Heilongjiang Province, as the largest production and supply base for high-quality soybeans in China, plays a vital role in optimizing the layout of soybean production and promoting the revitalization of the soybean industry. Soybean yield is used as a key indicator of soybean production. This study integrated soybean yield data from agricultural reclamation systems and local authorities. A variety of statistical analysis methods, such as barycenter analysis, the Mann–Kendall test, the space–time cube, and grey relational analysis, were used to research the spatiotemporal evolution and influencing factors of soybean production in Heilongjiang Province from 2011 to 2021. This paper revealed the spatiotemporal evolution mechanism and explored the reasons for the differences in the effects of influencing factors. The results were as follows. (1) During the period between 2011 and 2021, the center of gravity of county-level soybean yield in Heilongjiang Province moved towards the northwest over a distance of 16.82 km. The soybean yield in the province experienced a mutation in approximately 2018, from a downward trend to an upward trend. (2) The spatiotemporal hot spots of county-level soybean yield in Heilongjiang Province were concentrated along the line from Hailun to Aihui. The types of hot spots included consecutive hot spots, intensifying hot spots, sporadic hot spots, and new hot spots. (3) The spatiotemporal agglomeration patterns of county-level soybean yield in Heilongjiang Province included only high-high clusters, only low-low clusters, only high-low outliers and multiple types. (4) The temporal changes in soybean yield in various counties of Heilongjiang Province had obvious regional characteristics. (5) Socioeconomic factors had aftereffects on soybean planting decisions. (6) Sunlight hours, the price ratio of local soybeans to local maize, average temperature, the number of soybean patents, the price ratio of imported soybeans to local soybeans, soybean cultivation income, local soybean prices, and the number of newly established soybean enterprises were primary influencing factors. Precipitation and soybean import volume were secondary influencing factors. The income difference between maize and soybeans, crops-hitting disaster area, and maize yield were general influencing factors. This study aims to offer new pathways for alleviating the structural contradiction between soybean supply and demand and to provide a reference for the formulation of national soybean industry policies and food security strategies.

As one of the major new agricultural business entities, agricultural leading enterprises (ALEs) are responsible for ensuring national food security, leading agricultural and rural modernization, and increasing farmers’ employment prospects and incomes. From the perspective of headquarters and branches, this study used a point pattern analysis, the local Moran’s index, the rank-size rule, and the geographical detector to depict the spatial pattern of ALEs in Heilongjiang Province, detect influencing factors, and reveal the spatial layout mechanism. The main conclusions are as follows. (1) ALE headquarters and branches in Heilongjiang Province had different location requirements, and their layout orientation, clustering areas, and influencing factors were different. (2) The headquarters displayed a pronounced urban and agglomeration orientation, while branches exhibited a significant farm dependence and raw material orientation. (3) Both the headquarters and the branches showed a significant trend towards spatial clustering. The headquarters were mainly in the Harbin municipal district and surrounding counties, including Wuchang, Zhaodong, and Beilin, which showed a high–high cluster pattern. The branches were mainly in the Sanjiang Plain. Tongjiang, Fujin, Hulin, Mishan, Raohe, Baoqing, and Suibin showed a high–high cluster pattern, while the Harbin municipal district and Bei’an showed a high–low outlier pattern. (4) The ALEs’ regional connection network in Heilongjiang Province was radially distributed with the Harbin municipal district as the centre. The agricultural reclamation system deeply influenced it. (5) The number of supporting enterprises, number of permanent residents, gross domestic product, railway mileage, number of people with Bachelor’s degrees or above, and distance from the provincial capital were the main influencing factors of the headquarters spatial pattern in Heilongjiang Province. The number of state farms in Heilongjiang Province, the per capita grain yield, highway mileage, and distance from the provincial capital were the main influencing factors of the branch spatial patterns in Heilongjiang Province. The interaction results indicated that the explanatory power of two-factor interaction was stronger than that of a single factor regardless of headquarters or branches, and most interaction types were bilinear enhancements. This study aims to provide a decision-making reference for the long-term development of ALEs in Heilongjiang Province at the present stage and accelerate the development of agricultural industrialization in major grain-producing areas.

In the context of the increasingly intensified innovation competition, improving industrial innovation efficiency is the key to achieving the sustainable development of the old industrial base. This paper adopts the thinking of regional research to study the laws of industrial innovation in the old industrial base and takes the lock-in effect as the connection point between the industrial evolution history and industrial innovation efficiency. Based on the perspective of the lock-in effect, the three-stage industrial innovation model, the lock-in effect identification method, and the extended Porter model are creatively constructed. This paper chooses Jilin Province in Northeast China as a case, dissects the evolution history of industrial innovation in detail, and uses super-efficiency DEA, the Granger test, geographical detectors, and the panel regression method for quantitative analysis. The results show the following: (1) The lock-in effect faced by the industrial innovation of the old industrial base is significant, which has an impact on industrial innovation through industrial structure, enterprise composition, management system, and degree of marketization. (2) The lock-in effect causes the old industrial base to fall into an unhealthy circle in which it is difficult for industrial enterprises to obtain sufficient benefits through industrial innovation and the ability of industrial enterprises to absorb regional innovation resources is weakened. (3) The impact mechanism of industrial innovation in the old industrial base is very complex and the lock-in effect factors are not all negative. The improvement of industrial innovation in the old industrial base needs to increase the role of market forces, reform large traditional enterprises, and increase foreign economic ties. However, it also needs policy support, and it should avoid overly radical industrial transformation and enterprise strategies.

Geographical indication (GI) offers a unique protection scheme to preserve high-quality agricultural products and support rural sustainability at the territorial level. However, not all the areas with traditional agricultural products are acknowledged with a GI. Quantifying the contribution of each factor to geographical indication agricultural products (GIAPs) can facilitate the formulation of effective policies to improve rural livelihoods. In this study, the random forest (RF) model was applied to investigate the contribution of multi-perspective factors, including nature, society, agriculture and market, on the distribution of GIAPs, and examined the driving causes using interpretable approaches. The empirical findings demonstrate that the RF model is able to accurately capture most of the important factors characterizing GIAPs and to make out-of-sample predictions of the study units which obtain GIs. This study revealed that natural conditions and market demand were contributing aspects to the disparity of GIAPs in Northeast China. The order of determinants was the category of online GIAPs (CatOn) > the number of online GIAPs (NumOn) > the area of black soil (BlaSoil) > the distance to offline stores selling GIAPs (DisOff). Of these, GIAPs was lower than $y_{base}$ in parts of districts of Jilin and Heilongjiang Provinces when the area of black soil (BlaSoil) gradually increased. When the category and number of online GIAPs (CatOn and NumOn) were less than 20 and 5, respectively, GIAPs were enhanced, especially for 40% of the districts in Liaoning Province. Deepening understanding of GIAPs helps to better target and tailor sustainable development policies.