1. Socio-economic work plan for AGGP II
Rene Roy and Mfon Essien
Natural Resource Science and Bioresource Engineering
McGill University

2. Outline Socio-economic Objectives (AGGP II - the big picture)
Technology transfer activities
Socio-economic studies
Methodology
Data needs
Expected timeline: Work plan 2017/2018

3. Socio-Economic Objective – AGGP II
Partial budgeting of Beneficial management practices (BMPs) of water management systems related GHG mitigation and adaptation practices.
Identify and estimate the potential co-benefits of the BMPs
Assessment of Socio-economic factors influencing water demand and climate change.
Development of a regional GHG emission model
Encourage implementation of BMPs by farmers (technology transfer)
To inform decision makers of policy instruments or mechanism that would encourage adoption of BMPs

4. Technology transfer
To help the agronomists disseminating the information:
We will collaborate with the Centre de Référence en Agriculture et Agroalimentaire du Québec (CRAAQ) to publish partial budget of the BMPs (Availability by the end of 2018)
We will present workshop to professional groups and extension personel involved in farm consulting (Réseau Agri-conseil, Ordre des agronomes). (First seminar before the end of 2017)
We will collaborate with a progressist regional consultants to accelerate the dissemination of the BMP information to producers and better understand the BMP adoption (The collaboration will be prepared in 2017 and undertaken in )

5. Technology transfer To help producers adopting BMPs
We will approach Farm Management Canada to organize webinars about BMPs for Canadian producers (Webinar in Winter 2018)
We will hold workshops in collaboration with the Producteurs de Grain du Québec. (annual meetings: 2017, 2018, 2019, 2020)
We will publish documents on specialized websites such as Agri-Réseau and AAFC about how to implement the BMPs (One documents issued every year: 2017, 2018, 2019, 2020)
We will hold focus groups with producers and survey them to get information about the factors influencing the adoption of BMPs. (The coordination of this activity will be in conjunction with the researchers involved, and will be completed by the end of 2020)
Christian Overbeek, President (PGQ)

6. Social Benefit of Beneficial Management Practice
Total Economic Value
Direct use
Indirect use
Extractive use
Non-extractive use
Environmental services
Option value
Non-use value
Existence value
Bequest value
- Tap water
- Fishing
- Agriculture
- Recreative activities
- Landscape amenities
Greenhouse Gas control
Nutrient cycling
Water cycling
The possibility of using the good in the future
The intrinsic value of the good
The value that next generation could benefit from the use of the good

7. Net benefit of BMPs implementation
Implementing Best Water Management Practices in Agriculture
Private Benefit-cost analysis
Hydric impact
Economic Impact
Water Quality Index
Hydric pollutant emission
Value of goods and services from hydric services
BMPs Private Cost
Social Benefits
Net benefit of BMPs implementation =
Benefit transfer function
Atmospheric impact
Social Benefit-cost analysis
Value of goods and services from aesthetic and biodiversity services -
Other environmental
impact (Aesthetic, biodiversity, etc.)
Value of goods and services from atmospheric services

8. Why the need for research?
To efficiently undertake an integrated Bio-economic modelling of water management BMPs geared towards GHG mitigation and adaptation, that are adoptable by farmers and effectively communicate these BMPs to producers.

9. Objectives
To assess farm level profitability using a whole farm analysis and environmental evaluation from adoption of beneficial water management practices using LCA
Using a Decision Support System for Agricultural Technology (DSSAT) model to assess BMP potential in terms of profitability and impact on environment over time by calculating co-benefits from reduced GHG emissions, carbon sequestration, improved yield and water quality
Assess farmers’ perception and determinants of adoption of BMPs
Using a Multi-criteria analysis to conduct rigorous policy appraisal from the policy options given by the economic outcomes

10. Methodology Whole Farm Budget analysis Bio-Economic modelling
Life cycle Analysis (LCA): to estimate farm-level environmental and economic impact of installing water management system
Decision Support System for Agrotechnology Transfer (DSSAT): This modelling approach connects the bio-physical and socio-economic variables within a unified and coherent framework to produce a global assessment of crop production under climate change.
Regression or qualitative analysis: to assess the factors affecting farmers’ adoption of BMPs (Specifically, water management practices)

11. Methodology
Multi-Criteria Analysis (MCA): develop a decision making framework, which enables rigorous policy appraisals based on economic outcome of the BMPs towards mitigation of GHG emissions in a cost effective and sustainable manner, both at the farm and regional level. It will take three larger criteria in consideration i.e. economic, environmental and social with a ranking of the best alternative based on a weighting system. A sensitivity analysis would be employed to test the robustness of results.

12. Data needs Site Physical characteristics and information: Location
Farm size
Soil type
Topography
Seed variety
Planting date and Harvest date.
Annual tillage practices 2017 – 2020
Monthly precipitation and temperature data each year (2017 – 2020)
Historical and forecasted precipitation and temperature data (Environment Canada)
Lime application (kg/hectare) 2017 – 2020
Type of herbicide used and application rate (Kg/hectare)

13. Data needs
Years
Water system (AGGP study)
Water system BMP (AGGP Study)
Fertilizer Data
Inorganic
Organic
Year 1:2017
Year 2:2018
Year 3:2019
Year 4:2020
For NPV analysis
Or for inorganic or mineral soils.
Fertilizer data (kilogram/hectare): type, timing and quantity
E.g. NPK - Inorganic fertilizer, Cattle manure – Organic fertilizer

14. Data needs Yield Data (Tonnes/hectare):
Years
Preexisting condition or status quo e.g. if not FD. Data from region i.e. CRAAQ
Water system
Water system BMP
Year 1:2017
Year 2:2018
Year 3:2019
Year 4:2020
Yield Data (Tonnes/hectare):
i.e. yield data from each field site for Leamington, Sherrington, Harrow and St. Emmanuel for tomatoes, onion and corn and/or soybean respectively for each crop.
E.g. the table above is for Corn production with inorganic fertilizer in St. Emmanuel, there should be a replicate for organic fertilizer
FD: Field Drainage
CDSI: Controlled drainage with Sub-irrigation
For NPV analysis
Or for inorganic or mineral soils.

15. Data needs
Years
Water system
Water system BMP
Gas data
N20
CO2 CS
N2O
Year 1:2017
Year 2:2018
Year 3:2019
Year 4:2020
GHG emissions data (e.g. N20/Hectare): N20, CO2 – i.e. atmospheric or carbon sequestration data from each Water management treatment, under different crops (in each site) and fertilizer treatment i.e. for inorganic and organic fertilizer or soil types depending on the specific site.
CS= Carbon Sequestration
Sites: QC(St Emmanuelle - Corn and Soybean, Sherrington - Onions ), ON (Leamington - Tomatoe, Harrow - Corn and Soybean), NS (Truro – Dairy production)

16. Data needs
Years
Water system
Water system BMP
Water data
Water quality
Quantity of water irrigated or applied
Year 1:2017
Year 2:2018
Year 3:2019
Year 4:2020
Water Data: Water quality assessing quantity of nitrates and phosphorus in the water.
For NPV analysis
Or for inorganic or mineral soils.

17. Expected timeline Time Activity Remarks January – December, 2017
Socio-economic data collection and building farm budget
In progress
Socio-economic model development of LCA and DSSAT
April- October, 2017
Annual Socio-economic data collection: Farm and Regional Level
June- August, 2017
Data collection to obtain information for farmers workshops
January – March, 2018
Dissertation proposal

18. Study Area

19. Thank you Questions