Abstract:

[Objective] In the advancement of intensive agriculture, the contradiction between soil degradation and the demand for large-scale production has become increasingly pronounced, particularly in the core region of black soil in Northeast China. Long-term single-cropping patterns have caused soil structure damage and nutrient imbalance, severely threatening agricultural sustainability. Intensive rice cultivation has led to significant soil degradation, while the city must also balance national soybean planting mandates with large-scale production efficiency. However, existing planting planning methods predominantly focus on area optimization at the regional scale, lacking fine-grained characterization of plot-level spatial distribution, which easily results in fragmented layouts. Against this backdrop, a plot-scale multi-objective planting planning approach is developed to synergistically optimize contiguous crop distribution, soil restoration, practical production, and economic benefits, while ensuring national soybean planting tasks. This approach bridges macro-policy guidance and micro-production practices, providing scientific decision support for planting structure optimization and high-standard farmland construction in major grain-producing areas of Northeast China. [Methods] The multi-objective optimization model was established within a genetic algorithm framework, integrating connected component analysis to address plot-level spatial layout challenges. The model incorporated five indicators: economic benefit, soybean planting area, contiguous planting, crop rotation benefits, and the number of paddy-dryland conversions. The economic benefit objective was achieved by calculating the total income of crop combinations across all plots. A rigid threshold for soybean planting area was set to fulfill national mandates. The contiguous planting was evaluated using a connected-component-based method. The crop rotation benefits were scored according to predefined rotation rules. The paddy-dryland conversions were determined by counting changes in plot attributes. The model employed linear weighted summation to transform multi-objectives into a single objective for solution, generated high-quality initial populations via Latin hypercube sampling, and enhanced algorithm performance through connected-component-based crossover strategies and hybrid mutation strategies. Specifically, the crossover strategy was constructed based on connected component analysis: adjacent plots with the same crop were divided into connected regions, and partial regions were randomly selected for crop gene exchange between parent generations, ensuring offspring inherited spatial coherence from parents, avoiding layout fragmentation caused by traditional crossover, and improving the rationality of contiguous planting. The mutation strategies included three types: Soybean threshold guarantee, plot-based crop rotation rule adaptation, and connected components-based crop rotation rule adaptation, which synergistically ensured mutation diversity and policy objective adaptability. Take Fujin city, Heilongjiang province—a crucial national commercial grain base—as an example. Optimization was implemented using the distributed evolutionary algorithms in python (DEAP) library and validated through the simulation results of the four-year planting plan from 2020 to 2023. [Results and Discussions] Four years of simulation results demonstrated significant multi-objective balance in the optimized scheme. The contiguous index increased sharply from 0.477 in 2019 to 0.896 in 2020 and stabilized above 0.9 in subsequent years, effectively alleviating plot fragmentation and enhancing the feasibility of large-scale production. The economic benefits remained dynamically stable without significant decline, verifying the model's effectiveness in safeguarding production efficiency. The soybean planting area stably met national thresholds while achieving strategic expansion, strengthening food security. The simulation results of crop rotation benefits reached 0.998 in 2023, indicating effective promotion of scientific rotation patterns and enhanced soil health and sustainable production capacity. The optimization objective of minimizing paddy-dryland conversions considered practical production factors, achieving a good balance with crop rotation benefits and reflecting effective consideration of real-world production constraints. The evolutionary convergence curve showed the algorithm converged near the optimal solution, validating its convergence stability for this problem. In comparative experiments, compared with traditional plot-based strategies, this method outperformed in all optimization indicators except soybean planting area. Compared with the NSGA-II multi-objective algorithm, it showed significant advantages in contiguous planting and crop rotation benefits. Although minor gaps existed in economic benefits and paddy-dryland conversions compared to Nondominated Sorting Genetic Algorithm-Ⅱ (NSGA-II), the planting layout was more regular and less fragmented. [Conclusions] The multi-objective planting planning method based on connected components and genetic algorithms proposed in this study achieves scale conversion from macro policies to micro layouts, effectively balancing black soil protection and production benefits through intelligent algorithms. By embedding spatial topology constraints into genetic operations, it solves the fragmentation problem in traditional methods while adapting to policy-driven planting scenarios via single-objective weighting strategies. Four years of simulations and comparative experiments show that this method significantly improves contiguous planting, ensures soybean production, stabilizes economic benefits, optimizes rotation patterns, and reduces paddy-dryland conversions, providing a scientific and feasible planning scheme for agricultural production. Future research can be expanded in three directions. First, further optimizing genetic algorithm parameters and introducing technologies such as deep reinforcement learning to enhance algorithm performance. Second, integrating multi-source heterogeneous data to build dynamic parameter systems and strengthen model generalization. Third, extending the method to more agricultural regions such as southern hilly areas, adjusting constraints according to local topography and crop characteristics to achieve broader application value. The research findings can provide decision support for planting structure optimization and high-standard farmland construction in major grain-producing areas of Northeast China.

Key words: planting planning, genetic algorithm, contiguous planting, multi-objective optimization, black soil conservation