Dairy Industry Cost Structure, Productivity and Economic Efficiency: Enhancing Farm Level Long-term Competitiveness and Sustainability
Remaining competitive in the production of milk in Ontario is important because of the decline in the demand for some local dairy products due to 1) demographic changes and, 2) gradual increase in imports for milk components or dairy products imports. The recent TPP agreement that provides access to 3.25 per cent of Canada’s annual dairy production to its TPP partners (and CETA agreement) that underscore the long-term challenges facing Ontario’s dairy industry. The long-term competitiveness of the industry depends on the ability to have strong financial and economic performance, and these performance metrics are crucial in the development of strong public policies to support the growth of the industry. The purpose of this project is: 1) to estimate the hedonic cost(profit) and input distance functions for Ontario dairy farms; and 2) to examine the inter-farm cost/productivity spreads. The information generated in this study will be used in several policy simulations: e.g., from producer association perspective, it will assist in establishing competitive pricing of locally produced milk components (e.g., protein, fat) and in establishing the processor’s margin. The study provides insights for governments that is interested in long-term competitiveness and sustainability of the dairy industry.
Economic Analysis of Increasing Feed Efficiency and Reducing Greenhouse Gas (GHG) Emissions through Genomics: The Case of Ontario’s Dairy Industry
The purpose of this study is to identify and measure the social costs and benefits of selecting animals for increased feed efficiency and reduced greenhouse gas (GHG) emissions in the dairy industry. GHG emissions from the dairy sector are highly correlated with the quantity of feed consumed by herds. In Ontario, feed often represents the largest variable cost for these producers. Genomic selection enables producers to select for more feed efficient livestock, which produce lower GHG emissions and reduce feed costs. The objectives of the project are: 1) to estimate the likelihood of adopting genomics for feed efficiency at the farm level, 2) to identify factors that influence the adoption decisions given higher feed efficiency and lower GHG emissions and, 3) to examine the role the Ontario government may play (e.g., subsidy, education/extension, etc.) to encourage uptake of genomic selection for feed efficiency and reduced GHG emissions. With recent policy initiatives from the federal government on the topic of pricing emissions, it may be beneficial for dairy producers to proactively adopt technology that both increases their profits while reducing their environmental footprint.
Productive Efficiency and Competitiveness of Ontario Farms
This project explores the competitiveness of Ontario dairy farms. The long-term competitiveness in dairy production hinges on the ability of farms to keep production costs low through improvement in productivity. The purpose of this study is to estimate the productive efficiency of Ontario dairy farms, and to identify and assess key productivity or efficiency-enhancing factors. By doing so, we provide insights into performance bottlenecks that require further investigation, determine the overall scope for improvement, and provide a focal point for farm business management decisions and performance review. The use of farm level Canadian and the U.S. dairy data allowed us to compare the competitiveness of Ontario dairy farms with New York dairy farms, and to examine the effect of differences in dairy regulations and management practices in Canada and the U.S. on the competitiveness of individual farms. The project enhances the analytical capability within OMAFRA. The study provides OMAFRA staff and industry groups with a comprehensive performance benchmarking framework on which to base their recommendations (e.g., Towards Improved Profits) towards increased farm productivity, competitiveness or profitability. The framework used in the current study is applicable to other industries along the value chain.