WORKSHOP: DATA AND MODELLING FOR EVIDENCE-BASED POLICY

WORKSHOP: DATA AND MODELLING FOR EVIDENCE-BASED POLICY A joint Agriculture and Agri-Food Canada and Statistics Canada Workshop Friday June 24, 2016 Victoria, BC This workshop aims to increase awareness of the data, statistical tools and models available at AAFC and Statistics Canada to industry and academia which can be employed to support evidence-based policy making. Moderator- Nathan Niu, Chief, AAFC 8:00-8:15 Introductions and Opening Remarks Greg Strain, Director General, AAFC Mathieu Thomassin, Director, StatCan 8:15-9:15 Data for Evidence-Based Policy The purpose of this session is to highlight some of the data sources available to researchers. - Primary Agriculture Data Martin Beaulieu, Chief, StatCan - Farm Financial Data Cindy St-Germain, Chief, StatCan - Government Expenditures Data Luc Tanguay, Chief, AAFC 9:15-10:00 Discussion - Discussant - John Cranfield, University Guelph - Discussion to focus on data issues, such as ease of use and accessibility. 10:00-10:30 Networking Break 10:30-11:15 Models for Evidence-Based Policy This session will highlight several of AAFC s models that are used in the preparation of a variety of forecasts. - Food and Agriculture Regional Model Ashwina Aubeeluck, Economist, AAFC - Canadian Regional Agriculture Model Li Xue, Chief, AAFC 11:15-12:00 Discussion 12:00-13:00 Lunch - Discussant - Derek Brewin, University Manitoba Discussion to consider opportunities in which academia and AAFC may further collaborate to maximise the capability of AAFC s models. 13:00-14:45 A Partnership for Evidence-Based Policy This session will explore further opportunities for partnership between academia and AAFC and StatCan. - Accessing and Using Farm Level Data Martin Beaulieu, Chief, StatCan - Developing the Next Federal-Provincial- Territorial Agricultural Policy Frame Work Kara Beckles, Director, AAFC - Coordinating Agriculture Policy Research Initiatives, AAFC data and models Fabrice Nimpagaritse, Economist, AAFC - Selective AAFC Data and Models Booklet 14:45-15:00 Closing Remarks Greg Strain, Director General, AAFC Mathieu Thomassin, Director, StatCan - Canadian Agriculture Dynamic MicroSimulation Model Ryan McCullough, Senior Analyst, AAFC

Primary Agriculture Data Workshop for the Canadian Agricultural Economics Society: Data and modelling for evidence-based policy Martin S. Beaulieu June 24, 2016 1 Statistics Canada Statistique Canada

Objective To provide a brief overview on the farm level data available from the Census of Agriculture, Remote Sensing, Administrative Data and Commodity Data 2 Statistics Canada Statistique Canada

Census of Agriculture - Background Census of Agriculture is a unique source for: small/custom geographic areas enumerating rare and emerging commodities comprehensive and complete profile (farms and operators) Used to benchmark annual agriculture survey program estimates Provides frame information for the survey program Meet users needs for agricultural and environmental programs, health (pesticides), trade and crisis management (floods, disease outbreaks) 3 Statistics Canada Statistique Canada

Census of Agriculture - Scope The Census covers all farm operations producing agriculture products with the intent of selling them Collects information on all operations in Canada No minimum sales or size requirement 4 Statistics Canada Statistique Canada

Operators Adoption of technology Geographic location Succession Planning Farm Families Physical Inputs Social/ Human Provides a truly integrated profile of the physical, economic, social and environmental aspects of agriculture Certified Organic Crops and Livestock Business/ Economic Farm finances Land use Farm Management Practices Business Structure Capital Resources Water use Renewal Energy Environmental Labour Direct Marketing 5 Statistics Canada Statistique Canada

2016 Census Content 2016 Content Same as 2011 Farm operators Business structure Land use Inputs** Crops and livestock Finances* New Direct marketing New technology Succession planning Renewable energy Changes from 2011 Modified Machinery Farm labour Farm practices*** Remove Detailed farm expenses* Use of manure** Irrigation by crop type*** Detail organic*** 6 Statistics Canada Statistique Canada

Products on CANSIM 7 Statistics Canada Statistique Canada

Products on CANSIM 8 Statistics Canada Statistique Canada

Products on CANSIM 9 Statistics Canada Statistique Canada

Products on CANSIM Historical data 004-0001 To 004-0017 Socioeconomic data 004-0100 To 004-0129 Census of Agriculture, number and area of farms and farmland area by tenure, Canada and provinces, every 5 years (Number), 1921 to 2011 Census of Agriculture, number of farm operators by sex, age and paid non-farm work, Canada and provinces, every 5 years (Number), 1991 to 2011 Socioeconomic overview of the farm population, farms with one or more operators by household income classes in the year prior to the census, every 5 years (number), 2011 Socioeconomic overview of the farm population, farm operators and persons in the labour force by country of birth, every 5 years (number), 2011 2011 Census of Agriculture 004-0200 To 004-0242 Census of Agriculture, farms classified by the North American Industry Classification System (NAICS), every 5 years (number), 2011 Census of Agriculture, number of farm operators by paid non-farm work in the calendar year prior to the census, every 5 years (number), 2011 10 Statistics Canada Statistique Canada

Access to Census Data Custom tabulations Selected variables, different cross-tabs than CANSIM Toll-free 1-800-263-1136; 613-951-8116 infostats@statcan.gc.ca Census of Agriculture Longitudinal (1981-2011) Canadian Centre for Data Development and Economic Research (CDER) CDER@statcan.gc.ca 11 Statistics Canada Statistique Canada

Remote Sensing Data Crop Yield Modeling Satellite data: Normalized Difference Vegetation Index (1987 - present) Agroclimatic data Historical survey yields 1. 2. 12 Statistics Canada Statistique Canada

Crop yield model Replace survey with model to generate crop yield estimates Model preliminary crop yield estimates in advance of November Farm Survey estimates Yield model advantages Objective based Reduce respondent burden More cost effective Use of administrative data (crop insurance) 13 Statistics Canada Statistique Canada

Model-based Principal Field Crop Estimates Yield model - Published Sept 17, 2015 developed and tested on the crops that are typically published within the September Farm Survey accounts for approximately 98% of the agricultural land in Canada Alberta, Saskatchewan, Manitoba, Ontario and Quebec 3 weeks in advance of the September Farm Survey, and 11 weeks in advance of the November Farm Survey The Daily: http://www.statcan.gc.ca/daily-quotidien/150917/dq150917c-eng.htm CANSIM Table 001-0075: http://www5.statcan.gc.ca/cansim/home-accueil?lang=eng&p2=50&hpa 14 Statistics Canada Statistique Canada

Potential New Administrative Data Crop Insurance Preliminary analysis of the crop insurance data is encouraging CI data independently or combined with remote sensing Earth orbiting satellite data Different resolutions (spectral, spatial and temporal) Electrical consumption data (Smart Meter) Could be use it for identification of enterprises with high intensity use for their operations (greenhouses, hog barns, dairy, poultry, etc.) Big data, precision agriculture, crowdsourcing 15 Statistics Canada Statistique Canada

Agricultural Commodity Statistics Livestock (production, supply and demand) Dairy and Poultry Cattle, Hogs and Sheep Crops (area, yield, production, supply and demand) Grains, Oilseeds and Pulses Fruits and Vegetable Greenhouse, Sod and Nursery Maple, Honey, Mushrooms Aquaculture Fertilizer Food Per capita consumption 16 Statistics Canada Statistique Canada

Livestock: Summary of main commodities Cattle & Hogs Sheep Dairy Poultry Aquaculture Production Yes Yes Yes Yes Yes Stocks\Inventory Yes Yes Yes Yes Marketed (Q) Yes Yes Yes Yes Yes Trade Yes Yes Yes 1 1 Supply & Demand Yes Yes Yes Small area (CAR) 3 Yes Marketed ($) Derived Derived Yes Yes Yes Financial 2 2 2 2 Yes Notes: 1. Published by International Accounts & Trade Division by HS codes 2. From other survey program: e.g. Farm Financial Survey 3. Census Agricultural Region 17 Statistics Canada Statistique Canada

Livestock Survey/ Program Type of Data collection Frequency Sample & Micro-records Release (see appendix for list) Method of collection Cattle Production Supply/Demand Semi-annual Sample=7,500 Records=6,400 CANSIM Survey Admin. data Hogs Production Supply/Demand Semi-annual Sample=1,250 Records=1,050 CANSIM Survey Admin. data Sheep and Wool Production Supply/Demand Semi-annual Sample=1,250 Records=1,050 CANSIM Survey Admin. data Aquaculture Financial & qualitative data collected Annual Sample=10,000 Records=8,000 Publication & CANSIM Survey Admin. data 18 Statistics Canada Statistique Canada

Livestock Survey/ Program Type of Data collection Frequency Sample & Micro-records Release (see appendix for list) Method of collection Dairy Farm and commercial Production of milk, cheese, etc. Supply/demand Monthly Sample=30 Records=25 Factory production: 85 records CANSIM Survey Admin. data Poultry & Eggs Production Eggs and meat Monthly Annual CANSIM Admin. data Fur Statistics Production Supply/Demand Value Annual Sample=250 Records=150 CANSIM Survey Admin. data Stocks of Frozen Meat Inventory Quarterly Sample=100 Records=70 CANSIM Survey 19 Statistics Canada Statistique Canada

Crops: Summary of main commodities Grains Oilseeds Pulses/other Fruit & Veg Greenhouse Production Yes Yes Yes Yes Yes Area Yes Yes Yes Yes Yes Stocks Yes Yes Yes Deliveries/marketed Yes Yes Derived Yes Yes Trade\Inventories Yes Yes Yes 1 1 Supply & Demand Yes Yes Yes Small area (CAR) 3 Yes Yes Yes Marketed ($) Derived Derived Derived Yes Yes Financial 2 2 2 Yes Employment Yes Notes: 1. Published by International Accounts & Trade Division by HS codes 2. From other survey program: e.g. Farm Financial Survey 3. Census Agricultural Region 20 Statistics Canada Statistique Canada

Crops Survey/ Program Type of Data collection Frequency Sample & Microrecords Release Method of collection Field Crop Reporting Series Production, area Farm Stocks Annual 3 times a year Sample=26,800 Records=20,000 CANSIM Survey Admin. data Grain Marketing Data Trade, deliveries Commercial stocks Supply/Demand Monthly 3 times a year CANSIM Survey Admin. data Fruits and Vegetables Production, area Farm gate value Annual Sample=7,000 Records=6,300 CANSIM Survey Greenhouse, Sod and Nursery Production, area Operational costs Farm gate value Annual Sample=3,350 Records=3,000 CANSIM Survey 21 Statistics Canada Statistique Canada

Crops Survey/ Program Type of Data collection Frequency Sample & micro-records Release Method of collection Mushrooms Production Annual Sample=160 Records=80 CANSIM Survey Admin. data Potatoes Production, Area Farm gate value Semi-annual Sample=300 Records=250 CANSIM Survey Honey Production Farm gate value Annual CANSIM Admin. data Maple Production Farm gate value Annual Sample=700 Records=420 CANSIM Survey (ON,NB) Admin. data 22 Statistics Canada Statistique Canada

Other data Survey/ Program Type of Data collection Frequency Sample & Micro-records Release (see appendix for list) Method of collection Per Capita Food Statistics Food Availability Supply/Demand Annual CANSIM Survey Admin. data Fertilizer Shipments Production Shipments Inventory Quarterly Sample=30 Records=27 Publication and CANSIM Survey Joint Canada- USA-Mexico data release Production Formatted for comparability between countries Annual http://webpage.siap.gob.mx/i ndex.php Reformat of released data 23 Statistics Canada Statistique Canada

Questions/Contacts Remote Sensing and Geospatial Analysis Gordon Reichert gordon.reichert@canada.ca Analysis and Outreach Martin S. Beaulieu martin-s.beaulieu@canada.ca Commodity Cindy St-Germain cindy.st-germain@canada.ca 24 Statistics Canada Statistique Canada

Appendix Table _ Commodity on CANSIM 25 Statistics Canada Statistique Canada

Cattle, hogs and sheep (CANSIM) 003-0026 Cattle and calves, farm and meat production, annually 003-0032 Number of cattle, by class and farm type, annually 003-0099 Cattle and calves statistics, number of farms reporting and average number of cattle and calves per farm, semi-annually 003-0083 Cattle statistics, supply and disposition of cattle, annually 003-0085 Cattle and calves, number by class and calf crop, United States, annually 003-0028 Hogs, sheep and lambs, farm and meat production, annually 003-0105 Hogs and pigs statistics, inventory number by class and semi-annual period, United States, semi-annually 003-0104 Hogs and pigs statistics, inventory number by class and semi-annual period, United States and Canada, semi-annually 003-0103 Hogs statistics, number of farms reporting and average number of hogs per farm, semiannually 003-0100 Hogs statistics, number of hogs on farms at end of semi-annual period, semi-annually 003-0101 Hogs statistics, sows farrowed, pigs born and sows bred to farrow, semi-annually 003-0102 Hogs statistics, supply and disposition of hogs, semi-annually 003-0031 Number of sheep and lambs on farms, annually 003-0098 Sheep and lambs, number by class and lamb crop, United States, annually 003-0094 Sheep statistics, supply and disposition of sheep and lambs, annually 26 Statistics Canada Statistique Canada

Dairy and frozen meat (CANSIM) 003-0007 Supply and disposition of milk products in Canada, monthly 003-0008 Cash receipts from milk and cream sold off farms, monthly 003-0009 Production of selected butter products, monthly 003-0010 Production of selected products, by dairy manufacturers, monthly 003-0011 Milk production and utilization, monthly 003-0012 Commercial sales of milk and cream, monthly 003-0029 Production of concentrated milk products, monthly 003-0033 Stocks of specified dairy products, monthly 003-0034 Production of butterfat, monthly 003-0081 Stocks of frozen and chilled meats, domestic and imported, in cold storage, quarterly 003-0082 Stocks of frozen and chilled imported meats, quarterly 27 Statistics Canada Statistique Canada

Poultry and frozen poultry products (CANSIM) 003-0018 Production, disposition and farm value of poultry meat, annually 003-0019 Fowl and chicken meat production, weight and farm value, annually 003-0020 Production and disposition of eggs, annually 003-0021 Placement of chicks and turkey poults for production, monthly 003-0022 Production and disposition of eggs, monthly 003-0023 Stocks of frozen poultry meat, monthly 003-0024 Stocks of frozen eggs and edible dried eggs, monthly 003-0038 Report of processed eggs production in Canada, weekly 003-0039 Laying type pullet, chick placements, weekly 003-0079 Report of processed eggs production in Canada, annually 28 Statistics Canada Statistique Canada

Fertilizer, Aquaculture, Food and Mink (CANSIM) 001-0066 Canadian fertilizer inventories, by product type, annually 001-0067 Canadian fertilizer production, by product type and fertilizer year, cumulative data, annually 001-0068 Fertilizer shipments to Canadian agriculture and export markets, by product type and fertilizer year, cumulative data, annually 001-0069 Fertilizer shipments to Canadian agriculture markets, by nutrient content and fertilizer year, cumulative data, annually 003-0001 Aquaculture, production and value, annually 003-0003 Aquaculture economic statistics, value added account, annually 002-0010 Supply and disposition of food in Canada, annually 002-0011 Food available in Canada, annually 003-0014 Number and value of mink pelts produced, by colour type, annually 003-0015 Supply and disposition of mink and fox on fur farms, annually 29 Statistics Canada Statistique Canada

Grains, Oilseeds and Pulses (CANSIM) 001-0001 Producer deliveries of major grains, Canada and selected provinces, monthly (Tonnes), Aug 1966 to Mar 2016 001-0005 Crushing statistics of major oilseeds for Canada, monthly (Tonnes), Aug 1971 to Mar 2016 001-0010 Estimated areas, yield, production and average farm price of principal field crops, in metric units, annual, 1908 to 2016 001-0015 Exports of grains, by final destination, monthly (Tonnes), Jan 1922 to Feb 2016 001-0017 Estimated areas, yield, production, & total farm value of principal field crops, in imperial units, annual, 1908 to 2016 001-0040 Stocks of grain and oilseeds at March 31, July 31 and December 31, occasional (Tonnes), 1980 to 2015 001-0041 Supply and disposition of grains in Canada as of March 31, July 31, and December 31 (Metric tonnes), 1996 to 2015 001-0042 Supply and disposition of corn in Canada as of March 31, August 31 and December 31 (Metric tonnes), 1996 to 2015 001-0043 Farm supply and disposition of grains as of March 31, July 31 and December 31 (Metric tonnes), 2001 to 2015 001-0044 Milled wheat and wheat flour produced, Canada, monthly (Metric tonnes), Aug 1995 to Feb 2016 001-0071 Estimated areas, yield and production of principal field crops by Small Area Data Regions, annual, 1976 to 2015 001-0072 Estimated areas, yield, production of corn for grain & soybeans, using genetically modified seed, annual, 2000 to 2015 001-0073 Forage seed usage, by type of seed, in metric and imperial units, annual, 2008 to 2015 001-0075 Model-based Principal Field Crop Estimates, in metric and imperial units, annual, 2015 30 Statistics Canada Statistique Canada

Fruits, Vegetables & other Horticulture (CANSIM) 001-0009 Area, production and farm gate value of fresh and process fruit, annually 001-0013 Area, production and farm gate value of vegetables and process fruit, annually 001-0008 Production and farm value of maple products, annually 001-0007 Production and value of honey, annually 001-0012 Area, production and sales of mushooms, annually 001-0014 Area, production and farm value of potatoes, annual, 1908 to 2015 001-0045 Area, production and farm value of potatoes, by harvest season, United States, annual, 1998 to 2015 Can-US-Mex http://webpage.siap.gob.mx/index.php 31 Statistics Canada Statistique Canada

Greenhouse, Sod and Nursery (CANSIM) 001-0006 Production and value of greenhouses vegetables, annually 001-0046 Estimates of greenhouse total area and months of operation, annually 001-0047 Estimates of specialized greenhouse operations, greenhouse area, and months of operation, annually 001-0048 Production and sale of greenhouse flowers and plants by category, annually 001-0049 Production of potted plants, cut flowers, cuttings, by variety and tree seedlings, annually 001-0050 Channels of distribution for horticulture product sales and resales, annually 001-0051 Total value of greenhouse products, annually 001-0052 Greenhouse producers' operating expenses, annually 001-0053 Specialized greenhouse producers' operating expenses, annually 001-0054 Total greenhouse, sod and nursery employees, annually 001-0055 Total number of employees of specialized greenhouse operations, annually 001-0056 Estimates of nursery area, annually 001-0057 Nursery tree and plant production, annually 001-0058 Nursery stock sales and resales, annually 001-0059 Channels of distribution for nursery product sales and resales, annually 001-0060 Estimates of sod area, sales and resales, annually 001-0061 Nursery and sod producers' operating expenses, annually 32 Statistics Canada Statistique Canada

Farm Financial Data Workshop for the Canadian Agricultural Economics Society: Data and modelling for evidence-based policy Cindy St-Germain June 24, 2016

Objective To provide a brief overview on the type of farm financial statistics collected and disseminated by Statistics Canada s Canadian Agricultural Farm Financial Statistics Section 2 Statistics Canada Statistique Canada

CAFS Overview Canadian Agricultural Financial Statistics (CAFS) Farm Income, Expenses and Prices Farm Cash Receipts Agriculture Taxation Data Farm Financial Survey Input prices Product prices Net farm income Expenses Balance sheet Value added Debt Annual & Quarterly Farm cash receipts Farm operating revenues and expenses Total income of farm operators Total income of farm family Biennial survey Financial and balance sheet data Rotating content for the voluntary section. Capital (annual) 3 Statistics Canada Statistique Canada

System of Macroeconomic Accounts Net Cash Income Published: Farm cash receipts - quarterly Operating expenses and depreciation Income in kind Value of inventory change Net income - 3 measures Value of farm capital Farm debt outstanding Commodity prices Farm product price index Value added account Balance sheet Farm Cash Receipts Income in Kind Value of Inventory Change Program Payments Administrative Data on Marketings and Prices (65+ sources) Survey: Commodity Prices Surveys: Crops Livestock Animal Products Horticulture Census of Agriculture Administrative Data (65 + sources) Expenses Taxation Data and FFS 4 (Reconcile - every 5 years) Statistics Canada Statistique Canada

Farm Income and Prices Net Income Farm Cash Receipts (including program payments) - Operating expenses (after rebates) = Net Cash Income + Income in kind - Depreciation = Realized Net Income +/- Value of Inventory Change = Total Net Income 5 Statistics Canada Statistique Canada

Farm Income and Prices Aggregated statistical information relating to: farm product prices farm product price index farm cash receipts farm operating expenses and depreciation charges direct program payments value of farm capital value of inventory changes value per head farm debt outstanding income-in-kind net farm income balance sheet of the agricultural sector agriculture value added account 6 Statistics Canada Statistique Canada

Farm Income and Prices Exclusions Sales between farms within a province (e.g. Calf sold to cattle finisher) To avoid double-counting production (calf produced already accounted in cattle) Value added production by farm producers (e.g. apple to cider, etc) Services rendered by farmers (horse boarding, etc) Commodities harvested (as opposed to cultivated) (e.g. Wild blueberries, wild mushrooms) 7 Statistics Canada Statistique Canada

Farm Income and Prices Numerous data series from internal and external sources. Including survey and administrative data Main users: System of National Account (SNA) Agriculture Agri-Food Canada (AAFC) Provincial departments of agriculture The data feeds into the fiscal arrangement regulatory Used as part of the Growing Forward framework for program payments for farm producers. (AAFC) 8 Statistics Canada Statistique Canada

Farm Income and Prices Survey/ Program Frequency CANSIM Data sources Farm cash receipts Annual Quarterly 002-0001 002-0002 Surveys Admin. Data Direct payments to agriculture producers Total cash receipts from farming operations Annual 002-0076 Admin. Data Annual 002-0014 Survey Admin. Data Total cash receipts from farming operations, by item Annual 002-0015 Survey Admin. Data Farm income in kind, by item Annual 002-0012 Derived Value of inventory change Annual 002-0075 Survey Admin. Data Net farm income Annual 002-0009 Derived 9 Statistics Canada Statistique Canada

Farm Income and Prices Survey/ Program/ Data Frequency CANSIM Data sources Farm debt outstanding, classified by lender Annual 002-0008 Surveys Admin. Data Value of farm capital, at July 1 Annual 002-0007 Census Admin. Data Value per acre of farm land and buildings, at July 1 Annual 002-0003 Survey Admin. Data Value per head of livestock at July 1 Annual 003-0025 Survey Admin. Data Balance sheet of the agricultural sector, at December 31, and ratios Cattle and calves, farm and meat production Hogs, sheep and lambs, farm and meat production Annual 002-0020 Integrated Annual 003-0026 Survey Admin. Data Annual 003-0028 Survey Admin. Data 10 Statistics Canada Statistique Canada

Agriculture Prices and Indexes Monthly price series 300 farm-gate prices (average weighted price) Data availability announcement in the Daily Monthly Farm Product Price Index Released in the Daily and CANSIM Quarterly 11 Statistics Canada Statistique Canada

Agriculture Prices and Indexes Survey/ Program/ Data Frequency CANSIM Data sources Farm product prices, crops and livestock Monthly 002-0043 Surveys Admin. Data Farm product price index (FPPI) Monthly 002-0068 Admin. Data Farm product price index (FPPI) Annual 002-0069 Survey Admin. Data Farm product price index (FPPI) weights Monthly 002-0070 Survey Admin. Data Farm input price index Quarterly 328-0015 Survey Admin. Data 12 Statistics Canada Statistique Canada

Whole Farm Database Project Financial data using taxation data Operating Revenues and Operating Expenses: T1, T2, T3 Total income of farm operators Total income of farm families (biennial) Aggregated and farm-level data Number of variables: approximately 500 variables Sample size: approximately 30,000 T1 units plus all clean units that are not randomly selected (they passed the edits and are determined as being clean), approximately 8,000 T2s and a census of about 350 communal farming organizations. Custom tables: farm type, size, percentiles, typology (pension, lifestyle or low income), average per farm reporting, profitability 13 Statistics Canada Statistique Canada

Whole Farm Database Project Survey/ Program Frequency CANSIM Data sources Farms: detailed operating average revenues and expenses, ratios, distribution by category Annual 002-0044 to 002-0063 Appendix 1 Taxation Data Operators: detailed operating average revenues and expenses, incorporated, unincorporated, distribution by category Annual 002-0034 to 002-0042 Appendix 2 Taxation Data Farm families: net operating income and off-farm income by source, total and average, unincorporated, distribution by category Annual 002-0024 to 002-0033 Appendix 3 Taxation Data 14 Statistics Canada Statistique Canada

Farm Financial Survey 15 Financial structure Balance sheet Farm characteristic data Special module (e.g. on farm food safety; farm innovation) Capital investments and capital sales Off-farm income Growing Forward programs data (e.g. AgriStability, AgriInvest) Aggregated and farm-level data Number of variables: approximately 550, not including special module Sample size: approximately 10,000 Custom tabulations on request: farm type, size, percentiles, typology (pension, lifestyle or low income), average per farm reporting, profitability Statistics Canada Statistique Canada

Farm Financial Survey Data Frequency CANSIM Data sources Canadian and regional agricultural balance sheet (gross farm revenue equal to or greater than $25,000) Financial structure by farm type, average per farm (gross farm revenue equal to or greater than $25,000) Financial structure of farms by revenue class, average per farm (gross farm revenue equal to or greater than $25,000) Capital investment and capital sales of farms, average per farm (gross farm revenue equal to or greater than $25,000) Every 2 years 002-0071 Survey Every 2 years 002-0072 Survey Every 2 years 002-0073 Survey Every 2 years 002-0074 Survey 16 Statistics Canada Statistique Canada

Questions/Contacts Farm Income, Expenses and Prices Gail-Ann Breese gail-ann.breese@canada.ca Agriculture Taxation Data Michael Paju michael.paju@canada.ca Farm Cash Receipts Stephen Boyd stephen.boyd@canada.ca Farm Financial Survey Erin Kumar erin.kumar@canada.ca 17 Statistics Canada Statistique Canada

Appendix Table 1 002-0044 002-0045 002-0046 002-0047 002-0048 002-0049 002-0050 002-0051 002-0052 002-0053 002-0054 Detailed average operating revenues and expenses of farms, by farm type, incorporated and unincorporated sectors, Canada and provinces Detailed average operating revenues and expenses of farms, by revenue class, incorporated and unincorporated sectors, Canada Average operating revenues and expenses of farms, by revenue class, incorporated and unincorporated sectors, provinces Average operating revenues and expenses of farms, by revenue class and farm type, incorporated and unincorporated sectors, Canada Distribution of farms, by farm type and net operating income group, incorporated and unincorporated sectors, Canada and provinces Distribution of farms, by revenue class, farm type and net operating income group, incorporated and unincorporated sectors, Canada Average total agricultural sales of farms, by selected farm type, revenue class and degree of specialization, incorporated and unincorporated sectors, Canada Average total agricultural sales of farms, by selected farm type and revenue class, incorporated and unincorporated sectors, Canada Average net program payments and average net market income of farms, incorporated and unincorporated sectors, Canada and provinces Average net program payments and average net market income of farms, by farm type, incorporated and unincorporated sectors, Canada and provinces Average net program payments and average net market income of farms, by revenue class, incorporated and unincorporated sectors, Canada 002-0055 Financial ratios of farms, incorporated and unincorporated sectors, Canada and provinces 18 Statistics Canada Statistique Canada

Appendix Table 1 (end) 002-0056 Financial ratios of farms, by farm type, incorporated and unincorporated sectors, Canada 002-0057 Financial ratios of farms, by revenue class, incorporated and unincorporated sectors, Canada 002-0058 002-0059 002-0060 002-0061 002-0062 002-0063 Financial ratios of farms, by quartile boundary, incorporated and unincorporated sectors, Canada and provinces Financial ratios of farms, by farm type and quartile boundary, incorporated and unincorporated sectors, Canada Financial ratios of farms, by revenue class and quartile boundary, incorporated and unincorporated sectors, Canada Average net market income of farms, by income quintile, incorporated and unincorporated sectors, Canada and provinces Average net market income of farms, by farm type and income quintile, incorporated and unincorporated sectors, Canada Average net market income of farms, by revenue class and income quintile, incorporated and unincorporated sectors, Canada 19 Statistics Canada Statistique Canada

Appendix Table 2 002-0034 002-0035 002-0036 002-0037 Total and average off-farm income by source and total and average net operating income of farm operators, incorporated and unincorporated sectors Total and average off-farm income by source and total and average net operating income of farm operators by farm type, incorporated and unincorporated sectors Total and average off-farm income by source and total and average net operating income of farm operators by revenue class, incorporated and unincorporated Average off-farm income and average net operating income of farm operators by revenue class, incorporated and unincorporated sectors 002-0038 Average total income of farm operators by farm type, incorporated and unincorporated sectors 002-0039 002-0040 Average total income of farm operators by farm type and revenue class, incorporated and unincorporated sectors Distribution of farm operators by income group and farm type, with selected average incomes, unincorporated sector 002-0041 Average total income of farm operators by income quintile, unincorporated sector 002-0042 Average total income of farm operators by income quintile and farm type, unincorporated sector 20 Statistics Canada Statistique Canada

Appendix Table 3 002-0024 002-0025 002-0026 Total and average off-farm income by source and total and average net operating income of farm families, unincorporated sector Total and average off-farm income by source and total and average net operating income of farm families by farm type, unincorporated sector Total and average off-farm income by source and total and average net operating income of farm families by typology group, unincorporated sector 002-0027 Average total income of farm families by farm type, unincorporated sector 002-0028 Average income of farm families by source and family total income group, unincorporated sector 002-0029 Distribution of farm families and average total income by typology group, unincorporated sector 002-0030 Distribution of farm families and average total income by typology group and farm type, unincorporated sector 002-0031 Distribution of farm families by income group and family size, unincorporated sector 002-0032 Average total income of farm families by income quintile, unincorporated sector 002-0033 Average total income of farm families by income quintile and farm type, unincorporated sector 21 Statistics Canada Statistique Canada

Government Expenditures(GE) Data Data and Modelling for Evidence-Based Policy A joint Agriculture and Agri-Food Canada and Statistics Canada Workshop Agri-Food Support, Measurement & Analysis June 24, 2016 1

Purpose and Outline Purpose Present an overview of Government Expenditures (GE) and the type of information you can get from the GE database Outline Overview of GE results GE definition and Background information Some of the outputs from the GE database Strengths and Weakness of GE Future plans Collaboration 2

Billions How much government spend to support the agriculture and agri-food sector in Canada? 10 Federal and Provincial Government Expenditures in Support of the Agriculture and Agri-Food Sector, 1985-1986 to 2015-2016 9 8 7 6 5 4 3 2 1 0 Federal Provincial Source: AAFC calculations 3

1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 Billions GE is not only about how much governments spend but also how governments spend money to support the sector 8 Total Program Government Expenditures in Support of the Agriculture and Agri-Food Sector by Category, 1985-1986 to 2015-2016 7 6 5 4 3 2 1 0 Extension Education Innovative Practices, Products, and Technologies Rural and Regional Development Market Development, Promotion, and Trade International Development and Food Aid Safety and Control Measures Research Social and Labour Storage and Freight Assistance Financial Assistance Insurance and Compensation Cost Reduction Income Support and Stabilization Source: AAFC calculations 4

GE estimates how much governments spend to support the Canadian agri-food sector in a given fiscal year Government expenditures : Includes expenditures to support Primary agriculture, agriculture input subsidies and Food and beverage processing; Excludes expenditures to support aquaculture and fish processing. Comprehensive classification system to facilitate analysis: Source of funding: federal and provincial expenditures Category: Operating, capital, program and tax expenditures Sub-category: Program expenditures are broken down further into 14 sub categories (e.g., income support, Insurance and compensation, Research, Safety and Control Measures, Innovative practices, products and technologies, and Extension, etc.) Labels: BRM/Strategic initiatives, Direct and Indirect, Ad Hoc, Disaster, Sector (Primary/Processing), Environment, Department (Ag/Other departments). 5

GE is a national measure that estimates total government expenditures in Canada and by province It is a FPT initiative as an answer to Ministerial Request to provides sound data and analysis on government contribution and in measuring government support of the agri-food system A consistent way to gather expenditures (federal and provincial) for monitoring, analysis and comparison Methodology, Concepts and Classification System have been developed by the federal and provinces Source of information: Federal and Provincial Public Accounts, Budget Estimates, Financial Reports. Data is updated once a year. Last update in December 2015 2014-15: Figures based on Actuals 2015-16: Figures based on Budget Estimates 6

This unique source of information on expenditures in support of the agriculture and agri-food sector is used in different ways GE is currently use at the federal and provincial level to inform senior management on different issues (BRM/Non-BRM, Research expenditures, Ad Hoc programs, Environment expenditures, Development of agricultural policy framework, etc.) A source of information for OECD indicators of support (PSE and GSSE) and WTO Notification on Domestic Support. Aggregate data on GE is already available in AAFC publications 7

Information on GE can be available through an Excel Pivot table TYPE PROVINCE GE_NAME FUND SOURCE DEPARTMENT (Tous) (Tous) (Tous) (Tous) (Tous) (Tous) ($ '000) YEARF MC CATEG 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 A-OPERATING 2 328 698 2 388 548 2 551 730 2 435 972 2 314 966 2 006 287 A01-Departmental Operations and Management 425 004 409 398 558 614 451 049 333 993 306 579 A02-Research 362 798 357 795 350 434 362 487 301 593 268 698 A03-Policy and Programs 349 530 369 922 409 855 387 149 350 692 326 712 A04-Market Development, Promotion, and Trade 150 499 153 584 149 566 163 360 102 422 104 778 A05-Safety and Control Measures 828 560 882 750 876 242 890 446 939 308 692 937 A06-Rural and Regional Development 78 734 57 814 60 798 36 411 65 074 76 862 A07-Information and Statistical Services 24 704 44 436 30 033 26 527 103 294 93 250 A08-International Development and Food Aid 718 718 1 071 1 162 1 467 1 000 A09-Innovative Practices, Products, and Technologies -894-4 946-7 335 2 153 4 283 6 239 A10-Extension 108 993 117 031 122 402 115 229 112 839 129 231 A11-Social and Labour 51 46 50 B-CAPITAL 198 720 192 863 155 479 135 529 122 916 183 858 B01-Departmental Operations and Management 58 764 57 866 46 487 53 037 43 398 21 686 B02-Research 16 577 23 872 23 352 19 476 20 454 59 551 B03-Policy and Programs 758 557 496 880 569 552 B04-Market Development, Promotion, and Trade 502 279 566 656 148 195 B05-Safety and Control Measures 65 636 46 160 43 704 26 447 22 620 59 001 B06-Rural and Regional Development 50 706 60 583 39 444 34 723 35 376 42 727 B07-Information and Statistical Services 632 3 056 B09-Innovative Practices, Products, and Technologies 5 008 429 1 384 224 279 54 B10-Extension 138 62 45 86 73 93 C-PROGRAM 4 048 410 4 095 179 4 026 989 3 594 384 3 079 083 3 274 984 C01-Income Support and Stabilization 1 496 766 1 468 424 1 645 516 1 172 868 945 931 934 459 C02-Cost Reduction 22 855 6 970 23 142 8 594 7 583 10 383 C03-Insurance and Compensation 1 330 324 1 350 508 1 148 910 1 246 665 1 002 239 1 166 862 C04-Financial Assistance 93 177 129 891 112 558 112 834 62 594 65 419 C05-Storage and Freight Assistance 8 787 5 795 6 106 7 750 5 777 4 759 C06-Social and Labour 20 048 20 750 20 610 20 482 20 384 21 147 C07-Research 257 457 262 033 253 930 259 000 284 273 321 248 C08-Safety and Control Measures 124 246 117 409 109 134 90 979 113 146 110 523 C09-International Development and Food Aid 18 376 19 883 19 233 20 240 21 242 20 928 C10-Market Development, Promotion, and Trade 101 108 135 033 104 114 106 796 92 850 81 578 C11-Rural and Regional Development 210 870 179 199 181 000 170 294 145 945 146 922 C12-Innovative Practices, Products, and Technologies 116 693 146 530 147 216 151 549 148 267 166 995 C13-Education 123 796 125 365 126 695 132 822 129 259 124 060 C14-Extension 123 908 127 390 128 825 93 509 99 592 99 700 D-TAX 394 756 415 610 414 027 355 947 358 511 354 675 8 RECOVERIES -524 932-412 457-508 521-495 797-488 056-483 630 Total général 6 445 652 6 679 743 6 639 704 6 026 034 5 387 420 5 336 173

Strengths and Weaknesses of GE Strengths Include expenditures in support of the agriculture and agri-food sector (primary agriculture, agricultural input, food and beverage processing, and bio-based product industries). Classification system allows to monitor and analyse the level and composition of expenditures in support of the agri-food sector in a consistent way over time. Breakdown of the information available by province, source of funding (federal and provincial), categories, sub-categories and labels. Information available for more than 30 years. Weaknesses Do not include price support measures affecting the price that producers received from the market. Expenditures by program cannot be released No information at producer level No information by commodity. 9

Future Plans Continue to improve the GE database to answer current and future policy questions Labels Data on program expenditures Improve data accessibility Improve information on programs reported in the database (Program description) 10

Collaboration Make the data on Government expenditures available to academia for analysis with the agreement of provincial counterparts Work with academia on projects requiring data on expenditures in support of the agricultural sector 11

QUESTIONS? 12

ANNEXES 13

ANNEX 1 - Description Government Expenditures The Government Expenditures (GE) project is a federal-provincial initiative. GE estimates how much governments spend in support of the Canadian agri-food sector (primary agriculture, agricultural input industries and food and beverage processing) in a given fiscal. Data are available from 1985 onwards and by province. A methodology and a classification system have been developed to monitor and analyze government spending overtime in a consistent way. Source of funding: Federal or Provincial Main Categories: Operating, Capital, Program, Tax, Recoveries Sub-categories (14): Income Support & Stabilization, Insurance & Compensation, Research, Safety and Control Measures, Market Development, Innovative Practices/Products, Education, Extension, etc. Labels: Sector (Primary/Processing), Direct/Indirect, Environment, Departments (Agriculture/Others). Data are available from 1985 onwards and by province. Data reported in GE are based on Public Accounts and financial reports for Actuals and Budget Estimates for estimates year. As much as possible, the information reported in GE is broken down by program to allow a better classification. However, information by program is not publicly available. 2 - Projects of Interest to AAFC Agricultural policy objectives and the level and composition of governments spending in support to the agri-food sector 3 - Skills required and Data Access requirements GE data are available according to the characteristics described above. The skills required to manipulate the GE data require a good understanding of the GE methodology, concepts and classification system. The basic manipulation of the dataset requires: - Knowledge of Excel Pivot Tables 4 - Contact Luc Tanguay: Luc.Tanguay@agr.gc.ca or 613 773-2441 Last Updated: 2016-05-18 14

GE provides some information on support not available in other indicators of support GOV. EXPENDITURES PSE GSSE WTO NOTIFICATION DS AMS DIRECT PAYMENTS Purpose Measure how much governments spend on agriculture Measure how policies impact support to agriculture Type of measures All budgetary transfers Budgetary transfers Price support Operating expenditures Included PSE Not included GSSE Included - Research, Inspection, Extension, Marketing. Capital expenditures Included PSE Not included GSSE Included - Research, Inspection, Extension, Marketing. Program expenditures Included PSE Transfers to producers GSSE Transfers to sector Primary sector Included PSE Included GSSE Included Processing sector Included PSE Excluded GSSE First level: Included Information by province Yes PSE No GSSE No Information by program No PSE Yes GSSE No Measure domestic support to ensure support does not exceed the WTO commitment Budgetary transfers Price support Green box Included - Research, Inspection, Extension, Infrastructures Amber box Not included Green box Included - Research, Inspection, Extension, Infrastructures Amber box Not included Green box Included Amber box Included Green box Included Amber box Included Green box Included Amber box Included Green box No Amber box No Green box Yes Amber box Yes Measure how much producers receive Some budgetary transfers Not included Not included Included Payments to producers Included Excluded Yes Yes Information by commodity No PSE Yes, for some type of programs Green box No Amber box Yes No Information by source of funding (Fed, Prov) Yes Yes No No 15

The Food and Agriculture Regional Model (FARM) Models for Evidence-Based Policy Session Ashwina Aubeeluck AAFC-RAD-EMA June 24, 2016

Introduction: Economic Market Analysis Unit (EMA) Products The Medium Term Outlook (MTO) Forward looking analysis of domestic, international and trade policies and scenarios Two Partial Equilibrium Dynamic models: AAFC Food and Agriculture Regional Model (FARM) OECD - FAO AGLINK/COSIMO model International markets: AGLINK/COSIMO model Representation of supply, demand, trade and prices for major agricultural commodities. AAFC extends projections

Structure of the FARM model FARM (1200-1300 Equations) Grains & Oilseeds CPGOMOD Special crops (including bio-fuels and processed products) Hogs & Pork Cattle & Beef Mutton & Lamb Poultry & Eggs Milk & Dairy Products Farm Input Price Indices (FIPI) CPSPMOD PKMOD BFMOD SHMOD PTMOD DYMOD AGGMOD Farm input demand (Quantities) Cash receipts, expenses and farm income Consumer Price Index Value of Agriculture & Agri-Food Shipments Value of Agriculture & Agri-Food Trade

Process in creating FARM and the baseline Analysts are responsible for collecting the data for their commodity model. 4 Analysts revise and update the equations to reflect current market condition and policies for their model. Individual models are merged into one model (FARM). The model is calibrated and aligned to short term forecasts when creating the baseline (annual - 10 year projection). The baseline generated in FARM model is used as a benchmark for scenario analysis.

Examples of data sources Grain and oilseeds model (300~ equations) Statistics Canada, Provincial data (Winnipeg), ERS, States government, US energy Information administration, etc. Beef (200 ~ equations): Statistics Canada, Canfax, MISB red meat section (AAFC), USDA, Financière agricole du Québec, etc. Poultry (65 ~ equations): MISB Poultry Section (AAFC), Statistics Canada, GAC website, Chicken farmers of Canada, Egg Farmers of Canada, Turkey Farmers of Canada, USDA, etc.

Strengths Weaknesses Essential commodities are present Cross elasticities among the commodities create a dynamic model Reliable and used to make policy decisions Barriers to entry is high The model does not have horticulture Not disaggregated by province The model is maintained

End products The Medium Term Outlook projections are provided to the OECD in January. The Canadian Agricultural Outlook is published early in the new year. Scenarios are produced throughout the year using FARM (domestic model) and Aglink-Cosimo (International model). Examples of scenarios: Extreme weather scenarios, Border closure (from a disease), Changes in trade policy.

Growth rate (1990=1) Pork, beef and chicken (kg) Total (kg) Millions of hectares Examples of Economic and Market Analysis 40 Annual Meat Consumption Per Capita 95 25 Cultivated Area of Wheat and Coarse Grains vs. Oilseeds and Special Crops 35 30 25 90 85 80 75 20 15 10 20 1990 1994 1998 2002 2006 2010 2014 2018 2022 Pork Beef chicken Total Increase in meat prices in Canada 70 5 0 1990 1994 1998 2002 2006 2010 2014 2018 2022 Wheat & Coarse Grains Oilseeds & Special Crops 2.4 2.2 2 1.8 1.6 1.4 1.2 1 1990 1994 1998 2002 2006 2010 2014 2018 2022 Porc Bœuf Poulet Bilateral Trade Agreement (Korea and Canada) 2017-2023 Korea pork prices -10% Canadian hog prices 2.4% Slaughter of hogs 4% Korea Import of pigmeat from Canada 550% (330,000 mt) Korea Import of pigmeat from other countries -43% (145,000 mt) Canada Export of pigmeat 8.3%

Future Plans New types of analysis with current models Improve accessibility Bring in new graduates under the ECDP program Opportunities for employees to develop analytical skill and other key competencies

Collaboration Work with students/academics to undertake quantitative and qualitative analysis using models. Collaborative research papers on various commodities, trade scenarios and countries. Encourage academics to write papers on sensitive subjects. Peer review on results and papers

Annex 1 - Description Food and Agriculture Regional Model The Research and Analysis Directorate (RAD) at Agriculture and Agri-Food Canada (AAFC) carries out economic analysis and produces several key reports annually. The Medium Term Outlook (MTO) is one of such publications. The report provides detailed historical data and 10-year projections for each commodity and products (i.e., stocks, production, use, trade, prices, etc.). The econometric model called Farm and Agriculture Regional Model (FARM) is used to produce the projection for the domestic crops and livestock markets. FARM provides a medium term projections on the production, consumption and trade information of agricultural products. It sheds light on the food and non-food (e.g., feed, and biofuels) uses of the agricultural products. FARM provides an outlook of the agricultural land uses at national and regional levels (i.e., western and eastern Canada) Crops in the FARM model included are wheat, coarse grains (corn, barley, oats, etc.), oilseeds (canola, soybeans, etc.), special crops (lentils, field peas, etc.) Livestock and dairy sectors are important components of FARM model. The model includes beef, pork, chicken, sheep, turkey, egg, milk, cheese and other dairy products. FARM database uses data and information from multiple sources, such as, Statistics Canada, Organization of Economic Co-operation and Development (OECD), the US Department of Agriculture (USDA), etc. Using FARM, the Economic Market Analysis (EMA) group produces a baseline annually which takes into account the international and domestic market conditions and macro-economic situation. The baseline generated in the FARM model is used as a benchmark for scenario analysis (e.g., extreme weather, global price volatility, exchange rates, etc.). 2 - Projects of Interest to AAFC - Undertake improvements to the model to enhance representations of agricultural markets and initiate scenario type analysis, and issues of interest. 3 - Skills required and Data Access requirements Basic statistical concepts Knowledge of statistical package such as SPSS, SAS, Troll, etc. 4 - Contact Stéphan Gagné: stephan.gagne@agr.gc.ca or 613 773 2445 Carole Gendron : carole.gendron@agr.gc.ca or (613) 773-2443 Last Updated: 2016-05-09

Overview of the Canadian Regional Agricultural Model (CRAM) June 2016 Agricultural and Environmental Policy Analysis Section Research and Analysis Directorate

Outline of the presentation Model overview What types of research questions that CRAM can help answer Examples of application Summary 2

CRAM is a regional economic model of the Canadian agriculture sector A static partial equilibrium model of the Canadian Agriculture sector Constrained structural optimization of producer and consumer surplus Integrates all sectors of primary agriculture, some processing activities and transportation. 55 crop production regions and provincial level details for livestock A domestic model with linkages to international markets Covers rail and truck transportation from primary production areas to ports and the US Land and water are the only resource constraints. Calibrates exactly to production levels observed in the Census of Agriculture. Possible to develop a future version of the model which is aligned to the estimates in the Medium Term Outlook 3

CRAM covers all major production activities in the agriculture sector Cropping All major grains and oilseeds Special crops Forage production and pasture use Livestock Red meat (beef and hogs) Supply management (dairy and poultry) Processing Biofuels Oilseed crushing Livestock slaughter (beef and pork) Dairy products While horticulture is not included, potatoes are covered. 4

Risk version Recent additions/updates to CRAM Variance co-variance matrices were added to CRAM, which allows for the impact of risk to be explicitly modelled. Both price and yield uncertainties are included. Information is based on farm level data for most provinces. Red meat processing More realistic beef and pork cuts Industry cost structure, breakdown of processing cost for west/east, hog/beef Provincial commodity balance (update per cut) Product flows Domestic demand (east and west with new cuts) and export demand were created in the demand table. 5

Key features of CRAM Provides a very detailed snapshot of before and after a shock is provided to the model. A detailed regional breakdown of agricultural production allows for distributional impacts to be examined (i.e., interprovincial trade). Offers considerable flexibility for modeling value chains specific to Canadian agriculture. Linked to other models to provide additional capabilities: Crop growth and climate models Trade models such as AGLINK Agri-Environmental Indicator models Contributes to analysing a wide variety of topics, such as : Impacts of changes in the grain handling and transportation system on agricultural production Impacts of changes in agricultural production on natural resource use and environmental performance Impacts of BRM programs on agricultural production decisions The impact of a change in domestic policy on Canadian food processing and ripple effects back to farms 6

Technical aspects for collaborative projects CRAM is written in General Algebraic Modeling System (GAMS). The essential knowledge and skills required to use CRAM include: Knowledge of microeconomics theory, preferably in optimization theory; Familiarity with GAMS. CRAM documentation and learning materials CRAM documentation provides a detailed description of the model structure and theoretical framework A 101 learning version written in GAMS is available. 7

Application example: Canadian Wheat Board CRAM was used to assess the impacts of removing the CWB single desk selling authority. An Informa Economics study found that: Prices in export markets for board grains were lower than competitors. Logistical costs for board grains were significantly higher than for non-board crops Imposing these findings on CRAM showed that: Profits for crop producers increase by $850M, mostly from higher prices and lower marketing costs. A significant portion of this is capitalized into land values, with $330M flowing back to land owners who have rented their land out Areas of board grains increase relative to those of non-board grains and exports of board grains increase There is a negative impact on livestock producers who see increases in feed costs. Commodity Difference in crop area ('000 hectare) Percentage change in crop area Wheat 559 8% Durum 388 17% Barley (Feed) 89 7% Barley (Malt) 259 10% Flax -77-10% Canola -396-6% Field Peas -331-19% Hay -64-4% Region Difference in gross margin ($m ) Percentage change in gross margin BC 6.7 2% AB 346.2 26% SK 440.2 19% MB 56.7 9% WEST 849.8 19% 8

Application example: Impacts of Temporary Foreign Worker Program (TFWP) changes on the red meat value chain The June 2014 changes to the TFWP: Limit access to low-wage temporary foreign workers Reduce the duration of work permits Increase costs per worker While primary agriculture continues to receive an exemption from some of the key changes, strongest impacts are felt in the red meat, fruit and vegetable, seafood processing sectors. These changes are translated to different scenarios related to changes in labour cost and slaughter capacity, and plant closure for red meat processing sector. Key results: The TFWP changes would have significant negative impacts on the red meat industry, in particular on beef exports, with largest negative impacts felt in Alberta and Ontario. In all scenarios we see a switch away from value-added high quality beef exports to exporting more live animals. Even when primary agriculture is exempted from most of the key changes, the impacts go beyond food processing subsectors. 9

Application example: Climate change impacts CRAM was used to assess how changes in both variances and the means of crop yields due to climate change will impact producer responses. Estimated temperature changes for Canada (Hadley model) %Change - Hadley Spring Wheat Yield (CO2 = 452ppm) 1971-2000 to 2040-2069 CAR boundaries - 1986 CRAM boundaries (in red) - 2001 4807 4806 4612 4792 4805 4804 4791 4772 4782 4781 Legend 4803 4802 4801 4742 4771 4734 4762 4761 4752 4751 4605 4606 Prairie CARs %Change - Hadley 452 (-50%) - (-25%) POTAT ALFALFA HAY OTHER CORNS CORNG SOYBEA FLDPEAS LENTILS CANOLA FLAX OATS BARMT BARFD DURUM WHEAT National HAD3 Scenario: percentage difference in area 4741 4733 4722 4721 4712 4711 4702 4701 4604 4603 4602 4601 4607 4608 4611 4609 4610 April 23, 2008 (-25%) - (-10%) (-10%) - 10% 10% - 20% 20% - 30% HAD3 - Change in profits (crops only) Cram_regions_2004.shp Negative change No change Positive change N -10-5 0 5 10 15 20 W E Climate models crop models CRAM S 10

Application example: Future GHG emission estimates for agriculture Linking CRAM to a greenhouse gas (GHG) component, AAFC developed estimates of GHG emissions from the agriculture sector for the annual Emissions Trends Report published by Environment Canada. Emissions from the crops and livestock sector Emissions from Land Use, Land Use Change and Forestry (LULUCF), which includes the soil sink The estimates have consistently shown that: There is little expected growth in the emissions from cropping and livestock activities The contribution of the soil sink to reducing agricultural emissions is declining MTO CRAM the Canadian Economic and Emissions Model for Agriculture (CEEMA) 0-2 -4-6 -8-10 -12-14 -16 Emissions from agriculture Emissions from Cropland remaining Cropland (millions tons of CO2) 2011 2020 2030 11

Application example: Economic and environmental assessment of BRM programs In 2013 CRAM was used to assess the production and environmental impacts of AgriStability and AgriInsurance using the risk component of the model. Change in land use* and number of animals, and AEIs from 2011 baseline Crop area (1,000h) Beef (1,000h) Hogs (1,000h) Greenhouse Gases Wind Erosion Water Erosion Till erosion Residual Soil N AgriStability & AgriInsurance - - - - - - - - Scenario 1: -1587.78-67.84-24.9-1.41% -0.38% -0.03% -0.27% 1.4% No AgriStability, No AgriInsurance (-3.29%) (-0.6%) (-0.09%) Scenario 2: -96.21 43.71-2.48-1.15% -0.49% -0.01% -0.08% 3.1% No AgriInsurance (with AgriStability) (-0.2%) (0.4%) (0.00%) Scenario 3: -1571.74-67.84-23.67-1.40% -0.18% -0.06% -0.26% -1.2% No AgriStability (with AgriInsurance) (-3.25%) (-0.6%) (-0.09%) *A shift from G&O to more extensive use e.g. pasture. CRAM Agri-environmental Indicators (AEIs) 12

Mapping CRAM outputs in GIS CRAM s regional outputs make it ideal to present outputs in the form of maps. This form of presentation presents a large output of results in a visually friendly way. Various data maps can be overlaid on top of CRAM outputs to give different perspectives Farm type maps Population density Watersheds Environmental indicators Roads, railways, etc. Soil type Possibility of using geoprocessing tools to perform spatial analysis (e.g. impact of increasing buffer area around waterways on production) 13

CRAM regions and Residual Soil Nitrogen linked to agricultural farm extents 14

Strengths and weaknesses of CRAM Strengths Detailed regional breakdown of agricultural production allows for distributional impacts to be examined Covers all major commodities and accurately represents observed production levels Production responses are based on economic theory Provides a detailed breakdown of land use and type as well as water The flexible nature of the model allows for new modifications to be made for answering new policy questions (e.g. the recent upgrade of red meat processing component) A powerful tool for impact analysis of any changes to the policy or market conditions Weaknesses Cannot model decisions made over time or provide information on adjustment paths Limited characterization of international markets and needs to be linked with other models for true international analysis No direct linkages with the rest of the economy, cannot look at feedback effects between agriculture and other sectors 15

Future work and development for CRAM Explore opportunities to enhance the use of the model, in particular in the areas of value chain analysis and regional analysis. Add explicit labour components to the model in response to the sector s emerging labour and skill requirements. Further the analysis of economic implications and mitigation potential under various climate change policy designs for the sector. Streamline integrated modelling framework with CRAM, crop growth and climate models for analyzing climate change impacts and adaptation.

Questions and comments? Li Xue Agricultural and Environmental Policy Analysis Research and Analysis Directorate, AAFC li.xue@agr.gc.ca

ANNEX Canadian Regional Agriculture Model 1 - Description The Canadian Regional Agriculture Model (CRAM) is a sector (i.e. partial) equilibrium static model for Canadian agriculture written in General Algebraic Modeling System (GAMS). The model is disaggregated across both commodities and space (55 crop regions and 10 livestock regions). CRAM covers all major production activities in the agriculture sector, including: Cropping including all major grains and oilseeds, special crops, forage production and pasture use Livestock including beef and hogs, dairy and poultry production Some processing activities such as biofuel, oil crushing, red meat slaughter, dairy products Potato production CRAM is a non-linear optimization model maximizing producer plus consumer surplus less transport costs. Through a calibration process, the model calibrates exactly to production levels observed in the Census of Agriculture. The model currently reflects the baseline conditions for 2011. Key features: CRAM can provide a very detailed snapshot of before and after a shock is provided to the model. It covers both land and water resources and can provide details on agri-environmental impacts of agricultural production changes. A detailed regional breakdown of agricultural production allows for distributional impacts to be examined (i.e., interprovincial trade). It offers considerable flexibility for modeling value chains specific to Canadian agriculture. 2 - Projects of Interest to AAFC The model can be used to analyse a wide variety of topics. Some examples are: Impacts of changes in the grain handling and transportation system on agricultural production Regional impacts and interprovincial trade implications of international trade agreements The impact of a change in domestic policy on Canadian food processing and ripple effects back to farms 3 - Skills Required and Model and Data Access Requirements Access to CRAM is currently limited to AAFC employees and would require the user to be in place at AAFC Ottawa. CRAM documentation and a learning version can be provided before the access to the full model is granted. The essential knowledge and skills required to use CRAM include: Knowledge of microeconomics theory and preferably in optimization theory; Familiarity with GAMS. 4 - Contact Li Xue: Li.Xue@agr.gc.ca or 613 773 0910 Mohammad Shakeri: Mohammad.Shakeri@agr.gc.ca or (613) 773-2567 18

Canadian Agricultural Dynamic Microsimulation Model (CADMS) CAES Post Conference Workshop June 24, 2016

The Unique Dual Nature of the CADMS Model has Many Benefits A longitudinal farm financial database that represents all farms in Canada with sales greater than $10K. Utilizes information from the Farm Financial Survey, Taxfiler, Census of Agriculture and program administrative tax data CAIS and AgriStability. The Farm Financial Survey is a cross-sectional data set that is one of the few sources of detailed information on demographics, assets, liabilities, capital investments and non-farm incomes. Program administrative tax data provides very detailed revenue, expense and inventory data for participants, but no information about other aspects of the farming operation. Includes historical expense and sale items as well as forecast total sales and total expenses at farm level. Historical Database The historical database can be used for a variety of tasks, such as assessing farm structure, historical performance, the effectiveness of specific programs, modelling ex-post program response, for example. The agricultural sector is diversified (various farm types, sizes, and regions), and as such, access to disaggregated information is essential for decision making (i.e., Financial information on hog farms in Manitoba). Farm Income Forecast Aggregate forecasts, while valuable, provide information on overall farm income and financial situation at the national and provincial level, but does not provide a comprehensive understanding of the various situations facing producers. The CADMS model utilizes individual producer performance and historic variability to forecast farm-level revenues, expenses, and balance sheets, for the farm-level farm income forecast. 2

Disaggregated farm level data are essential in providing a more complete picture of the sector s performance Profit Margin Cumulative distributions can be used to observe distributional differences in farm performance indicators which is not possible by looking at averages only For example, while the average net market income is -$0.15 per dollar revenue for all cattle farms, more than 65% of farms are exceeding the average profit margin 1,0 0,5 0,0-0,5-1,0-1,5 Cumulative Distribution of Profit Margin, Cattle, 2003-08 Average 0 10 20 30 40 50 60 70 80 90 100 Percentile 65% AVERAGE 3

CADMS Provides Essential Information for a Variety of Analyses CADMS is capable of carrying out an extensive range of what is analysis and what if scenarios on measures of performance and can be used to inform policy design and program development Some examples include: Ontario and Quebec Greenhouse Analysis Egg Farm Cost of Production Analysis Impact of Program Payments using Break-Even Analysis Financial Situation and Yields of Potato Farms Price Scenario Analysis (Grain price shock) Interest Rate Scenario Analysis Extreme Weather Scenario 4

Percent of Farms CADMS Provides Essential Information for a Variety of Analyses Percent of Farms Below is one example of how CADMS was used to demonstrate the effect of program payments on break-even point of operations classified by farm type and revenue class 100 90 80 70 60 50 40 30 20 10 Less than $10,000 Percentage of Farms Breaking Even by Farm Type Excluding Program Payments (Average 2003-2008) $10,000 to $99,999 $100,000 to $249,999 Revenue Class $250,000 to $499,999 $500,000 to $999,999 Poultry Dairy Grains Hogs Cattle $1,000,000 and Over 100 90 80 70 60 50 40 30 20 10 Less than $10,000 Percentage of Farms Breaking Even by Farm Type Including Program Payments (Average 2003-2008) $10,000 to $99,999 $100,000 to $249,999 Revenue Class $250,000 to $499,999 $500,000 to $999,999 $1,000,000 and Over Grains Dairy Poultry Hogs Cattle 5

CADMS Provides Essential Information for a Variety of Analyses This slide is taken from a report prepared in 2015 examining the financial situation of Canadian potato farms. 700,000 600,000 500,000 Median net operating income of potato farms in the top tercile, selected areas, 2003 to 2012 NB PEI MB + AB 50,000 40,000 Median net operating income of potato farms in the bottom tercile, selected areas, 2003 to 2012 NB PEI MB+AB 400,000 30,000 300,000 20,000 200,000 100,000 10,000 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Note: The graphs are not adjusted for farm size. -10,000 The median of top earners in Manitoba (MB) and Alberta (AB) is higher than their counterparts in the New Brunswick (NB) and Prince Edward Island (PEI), and all have been increasing since 2003. The median of the bottom earners are similar among the three regions and there is no consistent trend up or down. 6

CADMS Key Capabilities and Limitations Capabilities Limitations Longitudinal farm-level financial database back to 2003 Representative of all farms in Canada Includes expenses, revenues, on/offfarm income, balance sheet (assets/liabilities), inventory, acreage Includes geographic location by province it may be possible to identify more specific locations Demographic information is limited i.e., years of experience, age, education Incomplete / Non-Standardized Production data No Entry and Exit information 7

How Can We Collaborate? CADMS has an exceptional capacity to undertake a range of analysis and AAFC is looking for partners in academia to maximise this potential. Possible avenues of collaboration would be: Or Undertake farm-level financial analysis (i.e., break-even analysis, cost of production, etc.) to address policy relevant research questions Explore the further development/enhancement of CADMS in areas such as forecasting balance sheets, or through consideration of information not currently utilized in the forecast model such as production data i.e., acreage, quantity produced as well as market price for commodities CADMS is well suited to short and medium-term graduate student research; however, given proprietary and confidentiality considerations, it needs to be accessed from AAFC. 8

Annex Canadian Agricultural Dynamic Microsimulation Model (CADMS) 1 - Description CADMS is a longitudinal farm financial database that represents all farms in Canada constructed from the Farm Financial Survey, Agriculture Taxation Data Program and BRM Program Participant financial data. Farm Financial Survey: Demographics, Assets, Liabilities, Capital investments and Non-farm incomes Taxation Data Program: Provides detailed estimates for revenues and expenses of farms and farm and off-farm income of farm operators and farm families BRM Program Administrative Tax Data: Detailed revenue, Expense and Inventory data 2 - Projects of Interest to AAFC Farm-level Financial Analysis - Financial performance by sector; Break-even analysis; Cost of production Development of CADMS balance sheet forecasting and through information not currently utilised in the forecast model 3 - Skills required and Data Access requirements Given the confidentiality constraints in place, the database must be accessed directly from AAFC; the data cannot be shared. Collaboration could include a researcher working at AAFC, or AAFC economists applying code to data and sharing results. Skills required would include: - Knowledge of econometrics - Knowledge of statistical package SAS 4 - Contact Nathan Niu: Nathan.Niu@agr.gc.ca or 613 773 2429 Last Updated: 2016-05-18 9

Accessing and Using Farm Level Data Workshop for the Canadian Agricultural Economics Society: Data and modelling for evidence-based policy Martin S. Beaulieu June 24, 2016

Objective To provide a brief overview on micro level data available at StatCan for research and discuss how to obtain access 2 Statistics Canada Statistique Canada

Canadian Centre for Data Development and Economic Research (CDER) CDER was created to allow Statistics Canada to make better use of its business data holdings without compromising confidentiality Set up at Statistics Canada HQ in Ottawa and launched in June 2011 for federal government researchers Access to CDER extended to academic and nonfederal government researchers in October 2012 3 Statistics Canada Statistique Canada

CDER Activities Provides analysts with secure access to business micro data for research-oriented projects that serve the mandate of Statistics Canada Provides analytical consulting services and support to users of business micro data Serves as a repository for business micro-data Leads the development of new business micro data 4 Statistics Canada Statistique Canada

CDER users Government and Government Agencies Agriculture and Agri-Food Canada Atlantic Canada Opportunity Agency Bank of Canada BC Ministry of Finance Business Development Bank of Canada Citizenship and Immigration Canada Conseil Emploi Métropole Emploi Québec Environment Canada Finance Canada Foreign Affairs, Trade and Development Industry Canada Parliamentary Budget Office Public Safety Canada National Research Council Natural Resources Canada Other Institutions 5 Statistics Canada Statistique Canada Brock University CD Howe Institute Canadian Venture Capital Association Ivey Business School Kellogg School of Management Laval University Ryerson University Stern School of Business Telfer School of Management University of British Columbia University of California University of Calgary University of Guelph University of Ottawa University of Texas University of Washington

Number of Research Projects 123 projects facilitated since the launch of CDER in 2011 105 from policy community 18 from Universities and non-government 18 Innovation, Science and Economic Development Canada Bank of Canada 19 60 Global Affairs Canada 9 17 Finance Canada, Environment Canada, Agriculture and Agri-foods Canada, ESDC, PBO, BDC, NRC, and BC Finance Universities and other non-government 6 Statistics Canada Statistique Canada

Research themes Firm financing Performance of venture capital in Canada, Financing profiles of R&D performers, Trends in financing activities among employer SMEs Innovation, firm strategies and firm performance Impact of management practices on performance, Drivers of innovation, Innovation and advanced technology on profitability, Value of intellectual property rights Firm profiles Performance of immigrant-owned young firms, Canadian firm size distribution, High growth and rapidly shrinking firms, Canada-U.S. productivity gap 7 Statistics Canada Statistique Canada

Research themes International trade Global value chains and productivity, Internationalization and the survival of firms, Canadian retailers to cross-border shopping, Characteristics of Canadian trading firms Evaluation of programs and policies The effectiveness of R&D direct grants versus R&D tax credits Canada Small Business Financing program s (CSBF) economic impact study and cost benefit analysis Analysing the impact of corporate taxes on growth of SMEs 8 Statistics Canada Statistique Canada

Data available at CDER 1) Stand-alone, research-ready data already in use Examples: Survey of Innovation and Business Strategies; T2 Corporate Income Tax; T2-Longitudinal Employment Analysis Program; McKay Taskforce database 2) Linkable File Environment (LFE) Specific variables from a set of files where linkages have been done, but files are so large that extractions are made upon request 3) Developmental datasets and linkages Analytical databases containing derived variables for specific analyses (examples: National Accounts Longitudinal Micro data File; T2-Longitudinal Administrative Database; Canadian Employer-Employee Database); additions to LFE; new standalone data 9 Statistics Canada Statistique Canada

Stand-alone data bases Includes survey, administrative data, and linked databases that have already been used at CDER Supporting documentation is available No associated data development costs included in charges for use at CDER Excludes: New data linkages Inclusion of additional variables from the same or different sources. For example, adding T2 variables on to T2-LEAP. Creation of micro data base from survey data. For example, creation of micro data from Functional Foods and Natural Health Products Survey 10 Statistics Canada Statistique Canada

Linkable File Environment LFE contains linked datasets from administrative and survey sources, due to the size of the databases, data are not stored as one database Depending on project, records with the required variables are extracted from the required databases and a custom research dataset is produced 11 Statistics Canada Statistique Canada

Datasets in the LFE Business Register (BR) Longitudinal Employment Analysis Program (LEAP) Incorporated business taxation data - General Index of Financial Information (GIFI) Unincorporated business taxation data (T1) Payroll Deduction Accounts (PD7) Value of Foreign Direct Investment Canadian Direct Investment Abroad Trade in Commercial Services 12 Statistics Canada Statistique Canada

Survey Datasets in the LFE Electronic Commerce and Technology Innovation Innovation and Business Strategy Advanced Technology Commercialization of Innovation Intellectual Property Management Financing and Growth of Small and Medium Enterprises Digital Technology and Internet Use 13 Statistics Canada Statistique Canada

Developmental datasets Includes: new linkages, creation of micro data from survey, micro data bases in progress Limited and incomplete documentation available Examples of data bases in progress: National Accounts Longitudinal Micro data File Canadian Employer-Employee Dynamics Database Surface Transportation File Longitudinal Census of Agriculture (1986-2011) National Pollutant Release Inventory Linkage to Annual Survey of Manufactures 14 Statistics Canada Statistique Canada

Canadian Employee-Employer Dynamics Database (CEEDD) Matched employer-employee database, 1983 to 2012 More variables, especially at the firm-level, are available after 2001 Covers the universe of individual tax filers, unincorporated businesses, and corporations in Canada. Links across various administrative tax files: T1 individual tax, T1 Family File, Immigrant Landing File, T4 employment remuneration, T1 unincorporated business, Record of Employment, temporary residents file, firm performance data from the National Accounts Longitudinal Micro data File 15 Statistics Canada Statistique Canada

CEEDD: Overview and Use Contains information for 3 main fields: Employees (demographic, immigrant status, employment income, etc.) Firms (business type, employment, payroll, revenue, expense, profit, workforce, etc.) Business owners (demographic, ownership type, ownership share, income from business) Possible Uses Self-employment and business ownership Interaction between workers and firms Immigrant business ownership Immigrants Initial Firm Allocation 16 Statistics Canada Statistique Canada

Surface Transportation File (STF) Motivation Transportation costs matter more and more as formal barriers to trade have fallen (Hummels 2007) Affect the size of markets available to firms, their locations decisions, and their size Principles guiding the construction of the STF Comprehensive. Includes the majority of goods movement in Canada and between Canada and the U.S. by value Flexible. Inter-regional movement of goods on logistics and trade basis Consistent. Identifiers (i.e., geography and commodity) are consistent through time and across modes 17 Statistics Canada Statistique Canada

Characteristics of the STF Modes: trucking and rail Period: 2002 to 2012 Shipments characterised by tonnage, value, revenue, (network) distance and commodity value of shipments = value/tonne x tonnes Transportation costs (revenue to carriers) measured on a level and ad valorem basis Geographically consistent and detailed origins and destinations Origins/destinations coded to the 2006 census geography Consolidated Census Subdivisions (CCSDs) Benchmarked to known provincial trade totals from the input-output accounts 18 Statistics Canada Statistique Canada

Future STF developments Coverage: Own account carriers to be added to the Transportation Commodity Origin and Destination Add other modes (Marine, pipeline, and air) Identifying shippers and receivers Possible uses Economic integration across sub-provincial regions Estimate the market potential (size) available to firms Firm trade networks Transportation demand modelling (e.g., how trade shocks are transmitted through the transport system?) 19 Statistics Canada Statistique Canada

Longitudinal Census of Agriculture (L-CEAG), 1986-2011 Motivation: Significant structural changes over 20+ years period in Canadian farming. Assist in better understanding the factors driving these changes A longitudinal file linking the 5-year censuses extending from 1986 to 2011 was constructed to examine farm dynamics in Canada. 20 Statistics Canada Statistique Canada

L-CEAG : Overview and Use Over 1.5 million records and 19 variables, including farm operator characteristics (e.g., age), farm type (e.g., NAICS), size (e.g., acres owned), economic variables (e.g., total gross farm receipts, wages and salaries), inputs (e.g., use of herbicides), technology (e.g., use of irrigation), and products (e.g., canola). Allows for analyses on farm dynamics over time: Farm entry and exit patterns through time Characteristics of entering, exiting, and continuing farms and how do they differ Profile of entering and exiting farms over time. Has this changed? Main factors leading to farm exit 21 Statistics Canada Statistique Canada

National Pollutant Release Inventory Linkage to the Annual Survey of Manufacturing National Pollutant Release Inventory Canada s legislated, publicly accessible inventory of pollutant releases (air, water and land), disposals and transfers for recycling Annual Survey of Manufacturing and Logging Variables available include: revenue, employment, salaries, cost of materials, cost of energy, commodity inputs & outputs Linkage for 2000-2012 period performed for Environment Canada Possible uses: Relationship between productivity and pollutant reduction Addition of data to study labour and health impacts 22 Statistics Canada Statistique Canada

Longitudinal Administrative Database Owner s of Canadian-controlled private corporations, business returns (T2), employment earnings from own business (T4), dividends (T5) Linkage of the Longitudinal Administrative Databank to specific variables from T2, T4 and T5 to support studies on income distributions and the income of families, including returns to business ownership Longitudinal Administrative Databank 20% sample of T1 Family File and Longitudinal Immigration Data Base, 1982 to 2012 23 Statistics Canada Statistique Canada

CDER Future directions Development of data tools to facilitate access to business micro data outside of headquarters Synthetic data, and disclosure limited data Continued development of core databases complete, longitudinal databases Improved documentation - record layouts, data description and user guides for stand-alone databases and some developmental databases Revamped website with more information on data sets available and documentation available Strengthening and automating confidentiality checking User driven data development 24 Statistics Canada Statistique Canada

Access for research purposes Remote access (not with sensitive business and taxation data) Submit research proposal to StatCan Staff Shared cost students-recruits Short analytical paper Joint research work Students-AAFC-StatCan Short analytical paper Submit research proposal to AAFC CDER 25 Statistics Canada Statistique Canada

Questions/Contacts Agriculture and Agri-Food Canada Katrin Nagelsmithz need to update Analysis and Outreach Martin S. Beaulieu martin-s.beaulieu@canada.ca Canadian Centre for Data Development and Economic Research CDER@statcan.gc.ca 26 Statistics Canada Statistique Canada

Developing The Next Federal-Provincial-Territorial Agricultural Policy Framework Update on timelines and engagement

Planning for the next agriculture policy framework is underway Developing a multilateral framework includes: Discussions on policy and program directions Multilateral negotiations between all FPT governments Negotiation of bilateral agreements with each PT for cost-shared strategic initiatives Development of a successor agreement as early as possible would improve results provides more certainty for program recipients when preparing applications 2

Key priorities from previous frameworks remain highly relevant for the long-term success of the sector Innovation - improving the sector s innovation capacity and knowledge transfer Market Access and Development - opening new markets and realizing their full potential Risk Management helping producers manage risks due to disasters and severe losses 3

In an increasingly connected world, the sector is continually exposed to broader economic and social challenges, both in Canada and abroad Food Processing - issues around productivity improvements, broader labour issues and manufacturing growth strategies, etc. Environment and Climate Change - protecting natural resources, while contributing to the sector s long-term competitiveness Social license - defining the role of government vis-à-vis consumer trust in food and the sector 4

Looking ahead, several key milestones define the path to the next framework FPT Ministerial Statement (mid-2016) Multilateral Agreement (signed mid- 2017) Launch of Next Policy Framework (April 1, 2018) FPT Working Groups FPT Stakeholder Engagement GF2 Annual Planning Meetings FPT Policy Analysis Multilateral Negotiations Bilateral Negotiations Towards Cost-Shared Strategic Initiatives Implementation BRM Mid-Term Review Multilateral Work Towards Renewed BRM Suite Agreement BRM Suite Implementation Late 2015 Early 2016 Late 2016 Early 2017 Late 2017 Spring 2018 April 2018 Major milestones: Ministerial policy statement (July 2016) Multilateral Framework Agreement (July 2017 or earlier) Bilateral agreements (March 2018 or earlier) 5

Stakeholder engagement will be important throughout development of the next framework Current phase of engagement (Pre-FPT Ministerial Policy Statement) Direction check with industry and stakeholders on experience with GF2 programming Next phase of engagement (Post-FPT Ministerial Policy Statement) More thorough engagement on key priorities outlined by FPT Ministers (risk management, innovation, etc.) and focusing on implications for future program direction Widening the participation through online channels, including social media promotion and engagement We will be engaging through existing channels and expanding into new areas 6

Early industry feedback is already pointing to opportunities for the next policy framework Ensuring continuity between frameworks Continuity of programming would provide funding stability Improving communications Good feedback and understanding of what GF2 includes Some lack of clarity of role of different governments Policy Priorities Multi-provincial or regional initiatives could be helpful Clearer priorities assist with decision making Program delivery Slow or unclear approval processes are a challenge Smoother transition and more continuity between frameworks would improve program impact and uptake 7

Now we d like to hear from you What is working well under GF2? Can you share some of your success stories? If you could make changes to GF2, what would they be? How would your business be impacted? What s new that needs to be reflected in the next policy framework? 8

Coordinated Agriculture Policy Research Initiatives WORKSHOP: DATA AND MODELLING FOR EVIDENCE-BASED POLICY A joint Agriculture and Agri-Food Canada and Statistics Canada Workshop 10-XXX-dp 1

Purpose To launch an initiative to improve collaborative research efforts with the academic community: Coordinated Agricultural Policy Research Initiatives (CAPRI) The overall objective of the initiative is to contribute to the development of independent evidence-based policy recommendations on issues affecting the Canadian agriculture and agri-food sector. 10-XXX-dp 2

Background Through past agricultural policy frameworks, AAFC supported programs aimed at creating strong linkages between academics across Canada and AAFC on agricultural policy research, in order to better integrate research from university experts and policy perspectives. APRN: 2004-2007 ERCA : 2009-2013 Development of the next policy framework is underway and there is a need to renew collaborative research efforts. While there are plans to propose a new Network-based program under the next policy framework in 2018, there is an immediate need to improve collaboration, enhance training opportunities and job possibilities for students. 10-XXX-dp 3

What is CAPRI? CAPRI is a program designed to increase collaboration between AAFC and the academic community by sponsoring projects that would be developed in collaboration with university professors and students as well AAFC staff The overall objective of the program is to improve collaboration with the academic community and enhance evidence-based policy development for the agriculture and agri-food sector in Canada 10-XXX-dp 4

Advantages for AAFC and Academic community AAFC and the academic community to be more in sync with respect to policy questions of immediate interest Training and developing of high qualified personnel for policy related jobs Students would work with AAFC personnel to develop policy briefs for AAFC audience Opportunity to place student with an organization where they could find future employment Increase AAFC policy capacity by fully exploiting available resources Quick and Easy to implement and to manage A contract between AAFC and researcher will be put in place for each AAFC-supported policy research. 10-XXX-dp 5

How CAPRI works? AAFC will make available resources including: data and models as well as departmental experts, to the academic community, in order to undertake short-term policy relevant research. Data and models would be made accessible on a project-by-project basis, data and models which may not otherwise be available outside of AAFC. Ideally, policy research would be undertaken as part of graduate thesis or major research project; but other collaborative projects would also be considered. 10-XXX-dp 6

Application Process - CAPRI Although applications are open throughout the year, AAFC has capacity to support limited number of projects per year Submission of project proposal Project proponent communicates with the appropriate AAFC contact for data or model to be used The objective of the early conversation with AAFC expert is to ensure a better understanding of the data/or models to be used as well as to ensure resource can actually help meet the of the objectives of the research proposal Discuss how the data could be accessed. There may be data access restrictions for non AAFC employees. Finalize and submit the project proposal to AAFC At least 3 months prior to the project commencing ( to allow for proper security and IT clearance) Contract Among other things, the contract establishes timelines for deliverable : A policy brief or a policy paper based on the analysis undertaken and a presentation to AAFC If necessary a small amount of funding per project may be provided and could be paid out into two installments; ( at the beginning of the project and the remaining once the project is completed) If required, site visits in Ottawa to interact with AAFC experts and to access required tools 10-XXX-dp 7