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. dave.shideler@okstate.edu. 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, dawn.thilmany@colostate.edu, 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 Dave.shideler@okstate.edu