The global agricultural system faces the challenge of ensuring sustainable food production while mitigating environmental degradation caused by industrial farming practices. Soil contamination, groundwater depletion, and land degradation pose significant threats. Fallowing, a technique involving temporarily ceasing cultivation, offers a potential solution, having been used globally to improve soil health and reduce negative externalities. In China, the government promotes fallowing as a cost-effective strategy to address ecological degradation and promote sustainable development. However, current policies lack guidance on optimizing fallow location for maximum cost-efficiency. This study addresses this gap by using a multi-criteria optimization algorithm to identify priority fallow areas across China, considering the trade-offs between environmental and economic benefits and costs. By incorporating soil pollution, groundwater overexploitation, land quality, and ecological protection redlines data, the study aims to determine the most cost-effective fallow strategies.

Existing studies on fallow implementation in China primarily focus on determining the appropriate fallow scale based on food demand or regional-scale analysis of a single environmental factor, often hampered by data limitations. Predictions of fallow ratios are made based on grain production potential, arable land holdings, and future food security constraints. However, these lack spatial analysis and fail to consider the spatial trade-offs between economic and environmental costs and benefits. While the concept of spatial trade-offs in land use decision-making is recognized, research on prioritizing fallow zones at a national scale and evaluating the cost-benefit trade-offs remains scarce. This study aims to bridge this gap by employing a national-scale spatial analysis to optimize fallow implementation.

This research utilizes a multi-criteria optimization algorithm to identify priority fallow areas in China. Four primary eco-environmental stressors are considered: soil pollution, groundwater overexploitation, cultivated land quality, and ecological protection redlines (EPR). Data on these stressors were compiled from various sources, including peer-reviewed articles, government reports, and national surveys. Soil pollution assessment utilizes a combined single-factor index and Nemerow integrated pollution index for eight pollutants (heavy metals, organic pollutants, and PAHs). Land quality grading is based on a 15-level classification system. Groundwater overexploitation assessment uses the water level amplitude method, comparing groundwater levels from 1979 and 2011. EPR delineation leverages existing guidelines and data from multiple sources. The optimization algorithm considers three objective functions: maximizing pollution control benefits (based on children's carcinogenic risk), maximizing environmental protection benefits (considering land quality, groundwater levels, and EPR), and minimizing fallow implementation costs (including crop production losses, subsidies, matching funds, and management costs). Five scenarios are simulated: prioritizing risk mitigation (PMS), prioritizing ecological civilization (PES), cost reduction (CRS), multiple benefits (MBS), and a comprehensive scenario (CFS) integrating all three objectives. The model incorporates a 20% upper limit for total fallow area to ensure national food security. The analysis is conducted using ArcGIS software.

The study reveals significant spatial variations in the distribution of priority fallow areas depending on the chosen scenario. For example, the PMS scenario prioritizes areas with high soil pollution in southeastern China, while PES focuses on areas with groundwater overexploitation and those within EPR zones in the north and central regions. CRS prioritizes areas in the north and west where fallow implementation costs are lowest. The MBS and CFS scenarios balance the benefits and costs, yielding more scattered distributions. A 20% fallow area prioritization under CFS could achieve 28.8% of pollution control benefits and 26.6% of environmental protection benefits with costs ranging from 65.5-95.6 billion USD. The total cost of implementing a 20% fallow strategy shows a substantial difference across scenarios, ranging from 56.6-79.4 billion USD in CRS to 380.4-588.7 billion USD in PMS. Compared to the CRS, cost savings in CFS could reach 323.8 billion USD at minimum and 509.3 billion USD at maximum, equating to 13.6% of China’s 2021 budget. The analysis highlights that location-specific fallow implementation is crucial for maximizing benefits and minimizing costs, with substantial variation in outcomes observed even for the same fallow percentage.

The findings demonstrate that a multi-objective approach and spatial differentiation are critical for efficient fallow implementation in China. The significant cost-benefit variations across scenarios underscore that simply focusing on area alone is insufficient for maximizing efficiency at the national scale. The location-specific results of the study provide crucial information for decision-makers to prioritize fallow areas based on the most pressing needs (soil pollution, ecological protection, and cost). The approach of this research can help guide future policy development by promoting cost-benefit efficiency, improving the effectiveness of soil pollution and land degradation prevention programs, and promoting sustainable land use practices. This research can inform the allocation of limited funds towards addressing the most critical environmental and economic issues.

This study presents a novel national-scale spatial analysis identifying priority areas for fallow implementation in China, optimizing the trade-offs between environmental and economic factors. The results emphasize the need for a multi-objective decision-making process and demonstrate significant cost savings with effective prioritization. Future research should incorporate additional factors, such as social and economic impacts on farmers' livelihoods, to refine the optimization model and guide the implementation of more effective and equitable fallow programs. Moreover, further research could explore the long-term ecological and economic benefits of different fallow strategies and modes.

The study acknowledges limitations concerning data quality and social implications. The soil pollution data used may have limitations in resolution and may include outliers, which could affect the pollution control benefit evaluation. While efforts were made to mitigate this using the [x/4, 4x] method, uncertainties remain. Furthermore, the study does not explicitly consider social factors, such as farmers' willingness to participate in fallow programs, which could affect the practical implementation of the recommendations. Future research could address these limitations by incorporating higher resolution data and incorporating social impact assessments.