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评估玉米叶中的作物气状况:使用叶绿素光参数的预测建模方法

Xiangzeng Meng^1,2, Shan Zhang^1,2, Lichun Wang^1,2

¹Institute of Agricultural Resource and Environment, Jilin Academy of Agricultural Sciences, 1363 Shengtai St, Changchun, 130033, Jilin, PR China.

|November 5, 2024

概括

使用叶绿素光技术对玉米进行精确的 (N) 评估,为优化施肥和促进可持续农业提供了一种非破坏性的方法. 这种方法提高了作物产量,减少了对环境的影响.

科学领域:

农业科学农业科学
植物生理学植物生理学
遥感遥感遥感遥感

背景情况:

化学肥料的使用急剧增加,超过了作物产量增加,突出了作物中精确 (N) 评估的关键需求.
目前的N评估方法往往是劳动密集型,对环境敏感,缺乏精度.
优化肥对于提高作物产量和减轻环境污染至关重要.

研究的目的:

开发和验证一种采用叶绿素光 (ChlF) 技术的模型,用于准确,非破坏性地评估玉米中的叶子 (N) 状态.
评估不同作物草管理策略和N应用率对叶子N含量和ChlF参数的影响.
确定关键的ChlF参数和补充的叶子特征,以提高N评估模型的准确性.

主要方法:

利用了玉米杂交Fumin 985的长期实验,在两个草管理策略和六个N应用率下采样.
采用部分最小平方回归 (PLSR) 模型,结合各种叶绿素光参数 (ChlF) 来预测叶子N含量 (N叶).
应用主要成分分析 (PCA) 来减少ChlF数据的维度,并使用ChlF和叶子特征的不同组合 (总色素含量,叶子干重) 来比较模型性能.

主要成果:

发现270公斤/ha-1的N应用率足以满足作物N需求.
叶子N含量,总色素含量 (TP) 和叶子干重 (DW叶子) 与N应用率显著相关,并影响了OJIP光动态.
与仅使用DW的模型相比,结合ChlF和TP的模型在预测N叶的准确性更高. 确定的关键ChlF参数包括ABS/RC, φ(Eo), ETo/CSm,以及 δ(Ro) /(1-δ(Ro)).

结论:

使用叶绿素光技术的非破坏性N叶检测对于玉米是可行的.
整合额外的叶子特征,如总色素含量,对于提高模型准确性至关重要.
这种技术在更大规模的应用需要考虑当地现场条件,以实现高效的管理和可持续的农业.

关键词:

叶绿素的光效应叶绿素光效应农作物草管理管理 N 应用率 N 应用率部分最小平方回归.春季玉米春季玉米

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