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

Final 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
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Project Information


Pasture-based dairies utilize a major strength of livestock production in the Southeastern US – the ability to produce quality forages during the majority of the calendar year. Pasture-based systems use irrigation to enhance forage production. However, little information exists on the fate of nitrogen and its impact on ground water quality, or on irrigated forage productivity when intensively grazed. The objectives of this study were to: 1) Screen warm and cool-season annual and perennial pasture species for seasonal forage productivity under grazing conditions, 2) monitor soil moisture content from irrigation practices to determine efficiency of irrigation practices, 3) Create a nitrogen “budget” to determine nitrogen losses, and 4) Develop educational and outreach information for training producers to be more efficient at, or transition into, pasture-based dairying. Research was conducted on two commercial farms located in the Coastal Plain of Georgia. Perennial forages were no-till established in paddock areas following herbicide application, and winter annual forages were no-till established as a double crop in bermudagrass paddocks. Soil moisture was measured electronically and irrigation efficiency was determined on two center pivots on each farm: one in which computer generated data was used to indicate irrigation needs and one in which producers used established irrigation practices. Nitrogen inputs were calculated from farm records and N losses calculated from nitrate efflux in soil water and ammonia losses measured in traps and PVC chambers. Data was used to supplement educational efforts specifically designed for pasture-based dairies.
The best pasture mixtures for year around forage production was a combination of bermudagrass overseeded with combinations of cereal rye, ryegrass, and crimson clover. Lack of participation on the part by producers did not permit assessment of irrigation efficiency at either farm. Lack of participation was a result of prioritization of other farm enterprises over irrigation management. Ammonia volatilization losses amounted to 2% and 1%; and leached nitrate, amounted to 12.5 and 0.3% of the total imported N from the two farms. UGA has held three intensive grazing management schools since 2008. One hundred and seven producers (representing 72 farms) in attended. We also conducted a comprehensive training for 108 Extension Agents, NRCS personnel, State Dept. of Ag officials, and other agricultural professionals from 8 states in SSARE’s region. The number of dairy cows on pasture-based dairies at the onset of this research project was less than 1% of the Georgia state herd, but in 2010 dairy cows on pasture-based dairies was approximately 15% of the Georgia herd and increasing at an exponential rate.

Project Objectives:
  1. 1. Screen warm and cool-season annual and perennial pasture species for seasonal forage productivity under grazing conditions.
    2. Determine moisture release curves for the major soil types found on cooperating grazing dairies and monitor soil moisture content from irrigation practices during periods of negative water balance.
    3. Create a farm-gate nitrogen “budget” to determine nitrogen dynamics and pollution potential, and whether pasture legumes can replace synthetic N fertilizer.
    4. Develop educational and outreach information for training producers to be more efficient at, or transition into, pasture-based dairying.

Over the past decade, there has been a trend in the dairy industry to shift from total confinement systems to managed intensive pasture systems because of improved economic competitiveness and improved lifestyle. Traditionally, improved economic competitiveness has been associated with increased farm output. But milk production in managed intensive grazing dairy systems decreases because of limited intake of nutrition available to grazers from pasture species. However, pasture based dairy systems reduced costs by being more efficient at using machinery, housing, bedding, feed, and labor which offsets the cost of reduced productivity to net a higher return. This intuitively suggests that as reliance on managed intensive grazing increases then farm efficiency would follow. If so, there would be a preponderance of dairy farms located in warmer climatic regimes in the US where climatic conditions permit longer grazing seasons. Yet, eight of the top ten dairy states in the US are located in the North where climatic conditions permit only limited grazing during summer months (Source: American Farm Bureau and USDA). Transitioning back into grazing systems indicates a shift in the dairy production paradigm and may signal that areas suited to long grazing seasons are poised for growth by increasing the number of intensively managed grazed dairies. A review of literature found that as grazing days increased, the need for tilled acreage and total acreage decreased as the net return (above expenses) per cow increased. Therefore, the changing face of the dairy industry is because of sustainability and profitability rather than dependence on government price subsidies.
Pasture based dairy farms are located in the Coastal Plains of South Georgia on land that was once in row crops. The soils are sandy loams typical of the Coastal Plain and have low cation exchange capacity, low water holding capacity, and compacted subsoils which are acidic and limit root growth. Many forages capable of supplying adequate nutrition to support the energy demands of a lactating dairy cow are not suited to these soils. Typically these dairy farms are planted to Tifton 85 bermudagrass for summer grazing which are over-seeded with cereal rye, ryegrass, and wheat for winter grazing. Use of pasture legumes is limited because of low summer productivity of adapted species, although some pastures have been seeded into improved white clover varieties. The lack of a well adapted legume species means that producers rely on mineral nitrogen for pasture fertilization for much of their nitrogen needs. Bermudagrass is more efficient at nitrogen utilization than is corn but the required rate of nitrogen application for bermudagrass production is similar to or surpasses that of corn. The production, transportation, and application of nitrogen fertilizer is an energy intensive process. The total energy input per pound of nitrogen applied as ammonium nitrate is estimated to be 36,000 Btus. The application of 200 lbs N/A on bermudagrass would require the Btu equivalent of 58 gallons of gasoline. Therefore, use of nitrogen fertilizer represents a significant on-farm energy demand and decreasing dependence on manufactured nitrogen fertilizers through inclusion of legumes or increased efficiency will be critical to the long-term sustainability of pasture-based dairies in the South.

Development of energy conservation practices on dairy farms comes at a time when high energy costs are placing financial strains on farmers dependent upon high-input agricultural production practices. We calculated the energy requirements for conventional dairies compared to that of a grazing dairy. Grazing dairies utilize 59% of the energy inputs as that of conventional dairies. Nitrogen fertilization and irrigation are the two largest inputs in grazing dairies, representing 43% of the total energy requirement. Thus, an energy budget analysis suggests that reducing nitrogen needs and prudent management of irrigation can reduce the energy requirements of grazing dairies and improve their efficiency. We interviewed our grass-based dairy cooperators and asked them to identify research goals that would help make their operations more sustainable. They identified three such goals: a) identify methods that can increase the use of forage legumes to reduce dependence on nitrogen fertilizer, b) develop efficient irrigation scheduling methods that keep soil hydrated without over-irrigation, and c) identify drought tolerant pasture mixtures that provide constant supplies of high quality forage. Thus, the research needs listed by producers can be viewed as a beta test for the energy budget model, and suggests that cost-analysis has led them to the same conclusion: energy consuming processes are a limitation to their profitability. Increasing rates of international and domestic energy consumption leaves little doubt that high energy costs will be the norm rather than the exception in the future. This project will provide assistance in developing prudent management practices to minimize those energy inputs.


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Materials and methods:

General: The cooperating dairy farms on which the research will be conducted are located in the Coastal Plains of Georgia. Both dairies rely exclusively on pasture resources with no conserved forages for their nutritional base. Greenstone Dairy is a 260 acre farm located approximately 30 miles southwest of Augusta, GA in the northern edge of the Coastal Plains commonly referred as the Sand Hills. Soils on this farm are members of the Orangeburg series with loamy-sand surface soils (0-25 cm) which lay over sandy-clay-loam subsoils (25-150 cm) and gentle slopes averaging approximately 3%. Grassy Flats dairy is a 300 acre farm located approximately 12 miles North of Quitman, GA near the Georgia-Florida border. Soils on this farm are members of the Tifton series with loamy-sand surface soils (0-23 cm) with sandy-loam and sandy-clay-loam subsoils (23-160 cm) and gentle slopes averaging approximately 5%. Both farms have three center pivot irrigation systems of which one on each farm will be used to develop an irrigation schedule (managed) for improved water utilization (see objective 2), and the other two will use current management (farm-managed) practices of daily applications of water.

Objective 1. Screen warm and cool-season annual and perennial pasture species for seasonal forage productivity under grazing conditions.

Traditionally, forages have been selected for persistence and/or yield distribution because they are desired by livestock producers and are easily measured. Recently forage breeders have changed their focus on breeding efforts to increase nutritive value and/or nitrogen fixation to mitigate poor quality forages and reduce reliance on nitrogen fertilizers. The Universities of Georgia and Florida, and USDA researchers located in these states, have collectively registered two tall fescue, two annual ryegrass, two bermudagrass, one bahiagrass, three red clover, three alfalfa, three white clover, and two perennial peanut germplasms or varieties in the past six years. While these forages have been examined individually for improved traits, they have not been evaluated to determine whether the traits for which they have been selected provide added value in a forage system. Therefore, improved cultivars of legumes (perennial peanut, red/white clovers, alfalfa), perennial grasses (tall fescue, bermudagrass, bahiagrass), and annual grasses (wheat, annual ryegrass, cereal rye) will be evaluated in replicated demonstration strips within existing forage systems on two dairy farms.

Forages will be planted in replicated plots within the managed center pivot and one farm-managed center pivot irrigation system on both farms. Cows will be permitted to graze areas planted to the forages. Exclusionary electric fencing will be used to provide estimates of forage yield and utilization as estimated by the difference of herbage mass inside and outside the exclusionary areas. Forages will be harvested bi-weekly, oven-dried, ground and analyzed for nitrogen, fiber constituents, and digestibility . The system will be modified for the warm season grasses by conducting a 36 hour digestion to predict neutral detergent fiber digestibility. Digestible nutrient yield will be calculated as the product of yield and digestibility. Yield, yield distribution, and digestible nutrient yield and distribution will be calculated for each species, and forages will be ranked for each month based upon their digestible nutrient yield. Nitrogen yield will be calculated as the product of dry matter yield and nitrogen content of the forage. Nitrogen removal will be calculated as the difference of nitrogen yield between the caged and uncaged areas of the plot. Nitrogen fixation will be estimated from the nitrogen yield of the legumes. Complementary forage mixtures will be established based upon results from years 1 and 2 to determine their compatibility.

Objective 2. Determine moisture release curves for the major soil types found on cooperating grazing dairies and monitor soil moisture content from irrigation practices during periods of negative water balance.

Some crops have rather elaborate predictive models to determine irrigation needs based upon the difference between expected crop water use and rainfall (Lascano, 2000; Hunsaker, 1999). These models require sophisticated weather information and technical expertise for estimating irrigation needs. They have also been developed for crop monocultures and aren’t well suited for multi-species crops. Grazing dairies choose to utilize multiple pasture species that complement one another because of yield distribution, forage quality (for milk production), and/or nitrogen fixation. Therefore it is unlikely that utilization of existing crop models will adequately predict water use and irrigation needs for grazing dairies because of the multi-species cultivation of pasture ecosystems and lack of predictive water use models for crop mixtures. Another way to develop irrigation scheduling is to monitor soil moisture content and irrigate when the soil reaches a specified dryness and terminate the event when the soil reaches a specified water content. Research in Nebraska showed that irrigation scheduling provided an average 35 percent savings in water and energy. In fuel costs alone, this is a per-season savings of about 550 kwh per acre for a center pivot sprinkler ( Assuming similar water and energy savings, this translates into energy savings of 137,500 kwh on a farm with 250 acres of irrigation.

Soil maps for each farm will examined and two center pivots from each farm with similar soils chosen. Irrigation scheduling will be developed on one of the two center pivot systems and on the other center pivot irrigation will be scheduled by the farm cooperators for water use comparisons. Soil moisture will be measured both in the managed and unmanaged center pivots at each farm. By monitoring the soil moisture content of the managed and non-managed irrigation systems we will be able to determine whether consistency and adequacy of the soil water content, as well as whether excessive soil water conditions that might lead to leaching prevail.

Ideally, measurements of soil water should be electronic, real-time, and interfaced with irrigation computer controls to trigger an irrigation event. Soil water content will be measured using time domain reflectometry (TDR) (Whalley, 1993) using Model CS625-L sensors (Campbell Scientific Inc., Logan, UT). TDR sensors will be calibrated gravimetrically in undisturbed soil blocks as described by Weitz et al., (1997). Soil blocks will be removed from the pivot areas on each farm and transferred to the laboratory. Soils will be saturated and drained to field capacity. TDR sensors will be placed into the soils and readings/soil weight will be taken every 12 hours as water evaporates from the soil surface. The soils will be saturated again and readings/soil weight taken until the soil reaches field capacity. The calibrated TDR sensors will be placed to a depth of 30 cm at 5 locations within in each of the selected center pivot systems. The sensors will be connected to a CR200-series data logger and transmitted to a RF401 radio modem connected to a CR10X control system.

Within the managed irrigation system, irrigation will turn on when the TDR sensors predict water content of the soils to be at 60% of field capacity. Sufficient water will be applied by irrigation to bring the water content of the soil back to field capacity. The cohort center pivot (managed by the producer) will also have TDR sensors placed in the field to monitor soil water content. Total water applied per acre will be calculated for the cohort pivot as well. Exclusionary cages will be placed in both pastures to protect the TDR sensors and transmitters.

Additional exclusionary cages used to measure forage yield on a monthly basis. Because livestock utilize the pastures with managed intensively grazed rotational systems, the forage within the fields will have the exclusionary cages place on the field just after a grazing cycle and will be removed after the following grazing event. Forage will be harvested from beneath the cages as well as outside the cages to estimate yield and forage utilization. The harvested forage will be oven-dried, ground, and analyzed for digestibility, neutral detergent fiber, and protein content. Thus we will determine whether the irrigation methods have an effect on the forage quantity and/or quality.

Objective 3. Create a farm-gate nitrogen “budget” to determine nitrogen dynamics and pollution potential, and whether pasture legumes can replace synthetic N fertilizer.

To carry out a nitrogen balance in each field, it will be necessary to determine the amount of N present in each field at the beginning and end of a measurement period, as well as inputs and outputs of N during the measurement period. Inputs of N will be mainly in the form of N fertilizers as well as manure N coming from outside each field. Outputs of N will be mainly in the form of N removed in milk and animals, as well as N losses through nitrate leaching and ammonia volatilization.

Amount of N in a field at a given time.
An estimate of the amount of N in a field at a given time can be obtained by sampling the standing stock of forage as well as the soil. Samples of forage will be collected from underneath cages at 10 locations in each field. These samples will be dried at 65° C, weighed, ground, and analyzed for total N. The total amount of N will be calculated by multiplying the N concentration in the forage by the dry matter yield.

Inorganic soil nitrogen will be determined from soil samples taken to a depth of 150 cm collected from ten locations in each field at the beginning and end of each growing period. A truck-mounted hydraulic probe will be used to remove a soil core (2.5 cm diameter) which will be divided in 15-cm increments down to the 30-cm depth, and in 30-increments thereafter. The soil will be air dried, ground, and extracted with 1M KCl (1:10, soil:KCl) to measure ammonium and nitrate concentrations by colorimetric procedures (Mulvaney, 1996). A larger diameter probe (4 cm) will be used at five locations in each field to remove cores that will be used for bulk density determinations. The inorganic N concentrations will be used together with bulk densities at each depth to determine the amounts of inorganic N (kg N ha-1) present at each depth.

Records will be kept of the amounts of N fertilizer applied to each field. Manure N coming from outside the field will be estimated based on the amount of time the animals spend outside the monitored field and the feed intake from outside the field.

Nitrogen Outputs
The main nitrogen outputs from the field can be estimated by measuring N removed in the form of milk and animals as well as nitrate leaching and ammonia volatilization.

Production records will be kept to measure the amount of milk leaving the farm. Subsamples of milk will be taken, a volume of milk freeze-dried to determine milk solid content and removal. The milk solid subsamples will be acid-digested and analyzed for nitrogen. Removal of nitrogen in milk will be calculated using the following equation:

N removal from milk = kg milk yield * g milk solids/kg milk * N content of milk solids

Nitrogen removal for maintenance and growth of grazing animals will be estimated from NRC guidelines using average body weight for the milking herd for first, second, and third lactation animals. To account for nitrate leaching, 10 cup lysimeters will be installed to a depth of 90 cm in each monitored field. The lysimeters will be kept under vacuum to collect samples of water percolating through the 90-cm depth. Water samples collected in the lysimeters will be removed weekly and analyzed for nitrate by a colorimetric procedure. Estimates of water flux through the 90-cm depth will be used together with nitrate concentrations to calculate nitrate leaching losses (kg N ha-1) during the measurement period.

Ammonia volatilization losses will be measured with the Integrated Horizontal Flux method. This method uses passive flux samplers consisting of two pairs of glass tubes (each tube 100 mm long, 10 mm outer diameter, 7 mm inner diameter) with a coating of oxalic acid on their inner surfaces. The tubes in each pair are connected by a piece of silicone tubing, and one of the tubes in each pair is connected to a nozzle (10 mm long glass tube glued to a steel disk with a 1 mm hole in the center) with another piece of silicon tubing.

As wind passes through the tubes, NH3 is trapped by the oxalic acid and forms ammonium oxalate, which is soluble in water and can be determined colorimetrically. The horizontal flux of NH3 through a plane perpendicular to the longitudinal axis of tubes facing in the same direction was used to calculate ammonia volatilization. Samplers were be placed at five heights on masts located at right angles on the periphery of each field to integrate losses at each height.

Objective 4. Develop educational and outreach information for training producers to be more efficient at, or transition into, pasture-based dairying.
In order to disseminate the new knowledge gained from the proposed research, we plan to publish peer-reviewed, refereed scientific journal articles and extension fact sheets for use by dairymen, ranchers, and the general public. We will seek to publish our research results in outlets available through the internet to extend availability as well as at national conferences for our peers. Fact sheets published from results of this project will be available at the University of Florida and University of Georgia extension websites, and would be available for "print on demand" through county extension offices in these states. In addition, due to our proximity to Alabama and South Carolina, we would organize one multi-state in-service training for county extension agents in a location convenient for participation from the region (FL, AL, GA, SC). We also plan to organize two dairy workshops conveniently located and advertised in the same region targeting dairymen in the area. Each year for the duration of the project, we will showcase our results on-site for field day participants at the sites of the studies.

End-user participation will be measured during field days and workshops. For both dairymen and professional educational events, evaluation would include surveys on subject matter covered during programs and usefulness of materials presented. Participants in these programs will be asked to evaluate new knowledge and skills obtained, and how these knowledge/skills could help them improve their or their clientele farm profitability and sustainability. Attempt will be made to contact workshop participants one year after the educational event they participated in, to measure degree of new practice/skill adoption. In particular, written project evaluations obtained from the participants of the regional in-service training and workshops would include measures of:

1. Knowledge gained in relation to issues of environmental quality and integrated natural resource management.
2. Skills learned to efficiently utilize non-renewable and on-farm resources for integrated forage/dairy cattle/environmental management.
3. Professional and team building skills developed for sustainable multi-state extension programming to enhance the economic viability of farm operations in the southeast region.
4. Networks developed with agricultural experts to enhance the quality of life for dairymen in the southeast region.

Research results and discussion:

Objective 1. Screen warm and cool-season annual and perennial pasture species for seasonal forage productivity under grazing conditions.

Initial stands of cool season perennial legumes and grasses were excellent 3 months after establishment. However, intensity and frequency of grazing in the pasture-based dairy system resulted in stand failure of these species within 1 year of establishment. Because of the lag between time of establishment and utilization of these forages combined with the expense of establishment, we concluded that the short duration in which these forages could be utilized does not justify their inclusion into pasture systems for intensive grazing systems that are on these dairies. Warm season perennial grasses, on the other hand, were persistent whether they were harvested at 10, 20, or 30-day intervals. Yield of bahia grass was greater than bermudagrass when harvested at 10 day intervals, there were no differences among warm season grass yields when harvested at 20 day intervals, but 30 days between harvests favored bermudagrass production (Table 1). Monthly distribution of forage growth depended on forage species. The species with the greatest yields and best compliment of growth were cereal rye, annual ryegrass, and bermudagrass (Figure 1). It is important to note that yields of pasture mixtures that were complementary, not additive. In other words, the monthly dry matter production of two species grown together was less than the sum of the two species grown in pure stands. Therefore, when attempting to plan forage systems for producers, appropriate experimentation must be established to test the productivity and distribution of specific species combinations and not assume that monthly productivity can be estimated by the sums of yield data when grown in monocultures.

Because forage production of winter annuals was tested by no-till establishment of research plots within bermudagrass pastures, we predicted the production distribution of combined warm season and winter annual forage systems by summing the forage production of each. The forage species combination with the greatest yields was cereal rye and annual ryegrass overseeded into Tifton 85 bermudagrass. The production curves predict that using this system will provide little to no forage during the month of October because a) the bermudagrass becomes dormant with the onset of cool nighttime temperatures and short daylengths during October, and b) the winter annuals are in a phase of establishment (Figure 2). Peaks of forage production occur during late spring and mid-summer, suggesting excess forage could be harvested for hay and fed back to cows during periods of low forage production.

Metabolizable energy (ME) of bahiagrass did not change regardless of the interval between harvest (Figure 3). However, the ME of bermudagrass decreased as interval between harvests changed from 10 to 20 days in length. The ME of bermudagrass did not change as the interval between harvests changed from 20 to 30 days. The winter annual forages had greater average ME than did the warm season perennial grasses (Table 2). Surprisingly, the ME of annual ryegrass was the least of the forages tested while wheat had the greatest ME. Regardless the ME of all winter annual forages are suitable for lactation. Therefore, selection of winter annual species for pasture systems should be selected based upon how the forage production matches with the needs of the milking herd.

Objective 2. Determine moisture release curves for the major soil types found on cooperating grazing dairies and monitor soil moisture content from irrigation practices during periods of negative water balance.
Soils maps were used to delineate major soil types on each farm. Soil moisture sensors were placed into the major soil types within each of two center pivot irrigation systems on each farm. One pivot was designated as a ‘control’ pivot, in which the producer was asked to manage irrigation with their normal practices. Soil water data was downloaded to a computer (located in the manager’s office) every 15 minutes by telemetry, but the producer was not permitted to see the data and could, therefore, not adjust irrigation based upon soil moisture data. The second irrigation pivot was designated as a ‘treatment’ pivot, in which the telemetric soil moisture data was projected on a farm map. The data was presented in such a way to illustrate % field capacity and the amount of irrigation needed to bring the volumetric water up to 90% field capacity. Producers were instructed to irrigate when soil moisture reached 60% of field capacity.

Producers did not implement irrigation scheduling as instructed. Reasons for failure to do so varied depending upon farm. One manager had troubles operating the laptop computer in his office and did not know how to program the pivot to vary water application based upon soil moisture information on the map. The second farm manager did not prioritize irrigation scheduling because of other farm operations that took precedence.

Objective 3. Create a nitrogen budget to determine when, and by how much, the dairies are deficient in nitrogen supply.

Ammonia Volatilization
In general, the amount of N lost through NH3 volatilization in each sampling period was less than 2.4 kg N ha-1 at both farms (Fig. 4). Overall NH3 losses amounted to 1% of the applied N at the Wrens farm and 2% of the applied N at the Quitman farm. When expressed on an area basis, our NH3 losses are at or below 8 kg N ha-1 (Table 3). Our data indicate that pasture-based dairies are likely to have lower NH3 losses than published data for confined dairies.

The low NH3 emissions found on both farms were surprising considering the Wrens farm applied twice the amount of N than the Quitman farm. Because these farms were both under center pivot irrigation, it is likely that both the UAN applied through the irrigation and the broadcast urea moved into the soil by irrigation water soon after N applicatoin. It is well documented that supplemental irrigation can play an important role in the rate of NH3 volatilization from N fertilizers. This effect was probably due to leaching of urea into the soil, thereby reducing NH3 losses. Although information on irrigation timing was not available for our study, the small NH3 losses suggest that irrigation applications occurred after each urea application at the Wrens farm.

It is likely that not all NH3 volatilized from our pastures was derived from applied N fertilizers. Ammonia is also lost from N in cattle manure and urine deposited on the surface of the pasture, with urine being the larger of these two sources. The application of poultry litter at the Wrens farm may have also contributed to the measured NH3 losses. The methods used herein could not distinguish between sources of NH3 emission, but the results suggest that these pasture-based dairies emit low NH3 to the atmosphere even in high N input scenarios. We attribute the low emissions to irrigation moving excreta N as well as fertilizer N into the soil.

Nitrate Leaching
The maximum NO3 - concentration observed in the cup lysimeters at the Wrens farm was 4.8 mg N/L. This occurred in January, when water flux through a 1-m depth was small (3.14 cm). Large water fluxes were estimated in October/November and March/April, but NO3 - concentrations in those months were low resulting in low amounts of leached NO3 - (0 to 0.5 kg N/ha/month). Total NO3 - losses for the monitored period (June 2008 to August 2009) at the Wrens farm amounted to 2 kg N/ha (Table 3), which correspond to 0.3% of the applied N. Total water flux at 1-m depth in the monitored period was 55 cm, which corresponds to 33% of the total amount of water (precipitation + irrigation) received.

Nitrate concentrations and water fluxes at a 1-m depth from the main soil types at the Quitman farm (Tifton, Stilson, and Fuquay soils). In general, NO3 - concentrations and water fluxes were greater than those at the Wrens farm. Maximum concentrations ranged from 12 to 27 mg N/L with maximum water fluxes as high as 30 cm/month. As a result, leached NO3 - ranged from 1 to 16 kg N/ha month-1 at the Quitman farm. Total NO3 - leaching losses for the monitored period (August 2008 to August 2009) amounted to 40 kg N/ha, which corresponds to 12.5% of the applied N (Table 3). Total water flux at 1-m depth in the monitored period ranged from 88 to 95 cm in the different soils, which represented 44 to 49 % of the total amount of water (precipitation + irrigation) received.

The Farm Gate Nitrogen Budget

a. Managed Inputs and Outputs
Managed inputs included fertilizer, grain, corn silage, and hay used. Rates and total amounts of N fertilizer varied between farms. At the Wrens Farm, monthly N application rates ranged from 16 to 55 kg N/ha for a total yearly rate of 491 kg N/ha. At the Quitman Farm, monthly N applications ranged from 14 to 43 kg N/ha, for a comparatively smaller yearly rate of 297 kg N/ha. Consequently, fertilizer N as % of total N inputs represented 79% at the Wrens farm and 41% at the Quitman farm. The smaller rate of N fertilizer applied at the Quitman farm was in part counterbalanced by a larger rate of N
input in the form of grain feed (383 kg N/ha versus 97 kg N/ha at the Wrens farm). Grain feed as % of total N inputs represented 16% at the Wrens farm and 53% at the Quitman farm. Thus, grain feed and fertilizer N accounted for most of the N input on both farms (95% at the Wrens farm and 94% at the Quitman farm). Hay and silage made up the rest of the N inputs.

Monthly N exported in milk ranged from 574 to 1,526 kg N for the Wrens farm, and from 405 to 956 kg N for the Quitman farm (data not shown). Total exported N in milk as a percentage of total N inputs was 15.8% for the Wrens farm and 17.1% for the Quitman farm
Thus, both farms used similar proportions of the input N to produce milk

Outputs in Environmental Losses

a. N2O Emissions
In the field study carried out at the Wrens farm, N2O emissions followed distinct patterns for each of the treatments. Emissions were greater from chambers containing urine and manure than from control chambers, with the larger rates of emissions occurring within the first 2 days after application. Although urine and manure emission rates peaked between 4 and 5 mg N2O m2/hr, overall emission rates remained at a lower rate for the majority of the study period. Carbon dioxide emissions were greater for the manure treatment than from the urine or control treatments for two days following application, but were not different from the control treatments thereafter. Between days 7 and 10, CO2 emission was smaller from the urine treatment than from the manure or control treatment. Estimating the total loss of N2O-N over two weeks for a grazed paddock resulted in 1.78 and 0.78 kg N/ha for Studies 1 and 2, respectively. In both studies, total N2O emission was greater from urine and manure treatments than from control treatments. Furthermore, in the second study, emission from urine was greater than emission from manure. This may have been due to more anaerobic conditions created by the manure application, as indicated by a greater emission of CO2.

The second study in this experiment had lower overall emissions, but a higher WFPS, which may have been due to the fact that under wetter conditions a larger proportion of the emitted N is converted to N2 gas rather than to N2O. In addition, an increase in WFPS would decrease nitrification, decreasing N2O emissions. Because the studies were located in different paddocks, it is possible that different soil characteristics, such as pH, bulk density, etc. could have played a role in the different results of both studies. Of the total inputs of N on the Wrens farm, N2O represented a 2.65% loss from the system (3.36% of the total applied fertilizer), with estimated monthly losses ranging from 0.52 to 3.13 kg N/ha (Table 4). This represented a loss of 16.5 kg N/ha for the year. These results are consistent with other studies conducted under similar conditions. Compared to other outputs in the N balance, N2O was second to least of the outputs from the system.

b. NH3 Volatilization
Monthly ammonia losses ranged from 0.5 to 4.1 kg/N ha at the Wrens farm, and from 0.1 to 1.5 kg N/ha at the Quitman farm (Table 4). When expressed as percentage of the total N inputs, total ammonia loss was 3.0% at the Wrens farm and 0.9% at the Quitman farm. Greater losses at the Wrens farm were likely due to the use of surface applied granular urea, in contrast to the Quitman farm, which used UAN solution through the irrigation system.

c. NO3 Leaching Losses
Monthly nitrate losses ranged from 0 to 0.5 kg N/ha for the Wrens farm and from 0 to 1.6 kg N/ha for the Quitman farm. Annual losses were estimated at 1.6 kg N/ha for the Wrens farm and at 38.3 kg N/ha for the Quitman farm (Table 4), which correspond to 0.3% and 5.3% of total N inputs, respectively. Larger water fluxes from the soil profiles, paired with significant NO3 concentrations were the driving factors for NO3 losses on both farms. Nitrate concentrations were highest on both farms between December and April. Although water fluxes during those months were not consistently high, losses from these months were still the main contributors to NO3 loss, with March and April being the highest. These elevated concentrations of NO3 during the winter were expected and is explained by slower plant growth during the winter, and lower relative efficiency of N uptake by plants. Typically cool season forages have shallower root depths compared to warm season forages. This coupled with higher rainfall during these months may leach NO3 past the rooting zone. The Quitman farm also received 40 cm (irrigation and precipitation) more than the Wrens Farm during the year. It is interesting to note that although N fertilization at the Wrens farm was not reduced during winter, NO3 leaching losses were low during winter.

d. Nitrogen Surplus
The N balances from both farms resulted in a surplus of unaccounted for N. Surplus N found on the Wrens farm was 375 kg N/ha/yr or 78% of the total input N. On the Quitman farm, surplus N was 559 kg N/ha or 77% of total N inputs (Table 4). Nitrogen surplus may include N in animals exported from the farm (culled cows and bull calves), increases in soil inorganic N, N losses in surface runoff, and N immobilized in soil organic fractions. Nitrogen amounts in animals exported from the farm and N losses in runoff were not estimated, but were probably small. Increases in inorganic N could not account for N surpluses because in both farms the amount of inorganic N in the soil decreased during the study. At the Wrens farm, the area-weighted inorganic N changed from 98 kg N/ha in the initial sampling to 55 kg N ha-1 in the final sampling, for an average loss of 43 kg N/ha. Similarly, at the Quitman farm, the area-weighted inorganic N changed from 65 kg N/ha in the initial sampling to 56 kg N/ha in the final sampling, for an average loss of 9 kg N/ha. Therefore, immobilized N was the main suspected source of unaccounted N.

Objective 4. Develop educational and outreach information for training producers to be more efficient at, or transition into, pasture-based dairying.

Educational materials were developed that included information developed from this research project, but as important was information and techniques that were gleaned during interactions with producers. For example, pasture management was not an objective of this research project, but while conducting research on-farm we observed various philosophies and strategies on pasture utilization. Use of rising plate meters to quantify forage mass is an integral component of the pasture-based farms, so we purchased electronic rising plate meters and developed calibration curves for each of the forage species tested. We have used the rising plate meters in pasture trials, observed pasture responses to management based upon RPM data, and have modified the way we manage pastures because of the utility of the RPM. Use of RPMs was not an objective in this project, but the technology we have developed using RPMs is a direct spin-off from this project and we have now included use of RPMs into our outreach programs. Meantime, we observed pasture management methods used by experienced pasture-based dairy managers, those transitioning into pasture-based systems, and those who are new to the pasture-based dairying paradigm. We have learned from successes and mistakes from our producer clientele and have modified our outreach efforts accordingly. We have also acquired a better understanding of animal management and the importance of a good animal genetics program to minimize the nutritional and environmental stresses associated with pasture-based dairying in the Coastal Plains of Georgia.

One other aspect of pasture-based dairy systems for which we have gained an appreciation are some of the associated intangibles. In particular is the lifestyle of the producers and how they view their systems. We have included producers into the outreach agendas to provide first-hand testimonials as to the family value of the pasture-based system. Producers who we have interacted with have given testimonials that a)” the pasture based system has provided me with an opportunity to employ my children and provide them with a future in the dairy industry”, b) mission statements such as the following: “Our mission statement is to provide the highest quality product we can, with the least cost, in a socially and environmentally benign system, that leaves something for the next generation”, and c) we have time to take the grandchildren on extended trips in our RV. While these statements are from seasoned veteran pasture-based dairy owners, they are indicative of the mindset of the system and illustrate the value of personal time.

Participation Summary

Educational & Outreach Activities

Participation Summary:

Education/outreach description:

The forage system and environmental data outlined in objectives 1 and 3 have been incorporated into our outreach efforts. Both extension and research scientists participate in the outreach programs for two reasons: 1) to provide the most current data to our producers to help them with their objectives of producing milk in an efficient and environmentally sustainable method, and 2) get feedback from the producers to provide assessments as to where we are lacking in efforts to make them more efficient and environmentally sustainable. An example of an outreach agenda is presented in Table 5.

The following is a summary of our outreach efforts over the past 2 years. The data and conclusions drawn from this SARE project directly contributed to a number of Extension programs and activities, including but not limited to, the following items:

1. Aiding the development of three modules in the Georgia Grazing School starting in 2008 and continuing through present Grazing Schools.
a. There were 107 producers (representing 72 farms) in attendance at the three Grazing Schools held since adding these modules.
b. These three modules include:
i. developing forage systems for year-long grazing,
ii. assessing nutrient management and implementing steps to minimize environmental impact,
iii. pasture wedge feed budgeting.

2. Providing the basis for a Grazing School for Milk Producers in Moultrie, GA in September 2010. There were 24 producers representing 14 pasture-based dairies in attendance. Seven of those in attendance were new or prospective pasture-based dairy producers, all of whom have started their dairies and are off to a great start.

3. Provided the basis for a program in December 2010 entitled “Grazing Dairy Cows on Winter Forage” for conventional dairy producers who are experimenting with or attempting to use high-quality pasture as part of the ration during the winter months as a way of transitioning to a confinement-grazing hybrid or full pasture-based dairy model. There were 15 producers in attendance at this meeting representing 5 dairies (averaging 1200 milk cows per dairy).

4. Provided the basis for a comprehensive training for 108 Extension Agents, NRCS personnel, State Dept. of Ag officials, and other agricultural professionals from 8 states in SSARE’s region. This two-day event included classroom and hands-on experiences at two pasture-based dairy farms in SE Georgia.

5. Provided data for numerous Extension bulletins, circulars, and fact sheets on two websites:
a. The UGA’s Forage Website: (average 67 unique visitors per day)
b. The UGA’s Management-Intensive Grazing Website: (average 15 unique visitors per day)

Project Outcomes

Project outcomes:

See section on Farmer Adoption

Economic Analysis

None conducted.

Farmer Adoption

We have tracked the number of dairy cows housed on pasture-based systems since the time the first pasture-based dairy operation started in the State, and compared the trend with the trend in total numbers of cows in the State dairy herd. The number of cows on pasture-based dairies is grew at a logarithmic rate since the University of Georgia began its research and outreach efforts (Figure 4). The rapid growth of dairy cows on pasture-based systems can be interpreted this that farmer adoption is high. However, we perceive that traditional producers are reluctant to consider, let alone adopt, pasture-based diary opportunities. Reasons for their recalcitrance to the technology is unclear.


Areas needing additional study

The reason resistance to adopting pasture-based dairy production is unclear. We know that limited crop, soil, and animal science research for pasture-based dairies has provided obstacles for producers to develop appropriate forage systems, and appreciate the changes in soil because of changing land use. Yield, quality, and production curves for the major forage species have been developed at the University of Georgia but animal intake and performance data are lacking. We also know that innovative producers who prescribe to the pasture-based production model expose themselves to uncertainties because of the lack of research, outreach, and social science support. Therefore, research areas for future studies include: 1) Develop producer-friendly pasture yield and quality distribution data to assist forage system planning; 2) Examine temporal changes in soil organic matter following conversion from row-crop to permanent pasture; 3) Investigate the attitudinal, fiscal and social characteristics of farmers who have adopted pasture-based dairying systems vs. those who are reluctant to shift to pasturing from conventional dairy operations, and 4) Develop novel educational/outreach materials for existing and next generation producers, farm consultants, and industry representatives.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.