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Significant increase in wintertime air temperature, especially the reduced cold extremes under climate change, might be beneficial to the winter survival of perennial crops. However, climate warming could result in less snowfall, reduced snow cover, as well as changes in climate conditions for fall hardening and winter thaws. How these changes might impact the risks of winter damages to overwintering crops, such as perennial forage crops requires a comprehensive assessment for proactively adapting to climate change in the agricultural sector, especially the beef and dairy industries. Based on the most up-to-date climate projections from a set of global climate models, we used a snow model and a suite of agroclimatic indices for perennial forage crops to assess potential changes in the risks of winter injury to perennial forage crops across Canada in the near-term (2030s), the mid-term (2050s), and the distant future (2070s). Our results show that the risk of exposure to extremely low temperatures (daily Tmin ≤ −15 °C) without snow protection is projected to decrease across Canada with improved conditions for fall hardening. However, winter thaws and rainfall are projected to increase, and this would increase the risk of winter injury due to loss of hardiness together with potential soil heaving and ice encasement. [ABSTRACT FROM AUTHOR]

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R2 = 0.76; crown closure R2 = 0.82; height R2 = 0.78) and tree species cover (e.g., Douglas fir R2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches. [ABSTRACT FROM AUTHOR]

Minimizing tillage has been promoted as an agricultural practice that may mitigate greenhouse gas emissions through carbon sequestration. However, there is some ambiguity regarding the effect of minimum tillage (MT) on emissions of other greenhouse gases, in particular soil nitrous oxide (N2O) emissions. To determine how effective MT could be in helping Canada mitigate greenhouse gas emissions, we used a meta-analysis to compare growing season N2O emissions from MT versus conventional tillage (CT). Overall, MT had 12% lower N2O emissions compared to CT (P = 0.03). However, there was high variability due to soil texture and growing season precipitation (GSP), with MT tending to emit more N2O than CT in climates where GSP exceeded 600 mm, particularly for soils with sand content less than 60%. Therefore, unless long-term tillage trials, which are urgently needed in eastern Canada, show a reduction in N2O emissions over time, MT should be used as a greenhouse gas mitigation measure only in dry climates or on sandy soils. [ABSTRACT FROM AUTHOR]

Crop yields directly affect carbon (C) inputs into soils. Tillage management can influence crop performance, and as such should be considered when quantifying soil organic carbon (SOC) change, and thus net greenhouse gas emissions from croplands for national greenhouse gas inventory reporting. We conducted a meta-analysis of the effects of no-tillage (NT) and conventional tillage (CT) on crop yields for multiple crop species, soil types, and climatic regions of Canada. Yield response to NT varied between western and eastern Canada. Regardless of crop type, experiment duration, soil texture, and residue management, experiments in eastern Canada showed an average of 6% lower yields (p ≤ 0.005) under NT compared to CT. In western Canada, crop type had an important effect on yields between NT and CT with wheat, canola, and legumes exhibiting 10% (p ≤ 0.001), 7% (p ≤ 0.05), and 9% (p ≤ 0.05) higher yields on average under NT compared to CT, respectively. In western Canada, higher yields would be reflected in a similar scale of higher C inputs to NT systems. A recent meta-analysis of the effects of tillage management on SOC in Canada showed an 8% higher storage of SOC under NT systems compared to CT, a difference limited to western Canadian soils. Incorporating the effect of tillage on C inputs will lead to improvements in the accuracy of the effects of tillage management on SOC change in Canadian cropland. The activity data can be improved by applying weightings to the yield data by site-specific assessment of tillage practices across Canada. [ABSTRACT FROM AUTHOR]