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
Farmers Markets -Local customers -Customers searching for multiple goods -Restaurants CSA -Informal production contract with households Farm Direct to Wholesale -Restaurants -Institutions -Specialty retail Multi-Farm CSA -Restaurants -Institutions -Specialty retail Roadside Stand and Online Sales -Loyal customers -Targeted visitors/tourists Food Hubs -Restaurants -Institutions -Specialty retail Farm Direct to Wholesale -Institutions (Farm to School) Traditional Distributor 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: http://www.extension.org/pages/70544/an-evolving-classification-scheme-of-local-food-business-models#.vvzobkbg-ix
Table 1: Market Typology Advantages & Disadvantages Market Orientation Customers Managerial Control Pricing Power Market Volume Potential Roadside Stand and Online Sales Farmers Markets Local, traveling and national households Local households, travelers Full control High Low to high 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 Food Hubs Traditional Distributor All buyers Local households and businesses Local businesses and institutions Shared control Medium Medium to High Shared to limited control Limited control and pricing power Medium Medium to High June 2016 Gaeta Italy
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
114 Case studies from over 200 when criteria to filter used 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),"Metaanalysis of US intermediated food markets: measuring what matters", British Food Journal, Vol. 118 Iss 5 pp. 1146 1162.
Table 4. Specific market outlets reported in case studies, sorted by prevalence % of viable businesses % of nonviable businesses (or unknown) Variable Direct market outlets*** Farmers market 11.76% 23.26% Community Supported 5.88% 5.88% Agriculture (CSA) 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),"Metaanalysis 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 Geography of farm vendors** % of viable businesses % of nonviable businesses (or unknown) 23.53% 9.30% Local ( 50 miles) 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% 11.0 9.30% 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 7. Location of markets and number of products Variable Geography of Markets** % of viable businesses % of nonviable businesses (or unknown) 5.88% 23.26% Local ( 50 miles) 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 17.0 5.88% 23.26%
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 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 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?
USDA ARMS sample of Local Food Farmers and Ranchers 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
Summary Statistics for Local Food Farmers and Ranchers, by Gross Cash Farm Income 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
$1-75,000 $75-350K $350-1 Million > $1 Million Quartile Labor Cost % Labor/ Gross Sales Asset Turnover Off-farm income Debt to Asset 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% 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% 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% 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%
How do top performers differ? 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.
Efficiency measures among quartiles 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.
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