More iban two-thirds of cropland in tbe UnitedStates is devoted to tbe production afiust four crop species-maize, wbcnt, soybeans, and cottonraising concerns thai bomogenization of the American agricultural landscape could facilitate widespread disease ami pest outbreaks, compromising tbe national food stipply. As a new component in national agricultural risk assessment, we employed a grapb-tbeoretic approach to examine the connectivity oj these crops across the United States. We used county crop acreage to evaluate the landscape resistance to Iransmission-tbe degree to wbicb host availability limits spread in any given rcgion^or pests or pathogens dependent on each crop. For organisms thai can disperse under conditions of lower host availability, maize and soybean arc bighly cotmectcd at a national scale, compared witb the more discrete regions of wheat and cotton production. Determining tbe scales at which connectivity becomes disrupted for organisms with different dispersal abilities may help target rapid-response regions and the development of strategic policies to enhance agricultural landscape heterogeneity

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stricting their application in retrospective or validation studies (Hutchinson, 1991). Crop growth models require solar irradiance as input data, yetThe need for solar irradiance data for crop models there are few places where such data are routinely measured. For has led researchers to develop a number of methods for locations where measured values are not available, solar irradiance simulating such data. For example, some crop modelers can be estimated using empirical models such as the Bristow-(e.g., Rosenthal et al., 1989) have incorporated stochas-Campbell (B-C) model. This study was conducted to assess the spatial and seasonal accuracy of the B-C model for midcontinental locations tic weather generators into their simulations. These in Kansas. A 30-year data set from Manhattan, KS, was used to weather generators simulate irradiance and other metecalibrate and evaluate unmodified and modified forms of the B-C orological and climatological inputs based on probabilismodel. The effect of seasonality was investigated by subdividing the tic criteria. This approach eliminates the need for meayearly data into two subsets, a high noontime solar elevation angle sured solar irradiance; however, it seems reasonable period, ranging from DOY 121 to 273, and a low noontime elevation that estimated, rather than randomly generated, solar angle period comprising the remainder of the year. The B-C model irradiance values would also result in improved yield eswas also evaluated without seasonal division of the year. The calitimates. brated models were then tested against measured solar irradiance A number of techniques are available for estimating values for 10 sites distributed across the state of Kansas. Results solar irradiance. These vary in sophistication from simindicate that, for the calibration site at Manhattan, irradiance was ple empirical formulations based on common weather more accurately estimated using a modified form of the B-C model. For the yearly data, root mean square error (RMSE) was 3.9 MJ m Ϫ2 or climate data to complex radiative transfer schemes d Ϫ1 (25% error), compared with 5.2 MJ m Ϫ2 d Ϫ1 (24% error) for the that explicitly model the absorption and scattering of high solar elevation angle period and 3.6 MJ m Ϫ2 d Ϫ1 (32% error) the solar beam as it passes through the atmosphere. for the low solar elevation angle period. The RMSE for the 10 test Hall, Kansas State University, Manhattan, KS 66506-0801; R.L. Vanwhere A, B, and C are empirical coefficients. Although derlip,

Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately $200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress.

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