Canadian Prairie agriculture, in general, is expected to benefit under climate change with increasing mean temperatures projected for the immediate future. However, a number of knowledge gaps still exist. Foremost among these is the measurement of the effects of extreme climate events in a given year as well as their long-term impact on the supply of agricultural products, and also the financial situation of farms. In addition, the economic impacts of climate change on livestock operations are relatively under-studied. In particular, knowledge of the impacts on Prairie beef cattle remains more guesswork than research-based evidence. This dissertation assesses the impact of changes in the normal climate as well as the impact of climate extremes by including projected inter-annual climate variability. The economic impact of these changes on crops, beef cattle activities and the viability of farms in mixed operation settings is measured. Correspondingly, this work presents alternative adaptation measures and their likely use in managing mixed farm operations for future extreme weather events. For the analysis, two study sites are selected: (1) the Oldman River Basin of Alberta, called Pincher Creek, and (2) the Swift Current Creek Basin of Saskatchewan, called Swift Current. This study is a part of a larger project entitled “Vulnerability and Adaptation to Climate Extremes in the Americas” and the study sites are intended to represent the project catchment areas in the provinces of Alberta and Saskatchewan.
I develop what I call a MF-CCE model (Mixed Farm model for the economic impact assessment of Climate Change and Extremes). The MF-CCE is a whole farm simulation model that integrates models of beef cattle production, crop production and climate changes into farm level economic decisions. Simulations are conducted over a 30-year period in each climate scenario: the first of these is a baseline climate scenario from 1971-2000, and I also simulate future climate change impacts for the 2041-2070 era. The modelled farms produce enough crops, hay and pasture to support the beef cattle feed demand. Pasture demand and supply are linked by specific pasture requirements and productivity. Beef herd feed grain demand and on-farm supply are linked by a linear programming optimization algorithm. Crop mix for the market is selected through the development of a multi-year linear programming problem that maximizes the present value of gross margins. Crop and hay productivity are estimated through the Food and Agriculture Organization’s (FAO’s) AquaCrop (version 3) modeling framework, while annual pasture productivity is estimated using the Forage Calculator for Native Rangeland obtained from the Saskatchewan Research Council (SRC). The AquaCrop is a water-driven crop simulation model, termed a crop water productivity (WP) model which simulates the yield response of herbaceous crops to water availability and use. The model is believed to be superior in simulating crop yield in the conditions where water is a key limiting factor in crop production (FAO, 2011).
Summarizing the results of the simulation, prairie crop production is expected to benefit under the simulated climate change scenario. Increases in crop productivity generate about 60% higher profits in the Pincher Creek site and about 57% more for the Swift Current site. Due to increases in grain and hay productivity, more area is made available to produce grain for the market. This effectively doubles the crop net return at the Pincher Creek site and triples the crop return at the Swift Current site.
A consideration of future pasture response to the climate change scenario is important in estimating climate change consequences for live beef production as well as on the economic return of a mixed farm. If the pasture productivity decreases, as assumed under the regular pasture yield scenario in the study, appropriate adaptation is necessary for the farm to benefit from future climate change. Under this scenario, beef production activities in the future are projected to gain by 50% in Pincher Creek and 40% in Swift Current compared to the baseline scenario. If pasture productivity under the future scenario increases in a manner similar to crop yield increases, existing pastureland will be enough to maintain beef herds into the future. In turn, this strategy will mitigate the cost of beef herd adaptation during climate extremes, and instead gains from beef cattle production would be 35% higher in Swift Current and 6% higher in Pincher Creek relative to gains under regular pasture yield conditions.
At the farm level, with beef cattle and crop production combined, substantial gains are projected for both of the study sites. Farm net profit is estimated to increase by more than 35% at the Pincher Creek site and more than 140% at the Swift Current site under the future scenario. Income risk will also be lower in this scenario, as highlighted by a lower coefficient of variation of net farm profit. Farm financial indicators tracked in this study – farm cash flow, family cash flow, and farm net worth – all indicate that the farm’s financial position will be much better in the future climate scenario. At the Pincher Creek site, a few problematic liquidity events are forecasted under the future climate scenario, but in light of significant improvements in other economic indicators, overall, this effect is negligible.
The appropriate choice of adaptation strategies for managing beef herds during extreme climate events plays an important role in determining the profitability of not only beef cattle activities, but also the financial position at the whole farm level. However, the choice of adaptations is contextual: the preference of adaptation strategy differs across activities, farms and period of study. For beef cattle activities, maintaining the beef herd without any compromise on herd size and implementing a regular feeding plan is preferred to other adaptation alternatives. At the whole farm level for the Pincher Creek site, culling the herd is preferred under the baseline scenario, while the purchasing feed option is preferred under the future climate scenario. At the Swift Current site, culling the herd is the preferred strategy under both scenarios.
Commodity prices and the cost of farm inputs profoundly affect the economic position of the farm under the future climate change scenario. If commodity prices and cost of production remain the same as under the baseline scenario, future farm net profit is estimated to be 50% higher for the Pincher Creek site and about 25% higher for the Swift Current site, compared to profits under projected future prices. This result implies that the pure effect of climate change could be much higher if costs and prices do not change.
Results of this dissertation indicate that average Prairie mixed farms, as represented by these study farms, remain economically viable under both the baseline and future scenarios. The results also suggest that the overall gain to these farms under a future climate change scenario would be positive. The potential severity of extreme climate events in the future, at least for the future scenario period simulated in this study, would not be significant enough to threaten the future economic viability of Prairie agriculture. However, the research also highlights the importance of policies that support farmers when they endure losses in years of extreme climate events. Further research on evaluating different Best Management Practices (BMPs) in dealing with droughts, for example, would be helpful in taking advantage of future climate change. Policy development to enhance the longer-term adaptive capacity of Prairie farmers, such as development of early warning systems for climate extremes, or the development of drought tolerant cultivars of crops and forages, would be most helpful in coping with climate extremes in the future.
| Identifer | oai:union.ndltd.org:USASK/oai:ecommons.usask.ca:10388/ETD-2016-01-2412 |
| Date | 2016 January 1900 |
| Contributors | Kulshreshtha, Suren N. |
| Source Sets | University of Saskatchewan Library |
| Language | English |
| Detected Language | English |
| Type | text, thesis |
Page generated in 0.0152 seconds