Understanding the Multiscale Relationships Between Grain Yields of Maize in China and Influencing Factors via Multiscale Geographically Weighted Regression Model

Maize is a key global food crop, with China being a major producer vital for global maize supply and food security. Accurately analyzing the relationships between grain yields of maize and influencing factors is crucial for enhancing crop production, evaluating arable land quality, and optimizing planting structure. However, when modeling those relationships, the coefficients of each influencing factor vary spatially and have different spatial scales, suggesting that the importance of the influencing factors is multiscale. Traditional global and local modeling methods such as ordinary least squares (OLS) and geographically weighted regression (GWR) models cannot accurately explain those multiscale spatial relationships. The multiscale GWR (MGWR) model, an extension of GWR, addresses these limitations by allowing each explanatory variable to have a unique spatial scale. By aligning the neighborhood structure of each variable with its corresponding spatial scale, MGWR improves the accuracy of local regression coefficient estimations, providing a more refined analysis of spatial heterogeneity. In this article, the multiscale importance of influencing factors on maize grain yield of the Chinese mainland was apportioned via MGWR model. Our findings verified that the relationships between maize grain yield and influencing factors differ at multiple spatial scales. The MGWR model can comprehensively apportion the importance of influencing factors at multiple scale, while global and local modeling methods provide biased estimations, with OLS method leaving large residues and GWR model attributing part contribution to spatially varying intercept terms. With the MGWR model, organic fertilizer and terrain aspect are globally important and their relationships with yields keep stationary; the relationships between yields and soil pH value, GDP, digital elevation model (DEM), and slope vary on a medium scale, presenting obvious regional differences; cultivation convenience and hydrothermal conditions affect yields at small scales. The comprehensive apportionment of multiscale relationships is an important guideline for the scientific management of agriculture and arable land resources.