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Submitted to: American Society of Animal Science Annual Meeting
Publication Type: Abstract Only Publication Acceptance Date: 4/2/2018 Publication Date: N/A Citation: N/A Interpretive Summary: Technical Abstract: My objective is to review methods for making meaningful forage quality assessments in large diverse landscapes to improve livestock management and to propose new assessment approaches. Livestock in regions where climate, soil, or both make forage crop cultivation impractical, graze vegetation that’s composed of mixtures of native and introduced plant species on large and diverse land units. While the term forage quality can include many aspects of nutritional values, here I limit it to digestibility and protein content. Forage quality on extensive landscapes can vary significantly in spatiotemporal dimensions and depends on such factors as plant species composition, soils, topography, management history, weather, and the species of grazer. Through grazing site, plant, and plant part selection a grazer’s diet can be markedly different in quality—typically greater—than the aggregate of available forage. From a grazing management perspective, forage quality assessment is used to 1) identify time periods when animals, through selection alone, are not able to meet nutritional requirements, 2) identify areas where sufficient forage quality levels are exceeded and possibly underutilized, and 3) characterize long-term quality trends that may, for example, be related to changes in species composition, climate, or range condition. The factors that effect spatiotemporal forage quality patterns should be considered when making quality assessments. Methods for assessing forage quality include vegetation sampling and forage analysis, remote sensing, fecal sampling, animal performance, and modeling. In this presentation, I will review the strengths and weaknesses of current methods and introduce recent animal sensor developments that provide forage quality insight. Finally, I propose methods for combining the animal sensor data with other datasets, for example resource maps, satellite imagery, and weather data, to develop landscape specific models to estimate forage quality now and weeks into the future. |
