Climate impact from agricultural management practices in the Canadian Prairies: Carbon equivalence due to albedo change.

It is generally accepted that land use and land management practices impact climate change through sequestration of carbon in soils, but modulation of surface energy budget can also be important. Using Landsat data to characterize cropland albedos in Canada's three prairie soil zones, this study estimates the atmospheric carbon equivalent drawdown of albedo radiative forcing for three management practices: 1) moving from conventional tillage to no-till, 2) eliminating summer fallow in crop rotations, and 3) growing crops with higher albedos. In a 50-year time horizon, conversion from conventional tillage to no-till results in a total equivalent atmospheric CO2 (CO2-eq) drawdown of 1.0-1.5 kg m-2, and conversion from summer fallow to crops results in CO2-eq drawdown of 1.1-2.4 kg m-2. Conversion of summer fallow to crops results in different magnitudes of CO2-eq drawdown depending on specific crops. Lentils, peas, and canola have relatively higher albedo than that of spring wheat and flax; hence, a larger magnitude of CO2-eq drawdown results when they replace summer fallow in the rotation. For the management changes from 1990 to 2019 for the whole Canadian Prairies, albedo changes induced a CO2-eq drawdown of about 179.3 ± 20.9 Tg due to increased area of no-till, and 101.6 ± 9.5 Tg due to reduced area under fallow. The study shows that the magnitudes of CO2-eq drawdown due to albedo change are comparable to that due to soil carbon sequestration. Therefore, it is important to account for cropland albedo changes in assessing the potential of agricultural management practices to mitigate climate change.

Effectiveness of using representative subsets of global climate models in future crop yield projections.

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040-2069 and 2070-2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9-4.7% for canola and 1.5-2.2% for spring wheat, and covers 61.8-91.1% and 66.1-80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Non-stationary response of rain-fed spring wheat yield to future climate change in northern latitudes.

The non-stationary response of crop growth to changes in hydro-climatic variables makes yield projection uncertain and the design and implementation of adaptation strategies debatable. This study simulated the time-varying behavior of the underlying cause-and-effect mechanisms affecting spring wheat yield (SWY) under various climate change and nitrogen (N) application scenarios in the Red Deer River basin in agricultural lands of the western Canadian Prairies. A calibrated and validated Soil and Water Assessment Tool and Analysis of Variance decomposition methods were utilized to assess the contribution of crop growth parameters, Global Climate Models, Representative Concentration Pathways, and downscaling techniques to the total SWY variance for the 2040-2064 period. The results showed that the cause-and-effect mechanisms, driving crop yield, shifted from water stress (W-stress) dominated (27 days of W-stress days) during the historical period to nitrogen stress (N-stress) dominated (27 to 35 N-stress days) in the future period. It was shown that while higher precipitation, warmer weather, and early snowmelts, along with elevated CO2 may favor SWY in cold regions in the future (up to 50% more yields in some sub-basins), the yield potentials may be limited by N-stress (only up to 0.7% yield increase in some sub-basins). The N-stress might be partially related to the N deficiency in the soil, which can be compensated by N fertilizer application. However, inadequate N uptake due to limited evapotranspiration under elevated atmospheric CO2 might pose restrictions to SWY potentials even in the least N deficient regions. This study uncovers important information on the understanding of spatiotemporal variability of hydrogeochemical processes driving crop yields and the non-stationary response of yields to changing climate. The results also underscore spatiotemporal variability of N-stress due to N deficiency in the soil or N uptake by crops, both of which may restrain SWY by changes in atmospheric CO2 concentrations in the future.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

Towards an improved methodology for modelling climate change impacts on cropping systems in cool climates.

Assessment of the impact of climate change on agricultural sustainability requires a robust full system estimation of the interdependent soil-plant-atmospheric processes coupled with dynamic farm management. The simplification or exclusion of major feedback mechanisms in modelling approaches can significantly affect model outcomes. Using a biogeochemical model, DNDCv.CAN, at three case-study locations in Canada, we quantified the impact of using commonly employed simplified modelling approaches on model estimates of crop yields, soil organic carbon (SOC) change and nitrogen (N) losses across 4 time periods (1981-2010, 2011-2040, 2041-2070, and 2071-2100). These approaches included using climate with only temperature and precipitation data, annual re-initialization of soil status, fixed fertilizer application rates, and fixed planting dates. These simplified approaches were compared to a more comprehensive reference approach that used detailed climate drivers, dynamic planting dates, dynamic fertilizer rates, and had a continuous estimation of SOC, N and water budgets. Alternative cultivars and rotational impacts were also investigated. At the semi-arid location, the fixed fertilizer, fixed planting date, and soil re-initialization approaches reduced spring wheat (Triticum aestivum L.) yield estimates by 40%, 25%, and 29%, respectively, in the 2071-2100 period relative to the comprehensive reference approach. At both sub-humid locations, the re-initialization of soil status significantly altered SOC levels, N leaching and N runoff in all three time periods from 2011 to 2100. At all locations, SOC levels were impacted when using simplified approaches relative to the reference approach, except for the fixed fertilizer approach at the sub-humid locations. Results indicate that simplified approaches often lack the necessary characterization of the feedbacks between climate, soil, crop and management that are critical for accurately assessing crop system behavior under future climate. We recommend that modellers improve their capabilities of simulating expected changes in agronomy over time and employ tools that consider robust soil-plant-atmospheric processes.

Long-term Trends in Corn Yields and Soil Carbon under Diversified Crop Rotations.

Agricultural practices such as including perennial alfalfa ( L.), winter wheat ( L.), or red clover ( L.) in corn ( L.) rotations can provide higher crop yields and increase soil organic C (SOC) over time. How well process-based biogeochemical models such as DeNitrification-DeComposition (DNDC) capture the beneficial effects of diversified cropping systems is unclear. To calibrate and validate DNDC for simulation of observed trends in corn yield and SOC, we used long-term trials: continuous corn (CC) and corn-oats ( L.)-alfalfa-alfalfa (COAA) for Woodslee, ON, 1959 to 2015; and CC, corn-corn-soybean [ (L.) Merr.]-soybean (CCSS), corn-corn-soybean-winter wheat (CCSW), corn-corn-soybean-winter wheat + red clover (CCSW+Rc), and corn-corn-alfalfa-alfalfa (CCAA) for Elora, ON, 1981 to 2015. Yield and SOC under 21st century conditions were projected under future climate scenarios from 2016 to 2100. The DNDC model was calibrated to improve crop N stress and was revised to estimate changes in water availability as a function of soil properties. This improved yield estimates for diversified rotations at Elora (mean absolute prediction error [MAPE] decreased from 13.4-15.5 to 10.9-14.6%) with lower errors for the three most diverse rotations. Significant improvements in yield estimates were also simulated at Woodslee for COAA, with MAPE decreasing from 24.0 to 16.6%. Predicted and observed SOC were in agreement for simpler rotations (CC or CCSS) at both sites (53.8 and 53.3 Mg C ha for Elora, 52.0 and 51.4 Mg C ha for Woodslee). Predicted SOC increased due to rotation diversification and was close to observed values (58.4 and 59 Mg C ha for Elora, 63 and 61.1 Mg C ha for Woodslee). Under future climate scenarios the diversified rotations mitigated crop water stress resulting in trends of higher yields and SOC content in comparison to simpler rotations.

Litter decay controlled by temperature, not soil properties, affecting future soil carbon.

Widespread global changes, including rising atmospheric CO2 concentrations, climate warming and loss of biodiversity, are predicted for this century; all of these will affect terrestrial ecosystem processes like plant litter decomposition. Conversely, increased plant litter decomposition can have potential carbon-cycle feedbacks on atmospheric CO2 levels, climate warming and biodiversity. But predicting litter decomposition is difficult because of many interacting factors related to the chemical, physical and biological properties of soil, as well as to climate and agricultural management practices. We applied 13 C-labelled plant litter to soil at ten sites spanning a 3500-km transect across the agricultural regions of Canada and measured its decomposition over five years. Despite large differences in soil type and climatic conditions, we found that the kinetics of litter decomposition were similar once the effect of temperature had been removed, indicating no measurable effect of soil properties. A two-pool exponential decay model expressing undecomposed carbon simply as a function of thermal time accurately described kinetics of decomposition. (R2  = 0.94; RMSE = 0.0508). Soil properties such as texture, cation exchange capacity, pH and moisture, although very different among sites, had minimal discernible influence on decomposition kinetics. Using this kinetic model under different climate change scenarios, we projected that the time required to decompose 50% of the litter (i.e. the labile fractions) would be reduced by 1-4 months, whereas time required to decompose 90% of the litter (including recalcitrant fractions) would be reduced by 1 year in cooler sites to as much as 2 years in warmer sites. These findings confirm quantitatively the sensitivity of litter decomposition to temperature increases and demonstrate how climate change may constrain future soil carbon storage, an effect apparently not influenced by soil properties.

How early can the seeding dates of spring wheat be under current and future climate in Saskatchewan, Canada?

BACKGROUND: Shorter growing season and water stress near wheat maturity are the main factors that presumably limit the yield potential of spring wheat due to late seeding in Saskatchewan, Canada. Advancing seeding dates can be a strategy to help producers mitigate the impact of climate change on spring wheat. It is unknown, however, how early farmers can seed while minimizing the risk of spring frost damage and the soil and machinery constraints. METHODOLOGY/PRINCIPAL FINDINGS: This paper explores early seeding dates of spring wheat on the Canadian Prairies under current and projected future climate. To achieve this, (i) weather records from 1961 to 1990 were gathered at three sites with different soil and climate conditions in Saskatchewan, Canada; (ii) four climate databases that included a baseline (treated as historic weather climate during the period of 1961-1990) and three climate change scenarios (2040-2069) developed by the Canadian global climate model (GCM) with the forcing of three greenhouse gas (GHG) emission scenarios (A2, A1B and B1); (iii) seeding dates of spring wheat (Triticum aestivum L.) under baseline and projected future climate were predicted. Compared with the historical record of seeding dates, the predicted seeding dates were advanced under baseline climate for all sites using our seeding date model. Driven by the predicted temperature increase of the scenarios compared with baseline climate, all climate change scenarios projected significantly earlier seeding dates than those currently used. Compared to the baseline conditions, there is no reduction in grain yield because precipitation increases during sensitive growth stages of wheat, suggesting that there is potential to shift seeding to an earlier date. The average advancement of seeding dates varied among sites and chosen scenarios. The Swift Current (south-west) site has the highest potential for earlier seeding (7 to 11 days) whereas such advancement was small in the Melfort (north-east, 2 to 4 days) region. CONCLUSIONS/SIGNIFICANCE: The extent of projected climate change in Saskatchewan indicates that growers in this region have the potential of earlier seeding. The results obtained in this study may be used for adaptation assessments of seeding dates under possible climate change to mitigate the impact of potential warming.