APEC Staff Papers
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Browsing APEC Staff Papers by Author "Bernard, John C."
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Item Modeling Nitrate Concentration in Ground Water Using Regression and Neural Networks(Department of Food and Resources Economics, 2003-01) Ramasamy, Nacha; Krishnan, Palaniappa; Bernard, John C.; Ritter, William F.Nitrate concentration in ground water is a major problem in specific agricultural areas. Using regression and neural networks, this study models nitrate concentration in ground water as a function of iron concentration in ground water, season and distance of the well from a poultry house. Results from both techniques are comparable and show that the distance of the well from a poultry house has a significant effect on nitrate concentration in groundwater.Item Modeling Nitrate Loading Rate in Delaware Lakes Using Regression and Neural Networks(Department of Food and Resources Economics, 2003-01) Sudhakar, Prachi; Krishnan, Palaniappa; Bernard, John C.; Ritter, William F.The objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models.Item Predictive Time Model of an Anglia Autoflow Mechanical Chicken Catching System(Department of Food and Resource Economics, 2003-10) Ramasamy, Saravanan; Benson, Eric R.; Bernard, John C.; Van Wicklen, Garrett L.In this project, a predictive time model was developed for an Anglia Autoflow mechanical chicken catching system. At the completion of poultry growout, hand labor is currently used to collect the birds from the house, although some integrators are beginning to incorporate mechanical catching equipment. Several regression models were investigated with the objective of predicting the time taken to catch the chicken. A regression model relating distance to total time (sum of packing time, catching time, movement to catching and movement to packing) provided the best performance. The model was based on data collected from poultry farms on the Delmarva Peninsula during a six-month period. Statistical Analysis System (SAS) and NeuroShell Easy Predictor were used to build the regression and neural network models respectively. Model adequacy was established by both visual inspection and statistical techniques. The models were validated with experimental results not incorporated into the initial model.