THE WHEAT AND STOCKER CATTLE ANALYZER: A MICROCOMPUTER DECISION AID FOR EVALUATING WHEAT PRODUCTION AND STOCKER CATTLE GRAZING DECISIONS

The Wheat and Stocker Cattle Analyzer is a microcomputer decision aid for evaluating interrelated wheat production and stocker cattle grazing decisions under yield, weight gain, and price uncertainty. An important feature of the model is that wheat commodity program provisions are incorporated into the analysis. A wide range of alternatives including wheat production for grain only, owned stocker cattle grazing, and wheat pasture leasing can be evaluated by the program.Crop Production/Industries, Livestock Production/Industries,

ECONOMIC IMPACT OF INTEGRATED PEST MANAGEMENT STRATEGIES FOR COTTON PRODUCTION IN THE COASTAL BEND REGION OF TEXAS

Crop Production/Industries,

Utilizing Forages to Program Steer Growth Patterns to Achieve Consistent Quality Beef

Many options are available for programming stocker cattle growth patterns through forage selection. In semi-arid south Texas rapid growth rates can be achieved by grazing irrigated small grains (oats, wheat and ryegrass) and slow growth rates are possible grazing native range pastures. Ryegrass (RG) nutrient quality indicates potential gains greater than 1.0 kg/d for steers, while typical winter native range (NR) pasture indicates gains of 0.45 kg/d or less. The purpose of this experiment was to quantify the impact of different programmed growth patterns on beef retail product especially size, marbling and tenderness

Intake, digestibility, and growth by steers compared under continuous and frontal grazing

Last updated: 10/22/201

Effect of Previous Stocking Rate of Rye-Ryegrass Pasture and Four Breed Types of Calves on Feedlot Performance

Last updated: 6/12/200

Previous Stocking Rate on Winter Pasture, Four Breed Types, and Feedlot Performance on Carcass Traits

Last updated: 6/12/200

Digitalization Platform for Mechanistic Modeling of Battery Cell Production

he application of batteries in electric vehicles and stationary energy-storage systems is widely seen as a promising enabler for a sustainable mobility and for the energy sector. Although significant improvements have been achieved in the last decade in terms of higher battery performance and lower production costs, there remains high potential to be tapped, especially along the battery production chain. However, the battery production process is highly complex due to numerous process–structure and structure–performance relationships along the process chain, many of which are not yet fully understood. In order to move away from expensive trial-and-error operations of production lines, a methodology is needed to provide knowledge-based decision support to improve the quality and throughput of battery production. In the present work, a framework is presented that combines a process chain model and a battery cell model to quantitatively predict the impact of processes on the final battery cell performance. The framework enables coupling of diverse mechanistic models for the individual processes and the battery cell in a generic container platform, ultimately providing a digital representation of a battery electrode and cell production line that allows optimal production settings to be identified in silico. The framework can be implemented as part of a cyber-physical production system to provide decision support and ultimately control of the production line, thus increasing the efficiency of the entire battery cell production process

Carcass Trait Effects from Environment, Growth Rate on Pasture, and Breedtypes

Last updated: 6/12/200

Grazing growth rate effects on subsequent feedlot and carcass traits in Braunvieh cross steers

Last updated: 6/12/200

Influence of Bos Taurus and Bos Indicus Breedtype on Production of Cortisol

Last updated: 6/12/200