Spatial heterogeneity and determinants of soybean yield in Northeast China

Northeast China (NEC) has been the major soybean-producing region in China. Hence, it is very necessary to explore the spatial heterogeneity of soybean yield per unit in the NEC, in order to fully meet the current import and export production and demand. In this study, a multi-feature random forest (RF)-based classification was used to extract the spatial pattern of soybeans in 2022 using the Google Earth Engine (GEE) platform. The time series leaf area index (LAI) data was also combined with the field-measured yield. A soybean yield estimation model was established to characterize the spatial heterogeneity of regional soybean yield per unit. A geographic detector model was used to quantitatively explore the influencing factors. The results show that: 1) The overall accuracy of the soybean planting area reached 89.48% after extraction, with the Kappa coefficient of 0.89, and the coefficient of determination R² was 0.92 between the soybean planting areas extracted from remote sensing and the statistical data of prefecture-level city. There was a marked spatial decrease in the planting area of soybeans from the northern to the southern NEC. The soybean planting areas were concentrated mainly in the Songnen Plain. Suihua City was found in the center of gravity for the soybean planting areas in the NEC. 2) The average soybean yield per unit was 2514.08 kg/hm² in the NEC. The coefficient of determination R² was 0.72, compared with the actual measured yield per unit. There was a significantly clustered spatial distribution of soybean yield per unit in the NEC. The areas with the high values were located mainly in the northern part of the NEC, whereas, the areas with the low values were in the southern. 3) Three dominant independent factors with the most pronounced spatial heterogeneity of soybean yield per unit were ranked in the descending order of the soil type, soil pH, and soybean subsidies, with q values of 0.27, 0.24, and 0.24, respectively. The three most significant interaction factors were to explain the spatial heterogeneity in the soybean yield per unit, including the interaction between mean annual rainfall and mean annual cumulative temperature, the interaction between mean annual rainfall and soybean subsidies, and the interaction between soil type and soybean subsidies, with q values of 0.44, 0.40 and 0.40, respectively. Six anthropogenic factors presented the significant impacts on the spatial heterogeneity of soybean yield per unit, namely soybean subsidies, soybean prices, agricultural irrigation area, total power of agricultural machinery, fertilizer prices, and illiteracy rate. Their optimal impact ranges varied significantly, where the optimal impact ranges were from 4801 to 7500 yuan/hm², from 5601 to 5800 yuan/t, from 13.6×10⁴ to 26.4×10⁴ hm², from 252×10⁴ to 436×10⁴ kW, from 2500 to 2602 yuan/t and from 1.4% to 1.8%, respectively. There was a significant spatial heterogeneity of soybean yield per unit in the NEC, with an overall decreasing trend from the north to the south. This variation trend can be primarily driven by natural factors also subjected to human intervention.