杂草抗性预测:基于田间历史的随机森林分析

IF 3.5 Q1 AGRONOMY
Janin Lepke, Johannes Herrmann, Nicolas Remy, Roland Beffa, Otto Richter
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引用次数: 0

摘要

近几十年来,除草剂抗药性已成为一个重大问题。由于诊断费用仍然昂贵,预测模型有助于评估抗药性演变的风险。本文基于德国霍恩洛赫(Hohenlohe)和法国香槟(Champagne)两个地区的田间历史数据,研究了杂草管理对禾本科植物Alopecurus myosuroides Huds对ALS抑制剂抗性演变的影响。使用随机森林方法,对大量的单一分析进行了变量重要性和性能测量,从而对 I 型误差、II 型误差、AUC 和准确性这四项性能指标进行了统计分析。结果表明,将霍恩洛厄的训练数据应用于香槟地区可以获得可接受的预测结果,反之亦然。结果表明,在几乎所有分析中,假阴性分类比假阳性分类更常见。根据来自霍恩洛厄和香槟的 A.myosuroides 样本的综合训练集,可以很准确地预测来自香槟数据集的 Lolium 植物的抗性状况。这表明这两种禾本科植物对 ALS 抑制剂的抗性进化密切相关。这项工作是基于田间杂草管理数据为用户提供简单除草剂抗性预测工具的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weed resistance prediction: a random forest analysis based on field histories
Herbicide resistance has become a major issue in recent decades. Because diagnostics is still expensive, prediction models are helping to assess risks of resistance evolution. In this paper the influence of weed management on the evolution of resistance of the grass Alopecurus myosuroides Huds to ALS-inhibitors is investigated based on field history data from two regions, Hohenlohe in Germany and Champagne in France respectively. Champagne data also comprise information on Lolium spp. Using a random forest method variable importance and performance measures were obtained for a large number of single analyses allowing for a statistical analysis of the four performance measures, type I error, type II error, AUC and accuracy. It could be shown that acceptable predictions can be obtained for training data from Hohenlohe applied to Champagne and vice versa. It turned out that in nearly all analyses false negative classifications are more frequent than false positive classifications. Based on a combined training set of A.myosuroides samples from Hohenlohe and Champagne resistance status of Lolium spp. from the Champagne dataset can be predicted with a good accuracy. This suggest that resistance evolution to ALS-inhibitors of the two grasses are closely related. This work is a first step to set a simple herbicide resistance prediction tool to the users based on field history weed management data.
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来源期刊
Frontiers in Agronomy
Frontiers in Agronomy Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
4.80
自引率
0.00%
发文量
123
审稿时长
13 weeks
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