Data mining brings new clarity to plant breeding, according to new Iowa State University study

February 11, 2019

By Fred Love, University News Service

AMES, Iowa – The immense number of possible hybrids that can be created from inbred corn plants can leave plant breeders wondering where to start when attempting to produce new crop varieties with desirable traits. But new research from an Iowa State University agronomist shows how advanced data mining techniques can enhance the efficiency of the process.

Development of a PhenoNet - an Integrated Robotic Network for Field-based Studies of Genotype x Environment Interactions

An award is made to Iowa State University to develop and deploy PhenoNet - an integrated robotic network for field-based studies of genotype crossed with environment (GxE) interactions. The core component of PhenoNet is a set of PhenoBots; lightweight robots that are able to autonomously navigate between crop rows using GPS and local range sensors while employing advanced sensing technologies to phenotype crop plants. The PhenoBots can measure indicators such as stalk size, plant height, leaf angle and tassel/inflorescence properties over time.

Low-cost nitrate sensors to populate genotype-informed yield prediction models for next generation breeders

Our civilization depends on continuously increasing levels of agricultural productivity, which itself depends on (among other things) the interplay of crop varieties and the environments in which these varieties are grown. Hence, to increase agricultural productivity and yield stability, it is necessary to develop improved crop varieties that deliver ever more yield, even under the variable weather conditions induced by global climate change, all the while minimizing the use of inputs such as fertilizers that are limiting, expensive or have undesirable ecological impacts.

Subscribe to RSS - genotype