Key Takeaways & Executive Findings
- •• • Lily BCF-Cd is significantly higher than maize BCF-Cd, with lily exceeding the medicinal limit of 0.3 mg·kg−1 in many samples, whereas maize remained below food safety thresholds, indicating differential risk and necessitating crop-specific management. • • Soil pH was identified as the most critical factor controlling BCF-Cd for both crops, with feature importance analysis showing pH dominance; this underscores pH management as a primary intervention for reducing Cd uptake. • • Random forest models with hyperparameter optimization achieved optimal predictive performance for BCF-Cd, enabling accurate spatial predictions for zoning; the models incorporated soil pH, Mn, OM, and ba as key predictors. • • The proposed zoning scheme, based on model predictions, balances economic benefits (lily has 5–10 times higher value per mu than maize) with safety, offering a practical framework for high-Cd cropland management.
Abstract
Cadmium (Cd) accumulation in crops is influenced by complex, crop-specific factors, posing challenges for the safe utilization of soils with elevated Cd levels. This study focused on a region with anomalously high soil Cd in northern Longshan County, Hunan Province, China. We systematically collected and analyzed Cd concentrations in the edible parts of lily (Lilium spp.) and maize (Zea mays L.), along with corresponding root-zone soil properties including Cd content, pH, and oxide levels. The bioconcentration factors (BCF-Cd) for lily and maize were compared, and their controlling factors were identified. Using random forest with hyperparameter optimization, optimal predictive models for BCF-Cd were developed for each crop. Results showed that lily BCF-Cd was significantly higher than that of maize. Key factors influencing BCF-Cd in both crops included soil pH, manganese (Mn), organic matter (OM), and the weathering-leaching coefficient (ba). Feature importance analysis identified soil pH as the most critical factor. Based on model predictions, a zoning scheme for safe arable land utilization was proposed to maximize land productivity while ensuring the medicinal safety of lily and food safety of maize. This study provides scientific support for enhancing food security and optimizing land resource use.
1. Introduction
Cadmium (Cd) contamination in agricultural soils poses a significant threat to food safety and human health, particularly in karst regions of southwestern China where geogenic Cd anomalies are prevalent. The complexity of soil-plant transfer, influenced by crop species and soil physicochemical properties, has hindered the development of universal safe-utilization strategies. Traditional empirical models often fail to capture nonlinear interactions, leading to either overly conservative land-use restrictions or unacceptable health risks.
This study addresses this bottleneck by employing a random forest machine learning approach, which inherently handles nonlinearity and feature interactions, to predict Cd bioconcentration factors (BCF-Cd) for lily and maize. By integrating comprehensive soil properties—pH, Mn, organic matter, and weathering-leaching coefficient—the models provide robust predictions that enable spatially explicit zoning. This approach not only identifies the most influential soil factors but also offers a data-driven pathway to maximize land productivity while ensuring crop safety, thereby resolving the trade-off between economic gain and health protection.
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DAI Liangliang, WU Wenbin, GONG Hao, ZHANG Jun, HU Xiangrong (2026). Safe Utilization of High Cadmium Cropland by Random Forest Based on Soil Properties. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025042203
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Frequently Asked Questions
What are the key soil properties that most strongly influence Cd bioconcentration in lily and maize, and how do they differ between the two crops?
Soil pH was identified as the most critical factor for both crops, with feature importance analysis showing pH dominance. Other significant factors included Mn, organic matter (OM), and weathering-leaching coefficient (ba). The influence of these factors may vary between lily and maize due to differences in root physiology and Cd uptake mechanisms, but pH consistently emerged as the primary control.
How were the random forest models optimized, and what predictive accuracy was achieved for BCF-Cd?
The models were optimized via hyperparameter tuning, though specific accuracy metrics (e.g., R², RMSE) are not detailed in the provided text. The study states that optimal predictive models were developed, indicating that the tuning process improved performance sufficiently for reliable zoning recommendations.
What is the practical significance of the zoning scheme proposed, and how does it balance economic benefits with safety?
The zoning scheme uses model predictions to delineate areas where lily cultivation is safe (BCF-Cd below the medicinal limit of 0.3 mg·kg−1) and where maize is safe for consumption. Given that lily has 5–10 times higher economic value per mu than maize, the scheme prioritizes lily in low-risk zones and maize in higher-risk zones, thereby maximizing economic returns while ensuring food and medicinal safety.
How do the BCF-Cd values for lily and maize compare, and what are the implications for risk assessment?
Lily BCF-Cd is significantly higher than maize BCF-Cd, indicating that lily accumulates more Cd from soil. This means that even in soils with moderate Cd levels, lily may exceed safety limits, whereas maize may remain safe. Therefore, risk assessments must be crop-specific, and land-use decisions should consider the differential accumulation potential.
What are the limitations of the random forest approach in this context, and how could the model be improved for broader applicability?
The random forest model is data-driven and may require extensive local soil and crop data for training. Its extrapolation to other regions with different soil types and climatic conditions may be limited. Improvements could include incorporating additional soil properties (e.g., clay content, redox potential) and crop physiological parameters, as well as validating the model across diverse geographical areas.
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