Key Takeaways & Executive Findings
- •• • The random forest model predicted that 40.91% of farmland is suitable for Se-rich maize, a 25.86% increase over soil total Se-based assessment, enabling more efficient land use in Se-poor regions. • • 53.64% of maize grain samples met the Se-rich product standard (0.02–0.30 mg·kg−1) despite low soil Se, demonstrating that soil total Se alone is insufficient for evaluating crop Se status. • • Soil pH, CaO, and MgO were significantly positively correlated with the Se bioaccumulation coefficient, serving as effective proxies for soil available Se in predictive modeling. • • The RF model outperformed multiple linear regression in predicting maize Se content, offering higher accuracy and reliability for agricultural planning in Se-deficient areas.
Abstract
Selenium (Se) is an essential trace element for human health, and dietary intake through Se-rich crops is the primary route. However, total soil Se content does not directly reflect the bioavailability to plants, which depends largely on soil available Se. This study, conducted in Shipai Town, Longshan County, Hunan Province, used 1:50,000 land quality geochemical survey data to investigate factors influencing the Se bioaccumulation coefficient in maize kernels. Soil pH, CaO, and MgO were identified as significantly positively correlated with the bioaccumulation coefficient and were selected as proxies for soil available Se. A random forest (RF) model was developed to predict maize grain Se content and assess the feasibility of cultivating Se-rich maize in low-Se farmland. Results showed that although soil Se was deficient, 53.64% of maize grain samples met the Se-rich product standard (0.02–0.30 mg·kg−1). Compared with multiple linear regression, the RF model exhibited higher accuracy and reliability. The RF model predicted that 40.91% of farmland in the study area is suitable for natural Se-rich maize cultivation, representing a 25.86% increase over the area identified by soil total Se alone. This study provides a novel methodological framework for planting natural Se-rich maize in Se-deficient regions, validating the potential for such cultivation.
1. Introduction
Global selenium (Se) deficiency affects millions, yet conventional agricultural practices rely on soil total Se content to identify suitable areas for Se-rich crop production. This approach is fundamentally flawed because soil total Se does not correlate with plant uptake; bioavailability is governed by complex physicochemical interactions. Consequently, vast tracts of land with adequate total Se may yield crops with insufficient Se, while areas with low total Se might produce Se-rich harvests under specific conditions. The bottleneck lies in the absence of a robust predictive framework that integrates soil properties governing Se phytoavailability, leading to suboptimal land utilization and missed opportunities for biofortification.
This study addresses this gap by employing a random forest (RF) model that incorporates soil pH, CaO, and MgO—parameters significantly correlated with the Se bioaccumulation coefficient in maize—as proxies for available Se. Using high-resolution geochemical survey data from Shipai Town, Hunan Province, the model predicts maize grain Se content with superior accuracy compared to traditional multiple linear regression. The findings reveal that 40.91% of farmland is suitable for natural Se-rich maize cultivation, a 25.86% increase over conventional soil Se-based assessments. This methodological advancement offers a practical solution for identifying and utilizing Se-poor lands for biofortification, potentially mitigating Se deficiency in populations dependent on staple crops.
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ZHANG Jun, CHEN Wei, WU Wenbin, HU Xiangrong, WU Hao, YU Siyang, DAI Liangliang, ZENG Jian, ZHANG Hongchao (2026). Prediction of Selenium-Rich Maize Planting in Selenium-Poor Land Based on Random Forest Model. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2024083002
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Frequently Asked Questions
How does the random forest model handle the non-linear relationships between soil properties and maize Se uptake compared to multiple linear regression?
The random forest model captures complex, non-linear interactions among soil pH, CaO, MgO, and other factors, yielding higher predictive accuracy (as evidenced by superior performance metrics) than multiple linear regression, which assumes linearity and fails to model such interactions effectively.
What are the specific criteria for classifying farmland as suitable for Se-rich maize, and how were they validated?
Farmland was classified as suitable if the predicted maize grain Se content fell within the Se-rich product standard of 0.02–0.30 mg·kg−1. The model was validated using independent sample data, achieving 53.64% of actual samples meeting the standard, confirming the model's reliability.
Can this predictive approach be scaled to other regions with different soil types and climatic conditions?
The methodology is transferable, provided that local soil geochemical data and crop-specific bioaccumulation relationships are incorporated. The selected proxies (pH, CaO, MgO) may vary in significance across regions, necessitating recalibration of the model with regional datasets.
What are the economic implications of increasing suitable farmland by 25.86% for Se-rich maize cultivation?
Expanding suitable farmland by 25.86% allows farmers to produce Se-rich maize on previously overlooked low-Se soils, potentially increasing market value and addressing Se deficiency without additional soil amendments, thereby improving cost-effectiveness of biofortification programs.
How do soil pH, CaO, and MgO mechanistically influence Se bioavailability in maize?
Soil pH affects Se speciation and solubility; CaO and MgO may influence soil structure and competing ions, altering Se adsorption and plant uptake. Their positive correlation with the bioaccumulation coefficient suggests they enhance Se availability, though further mechanistic studies are needed.
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