Agriculture : Mechanization


System for Accurately Forecasting Prices and Other Attributes of Agricultural Commodities

UW-Madison researchers have developed a method and system for forecasting agricultural commodity prices and amounts of consumption, production and trade flows across regions, under a variety of scenarios. The method involves use of a general-purpose computer and employs a multi-component spatial equilibrium function that approximates an inter-regional market in agricultural commodities. The method generally involves first creating an inputs database that contains definitions of the regions and forecast scenarios. The inputs database also contains several years of industry data, including commodity prices and amounts of consumption, production, and trade flow in the regions. The function is refined and solved by maximizing a consumer and producer surplus net of all transaction costs to generate the forecasts. To further refine the forecasts, the method may be solved for an optimal amount of intermediate commodities consumed in the making of the final processed commodities.

Self-Propelled Harvesting Apparatus for Better Plant Drying and Digestibility

A UW–Madison researcher and others have developed a self-propelled harvesting apparatus for severely conditioning and processing plant material. The machine operates over a field using a pair of cylindrical crushing rolls and a rotor with radial fins for impacting. After material is crushed and impacted, a hood directs it for further impacting. A press assembly presses and discharges a mat of the compressed material onto the stubble. Upper and lower conveyance belts are arranged in an S-configuration, and texturing of the surface provides pockets of moisture and prevents sliding of plant material.

Predicting the Nutritional Quality of Corn Silage by Near Infrared Reflectance Spectrophotometry Equations

UW–Madison researchers have developed prediction equations that can estimate corn silage nutrition based on the unique light patterns of different chemical components using near-infrared reflectance spectrophotometry (NIRS).

The NIRS equations are calibrated on data obtained by traditional wet lab analyses of silage from the northern Corn Belt. Values of acid detergent fiber (ADF), neutral detergent fiber (NDF), in vitro true digestibility (IVTD) and crude protein serve as base parameters to assess all subsequent NIRS scans of dried ground samples.