澳大利亚东部 Argemone mexicana、Brassica tournefortii 和 Rapistrum rugosum 的出现模式

IF 2.4 3区 农林科学 Q1 AGRONOMY Gesunde Pflanzen Pub Date : 2024-07-04 DOI:10.1007/s10343-024-01003-w
Gulshan Mahajan, Bhagirath Singh Chauhan
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引用次数: 0

摘要

一项研究评估了利用累积生长度日(CGDD)预测三种杂草出苗周期的潜力:Argemone mexicana、Brassica tournefortii 和 Rapistrum rugosum。通过在土壤表面放置 200 颗每种杂草的新鲜种子,定期监测杂草的出苗情况。杂草萌发数据采用三参数半正方形贡珀兹模型进行拟合。根据气温和降雨量的季节性变化,50% 的墨西哥豚草出苗所需的 CGDD 为 3380 至 5302。大部分萌发出现在 3 月至 6 月。A. mexicana 的种子有休眠现象,大多数种子在第二季萌发。根据温度和降雨强度的季节性变化,B. tournefortii 50%萌发所需的 CGDD 为 824 至 2311。大多数 B. tournefortii 在第一季(2 月至 6 月)萌发,表明种子几乎没有休眠。R. rugosum 50%萌发所需的 CGDD 为 2242 至 2699,取决于天气参数(温度和降雨量)。R. rugosum 的主要出苗期为 2 月至 6 月,60% 的种子在第一季发芽,40% 的种子在第二季发芽,这表明种子存在休眠。对三种杂草出苗模式的模型验证的决定系数为 85%,表明 CGDD 可以很好地预测这些杂草的出苗。这些结果表明,根据 CGDD 和降雨模式预测三种杂草的出苗情况将有助于种植者做出更好的杂草管理决策。
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Emergence Pattern of Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum in Eastern Australia

A study assessed the potential for using cumulative growing degree days (CGDD) to predict the weed emergence periodicity of three weed species: Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum. Weed emergence was monitored regularly by placing 200 fresh seeds of each weed species on the soil surface. Weed emergence data was fit using a three-parameter sigmoidal Gompertz model. The CGDD required for 50% emergence of A. mexicana ranged from 3380 to 5302, depending upon the seasonal variation in temperature and rainfall. The majority of emergence appeared from March to June. The seeds of A. mexicana exhibited dormancy, as the majority of seeds germinated in the second season. The CGDD required for 50% emergence of B. tournefortii ranged from 824 to 2311, depending upon the seasonal variation in temperature and intensity of rainfall. Most cohorts of B. tournefortii appeared in the first season from February to June, indicating little dormancy in seeds. The CGDD required for 50% emergence of R. rugosum ranged from 2242 to 2699, depending upon weather parameters (temperature and rainfall). The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season, indicating dormancy in seeds. The coefficients of determination for the model verification on the emergence pattern of three weeds were > 85%, suggesting that CGDD are good predictors for the emergence of these weeds. These results suggest that forecasting the emergence of three weed species on the basis of CGDD and rainfall patterns will help growers to make better weed management decisions.

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来源期刊
Gesunde Pflanzen
Gesunde Pflanzen 农林科学-农艺学
CiteScore
3.50
自引率
25.80%
发文量
152
审稿时长
>12 weeks
期刊介绍: Gesunde Pflanzen publiziert praxisbezogene Beiträge zum Pflanzenschutz in Landwirtschaft, Forstwirtschaft, Gartenbau und öffentlichem Grün und seinen Bezügen zum Umwelt- und Verbraucherschutz sowie zu Rechtsfragen. Das Themenspektrum reicht von der Bestimmung der Schadorganismen über Maßnahmen und Verfahren zur Minderung des Befallsrisikos bis hin zur Entwicklung und Anwendung nicht-chemischer und chemischer Bekämpfungsstrategien und -verfahren, aber auch zu Fragen der Auswirkungen des Pflanzenschutzes auf die Umwelt, die Sicherung der Ernährung sowie zu allgemeinen Fragen wie Nutzen und Risiken und zur Entwicklung neuer Technologien. Jedes Heft enthält Originalbeiträge renommierter Wissenschaftler, aktuelle Informationen von Verbänden sowie aus der Industrie, Pressemitteilungen und Personalia. Damit bietet die Zeitschrift vor allem Behörden und Anwendern im Agrarsektor und Verbraucherschutz fundierte Praxisunterstützung auf wissenschaftlichem Niveau.
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