Role of Basis in Grain Marketing Decisions

1. Forecasting Basis Using Kriging Extrapolation and Markov Chains: Which is More Accurate?Ward E. NefsteadAssociate Professor&Ext. EconomistU. of Minnesota

2. Role of Basis in Grain Marketing Decisions • Knowledge of Basis allows decisionmaker to estimate local prices in the future • Forward contract prices can be evaluated relative to normal basis • Basis levels can signal changes in future prices-narrow vs. wide

3. Components of Basis • Transportation cost • Interest rates • Storage and related costs • Competition among elevators

4. Law of One Price Revisited • Prices should vary based on transportation costs only given assumptions of perfect competition and a uniform product. • Narrow basis nearest resale(terminal) points and wider basis further away from resale • Kevin McNew estimates spatial basis points with an equation-based on flow pattern to market

5. Structural Change changes Basis Patterns • Growth of ethanol, other processing plants creates a narrow basis • Basis in narrowest along NW and SW Minnesota vs. traditions pattern-along Mississippi River- SE Minnesota

6. Minnesota Corn Basis 2004

7. Small Changes in Basis not easily visible in Macro-spatial basis patterns • AgManager, CARD products use data smoothing techniques to average basis • Precision Ag Software- Vesper- allow small area basis changes • Variograms show small area changes

10. Computer Software for Basis Comparison • ESRI- Spatial Analyst,Business Map Pro • Spreadsheet- Basis Tool • Other

11. Methods of Forecasting Basis • Traditional- use 3 or 5 year average at location. Assumption-past pattern will feature regression toward mean. Changes in structure, costs will dramatically alter future basis patterns.

12. Other Basis Forecasting Methods • Markov Chains- allows an evolving structure to be included in forecast. Several states are possible * Large supply, weak demand(state 1) * Large supply, normal or strong demand(state 2) * Small supply, weak demand(state 3) * Small supply, normal or strong demand(state 4) * Normal supply ,normal demand(state 5)

13. State Diagram

14. Markov Chains identify which state will predominate • Probabilities of movement from state to state can be estimated • Narrow or wider basis will be associated with each state-example at MN location(Hutchinson)-corn average • * state 1-..70 • * state 2- .50 • * state 3- .45 • * state 4- .30 • *state 5- .35

15. State Progression 1-5

16. Markov Chain software • Measures the transition probabilities • New state probabilities can be estimated • Spreadsheet based software

22. Another Method of Basis Forecasting- Kriging Extrapolation • Spatial software uses Kriging methods • Kriging and related measures- estimate surfaces • Use of ESRI Spatial Analyst and other programs allows surface estimation • Changes in surfaces can be projected forward on a weighted basis

23. Vesper-Used for Kriging

24. Kriging • Uses data points for project a surface • Variations are used- CoKriging ,etc • Variograms show the change in the surface with distance

27. Kriging price surface • Minnesota price surface • Iowa price surface • Changes in basis-3 years(Ag Manager, other) • Surface can be extrapolated(projected ahead)

29. Kriging Surface Extrapolation • Changes must be weighted • Example weight changes for the past five years • Point forecasts are smoothed to create “new” projected surface

30. corn5596355350560555626556378

31. How has Basis Changed • Increased competition for supplies- feed vs other uses • Changes in rail and other surface transportation- DME railroad • Growth of ethanol and biodiesel plants • Higher energy costs(widen basis)

32. Quicktime video of spatial changes

41. Now, time for comparison- Which is more accurate in forecasting 2006 MN Basis- One locationCompare Traditional, Markov, and Kriging Methods

42. 1 stepTransition Matrix

44. Hutchinson Mn Spot Corn Basis • Traditional • Markov estimation • Kriging Point/Surface Which is Best? You Decide!

45. Following estimates of 2007 Basis at Hutchinson ,MN

46. proj.basis-5 yr ave0.55650.231840.602040.581280.64108

47. Kriging Extrapolation0.5567470.2379520.5891250.5691090.631987

49. Markov estimate.52500.55250.5150.4750.45

50. Farmer Consultations/Meetings • Review traditional basis for area • Present alternate methods of basis forecasting • Discuss impact of alternative forecasts on grain marketing decisions