Sampling for Decision Making in Crop Loss Assessment and Pest Management Sampling in Precision IPM: When the Objective Is a Map

Fleischer, S. J., Blom, P. E. , and Weisz, R. 1999. Sampling in precision IPM: When the objective is a map. Phytopathology 89:1112-1118. Measuring and understanding spatial variation of pests is a fundamental component of population dynamics. The resulting maps can drive spatially variable pest management, which we define as precision integrated pest management (IPM). Precision IPM has the potential to reduce insecticide use and slow the rate of resistance development because of the creation of temporally dynamic refuges. This approach to IPM requires sampling in which the objective is to measure spatial variation and map pest density or pressure. Interpolation of spatially referenced data is reviewed, and the influence of sampling design is suggested to be cr itical to the mapped visualization. Spatial sampling created problems with poor precision and small sample sizes that were partially alleviated with choosing sampling units based on their geostatistical properties, adopting global positioning system technology, and mapping local means. Mapping the probability of exceeding a threshold with indicator kriging is discussed as a decision-making tool for precision IPM. The different types of sampling patterns to deploy are discussed relative to the pest mapping objective. Additional keywords: geostatistics, precision agriculture. The early graphical development of the economic threshold (Fig. 4 in literature citation 12;28) conceptualizes a series of pest density maps marching through time. The entire map is above or below a general equilibrium density at different points in time, but at all points in time, parts of the population are shaded as above, and other parts below, an economic threshold. To facilitate integrated pest management (IPM), we simplify the map into a flat plane estimated by the mean and we sample to categorize the mean relative to a threshold or management criterion. This approach has been used to determine when, and when not, to apply management inputs. In other words, it has worked to help manage temporal variation. Today, we find ourselves in a world focused on environmental issues in which geospatial information technologies are becoming increasingly accessible. When applied to agriculture, these technologies create the opportunity for management of both temporal and spatial variation (19). Allen et al. (1) conjectured what scouting might look like in the future if we were to apply a wide array of new technologies. Many of these technologies exist today, are rapidly advancing, and could be used to map pest populations within fields. We can capture greater biological realism by recognizing that populations vary in space, but this requires that the objective of the sampling be redefined as mapping the population rather than as estimating or categorizing the mean. In this discussion, we focus on point-based sampling within agricultural fields. We recognize that the scale of the map should depend upon the biological organism being mapped, the agricultural or forested ecosystem being considered, and the intended use of the map. In entomology, there are more and further developed examples of mapping at a landscape or regional scale in forested or rangeland ecosystems (15) than in production agriculture, and the logical and technological tie to area-wide pest management pro