For approximately 55 years, computer models have been used as Decision Support Systems (DSS) to apply scientific knowledge to virtually every branch of science: from life sciences (e.g., development of the molecular structure of drugs and the management and planning for sustainable production of foods) to earth sciences (e.g., space exploration and global warming). Humankind has benefited tremendously by using DSS in specific areas for which experimentation is practically impossible or infeasible. Decision Support Systems (also referred to as Smart Decision Tools) can be broadly categorized into five classes: communication-driven, data-driven, document-driven, knowledge-driven, and model-driven (D. J. Powers ). In the late 1960s, data-driven and model-driven DSS were built based on scientific knowledge, theory development, and operational research concepts. However, it was not until the advancement of microcomputers and software in the mid-1980s that DSS became user-friendly and started being applied practically. The development of DSS was tightly connected to the evolution of the architecture and processing power of microcomputers.
DSS have positively influenced several sectors in agriculture. The predictive power of DSS has contributed to improvements in the productivity and profitability of many agriculture-oriented companies. With DSS, users can evaluate many production alternatives and choose the best solution for each specific condition and desired outcome. For decades, animal scientists have taken advantage of DSS computer models. These systems allow users to appraise feed biological, nutritive, and substitution values; determine quantity and quality of feed required to support different animals’ physiological needs; and estimate animal performance for given values of intake and feed quality.
Since 2006, the Ruminant Nutrition Laboratory of the Department of Animal Science at Texas A&M University has offered different computer models. We designed these computer models to help producers, consultants, researchers, and students on issues related to the nutrition of large and small ruminants, help feedlot managers to achieve maximum profit, provide advanced modeling techniques, and deliver a complete system for assessing the quality of feeds for ruminants. Some of these models offer solutions for specific problems (e.g., Cattle Value Discovery System—CVDS, Large Ruminant Nutrition System––LRNS, Small Ruminant Nutrition System––SRNS) while others can be broadly used in many different areas of research (e.g., Model Evaluation System—MES).
Ruminant animals are widely utilized to convert human-inedible feedstuffs to nutritious food under widely varying conditions around the world. The goals of enhancing ruminant nutrition are to improve productivity, reduce resource use, and protect the environment. However, scientists often have to extrapolate nutrient requirements and feed values developed under standardized, controlled, laboratory research conditions to all combinations of cattle types, feeds, and environmental and management conditions. In these cases, DSS can be used as virtual simulators to predict nutritional requirements and feed utilization in a variety of production settings.
The complete article can be downloaded from here.
Luis Tedeschi (Texas A&M Univ.) and Danny Fox (Cornell Univ.)