- Animal Products: dairy
- Animal Production: grazing management
Winter annual and warm season perennial forage species were tested in various combinations to determine yield distribution and quality over 3 years. Production curves were matched with livestock requirements to establish a forage “balance sheet”. The balance sheet is useful in predicting when forage deficiencies are likely to occur and provide insight as to the impact of how the breeding/calving season will impact forage supply during periods of peak demand. However, grazing experiments determined that forage balance sheet data did not provide sufficient detail on winter annual species to predict best species selection or planting configurations for maximum animal product.
Grazing dairies do not import as many nutrients onto farm premises as confinement dairies because they utilize pastures rather than stored feedstuffs to meet nutritional needs of livestock (Norman and Hare, 2005). Because fewer nutrients are imported in feedstuffs means there is less nutrient loading on the land resources (Lanyon, 1991). Thus, grazing dairies are more sustainable and profitable than confinement-based systems (Johnson, 2002). Dairy cows have a very high energy demand during lactation (Holmes et al., 2007). Therefore, the success of a grazing dairy is predicated on a forage program that will provide high quality forage to meet the energy demands of the lactating cow. Selecting forage species to include into a forage program is a complex issue since neither quality nor quantity of forage is constant (Ball et al., 2007). Immature forages have extremely high forage quality and contain the necessary energy availability to support lactating cows. But, forage supply is low when forages are immature and grazing risks diminishing the stand of the crop by damaging its growing points. As the forage matures, the amount of mass increases, but the quality decreases to unacceptable levels for lactating cows. Optimally, a forage program is one which provides the most consistent amount of energy that meets the needs of lactating cows. When forages can not meet energy demands of lactating cows, supplemental forages and/or rations must be used (Holmes et al., 2007).
It is well understood that a combination of cool- and warm-season forage species are needed to provide dietary needs to lactating cows in the southeastern US. Forage yield information is available for dry-land conditions, but grazing dairies use irrigation to keep forage supply constant. We completed a 3-year study to develop data for yield and growth rates of cool- and warm-season forage species using grazing dairy farms for our research sites. The data we are generating will be amalgamated into a spreadsheet to predict irrigated a) growth rate and b) seasonal productivity of the major forage species grown in the Southeastern US. The spreadsheet is being used to assist grazing dairy producers in planning forage programs.
Unfortunately, the utility of the spreadsheet that was developed had limitations because we did not have sufficient forage quality data to predict periods of excess or deficit energy in the forage. Forage quality data is necessary to assist producers in predicting performance of lactating cows when grazing forages, when (ie what plant stage) surplus forages can be conserved (in periods of peak production) and when forage supplementation will best serve the needs of livestock with high energy demands (eg young stock and lactating cows). The purpose of this project was to conduct forage quality analyses on forage samples from our on-farm research efforts to develop a more comprehensive forage systems planning tool for grazing systems.
Project objectives:div style="margin-left:1em;">
The overall objective of the project is to build a data base from which pasture systems can be planned to optimize production on dairies. The specific objectives are to: 1) create a data base than can be used to estimate monthly forage production for species commonly grown in the Coastal Plain of Georgia, 2) Test forages for quality to determine which make the best combinations (combined with production data) for forage systems, and 3)