Quantitative evaluation of soil health based on a minimum dataset under various short-term crop rotations on the Canadian prairies.

Maintaining and improving soil health (SH) is essential for the long-term sustainability and productivity of agriculture, notably in the face of climate change. This study addressed the challenge of selecting appropriate soil indicators, scoring methods, and indexing approaches for SH evaluation under no-till crop rotations. This study aimed to develop minimum datasets (MDS) and assess SH in six crop rotations (denoted as conventional, diversified, high-risk and high-reward, market-driven, pulse-oilseed intensified, and soil health-enhanced rotations) at three sites on the Canadian prairies. Fourteen soil indicators in the total dataset (TDS) were examined, encompassing both chemical (0-7.5 cm depth) and physical (5-10 cm depth) properties. Principal component analysis (PCA) identified MDSs from the TDS. Two scoring [linear (L) and non-linear (NL)] and two SH indexing approaches [additive (A) and weighted additive (WA)] were used to calculate the SH index (SHI). One-way ANOVA evaluated the SHI among crop rotations. The PCA revealed variations in the number of indicators in the MDS across sites, with soil organic carbon, bulk density, macroporosity, and plant-available water capacity as the common indicators for MDS across sites. Other indicators such as particulate organic matter carbon, aggregate stability, field capacity, and microporosity were found to be important, depending on the site. The non-linear weighted additive SH indexing (SHI.NLWA) proved to be the most sensitive and effective for differentiating among crop rotations in the short-term across study sites (R2 = 0.89-0.94, P < 0.05). Crop rotations significantly influenced SHI, with the diversified and high-risk and high-reward rotations having the highest SHI at Lethbridge and Scott, respectively. Overall, the diversified rotation at Lethbridge and Swift Current, along with the high-risk and high-reward rotation at Scott, exhibited better soil function than other rotations. Monitoring SHI over time and selecting crop rotations that improve SH can collectively enhance soil functions and agroecosystem productivity.

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.