Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale
Boyang Huang, Qixin Lü, Zhi-Xian Tang, Zhong Tang, Hongping Chen, Xinping Yang, Fang‐Jie Zhao, Peng Wang
Abstract
= 0.64). Scenario simulations revealed that liming soil to a target pH of 6.5 could be one of the most cost-effective approaches to reduce the exceedance of Cd in rice grain. Taken together, these results show that machine learning methods can be used to predict Cd concentration in rice grain reliably at a regional scale and to support soil management and safe rice production.
Topics & Concepts
Scale (ratio)CadmiumEnvironmental scienceSoil scienceAgricultural engineeringMaterials scienceEngineeringMetallurgyGeographyCartographyHeavy metals in environmentGeochemistry and Geologic MappingRadioactivity and Radon Measurements