Beauchêne Katia, Leroy Fabien, Fournier Antoine, Huet Céline, Bonnefoy Michel, Lorgeou Josiane, de Solan Benoît, Piquemal Benoît, Thomas Samuel, Cohan Jean-Pierre
ARVALIS - Institut du Végétal, Ouzouer-le-Marché, France.
ARVALIS - Institut du Végétal, Boigneville, France.
Front Plant Sci. 2019 Jul 16;10:904. doi: 10.3389/fpls.2019.00904. eCollection 2019.
In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops' phenotype. In this paper, we will highlight the PhénoField production capability using data from 2017-wheat trial. The innovative approach of the PhénoField platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.
为了评估田间条件下水分亏缺的影响,研究人员或育种者必须在非常有限的田间环境中建立大型实验网络。经验表明,由于气候条件不允许测试胁迫情景,一半的田间试验并不相关。PhénoField平台是欧盟首个基于田间的基础设施,可确保大量地块免受降雨影响,并能对作物表型进行非侵入式采集。在本文中,我们将利用2017年小麦试验的数据突出展示PhénoField的生产能力。PhénoField平台的创新方法包括使用自动灌溉防雨棚以及高通量田间表型分析,以完善传统表型分析和微气象密集测量。首先,为了测试各种非生物胁迫,自动移动防雨棚通过每天控制所需限制因素对评估作物的施用量和施用时期,实现对施肥或灌溉的精细管理。这种管理基于微气象测量以及碳、水和氮作物预算模拟。此外,由于受控条件下的高通量植物表型分析已经相当成熟,通过配备各种光学传感器的表型龙门架,可以在田间条件下进行可比评估。这种方法能够获取与手动测量相比相似或创新的变量,并且其动态分析能力也很突出。因此,基因型与环境之间的相互作用可以被解读并更详细地描述,因为这不仅能获取环境数据,还能获取植物对水分和氮素限制条件的响应。进一步的数据分析可以获取各种指标动力学的曲线参数,这些参数对于胁迫条件下植物行为的整合性和相关性更强。PhénoField平台的所有这些特性为改进各类作物模型、在整个生长周期中精细表征品种行为以及评估更适合特定研究问题的特定传感器开辟了道路。