Dave Shideler, Oklahoma State University Collaborators: Allie Bauman, Becca Jablonski and Dawn Thilmany, Colorado State University Acknowledgement: We gratefully acknowledge the financial support for this project from USDA-NIFA Award Number 2014-68006-21871
Dave Shideler
Department of Agricultural Economics, Oklahoma State University, 323 Ag Hall, Stillwater, OK 74078. [email protected]. 405-744-6170
Allie Bauman, Becca Jablonski and Dawn Thilmany Department of Ag and Resource Economics, B325
Clark, Colorado State University, Fort Collins CO 80523-1172, [email protected], 970-491-7220
Blake Angelo, Manager of Food System Development, City of Denver
Growing public interest leading to resources
Low et al, 2015; Martinez et al., 2010; Union of Concerned Scientists, 2013
USDA-RD’s “Running a Food Hub” series
National Food Hub Survey, Business Assessment Kit
Need to assess different market strategies Initial results
Local Foods and Small Farms
Source: Vogel and Jablonski 2015
Source: Schmit and LeRoux 2014
Source: Schmit and LeRoux 2014
Driven by results from foundational research
Direct Marketing
• Very Small
• High Value added
Value Food Chains
• Higher Volume
• High Value
Trouble Zone
• Lower Volume
• Low Value Added
Commodity
• High Volume
• Low Value Added
Modified from: Stephenson, Agriculture of the Middle
Farm Direct to Wholesale
-Institutions (Farm to School)
Farmers Markets -Local customers
-Customers searching for multiple goods
-Restaurants
CSA -Informal production contract with households
Roadside Stand and Online Sales
-Loyal customers
-Targeted visitors/tourists
Farm Direct to Wholesale
-Restaurants
-Institutions
-Specialty retail Multi-Farm CSA -Restaurants
-Institutions
-Specialty retail
Food Hubs -Restaurants
-Institutions
-Specialty retail
Traditional Distributor
http://www.extension.org/pages/70544/an-evolving-classification-scheme-of-local-food-business-models#.VVZOBkbG-ix
Bauman, A, D. Shideler, D. Thilmany, M. Taylor and B. Angelo, An Evolving Classification
Scheme of Local Food Business Models. eXtension CLRFS Resource page. May 2014 online:
June 2016 Gaeta Italy
Market Orientation Customers Managerial Control Pricing
Power
Market Volume
Potential
Roadside Stand and
Online Sales
Local, traveling and
national households
Full control High Low to high
Farmers Markets Local households,
travelers
Full control High Low to medium
CSA Local households Full control Medium Low
Farm Direct to
Wholesale
Local, independent
businesses, institutions
Full control Medium Medium
Multi-Farm CSA Local households and
businesses
Shared control Medium Medium to High
Food Hubs Local businesses and
institutions
Shared to limited
control
Medium Medium to High
Traditional Distributor All buyers Limited control and
pricing power
Table 1: Market Typology Advantages & Disadvantages
There is a likely tradeoff between volume of sales and two key management factors: 1) Managerial control retained by producers 2) Pricing power of producers Is there an “optimal” place on continuum for an operation?
June 2016 Gaeta Italy
Profitability % Records
Highly profitable (over 5% net profit) 0.00%
Profitable (between 2% and 5% net profit) 5.83%
Breakeven (between 0% and 2% net profit) 10.68%
Cash flow neutral (total expenses equal revenues) 0.97%
Net loss (total expenses exceed revenues) 5.83%
Unsustainable loss (variable expenses exceed revenues) 0.97%
Unknown 75.73%
Blake E. Angelo Becca B.R. Jablonski Dawn Thilmany , (2016),"Meta-
analysis of US intermediated food markets: measuring what matters",
British Food Journal, Vol. 118 Iss 5 pp. 1146 – 1162.
114 Case studies from over 200 when criteria to filter used
Table 4. Specific market outlets reported in case studies, sorted by prevalence
Variable
% of viable
businesses
% of nonviable
businesses (or
unknown)
Direct market
outlets*** Farmers’ market 11.76% 23.26%
Community Supported
Agriculture (CSA) 5.88% 5.88%
Internet/mail order sales 11.76% 17.44%
Buying clubs 11.76% 9.30%
Farm stand/store 11.76% 10.47%
Delivery to customers 5.88% 11.63%
Intermediated market
outlets** Grocery retail 76.47% 46.51%
Restaurant 41.18% 46.51%
Institution 5.88% 37.21%
Distributors 29.41% 20.93%
Other 5.88% 11.63%
Value-added processing 11.76% 5.81%
Note: Asterisks indicate respective significance levels: * α = 0.10; **α = 0.05; ***α = 0.01.
Chi squared tests were performed to test differences among samples for reported use of direct
market outlets and intermediated market outlets categories.
Blake E. Angelo Becca B.R. Jablonski Dawn Thilmany , (2016),"Meta-
analysis of US intermediated food markets: measuring what matters",
British Food Journal, Vol. 118 Iss 5 pp. 1146 – 1162.
Table 6. Location and number of farm vendors
Variable
% of viable
businesses
% of nonviable
businesses (or
unknown)
Geography of
farm
vendors** Local (≤50 miles)
23.53% 9.30%
Near Regional (>50-<250
miles) 23.53% 19.77%
Far Regional (250-400 miles,
or within state) 11.76% 18.60%
Multi-state (>400 miles or
outside of state) 23.53% 16.28%
International (outside of US) 5.88% 3.49%
Unknown 23.53% 9.30%
Table 7. Location of markets and number of products
Variable
% of viable
businesses
% of nonviable
businesses (or
unknown)
Geography
of
Markets** Local (≤50 miles)
5.88% 23.26%
Near Regional (>50-<250 miles) 11.76% 6.98%
Far Regional (250-400 miles, or
within state) 11.76% 9.30%
Multi-state (> 400 miles or outside of
state) 47.06% 32.56%
International (outside of US) 5.88% 1.16%
Unknown 5.88% 23.26%
11.0
17.0
Blake E. Angelo Becca B.R.
Jablonski Dawn Thilmany ,
(2016),"Meta-analysis of US
intermediated food markets:
measuring what matters",
British Food Journal, Vol. 118
Iss 5 pp. 1146 – 1162.
Blake E. Angelo Becca B.R. Jablonski Dawn Thilmany , (2016),"Meta-analysis of US intermediated food markets: measuring what matters", British Food Journal, Vol. 118 Iss 5 pp. 1146 – 1162.
Essential Elements Economic Viability Analysis Data
Wealth Creation Analysis Metrics
Enterprise scope, size and organizational factors
Name, revenues, product/ service portfolio, employees, legal structure, governance model, year of establishment
Gross margin, net income, asset value, debt level (or ratio), labor expenditures, portfolio shares of key product lines
Mission statement, commitments to community partners (environmental, cultural, political, education)
Competitive advantage
Market orientation, differentiation scheme, key alliances, networks and partners, scale relative to industry average
Sales attributed to partners/alliances, financial ratios benchmarked to industry averages
Specific evidence of business alliances or partnerships that are aligned with mission or strategic position
Essential Elements Economic Viability Analysis Data
Wealth Creation Analysis Metrics
Marketing strategy, channels and pricing strategies
Number of market channels, share through major channels, relative price points (broadly defined)
Price premia (actual or goals with specific number for key products), returns to promotions or differentiation strategies
Sales driven by key partners or alliances, share of sales pledged to community orgs, price discounts or allowances for allied businesses
Sustainability and/or growth strategy
Intended expansion in geographic markets (vendors or markets), new initiatives to differentiate product lines or coordinate in new market channels
Year over year sales growth, planned investments in capital or work force, payback period expectations on market expansion plans or investments
Evidence that linkages generate specific social and political capital (lower transaction costs, access to new markets, favorable zoning)
Blake E. Angelo Becca B.R. Jablonski Dawn Thilmany , (2016),"Meta-analysis of US intermediated food markets: measuring what matters", British Food Journal, Vol. 118 Iss 5 pp. 1146 – 1162.
Essential Elements Economic Viability Analysis Data
Wealth Creation Analysis Metrics
Challenges and potential threats
Number of new competitors, regulatory compliance issues, loss of market channels/partners, cost pressures
Evidence of lower prices or margins, cost inflation, estimates of costs to comply with regulations (food safety, liability, environmental impacts)
Negative spillovers, unintended over competition from proliferation in certain regions, regulatory scrutiny (food safety or zoning concerns)
Blake E. Angelo Becca B.R. Jablonski Dawn Thilmany , (2016),"Meta-analysis of US intermediated food markets: measuring what matters", British Food Journal, Vol. 118 Iss 5 pp. 1146 – 1162.
What can we learn about differences in Key factors and how they relate to financial viability?
No. of observations Population size
Market Channel
D2C 664 124,186
Intermediated 136 11,703
D2CIntermediated 213 24,012
Alllocalfood 1,013 159,901
Nonlocalfood 16,416 1,935,568
Local food producers by farm scale (GCFI)
1kto75k 534 112,563
75kto350k 214 21,104
350to1Million 104 3,922
Million and higher 107 3,607
USDA ARMS sample of Local Food Farmers and Ranchers
Under $75,000 $75-350,000 $350,000 and above
ROA -55.91 (26.12)
1.29 (2.45)
13.63 (2.35)
Labor Share of Exp 0.08 (0.01)
0.20 (0.02)
0.30 (0.02)
Fuel Share of Exp 0.13 (0.01)
0.12 (0.01)
0.08 (0.00)
Utilities Share of Exp 0.10 (0.01)
0.07 (0.01)
0.05 (0.01)
Rent Share of Exp 0.08 (0.01)
0.21 (0.02)
0.34 (0.02)
Localfruitveg 0.33 (0.02)
0.33 (0.03)
0.34 (0.03)
Localfieldcrop 0.03 (0.01)
0.10 (0.02)
0.13 (0.02)
Localanimal 0.34 (0.02)
0.27 (0.03)
0.22 (0.03)
Observations 516 203 186
Summary Statistics for Local Food Farmers and Ranchers,
by Gross Cash Farm Income
Quartile Labor Cost % Labor/ Gross Sales Asset Turnover
Off-farm
income
Debt to
Asset
$1-75,000
Q1 7% 62.840 85% $ 62,818 12%
Q2 8% 27.391 8% $ 68,718 11%
Q3 7% 22.862 6% $ 65,099 7%
Q4 8% 15.994 14% $ 76,291 3%
$75-350K
Q1 18% 23.869 41% $ 30,914 23%
Q2 22% 29.206 14% $ 34,744 9%
Q3 23% 14.729 16% $ 56,246 10%
Q4 22% 26.087 85% $ 52,203 17%
$350-1 Million
Q1 32% 23.296 52% $ 57,087 32%
Q2 25% 14.326 25% $ 34,850 10%
Q3 26% 59.035 39% $ 54,567 9%
Q4 34% 61.544 163% $ 26,232 28%
> $1 Million
Q1 35% 6.852 42% $ 30,010 21%
Q2 30% 15.570 42% $ 39,773 11%
Q3 32% 12.616 67% $ 40,095 25%
Q4 42% 8.640 166% $ 54,460 30%
Farms with greater scale (over $350,000 but less than one million in gross income) Over half of the sample is operating at a
profitable level at this scale. Debt use bimodal
Best and worst performing farmers relatively higher levels of debt.
One could imagine a situation where the poorest performing operations see debt as a solution for cash flow shortfalls,
Whereas the best performing operations see debt financing as an opportunity for faster growth.
How do top performers differ?
Asset turnover generally highest among best ROA farms Exception is efficiency in smallest
sales class, perhaps due to low capital investments
Also have high labor productivity Labor productivity important, but
less so for those grossing over $1 million Perhaps this is related to transition
to wholesale markets that require more capital investments.
Efficiency measures among quartiles
Translating these results into Extension pieces for practitioners, government officials, and financial institutions
Webinars Case Studies in conjunction with the AMS
Toolkit (www.localfoodeconomics.com) Pre-conference
Website: www.localfoodeconomics.com/benchmarks
Dave Shideler Oklahoma State University [email protected]