SinoGreenTech Academic Portal
DL
Verified CAS / Academic Author2 Decoded Studies

Prof. DAI Liangliang

Changsha Natural Resources Comprehensive Survey Center, China Geological Survey, Changsha, 410600, China

Co-Affiliations:Changsha General Survey of Natural Resources Center, China Geological Survey, Changsha 410625, China

Research Publications & English Decoded Briefs

Showing 2 publications
Environmental Chemistry2026DOI: 10.7524/j.issn.0254-6108.2024083002

Prediction of Selenium-Rich Maize Planting in Selenium-Poor Land Based on Random Forest Model

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.

Environmental Chemistry2026DOI: 10.7524/j.issn.0254-6108.2025042203

Safe Utilization of High Cadmium Cropland by Random Forest Based on Soil Properties

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.