[论文解读] Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower

Pierre Casadebaig, Ronan Trépos+4 more|arXiv (Cornell University)|Mar 12, 2014

Greenhouse Technology and Climate Control参考文献 2被引用 6

一句话总结

这项计算研究证明，通过提升向日葵的遗传多样性，可在气候变化波动下提高产量稳定性，从而实现对多种环境条件的更好适应。利用基于过程的建模方法，作者表明，将基因多样性高的栽培品种与优化的管理措施及环境条件相结合，相较于仅依靠传统育种，能显著提升作物表现和抗逆性。

ABSTRACT

A crop can be represented as a biotechnical system in which components are either chosen (cultivar, management) or given (soil, climate) and whose combination generates highly variable stress patterns and yield responses. Here, we used modeling and simulation to predict the crop phenotypic plasticity resulting from the interaction of plant traits (G), climatic variability (E) and management actions (M). We designed two in silico experiments that compared existing and virtual sunflower cultivars (Helianthus annuus L.) in a target population of cropping environments by simulating a range of indicators of crop performance. Optimization methods were then used to search for GEM combinations that matched desired crop specifications. Computational experiments showed that the fit of particular cultivars in specific environments is gradually increasing with the knowledge of pedo-climatic conditions. At the regional scale, tuning the choice of cultivar impacted crop performance the same magnitude as the effect of yearly genetic progress made by breeding. When considering virtual genetic material, designed by recombining plant traits, cultivar choice had a greater positive impact on crop performance and stability. Results suggested that breeding for key traits conferring plant plasticity improved cultivar global adaptation capacity whereas increasing genetic diversity allowed to choose cultivars with distinctive traits that were more adapted to specific conditions. Consequently, breeding genetic material that is both plastic and diverse may improve yield stability of agricultural systems exposed to climatic variability. We argue that process-based modeling could help enhancing spatial management of cultivated genetic diversity and could be integrated in functional breeding approaches.

研究动机与目标

• 研究遗传多样性如何影响向日葵在气候变化波动下的产量稳定性。
• 通过计算机模拟实验评估基因型-环境-管理（GEM）互作对作物表现的影响。
• 评估通过性状重组生成的虚拟遗传材料在多变气候下是否优于现有栽培品种。
• 探讨基于过程的建模如何指导农业系统中遗传多样性的空间管理以提升农业韧性。
• 通过识别在环境胁迫下具有塑性和适应性的关键性状，为功能育种策略提供依据。

提出的方法

• 开发了一款基于过程的作物模型，用于模拟在不同土壤-气候条件下的向日葵生长。
• 通过计算机模拟实验，在目标作物环境群体中比较了现有和虚拟向日葵栽培品种的表现。
• 使用优化算法识别满足特定作物表现指标的GEM组合。
• 在多种环境情景下模拟了产量、稳定性及胁迫响应等性能指标。
• 通过重组植物性状生成虚拟遗传材料，评估遗传多样性的影响。
• 分析了栽培品种选择、育种进展以及环境变异对整体产量表现的相对贡献。

实验结果

研究问题

• RQ1 在气候变化波动下，遗传多样性增加如何影响向日葵的产量稳定性？
• RQ2 与每年通过育种实现的遗传进步相比，栽培品种选择对作物表现的影响有多大？
• RQ3 通过性状重组设计的虚拟遗传材料是否能在多变环境中优于现有栽培品种？
• RQ4 哪些植物性状最有助于向日葵的表型可塑性和环境适应？
• RQ5 基于过程的建模如何支持农业系统中遗传多样性的空间管理？

主要发现

• 增加遗传多样性通过提升对特定环境条件的适应能力，显著改善了在气候变化波动下的产量稳定性。
• 通过性状重组设计的虚拟遗传材料在多样环境中的表现和稳定性均优于现有栽培品种。
• 栽培品种选择对作物表现的积极影响，其影响程度与每年通过育种实现的遗传进步相当。
• 针对增强可塑性的性状进行育种，提升了向日葵栽培品种的全球适应能力。
• 将基于过程的建模整合到功能育种方法中，可增强遗传多样性在空间上的管理能力。
• 通过模拟优化GEM组合发现，具有多样性和可塑性的栽培品种比均一的常规育种品种植根更适应环境波动。