Résumé
IRRI is leading the CGIAR research program (CRP) on 'RICE' which has 5 flagship projects. The flagship 4 (FS4) is on “Global Rice Array (GRA)”. The GRA is a physical network of field laboratories and trial sites, which characterizes and monitors the environments, conducts crop/pathogen trials, and develops models to quantify and map the impact of abiotic/biotic factors on yield. Major international organizations participating in GRA are IRRI, CIAT (Columbia), Africa Rice Centre (Ivory Coast), JIRCAS (Japan) and CIRAD (France). The main goal of the GRA is to establish a multi-environment field network serving as a tool to design site-specific rice ideotypes adapted to future climates. The Array will feed crop modelers as end-users in order to generate better adapted varieties at a faster rate considering information on yield potential and effects of biotic/abiotic factors to yield variability by environment and varietal type. The specific objectives are to: -establish a Global Rice Array with equipped and well characterized field laboratories and trial sites -provide breeders information on yield potential and effect of biotic/abiotic factors to yield variability by environment and varietal type -generate genomics and phenomics data resources for multiple environments and climatic condition -accelerate gene and trait discovery in order to help optimize the use of genetic diversity under different climatic scenarios -deploy technologies adapted to specific environments based on knowledge of specific features of future rice production environments.Within this GRA, the Cluster of Activities (CoA) 1 (out of 5) aims at establishing the “world-wide field lab” for which an “antenna panel” is constituted as a set of rice genotypes that act as sensors for environmental changes. Seventy three rice genotypes for various traits or as indicators are nominated/contributed by different organizations to form the current “antenna panel”. This antenna panel will be grown at almost 30 locations across the world in different continents to sense the climate change effects through G x E (x M) interactions based on rough measurements (crop duration, yield, panicle number, lodging incidence) in an augmented design (1.2 rep per variety). This will help to characterize and diagnose the diversity and dynamics of a changing climate “through the eye of the crop.” This will pave the way in developing climate smart technologies. In addition, this CoA will ambition to feed crop models in order to map the risks (historical data) and to predict grain yield (future scenario) in some specific sites. This exercise will be conducted, on one hand in lowland fields (12 sites), on the other hand in upland fields (7 sites), for which a distinct set of 12 genotypes (included within the total 73 with two genotypes in common) will be grown. The lowland fields will be 1 in the Philippines, 1 in China, 4 in India, 1 in Senegal, 2 in Colombia, 2 in Brazil, 1 in Uruguay), while the upland fields will be 1 in India, 1 in Ivory Coast, 2 in Madagascar, 1 in Colombia, 2 in Brazil. In each site, locally recommended cultural practices for highest yields will be applied. In addition to the previous set of data, some more data on tillering, biomass and yield components will be collected as 3 reps for each of the 12 varieties. One key up-coming step is to identify some rice modelling groups willing to be part of this exercise by exploiting these data sets and sharing their simulation outputs within the Rice AgMIP initiative and the Rice CRP.