Spatial variation analysis of the effect of wheat and maize crop
rotation on soil organic carbon in the Sushui River Basin
Abstract
Objective of investigation: Enhancing soil organic carbon (SOC)
content in farmland is crucial for soil quality maintenance and food
security. Crop rotation plays a significant role in this regard.
However, the relationship between crop rotation and SOC sequestration
remains unclear. This study focused on the effects of wheat
−maize crop rotation on SOC in the Sushui River Basin in
2017–2021. Experimental material: soil organic carbon (SOC)
content of monoculture wheat (47), monoculture maize (30), and
wheat-maize rotation (35) sites in 2017 and 2021 cropland quality
monitoring sites. Influencing factors were average temperature and
rainfall during the study period, topographic factors, soil factors, and
anthropogenic factors dominated by cropping systems. Method of
investigation: Geographically Weighted Regression and Geodetector were
used to explore the spatial effects of major food cropping systems on
SOC trends and their interactions with other factors. Data
collection: Soil, climate, and terrain data were obtained from Shanxi
Province Cropland Quality Monitoring Data, Yuncheng Meteorological
Bureau, and the Geospatial Data Cloud website of the Computer Network
Information Center of the Chinese Academy of Sciences, respectively.
Results: Analysis of variance (ANOVA) indicated significant
differences only between wheat and maize monocrops. Altitude,
temperature, rainfall, and pH were the main factors affecting SOC
spatial heterogeneity. Although the impact of the cropping system alone
on spatial heterogeneity was not significant, the influence increased
after interactions with other factors. Concerning SOC variation,
wheat–maize rotation had a trade-off effect with elevation and
synergistic effects with rainfall and pH. It displayed a synergistic
effect with temperature in the southwest and a trade-off effect in the
northeast. Conclusions: The degrees of trade-offs and synergy
varied spatially among all interacting factors. Our results provide
valuable insights that could facilitate optimization of planting layout
and improvement of farmland management schemes.