Exploring the links between genetic and phenotypic traits is an important area of research in agronomy. One of the main objectives of this is to accelerate the development of important traits that can positively impact the agricultural economy. However, due to the existence of complex molecular interactions, to gain complete understanding will warrant data analyses performed at different molecular and environmental levels for a given (plant) subject. For instance, to understand how rice genes involved in metabolism or signaling of growth regulators control the rice panicle architecture. While high-throughput technologies have played a key role in accelerating and generating the much-needed data, these can only partially capture the dynamics in genotypephenotype relations. Consequently, our knowledge of the complex relationships between the different molecular actors responsible for the expression of the phenome in various plant systems remains fragmented. Hence, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD [1] (www.agrold.org), a knowledge graph system that exploits the Semantic Web technology and FAIR principles [2], to integrate information to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, Arabidopsis and in this way facilitating the formulation of new scientific hypotheses. We present some integration results of the project, which currently focused on genomics, proteomics and phenomics. AgroLD is now an RDF knowledge base of 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources –such as Ensembl plants [3], Gramene.org [4] and TropGeneDB [5]– with 15 ontologies –such as the Gene Ontology [6] and Plant Ontology [7]. Our objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to