Examining the effect of soil organic carbon on major Canadian Prairie crop yields with predictive soil mapping

Abstract

Maintaining soil organic carbon (SOC) is critical for global food security as it is essential for soil functions that sustain crop yields. There has been an increase in predictive soil mapping, which when combined with extensive crop yield datasets, enables a better understanding of crop yield and SOC relationships. This study focused on updating maps of SOC content in Saskatchewan using recently digitized historical SOC datasets and predictive soil mapping, and using the maps to examine the relationship between SOC and crop yield. A database of 5014 SOC values was used to map SOC contents using a Random Forest model and a range of environmental covariates. The final SOC model had a R² of 0.48, root mean square error of 0.98%, concordance correlation coefficient of 0.67, and a bias of 0.12%. The relationship between mapped SOC values and crop yield data, with 100,000–200,000 records depending on crop type, was then assessed using a linear mixed effects model after normalizing the data by rural municipality to remove broad-scale climate effects. Overall, an increase in SOC by 1% led to an increase on average of 263 kg ha⁻¹ for wheat (Triticum aestivum L.), 293 kg ha⁻¹ for barley (Hordeum vulgare L.), 133 kg ha⁻¹ for canola (Brassica napus L.), and 135 kg ha⁻¹ for field peas (Pisum sativum L.). These results show that increasing SOC was associated with greater yields for four major crops in Saskatchewan, with the largest gains occurring when the initial SOC contents are lower.