Assessing the Economics of Crop Choices on a Start-up Market Farm

1. Assessing the Economics of Crop Choices on a Start-up Market Farm January 16, 2012 Eric & Joanna Reuter Chert Hollow Farm, LLC

2. Brief farm overview

3. Chert Hollow Farm, LLC • Farm founded in 2007 • Certified-organic produce since 2009 Columbia Farmers Market • Customer counts up to ~4,000 (Saturday morning) • 80+ vendors • Farm also sells to restaurants • In 2012 farm converts to CSA

4. Intensive vegetable production in permanent, no-till beds, >1 crop/year Two vegetable growing areas, ~1.5 acres total, space limited Diversification/ on-farm fertility

5. Our background & perspective • Geologists/natural scientists by training • Think on long time scales • Data oriented • Focus on soil health & ecology • Respect for the complexity of the ecosystem processes that provide the context of our farming Eric: Full-time farmer since 2007 Joanna: Full-time farmer since 2009 • Employees: part time, paid in product

6. Crop profitability assessment Acknowledgment of Wiswall’s approach

7. An excellent resource with methods to assess crop profitability (& much more): • Crop enterprise budgets account for cost of: • Labor • Machinery • Input/products • Overhead • & more

8. An excellent resource with methods to assess crop profitability (& much more): • However: • Assumes extensive & detailed records that a new farm may not yet have • Even an established farm may not have full detail of records desired, especially for time budgets

9. Crop income comparison model Our approach

10. What is a model? • Simplified version of reality • Captures key features, leaves out some details • Useful in planning

11. Crop income comparison model • Goal: • To analyze and explore the economics of crop choices in a relative context for use as an aid in crop planning • What it will look like: • A spreadsheet listing crops with data and calculations that capture the essence of crop income considerations for a farm • To derive calculations, start simple and add improvements: • Estimate gross income potential • Account for the fact that not all of the harvested crop is likely to sell • Account for expenses of producing specific crops, ignoring overhead • Account for growing time (quick vs. long-season crops) • Data sources: • For best results, use on-farm data for yield, price, and other factors • For new crops, or if records are incomplete, use published data and/or estimates

12. Simple crop income assessment: price [$]__ yield [weight] marketable yield [weight] area [area] gross income potential [$] area [area] = * [units] are in brackets • Use published data to assess potential new crops

13. Simple crop income assessment: price [$]__ yield [weight] marketable yield [weight] area [area] gross income potential [$] area [area] = * [units] are in brackets • Use published data to assess potential new crops

14. Crop comparison example price [$]__ yield [weight] marketable yield [weight] area [area] gross income potential [$] area [area] = *

15. Account for the fact that not all of the harvested crop is likely to sell • Of the marketable crop that makes it as far as the packing shed, how much is actually sold? • Define the sales factor as the percentage of marketable crop that is sold Started with: price [$]__ yield [weight] marketable yield [weight] area [area] gross income potential [$] area [area] = * Incorporate sales factor: price [$]_ yield [wt] marketable yield [wt] area [area] expected gross income [$] area [area] sales factor (%) = * *

16. Sales factor: % of crop sold • This number may be hard to pin down for many crops, as it may vary considerably over time, but its presence is a reminder that income projections should not be based on total harvestable yield. • Dependent on: • Storage characteristics of item • Okra = highly perishable, sell ASAP or lose • Garlic = highly storable, sell over period of months • Supply; the quantity ... • ...that we offer (more or less predictable/controllable) • ...that competitors offer (more of a wildcard) • ...that the food system offers (example: hardneck garlic) • How item is displayed/offered for sale • Bundles vs. bulk • Often hard to sell out 100% because of small-stand syndrome • Demand • Customer preferences/whims; food fads • Edamame...chef told us he was “moving beyond the Japanese food craze”

17. Crop comparison example price [$]_ yield [wt] marketable yield [wt] area [area] expected gross income [$] area [area] sales factor (%) = * *

18. This is just gross. What about net?

19. - = net income expenses gross income • To make crop choices, do not necessarily need to know true net • Ignore overhead expenses • Ignore “base” crop expenses—expenses that are more or less similar for every crop and thus cancel out when considering relativecrop profitability

20. Classifying expenses Examples Classification of crop expenses may vary among farms Only need crop-specific expenses for relative crop income assessment

21. Classifying expenses • Classification of crop-specific expenses may vary among farms • Is the cost different enough between different crops to be worth accounting for in detail? • Subdivide labor expenses from other expenses (products/inputs) • Estimate as needed for time budgets • Our independent estimates were very close to each other for the five example crops

22. An important equation, but not the subject of this presentation: - = net income expenses gross income Instead, use this: - = relative crop income crop-specific expenses gross income Normalize by area: crop-specific expenses [$] area [area] expected gross income [$] area [area] relative crop income [$] area [area] = - Substitute previously derived equation for expected gross income/area: relative crop income [$] area [area] = crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] - * *

23. Crop comparison example relative crop income [$] area [area] = crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] • REINSERT TABLE HERE - * *

24. Account for growing time • Number of days in ground (or allocated to the crop) • To estimate, use “days to maturity” data from seed catalogs (plus a buffer, esp. for crops with long harvest period) relative crop income [$]_ area [area] * time [time] = ( ) crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] - * * time in ground [time]

25. Crop comparison example relative crop income [$]_ area [area] * time [time] = ( ) crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] - * * time in ground [time]

26. Additional considerations for crop choice • Other factors that are not quantified by this model: • On the spectrum of heavy feeder to nitrogen fixer, where does crop fall? • Do leftovers have value even if they don’t sell? • For our pantry, employee pay, food bank, pig/goats/chicken, or the compost pile? • What additional expenses might be incurred as a result of growing decisions (& are these costs worth it if they might be avoided with other crop combinations?): • Will extra cooler space or a new trailer be needed because of too much bulk at a given time? • Do specific crops or combination of crops increase the farm’s resilience to extreme weather? • How is work load distributed in crop production? • Heavy up front (work happens even if crop fails) • Heavy at harvest (work only happens if crop is successful)

27. Data sources • Our farm’s yield data vs. generic yield data • Same prices, sales factors, and crop-specific expenses, used in both calculations

28. Data exploration • Once data are in a spreadsheet, there are many opportunities for data exploration • Ask the question: What can be done to make a crop more profitable? • Would we be better off raising the price & selling less? • Would reducing picking frequency and accepting more overgrowns improve profitability through lower labor requirements? • Would reducing production to ensure regular sell-outs improve profitability? • Is trellising worth the hassle? (Compare yields & time budgets for bush & pole beans or peas)

29. Record-keeping suggestions

30. Using on-farm data relative crop income [$]_ area [area] * time [time] = ) • Data needs: • Yield • Area planted • Price • Sales factor • Time a crop is in ground • Crop-specific expenses: • Products/inputs • Labor ( crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] - * * time in ground [time] • Why collect your own data? • Results are as good as the data you put in • Better understanding of strengths & limitations of the numbers • Way to assess year-to-year variation

31. Area planted: Planting plan &/or seeding/transplant record sheets

32. Yield data: Harvest record sheet • Developing an effective record sheet is an iterative process • If it is not being used or if it does not have the information needed, it is not effective; change it • Pitfall: inconsistent units • Try to use consistent units (weight, volume, or count) on harvest and sales records • Label containers with tare weight • Also record conversion factors between weight & volume (for example, 1 pint edamame = 0.5 lbs) or weight & count (x peppers = y pounds) so can convert if needed

33. Time in ground Start date End date: Look for date of last harvest

34. Price data: Market records & invoices market prices (include sales tax) restaurant prices

35. Sales factor: Market record sheets&/or compilation of harvest & sales records

36. Crop-specific expenses • Compiled from various sources: • Receipts, bank records (need these for the IRS anyway) • Records of materials used (twine, irrigation, etc.) • Labor expenses: • Employee time sheets • Task logs, crop journal

37. Record-keeping summary • Plan record keeping to fulfill your data needs (not just what the certifiers or bureaucrats require) • Harness the records that you have • Analyze data even if on-farm records aren’t perfect; fill in with estimates or use off-farm data as needed • Improve record-keeping methods each year

38. How many types of crops? Reasons to diversify Reasons to simplify • Risk management (Grohsgal, Growing for Market, Oct. & Nov. 2006) • Work load distribution over time • Rotational considerations (nutrient use, disease) • Enjoyment of growing, experimenting with, & eating diverse plants • Not enough market to make a full living off of very few crops • Efficiency in: • Planning • Record keeping • Planting • Harvest • Crop profitability analysis • Customers often can’t absorb full diversity of complex stand in a busy market

39. Crop income comparison model relative crop income [$]_ area [area] * time [time] = • This method is: • Flexible enough to be tailored to farm-specific methods & conditions • Useful for assessing crops even if they have not been grown on a farm • Best with good on-farm records, but still useful even if farm records are incomplete or imperfect • Useful for considering ways to improve profitability of a given crop ( ) crop-specific expenses [$] area [area] sales factor (%) price [$]_ yield [wt] marketable yield [wt] area [area] - * * time in ground [time]

40. Questions? www.cherthollowfarm.com contactus@cherthollowfarm.com