The identification and optimization of local barrier factors affecting soil organic carbon (SOC) in the black soil region of Northeast China are critical for enhancing soil carbon sequestration, reducing emissions, and protecting black soil resources. However, existing studies employing the Shapley algorithm to identify local influencing factors often neglect the spatial dependency of SOC and fail to provide quantitative optimization strategies for these barriers, which limits the accuracy and practical applicability of their results. To address these gaps, this study takes Kedong County in Heilongjiang Province as a case study and proposes a novel integration of the Extreme Gradient Boosting (XGBoost) model with the Geoshapley algorithm. This approach effectively identifies localized barrier factors influencing SOC, determines optimal target values for each factor, and estimates the potential improvement in SOC levels following optimization. The methodology involved collecting and preprocessing spatial data on SOC and 19 potential influencing factors based on the Scorpan framework, covering soil properties, climate, organisms, topography, and spatial location. The XGBoost model was used to capture complex nonlinear relationships between SOC and environmental variables, while the Geoshapley algorithm was applied to account for spatial dependence and interaction effects, providing more accurate estimates of factor importance and enabling local interpretation of model predictions. The genetic algorithm was used for variable selection to avoid overfitting and reduce dimensionality. Comprehensive results demonstrated that the XGBoost model achieved exceptional performance, with fitting and prediction R² values of 0.99 and 0.93, respectively, indicating strong explanatory power and generalization capability. The Geoshapley analysis provided several key findings:1) Spatial location was identified as the second most important factor, accounting for substantial variation in SOC distribution, which confirms the necessity of incorporating spatial effects in SOC modeling; 2) The spatial distribution of Geoshapley values for key variables revealed distinct regional patterns, with AN showing higher values in eastern areas, indicating its positive contribution to SOC accumulation in these regions, while AK and AP displayed more negative values in western parts, suggesting their inhibitory effects on SOC in these locations; 3) In western regions, SOC was primarily constrained by low AN and reduced annual rainfall, combined with excessively high AK and elevated AP; 4) Eastern SOC levels were limited by significant topographic relief, coupled with low road density and limited access to water bodies; 5) Partial dependence plots identified clear threshold effects: SOC reached peak values when AN, AK, AP, and the distance to roads and water bodies reached 300 mg/kg, 231 mg/kg, 52 mg/kg, 8 467 m and 180 m, respectively. Implementation of the optimization strategy based on these thresholds is projected to increase mean SOC content across the study area from an initial 32.52 g/kg to 41.62 g/kg—a significant increase of 27.98%. The western regions showed the most substantial potential improvement, with some areas gaining over 15 g/kg, while eastern areas remained stable with only minimal adjustments, indicating region-specific responsiveness to management interventions. This study underscores the critical importance of integrating spatial dependency into SOC modeling and highlights the advantages of the Geoshapley algorithm in improving interpretation accuracy over conventional SHAP methods. The spatial patterns of Geoshapley values provide valuable insights into the region-specific mechanisms governing SOC accumulation. The findings provide actionable insights for tailoring local soil management practices and agricultural strategies, such as site-specific fertilization and improved irrigation infrastructures. Furthermore, the methodology offers a scalable framework for identifying and optimizing barrier factors of soil attributes in other regions, supporting global efforts toward sustainable land use and climate change mitigation. The approach demonstrates how advanced spatial machine learning techniques can bridge the gap between theoretical modeling and practical agricultural management, enabling more precise and effective soil conservation strategies in ecologically vulnerable regions.

Cultivated land is the most important carrier of grain production. Quantity, quality, and utilization rate can also determine the production capacity of grain for national grain security. Among them, agricultural carbon emission has been one of the important sources of greenhouse gases. There is a complex logical relationship among land use, land use carbon reduction, and grain production under the strategic context of climate change response to the overall layout of ecological civilization. In response to the growing demand for grain, agricultural production factors in the short term have caused ecological pollution and carbon emissions in cultivated land. The demand for diversified agricultural products has also intensified the excessive investment of production resources, further deteriorating the ecological environment of cultivated land and carbon emissions. Therefore, this study aims to explore the relationship between cultivated land utilization and grain production in the context of carbon emission constraints, in order to reveal the complex logical relationship among the three elements of "soil-carbon-grain". The challenges were also proposed to sustainably utilize the cultivated land under the goals of grain security and carbon emission reduction. Furthermore, the optimal paths were constructed to promote the green transformation and upgrading of cultivated land use for better grain production. The results showed that: (1) A complex "soil-carbon-grain" factor system was obtained to form the logical connection between cultivated land utilization and grain production under carbon emission constraints. (2) The carbon reduction was implemented to utilize the cultivated land under current agricultural production. There were uncertain impacts on national grain security. But the ever-increasing grain production still resulted in a large amount of carbon emissions. (3) There was a better balance between cultivated land utilization and grain production under carbon emission constraints. A coordinated optimal path was established for the "soil-carbon-grain" elements from three aspects: cultivated land protection, carbon reduction, and grain production. The cultivated land resources were allocated to construct a carbon trading market for the structure of grain production. According to the current agricultural production, it was still feasible to coordinate the "Carbon Peaking and Carbon Neutrality" goals with grain security goals, although there was a high degree of uncertainty on carbon emissions that were reduced from cultivated land utilization. The proportion and quantity of input were optimized from the factors of grain production, in order to implement the "Trinity" protection system for the cultivated land, differentiated carbon reduction for cultivated land use, and stable support of agricultural funding. A mature trading market of agricultural carbon was established to innovate the green technologies of agricultural production. The carbon emissions were reduced from the cultivated land use for the national grain security, the carbon sequestration emission reduction potential of cultivated land, and grain production. It is very necessary to plan the spatial patterns of cultivated land using various policy tools, in order to promote the sustainable and synergistic development of low-carbon and green utilization of cultivated land and national food security.