Classifying Agricultural Operations for the Farm Environmental Management Survey

1. Classifying Agricultural Operations for the Farm Environmental Management Survey Third International Conference on Establishment Surveys June 2007 Martin Pantel Business Surveys Methods Division

2. Outline • The 2006 Farm Environmental Management Survey (FEMS) • Targets both Crop and Livestock farms • Solutions • NAICS coding system • New strategy • Some results

3. Background: FEMS 2006 • Agriculture and Agri-Food Canada initiative • Environmental management in the media • Importance of agricultural industry in Canada • Support the industry’s environmental initiatives, address federal and provincial policy needs, and guide sustainable development actions in Canada’s agriculture sector. • Use of 2006 Census of Agriculture data • CATI, ≈ 20,000 farms

4. Background: FEMS 2006 • Some farms excluded from FEMS: • Total gross sales of less than $10,000 • Institutional farms (prisons, research stations, colleges, etc.) • Farms located on Indian reserves • Insufficient livestock inventory / crop area • Greenhouse/sod/nursery operations • Farms in the 3 northern territories.

5. Background: FEMS 2006 • Crop vs. Livestock: Different issues • Crop and Nutrient Management, Pesticide Application • Livestock Inventories and Buildings, Manure, Grazing Livestock Management • Some common issues: Land and Water Management, Wildlife Damage, Waste Management and Hazardous Materials, Environmental Farm Plan

6. Problem: Crop or Livestock? • Frame includes Crop and Livestock farms • Two questionnaires - nobody can receive both • What about the farm that has 30 cows and 50 acres of canola? • Force it into a group based on some criteria • Create a “Mixed” category

7. Solution 1 - NAICS • The North American Industrial Classification System (NAICS) • Well-established system • Based on main product or group of products, generating at least 50% of the total cash receipts for the farm.

8. Solution 1 - NAICS • NAICS categories • Cattle ranching and farming • Hog and pig farming • Poultry and egg production • Sheep and goat farming • Other animal production • Oilseed and grain farming • Vegetable and melon farming • Fruit and tree nut farming • Greenhouse, nursery and floriculture production • Other crop farming

9. Solution 1 - NAICS Coverage, bias problems? • Results inferred upon the survey population; but how close is it to the target population? wheat CROPS • • • • LIVESTOCK • • •

10. Solution 2 - New approach • Allow for Mixed farms • Crop population: C+M • Livestock population: L+M C M L

11. Solution 2 - New approach • Summary measures • Cropland: sum of acres for crops of interest (wheat, grain, oilseeds, hay, potatoes, fruits, vegetables, other field crops) • Animal Units (or AUnits): Combines the livestock types of interest (cattle, hogs, poultry) on a uniform scale. • 1 AUnit ≈ 1 cow ≈ 5 hogs ≈ 200 chickens

12. Solution 2 - New approach • Classification • A large proportion of any commodity will be produced by a small number of large farms. (e.g. the largest 60% of farms have 95% of the livestock). • Find each farm’s percentile according to each commodity (a “small” livestock farm may still be important for poultry).

13. Solution 2 - New approach • Classification (cont.) • Compare the largest of the farm’s percentiles to the threshold for that province, to see if the farm should be flagged as an important contributor for that type (Crop / Livestock). • If the farm is flagged for both types, it is classified as a Mixed farm.

14. Solution 2 - New approach • Example

15. Solution 2 - New approach • Example (cont.) • Total AUnits in the province = 96.25 • 95% of the total = 91.44 • How many farms do we need?

16. Solution 2 - New approach • Example (cont.) • By taking the 3 largest of the 5 farms (or 60% of them), 95% of the AUnits are covered. • Set this threshold (0.60) aside for now. • Each farm can get up to four percentiles: one for each livestock commodity they produce and one for AUnits as a whole. • First, sort the cattle farms in increasing order of cattle inventory and find their percentiles.

17. Solution 2 - New approach • Example (cont.) • Note: P_Cattle = FarmRank / NumFarms • Merge into the original dataset

18. Solution 2 - New approach • Example (cont.)

19. Solution 2 - New approach • Example (cont.) • Repeat the same procedure for the Hog and Poultry producers, as well as for the summary measure AUnits. • Re-merge the results, and compare the largest percentile against the threshold of 0.60.

20. Solution 2 - New approach Example (cont.)

21. Solution 2 - New approach • Repeat the same procedure for crops: • Find the threshold (for 95% of Cropland). • Calculate the percentiles according to the 8 crops of interest, and according to the summary measure. • Assign a “Crop flag” accordingly. • Using the two flags, determine if each farm is Crop, Livestock or Mixed.

22. Solution 2 - New approach • Example (cont.)

23. Solution 2 - New approach NC=79,830 NM=35,030 NL=39,976 N=154,836 ↓ ↓ nL + nML = 5,782 + 4,377 = 10,159 nC + nMC = 7,454 + 3,099 = 10,553 n=20,712

24. Solution 2 - New approach • Sampling from the Mixed pool • Response burden: cannot sample the same farm for both questionnaires. • Random number assigned to each farm, list of farms sorted accordingly. • Crop / Livestock farms are selected from opposite ends of the list.

25. Results – New approach vs. NAICS • Coverage CROPS wheat • • • • • • • LIVESTOCK

26. Results – New approach vs. NAICS

27. Next Steps • Weighting, Estimation • Two distinct surveys • Crop + Livestock ≠ Canada • Some Canada-wide data available (e.g. Environmental Farm Plan) • Dissemination

28. For more details… Martin Pantel (613) 951-3029 Martin.Pantel@statcan.ca