优化作物品种混合用于病毒性疾病管理:以木薯病毒流行为例研究。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-18 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1012842
Israël Tankam Chedjou, Ruairí Donnelly, Christopher A Gilligan
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引用次数: 0

摘要

木薯病毒性疾病,包括木薯花叶病(CMD)和木薯褐条病(CBSD),对全球粮食安全构成重大威胁,特别是在撒哈拉以南非洲。本研究通过引入CropMix这一新颖的基于网络的应用程序,探索了品种混合作为可持续疾病管理策略的潜力。该应用程序编码了一个灵活的虫媒植物病原体传播模型,以预测和优化品种混合情况下的产量。例如,我们使用该应用程序来评估抗病毒木薯品种(在没有病害的情况下产量可能较低)保护更敏感品种免受CMD和CBSD侵害的能力,我们还考虑了涉及耐病毒品种和非寄主作物的混合物。对于CMD,木薯花叶病毒(begomvirus属)的高传播率限制了混合物的效果,无论粉虱的压力如何,敏感的单一栽培比敏感的抗性混合物的产量更高。相比之下,对于CBSD,品种混合显示出实质性的效益,抗性品种屏蔽了易感品种,减轻了中高虫害压力下的严重产量损失。涉及非寄主作物的管理战略和诸如防虫等补充性控制措施可以进一步提高结果。该模型的简单性和适应性使其适合针对各种虫媒作物病毒病和农业生态环境提出量身定制的建议。该研究强调需要整合真实世界的数据和参与性框架,以完善和实施疾病管理战略。我们讨论了农艺潜力和农民可接受性之间的关键平衡,强调了合作努力确保可持续木薯生产的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing crop varietal mixtures for viral disease management: A case study on cassava virus epidemics.

Cassava viral diseases, including Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD), pose significant threats to global food security, particularly in sub-Saharan Africa. This study explores the potential of varietal mixtures as a sustainable disease management strategy by introducing CropMix, a novel web-based application. The application encodes a flexible insect-borne plant pathogen transmission model to predict and optimize yields under scenarios of varietal mixtures. For instance, we use the application to evaluate the ability of virus-resistant cassava varieties (which may have lower yields in the absence of disease) to protect more susceptible varieties against CMD and CBSD, and we also consider mixtures involving tolerant varieties and non-host crops. For CMD, the high transmission rates of cassava mosaic virus (genus Begomovirus) limits the efficacy of mixtures, with susceptible monocultures emerging as more productive than susceptible-resistant mixtures whatever the whitefly pressure. In contrast, for CBSD, varietal mixtures demonstrate substantial benefits, with resistant varieties shielding susceptible ones and mitigating severe yield losses under moderate or high insect pressure. Management strategies involving non-host crops and complementary control measures, such as roguing, can further enhance outcomes. The model's simplicity and adaptability make it suitable for tailoring recommendations to diverse insect-borne crop viral diseases and agroecological contexts. The study emphasizes the need for integrating real-world data and participatory frameworks to refine and implement disease management strategies. We discuss the critical balance between agronomic potential and farmer acceptability, underscoring the importance of collaborative efforts to ensure sustainable cassava production.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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