AgriProfit$ Business Analysis and Research Program Anatoliy Oginskyy, ML Manglai

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1 AgriProfit$ Business Analysis and Research Program Anatoliy Oginskyy, ML Manglai June 23 16, 2017

2 Outline AgriProfit$ Business Analysis and Research Program (AgriProfit$) 20-Year Historic Cow-Calf Economic, Production, and Financial Benchmarks AgriProfit$ GHG Simulation Module (Phase 1)

3 A cost of production program to inform public and help farmers with decision making Started in 1990 What is it? Covers all crops, cow-calf, drylot, and grasser enterprises Based on producers accounting, financial, and production information collected by interviewers Methodology reflects the accrual-adjusted method Produces customized reports (balance sheet, income statement, enterprise analyses) and provincial benchmarks

4 How does it work? A list of participants is formed and farm visits are scheduled During the farms visits financial and production data are collected in electronic form by trained interviewers Data are received and processed in the office Customized economic reports are mailed to the participants Individual meetings with participants are scheduled on request Sample benchmarks are produced, published, and send to participants

5 Software and technology User Interface Data transfers Reporting Benchmarking Import Export Data checking Support utilities Reporting

6 Benefits for participants A detailed financial analysis of their farms (i.e. balance sheet and income statement) A detailed economic analysis by enterprise (i.e. cow/calf, cereal, and oilseeds) Productions costs and returns on a per unit output (bushel, tonne) and unit of investment (acre, cow) basis Benchmarks reports comparable by region and soil zone AgriProfit$ applied research information and bulletins

7 Example of cash expenses data table Return to Farm Help Producer: Farm Name: John Smith John Smith Ltd Expenses - Cash Basis (2016) Code General Farm Description Total ($) Cereals/ Oilseeds Percent Allocated to: Forage Pasture Cow-Calf Beef Drylot Other Farm Non-Farm/ Personal Total % (must be 100) Calculated Total Example: Fuel, Oil & Lube (85) Repair & Maintenance - Buildings (81) Repair & Maintenance - Machinery (82) Custom Work and/or Machine Rental (83) Other Trucking Costs - not on module (84) Fuel, Oil & Lube (85) Small Tools & Miscellaneous (86) Land Tax (87) Vehicle License/Reg. & Insurance (88) Bldgs/Lvstk Licenses & Insurance (89) 4317 Hi: Office Supplies (90) Utilities - Natural Gas/Propane (91) Utilities - Electricity (92) Utilities - Phone (93) Hi: Hi: Professional Fees (94) Association Dues & Advertising (95) Travel (96) Tack, Horse, Guard Dog (97) Other (98)

8 g Information Gestation Information Example of cow-calf breeding/calving data table Return to Help Cow-Calf Enterprise (2016) 9 A Breeding Season (brought from previous year) Information - Pasture 2015 Producer: John Smith Farm Name: John Smith Ltd Cows Heifers Total Number of Bulls Used Date bulls turned out July 15, 2016 (mm/dd/yyyy) July 15, 2016 (mm/dd/yyyy) Trigger date April 13, 2017 (mm/dd/yyyy) April 13, 2017 (mm/dd/yyyy) Date bulls removed/pulled from pasture October 8, 2016 (mm/dd/yyyy) September 15, 2016 (mm/dd/yyyy) B Females Exposed (Number Pregnancy Checked) Less: - Open Females Sold (Cull) Died Prior to Calving Bred Cows Sold (include culls) Add: + Bred Females Purchased Less: - Open or Kept to be Re-Bred C 'Expected' to Calve in D Calving Information - Spring 2016 Cows Heifers Total Date of First Calf April 20, 2016 (mm/dd/yyyy) April 20, 2016 (mm/dd/yyyy) Cows Calved Before Trigger Date Cycle 1 (day 1-21) Cycle 2 (day 22-42) Cycle 3 (day 43-63) Cycle 4 (day 64+) Total Date of Last Calf June 30, 2016 (mm/dd/yyyy) June 30, 2016 (mm/dd/yyyy) Calving Period (days)

9 Example of crop and rations inventories data table Producer: John Smith Return to Farm Help Farm Nam John Smith Ltd Crop and Rations Inventories (2016) What Percentage (%) of STRAW is CONSUMED as feed? Code Crop Farmer Descriptio n Unit Type lbs/bale % Moisture # of units $/unit total # of units # of units $/unit # of units # of units $/unit Calculated Total Commodity Opening Inventory Production Purchased Feed Sales DM In take 24 lb/au/d Cow-Calf Example: Feed Barley (8) Bushel Alfalfa Hay (200) Tonne $ $ , Alfalfa Hay (200) Tonne $ $ Mixed Grass Hay (214) Tonne $ $ Greenfeed (230) Tonne $ $ Other Silage (234) Tonne $ $ Grain Straw - General/Bedding (240) Tonne $ $ Fed to

10 Field/ Pasture No. Code Example of grazing data table Return to Help Grazing Information (2016) Producer: John Smith Farm NamJohn Smith Ltd Crop Farmer Descriptio n Total Acres Number of Days Date In (mm-ddyyyy) Cows or C/C Pairs Bred Heifers Repl't Heifers Bulls Grass Ylg's Horses Other G raz ing Load Hi: 8 AUM /hd 0 AUM /hd 3 AUM /hd 2 AUM /hd 0 AUM /hd E nter Avg W eights here in Lb/Hd: Example: Code Alfalfa/Grass Pasture (300) North Field P2 305 Tame Grass Pasture (305) Nancy's /09/ /01/ P2 305 Tame Grass Pasture (305) Nancy's /31/ /29/ P1 305 Tame Grass Pasture (305) home /04/ /01/ P1 305 Tame Grass Pasture (305) home /05/ /02/ P1 305 Tame Grass Pasture (305) home /31/ /02/ P3 305 Tame Grass Pasture (305) Brians /09/ /31/ P3 305 Tame Grass Pasture (305) Brians /31/ /22/ P4 312 Annual Cereal (312) /01/ /31/ P6 312 Annual Cereal (312) /02/ /23/ P6 312 Annual Cereal (312) /30/ /24/ P5 312 Annual Cereal (312) /24/ /31/ Or Date Out (mm-dd-yyyy) Number of Head

11 Example of cattle purchases data table Back to CowCalf Help Producer: Farm Name: John Smith John Smith Ltd Production & Breeding Stock Purchases (Transferred In) Code Livestock Category Sale / Transfer Date (mm/dd/yyyy) No. of Head Trucking Calculated Total , ,025 Breeding Herd Purchased for Cash Cash/ CMV Example: Bulls June 15, Cash by Total OR by Average Deductions ($) Gross Value ($) (enter 0 if no expenses) Total Weight (lbs) Total Gross Value ($) Average Weight (lbs/hd) 61 Open Heifers (61) March 17, Cash 0.0 $115, $115, $0.00 $0.00 $115, $1, Lo: 2 61 Open Heifers (61) November 17, Cash 0.0 $51, $51, $0.00 $0.00 $51, $1, Lo: 2 43 Bred Cows (43) November 17, Cash 0.0 $31, ,040.0 $31, $0.00 $0.00 $31, ,040.0 $1, $ Bulls (41) December 7, Cash 0.0 $5, ,500.0 $5, $0.00 $0.00 $5, ,500.0 $5, Hi: 4 Average Value ($/lb) Other Net Value ($) Average Weight (lb/hd) Average Value ($/Hd) Average Value ($/lb)

12 Examples of reports

13 Examples of provincial benchmarks

14 Benefits for Alberta public Up-to-date high quality financial and production information available for general public and government agencies. Collected production information is very detailed that makes it unique in Canada and internationally AgriProfit$ applied research media publications like newsletters bulletins, and presentations More then 20-year comprehensive dataset that can be used for indepth trend exploration, policy analysis, modeling, and forecasting

15 AgriSustainability Future developments Extending business analysis capabilities Flexibility in surveying emerging issues, mgmt. practices, and policies/regulations Historical enterprise (cow/calf, crops, etc.) and thematic benchmarking (feed rations, breeding genetics, etc. ) Sustainability benchmarking by combining social, economic, and environmental indicators

16 www1.agric.gov.ab.ca/general/progserv.nsf/all/pgmsrv385 Whom to contact

17 Cow/Calf Historic Benchmarks Motivation Increase the amount of information derived from the collected data by by generating and maintaining general purpose and thematic historic benchmarks for public and government use, and for in-depth trend exploration, modeling, and forecasting

18 Does the sample reflect external conditions and shocks? $/Lb Weaned 3,00 2,50 Revenues, Costs, Profitability, and Prices 2,00 1,50 1,00 0,50 0,00 (0,50) (1,00) (1,50) Revenue ($/lb W) 0,78 1,29 1,05 1,34 1,57 1,29 1,01 0,51 0,97 1,32 0,95 0,79 0,96 0,98 1,46 1,69 1,45 1,52 2,65 1,70 Total Cost ($/lb W) 1,28 1,47 1,23 1,11 1,13 1,27 1,47 1,45 1,16 1,15 1,09 1,12 1,13 1,15 1,21 1,24 1,34 1,27 1,52 1,48 Net Return ($/lb W) (0,50) (0,17) (0,18) 0,22 0,44 0,02 (0,46) (0,94) (0,20) 0,17 (0,14) (0,33) (0,17) (0,18) 0,25 0,45 0,11 0,24 1,14 0,21 November Price ($/lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59

19 Does the sample reflect external conditions and shocks? $/Lb Weaned 3,00 2,50 2,00 Revenues, Costs, Profitability, and Prices + correlation between profitability and prices 1,50 1,00 0,50 0,00 (0,50) (1,00) (1,50) Revenue ($/lb W) 0,78 1,29 1,05 1,34 1,57 1,29 1,01 0,51 0,97 1,32 0,95 0,79 0,96 0,98 1,46 1,69 1,45 1,52 2,65 1,70 Total Cost ($/lb W) 1,28 1,47 1,23 1,11 1,13 1,27 1,47 1,45 1,16 1,15 1,09 1,12 1,13 1,15 1,21 1,24 1,34 1,27 1,52 1,48 Net Return ($/lb W) (0,50) (0,17) (0,18) 0,22 0,44 0,02 (0,46) (0,94) (0,20) 0,17 (0,14) (0,33) (0,17) (0,18) 0,25 0,45 0,11 0,24 1,14 0,21 November Price ($/lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59

20 Does the sample reflect external conditions and shocks? $/Lb Weaned 3,00 Revenues, Costs, Profitability, and Prices 2,50 2,00 + correlation between profitability and prices BSE outbreak 2014 price increase 1,50 1,00 0,50 0,00 (0,50) (1,00) (1,50) Revenue ($/lb W) 0,78 1,29 1,05 1,34 1,57 1,29 1,01 0,51 0,97 1,32 0,95 0,79 0,96 0,98 1,46 1,69 1,45 1,52 2,65 1,70 Total Cost ($/lb W) 1,28 1,47 1,23 1,11 1,13 1,27 1,47 1,45 1,16 1,15 1,09 1,12 1,13 1,15 1,21 1,24 1,34 1,27 1,52 1,48 Net Return ($/lb W) (0,50) (0,17) (0,18) 0,22 0,44 0,02 (0,46) (0,94) (0,20) 0,17 (0,14) (0,33) (0,17) (0,18) 0,25 0,45 0,11 0,24 1,14 0,21 November Price ($/lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59

21 Does the sample reflect external conditions and shocks? $/Lb Weaned 3,00 Revenues, Costs, Profitability, and Prices 2,50 + correlation between profitability and prices BSE outbreak 2014 price increase 2,00 1,50 1,00 0,50 0,00 (0,50) (1,00) Cost of the outbreak (1,50) Revenue ($/lb W) 0,78 1,29 1,05 1,34 1,57 1,29 1,01 0,51 0,97 1,32 0,95 0,79 0,96 0,98 1,46 1,69 1,45 1,52 2,65 1,70 Total Cost ($/lb W) 1,28 1,47 1,23 1,11 1,13 1,27 1,47 1,45 1,16 1,15 1,09 1,12 1,13 1,15 1,21 1,24 1,34 1,27 1,52 1,48 Net Return ($/lb W) (0,50) (0,17) (0,18) 0,22 0,44 0,02 (0,46) (0,94) (0,20) 0,17 (0,14) (0,33) (0,17) (0,18) 0,25 0,45 0,11 0,24 1,14 0,21 November Price ($/lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59

22 Can the price be used to estimate farm financial health? Ratio $/lb W 1,50 Financial Ratios, and Prices 3,00 1,00 2,50 0,50 2,00 1,50 0,00 1,00 (0,50) 0,50 (1,00) Expense/Revenue 0,61 0,54 0,63 0,52 0,42 0,58 0,73 1,34 0,64 0,59 0,59 0,67 0,63 0,68 0,52 0,51 0,62 0,67 0,35 0,58 Return/Assets 0,00 0,20 0,20 0,30 0,50 0,50 0,20-0,60 0,30 0,40 0,20 0,20 0,20 0,20 0,40 0,56 0,25 0,13 0,87 0,27 Price ($/100 lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59 0,00

23 Can the price be used to estimate farm financial health? Ratio $/lb W 1,50 Revenues, Costs, Profitability, and Prices 3,00 1,00 0,50 - correlation between Operating Expense Ratio and prices 2,50 2,00 1,50 0,00 1,00 (0,50) 0,50 (1,00) Expense/Revenue 0,61 0,54 0,63 0,52 0,42 0,58 0,73 1,34 0,64 0,59 0,59 0,67 0,63 0,68 0,52 0,51 0,62 0,67 0,35 0,58 Return/Assets 0,00 0,20 0,20 0,30 0,50 0,50 0,20-0,60 0,30 0,40 0,20 0,20 0,20 0,20 0,40 0,56 0,25 0,13 0,87 0,27 Price ($/100 lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59 0,00

24 Can the price be used to estimate farm financial health? Ratio $/lb W 1,50 1,00 Revenues, Costs, Profitability, and Prices - correlation between Operating Expense Ratio and prices 2014 price increase 3,00 2,50 0,50 2,00 1,50 0,00 1,00 (0,50) BSE outbreak 0,50 (1,00) Expense/Revenue 0,61 0,54 0,63 0,52 0,42 0,58 0,73 1,34 0,64 0,59 0,59 0,67 0,63 0,68 0,52 0,51 0,62 0,67 0,35 0,58 Return/Assets 0,00 0,20 0,20 0,30 0,50 0,50 0,20-0,60 0,30 0,40 0,20 0,20 0,20 0,20 0,40 0,56 0,25 0,13 0,87 0,27 Price ($/100 lb W) 0,80 1,11 1,15 1,33 1,49 1,35 1,20 1,15 0,97 1,29 1,10 0,94 0,99 0,99 1,26 1,50 1,48 1,53 2,72 2,59 0,00

25 Does data support that extended grazing decreases feed cost? Grazing Days and Feeds Costs Feed Cost ($/wcow) Grazing (day) Total Cost ($/wcow)

26 Does data support that extended grazing decreases feed cost? Grazing Days and Feeds Costs correlation between grazing days and feeds cost correlation between feeds cost and total cost Feed Cost ($/wcow) Grazing (day) Total Cost ($/wcow)

27 AP$ GHG Module CO 2 N 2 O CH 4 Motivation Increase an amount of information derived from the collected data by assessing the environmental impacts of the surveyed production systems and technologies

28 AP$ GHG Module CO 2 N 2 O CH 4 Approach Build a separate GHG emission module Utilize the IPCC methodology (Phase 1) adjusted for Canadian/Alberta conditions (Phase 2)

29 Losses AP$ GHG Module CO 2 N 2 O CH 4 Conceptual Model CO 2 CH 4 N 2 O Hay Pasture Canola Imports Hay Feeds Livestock Manure Product Wheat Exports Hay Grain Barley

30 AP$ GHG Module CO 2 N 2 O CH 4 Emission Inventory Feed Production Livestock Production Fuel Drying/ Extraction Fertilizers Manure Application (F,G) Crop Residue Enteric (F,G) Manure Storage CO 2 CO 2 N 2 O(d,i) N 2 O(d,i) N 2 O(d) CH 4 CH 4 (f,g) N 2 O(d,i) Greenhouse Gases Inventory

31 AP$ GHG Module CO 2 N 2 O CH 4 Empirical Model: Equations Methane (CH 4 ) GE GE CH CH4( ferm) MJ 2KG 2 4 N CH M 3 KG CH4 ( manure) VS 4 MAX storloss 2 Carbon Dioxide (CO 2 ) ( tractor ) FUEL litre L ( dry / extract ) GAS / ELECT L KW CO2 2CO 2 CO 2 / 2CO 2 DE% VS GE ASH GE URINE frac DMKG2 MJ Nitrous Oxide (N 2 O) N applic EFNapplic N2O N N O N N EF N O N O N2O ( fert) 2 N O ( fert) direct 2 2 indirect applic volfrac Nvol 2 N2 2 N2Odirect ( manure) N EF N O 2N2O N2O ( manure) excret volatfrac Ndeposit 2 N2 excret Nexcret 2 N N N EF N O N O indirect 2 N EF N O N O N2O ( pasture) excret Nexcret 2 N 2 N O ( pasture) N N EF N direct 2 2 indirect excret volatfrac Ndeposit N2O N2N2 2 O resid RESYLD RESYLD DM YLD DM DM O N concfrac RESID ratio( 0.2ifbelow YLD above/ below) ratio

32 AP$ GHG Module CO 2 N 2 O CH 4 Empirical Model: Parameters Parameter Value Reference Default Emission Factors for Direct N2O Emissions EF1 [fertilizer, manure, residue] (kg N2O-N/kg of N) 0.01 IPCC, Ch.11, EF [manure] (kg N2O-N / kg N excreted) IPCC, Ch.10, N Retention 0.07 IPCC, Ch10, Default Emission, Volatilization and Leaching Factors for Indirect Soil N2O Emissions Frac_GASM [Volatilization] (kg NH3-N+Nox-N) (kg N applied or deposited) 0.2 IPCC, Ch.11, EF4 [Volatilization] (kg N2O-N) / (kg of NH3-N+Nox-N volitalized) 0.01 IPCC, Ch.11, Frac_GASF [Volatilization from fertilizer] (kg NH3-N + NOx-N) / (kg N applied) 0.1 IPCC, Ch.11, Frac_GASM [Volatilization from manure] (kg NH3-N+Nox-N) (kg N) 0.2 IPCC, Ch.11, Volatilization Losses (kg N2O-N) / (kg of NH3-N+Nox-N volitalized) 0.45 IPCC, Ch.10, General Nitrous Oxide (N2O) Simulation Conversions Conversion from N2O-N to N2O IPCC, Ch. 11, Coefficient to account for ratio of above and below residue yields 0.2 Holos: Methodology and Algorithms for Version 1.1.x, 75

33 AP$ GHG Module CO 2 N 2 O CH 4 Cow-Calf Enterprise Assumptions Only primary sources of energy are used The end (functional) product is weaned calves evaluated at farm gate Feeds, manure, and residues are calculated and allocated at the farm of production origin

34 kg CO2-e/AU kg CO2-e/kg WLW AP$ GHG Module CO 2 N 2 O CH Historic CO 2 -e Footprint CH 4 as a major driver of emission CH4 N2O CO2x10 GWI (CO2-e) Historic CO 2 -e Composition CN 4 (GE) contribution 100% 80% 60% 40% 20% 0% CH4 N2O CO2x10

35 CH 4 (kg/au) AP$ GHG Module CO 2 N 2 O CH Feed Gross Energy vs. CH 4 Emissions Linear dependence of CN 4 emission on feed rations composition Feed GE (MJ/AU)

36 GWI (Tonne CO2-e) GWI (Tonne CO2-e) WLW (Tonne) AP$ GHG Module CO 2 N 2 O CH Weaned Calves Production and CO 2 Emissions + correlation between production level and emissions GWI (Tonnes CO2-e) WLW (Tonnes) WLW (Tonne) Linear dependence of CO 2 -e emission on weaned calves production

37 Crops Assumptions Only primary sources of energy are used The end (functional) product is harvested yield evaluated at the farm gate Manure, and residues are calculated and allocated at the farm of production origin Emissions from biological nitrogen fixation and its residue decay are not accounted for AP$ GHG Module CO 2 N 2 O CH 4 Grasslands do not contribute to N 2 O emission

38 kg CO2-e/ha AP$ GHG Module CO 2 N 2 O CH 4 N 2 O as a major driver of emission Crop Historic Emissions N2O CO2x10 GWI 100% 48,8 90% 116,6 114,1 122,1 75,5 136,1 80% N 2 O contribution 107,9 154,3 126,7 122,7 120,5 120,8 152,1 165,9 151,0 151,1 164,8 143,3 84,0 152,5 70% 60% 50% 534,2 40% 336,0 305,6 323,7 262,7 359,8 315,1 456,3 410,3 431,9 383,5 368,8 451,5 422,6 30% 461,6 483,1 536,2 545,3 521,0 528,5 20% 10% 0% KG CO2-E PER HA

39 kg CO2-e/ha AP$ GHG Module CO 2 N 2 O CH 4 Historic CO 2 -e Emissions by Crop Is there a trend? ,3 50,5 29,9 64,3 47,2 95,8 51,5 35,6 38,7 54,5 26,5 33,1 28,3 27,8 34,7 31,2 39,5 39,3 91,7 56, Canola Feed Barley Hay Synthetic N Applied (kg/ha) Fertilizer application as a driver Canola Feed Barley Hay

40 AP$ GHG Module CO 2 N 2 O CH 4 Next Steps Regional assessment Irrigated lands Drylots and Grassers Carbon storage, tillage, indirect N 2 O from leaching

41 AgriProfit$ Business Analysis and Research Thank You!