Improved efficiency of grazing dairies using complementary pasture species and irrigation scheduling

2010 Annual Report for LS07-196

Project Type: Research and Education
Funds awarded in 2007: $210,000.00
Projected End Date: 12/31/2009
Region: Southern
State: Georgia
Principal Investigator:
Dr. Nicholas Hill
University of Georiga

Improved efficiency of grazing dairies using complementary pasture species and irrigation scheduling


Soil, climatic, forage, and irrigation resources make the coastal plain of the southeastern US an ideal location for high producing livestock systems. However, adding irrigation to the pasture system has changed the paradigm and forage production information is lacking within this system. Three perennial summer grasses, five winter annual grasses, and mixtures of the winter annuals were tested in small plots within irrigated pastures in the coastal plain of Georgia. Plots were harvested regularly to estimate forage and energy yield of each over three years. Electronic rising plate meter (RPM) measurements were taken prior to harvest and the forage yield information used to calibrate the meter for each species. Steer performance was tested in a grazing trial with treatments of annual ryegrass, and binary mixtures of annual ryegrass and cereal rye planted using two different configurations. Forage availability was measured using the RPM and maintained between 1500 and 3500 kg ha-1 by adjusting numbers of steers on pasture using a put and take system. ‘Tifton-85’ bermdugrass (Cynodon spp.) had greater forage and energy yield than other warm season perennial grasses. A monoculture of ‘Marshall’ annual ryegrass (Lolium multiflorum L.) or binary culture of annual ryegrass and ‘Wrens abruzzi’ cereal rye (Secale cereale L.) gave uniform distribution of high quality forage in small plots. However, the grazing experiment indicated that a monoculture of annual ryegrass gave the best and most consistent animal performance, thus making it the least risky choice of winter forage to complement bermudagrass.

Objectives/Performance Targets

The purpose of this research effort was to: 1) quantify yield distribution and quality of forage species throughout the growing season when grown under irrigated, 2) calibrate a RPM to the various forage species tested to provide a means of obtaining temporal changes in forage growth, and 3) validate a winter-annual forage system designed from objective 1 using growing steers.


Objective 1. Quantifying yield distribution and quality of forage species throughout the growing season when grown under irrigation. Warm Season Perennial Grasses. Dry forage yield increased for all forages as harvest interval increased (Table 1). However, the increase in dry forage yield was less for TifQuik bahiagrass than either Tifton-85 or Coastross II bermudagrass. TifQuik bahiagrass had greater dry forage yield when harvested a 10-day intervals than either of the bermudagrass cultivars but Tifton-85 bermudagrass had greater dry forage mass than either Coastcross II bermudagrass or TifQuik bahiagrass at the 20 and 30-day harvest intervals. Contamination of common bermudagrass in the Coastcross II bermudagrass at the time of sprigging limited its yield potential. The interval between harvests impacted the proportion of dry forage yield over time (Table 2). Increasing the harvest interval from 10 to 20 or 30 d increased the proportion of dry matter produced in June and reduced the proportion of forage produced in July in all forage species. The proportion of dry forage yield in August and September was not affected by harvest interval among any of the warm season grasses tested. Metabolizable energy was used as an indicator of forage quality instead of fiber, digestibility, or protein because energy is considered the limiting variable to animal productivity under pasture conditions (Mertens, 1994; Moore, 1994) and it is the standard for expressing feed value in management intensive grazing systems (Milner et al., 2011). Near infrared reflectance spectroscopy is also an acceptable method of predicting metabolizable energy (Phillips et al., 2011; Milner et al., 2011). Metabolizable energy was lower in bahiagrass than in the two bermudagrass cultivars regardless of harvest interval. Interestingly, ME did not change in bahiagrass forage regardless of the harvest interval but, as expected, decreased with the number of days between harvests in the bermudagrass cultivars. Regardless, the metabolizable energy values of the warm season grasses are insufficient for high-producing milk cows or rapidly growing animals and will need supplementation to provide adequate energy (Holmes et al., 2012). Winter Annual Grasses. Annual ryegrass out-yielded all other winter annual forages tested (Table 5). Cereal rye was the second highest yielding cool-season species tested, followed by oats and wheat. Triticale had the lowest yield of the cool-season annual species tested. Distribution of yield varied among the forage species tested. Triticale had the greatest proportion of fall growth compared to the other species (Table 5). Oats and cereal rye also had good fall growth, but oats produced little winter growth compared to cereal rye. Fall and winter growth of annual ryegrass was lower than cereal rye, but it had proportionately more forage growth in March, April, and May compared with the other forage species except wheat. Eighty percent of wheat yield occurred in March. The metabolizable energy content of the cool-season annual grasses was adequate to support energy demands by growing or lactating cattle during periods when the forages were in vegetative stages of growth (Table 6) (Holmes et al., 2012). However, metabolizable energy decreased in the spring when seed heads emerged and were below values necessary for high producing cattle. Lack of uniform forage production and quality (i.e., metabolizable energy) suggest that mixtures of these species may be necessary to provide adequate quality forage for animal classes in which availability of high quality forage is of concern. Therefore, the energy yield of the forages was calculated to provide the best approximation to supply adequate forage for high producing cattle and determine which mixtures might give the most uniform distribution of energy availability. Annual ryegrass produced the greatest energy yield among all winter annuals, but the forage species with the greatest energy yield varied among months (Table 7). Cereal rye, oats, and triticale had the greatest energy yield in the fall of the year, but cereal rye had greater energy yield than all other forages during December and February. Wheat had the greatest energy yield during March followed by annual ryegrass, both of which had greater energy yields than all other forage species. Annual ryegrass had greater energy yield than the remaining forage species during April and May. These data suggest that annual ryegrass is the forage of choice to supply the greatest amount of metabolizable energy, especially during late spring. However, the lack of energy production during fall and winter indicate that pasture mixtures with other species with greater fall energy yield might be beneficial if the goal is to meet the demands of high producing cattle at that time of the year. Thus, average energy yields of annual ryegrass and other forage species were calculated to estimate monthly distribution for each. A combination of cereal rye and annual ryegrass gave the greatest energy yield during the fall and winter months, but a wheat-annual ryegrass combination gave greatest yield in March (Figure 1). A monoculture of ryegrass gave the greatest late season energy yield. Thus, consistency of forage production by annual ryegrass and cereal rye mixtures suggest this pasture mixture would be best suited for producers. However, for some producers the preference for winter pasture is a monoculture of annual ryegrass because of potential competition between the two during portions of the winter months. This gave rise to the grazing trail (Objective 3) that compares pasture mixtures of annual ryegrass and cereal rye planted in different configurations and compared to a monoculture of annual ryegrass to determine which provided the greatest liveweight gain by steers (Objective 3). Objective 2. Calibrating a RPM to estimate yield of forage species. One of the tenets of intensively managed pasture systems is being able to obtain a real-time estimate of forage mass. Changing sward conditions can have an impact on dry mater intake and, thus, animal performance (Curran et al, 2010) and having a real-time estimate of forage mass is critical to optimize pasture utilization. Rising plate meters are an inexpensive system that enables producers to accurately estimate available forage and make management decisions regarding defoliation events (Lile et al., 2001). Computer models using data from RPMs have been used to combine forage mass with pasture growth rates to provide assistance in animal allocation and feed supplementation (Dobos and Fulkerson, 2004; Romera et al, 2010). However, calibration equations for commercial RPMs are not universal and they should be calibrated to accommodate local pasture species and growing conditions (Sanderson et al., 2001). Best fit regression equations for the RPM evaluated in this study were all linear (Table 8). Calibration equations with coefficients of determination equal to or greater than 0.73 or greater have been successful at predicting forage mass in practical settings on farms (Thomson et al., 2001). Coefficients of determination in this experiment were at or above 0.73 for all equations except the equation for the cool season annuals when they were in the reproductive stage of development. Therefore, the equations were suitable for monitoring forage mass when evaluating these species in the grazing portion of this research project. Objective 3. Validation of a winter annual forage system using growing steers. Environmental conditions varied among years in which the grazing experiments were used to test the forage systems. Although temperatures were cool in January, but were similar to the 30-year average for the remainder of the grazing season in 2011 (Fig. 2). Rainfall was below the 30-year average in January, near normal from February through April, but almost non-existent in May of 2011 (Fig. 3). Temperatures were above the 30-year average (Fig. 2) and rainfall below the 30-year average (Fig. 3) for all months in 2012. Temperatures were above normal in January, 2013 but were below the 30-year average for the remainder of the grazing season (Fig. 2). Rainfall was below the 30-year average in January, near the 30-year average in March and April, and above the 30-year average in February and May of 2013 (Fig. 3). Therefore, climatic conditions for the 3 grazing seasons could be summarized as essentially normal rainfall and temperatures in 2011, warmer and drier than normal in 2012, and cooler and wetter than normal in 2013. Varying climatic conditions can present challenges to livestock producers who wish to optimize utilization of their forage resources. Precipitation encourages cool-season grass growth when temperature does not limit plant growth, though high temperatures limit cool-season grass growth, especially under limited rainfall conditions (Smoliak, 1986). Thus, having an accurately calibrated RPM provided real-time assessments of pasture conditions regardless of climatic conditions. Having accurate RPM calibrations permitted us to accurately measure the available dry matter at any given time during the grazing cycle. Having an accurate assessment of forage availability was important because maximum forage growth for cool season grasses occurs within a target range of 1500 and 3500 kg ha-1 available forage (Thompson et al., 2001). Cool season forages grow at liner rates within these target limits and maintaining pastures accordingly reduces the lag effect of under and/or overutilization. Failure to keep forages in a linear state of growth accentuates losses from periods of reduced growth. Inefficiencies associated with inaccurate forage measurements result in on-farm economic losses (Sanderson et al., 2001). Available forage was maintained within the optimum target range by using weekly RPM measurements. The RPM data were used to determine whether steers were consuming forage at a rate similar to the rate of growth, or whether grass growth was less or more than the rate of consumption. Decisions as to adding or removing grazer animals were made based upon the RPM data and long-range (10-day) temperature predictions. Average available forage values were maintained within the 1500 to 3500 kg ha-1 target range for all pasture treatments. As such, animal response data were likely to be a representation of the true potential of the growing steers on the forages compared (Table 9). Average daily gain (ADG) was not different among the forage treatments in 2011 and 2013, but steers grazing annual ryegrass had higher ADG than the other pasture treatments in 2012. Pasture treatments containing cereal rye provided the greatest number of grazing days in 2011 but in 2012 annual ryegrass had more steer grazing days than did either of the other pasture treatments. Pastures planted to alternating rows of annual ryegrass and cereal rye had the fewest grazing days in 2012. Pastures planted to alternating rows had the greatest, and annual ryegrass the least, number of grazing days in 2013. Gains per hectare were similar among pasture treatments in 2011 but annual ryegrass gave the greatest gain per hectare in 2012. Gain per hectare among pasture treatments containing cereal rye was not different in 2012. The treatment containing alternating rows of annual ryegrass and cereal rye gave the most gain per hectare in 2013. When averaged across all years, annual ryegrass gave the greatest ADG but gains per hectare and grazing days did not differ among pasture treatments. Other than ADG, animal responses were not different from one another for the 3-year means of the pasture treatments (Table 9). It could be concluded that monocultures of annual ryegrass is the logical choice for winter annual pastures because of the ADG and gain ha-1 are both numerically greater than the other pasture treatments. These means are based upon data in which there was significant year-to-year variation for each treatment. Analysis variation in production systems is used as a means of assessing economic risk in a) real estate investment analysis (Karyoli and Sanders, 1998), b) finding ways to avert risk during “productivity shocks” and countercyclical variation (Storesletten et al., 2001), and c) determining the effects of global climate change on economic stability of low-income populations (Parry et al., 2004). Increased variation in productivity increased economic risk regardless of the economic assessments under those three diverse scenarios. Inasmuch as variation in production is universally associated with risk, variation of the pasture systems in this experiment was assessed to risk. Standard deviations (SD) and coefficients of variation were calculated from the yearly means of the animal production as measures of variation for each pasture system. The mean CV and associated SD were calculated from the CVs for ADG, gain ha-1 and grazing days to assess the overall stability of each pasture treatment. There was variation among the CVs within any one animal production parameter and the lowest CV value varied among the pasture treatments depending upon the animal production parameter (Table 10). This would indicate that the pasture system of choice would depend upon which animal production variable was most valued by producers. However, the overall mean CVs for the monoculture of annual ryegrass and the mixed culture of one-half the ryegrass and one half cereal rye were not different from one another. But, the standard deviations of the CV values for all production parameters for the annual ryegrass was approximately one-fifth of that associated with the treatments with mixtures of cereal rye. Thus, production using the annual ryegrass pasture treatment had the least variation, and was therefore the least risky, when all animal production parameters were considered.

Impacts and Contributions/Outcomes

The results of this study indicate that the combination of greater yields and metabolizable energy in Tifton 85 bermudagrass make it a logical choice for a perennial warm season forage in the coastal plain of Georgia. Since the forage distribution of bermudagrass is limited to summer months, winter annual forages were tested to see which provided the best options to extend the grazing season for high producing livestock such as stocker steers or dairy cows. On-farm small plot research suggested that the yield distribution of a pasture mixture of annual ryegrass and cereal rye gave a yield distribution which favored grazing for extended periods of time. However, the small plot research also indicated that monocultures of annual ryegrass provided a greater energy yield than did a binary mixture of annual ryegrass and cereal rye. Determining which winter annual pasture gave the greatest amount of animal yield with the least amount of risk was tested in a grazing trial in which the forage was managed using pre-calibrated rising plate meters to maintain linear growth rates. Results of three years of grazing studies indicated that annual ryegrass gave the best animal performance with the least amount of risk.


Dennis Hancock

[email protected]
Assistant Professor
University of Georgia
Dept. Crop and Soil Sciences
3111 Miller Plant Sciences Building
Athens, GA 30602
Office Phone: 7065421529
Ann Blount

[email protected]
Associate Professor
N. Florida Research and Education Center
3925 Highway 71
Marianna, FL 32446
Office Phone: 8504829849
Miguel Cabrera

[email protected]
University of Georgia
Dept. Crop and Soil Sciences
3111 Miller Plant Sciences
Athens, GA 30602
Office Phone: 7065428332
Cheryl Makowiak

[email protected]
Assistant Professor
North Florida Research and Education Center
155 Research Road
Quincy, FL 32351
Office Phone: 8508757126
Kerry Chestnut

Greenstone Dairy
2111 Adams Road
Wrens, GA 30818
Office Phone: 7063612343
Al and Desiree Wehner

[email protected]
164 Jersey Drive
Grassy Flats Dairy
Quitman, GA 31643
Office Phone: 2292639830