Final Report for LS11-243
Project Information
This multi-disciplinary project assisted Southeastern dairy producers in achieving their goal of becoming more sustainable by developing representative economic models for a pasture-based dairy system, a confinement dairy system, and a pasture-based and confined feeding hybrid system. Using information from individuals and groups of pasture-based and conventional dairy producers, we identified production and financial management practices that serve as benchmarks for comparison and allowed us to assess the sustainability of these three economic models. We also worked to identify barriers to converting from a conventional dairy to a pasture-based production system.
After assembling an advisory panel of grazing dairy families and others, we began to:
- Collect, analyze, and report to dairy families financial and production benchmark information annually from 60 grazing and conventional dairy farms in Georgia and Florida. Results of this benchmark information were (and will be) used to help dairies compare their specific operation to the group, especially in certain key performance indicators (KPI) including production, profitability and labor utilization.
- Facilitate periodic meetings between groups of grazing dairy producers two-four times per year where participants share knowledge and questions as well as receiving information from university and other professional consultants.
- Document current dairy grazing and environmental stewardship practices in Georgia and Florida via a comprehensive and detailed survey instrument collecting production, management, and financial data of approximately 40 grazing dairy farms and 20 comparable conventional dairy farms.
- Use the annual benchmark data in conjunction with the comprehensive survey to identify practices or systems that are predictors of profitability, financial robustness, personal sustainability and environmental sustainability.
- Survey current conventional and pasture-based dairies in Florida and Georgia to determine impediments or barriers to current conventional producers adopting pasture-based dairying.
- Utilize information obtained in Objectives 1-5 to create and distribute printed and electronic educational materials for grazing dairy farms or for conventional farms that are considering switching to a pasture-based system. Examples of these materials include:
- Pasture-based dairy budgets.
- Computerized planning tools/decision-aids that help to evaluate what-if scenarios, and
- Extension fact sheets that discuss numerous economic and production facets of grazing dairy production. Some of the fact sheets will also cover items to address when considering converting from a conventional to a pasture-based dairy.
The sustainability of the dairy industry in the Southeastern U.S. is threatened. Most of the dairy farms in this region are conventional dairies with cows typically concentrated in a relatively small area and fed a diet of corn silage or other stored forages, and concentrated feeds. This system is very useful for maximizing milk production per cow and producing affordable milk for consumers when feed prices are low. However, numerous recent global and domestic events have led to sharp increases in feed and fuel prices and lower milk prices. The results have been disastrous with many dairies exiting the business. Though feed and fuel prices have moderated in the past 18 months, a significant amount of volatility in grain and fuel prices are expected to continue at least until 2017.
It is imperative then that dairy families find ways to become more sustainable. This crisis has led to a renewed interest in pasture-based dairy systems. While overall milk production is typically lower in these systems, total overhead costs are generally much lower. These operations also report reduced labor, machinery and equipment requirements and face fewer risks from price volatility in feed requirements. This system is also viewed by many as being more environmentally sustainable and socially acceptable. It also lends itself well to marketing locally-produced value-added dairy products. Given these advantages, there is considerable interest by families with conventional dairies to change to more pasture-based systems. However, a lack of financial and technical information regarding these systems limits their adoption. This dire situation has led groups of both pasture-based dairies and conventional small to mid-size dairy producers to appeal to the University of Georgia and University of Florida for financial and production management assistance with hopes of becoming more sustainable.
Cooperators
Research
We first identified a steering committee of pasture-based dairy producers in Georgia and Florida, which was a subset of a group with which we have been working. The group includes several of the leading dairy producers in both states. The committee provided guidance and input on survey instruments, decision-aids, and tools as well as helping set the topics for the periodic meetings as presented below.
Objective 1: Participants were recruited through the Florida/Georgia Dairy Graziers' Group, dairy producer associations (Georgia Milk Producers, Southeast Milk Inc., Farm Bureau), veterinarians and nutritionists, and others as identified by our producer panel. Following an initial recruitment period, orientation sessions were conducted for participating farm families to make them aware of what records they need to keep as well as the preferred format. Producers also received their unique and secret benchmark identification number (BIN).
To collect the annual financial and production benchmark information we utilized the computer program FINPACK (University of Minnesota Center for Farm Financial Management (CFFM)). The FINPACK program’s annual financial analysis tool, FINAN, was used as the main focus of this project. FINAN combines beginning and ending balance sheets along with production and financial records to arrive at a realistic picture of what occurred on the farm in that year. Therein, enterprise budgets for the milking herd, replacement heifers and forages were developed.
After each year's data was collected and entered into FINAN, the project team utilized the package RANKEM (developed by CFFM) to compile the individual records and then sort them according to several KPI including pounds of milk produced per cow, cost per hundredweight, returns over feed cost, labor costs per cow, etc. RANKEM was then utilized to generate individual reports for each BIN that shows how the individual farm compares to the group.
Objective 2: We met with the dairy families annually in Georgia and Florida to review/provide an update on our project’s progress. The aim of this meeting was to review the benchmark results from the previous year and offer suggestions on ways to improve on specific KPI while keeping the systems concept in mind. This culminated in final program summary at the Mid-Atlantic Dairy Grazing Conference in Perry, GA in November 2014, wherein several of the GA and FL dairy families attended to review the FINAN results and survey results.
Objective 3: We initiated a comprehensive production practice survey of 20 grazing-based dairies in Florida, 20 grazing-based dairies in Georgia, and 10 comparable conventional dairies in both Florida and Georgia. The survey was extensively based on the survey developed by Dr. Victor Cabrera, University of Wisconsin, which was developed for organic grazing dairies in Wisconsin. The survey consisted of 62 questions covering 7 areas, including farm business structure, young stock management, milking herd management, pasture and crop management, feeding management, manure and nutrient management, and environment and sustainability. The survey focused on the year from summer 2011 to spring 2012. Dairy farmers in Florida and Georgia were invited by telephone calls, emails and announcements in newsletters to participate. Data were collected by personal interviews from September 2012 to March 2013, and analyzed using Microsoft Excel. The survey results were analyzed using standard statistical methods, including regression and ANOVA.
Objective 4: After two years of collecting benchmark information and an assessment of the initial dairy systems was completed, the project team leader analyzed the data and offered an assessment of what made some systems more sustainable given various economic and environmental factors. From the benchmark dataset, three representative farms from the data (one pasture-based, one conventional, and one hybrid) were established. These farms were representative (in terms of forage/feeding systems, investment costs, available labor, etc.) of the benchmark farms. Simulation analyses were then conducted utilizing the SIMETAR program to simulate changes in milk prices, fertilizer and purchased feed prices, and the resulting impacts on net income, total operation cost, cost/cwt. milk produced.
Objective 5. To determine the impediments or barriers to the adoption of pasture-based dairy systems, a survey instrument was developed and distributed to current dairy producers. This survey asked respondents questions related to their current production system, herd size, milk production per cow, if they have always utilized this system, and have they ever considered changing to a pasture-based system if they are currently using conventional methods. At the beginning of the survey, respondents were provided definitions of conventional and pasture-based dairying systems. Based on their response to their current situation, subjects were then asked a series of questions related to their attitudes regarding pasture-based dairying and why they have or have not adopted pasture-based methods.
Objective 6. As the project progressed, we took the financial, production, and environmental information we gathered and developed electronic information for dairy families to use. These budgets include not only projected milk production, income and expenses, but also include realistic estimates of facilities cost, land cost, forage types and amounts, labor requirements and other very important decision-making criteria that were not previously available for this region. We also develop extension materials (decision aids, media, etc.) that can be used to assist pasture-based dairies or those considering changing to this system. These decision aids and extension materials were presented in a series of seven trainings conducted in Florida and Georgia in the winter of 2015. Dates and locations of the workshops were as follows:
- January 26: Oglethorpe, GA
- January 27: Eatonton, GA
- January 28: Live Oak, FL
- January 29: Quitman, GA
- January 30: Marianna, FL
- February 3: Waynesboro, GA
- February 12: Okeechobee, FL
Objective 1: Benchmarking - In 2011 there were seven farms that signed up for the benchmarking service. This number was considerably less than the program goal. Several reasons contribute to the lack of participation including producer reluctance and personnel changes related to the original collaborators. It should be noted that the farms presented here are a mixture of conventional and grazing/hybrid dairy farms. Because of the small sample size, it is impossible to make any inferences as to which production system is the most profitable. Curiously, most of the farms have a relatively low depreciation and interest expense percentage of dollars generated. This is most likely due to the fact that most of the farms have a very low debt-to-asset ratio. It is also worth acknowledging that that the mean ROA was a very strong 34%. Since it became apparent that we would fall short of our target number of producers, we decided to focus our efforts on objective 6 and developing financial management tools that southeastern dairy producers can utilize to help them in their operations.
Objective 2: Reporting benchmarking to the dairy producers to stimulate a feedback loop - Following an initial presentation at the Georgia Dairy Producers Conference in January 2013, five workshops were scheduled around Georgia to teach dairy farmers about key business management principles including goal development, farm planning, and annual evaluations for key performance indicators (KPI). Workshops were held in Clermont, Greensboro, Montezuma, Pennington, and Tifton. Approximately 75 producers and their families attended these workshops. In these workshops, producers were taught how to develop SMART (specific, measurable, attainable, realistic, and time-bound) goals as well as how to monitor and compare financial KPI. Attendees learned about the three legs of financial sustainability (profitability, liquidity, and solvency) and how they could measure these on their operations. To facilitate learning, an example from the Ohio State University’s “15 Measures of Dairy Farm Competitiveness” was adapted to the workshop with learners working through the examples to reinforce the concepts taught. Feedback from attendees indicated that the program was well-received and very useful. Additional feedback was incorporated into materials and trainings developed to fulfill Objective 6.
Objective 3: Production practice survey - The survey was completed by 23 dairy farms, involving approximately 29,000 cows and 17,000 heifers, about 15% of all dairy cows in Florida and Georgia. Rotational stocking was employed by 13 (57%) of the respondents. During the warm season, all 23 farms grew warm-season perennial grasses, and during the cool season, 18 farms grew cool-season annual grasses. The total area of warm-season perennial grassland was 5,012 ha, with mixed-species pastures occupying 2,630 ha (52%) and non-mixed pastures occupying the remaining 2,382 ha. Of the non-mixed grass pastures, areas were: 878 ha (37%) of bermudagrass (Cynodon spp.), which included Tifton 85, common bermudagrass, Florakirk bermudagrass and coastal bermudagrass; 1,114 ha (47%) of stargrass (Cynodon nlemfuensis); 100 ha (4%) of limpograss (Hemarthria altissima); and 289 ha (12%) of bahiagrass (Paspalum notatum), including cv. Pensacola, Tifton 9 and Argentine. The total area of cool-season annual grasses was 1,475 ha, with mixed cool-season annual grasses on 878 ha (59%) and non-mixed cool-season annual grasses on 678 ha (41%). Of the non-mixed grasses, oats (Avena sativa) was the most common (482 ha, 71%), followed by triticale (x Triticosecale spp.) on 144 ha (21%) and annual ryegrass (Lolium multiflorum) on 52 ha (8%). The most popular mixture of cool-season grasses was annual ryegrass and oats, established on 374 ha (43%). Warm-season annual grasses were established on 2,358 ha, with corn (Zea mays) on 938 ha (40%), sorghum (Sorghum bicolor) on 850 ha (36%), crabgrass (Digitaria sanguinalis) on 400 ha (17%) and pearl millet (Pennisetum glaucum) on 168 ha (7%).
Thirteen farms (57%) treated fall armyworm (Spodoptera frugiperda) with pesticide, while 16 farms (70%) controlled weeds with herbicides and 11 farms also used a machine to cut weeds. No manure or commercial fertilizer was used on grass paddocks on 10 farms (43%), while 3 farms (13%) used only commercial fertilizer, 1 farm (4%) used liquid manure only, 1 farm (4%) used solid and liquid manure, 3 farms (13%) used liquid manure and commercial fertilizer, and 5 farms (22%) used solid and liquid manure plus commercial fertilizer. Ten farms (43%) applied no manure or fertilizer to cropland, 9 farms (39%) used all their liquid and solid manure plus commercial fertilizer, 3 farms (13%) used only liquid and solid manure, and only 1 farm (4%) used liquid manure and commercial fertilizer on cropland.
Average milk production was 27 ± 7 kg/cow/d during the winter and 20 ± 7 kg/cow/d during the summer. The rolling herd mean yield was 7,794 ± 1,773 kg/cow/yr. Average somatic cell count was 246,292 ± 69,614 cells/mL during the winter and 365,292 ± 78,587 cells/mL during the summer. Six farms (26%) utilized a year-round breeding strategy, while the remaining farms practiced various seasonal breeding strategies. Three farms (20%) employed 100% seasonal breeding. The most calvings were reported in October (11 farms, 48%), while 14 farms (61%) reported the fewest calvings in August. Non-breeding periods were reported by 18 farms (78%). Summer breeding was avoided owing to low conception rates; summer calving was avoided be-cause of calving problems at this time (9 farms, 50%), and cows were not bred during October−November (11 farms, 61%) to avoid calving during the summer.
Objective 5. Barriers to the adoption of pasture-based dairy systems – The survey received 120 responses, of which 67% were from Georgia and 33% were from Florida. Of the respondents, 92% declared themselves to be white, which is similar to the demographics of dairy ownership in these two states.
One of the revealing statistics is that only 25% of respondents indicated that their operations had low debt (<10% debt as a percentage of total assets; Fig. 1). Approximately 67% reported they were using pasture as a source of forage for their milking cows during 2012 (the year of the survey). Of this group, 37%, 30%, and 33% indicated they had been dairying with cows on pasture for less than 10 years, 10-20 years, or more than 20 years, respectively. When asked how concerned they were about the long-term survival of their dairy farms, the most common factors that caused them very or extremely concerned was milk production costs (86.3% of the respondents). The least concerning factor was pressure from land development (Fig. 2). For those dairymen that switched to more of a pasture-based dairy model, 70.7%, 58.7%, and 57.7% indicated that the change had had a positive or highly positive improvement on herd health, environmental impact, and financial performance, respectively. The impact of the switch to a pasture-based dairying model was believed to have a positive or highly positive improvement on family quality of life and contribution to the local community by 44.0% and 27.1%, respectively. In these situations, 44.0% and 65.8% of the respondents indicated that the change had no positive or negative effect on the family’s quality of life and contribution to the local community.
Objective 6. Development of extension materials (decision aids, media, etc.) and workshops to assist pasture-based dairies or those considering changing to this system. Three economic models were developed that would assist producers who were attempting to compare the various dairy models. These included economic models (spreadsheet decision aids) for a 600-cow pasture based dairy (all or mostly pasture), a 600-cow hybrid (combination of pasture and concentrate/silage feeding), and a 2,500-cow conventional (concentrate/silage feeding) dairy. Early drafts of these economic models were presented to the Florida/Georgia Dairy Graziers' Group at the Mid-Atlantic Dairy Grazing Conference in November 2014. Their critique and suggestions for improvement were incorporated prior to the presentation and demonstration of these economic models at seven workshops across Georgia and Florida in the winter of 2015 at the locations described in the Materials and Methods.
The seven trainings attracted 40 participants including producers, extension agents, nutritionists, farm credit lenders and consultants. More than 70% of participants were producers that represented operations in 13 different counties throughout Georgia and Florida. Nearly half of the operations were small and less than 500 head with an additional 30% of operations having less than 1200 head; three operations in attendance had more than 2500 head. Sixty percent of operations classified themselves as a hybrid operation and nearly 30% considered themselves a conventional operation. Three operations were strictly grazing style. Figure 3 summarizes the herd size based on the type of operation.
All participant operations in west Georgia had less than 500 head in a hybrid model and one operation was strictly grazing. Operations in north Florida followed suit with participant operations being small and hybrid style. Operations in south and west Georgia had the largest variety in terms of herd size and style of operation. South Georgia operations were either of medium size (500-1200) or large with over 2500 head. Medium sized operations represented 71% of the operations present and were all hybrids. With the large herd operations, one was strictly conventional while the other was classified as grazing. Half of the operations in west Georgia were small and conventional. The largest (more than 2500 head) was also conventional while the medium size operation was a hybrid. In middle Georgia, the larger operations (more than 500 head) were all conventional while the smaller operations had both a grazing and a hybrid operation. Operations in the Florida panhandle were all classified as hybrid. Two-thirds of the operations had less than 500 head while the remaining operation had between 1200 and 2500 head. The operation in south Florida was a conventional dairy with medium sized herd (500-1200 head). Figures 4 and 5 summarize herd size and type of operation based on location.
Subsequently, these decision-aids have been made available online on the University of Georgia Applied and Agricultural Economics’ Beef and Dairy Budgets webpage (http://www.agecon.uga.edu/extension/budgets/beef-dairy/index.html) and these products have been promoted to southeastern dairymen at the Southern Dairy Conference, GA/FL Corn Silage Field Day, the Georgia Grazing School, and as part of at least 15 other dairy extension meetings.
- Figure 1. Participants self-classification of debt level as a percentage of total farm assets.
- Figure 3. Self-reported herd size of the conventional (confinement), pasture-based, and hybrid dairy operations who attended the seven workshops.
- Figure 4. The herd size in operations represented at the seven locations where the workshops were held.
- Figure 5. The type of operations represented at the seven locations where the workshops were held.
- Figure 2. Self-reported rating of concern over long-term threats to the survival of the participants’ dairy farms.
Educational & Outreach Activities
Participation Summary:
Economic Dairy Models
- 600 Cow Grazing (all or mostly pasture) (http://www.agecon.uga.edu/extension/budgets/beef-dairy/documents/UGA600GrazingDairy-Final.xlsx)
- 600 Cow Hybrid (combination of pasture and concentrate/silage feeding) (http://www.agecon.uga.edu/extension/budgets/beef-dairy/documents/UGA600HybridDairy-Final.xlsx)
- 2,500 Head Conventional (http://www.agecon.uga.edu/extension/budgets/beef-dairy/documents/UGA2500Conventional-final.xlsx)
Project Outcomes
In an exit survey following the seven workshops to accomplish objective 6, producers were asked if they were considering changing or modifying their operation (pasture vs. confined feeding regime, herd size, type, etc.) and just over half of the producers responded “yes.” Producers indicated the top three reasons for changing or modifying their operation are potential profits, change in herd size, and the cost of feed. All but one participant indicated their desire to use the budgets and decision aids to make future decisions on their operation. Producers mentioned using the budgets as a way to determine herd size, analyze raised versus purchased costs for feeds, and evaluate total costs. Farm credit lenders found the budgets to be helpful in determining whether or not to lend, evaluating cash flows and provided a useful tool for their customers. Producers, lenders and extension agents found the “what if” scenarios the most beneficial and indicated being able to change certain variables and analyze the outcome as one of the best features of the budget. Overall, all the participants found the trainings useful and the budget easy to understand. The majority of participants said this training greatly improved their interest in the topic and increased their knowledge of using spreadsheets and calculations to improve their operation’s efficiency.
Economic Analysis
N/A
Farmer Adoption
Though we do not have an ability to quantify the number of downloads for the decision aids, we know that at least 15 dairymen in GA have used these economic models to aid in their decisions. Since developing the economic models and providing the workshops earlier this year, we have used them with 4 dairymen who have transitioned from a conventional dairy model to a hybrid or a pasture-based dairy model.
Areas needing additional study
One of the major deficits of information for the pasture-based dairy operations is the development of forage systems that optimize the balance between acreage that is in permanent pasture and acreage planted to winter and summer annual species each year. Further, weed management on pasture-based dairies is one of the most limiting factors. The management of spiny amaranth and goosegrass in these pastures with limited herbicide options and challenging plant back intervals for those herbicides that do exist have resulted in the loss or renovation of several paddocks within existing pasture-based dairy operations.
"This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, through the Southern Sustainable Agriculture Research and Education program under subaward number LS11-243. USDA is an equal opportunity employer and service provider.