Gabi-Energy: Biomass production in maize - genomics guided breeding of energy maize and a systems-oriented analysis

The rising gap between the surging global oil demand and the available fossil fuel reserves has initiated worldwide efforts to develop alternative energy sources based on plant biomass. Along these lines GABI-ENERGY aims at raising the biomass of maize plants for bio-energy production using varieties adapted to the aeroclimatic conditions of Central Europe.

The breeding of maize varieties with high biomass is achieved through two complementary approaches: (A) a deep understanding of the genetic and biochemical/physiological basis of biomass accumulation in maize and (B) the identification of chilling tolerance and late maturity genes by a genomics-guided strategy. A consortium of scientific Institutions (Heinrich-Heine-University of Düsseldorf, University of Potsdam,  Max-Planck-Institute for Molecular Plant Physiology in Potsdam-Golm, University of Hohenheim) are cooperating together with the private partner KWS SAAT AG.

The University of Hohenheim compiled a broad germplasm collection of 290 maize inbred lines and their corresponding testcross progenies using two different testers. This collection was phenotyped under field conditions at several locations for different agronomic traits including dry matter yield and dry matter concentration, flowering time, early vigor and plant height in 2008 and 2009. Additionally, the early plant biomass development was assessed using a new, high throughput non-invasive phenotyping platform based on light curtains and spectral reflectance technology. The specific biogas yield and the chemical composition of the harvested maize plants were determined and calibration methods based on NIRS will be developed for a high throughput screening of these traits.

The phenotypic data will be used to identify the optimum ideotype for energy maize and the best breeding strategy. Relations of biomass accumulation under field conditions with metabolic profiles will be analyzed by correlation analyses, association mapping will be used to link genotypic (60k SNP marker chip) with the phenotypic data. The genetic markers and biomarkers identified will support the breeding of energy maize.