东北地区大豆单产空间分异特征及其影响因素分析

作为中国最重要的大豆主产区，东北地区的大豆生产成为解决大豆进出口产需问题的核心。明晰东北地区大豆单产空间分异特征及其影响因素，对于保卫“黑土粮仓”和夯实粮食安全“压舱石”具有重要意义。该研究基于谷歌地球引擎（Google Earth Engine，GEE）平台，采用多特征随机森林的分类方法，提取2022年大豆种植空间分布信息，结合多时相叶面积指数（leaf area index，LAI）数据与实测产量建立大豆估产模型，明晰区域大豆单产空间分异特征，运用地理探测器定量解析大豆单产空间分异特征的影响因素。结果表明：1）2022年东北地区大豆种植面积的总体提取精度达89.48%，Kappa系数为0.89，与统计数据之间的决定系数（R²）为0.92。大豆种植面积由北向南递减，大豆种植区主要分布于松嫩平原地区，重心位于绥化市。2）2022年东北地区大豆平均单产为2514.08 kg/hm²，与实测产量之间的R²为0.72。大豆单产空间分布集聚性显著，呈现出北高南低的分布特征。3）土壤类型、土壤pH值和大豆补贴是解释大豆单产空间分异特征最重要的3种单因子，q值分别为0.27、0.24和0.24。年均降雨∩年均积温、年均降雨∩大豆补贴以及土壤类型∩大豆补贴是解释大豆单产空间分异特征最重要的3对交互因子，q值分别为0.44、0.40和0.40。人为因素对大豆单产差异影响显著，大豆补贴、大豆粮价、农业灌溉面积、农业机械总动力、肥料价格和文盲率的最佳影响范围分别为4801～7500元/hm²、5601～5800元/t、13.6×10⁴～26.4×10⁴ hm²、252×10⁴～436×10⁴ kW、2500～2602元/t以及1.4%～1.8%。东北地区大豆单产在空间上呈现由北向南递减的趋势，具有显著的空间异质性。大豆单产空间分异受自然因素与人为因素的共同影响，自然因素起主导作用，人为因素起调控作用。

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.