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.
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)
Perennial peanut, bahiagrass, and bermudagrass were established at pasture-based dairies in the summer of 2007. Irrigation was used to supplement plant needs when soil moisture fell below 40% of available water. Plots were harvested at 10, 20, and 30 day intervals throughout the summer of 2008, 2009, and 2010, subsamples dried in an oven, and dry matter yields calculated. Cereal rye, triticale, wheat, oats, and annual ryegrass plots were established into bermudagrass pastures in October of each year and harvested on schedules typical of grazing rotations used by the producer. Monthly growth rates and yields were calculated for each species using the means across years. The data was entered into a forage spread sheet. The spread sheet was designed so a producer can select a forage species, assign acreage to the forage, and the forage growth rate and yield of the acreage will automatically be calculated. The program was designed so that additional forages can be selected, acreages assigned, and yields and growth rates of those forages also calculated. By conducting an iterative process of selecting forages and acreages, producers can now calculate a monthly forage budget. They can also enter the number of dry and lactating cows and the forage spreadsheet will automatically calculate periods of deficits and surplus that producers can plan around by harvesting excess forage (during periods of surplus) and feeding it back to the herd (during periods of deficit).
All harvested forage samples were ground to pass a 1-mm sieve and 20 g of forage reserved for laboratory analysis. Samples were scanned using near-infrared reflectance spectroscopy, and calibrated from chemical analyses for protein, fat, neutral detergent fiber, acid detergent fiber, and lignin using the methods of Van Soest et al. (1991). Metabolizable energy (ME) was calculated using the formulas of Conrad et al. (1984) and Harlan et al. (1991). These methods were selected because they have been used over a wide range of legume hay, cool and warm season grass hay, winter annual hay, and grains and have good agreement between predicted values (from the formulas) and animal studies measuring energy. Metabolizable energy
yield (ME*dry matter yield) were calculated for each forage species and data entered into the spreadsheet to predict the pasture species combinations that were most likely to give greatest animal product per unit of pasture area. ME yield data indicated that a pasture mixture of 50% annual ryegrass and 50% cereal rye would give the greatest yield of animal product per unit land area. Pastures of annual ryegrass only, 50% cereal rye and 50% annual ryegrass planted in alternating rows, and 50% cereal rye and 50% annual ryegrass planted in monoculture within a pasture were established in the fall of 2010 and 2011 to beta test whether the forage spreadsheet gave precise information. The forage was grazed once sufficient mass (3500 kg/ha) accumulated. Forage was managed for maximum output by maintaining available forage between 1500 and 2500 kg/ha using a put-and-take stocking system with steers. Animal grazing days, average daily gain (ADG) and gain per acre were calculated for each forage system.
a. Summer perennials, Perennial peanut was slow to establish and did not withstand the repeated defoliation regime that was typical of pasture-based dairies. Thus bermudagrass and bahiagrass were the only perennial species evaluated for yield and forage quality. Tifquick bahiagrass had greater yields than either bermudagrass when harvested every 10 days, but Tifton 85 bermudagrass had greater yields than Coastcross II bermudagrass or Tifquick bahiagrass at the 20 and 30 day harvest intervals (Table 1) but proportional yields among the 3 grasses were similar regardless of harvest frequency (Table 2). However, forage quality of both bermudagrass cultivars were greater than bahiagrass (Table 3). This resulted in similar ME yields for the three entries at the 10 day harvest interval, and the greatest ME yield for Tifton 85 bermudagrass at the 20 and 30 day harvest intervals. Bahiagrass ME yield was least at the 20 and 30 day harvest intervals. These data confirm observations by producers that Tifton 85 bermudagrass provided the best perennial warm season forage option for pasture-based dairies.
b. Winter annuals: Annual ryegrass had the greatest yield of all winter annual forages tested, followed by cereal rye (Table 4). Oats and wheat yielded least of the winter annuals tested. However, cereal rye had a greater proportion of yield in the fall and winter months than annual ryegrass (Table 6). Annual ryegrass had better yield distribution in the spring than the other winter annual forages and mixtures of the two gave greater yields than either grown in monoculture (data not shown). Forage quality of the winter annuals was least in the late spring, but were similar among forage specie regardless of month of production (Table 6). Therefore distribution of energy yield was closely associated with the forage dry matter yield. Collectively the data indicate that a mixture of cereal rye and ryegrass will give greater forage quality than any other species combination, or any species monoculture, and this species combination will give the greatest animal product (milk or meat).
Data projecting that a mixture of annual ryegrass and cereal rye would give greatest animal product was counterintuitive to the observations of our cooperators. Our cooperators consistently stated that a monoculture of annual ryegrass gives better livestock performance than when in combinations with other winter annual grasses. Thus we conducted a grazing experiment as a beta test for the forage spreadsheet. The grazing experiment was conducted over a two-year period, with each year having dramatically different climatic conditions. Temperatures in the fall of 2010 were normal during the establishment of the pastures, but turned colder than normal during the winter months. Thus, grazing did not commence until the 5 February 2011. Fall temperatures were above normal in 2011 followed by above normal temperatures in the winter months of 2012. Pastures established more rapidly in the fall of 2011 and grazing commenced on 9 January 2012. Steer performance was not different among pasture treatments in 2011 for any variable measured (Table 7). However, treatment effects were dramatically different in 2012 than in 2011. Initial weights of steers were not different among pasture treatments, average daily gain (ADG) and grazing days per hectare (Grazing d) were dramatically better for monocultures of annual ryegrass pastures than for either of the pasture mixture. This resulted in greater gain per head (Gain/hd) and gain per hectare (Gain/ha) for the annual ryegrass pastures. There were no differences among the two pasture mixture treatments except that grazing days was slightly less in the treatment in which the pasture mixture was planted in alternating rows.
Educational & Outreach Activities
Data from this grant have been used in grazing schools designed for pasture-based dairies to illustrate how pasture species selection affects forage supply. Roughly 170 producers and county agents have attended the schools and received forage systems training.
N.S. Hill, D. Hancock, and D.E. Kissel. 2012. Using Gaming to Develop Forage Systems for Pasture-based Dairies. Southern Branch ASA Meetings. Birmingham, AL.
This research provides opportunities to provide yield data to producers who wish to maximize grazing opportunities for livestock operations. While the data is not perfect, it is an excellent format from which livestock producers can plan forage systems rather than subjective assessments or innuendo. Producers have used the forage production curves to develop forage systems for dairy, equine, cow/calf, and stocker production situations. The increase in the number of cows on pasture-based dairies is perhaps the best estimate of outcome (Figure 1).
No economic analysis was conducted for this research project.
Areas needing additional study
Without doubt, the efficiency of pasture-based dairy systems is a driving force for the increase in the number of dairy cows housed on these systems (Figure 1). However, as producers in other areas of the nation adopt this technology, it will be incumbent for research and outreach programs to develop relevant information to keep these producers competitive. The research conducted herein demonstrates the quantum need for additional grazing research to estimate livestock performance for dairies.