Spatial Modeling of Soil Heterogeneities and their Impacts on Soil-Phosphorus Losses in a Quebec Watershed By Alaba Boluwade Department of Bioresource Engineering, McGill University, QC, Canada Supervisor: Prof. C. A. Madramootoo Dean, Faculty of Agric. And Environmental Science, McGill University, Quebec, Canada.

Presentation Outline Brief Introduction Study Area Hydrologic Modeling Problem Statement Research Objectives Research Methodology Field Sampling Acknowledgement 2

Brief Introduction 3 (adapted from Pierzynski et al., 1994)

Study Area 4

Pike River Upstream 386km 2 Forest = 54% Grassland = 20% Walbridge Upstream and Downstream Pike River Downstream 563km 2 Forest 44%, Grassland 20% and Corn 16% Castor Watershed: 3 Subbasins Grassland 28%, Corn 44%, 12 km 2, Cereals 20% Source: Beaudin et al

Hydrologic Modeling: Hydrologic Cycle 6 Source: hydrosphere/

Hydrologic Modeling This is the mathematical representation of the flow of water and its constituents on the land surface or subsurface environment There is a tight relationship between GIS and hydrologic models

Hydrologic Modeling:SWAT 8 SWAT: Soil and Water Assessment Tool Modeling program developed by the USDA/ARS

Spatial Input for Castor Watershed. 9

Problem Statement The major challenge with this distributed model and others is that there is no clear and specific procedures on what level of details are needed (in this spatial input) for representing the spatial heterogeneities of the soil properties. 10

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Research Objectives: Overall Objective: Quantification and evaluation of the impacts of actual-field observed spatial soil heterogeneities and dynamics on prediction of phosphorus loss in a watershed in Southern Quebec, Canada using a 2-dimensional, physically based model(SWAT). 12

Specific Objectives: Develop a stochastic Markov Chain Monte Carlo (MCMC) method to represent the spatial variation of soil properties for a physically based model Geospatial quantification of the soil-test phosphorus using various Geostatistical techniques 13

Research Methodology 14

Defining a sampling strategy Sampling strategies: greater sampling density = greater accuracy of the data Sampling density vs. accuracy gains: storage and processing power with spatio-temporal variation 15

Stratified Sampling in MATLAB using “fmincon” function Minimization function for cost and sample size Based on proportions of the strata Weights for the strata = , , Cost per sample is 4.5 dollars Sample Size= =480 16

17 Sampling Strategy

Sampled Soil Properties: Organic Carbon Bulk Density Clay Content (Particle size distribution) Soil pH Water Soluble Phosphorus Soil Test Phosphorus Hydraulic Conductivity 18

Finally, the research should recommend Nutrient use efficiency Net returns to farmers Reduction in overall nutrient loss from agricultural field Classification of management zones in form of polygon Adaptive agricultural management practices Optimization of the soil Phosphorus-Index for land-use and ecosystem management 19

Acknowledgement 20 Prof. Chandra Madramootoo (Supervisor) Dr. Aubert Michaud (IRDA, Quebec) Isabelle Beaudin (IRDA, Quebec)

Thank you and Questions 21