Optimal Fertilizer Application Decision Under Crop Price and Weather Uncertainty, Haldimand—Norfolk District, Ontario

The main goal of this research is to model the farmers’ optimal agricultural fertilization behaviour under two uncertainties: 1) crop price uncertainty and 2) weather uncertainty. The weather uncertainty takes into consideration two factors: precipitation and temperature. Understanding the factors that influence farmers’ decisions on fertilizer application could help to update the recommended fertilizer application rate and support the design of best management practices in fertilizer application. This understanding of farmers' behaviour under uncertainty, can then inform nutrient control strategies, aimed at reducing nutrient inputs to the Great Lakes. This data set is collected to support the project titled “Lake Futures: Enhancing Adaptive Capacity and Resilience of Lakes and Their Watersheds: Work Package 4". Lake Futures is a Pillar 3 project under the Global Water Futures Program funded by Canada First Research Excellence Fund.

Daily precipitation and temperature data for the London International Airport weather station site will be downloaded from the Environment Canada website. London's climate data will be used as it is the closest location with long term data near the study area. Climate data will be used to calculate growing-degree days and to model weather regimes. Corn yield data will be extracted from existing literature, in the Haldimand—Norfolk electoral district, to help develop a yield response model. The parameterisation for the crop growth model will be collected from available literature. Then, a statistical model will be built to represent the farmers' fertilizer application problem, and a numerical relationship will be derived from using the model.

Insley, M., & Yang, X. (2019). Optimal Fertilizer Application Decision Under Crop Price and Weather Uncertainty, Haldimand—Norfolk District, Ontario. Waterloo, Canada: Canadian Cryospheric Information Network (CCIN). (Unpublished Data).

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