Crops feed the world's population and shape human civilization. The improvement of crop
productivity has been ongoing for almost 10,000 years and has evolved from an experience …

Our agricultural systems are now in urgent need to secure food for a growing world
population. To meet this challenge, we need a better characterization of plant genetic and …

LightGBM is an ensemble model of decision trees for classification and regression
prediction. We demonstrate its utility in genomic selection-assisted breeding with a large …

Background In maize hybrid breeding, complementary pools of parental lines with reshuffled
genetic variants are established for superior hybrid performance. To comprehensively …

Maize (Zea mays) is one of the most important crops in the world. However, few
agronomically important maize genes have been cloned and used for trait improvement, due …

Abstract Background Maize (Zea mays L.) is at the vanguard facing the upcoming breeding
challenges. However, both a super pan-genome for the Zea genus and a comprehensive …

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement
in plants. Traditional methods typically use linear regression models with clear assumptions; …

Taking advantage of natural variation promotes our understanding of phenotypic diversity
and trait evolution, ultimately accelerating plant breeding, in which the identification of …

Low grain moisture at harvest is crucial for safe production, transport and storage, but the
genetic architecture of this trait in maize (Zea mays) remains elusive. Here, we measured the …

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits.
We propose an integrative multi-trait breeding strategy via machine learning algorithm …