Simulating Farmer Adoption of Agricultural Best Management Practices in the Upper Medway Creek Subwatershed at Upper Thames River Watershed, Ontario

The main goal of this project is to investigate how agriculture and food production systems may change in the future, in response to risks and uncertainties, brought on by different climate stressors and socio-economic drivers. An Agent-based Model (ABM) framework was developed to serve as a proof of concept study conducted to assess the impacts of different socio-economic and environmental factors on farmers decision-making process of the adoption of Best Management Practices (BMP). The developed ABM will also help to model the dynamics of the coupled human-environmental system in affecting phosphorus transportation in agricultural watersheds using an ABM approach, coupled with a hydrological model. This data set is collected for the project titled “Agricultural Water Futures in Canada: Stressors and Solutions is a Pillar 3 project under the Global Water Futures Program funded by Canada First Research Excellence Fund.

An ABM was developed to simulate the farmer decision-making process and estimate adoption rates of different BMPs in response to different socio-economic and environmental situations. Six BMPs, including the reduced tillage, the no-till system, grassed waterways, riparian buffer strips, Water and Sediment Control Basin, and windbreaks were focused. Annual Crop Inventory data from 2011 to 2015 were obtained from the Agriculture and Agri Food Canada (AAFC), and were used as land-cover data in this study. It is a set of raster data that shows the distribution of different crop types in Canada at a spatial resolution of 30m. The soil type data was obtained from the Soil Survey Complex dataset, provided by the Agriculture Food and Rural Affairs. The digital elevation model (DEM) data was obtained from the Ministry of Natural Resources and Forestry, Southwestern Ontario Orthophotography Project (SWOOP) 2015 Digital Elevation Model. It is a set of raster graphics with a spatial resolution of 2m that represents the elevation of earth’s surface. The criteria and requirements for installing each BMP were acquired from existing publications. The results obtained from the ABM were compared with results generated from a random generator, which indicates that the model does not make random decisions. The results demonstrated that the developed ABM is robust in simulating farmers’ decision making on BMP application within the Upper Medway Creek Subwatershed. According to the sensitivity analysis, providing subsidies and improving knowledge level of BMPs have positive effects on the implementations of certain BMPs in general.

Liuyi, G. (2018). Simulating Farmer Adoption of Agricultural Best Management Practices in the Upper Medway Creek Subwatershed at Upper Thames River Watershed, Ontario. Waterloo, Canada: Canadian Cryospheric Information Network (CCIN). (http://hdl.handle.net/10012/14003).

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