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一个框架来识别耕地中的积问题:整合现场调查,传统土壤地图和机器学习模型

Xingjie Yin¹, Haile Zhao¹, Yuchao Luo¹

¹College of Land Science and Technology, China Agricultural University, Beijing, China.

|May 30, 2025

概括

在中国北部,积问题 (CAP) 显著降低了作物产量. 基于树的机器学习模型有效地预测了CAP,这与特定的地形和较高的土壤侵蚀风险有关.

科学领域:

农业科学农业科学
土壤科学土壤科学
环境科学环境科学

背景情况:

积累问题 (CAP) 对中国北部的肉土壤地区的农业生产力构成重大威胁.
识别和减轻加农政策对于提高土壤质量和作物产量至关重要.

研究的目的:

用老土壤地图构建积问题 (CAP) 数据集,在Aohan Banner,Chifeng市.
评估各种机器学习模型在CAP识别中的预测性能.
分析地形对农业农产品的发生和分布的影响.

主要方法:

现场研究和数据收集在Aohan旗,奇峰市.
开发一个利用传统土壤地图的CAP数据集.
机器学习模型 (BRT,XGBoost) 的应用和评估,用于CAP预测.
空间分析和回归建模以评估地形影响.

主要成果:

积问题 (CAP) 在干旱地区普遍存在,影响了58%的受访农民,产量减少了84%.
基于树的模型,特别是BRT和XGBoost,在预测CAP方面表现出色,BRT在绘制地图方面表现出色.
在空间上,CAP集中在东部和中部的Aohan Banner,与山坡,山脊和山峰相关,并且与地形变量 (MRVBF,GEO) 有负相关性.
地形变量和CAP概率之间存在强烈的负相关性,在易受土壤侵蚀的地区发生率增加.

结论:

机器学习模型,特别是BRT,为区域土壤评估中识别和绘制积问题 (CAP) 提供了有效的工具.
地形因素和土壤侵蚀风险是预测CAP发生的重要指标.
研究结果为针对性土壤管理策略和受影响农业地区未来研究方向提供了关键的见解.

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