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Agroecological models have the potential to contribute to both the fight against climate change and a shift away from the dominant food system. In this article, I discuss the challenges that ecological farmers in Canada are facing in terms of scaling out agricultural systems that will help to reduce greenhouse gas emissions and sequester carbon. I draw on Gramscian theories to argue that alliance-building is required in order to advance a counter-hegemonic agroecology in Canada, with those alliances going beyond narrowly conceived class-based interests. I suggest that the challenges farmers are facing highlight the need for a just transition in agriculture, and that the social transformation that this would entail means that proponents of agroecology must consider the positionalities of environmentalists, scholars, farmers and farm workers, and Indigenous peoples across the country. [ABSTRACT FROM AUTHOR]

In this country report, I offer a resident-outsider's perspective on the recent history and current landscape of Canadian feminist geography. I highlight the institutional framework that showcases Canadian feminist geography: the Suzanne Mackenzie Memorial Lectures, the biennial events put on at the Canadian Association of Geographers meeting with the support of the Canadian Women and Geography Study Group/Groupe d'étude sur les femmes et la géographie (CWAG). I discuss recent community-building efforts, including the Great Lakes Feminist Geography Collective, and scholarly workshops, and point to the creative outputs that have emerged from these collective workspaces. I point to a variety of Canadian feminist geographers who have laid the groundwork for the diverse field that exists today, as well as some who are re-making the field through the use of other ontological and methodological frameworks. I conclude with a commentary on the importance of community- and alliance-building, especially in the face of challenges like structural injustice, generational transition, and even physical distance. [ABSTRACT FROM AUTHOR]

We have designed a system that predicts species richness in the mixed wood boreal forest of Canada. The system is based on a simple multivariate linear model that uses four landscape characteristics as independent variables: canopy species type, distance from the nearest ridgeline, time since the last fire and canopy stem density. The model is shown to provide statistically significant estimates of richness when using observed independent variables. We developed models for estimating the four landscape characteristics from geospatial data consisting of remotely sensed imagery and a digital elevation model. We ran the model at the stand scale and the pixel scale and found that stand scale predictions were be more accurate that pixel scale predictions. We produced a map of vegetation species richness for Prince Alberta National Park in central Saskatchewan Canada that is consistent with our expectations. We also estimated the uncertainty in the four landscape characteristic estimates and developed a methodology for propagating this uncertainty through the system to produce estimates of uncertainty in the pixel-based richness predictions. While the uncertainty is significant, the estimation and management of uncertainty in a mapping system of this type represents an innovation. [ABSTRACT FROM AUTHOR]