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Nutrient Requirements for Horses

This Shiny application is an interactive decision-support tool for calculating nutrient requirements and ration adequacy for horses using the equations and recommendations from the Nutrient Requirements of Horses, Sixth Revised Edition (NRC, 2007). It is intended to complement, not replace, the book by helping users apply its models to individual horses and practical rations.

Data source: The app uses NRC (2007) equations to estimate daily nutrient requirements for different classes of horses (maintenance, growing, pregnant, lactating, working/training, and stallions). Feed composition values in the library are drawn from NRC publications for horses and other species (e.g., beef, swine, dairy cattle).

Core calculator: Users select animal type, enter mature and current body weight, and provide additional information when needed (age, gestation month, lactation month, or work intensity). Feeds are chosen from the library (including user-defined custom feeds), amounts are entered in kg, and the app computes daily nutrient requirements, nutrient supply, balance (deficit or excess), and percentage of requirements met for macro nutrients.

Scenarios and custom feeds: Scenarios allow saving and reloading complete combinations of animal information and feed selections for easy reuse. Custom feeds can be created by entering nutrient composition (DM, DE, CP, Lys, Ca, P, Na, Cl, K) when a feed is not present in the default library; these custom feeds then appear under the “Custom” feed type and can be used like any other feed.

Data management: All scenarios and custom feeds are stored in an on-disk RDS file and can be backed up or moved between computers using JSON export/import. Exported files contain all user data (scenarios + custom feeds) and can be re-imported to restore or append data.

Reference: National Research Council (NRC). 2007. Nutrient Requirements of Horses, Sixth Revised Edition. National Academies Press.

 

We welcome your feedback!

If you identify any mistakes, errors that need correction, or have suggestions to improve the model's predictive accuracy, please don’t hesitate to contact us. We are also interested in hearing your thoughts about the tool and its usefulness in your work.