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使用随机森林算法,模拟在缺乏Se的农田种植富含Se玉米的可行性

Jun Zhang¹, Zhitao Huo¹, Cong Mao¹

¹Changsha General Survey of Natural Resources Center, Changsha, China.

Environmental geochemistry and health

|January 16, 2024

概括

对健康至关重要,这项研究发现,土壤pH,CaO和MgO影响玉米中的生物积累. 这项研究确定了在缺乏的地区适合种植自然丰富的玉米的土地.

科学领域:

农业科学农业科学
环境地质化学环境地质化学
营养科学营养科学

背景情况:

(Se) 是一种对人类健康至关重要的微量元素.
植物的吸收取决于土壤的可用Se,而不是总含量,使Se丰富的作物可以在缺乏的地区种植.
了解玉米等作物中的Se生物积累是解决区域Se缺陷的关键.

研究的目的:

研究影响玉米种子中Se生物积累系数的因素.
利用地化学调查数据,开发玉米种子Se含量的预测模型.
在缺乏Se的地区确定适合种植天然富含Se的玉米的农田.

主要方法:

在1:50,000尺度上进行了土地质量地化学调查.
采用随机森林 (RF) 和多重线性回归 (MLR) 模型来预测玉米种子的Se含量.
分析了土壤特性 (pH,CaO,MgO) 对Se生物积累的影响.

主要成果:

研究区的土壤缺乏Se,但54%的玉米谷物样本符合Se丰富标准.
土壤pH,CaO和MgO含量显著影响了玉米种子中的Se生物丰富系数 (分别为0.54,0.42,0.35影响水平).
射频模型在预测玉米的Se含量方面表现出高于MLR的精度,确定了41%的农田有利于种植Se丰富的玉米.

结论:

引入了一种方法框架,用于在缺乏Se的地区种植天然富含Se的玉米.
射频模型提高了适合种植Se丰富玉米的土地的识别,与土壤单独的Se含量相比,潜在种植面积增加了26%.
这种方法支持丰富食品来源的生产,并解决与缺乏相关的公共卫生问题.

关键词:

农业产品规划农业产品规划预测模型是一个预测模型.随机森林模型是一个随机森林模型.这就是生物积累.土壤的Se水平.

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