欧亚国际

摘要  

因资源有限以及环境变化等因素，对食品、饲料以及能源的需求日益增长，已成为挑战因素。进行耐胁迫、耐旱育种是育种者特别面临的较重要的挑战之一。比较筛查海量候选植物和候选基因以及研究其与环境互作是该任务主要瓶颈之一。我在干旱条件下，我们建议在预田间筛查（表型）的性状时要考虑4个主要因素： (i)何时、何地以及何种条件下进行表型研究；(ii)要进行表型研究的性状； (iii) 如何进行表型研究（何种方法）；以及(iv)如何将采集数据转换为可用于实践决策的知识。我们对一些常见错误进行了描述，包括采用不充分表型方法、不正确术语以及不当使用非相关性状作为标记用于耐旱研究。我们建议采用较多非成像相关、基于生理的、高通量表型系统，结合土壤-植物-大气连续体(SPAC)测量方法，调整植物反应模型来适应持续和波动的环境条件，应深入对其进行研究以便将其作为表型工具，以便较好理解和鉴别植物胁迫反应。我们相信很多今天的表型挑战将来会通过技术和自动化加以解决，留给我们的主要挑战是将大量累积信息转化为有意义知识和决策工具。

额外关键词：干旱胁迫、耐旱、建模、筛查、压力生理学

The advantages of functional phenotyping in pre-field screening  for drought-tolerant crops

Abstract. 

Increasing worldwide demand for food, feed and fuel presents a challenge in light of limited resources and climatic challenges. Breeding for stress tolerance and drought tolerance, in particular, is one the most challenging tasks facing breeders. The comparative screening of immense numbers of plant and gene candidates and their interactions with the environment represents a major bottleneck in this process. We suggest four key components to be considered in pre-field screens (phenotyping) for complex traits under drought conditions: (i) where, when and under which conditions to phenotype; (ii) which traits to phenotype; (iii) how to phenotype (which method); and (iv) how to translate collected data into knowledge that can be used to make practical decisions. We describe some common pitfalls, including inadequate phenotyping methods, incorrect terminology and the inappropriate use of non-relevant traits as markers for drought tolerance. We also suggest the use of more non-imaging, physiology-based, high-throughput phenotyping systems, which, used in combination with soil–plant–atmosphere continuum (SPAC) measurements and fitting models of plant responses to continuous and fluctuating environmental conditions, should be further investigated in order to serve as a phenotyping tool to better understand and characterise plant stress response. In the future, we assume that many of today’s phenotyping challenges will be solved by technology and automation, leaving us with the main challenge of translating large amounts of accumulated data into meaningful knowledge and decision making tools.

Additional keywords: drought stress, drought tolerance, modeling, modelling, screening, stress physiology.