INSURING AGAINST LOSSES FROM TRANSGENIC CONTAMINATION

Concerns about contamination of the food supply and the financial losses that would result have limited the promise of certain genetically engineered plants. This article addresses the situation by constructing an insurance pricing model to protect against those losses. The model first estimates the physical dispersal of corn pollen subject to a number of parameters. This physical distribution is then used to calculate the premium for fair valued insurance that would be necessary to destroy contaminated fields. The flexible framework can be readily adapted to other crops, management practices, and regions.contemporaneous fertility, insurance, Lagrangian stochastic model, pharmaceutical-corn, pollen dispersal, Crop Production/Industries, Risk and Uncertainty,

Classifying Rural and Small Urban Transit Agencies

In this paper, rural and small urban transit agencies are classified into peer groups using hierarchical cluster analysis and data from the Rural National Transit Database (Rural NTD). The objective is to provide a basis for the comparison of individual agency to peer group performance as well as econometric analysis between and within peer groups. Rural and small urban transit agencies are first assigned to three groups by service provided: demand-response, fixed-route, and demand-response and fixed-route service. A fourth group is created to accommodate the large number of transit agencies providing demand-response service that did not report vehicle-hour data. The four groups are then clustered using vehicle-mile, vehicle-hour (where available), and fleet size variables. Operating statistics for each cluster by group are presented. The process for comparing individual agency performance to its respective cluster is described. The Rural NTD demonstrates its usefulness as a consistent, uniform national dataset. However, additional service area information would accommodate clustering based on exogenous as opposed to endogenous variables as is necessary with the current data set

North Dakota Potato Industry

In an attempt to increase net returns from farming efforts and stabilize agricultural commodities, rural communities are viewing value-added processing as a possible solution. Many attempts have been made at the value-added concept, and recently adding potato production under irrigation has changed farming for some North Dakota producers. The potato industry is meeting consumer demands for more efficient and less time consuming methods of cooking by offering a wider variety of convenient processed potato items. The different uses of potatoes determines processor locations and movement of the raw product. However, the location of processing plants and warehouses impact highway demand and truck use. A network flow model was developed to estimate the truck traffic generated by the potato industry. The model uses some of the steps implemented by Denver Tolliver of UGPTI in developing a Prototype Corn Highway Network Model for Southeastern North Dakota. A network model is a representation of supply and destination nodes and the transportation links. The most important findings is the reduction in production in northeastern North Dakota, the traditional location of potato production and the introduction of irrigated acreage in the central and south central part of the state. The processors demand a uniform quality product which can best be controlled under irrigation. Continued irrigation development will increase tonnage product from the land. This production may not be potatoes, but whatever the crop, the additional tonnage will have greater impacts on the North Dakota highway system. Development of flow models to coincide with NASS production data will provide valuable insight for North Dakota highway planners

Organizing Transit in Small Urban and Rural Communities

The justification of government support of rural transit on the basis of the presence of increasing returns to scale and the most efficient regional organization of transit is investigated. Returns to density, size, and scope at most levels of output were found. Cost subadditivity, where a monopoly firm can provide service at a lower cost than two firms, was found for many, but not all observations. The presence of natural monopoly in rural transit in a strict sense is rejected. The findings and implications are directly applicable to rural transit in North Dakota and should be helpful in informing future federal policy as well as rural transit policy, service design, and operation in other states

Rural School Vehicle Routing Problem

The school bus routing problem traditionally has been defined in an urban context. However, because of the unique attributes of the problem in rural areas, traditional heuristic methods for solving the problem may produce impractical results. In many cases, these characteristics also provide the opportunity to investigate what size and mix of vehicles, whether large or small buses, conforming vans, or other modes, are most efficient. In addition, these vehicles may be further differentiated by the presence of equipment for transporting students with special needs. To address this situation, a mathematical model of the problem was constructed and a new heuristic was developed. This heuristic consists of two parts: constructing the initial route and then improving it by using a fixed tenure tabu search algorithm. This rural routing heuristic, in addition to several existing ones, is then applied to a randomly generated school district with rural characteristics. For the relevant measure, a function of student ride time, the new heuristic provides a set of routes superior to those produced by existing methods. Because ride times produced by the new heuristic are lower than those for routes generated by existing methods, the likelihood of injury to students may decrease. Also, with the cost of operation for each route calculated in dollars, a comparison of solutions in financial, as well as temporal, terms is possible

A stochastic model of maize gene dispersal

The spatial distribution of maize genes is predicted using a two dimensional Lagrangian stochastic (LS) model that meets sub-inertial and well-mixed conditions. Using wind data from central Iowa, a Weibull distribution is fit. From this distribution, a series of Monte Carlo simulations are run using the LS model in three forms. The final two forms of the model are based on USDA regulations for the production of pharmaceutical producing corn, demonstrating the model's suitability for analyzing the risk of transgene dispersal in maize. Special considerations are given to the large size of maize pollen, the likelihood of pollen seepage given the use of biological inhibitors of gene release, and the possibility of contemporaneous fertility of neighboring corn fields.</p

The Stochastic School Transportation Problem

The efficient transportation of elementary and high school students within the United States has long been of interest to academics and practitioners alike. Research in the field has closely followed the development of solution techniques to general network routing problems by appropriately altering these models and the associated algorithms to address the subtleties of a particular bus routing problem. In this paper, a mathematical model that accommodates many of the sources and impacts of uncertainty is presented. Due to the impact of time windows on routing, traditional solution heuristics are unsuitable. However, a number of regular stochastic events that arise in the design and operation of pupil transportation are discussed and general solution outlines are presented

Classifying rural and small urban Transit Agencies

Rural and small urban transit agencies are classified into peer groups by the hierarchical cluster analysis and data from the Rural National Transit Database. The objective is to provide a basis for the comparison of individual agency performance with peer group performance as well as econometric analysis between and within peer groups. Rural and small urban transit agencies are first assigned to three groups by service provided: demand-response, fixed-route, and demand-response and fixed-route service. A fourth group is created to accommodate the many transit agencies providing demand-response service that did not report vehicle hour data. The four groups are then clustered by using vehicle mile, vehicle hour (when available), and fleet size variables. Operating statistics for each cluster by group are presented. The process for comparing individual agency performance with its respective cluster is described. The Rural National Transit Database demonstrates its usefulness as a consistent, uniform national data set. However, additional service area information would accommodate clustering based on exogenous, as opposed to endogenous, variables, as necessary with the current data set