Abstract：
Selenium (Se) is an essential element for human and animal health and has antioxidant, anticancer, and antiviral effects. However, more than 100 million people in China do not have enough Se in their diets, resulting in a state of low Se in the human body. Since the absorption of Se by crop seeds depends not only on the Se content in soil, there are many omissions and misjudgments in the division of Se-rich producing areas. Soil pH, total iron oxide content (TFe2O3), soil organic matter (SOM), and P and S contents were the main factors affecting Se migration and transformation in the soil-rice system. In this study, we compared the performance of the back propagation neural network (BP network) and multiple linear regression (MLR) using 177 pairs of soil-rice samples. Our results showed that the BP network had higher accuracy than MLR. The accuracy and precision of the prediction data met the requirements, and the prediction data were reliable. Based on the Se data of surface paddy fields, 26,900 ha of Se-rich rice planting area was planned using this model, accounting for 77% of the paddy field area. In the planned Se-rich area for rice, the proportion of soil Se content greater than 0.4 mg·kg-1 was only 5.29%. Our research is of great significance for the development of Se-rich lands.
摘要：
硒(Se)是人类和动物健康的必需元素,抗癌，和抗病毒作用。然而,中国超过1亿人的饮食中没有足够的硒，导致人体内低硒的状态。由于作物种子对硒的吸收不仅取决于土壤中的硒含量，富硒产区的划分存在许多遗漏和误判。土壤pH值,总氧化铁含量(TFe2O3)，土壤有机质（SOM），P和S含量是影响土壤-水稻系统中硒迁移和转化的主要因素。在这项研究中,我们使用177对土壤-水稻样品比较了反向传播神经网络（BP网络）和多元线性回归（MLR）的性能。我们的结果表明，BP网络比MLR具有更高的精度。预测数据的准确性和精度达到要求，预测数据可靠。基于表层水田的硒数据，使用该模型规划了26900公顷的富硒水稻种植面积，占水田面积的77%。在计划的水稻富硒区，土壤硒含量大于0.4mg·kg-1的比例仅为5.29%。本研究对富硒土地的开发具有重要意义。
