Understanding Quality Determinants in Pea Seeds to improve market opportunities that promote sustainable agriculture (QDiPS)

Lead Research Organisation: John Innes Centre
Department Name: Contracts Office


Seed quality traits can act as a driver for making changes to agricultural systems, such that sustainability might be increased substantially through greater use of pulse crops in rotations. Premium markets exist for pulses of high quality, which can impact on choices in rotations. This project will identify the scientific basis for seed quality parameters in the three main pea crops for human food use – vining, canning and dried pulses. The project will identify compounds that are associated with quality and provide information on how these change in seeds as they mature and under different growing conditions. The work will link genetic and metabolomic studies with industrial assessments of quality, through studies of materials harvested according to current industrial standards by the industrial partners. Genetically marked lines will be used for metabolite profiling and for industrial sensory analysis. In this way, genes and markers linked to quality will be identified. The role of candidate genes will be explored by industrial analysis of variant lines.
Linking quality characters to biochemical and genetic information will facilitate the rapid identification of superior lines, through deployment of genetic markers in marker assisted selection. This will lead to greater efficiency in breeding programmes aimed at quality food markets. It will also provide opportunity to develop more robust methods for assessing maturity rapidly in the field. The identity of genes associated with quality will provide opportunities to manipulate the genes in the biochemical pathways and to identify novel sources of variation within germplasm. The impact of improvements to systems for quality assessment in pulses, and the reality of meeting current and increased market demands, on UK sustainable agriculture, will be explored. Predictive modelling of changes to rotations, and land-use efficiency data will be studied, based on socio-economic data in relation to climate change.


10 25 50