1. Linking Agri-Environmental Water Quality Indicators (AEWQIs) to Policy: the Canadian Experience Trilateral Cooperation to Promote the Protection of Water Quality through Sustainable Agriculture Banff, Alberta October 7 – 10, 2003 Allan J. Cessna and Bruce Junkins Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada

2. Why Risk Indicators? Monitoring for a range of contaminants (especially pesticides) for the whole country is very expensive Wanted contaminant information that was specific to agriculture

3. Why Risk Indicators? (continued) Can link agri-environmental indicators to economic models which allows us to build scenarios on policy and economic outcomes Forward looking - Can assess impacts of policies before they are put in place Often there is a lag of several years between when a policy is implemented and the effects of the policy can be measured

4. Why Risk Indicators? (continued) Can investigate adoption rates (eg., beneficial management practices) per dollars spent Get more information than just a trend Although monitoring information is not available on a national basis, monitoring information is available on regional basis to permit validation of the models

5. History of AEWQIs in Canada In 1993, under the Agri-Environmental Indicator Project, work was initiated on 2 AEWQIs: risk of water contamination by N and by risk of water contamination P In 2001, under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP), work was continued on the N and P indicators, and development of a pesticides indicator and a pathogens indicator was initiated

6. Indicators of Risk of Water Contamination The main data source for inputs to the indicators is the Census of Agriculture which covers all agricultural regions of Canada (Available at 5-yr intervals). All four water quality indicators will be calculated at the Soil Landscapes of Canada polygon level (1: 1 000 000). Nationally, there are 3,267 agricultural polygons for which data are reported in the Census of Agriculture

7. Indicator of Risk of Water Contamination by Nitrogen (IROWC-N)

8. Crop, Animal, Soil, Weather, N fertilizer Inputs: Agricultural Production System, Input: Policy Scenarios Fig. 1. Data flow of integrated modelling Canadian Agriculture Nitrogen Budget CANB Model Canadian Regional Agricultural Model (CRAM) Data handling tools EasyGrapherScalingUp Canadian Soil Information System (CanSIS) ArcViewMaps Outputs: RSN IROWC-N Components

9. 350074 Map 1. Residual Soil Nitrogen (RSN) at the SLC scale (2008 business as usual scenario)

10. Fig. 2. RSN and IROWCN at the provincial scale

11. Fig. 3. Time trend of RSN at the national scale

12. Indicator of Risk of Water Contamination by Phosphorus (IROWC-P)

13. Some Characteristics of IROWC-P IROWC-P was adapted and combined with aspects of IROWC-N and PI (Phosphorus Index) (Lemunyon and Gilbert, 1993). The 3 principal components of IROWC-P are:  P transport,  P status  Annual P balance

14. Suggested Improvements of IROWC-P (2003-2008) Because sufficient soil P status data are available only for the province of Quebec, IROWC-P has thus far been calculated only for Quebec. The goal now is to improve the indicator by:  incorporating measured P sorption capacity values for all dominant soil series and extrapolated values for all sub-dominant soil series on a national basis  incorporating an hydrologic component

15. Indicators of Risk of Water Contamination by Pesticides (IROWC-Pest) and Pathogens (IROWC-Path)

16. Approaches to Developing IROWC-Pest and IROWC-Path Initial emphasis will be to develop indicators for surface water Existing models, that estimate pesticide and pathogen movement in water and pesticide movement in air will be used where possible The feasibility of using an hydrology component common not only to IROWC-Pest and IROWC- Path but also to IROWC-N and IROWC-P will be explored

17. Linking Agri-Environmental Indicators to Policy Models

18. Multidisciplinary approach to develop and apply integrated economic/environmental models to bring resource science to the policy table to analyze how:  Economic policies and market signals affect the environment  Environmental regulations and international agreements affect economic performance  New technologies impact both economic and environmental performance Objective

19. Promote Sustainable and Profitable Resource Use Soil Quality erosion soil carbon nitrogen salinization compaction Soil Quality erosion soil carbon nitrogen salinization compaction Water Quality nitrogen phosphorous pesticides pathogens Water Quality nitrogen phosphorous pesticides pathogens Air Quality greenhouse gases (CO 2, N 2 O, CH 4 ) odours particulates Air Quality greenhouse gases (CO 2, N 2 O, CH 4 ) odours particulates Biodiversity habitat use species at risk Biodiversity habitat use species at risk Nutrient Balance carbon cycle nitrogen cycle Nutrient Balance carbon cycle nitrogen cycle Farm Resource Management land use crops livestock Farm Resource Management land use crops livestock Farm Environmental Planning: Managing land and water, nutrients, and pests Farm Environmental Planning: Managing land and water, nutrients, and pests

20. Canadian Regional Agricultural Model (CRAM)  Economic model used as policy tool at AAFC for many years  Static, non-linear optimization model  Integrates all sectors of primary agriculture on regional basis CRAM generates a significant amount of information  Land use change for major activities (cropland, hayland, tame pasture, native pasture)  Area of major crops (cereals, oilseeds, specialty crops)  Summerfallow and tillage practices (West)  Livestock numbers (beef, pork, dairy, poultry)  Economic impact on both producers and consumers Changing activity levels in CRAM in terms of land use, land use management and animal production will affect environmental outcomes Changing activity levels in CRAM in terms of land use, land use management and animal production will affect environmental outcomes Policy Model – CRAM

21. Policy (economic) Model Policy (economic) Model Economic Impacts Resource Allocations -cropping patterns -tillage practices -livestock numbers Resource Allocations -cropping patterns -tillage practices -livestock numbers Policy Decision Other Economic/Environmental Considerations feedback Environmental Impacts Agri-Environmental Indicator (AEI) Models Agri-Environmental Indicator (AEI) Models Economic Parameters Technology Farm Management Practices Physical Resource Base Economic Parameters Technology Farm Management Practices Physical Resource Base Policy Scenario Scientific Knowledge Environmental Data -F/P/T gov’t -Industry -Academics Scientific Knowledge Environmental Data -F/P/T gov’t -Industry -Academics Integrated Economic/Environmental Analysis

22. F/P/T commitment to set specific environmental outcome targets Use existing economic and AEI models to quantify expected outcomes Select and analyze potential farm actions for improving environmental performance Provide scientifically based quantitative analysis to assist process of establishing provincial environmental targets under APF Application: Agricultural Policy Framework (APF) - Provincial Environmental Targets

23. Risk of soil erosion from water (crop, tillage,soil) Risk of soil erosion from wind (Prairies) (crop, tillage, summerfallow, soil) Residual soil nitrogen (crop, N fertilizer, manure) Risk of water contamination from nitrogen (East) (residual N, precipitation, transpiration) Soil Carbon (tillage, crop, soil) Greenhouse gases (Sinks and emission reductions) (CO 2, CH 4, N 2 O) Biodiversity in terms of wildlife habitat (land use) Suite of AEIs for APF Analysis (Key Drivers)

24. Soil Management  Increased use of conservation tillage (no-till)  Decreased use of summerfallow  Increased use of forage in rotations  Conversion of marginal land to permanent cover Pasture Management  Increased use of complimentary and rotational grazing Nutrient Management  Better management of matching N applied to crop requirements Livestock Management  Improve management of protein in diets Shelterbelts and Plantation Forestry  Increased use of forestry on marginal agricultural land Scenarios Selected for APF Analysis (BMPs – Beneficial Management Practices)

25. RSN in Response to Policy Scenarios

26. Results of APF Analysis National Summary of the Percentage Change in AEIs from 2008 BAU for Low, Medium and High Adoption Rates

27. Enhancements to CRAM  Add water component  Update data and structure for livestock and crops  Improve regional coverage (Ontario, Quebec, B.C.)  Improve cost structure Address data gaps  Data Warehouse  Farm Environmental Management Survey Linkages to AEI models  Refinement of existing AEIs  Need for additional AEIs  AEIs must be responsive to BMPs  Feedback linkages between economic and environmental components Future Directions : Model Development

28. Future Directions : Spatial Issues LUAM CRAM crop production regions Land Use Allocation Model

29. Climate Change  Domestic Emissions Trading/Offset system  Mitigation  Impacts and adaptation  Environmental Co-benefits Environmental Assessments  World Trade Organization negotiations  Agriculture programs and policies APF environmental outcome targets Medium Term Policy Baseline Future Directions : Policy Analysis

30. AEIs Economics and Markets Science Policy Scenario Integrated Models Economic Outcomes Environmental Outcomes (air, soil, water, biodiversity) Environmental Outcomes (air, soil, water, biodiversity) Economic Valuation Trade-Off Analysis Input to Policy Evaluation and Development Process National Agri-Environmental Health Analysis and Reporting Program (NAHARP) - Linking Science to Policy -