Abstract: Spring maize is the primary food crop in Northeast China. Researching and analyzing the spatial difference in yield under diverse climate and soil models holds immense significance in guiding agricultural production and ensuring food security. In this study, a total of 13 key influencing factors were selected from the three aspects of soil, topography and climate, and a multivariate stepwise linear regression model was constructed for spring maize per unit yield and 13 influencing factors by using multivariate stepwise regression analysis. Then the spatial distribution of spring maize per unit area yield in Northeast China was analyzed by using ArcGIS software to rasterize the spring maize yield per unit area. The results showed that: (1) the spatialized data (with a spatial resolution of 1km) calculated by the multiple linear regression model are basically consistent with the statistical data of spring maize yield per unit area. The spring maize yield in Northeast China is between 2482.49-10147.10kg·ha⁻¹. (2) The spatial distribution map of spring maize yield objectively reflects that the spatial distribution trend of spring maize yield, which generally shows a pattern of decreasing from the central to the surrounding areas. This study accurately obtained the grid-scale simulation results of spring maize yield per unit area spatialization in Northeast China (the average relative error is 1.45%), which provides a method reference for the optimization of agricultural production layout and decision-making in Northeast China.

Key words: Maize yield, Spatialization, Multiple linear regression, Northeast China