One of the major challenges in plant breeding is the differential response of genotypes from one environment to another, or phenotypic plasticity. Although many methods have been developed, these methods lack the forecasting capacity, and our understanding of genomic and environmental determinants is still limited. We have recently established an analytical framework to answer long-standing questions in phenotypic plasticity and genotype by environment interaction. The essence of this framework is to combine knowledge from physiology, genetics, and statistics to identify the determinants rather than model fitting. It is about identifying hidden patterns and biological insights and enhancing forecasting capacities and gene effect continuum profiling.Our long-term goal is to significantly enrich our understanding of genes and environmental factors underlying phenotypic plasticity and translate this understanding to application by enabling the in-season, on-target crop performance prediction and informing plant breeders about exploring genetic and environmental space. We hypothesize that the long-desired independent environmental indices can be identified to exploit the patterns underlying crop performance variation. Toward this goal, we designed four components of this project. 1) Develop an open-source package and host workshops to facilitate the community to dissect flowering-time plasticity. These workshops will not only provide lectures to explain the background knowledge and new insights in phenotypic plasticity, but also train attendees to practice with prepared datasets and the open-source package. 2) Identify genomic and environmental determinants underlying phenotypic plasticity of agronomically important traits from diverse populations. For multi-environmental trial data for different traits, an environmental index will be sought to quantify the overall environment and approximate what is reflected by the overall performance of the population of genotypes. With the obtained environmental index, performance data of genotypes are then regressed to extract two parameters to connect with genome-wide genetic variants for genetic determinants identification. 3) Integrate crop growth model to quantify environments and dissect phenotypic plasticity. Sensitivity analysis will be conducted to identify major environmental factors that affect the performance of genotypes. Performance prediction by combining the strengths of crop growth model and the phenotypic-plasticity approach will be pursued. 4) Explore ways to understand the environmental factors underlying the performance trial and optimize breeder's design of testing and reference set. Performance data obtained directly from two collaboration breeders will be analyzed and specific recommendations will be made about how each testing location captures the range of major environmental index and the distribution, and what are the possible representative subset of testing locations when resource is limited.This project is expected togain an improvedunderstanding of genes and environmental factors underlying phenotypic plasticity; achieve an enhanced ability to make in-season, on-target crop performance prediction; and develop an improved analytics to help plant breeders optimize the process of exploring genetic and environmental space.