Long-term study examines the impact of climate and terrain on soil nutrients in semi-arid Canadian Prairies, suggesting improved nutrient management strategies.
This study provides rare 18-year evidence on how climate and terrain interact with input systems to shape soil nitrate (NO₃⁻–N) and available phosphorus (available P) dynamics in the semi-arid Canadian Prairies. A long-term field experiment was conducted from 1994 to 2013 at Scott, Saskatchewan, Canada, to assess three input systems (organic (ORG), reduced (RED), and high (HI)) and three cropping diversity levels under variable growing season precipitation (GSP), temperature, and terrain attributes. Machine-learning (ML) importance analysis indicated that environmental variables dominated predictor rankings for soil nutrient status. For NO₃⁻–N, monthly precipitation and growing degree day (GDD) variables contributed the largest relative importance shares, with individual terms reaching about 30 and 33%, respectively, whereas terrain attributes collectively accounted for up to about 50% of relative ML importance for available P. Subsequent GLM and mixed-model analyses similarly showed smaller management contributions overall than environmental covariates, with input-system effects generally accounting for only modest shares of explained variation or mixed total effects, depending on response variable and model. NO₃⁻–N declined over time, while available P accumulated in reduced-input systems, exceeding high-input systems by the end of the study. We concluded that environmental factors were the key drivers of long-term soil nutrient dynamics in this study. This work is among the first to integrate machine learning with long-term field data to quantify climate–terrain–management interactions for long-term soil nutrient dynamics, offering actionable guidance for precision agronomy. Incorporating environmental factors alongside fixed treatment effects in analyses of crop inputs and rotational diversity is needed to optimize fertilizer management strategies.
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Lychuk et al. (2026) studied this question.
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