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.

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.