Strengthening Opportunities Along the Meat Supply Chain to Promote Sustainable Agriculture in Intermountain States

Progress report for SW21-921

Project Type: Research and Education
Funds awarded in 2021: $349,994.00
Projected End Date: 09/30/2024
Host Institution Award ID: G325-21-W8612
Grant Recipients: Colorado State University; Oregon State University; Montana State University; University of Wyoming; Cornell University
Region: Western
State: Colorado
Principal Investigator:
Becca Jablonski
Colorado State University
Co-Investigators:
Thomas Bass
Montana State University
Cody Gifford
University of Wyoming
Jennifer Martin
Colorado State University
Martha Sullins
Colorado State University Extension
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Project Information

Summary:

Evolving consumer interest in more localized and sustainable meat production, particularly in the time of COVID-19, is creating new market opportunities for livestock producers, meat processors and other meat supply chain participants. Studies throughout the United States have documented consumer willingness to pay for sustainability attributes and specialized production and handling methods, but the skills and resources needed for producing and processing livestock, and selling meat products profitably into local food markets (defined as direct and intermediated market channels) are difficult to access and adapt to values-based, consumer-focused business models. 

Leveraging the Niche Meat Processor’s Assistance Network’s resources, as well as Cornell University’s NESARE-funded work in Massachusetts, this project will create financial benchmark and pricing resources that support advanced training curriculum and improve meat supply chain coordination in Wyoming, Colorado and Montana. We will do this by integrating restricted access farm financial data into educational programs and materials, thereby increasing producers' access to and ability to act on local food market channel opportunities that best meet the comparative advantage of their operation. In addition, we will convene stakeholders throughout the meat supply chain in an effort to improve coordination, reduce transaction costs, and thus improve producer profitability. Specifically, this portfolio of education and outreach activities will include: 1) two regional meat conferences (modeled after the highly successful Carolina Meat Conference); 2) online meat school classes to reduce risk and support producer profitability in new markets; and 3) augmenting Cornell’s online pricing tool with cost of production data to support improved decision making around markets channel selection. 

This project includes two research objectives (RO) and three education objectives (EO): RO1) Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders; RO2) Examine multiple years of USDA ARMS data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers in the Western U.S. The Education Objectives include: EO1) Enhance at least 60% of producer attendees’ knowledge of and connections to meat supply chain partners within Colorado, Montana and Wyoming through a multi-state meat conference held in 2021 and 2023; EO2) Assist at least 90 livestock producers (30 in each state) in identifying and managing production, processing and marketing risks to their meat businesses through targeted classes on sustainable livestock production practices, strategies for improving meat processing, and identifying and building new markets for their meat products, with at least 60% improving the profitability of an existing or new marketing channel; and EO3) Encourage and instruct at least 30 livestock producers (10 in each of 3 states) to understand and estimate their costs of production, and develop retail and wholesale pricing models to assess and improve their profitability in different market channels.

Project Objectives:

Research Objectives:

  1. Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 ranchers/producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders. 
  2. Examine multiple years of USDA ARMS data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers/ranchers in the Western U.S.  

Education Objectives:

  1. Enhance at least 60% of livestock producer/rancher attendees’ knowledge of and connections to meat supply chain partners within Colorado, Montana and Wyoming through a multi-state meat conference held in 2021 and 2023.
  2. Assist at least 90 livestock producers (30 in each state) in identifying and managing production, processing and marketing risks to their meat businesses through targeted classes on sustainable livestock production practices, strategies for improving meat processing, and identifying and building new markets for their meat products, with at least 60% improving the profitability of an existing or new marketing channel. 
  3. Encourage and instruct at least 30 livestock producers (10 in each of 3 states) to understand and estimate their costs of production, and develop retail and wholesale pricing models to assess and improve their profitability in different market channels.

Cooperators

Click linked name(s) to expand/collapse or show everyone's info
  • Allie Bauman (Researcher)
  • BJ Bender - Technical Advisor - Producer
  • Jeremy Burkett - Technical Advisor - Producer
  • Wes Henthorne - Technical Advisor - Producer
  • Joyce Kelly - Technical Advisor - Producer
  • Adrienne Larrew - Technical Advisor - Producer
  • Matt LeRoux - Technical Advisor
  • Anne Miller - Technical Advisor
  • Dr. Todd Schmit (Researcher)
  • Jeff Smith - Technical Advisor - Producer
  • Rebecca Thistlethwaite - Technical Advisor (Educator)

Research

Hypothesis:
  1. Most producers do not know their costs of production. Providing information on costs of production that they can enter into the pricing tool and use to set pricing will lead to better decision-making and thus make their operations more profitable and sustainable.
  2. There are noticeable disconnects throughout the supply chain. Through intentionally designing regular convenings there are opportunities to build trust that will strengthen regional meat supply chains and result in improved sustainability outcomes for ranchers, processors, and regional communities and economies. 
Materials and methods:

RO1. Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 ranchers/producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders. 

As the number of opportunities for values-based procurement and claims-based markets grow, for livestock producers to effectively benefit from these opportunities there must be enhanced communication and understanding across supply chains - from production to consumers. This becomes more of a challenge as meat supply chains are highly consolidated (e.g., MacDonald et al. 2000), limiting direct communication between producers and consumers across the supply chain. Carolan (2020), for example, conducted interviews with individuals engaged in shaping urban food policy and institutional procurement plans in Denver, and rural Colorado farmers and ranchers. He found important differences across the two groups in terms of conceptions of “good food” and what it means to be a “good farmer”; reconciling these requires mediation. 

Accordingly, we are proposing the rigorous evaluation of a meat conference, to be held twice during the grant period. The purpose of this research objective is to evaluate how an event that intentionally brings together stakeholders along the meat supply chain and incorporates activities designed to build bridges can result in measurable changes in connections and relationships, including across rural and urban stakeholders. To do this we will follow methods used by Brasier and Goetz (2010) and Love et al. (2020). Brasier and Goetz (2010) analyzed the number of new connections made at a local foods conference, particularly for those with few professional linkages prior to the conference. Love et al. (2020) analyzed how different educational curriculum in a construction management program impacted social networks. 

Data: Data collection will center around the meat conferences held in year one quarter four in Colorado, and year three quarter four in Montana. For each meat conference, registrants will be surveyed before and after the event. We expect that at least 200 individuals will participate in each year of the meat conference, and participation in the pre survey will be required for all attendees to attend the event. To improve the response rate following the event, we will raffle an iPad. Accordingly, we estimate that at least 100 individuals will participate in both the pre and post survey, thus providing useable data for our social network analysis. 

Participants will be asked to identify the types of relationships they have with different types of stakeholders across the meat supply chain before and after the event. For each question, they will add an individual name and organization name with whom they have had a written or verbal exchange about something pertaining to meat within the past year. Who they identify will be piped through to future sections of this survey where they will be asked to answer additional questions about these individuals/organizations. 

Additionally, respondents will be asked questions regarding their view of different claims-based language and labels including “sustainable” “regenerative” and “grass-fed”. This will help us to discern if changes in values, production practices, or market opportunities were related to changing supply chain relationships. Finally, producers will be asked pre/post questions related to pre/post markets, prices, and profitability to enable us to understand if new relationships were formed that impacted sales.

Methods: Social network data will be analyzed statistically using UCInet (Borgatti and Freeman 2002), and social network diagrams will be created using Visone (Brandes and Wagner 2004). Density, a measure of the number of actual connections in a network divided by the number of possible connections (Giuffre 2013), will be used to measure ‘within’ network connections and to explain network cohesion. In this research, we will use density to compare changes from the pre/post surveys. Additionally, we will calculate fragmentation in the network, a whole network statistic that measures the proportion of pairs of nodes that cannot reach each other (Hanneman and Riddle 2005). 

Three network statistics will be used to evaluate the network as well as changes within and across the network. First, average degree is a measure of the number of connections for each actor in the network (Giuffre 2013). Second (described previously), we will calculate the fragmentation in the network. Third, Freeman’s centralization will be used to characterize the overall structure of a network through “in” and “out” measures of centrality. When a network is more centralized, it indicates that the few central individuals are more influential than those on the periphery. In contrast, a network with low centrality shows that social relationships and influence are more evenly balanced across the network. 

Baran (1964) described the difference between three ideal types of network structures: a) a centralized star-like network, b) a decentralized network, and c) a distributed un-centralized network. The key characteristic of a centralized network is its star-like pattern. Centralized star-like networks are vulnerable because the removal of a key central node can cause all other nodes to become disconnected from each other (this is what we might expect to see in the ‘pre’ meat supply chain, as it is generally only the processor or distributor that communicates with both the livestock producer and buyer). A decentralized network also has a star-like pattern, and is still considered a vulnerable or imbalanced network because the central nodes have more influence. In contrast, in a distributed network, all nodes can connect and communicate with other nodes, thus reducing the imbalance of power found in centralized networks. If the meat conference is successful in strengthening ties throughout the supply chain, this is the type of result we would expect to see.

RO2. Examine multiple years of USDA ARMS data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers/ranchers in the Western U.S. 

Recent national research on the profitability impacts of sales through local food markets (e.g., Bauman et al. 2018a, 2018b; Jablonski et al. 2020) provides important preliminary information for producers making determinations about market channel selection. Recent research by Jablonski and Bauman, for example, use USDA ARMS data to create financial benchmarks for farms and ranches with sales through local food markets, provide preliminary estimates of profitability implications of local food marketing strategies, investigate the role of human capital measured in terms of wage rates and labor expenditures, and evaluate the financial efficiency of local food producers (fact sheets based off of peer-reviewed research can be found here: https://localfoodeconomics.com/benchmarks/#factsheetsection). As an example of some of the analysis used in this research, Bauman et al. (2018) divide the sample by gross cash farm income, as well as quartile within each sales class (i.e., quartile four is the most profitable and quartile one the least profitable) to investigate the relationship between key financial characteristics and profitability for farms selling through local markets. Across sales class ($1,000-$74,000, $75,000-$349,999, $350,000-$1M, and >$1M), each of the quartile groups are significantly different at the one percent level. They find that among the top performing quartiles, direct-to-consumer only marketers had lower return on assets (their measure of profitability) compared to top performers using intermediated only markets or a combination of direct-to-consumer and intermediated markets. Bauman et al. (2019) explored the financial efficiency of farms and ranches that sell through local food markets to better understand what factors could improve their viability and performance. Profit efficiency frontier maps the combinations of fixed and variable inputs that are utilized by the most profitable local food producers. They find that, on average, a farm or ranch could increase profit by about 133% by improving efficiency (i.e,. utilizing a different combination of fixed and variable inputs). Overall, most farms and ranches using local food markets are not producing on the efficiency frontier and could realize significant improvements in profitability with changes in their operation. 

Though this preliminary analysis is important - and points to the importance of understanding the relationship between market channel selection and production characteristics - more in-depth analysis is needed to provide information to producers that can meaningfully inform market channel selection. This research objective will utilize the USDA ARMS data, focused on livestock producers in the Western US, to examine the relationship between market channel selection and production, demographic and locational characteristics. 

Data: The USDA ARMS is USDA’s primary source of information on the financial condition, production practices, and resource use of America’s farm businesses and the economic well-being of America’s farm households. The ARMS is a nationally representative survey that targets about 30,000 farms annually, including operations of all sizes and commodities (Kachova 2013; USDA ERS 2020). ARMS data are restricted-access, and Jablonski and Bauman have been approved by the USDA National Agricultural Statistics Service (NASS) and Economic Research Service (ERS) to utilize the data. Access is available via NORC at the University of Chicago’s secured network, which can be accessed remotely.  

Since 2008, the ARMS includes questions about farm sales through local food channels and provides a sufficiently large sample of producers participating in these markets (Low and Vogel 2011). The 2013-2016, and 2019 Phase III data include more detailed questions about local food channels. ARMS participants were asked to report if they produced, raised, or grew commodities for human consumption that were sold directly to (1) individual consumers, (2) retail outlets, or (3) institutions. Subsequently, they are asked to provide the monetary amounts received from selling (1) directly to consumers at farmers markets; (2) directly to consumers from on-farm stores, u-picks, roadside stands, or CSAs; (3) to local retail outlets such as restaurants or grocery stores; (4) to regional distributors such as food hubs; or (5) to local institutional outlets such as schools or hospitals (USDA ERS 2019). However, due to sample size limitations, we will aggregate sales to (4) and (5). 

PI Jablonski and Bauman have utilized these data extensively to analyze profitability impacts of farms and ranches selling through local food markets (Bauman et al. 2018, 2019; Jablonski 2020). However, they have not yet analyzed farm-level profitability implications for ranchers specifically. In addition, the ARMS has more detailed information on cattle compared to other species, so we will leverage these data to provide detailed estimated costs of production. 

Methods: We use two methodological approaches to analyze our data. In both methods, we focus on a subsample of livestock producers/ranchers from ARMS that are located in the Western region of the U.S.. First, we follow Bauman et al. (2019) and divide our sample into quantiles segmented by their profitability performance (defined as return on assets) to understand how strong and weak performance may vary based on market channel decisions, operation / producer characteristics (including variable expenses, labor share of variable expenses, land tenure, beginning farmer status, debt utilization, producer age, producer education, etc.), and locational variables (including ruralness as defined by the rural-urban continuum code). This analysis is largely descriptive, but allows for results that are easily digestible by Extension and producer audiences. 

Second, we use a more rigorous approach following Park (2015): unconditional quantile regression (UQR). UQR allows one to evaluate the impact of changes in the distribution of the explanatory variables (in our case operation / producer characteristics, market channel selection, and location variables) on quantiles of the unconditional (marginal) distribution of an outcome variable (in our case, profitability, defined as return on assets) (Firpo et al. 2009). UQR is an appropriate approach as it enables us to provide more refined estimates of the relationship between the operation/producer characteristics, market channel selection, and location variables, across the entire distribution of return on assets. Though these results will be a bit more complicated to explain to Extension and producer audiences, they will provide important checks on the descriptive findings/analysis.  

For both RO1 and RO2, results will be presented and vetted at academic conferences and published in peer-reviewed journals. Additionally, for RO1 we will present results to extension and other technical assistance audiences with recommendations for future convenings or attempts to strengthen relationships across diverse stakeholder groups. For RO2 we will make results available to extension and producer audiences via factsheets on the localfoodeconomics.com website as well as through the meat school classes and online pricing tool (see education plan below).

Research results and discussion:

The research for this project is still ongoing. 

Participation Summary

Research Outcomes

Recommendations for sustainable agricultural production and future research:

Meat Summit Research-Related Outcomes (text)

Our research objective with the Mountain Meat Summit was to evaluate how this event could result in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders. Building networks and connections took place over two days through:

  1. Tours of three different scales of livestock and meat enterprises; 
  2. Exposure for Mountain Meat Summit participants to industry sector updates from CattleFax, the US Meat Export Federation, and CSU AgNext; 
  3. Panel discussions with multiple stakeholders, across geographies (for example, two states’ departments of Agriculture and meat producers from the Western region); 
  4. Curbside consulting opportunities representing nine different technical assistance providers who could help Summit participants identify new resources to build new plants or markets or improve existing resource allocation for their operations; and
  5. Multiple networking sessions throughout the second day’s program at the Colorado State University campus in Fort Collins. 

Of 173 attendees at the two-day event, we obtained 93 usable responses for the pre-survey and 50 usable responses for the post-survey. Linking pre and post survey respondents, we obtained 38 respondents for whom we could measure specific outcomes from participating in the conference. For example, 8 ranchers/producers responded to the pre and post-surveys indicating they made 102 new meat supply chain connections, primarily with other ranchers. Interestingly, educators and researchers made the greatest number of new meat supply chain connections, followed by ranchers and then by input suppliers and service providers. The table below summarizes some of these results.

Number of new meat supply chain connections by role in the livestock/meat industry.

 

 

Ranchers

Processors

Retailers

Restaurants

Input suppliers, service providers

Educators, researchers

Ranchers

(n=8)

Total

30

11

10

21

9

21

Average

3.8

2.8

3.3

5.3

3.0

3.5

Processor (n=5)

Total

16

10

7

0

4

15

Average

5.3

5.0

7.0

-

2.0

3.0

Retailers (n=3)

Total

7

5

5

3

3

12

Average

3.5

2.5

5.0

3.0

3.0

4.0

Input suppliers/ service providers (n=8)

Total

26

28

6

0

1

27

Average

5.2

4.7

3.0

-

1.0

3.9

Educator/ researcher (n=14)

Total

30

21

10

3

13

36

Average

3.5

3.7

1.7

3.0

2.2

3.7

We also looked the level engagement that certain supply chain stakeholders had before the event, based on how often they connected with other supply chain actors at intervals of: 1) more than one time per month; 2) 5-10 times per year; 3) less than 5 times per year and 4) no interaction at all.

For example, ranchers with different levels of engagement in the supply chain before the Summit made significant connections with new stakeholders, especially among those who came to the event with a high level of connectivity (more than one time per month). Those individuals generally made between 3 and 5 new connections, on average, with other ranchers, processors, buyers, restaurants and educators/researchers. Those with moderate connectivity before the event made fewer new connections overall, but those with low connectivity engaged with other ranchers, new processors, service providers and educators/researchers. Lastly, those participants with no pre-event connections met some new supply chain actors with whom they intend to connect in the coming months.

We saw similar patterns across processors and buyers who attended the Summit. Processors in the high engagement category gained a variety of new connections at the conference, including with ranchers, other processors, restaurants, and educators/researchers. Those with low pre-event connectivity (that is interactions of fewer than 5 times per year) made significant new connections to build on in the coming months. Finally, buyers (retail and wholesale) made significant new connections across all stakeholder groups except input and service providers (and these may be less relevant to their role in the supply chain). Even those who reported low to no pre-event interaction with supply chain actors connected with ranchers and processors. In sum, the Mountain Meat Summit provided multiple ways for supply chain participants to forge new connections, whether they attended the event with a broad set of connections or they used the Summit to grow their engagement in the sector.

We should note that we had originally planned on conducting a complete social network analysis to be able to map out the direction and strength of existing and new connections resulting from the Summit. However, this approach was limited by a sponsor request to refrain from contacting certain participants (primarily students and those who typically attended the International Livestock Forum (our complementary event). This necessarily limited our pre- and post-Summit survey to a much smaller group of participants. 

Calculator Research-Related Outcomes (text)

Much of this year was spent understanding the data that are available nationally within the Census of Agriculture. Though we have not yet worked on the education portion of this effort, and thus haven’t “tested” our calculator, we provide a detailed description about the data we use and choices that we made to better understand specific types of ranchers and to provide information so that they can make more informed pricing and marketing decisions. 

Data

We used 2017 Census of Agriculture microdata to estimate the average cost of production for livestock operations selling through local food market channels (including direct-to-consumer and intermediated markets). In the Census of Agriculture, a farm is defined as a place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year. This very liberal definition of a farm captures a wide range of farms, including those that are not aspiring to be commercially viable operations. To capture only commercial operations, we drop all observations with less than $1,000 in sales.

Our sample consists only of non-diversified operations, i.e., operations that sell one species and no crops, so we can attribute all costs of production to only the species of interest. Farms are allowed to grow crops but have zero sales, implying that those costs accrue to feed requirements for the livestock enterprise. To capture cost of production differences across scale, we separate our sample, based on the number of head sold in a year, into small operations (<25th quantile), mid-size operations (25th to 75th quantile) and large operations (76th to 95th quantile). We drop observations above the 95th percentile as outliers. Species groupings include (1) non-dairy cattle, (2) hogs and pigs, and (3) sheep, lamb, and meat goats. For non-dairy cattle operations, we drop operations selling zero cattle weighing 500 pounds or more. We provide the expenses as a cost per head by species, scale, market channel, and region.

Methods

Given the large differences in costs across scale, we separate our sample by scale, based on head sold. For each species, we have three categories: small operations (<25th quantile), mid-size operations (25th to 75th quantile) and the large operations (76th to 95th quantile). We drop observations above the 95th percentile due to outliers.

  •         Cattle: Cattle sold or moved from this operation in 2017, including calves weighing 500 pounds or more, local only

o   Small scale (<25th quantile): <2 head

o   Mid-scale (25th to 75th quantile): 2 to 8 head

o   Large scale (76th to 95th quantile): 9 to 30 head

  •         Hogs and pigs: Number of hogs and pigs sold or moved from this operation during 2017,
    including feeder pigs, local only

o   Small scale (<25th quantile): <6 head

o   Mid-scale (25th to 75th quantile): 6 to 27 head

o   Large scale (76th to 95th quantile): 28 to 200 head

  •         Sheep, lamb and meat goats: Number of sheep, lamb, and meat goats sold or moved from this operation during 2017, local only

o   Small scale (<25th quantile): <10 head

o   Mid-scale (25th to 75th quantile): 10 to 23 head

o   Large scale (76th to 95th quantile): 24 to 62 head

Our sample consists of only those producers selling exclusively through local food market channels (i.e., they do not sell through both local and commodity market channels). Local food market channels include both direct-to-consumer market channels (i.e., farmers market, on-farm stores or farm stands, roadside stands or stores, u-pick, CSA, and online marketplaces) and intermediated market channels (i.e., supermarkets, supercenters, restaurants, caterers, independently owned grocery stores, food cooperatives, K-12 schools, colleges or universities, hospitals, workplace cafeterias, prisons, and food banks). Due to sample size issues, we do not disaggregate direct-to-consumer and intermediated within our local food market channel category.

All estimates are broken out by region as well as provided nationally. Regions are based on the census regions but with the west disaggregated into census divisions. Regions include Pacific (WA, OR, CA), Mountain (ID, NV, MT, WY, UT, AZ, CO, NM), Midwest (ND, SD, NE, KS, MN, IA, MO, WI, IL, MI, IN, OH), South (OK, TX, AR, LA, KY, TN, MS, AL, WV, VA, NC, SC, GA, FL, MD, DE), Northeast (NY, PA, NJ, VT, NH, MA, CT, RI, ME). We also include data for cattle producers in Colorado as the sample size allows for a state-level estimate.

Production expenses

Table 1 provides all production expense variables asked in the 2017 Census of Agriculture and the expense categories used in this analysis. Some expense categories were grouped together for those categories for which producers were less likely to have recorded their expenses disaggregated.

Each expense is provided on a per head basis. We report both the total production expense, total expense per head, and the expense in each category per head, including benchmark ranges.

Table 1. Description of production expenses

Variable

Production expense description

k1501 + k1502 + k1503

Fertilizer, chemicals, seeds, and plants

k1504 + k1505

Breeding stock, other livestock purchased or leased

k1506

Feed

k1507

Gas, fuel, oil

k1508

Utilities

k1509

Repairs and maintenance

k1510 + k1511+ k1512

Hired labor, contract labor, custom work, and machine hire

k1513 + k1514 + k1517[3]

Cash rent for land and buildings, rent, lease expense for machinery, property taxes

k1513

Cash rent for land and buildings – including grazing fees

k1514 + k1517

Rent, lease expense for machinery, equipment and farm share of vehicles, and property taxes

k1515 + k1516

Interest paid on real estate debt, interest paid on non-real estate debt

k1518 + k1935

Other production expenses, medical expense (medical supplies, veterinary, and custom services for livestock)

Sum of all expenses above

Total production expense

Detailed description of how commodity variables were calculated

Cattle

Cattle operations = if gross value of sales from cattle and calves (including sales under production contract) is equal to total value of production, then 1 otherwise 0

 Hogs and pigs

Hog and pig operations = if gross value of sales from hogs and pigs (including sales under production contract) is equal to total value of production, then 1 otherwise 0

 Sheep, lamb and meat goats

Sheep, lamb and goat operations = if gross value of sales from sheep, lamb, and meat goats (not including sales under production contract) is equal to total value of production, then 1 otherwise 0

6 Grants received that built upon this project
8 New working collaborations

Education and Outreach

1 Published press articles, newsletters
1 Tours
1 Webinars / talks / presentations
1 Workshop field days

Participation Summary:

130 Farmers participated
245 Ag professionals participated
Education and outreach methods and analyses:

These are described in the previous research section.

Education and outreach results:

Meat School Education-Related Outcomes

After taking the 8-week class, we asked participants to evaluate: 1) the degree to which they had learned key course concepts; 2) new connections to meat supply chain stakeholders for their businesses; and 3) new marketing strategies that they would employ. We obtained responses from 63 participants for a response rate of 31%.

Participants noted that they gained the most knowledge in maximizing the carcass value of their animals for market and pricing their products for different markets (where participants rated their knowledge gained at 2.49 out of a 3-point scale. Participants indicated that they gained less knowledge in influencing pasture quality to improve animal nutrition and meat quality (1.98 average knowledge gained) and balancing an animal's nutritional needs with the feedstuff available (2.11 average knowledge gained). However, years in production significantly influenced respondents' appraisal of their knowledge gained during the Western Meat School. For example, those with less than 2 years of experience in meat production said that they learned the most in the areas of influencing pasture quality to improve animal nutrition and meat quality,  and balancing an animal's nutritional needs with the feedstuff available, while those with more than 20 years of experience said they learned the least in these two areas. In fact, in all but 3 topic areas, the newest producers said they learned the most compared to the rest of class participants, and those with the most experience (especially those with between 10-20 years in business) said they learned the most about maximizing their carcass value.

Overall, Western Meat School participants reported gaining an average of 20 new connections at the completion of the class, with those in the business the longest acquiring the greatest number of new supply chain connections (nearly 30), compared to the newest producers (nearly 15, or half as many).

Year of experience in livestock production

Average number of new connections

Number of respondents

Less than 2 years

14.67

6

2-10 years

17.17

24

10-20 years

15.00

14

More than 20 years

29.84

19

Average for all participants

20.3

63

Lastly, Western Meat School participants indicated the changes they intended to make as a result of their participation in the class, ranking communications and branding as the most likely and production diversification to increase product selection the least likely. 

Participants’ market-oriented business changes 

Those with the most business experience indicated they were interested in more vertical integration of processing or distribution operations, while those with the least experience focused more on defining their businesses and marketing lower value cuts.

Participants’ market-oriented business changes, by years of experience

Meat Summit Education-Related Outcomes

Overall, the event helped participants gain knowledge about the business constraints they had identified prior to attending. For example, processors indicated that they had 3 barriers addressed that they had identified, while ranchers and educators/researchers gained information on more than two of their constraints. Retailers and other buyers, in addition to input suppliers and service providers, had fewer than 2 barriers to business expansion addressed by the event (see table below).

Barriers to business expansion identified by participants

 

Ave number of barriers addressed by conference

Understanding customer tastes & preferences

Information on product differentiation

Processing options

Regulations around meat sales

Food safety

Pricing for profitability

Labor

Improving technology

Accessing capital

Ranchers

2.4

11%

14%

20%

33%

0%

36%

0%

20%

22%

Processors

3.0

21%

14%

13%

17%

0%

14%

0%

20%

22%

Retailers

1.7

0%

14%

13%

0%

0%

14%

0%

0%

0%

Input suppliers, service providers

2.0

32%

29%

13%

17%

50%

0%

0%

30%

22%

Educators/ researchers

2.5

37%

29%

40%

33%

50%

36%

100%

30%

33%

Notes: 1) Although we asked about restaurant buyers and chefs, none attended the event; 2) no one who identified transportation as a barrier also acknowledged having gained any information about it, therefore these two elements were eliminated from the table above.

In the post survey we asked participants to list three new business activities they planned to engage in over the next six months as a result of attending the ILF/MMS event. More than one-fifth mentioned activities related to improving or maintaining the networking that they began at the ILF/MMS event, while others indicated they wanted to offer education based on information they gained. The table below summarizes these responses, indicating a variety of actionable business activities that participants would pursue.

 

New business activity

Percent of total responses

Improve/maintain networking

22%

Offer new education based on ideas gained

19%

Expand/explore new markets

17%

Explore grants/funding opportunities

11%

Improve own business practices

11%

Look at new/improved processing options

7%

Use industry publications/info to stay up to date

6%

Use Cornell Meat Price Calculator

4%

Address workforce issues

2%

Address biosecurity

2%

Calculator Education-Related Outcomes

Based on the above-mentioned activities, we now understand the cost of production associated with different species, scale, and market channel. Over the next year, we will work to finalize a draft of the online calculator. Preliminary testing will take place in the fall of 2023. 

Education and Outreach Outcomes

Recommendations for education and outreach:

We are still working on the education and outreach recommendations and will have these ready for 2024.

Key areas taught:
  • In the first year of the project we have focused on working with our advisory committee to plan the educational approach, as well as to meet with project partners, and conduct the research that lays the foundation for the Meat Summit, Meat School, and pricing calculator. We have also explored how to modify educational content delivery based on new opportunities to use online platforms and thus reach a wider audience. 

Information Products

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.