 1979) (also "economic injury level" - Stern, 1959) | ||||||
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4.4.2.4. | "The pest population at which pest control measures must be taken to prevent the pest population from rising to the economic injury level" (Stern, 1959) (also "action threshold") | ||||||
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4.4.3. | Recognizing that some authors used very different definitions for the terms "economic threshold" and "economic injury level," we have to be careful in reading the IPM literature. | ||||||
4.4.4. | In recent years Larry Pedigo has straightened out some of the confusion. In his 1989 textbook "Entomology and Integrated Pest Management," he revived Stern's original definition of the economic injury level, and to make it a practical management tool, he expressed it in terms of variables that can be estimated empirically. | ||||||
4.4.4.1. | Pedigo's definition of the economic injury level (EIL) is derived from the decision criterion in partial budget analysis: | ||||||
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4.4.4.2. | Considering that the partial revenue is the yield loss prevented by controlling the insect population, and simplifying the partial cost to only the cost of the insect control (which is probably valid in most cases), the above inequality can be written | ||||||
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where "loss" is the proportion of the yield lost per insect. Rearranging terms, we get | |||||||
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Note that this assumes that the loss is directly proportional
to the insect population, or in other words, loss is a linear function
of insect population. Pedigo expressed his definition of economic injury
level as
EIL = C/(VIDK) where:
EIL = economic injury level This equation expands the simple proportion of yield lost per insect into a term for "Injury", which represents the physiological effects of insect feeding, "damage", which is a measurable loss in yield or quality per unit of injury, and a dimensionless constant, K, which represents the proportionate reduction in injury as the result of the insect control. |
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4.4.4.3. | Empirically determined economic injury levels have proven very useful in reducing the numbers of insecticide sprays that are necessary for controlling many insect pest species. It is a reactive rather than a proactive approach and therefore may not be applicable to pests whose populations develop too rapidly to be managed by any reactive means (e.g., many plant pathogens). | ||||||
4.5. | Optimization | ||||||
4.5.1 | Introduction | ||||||
4.5.1.1. | Until now we have discussed management decisions only a single management variable at a single moment in time. | ||||||
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4.5.1.2. | Optimization is a systematic procedure for finding the "best" solution (or solutions!) to a complex problem | ||||||
4.5.1.3. | It is necessary to explicitly state the objective | ||||||
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4.5.2. | Simulation | ||||||
4.5.2.1. | By repeated execution of a computer simulation model with different values for the input variables | ||||||
4.5.2.2. | e.g., suppose we have 3 partially resistant cultivars which require different levels of fungicide application to control a fungus disease | ||||||
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4.5.2.3. | The simulation approach is usually faster and cheaper than doing the optimization empirically in the field | ||||||
4.5.2.4. | The simulation approach is limited by the number of runs of the simulation that is feasible | ||||||
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4.5.3. | Linear programming | ||||||
4.5.3.1. | Programming here refers to planning, not computer programming | ||||||
4.5.3.2. | Is used to solve problems of allocation (e.g., allocation of land area in a crop rotation plan, allocation of effort in a pest monitoring scheme, etc.) | ||||||
4.5.3.3. | The procedures are illustrated in the following simple example: | ||||||
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4.5.3.4. | Computer software exists for a wide range of linear programming applications | ||||||
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4.5.4. | Dynamic programming | ||||||
4.5.4.1. | Particularly useful for solving sequential decision problems | ||||||
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4.5.4.2. | A simple 2-decision-period example | ||||||
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4.5.4.3. | The number of possible decision combinations increases with the power of the number of decision points | ||||||
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4.5.4.4. | The dynamic programming technique can handle nonlinear models, stochastic models, and a large, but limited, number of decision variables | ||||||
4.5.4.5. | Dynamic programming can be used where the system can be adequately modeled with a relatively modest number of variables | ||||||
4.6. | Economics of public pest management programs |
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