Edmund W. Schuster

Controlling the Risk for an Agricultural Harvest

Gathering the harvest represents a complex managerial problem for agricultural cooperatives involved in harvesting and processing operations: balancing the risk of overinvestment with the risk of underproduction. The rate to harvest crops and the corresponding capital investment are critical strategic decisions in situations where poor weather conditions present a risk of crop loss. In this article, we discuss a case study of the Concord grape harvest and develop a mathematical model to control harvest risk. The model involves differentiation of a joint probability distribution that represents risks associated with the length of the harvest season and the size of the crop. This approach is becoming popular as a means of dealing with complex problems involving operational and supply chain risk. Significant cost avoidance, in the millions of dollars, results from practical implementation of the Harvest Model. Using real data, we found that the Harvest Model provides lower-cost solutions in situations involving moderate variability in both the length of season and the crop size as compared to solutions based on imposed risk policies determined by management.

Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques

Advances in many areas of sensing technologies and the widespread use and greater accuracy of global positioning systems offer the prospect of improving agricultural productivity through the intensive use of data. By nature, agriculture is a spatial science characterized by significant variability in terms of yield and concentration of pests and plant diseases. Consequently, precision agriculture seeks to improve the effectiveness of various types of sensing information to give the grower more data and the ability to design the specific treatments for site-specific management of inputs and outputs. The intensive use of data in agriculture is at a relatively early stage and there remains much opportunity to refine modeling approaches and to build information infrastructure. With the overall goal of optimizing inputs to achieve the maximum output in terms of yield, this paper focuses on the application of a clustering algorithm to field data with the goal to identify management zones. We employ two sets of attributes, first yield and second field properties like slope and electrical conductivity to delineate the management zones. By definition, a management zone is a contiguous area defined by one or more features and may take on many different shapes. Building on the established machine learning approach of k-means clustering, we successfully identify a near optimal number of management zones for a cotton field.

Raw Material Management at Welchs, Inc.

Welchs, a large grape-processing company owned by a grower cooperative, faced complex logistics in planning recipes for products sold in retail stores. The recently installed integrated MRP and cost-accounting systems did not include ways to calculate recipes at optimal cost based on plant-raw-material and capacity constraints. An imbalance of supply and demand further complicated this problem in raw-materials management. The cross-functional team in charge of managing raw materials spent increasing a mounts of time deciding what recipes to use at each plant. We formulated the problem as a linear program model and used spreadsheet optimization to incorporate the model in daily decision making. The company has run the model each month since 1994 to provide senior management with information on the optimal logistics plan. This simple application saved Welchs between

The Open System for Master Production Scheduling: Information Technology for Semantic Connections between Data and Mathematical Models

130,000 to

The Illuminating Role of Laser Scanning Digital Elevation Models in Precision Agriculture Experimental Designs - An Agro-Ecology Perspective

170,000 during the first year.

Applications of nanomaterials in agricultural production and crop protection: A review

Commonly provided by ERP vendors, master production scheduling (MPS) systems often strive to meet the needs of a large user base while limiting software functionality. Subsequently, business process reengineering becomes the means for firms to adapt to MPS software packages. This article develops a flexible approach for MPS delivery as an alternative to packaged software. The article examines the general case of open system architecture to deliver a specific master scheduling model to end-users. The open system approach fulfills a goal to standardize and speed the process of modeling in practice by creating a supply network for mathematical models that is searchable across the Internet with precision. The value lies on quickly putting state-of-the-art modeling in the hands of many users with no local computer implementation other than downloading an Excel spreadsheet.

Global RFID: The Value of the EPCglobal Network for Supply Chain Management

Jeffrey Willers, Darrin Roberts, Charles O’Hara, George Milliken, Kenneth Hood, John Walters and Edmund Schuster Genetics and Precision Agriculture Research Unit, USDA-ARS, Mississippi State, Mississippi, Department of Plant and Soil Sciences, Mississippi State University, Mississippi, Geosystems Research Institute, Mississippi State University, Spatial Information Solutions, Starkville, Mississippi, Department of Statistics, Kansas State University, Milliken Associates, Manhattan, Kansas, Perthshire Farms, Gunnison, Mississippi, InTime, Inc., Cleveland, Mississippi Aggeos, Inc., Fulton, Mississippi, Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA