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通过使用可解释的机器学习来识别飞灰和有机修改下的大米生产率的土壤驱动因素

Soumyajeet Pradhan¹, Prasanna Kumar Samant¹, Rabindra Kumar Nayak¹

¹Department of Soil Science and Agricultural Chemistry, College of Agriculture, Odisha University Agriculture and Technology (OUAT), Bhubaneswar, 751003, Odisha, India.

|November 19, 2025

概括

飞灰与有机物和NPK肥料相结合,在酸性土壤中显著提高了大米产量. 可解释的机器学习确定了关键的土壤特性和微生物酶,推动了这种改善的土壤健康和作物生产率.

科学领域:

农学和土壤科学农学和土壤科学
环境科学环境科学
农业工程农业工程

背景情况:

热带酸性土壤遭受土壤质量差和营养缺乏,限制了大米的生产.
飞 (FA),一个工业废物,显示出作为土壤修改的潜力,但其对土壤产量动态的影响需要进一步调查.
非线性土壤-产量关系是复杂的,用传统的统计方法分析是具有挑战性的.

研究的目的:

评估飞灰,农场 (FYM) 和NPK肥料对热带酸性土壤中的土壤特性和大米产量的综合影响.
通过使用可解释的机器学习 (ML) 来识别影响大米生产力的关键土壤预测因素.
了解土壤特性和产量反应之间的机械相互作用,以实现综合营养管理.

主要方法:

进行了一项实地实验,使用不同速率的飞 (10-40t ha-1),FYM (5t ha-1),NPK肥料.
分析了土壤的物理化学和生物特性,包括酶活动.
使用可解释的机器学习模型,确定了大米产量的重要土壤预测因素,并分析了它们的相互作用.

主要成果:

结合40t ha-1飞灰,FYM和NPK (FA40 + FYM + NPK) 的处理导致了最高的谷物产量 (54.0 q ha-1),比单独的NPK增加了38.5%.
这种最佳处理显著改善了土壤的多孔性 (45.5%),水容量 (37.8%),可用的N (212.9公斤ha-1),可用的P (19.6公斤ha-1),以及微生物酶活动 (尿酶和β-葡萄糖酶).
机器学习确定了β-葡萄糖酶,有机碳,尿素酶,可用的和粘土含量作为关键产量预测因素,在碳周转和营养物质矿化之间观察到协同作用.

结论:

与FYM和NPK集成应用飞灰 (20-40t ha-1),有效地提高了土壤功能,并维持了酸性热带土壤中的大米生产率.
可解释的ML模型为土壤健康评估和为酸性农业生态系统优化化肥料策略提供了关键的机械洞察力.
这种方法为管理退化土壤和提高农业产量提供了可持续的途径.

关键词:

酸性土壤是一种酸性土壤.有条件的随机森林.FYM FYM是指一个年轻人.有机碳有机碳是有机碳的组成部分.土壤的健康状况这就是β-glucosidase.

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