Final Report for GNC10-133
Project Information
Individuals motivated to become involved in agriculture may find that a small scale livestock enterprise can fit within their resource constraints. Enterprise budgets yielded profits of -$116.11, -$158.69, $6.37, $82.99, and -$2.38 per unit for cow-calf, dairy steer, sheep, goat, and turkey operations respectively. The negative profit projections from this analysis provide evidence that small scale livestock operations often do not make sense when using profitability as the sole entry criteria. A Comparative Decision Support matrix provides additional comparison information for individuals making small scale livestock entry decisions. http://www.agecon.purdue.edu/newventures/cds/
Introduction:
Livestock enterprises have the potential to be profitable for the producer and contribute to the welfare and sustainability of the surrounding rural community. In many cases small scale livestock operations are attractive to producers because they have a small amount of land or financial capital to invest, they want to add the livestock operation to an existing farm operation as a form of diversity, they have other employment and the livestock operation is a part time effort, or they simply want to get involved in agriculture because of alternative motives such as to provide their children responsibility or to improve their quality of life.
Species from large ruminants, small ruminants, poultry, swine, and exotic classifications were evaluated for this study. The number of enterprises chosen reflect the timeframe of the project. Subsequently cow-calf, dairy steers, sheep, goats, and turkeys were chosen. The rationale of choosing this subset of enterprises comes from the predicted need for a diverse set of species that are readily available in the North Central region and that can complement different resource endowments. The objective of the project was to provide results that are easily employed and apply to the broadest number of individuals.
This set of enterprises takes advantage of specific opportunities inherent in each species. Sheep and cow-calf enterprises were chosen for their success on marginal lands and their key differences in size, cost, expertise, and needed equipment. Finishing dairy steers is an enterprise that has not had a large amount of research and is a growing opportunity particularly in the North Central region. With a number of traditionally strong dairy states (Wisconsin, Michigan, and Minnesota) in the North Central region an abundance of dairy steers is expected in the future. Further, Indiana has experienced recent growth in dairy; therefore, it is expected that the number of dairy steers available in the region will continue to grow, perhaps even at an increasing rate. Goats are included because of their potential success in ethnic markets, brush control attributes, and price premium per pound capabilities compared to other red meat. Turkeys can be raised on small acreage and are becoming an important addition to the growing Farmer’s Market trend.
The outcomes from this project include a set of tools to assist individuals when choosing a livestock enterprise that complements their resources rather than purely focusing on profitability as a decision criteria (although profitability is included in this study). The decision support matrix is an important tool that is structured to reflect the opportunities available in the North Central region. When decision makers are able to make better investment decisions there is a greater chance they will be successful, which in turn has a positive long term impact on the quality of life of the participants and the economies of the rural communities where they live.
SARE funded a project to assist farmers with enterprise diversification. The conclusion from that project was that there is a need for additional study to produce business planning curriculum that fit into the busy lifestyles of farmers or individuals looking to start an enterprise (Jost, 2003). The project reported on here compliments Jost’s enterprise diversification project by providing budgets and an easy-to-use decision support matrix that will help individuals apply the methods suggested by Jost. Another project funded by SARE attempted to assist individuals in planning small farms for pleasure and profit; however, the final report indicated that materials (which had to be purchased) were not adopted by farmers and that budgets needed to be updated (Macher, 2005). Current materials, that are easy to use and readily available, are a significant output from this project. The project reported on here benefits from Kollock’s (2006) project to increase the demand for small scale agricultural products and Muntz’s (2007) project that helps individuals begin small scale poultry operations. There are many budgets for enterprises available, which were used in this study from Iowa State, Michigan State and the University of Minnesota. The objective of this project was to tailor broad budgets to a small scale enterprise specifically in the North Central region which makes them more relevant to the population that is being served.
The objectives and performance targets laid out by the project proposal are discussed and the resulting activities and timeframe followed to complete the objectives are discussed in this section.
Several short term expected outcomes were identified in this proposal for this project. These reflect the method of collecting information to develop outputs for the realization of the proposed intermediate/long term outcomes.
The first expected outcome was to create a framework for a productive orientation call with farmer participants. In order to attain this outcome, the research team sought Institutional Review Board (IRB) approval at Purdue University. This approval process entailed generating the framework for the entirety of the human subject participation in this research. This framework consisted of recruitment email text, orientation call script and questions, farm visit script and questions, and description of human subject privacy and compensation. The IRB proposal was approved June 2011.
The second expected outcome was to gain base knowledge of livestock enterprises through an orientation call with farmer participants. During this portion of the project, farmer participants were identified and recruited in accordance with the process identified in the IRB proposal. After the farmer participants were identified, an orientation call was conducted with each participant to provide an overview of the research and expectations of farm visits. Additionally, an overview of the participants’ livestock operation was discussed to tailor farm visits accordingly. These calls were completed during September 2011.
Another expected outcome was to have productive field visits. Field visits entailed completion of the survey from the IRB proposal as well as a tour of the livestock enterprise facility. The first visit took place on 9/29/11 in Minnesota at a turkey operation by Anna Lee Allcorn and Dr. Joan Fulton. The Indiana sheep operation was visited by Anna Lee Allcorn and Dr. Nicole Olynk on 10/7/11. A Kansas cow-calf operation was visited by Anna Lee Allcorn and Dr. Olynk on 10/28/11. Finally, an Indiana goat operation was visited on 11/30/11 by Anna Lee Allcorn and Dr. Olynk. Each visit lasted approximately three hours and the participants were compensated $150 for their time. These visits provided the information needed to complete the next expected outcome of gaining in-depth knowledge of livestock enterprises.
The final short term expected outcomes were to clarify any questions after the field visits and to verify usability and accuracy of the tools created as a result of the project. Follow-up communication was conducted on an as-needed basis with each of the participants. Ten secondary farmer participants (those who did not have a farm visit, but were asked to verify usability of tools) were identified and provided valuable feedback on enterprise budgets. Additionally, the project was presented to a group of thirty Purdue agribusiness students and faculty to get their insights and feedback on the economic framework and decision tool design. From these outcomes enterprise budgets and decision assistance tools were designed.
Intermediate goals established in the project proposal are designed to utilize the information and outputs of the short term expected outcomes.
The first intermediate expected outcome was for this project to be useful for individuals when planning for their venture. Enterprise budgets were the output by which this outcome was evaluated. As discussed in the short term outcomes, farmer participants reviewed the enterprise budgets for accuracy and usability, additionally Purdue Extension faculty reviewed these outputs. The Materials and Methods section of this report gives a more in-depth methodology of enterprise budget creation.
The second intermediate expected outcome was to provide insight for decisions based on what resources are needed along with what resources the individual already has. This information is reflected in the investment portion of the enterprise budgets as well as in the qualitative section of the Comparative Decision Support matrix. This expected outcome was originally proposed to be in a separate Land, Labor, and Capital Needs Summary. However, when the project evolved to have a more sophisticated decision support matrix it was determined that the Land, Labor, and Capital Needs Summary should be merged with the matrix and budgets.
Assisting individuals in choosing the best livestock enterprise, thus increasing enterprise success was also among the intermediate expected outcomes of the project. The Comparative Decision Support (CDS) matrix was created to assist the user determine the livestock enterprise that complemented their lifestyle. A secondary tool (CDS2) was designed to provide more in depth, personalized analysis for the user. Further information on the methodology used to create this tool can be found in the Materials and Methods section of this report.
In addition to creating tools, this project had expected outcomes of sharing the tools with the public and creating Extension awareness of the tools. This was done in four ways rather than the two original projected avenues. First, the tools are interactive and thus a website was designed to make them readily available to the public. This website went live during the month of March, and is being updated as publications are completed. Second, this website houses not only the tools but also informational videos containing useful information and interpretation. Third, six Extension publications were written and will be available from the website to provide further information for individuals and Extension educators. These Extension publications are in the final stages of the extensive internal expert review process. Drafts are available currently on the website, and will be distributed more broadly as soon as the final edits are approved. This set of publications along with a flyer marketing the online component of the research will be emailed to all Indiana Extension educators when the Extension publications are finalized. Fourth, two presentations discussing this project’s outputs were made on the Purdue campus reaching approximately seventy individuals (presentation dates: 2/8/12 and 3/8/12). These presentations employed Turning Point Audience Response Systems to highlight the interactive nature of the tool package.
The proposal also had expected outcomes to report the findings of the research and present publishable material through Anna Lee Allcorn’s masters thesis. She successfully defended her thesis on 3/2/12. In addition to a successful thesis document and defense, this research has been accepted for an oral presentation at the 6th Annual National Small Farms Conference in September 2012.
The final expected outcome of this project is to share information on the SARE Project database which will be completed with the submission of this report.
Cooperators
Research
Individuals seek to maximize their own expected utility. While profit or income is generally assumed to impact utility, other non-monetary factors are also assumed to impact utility, like leisure time, allocation of resources between the livestock business and the family, and other potential factors. Expected utility, (EU) is defined as follows,
Equation 1. Expected Utility of Small Scale Livestock Enterprise
Expected Utility=f(R_Family,R_Business,Leisure,Income_Family,Profit_Business,?)
where,
RFamily = the resources allocated to the family,
RBusiness = the resources allocated to the business (livestock operation),
Leisure = the proportion of time not devoted to the business or employment,
IncomeFamily = the income earned from sources other than the livestock operation,
ProfitBusiness = the profit generated from the livestock operations, and
? = other factors impacting utility.
This expected utility served as the framework for the research design. Producer surveys were utilized to increase our understanding of the error term (?). Producer responses helped identify potential factors impacting utility that are not captured in the traditional profit, resource, and time allocation utility model; however, this term also captures factors impacting utility that are not included in this research. The enterprise budgets address the profitability of the business. The Comparative Decision Support (CDS) matrix takes the resources of the family to be allocated to the business to return information on the type of enterprises that could be supported with those resources. The CDS seeks to give the user the ability to maximize their expected utility by providing information relating to each of these sources of utility derivation.
Producer surveys and farm visits were used as the mechanism to identify other factors (?) potentially impacting utility. Producer surveys also provided insight and guidance for the formulation of the decision support tools (budgets and CDS).
Potential producers were identified as possible participants by way of previous relationships with Purdue faculty. Formal recruitment to participate in the research was done via email to small scale livestock enterprise owners.
A producer was identified for each species represented in the study: cow-calf, dairy steer, sheep, goat, and turkey. Producers were asked to participate in an orientation call and a field visit survey. The orientation call took place in September and was used to brief participants on the project. The survey delivery method was an in-person interview. The field visit surveys took place from September to November and lasted approximately a half day per interview. During each visit the same standard questions were asked along with open-ended questions to allow the respondents to share information about their operation that they were comfortable sharing. Additionally, enterprise specific questions regarding production practices and input needs were included. Relationships with agricultural and business service organizations were explored to gain insight into where small scale livestock producers gather information. Financial practices such as loans were explored with ranges of investment magnitudes in order to protect sensitive information, but still gather useful data. Seasonality of enterprises were explored with questions regarding labor hours and number of people involved during busiest/slowest times of the year. Participants were asked about marketing practices as well as current trends in the industry. Survey questions were approved by the Purdue Institutional Review Board (IRB Protocol #1103010958).
Enterprise budgets for the species included in the study were constructed in Excel (Microsoft, Redmond, WA) using the format of the Iowa State University 2010 Enterprise Budgets (Ellis et al., 2010). This format consisted of an operating budget with revenue, variable costs with a subcategory of feed costs, and fixed costs. Additionally, a separate investment budget was constructed—the fixed costs in the operating budget reflect estimated annual replacement costs for breeding stock and depreciation, interest, taxes and insurance on the facilities and machinery investment.
Each generated enterprise budget relied on an identified budget from the literature to initially define the input needs of the operation. The cow-calf and sheep operations’ input needs were based on the Iowa State University budgets (Ellis et al., 2010). The dairy steer budget reflected Robert Tigner’s 2005 budget (Tigner 2005). The goat budget utilized the Virginia Extension meat goat budget to identify input needs and the turkey budget requirements were based on the Pennsylvania State University budget (Callan et al., 2011; Hulet et al., 2004). In addition to using these identified budgets to identify the input needs of the enterprises, these budgets’ predefined scales were assumed as the generated budgets’ scale in order to produce an enterprise consistent with the original scale of the inputs.
Generated budgets deviated from the identified base budgets in literature for two reasons: the base budget did not include investment expenses in the format needed to be consistent with the Iowa State format chosen, or the prices of the base budget were not current.
Investment expenses were inconsistent with the chosen format in the case of the dairy steer operation, which did not include background numbers on investments made for the operation. The facilities and machinery estimate from the cow-calf budget were taken as realistic needs for the operation with the exception of hay equipment, which was excluded from the dairy steer investment list. The goat investment followed closely with the base budget and reformatted to be consistent; however, the facilities and machinery investment were taken from the sheep budget for the same reasons as previously mentioned with the dairy steer investment.
Prices for feeder livestock purchases (dairy steers and poults), feed inputs, and animal sale prices are described below, and reflect current prices. Feed input prices are consistent across enterprise budgets for consistency and ease of comparison. Additionally all prices are housed in a separate worksheet in the budget file and linked to enterprise budgets for ease of updating consistently.
After budgets were formulated, producers were asked to review the investment, inputs, and pricing to identify potential inconsistencies with actual production practices. The dairy steer budget was identified, through discussions with producers, as problematic in the area of feed inputs. The producer, K.L. Allcorn (Quicksand Inc., Olton, Texas, personal communication) had concerns that the feed intake was not sufficient to produce the proposed gain. After consulting an animal nutritionist, Allcorn proposed a ration consisting of 85 percent corn and 15 percent soybeans for a total intake of 32 bushel of corn and 340 pounds of soybean meal over 150 days for an average daily gain of 2.33 pounds per day.
Sensitivity analysis of enterprise budgets was conducted using a stochastic modeling approach. Five years of price data (2007-2011) for the primary feed inputs and livestock was utilized in this analysis.
Soybean meal price was changed from a per ton price to a per hundredweight price to be consistent with the scale used in the enterprise budgets. Also, the turkey complete grower price was transformed from a per ton price to a per 50 pound price. Dairy steer data was taken from a weekly report and averaged to find a monthly price. Monthly prices were put into Stata software (Intercooled Stata for Windows, version 12.0, Stata Corporation, College Station, TX), which returned the correlation between variables.
Certain variables were identified as the key prices in the enterprise budgets and were made stochastic in this analysis (variables included: corn, soybean meal 48%, hay, turkey complete grower, dairy steer 350 lbs, poults, calf 500 lbs, dairy steer 700 lbs, sheep 125 lbs, goat 80 lbs, and turkey 15 lbs). Triangular distributions were assumed for this analysis, which rely on an input of minimum, most likely, and maximum parameters. This information was taken from the five years of data previously discussed. The most likely value was assumed to be the mean of the variable observations.
Stochastic modeling was conducted using @RISK Version 5.7 (Palisade Corp., Newfield, NY). The stochastic variables were parameterized with triangular distributions. Profit for each enterprise (cow-calf, dairy steer, sheep, goat, turkey) was tracked by @RISK. 10,0000 iterations were run and included in the output for evaluation.
The Comparative Decision Support Matrix (CDS) was developed utilizing the information learned from farmer interviews, during field visits, and through the construction of the enterprise budgets. The CDS is an interactive tool that reflects user input to formulate an individualized matrix. Both quantitative and qualitative decision criteria sections are incorporated into the matrix so the outputs reflect this broad set of objectives.
In the quantitative section input concerning the intended investment and number of acres to be dedicated to the enterprise is inserted by the user. This information is used to determine the maximum number of head per enterprise the user could support with the resources they wish to employ. The initial investment per unit is assumed to be linear within a defined bound and is the sum of breeding herd investment per unit and facilities and machinery investment per unit. Dairy steers and turkeys do not have a breeding herd investment; however, the cost of the initial purchase of 350 pound dairy steers or poults are included in the initial investment calculation. The initial investment number entered by the user is then divided by the initial investment per unit to find the maximum number of units that could be financed. The user’s inputted number of acres to devote to the enterprise is divided by the number of acres per unit to find the maximum number of units that could be contained on the land. The minimum of these calculations is taken as the maximum number of units that can be supported by the user’s resources. Due to the scale limitations of the enterprise budgets, an upper bound was introduced to the maximum number of units calculation.
The upper bound on the maximum number of units reflects the limitations of the static budgets to adequately show the impact of scale on the operation. The budgets are scale specific meaning that the expenses and fixed costs are estimated with an assumed number of units, thus the budgets represent that specific enterprise and operating decisions/assumptions at a point in time. It is acknowledged that the budgets are not accurate when increasing or decreasing the scale. The budgets generated in this research are designed to encompass general operating decisions based on literature and producer insight. While the budgets are not accurate when scale is altered, the CDS matrix operates on the assumption that within certain bounds the budgets reflect realistic expectations on a linearly changing per unit basis. Bounds were determined following discussions with producers and evaluation of fixed cost components.
The CDS matrix shows the resource(s) with binding constraint(s) and resulting slack value of non-binding constraint(s) in the maximum number of units, initial investment, and acreage used categories. The initial investment needed is the previously defined initial investment per unit multiplied by the maximum number of units. The acreage needed is the acreage per unit multiplied by the maximum number of units. Operating expenses is the total of all costs taken from the enterprise budget (this figure does not include breeding stock or facilities and machinery investment) multiplied by the maximum number of units. For the dairy steer and turkey operations this provides a double counting of purchase of input animals for the first operating period. This is addressed by including operating expenses for the first year found by subtracting the cost of the dairy steer or poult multiplied by the maximum number of units from the operating expenses. Feed costs as a percentage of operating expenses is found by taking the subtotaled feed costs from the enterprise budgets and dividing by the operating expenses (not operating expenses for the first year). Estimated profitability is found by multiplying the profit from the enterprise budgets by the maximum number of units. Total labor hours is found by multiplying the labor hours per unit from the enterprise budgets by the maximum number of units.
The qualitative section of the CDS is to assist users further in matching their resource endowment to an enterprise, including the need for a permanent structure/building, labor intensity associated with this operation, the ease of entry and exit, and market access.
A second worksheet entitled CDS2 is included with the CDS that allows the user to conduct their own sensitivity analysis. The user defines the number of units per enterprise by species and inputs prices for key feed inputs and livestock selling prices along with labor. Using this information, total revenue, feeder animal costs, feed costs, total variable costs, total fixed costs, and profit is found. These calculations are done in a hidden worksheet in the same manner as the CDS.
In this section the outputs of this research are described, including, enterprise budgets generated for five livestock operations; cow-calf, dairy steers, sheep, goats, and turkeys. An overview of potential marketing opportunities for each enterprise and the necessary premium needed to generate a breakeven is also provided. Key features of the Comparative Decision Support (CDS) matrix and matrix are summarized. Input from producer interviews is contained in the budget overview and the discussion surrounding the CDS. Please note that the actual budgets and CDS matrix are not contained in this report, but can be found on the project website.
Enterprise budgets were an output of this research as well as an input to the Comparative Decision Support matrix. The enterprise budgets were designed with assumed operating decisions. Also, recall that the prices of inputs are assumed to remain constant across all livestock enterprises. The enterprise budgets that follow are the base case for the described CDS. Budgets can be altered in the Excel document with the user’s estimates and is reflected in the CDS and budget calculations.
The cow-calf enterprise budget relies heavily on the Iowa State University cow-calf budget. This enterprise is defined as a hay and pasture operation that markets calves at 500 pounds.
With the assumed production decisions and prices this budget shows an operating loss of $116.11 per cow unit. This budget allocates 8 hours of labor at $14 per hour which reflects an opportunity cost of time; if labor was excluded from this budget the enterprise losses would be reduced to $4.11 per cow unit. Corn stalks comprise a small portion of the budget; however, if the operator has corn stalks as a by-product of other farm operations this would positively impact the bottom line. This thought process could also be applied to pasture not being utilized, which would have a more substantial positive impact the bottom line. Also, note that the assumed hay price is a national price, but the user’s actual price is highly dependent on the local market and would have a significant impact on the profitability of the enterprise. For example, the price of hay is assumed to be $150 per ton in this budget; however, if a producer in Indiana wanted to purchase hay they might have to pay up to $240 per ton. This increase in hay price would negatively impact profit moving from -$116.11 per unit to -$313.62 per unit.
Cow-calf producers traditionally market calves through on-farm/private treaty agreements or livestock auctions, which is the marketing method assumed by the enterprise budget. Producers may be able to capture a niche market by providing calves raised with specific characteristics. Some niche markets for beef include: grass-fed, locally-raised, hormone-free, organic, natural, family farm, or humanely-produced (Holland 2001). Marketing calves with these characteristics can change the operating expenses of the enterprise as well as the expected revenue generated when calves are sold. Finding a buyer to compensate for the additional expenses of production and increase the profitability of the enterprise from the traditional market may require additional labor hours. This study does not explore the additional expenses required to produce for these niche markets nor does it estimate price premiums for niche production qualities to determine profitability as this data is not readily available and is highly variable.
Farms with existing operations seeking to diversify may find that they have already accounted for the expenses of machinery or other fixed costs and will not consider these expenses in profit calculations. Also, use of by-products from farm operations as feed inputs can result in a change in input requirements and estimated expenses. Adjusting budget input to reflect existing resources is recommended to users of these enterprise budgets to estimate the most accurate budget for planning.
It is also noted that the calf selling price to breakeven would be $190.25/cwt. This means a producer would require a premium of $32.25/cwt. over the assumed market price to break even.
The dairy steer enterprise budget is a compilation of budgets from Iowa State University (2010), Robert Tigner (2005), and producer interviews. This enterprise is defined as purchasing dairy steers at a starting weight of 350 pounds and marketing at 700 pounds.
With the assumed production decisions and prices this budget shows an operating loss of $158.69 per head. It is important to acknowledge that the assumed purchase and sale prices are based on Kentucky state reporting rather than a national average, due to the lack of national data for dairy steers. Local dairy steer prices are sure to have an impact on the bottom line of this enterprise.
Dairy steer producers traditionally market steers through livestock auctions, which is the marketing method assumed by the enterprise budget. It is recognized that many producers continue to feed dairy steer in a finishing program and market directly with packers. Finishing dairy steers is not included in this budget; however, a producer could potentially find a niche market for lean freezer beef based on the lean meat of dairy steers. Additionally, producers may be able to capture a niche market by providing steers produced with specific characteristics like grass-fed or hormone-free, but this will change production decisions and costs, as well as potential revenues from those assumed in this budget. Again, finding a buyer to compensate for the additional expenses of production and increase the profitability of the enterprise from the traditional market may require additional labor hours.
The steer selling price to breakeven would be $109.12/cwt. This means a producer would require a premium of $22.67/cwt. over the assumed market price to break even.
The sheep enterprise budget is primarily designed from the Iowa State University sheep budget. This enterprise is defined as an ewe flock that lambs in January and February and markets lambs at 125 pounds.
With the assumed production decisions and prices this budget shows an operating profit of $6.37 per ewe unit. When evaluating this budget it is important to note the 5 labor hours per ewe unit at $14 per hour reflecting the opportunity cost of the operator’s time. If this were not included in the budget, an operating profit of $76.37 per ewe unit could be realized.
Sheep producers traditionally market lambs through on-farm/private treaty agreements or livestock auctions, which is the marketing method assumed by the enterprise budget. Producers may be able to capture the ethnic niche market by providing lambs produced with specific characteristics or killed in a specific manor. Marketing to an ethnic market requires knowledge of production practices and access to the ethnic population. Freezer lamb and lamb sold at farmer’s markets are also methods of marketing that could potentially return a premium price.
The goat enterprise budget was compiled primarily using the Virginia Extension goat budget. This enterprise is defined as a doe herd that markets meat goats at 80 pounds.
With the assumed production decisions and prices this budget shows an operating profit of $82.99 per doe unit. This is the most profitable enterprise from those included in this research reflecting the growing demand for goat meat in the marketplace along with the second lowest feed cost as a percentage of operating expenses.
Meat goat producers traditionally market goats through on-farm/private treaty agreements or livestock auctions, which is the marketing method assumed by the enterprise budget. A growing market for meat goats is ethnic markets, which in many cases can be produced in the same way as traditionally marketed goat. In some cases specific characteristics of the goats or killing in a specific manor in required to meet the ethnic market. As with lambs, marketing to an ethnic market requires knowledge of production practices and access to the ethnic population in order to realize a potential premium.
The turkey enterprise budget uses the Pennsylvania State University turkey budget as a baseline for input quantities. This enterprise is defined as a meat hen production operation purchasing poults at 1 day old and marketing at 14 weeks.
This budget shows an operating loss of $2.38 per bird with assumed production decisions and prices.
Producer interviews yielded that small-scale turkey operations are often not able to forward contract because of the vertical integration in the poultry industry. In addition it is noted that in most markets there is a monopoly in poult sales and a monopsony in processing, creating challenges for the producer. Due to the nature of this industry, it important that small scale producers not wishing to participate in contract feeding find a local market. Turkeys can be home-slaughtered within the guidelines of FSIS. Also, seasonal demand for turkeys at Thanksgiving can provide a fresh market niche for producers. Free-range, natural, hormone-free, never frozen, and local characteristics are all possible niche markets for turkeys. Identifying a niche market with buyers to compensate for the additional expenses of production and increase the profitability of the enterprise from the traditional market may require additional labor hours.
A hen selling price of $0.91/lb. is necessary to breakeven. This means a producer would require a premium of $0.16/lb. over the assumed market price to breakeven.
This research culminates with the Comparative Decision Support (CDS) matrix. The matrix was developed utilizing the enterprise budgets discussed in the previous section for the upper section of the CDS. Producer interviews provided the background for the qualitative portion in the lower section of the matrix. The individual looking to begin a small scale livestock enterprise, or the user as they will be referred to for the remainder of this report, enters investment and acreage parameters into the questionnaire portion of the Excel worksheet. The enterprise budgets and user parameters feed into a hidden calculation worksheet, which calculates the information that is shown in the CDS. Pertinent definitions and notes regarding the information found in the CDS are described in the Notes and Definitions summary contained in the same worksheet as the CDS.
The CDS reflects the idea of binding constraints in the calculations, which are common in linear programming analysis. For instance, when the user input is $5,000 investment and 5 acres, the cow-calf and dairy steer enterprises have acreage as the binding constraint and slack in investment. Sheep, goats, and turkeys are bound by the investment constraint with slack in additional acreage that could be devoted to the enterprise. In this case, the upper bound on maximum number of units constraint is not binding for any enterprise. In addition to an upper bound, the CDS also returns a no entry option when the user investment or acreage input is insufficient to begin an enterprise.
As discussed, the CDS returns an individualized output based on user answers to the questionnaire. In addition to this individualized output, the user can change the budgets contained in the Excel workbook to reflect their own price estimates and production practices. Any changes made to the budgets will be reflected in the CDS because of the linked cells and formulas contained in the calculation sheet. This allows the CDS tool to be tailored to reflect the resources the user already has, potential production decisions and local price estimates.
Educational & Outreach Activities
Participation Summary:
This project resulted in Anna Lee Allcorn’s masters thesis titled Economic Based Decision Support to Promote Sustainable Small Scale Livestock Enterprises to Potential Industry Entrants, at Purdue University. Additionally, six Extension publications that take individuals through the small scale livestock enterprise entry decision process and provide enterprise budget overviews were published. Two informative videos featuring decision assistance tools, how to use the tools, and interpretation of results were produced. A website featuring the interactive Comparative Decision Support tool package, video demonstrations, and Extension publications is available to the public. Two presentations were held on the Purdue campus using immediate audience response systems to highlight the interactive components of the decision assistance tool package. Finally, this research has been accepted as an oral presentation at the 6th Annual National Small Farms Conference to be held in September 2012.
Project Outcomes
Economic Analysis
In addition to the economic analysis inherent in the nature of this research, further economic sensitivity analysis was built into the project. This analysis was performed on both the enterprise budgets and the CDS. This analysis is described in this section.
Stochastic modeling in @RISK was used to generate 10,000 iterations, which were used for the sensitivity analysis of the generated enterprise budgets. Profit for each enterprise was tracked for each iteration modeled. Stochastic variables included: prices for corn, soybean meal 48%, hay, turkey complete grower, dairy steer (350 lbs), poults, calves, dairy steer (700 lbs), sheep, goat, and turkey. All other variables included in the enterprise budgets were held constant.
This analysis showed that under the current production decision assumptions each enterprise, with the exception of cow-calf, has a positive maximum potential profit. The negative profit from the cow-calf enterprise is not that surprising when comparing to the past five years of negative profits in Kansas (Dhuyvetter 2011). When looking at the detailed statistics it becomes apparent that the positive profit for dairy steers and turkeys occur in less than 5 percent of the iterations. Labor cost, which was not a stochastic variable in this model, was valued at $14 per hour. A second run of the model was performed with a labor cost of $0 per hour to show profit while ignoring the opportunity cost of time.
By taking out the cost of labor all enterprises have the potential to realize a positive profit according to this output. Sheep and goats are still the only species that return a positive profit a majority of the time. Cow-calf and dairy steer enterprises return a positive profit less than 5 percent of the time. Turkey turns a positive profit in less than 20 percent of the iterations.
This analysis reflects the limited potential profitability for cow-calf, dairy steer, and turkey enterprises. This analysis further instills that scale and production decisions play an important role in profitability for livestock enterprises. Cow-calf, dairy steer, and turkey enterprises operating in a traditional marketing environment, as assumed in this research, will most likely realize a negative profit. Sheep and goats have more potential for profitability.
The second worksheet, titled CDS2, to be used in conjunction with the CDS is a tool to allow the user to conduct their own sensitivity analysis using their knowledge of local markets and prices. The user inputs information into the worksheet in steps.
This part of the CDS tool package asks the user to define the size of their operation. This allows the sheet to calculate a profit/loss statement consistent with the user’s intended scale. In the CDS, the user inputs investment and acreage parameters and the matrix is based on the maximum number of units these parameters can support. The user may not want 4,500 turkeys even though their input parameters would support that, which is the reason the CDS2 has the user define the parameters of scale.
The worksheet contains a box with instructions and a summary of the minimum, average, and maximum prices for the variables that were identified as stochastic in the enterprise budget sensitivity analysis. Also, the information box includes the assumed prices used in the CDS and enterprise budgets. This information is included so the user will have some idea of the national market and do a best/worst case scenario even if they have limited knowledge about the local market.
The output of the CDS2 is a profit/loss summary estimated with the user inputs and enterprise budget information. This portion of the decision assistance tools introduces the idea of sensitivity analysis to the user in a simple, straightforward and interactive tool. The CDS2 allows the user to see across species the impact of price changes and premium prices from niche marketing activities.
Farmer Adoption
This research has reached the five farmer participants that hosted the research team for field visits. Another ten farmers were involved in the material review process. The two presentations held on the Purdue campus reached an additional seventy individuals. A marketing campaign launched via email and postal mail began in the summer of 2012 to inform Extension educators across Indiana about the new tools available to their constituents. The number of Extension educators reached is estimated to be ninety individuals. Finally, the oral presentation at the 6th Annual National Small Farms Conference is expected to draw fifty people per breakout session.
Areas needing additional study
Enterprise budgets rely on assumed production decisions and prices, which vary across geographic regions. This research sought to provide a tool that was relevant for the north central region; however, even in this subset of states production decisions and prices are not homogenous. Furthermore, the data used in this research was often national in scope because of the limitations in availability of data. This limitation was addressed by making the tool simple for the user to change price and quantity assumptions in the enterprise budgets.
For the purpose of this research, it was assumed that the per unit enterprise budgets were linear within a defined range. In other words, the cost of production did not exhibit economies of scale, rather each unit increase had the same cost as the previous unit. Further research would be beneficial to provide a tool that reflected scaling budgets with differing fixed costs, which would be consistent with economies of scale. The challenge faced in this study was to not only make the decision support tool realistic with the budgeting, but also to make it accessible and easy to use, which was why the linear approach was favored.
This study provides a useful framework for comparing any assortment of livestock enterprises including niche-marketing based enterprises. The same framework could also be altered for crop enterprise comparisons or potentially to compare crop and livestock enterprises in the same matrix. The framework could be used in comparing other small business endeavors by slightly modifying the attributes associated with the industry being compared.
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